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I use latent variable models including Multidimensional Item Response Theory (MIRT) to model responses to typical performance tests including ipsative questionnaires, and to model response biases in self-report measures and in feedback reports to individuals and organisations.<br /><span class="u-fw700">Supervisors: </span>Alberto Maydeu-Olivares<br /><span class="u-fw700">Phone: </span>+44 1227 823097<br /><b>Address: </b>School of Psychology <br />University of Kent <br />Canterbury, Kent <br />CT2 7NP<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://westminster.academia.edu/AngelaMansi"><img 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data-component-name="Pill" data-props="{"color":"gray","children":["Longitudinal data analysis"]}" data-trace="false" data-dom-id="Pill-react-component-2775ad58-368b-4225-8632-a11c299cfb2d"></div> <div id="Pill-react-component-2775ad58-368b-4225-8632-a11c299cfb2d"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="519455" href="https://www.academia.edu/Documents/in/Forced-Choice_Questionnaires"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Forced-Choice Questionnaires"]}" data-trace="false" data-dom-id="Pill-react-component-5d294b10-4f60-4697-8d59-ea49ebccd473"></div> <div id="Pill-react-component-5d294b10-4f60-4697-8d59-ea49ebccd473"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="519455" href="https://www.academia.edu/Documents/in/Response_Biases_and_Faking"><div 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style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Anna Brown</h3></div><div class="js-work-strip profile--work_container" data-work-id="119870659"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/119870659/Mixed_Keying_or_Desirability_Matching_in_the_Construction_of_Forced_Choice_Measures_An_Empirical_Investigation_and_Practical_Recommendations"><img alt="Research paper thumbnail of Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? An Empirical Investigation and Practical Recommendations" class="work-thumbnail" src="https://attachments.academia-assets.com/115190109/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/119870659/Mixed_Keying_or_Desirability_Matching_in_the_Construction_of_Forced_Choice_Measures_An_Empirical_Investigation_and_Practical_Recommendations">Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? An Empirical Investigation and Practical Recommendations</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://kent.academia.edu/AnnaBrown">Anna Brown</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LiMT4">Li MT</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LingyueLi2">Lingyue Li</a></span></div><div class="wp-workCard_item"><span>Organizational Research Methods</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Forced-choice (FC) measures are becoming increasingly popular as an alternative to single-stateme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Forced-choice (FC) measures are becoming increasingly popular as an alternative to single-statement (SS) measures. However, to ensure the practical usefulness of an FC measure, it is crucial to address the tension between psychometric properties and faking resistance by balancing mixed keying and social desirability matching. It is currently unknown from an empirical perspective whether the two design criteria can be reconciled, and how they impact respondent reactions. By conducting a two-wave experimental design, we constructed four FC measures with varying degrees of mixed-keying and social desirability matching from the same statement pool and investigated their differences in terms of psychometric properties, faking resistance, and respondent reactions. Results showed that all FC measures demonstrated comparable reliability and induced similar respondent reactions. FC measures with stricter social desirability matching were more faking resistant, while FC measures with more mixed keyed blocks had higher convergent validity with the SS measure and displayed similar discriminant and criterion-related validity profiles as the SS benchmark. More importantly, we found that it is possible to strike a balance between social desirability matching and mixed keying, such that FC measures can have adequate psychometric properties and faking resistance. A 7-step recommendation and a tutorial based on the autoFC R package (Li et al., 2022) were provided to help readers construct their own FC measures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34be8b64c220151b47b97155f4a4d703" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115190109,"asset_id":119870659,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115190109/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="119870659"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119870659"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119870659; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119870659]").text(description); $(".js-view-count[data-work-id=119870659]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119870659; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119870659']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "34be8b64c220151b47b97155f4a4d703" } } $('.js-work-strip[data-work-id=119870659]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119870659,"title":"Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? 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A response process model of faking on multidimensional forced-choice personality assessments" class="work-thumbnail" src="https://attachments.academia-assets.com/115189970/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/119870209/How_do_applicants_fake_A_response_process_model_of_faking_on_multidimensional_forced_choice_personality_assessments">How do applicants fake? A response process model of faking on multidimensional forced-choice personality assessments</a></div><div class="wp-workCard_item"><span>International Journal of Selection and Assessment</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Faking on personality assessments remains an unsolved issue, raising major concerns regarding the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Faking on personality assessments remains an unsolved issue, raising major<br />concerns regarding their validity and fairness. Although there is a large body of<br />quantitative research investigating the response process of faking on personality<br />assessments, for both rating scales (RS) and multidimensional forced choice (MFC), only a few studies have yet qualitatively investigated the faking cognitions when responding to MFC in a high‐stakes context (e.g., Sass et al., 2020). Yet, it could be argued that only when we have a process model that adequately describes the response decisions in high stakes, can we begin to extract valid and useful<br />information from assessments. Thus, this qualitative study investigated the faking<br />cognitions when responding to MFC personality assessment in a high‐stakes<br />context. Through cognitive interviews with N = 32 participants, we explored and<br />identified factors influencing the test‐takers' decisions regarding specific items and<br />blocks, and factors influencing the willingness to engage in faking in general. Based<br />on these findings, we propose a new response process model of faking forced-choice items, the Activate‐Rank‐Edit‐Submit (A‐R‐E‐S) model. We also make four<br />recommendations for practice of high‐stakes assessments using MFC.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="12499b7a0812019873f794d03f5c290f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115189970,"asset_id":119870209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115189970/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="119870209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119870209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119870209; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119870209]").text(description); $(".js-view-count[data-work-id=119870209]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119870209; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119870209']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "12499b7a0812019873f794d03f5c290f" } } $('.js-work-strip[data-work-id=119870209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119870209,"title":"How do applicants fake? A response process model of faking on multidimensional forced-choice personality assessments","internal_url":"https://www.academia.edu/119870209/How_do_applicants_fake_A_response_process_model_of_faking_on_multidimensional_forced_choice_personality_assessments","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":115189970,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115189970/thumbnails/1.jpg","file_name":"Int_J_Selection_Assessment_2022_Fuechtenhans_How_do_applicants_fake.pdf","download_url":"https://www.academia.edu/attachments/115189970/download_file","bulk_download_file_name":"How_do_applicants_fake_A_response_proces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115189970/Int_J_Selection_Assessment_2022_Fuechtenhans_How_do_applicants_fake-libre.pdf?1716465331=\u0026response-content-disposition=attachment%3B+filename%3DHow_do_applicants_fake_A_response_proces.pdf\u0026Expires=1740454704\u0026Signature=AuiDUS8BYJw~sUrgDNdFr~qQdoouqewGGziNBN9Ru7k3iRiJRfKRrn~Z8jaByL2ZjF~hlDOdMByWqu2go6e17rP8nQVThWQ7Wf8LVGQCuFbqDs~edsFK5zmQZdbXicGG9L1ejfjBPe8J4g~sQKvmDS12mcahXPfz3jmw6iU3qfhP7ZsRHRzyyth8MoslLx9GWjlDDdiCty9rwKU671gbB7aN5GFMr96m8Ct2umO6vj9zkUq8VNyOxdr4pV0RvNSuU75A6WwLSDEeYllAZoysnjwoaxA0UlMROYiENZFlmAwhLp95aNfVUROYRnoS76lcTGAtaOGHvg8jpLauXv1MUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="96221300"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design"><img alt="Research paper thumbnail of BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design">BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Risk behaviors such as substance use or deviance are often limited to the early stages of the lif...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Risk behaviors such as substance use or deviance are often limited to the early stages of the life course. Whereas the onset of risk behavior is well studied, less is currently known about the decline and timing of cessation of risk behaviors of different domains during young adulthood. Prevalence and longitudinal developmental patterning of alcohol use, drinking to the point of drunkenness, smoking, cannabis use, deviance, and HIV-related sexual risk behavior were compared in a Swiss community sample (N 2,843). Using a longitudinal cohort-sequential approach to link multiple assessments with 3 waves of data for each individual, the studied period spanned the ages of 16 to 29 years. Although smoking had a higher prevalence, both smoking and drinking up to the point of drunkenness followed an inverted U-shaped curve. Alcohol consumption was also best described by a quadratic model, though largely stable at a high level through the late 20s. Sexual risk behavior increased slowly from ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96221300"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96221300"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96221300; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=96221300]").text(description); $(".js-view-count[data-work-id=96221300]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 96221300; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='96221300']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=96221300]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":96221300,"title":"BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design","internal_url":"https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="96221270"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2"><img alt="Research paper thumbnail of Shorter Personality Questionnaires: A User’s Guide Part 2" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2">Shorter Personality Questionnaires: A User’s Guide Part 2</a></div><div class="wp-workCard_item"><span>Assessment and Development Matters</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The final part of this two-part series summarises some of the issues involved in determining the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The final part of this two-part series summarises some of the issues involved in determining the correct length of assessment in a personality questionnaire (PQ). Part 1 discussed general issues that face test designers; this article covers some more modern solutions and provides practical messages for practitioners.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96221270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96221270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96221270; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=96221270]").text(description); $(".js-view-count[data-work-id=96221270]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 96221270; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='96221270']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=96221270]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":96221270,"title":"Shorter Personality Questionnaires: A User’s Guide Part 2","internal_url":"https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="90285571"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire"><img alt="Research paper thumbnail of RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire">RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms of approach, avoid, or equivocate (i.e., neither approach nor avoid). While there are numerous theories of personality, temperament, and character, few seem to take advan-tage of parsimonious taxonomy. The present study sought to implement this taxonomy by creating a questionnaire based on a categorization of behavioral temperaments/tendencies first identified in Buddhist accounts over fifteen hundred years ago. Items were developed using historical and contemporary texts of the behavioral temperaments, described as “Greedy/Faithful”, “Aversive/Discerning”, and “Deluded/Speculative”. To both maintain this categorical typology and benefit from the advantageous properties of forced-choice response format (e.g., reduction of response biases), binary pairwise preferences for items were modeled using Latent Class Analysis (LCA). One sample (n1 = 394) was used to esti-mate the item parameters,...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="90285571"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90285571"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90285571; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90285571]").text(description); $(".js-view-count[data-work-id=90285571]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 90285571; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='90285571']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=90285571]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90285571,"title":"RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire","internal_url":"https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="90285567"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/90285567/Planning_a_Career_in_Psychological_Assessment"><img alt="Research paper thumbnail of Planning a Career in Psychological Assessment" class="work-thumbnail" src="https://attachments.academia-assets.com/93890227/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/90285567/Planning_a_Career_in_Psychological_Assessment">Planning a Career in Psychological Assessment</a></div><div class="wp-workCard_item"><span>European Journal of Psychological Assessment</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two aims that are explicitly formulated in the mission statement of the European Association of P...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two aims that are explicitly formulated in the mission statement of the European Association of Psychological Assessment (EAPA) are to "promote the interest of young professionals and scientists in the science of psychological assessment" and to "create opportunities for scientific exchange about psychological assessment" (EAPA, 2021). In an effort to address these goals, EAPA held its first-ever Winter School 1 bringing early career researchers (ECRs) and experts together. The school was hosted by the Martin Luther University Halle-Wittenberg on May 7, 2021 (online due to the pandemic situation). In this Editorial, we highlight some of the issues discussed, advice and concerns expressed about what to consider when planning a career in psychological assessment. While this cannot be a comprehensive guide, it should be seen as a starting point for a more extensive discussion aimed at helping emerging scholars in the field. Of note, the European Journal of Psychological Assessment, EJPA, as the flagship journal of EAPA, often receives submissions from ECRs (as first authors) and shares the EAPA's commitment toward supporting them through high-quality and swift review processes. We hope that this Editorial helps to serve as an additional building block in supporting young scholars at the beginning of their career. 1 Skepticism against holding a Winter School in May can be alleviated: The temperature in Halle was 6 degrees Celsius to provide the suitable ambience to rightfully call it Winter School.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d83d0da9d570a6b5588e27243cfa7eed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93890227,"asset_id":90285567,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93890227/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="90285567"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90285567"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90285567; 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Within-Subject Trifactor Mixture Modeling Applied to BIDR Responses" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/85575023/Can_Faking_Be_Measured_With_Dedicated_Validity_Scales_Within_Subject_Trifactor_Mixture_Modeling_Applied_to_BIDR_Responses">Can Faking Be Measured With Dedicated Validity Scales? Within-Subject Trifactor Mixture Modeling Applied to BIDR Responses</a></div><div class="wp-workCard_item"><span>Assessment</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) u...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) under answer honest and instructed faking conditions in a within-subjects design. We analyze these data with a novel application of trifactor modeling that models the two substantive factors measured by the BIDR—self-deceptive enhancement (SDE) and impression management (IM), condition-related common factors, and item-specific factors. The model permits examination of invariance and change within subjects across conditions. Participants were able to significantly increase their SDE and IM in the instructed faking condition relative to the honest response condition. Mixture modeling confirmed the existence of a theoretical two-class solution comprised of approximately two thirds of “compliers” and one third of “noncompliers.” Factor scores had good determinacy and correlations with observed scores were near unity for continuous scoring, supporting observed score interpretations of BIDR sca...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="85575023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="85575023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85575023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85575023]").text(description); $(".js-view-count[data-work-id=85575023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85575023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85575023']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=85575023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85575023,"title":"Can Faking Be Measured With Dedicated Validity Scales? 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="85574970"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/85574970/Multidimensional_Forced_Choice_CAT_With_Dominance_Items_An_Empirical_Comparison_With_Optimal_Static_Testing_Under_Different_Desirability_Matching"><img alt="Research paper thumbnail of Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching" class="work-thumbnail" src="https://attachments.academia-assets.com/90231557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/85574970/Multidimensional_Forced_Choice_CAT_With_Dominance_Items_An_Empirical_Comparison_With_Optimal_Static_Testing_Under_Different_Desirability_Matching">Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching</a></div><div class="wp-workCard_item"><span>Educational and Psychological Measurement</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by simulations and lacking in empirical deployment. This empirical study trialed a FC CAT with dominance items described by the Thurstonian Item Response Theory model with research participants. This study investigated important practical issues such as the implications of adaptive item selection and social desirability balancing criteria on score distributions, measurement accuracy and participant perceptions. Moreover, nonadaptive but optimal tests of similar design were trialed alongside the CATs to provide a baseline for comparison, helping to quantify the return on investment when converting an otherwise-optimized static assessment into an adaptive...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4da697b49f3e341b0cd8d900d7269888" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90231557,"asset_id":85574970,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90231557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="85574970"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="85574970"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85574970; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85574970]").text(description); $(".js-view-count[data-work-id=85574970]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85574970; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85574970']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="82361235"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/82361235/Investigating_the_Normativity_of_Trait_Estimates_from_Multidimensional_Forced_Choice_Data"><img alt="Research paper thumbnail of Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data" class="work-thumbnail" src="https://attachments.academia-assets.com/88096123/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/82361235/Investigating_the_Normativity_of_Trait_Estimates_from_Multidimensional_Forced_Choice_Data">Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data</a></div><div class="wp-workCard_item"><span>Multivariate Behavioral Research</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6023b734bf5619a3ea1292a9919cf54c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88096123,"asset_id":82361235,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88096123/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82361235"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82361235"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82361235; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82361235]").text(description); $(".js-view-count[data-work-id=82361235]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82361235; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82361235']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6023b734bf5619a3ea1292a9919cf54c" } } $('.js-work-strip[data-work-id=82361235]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82361235,"title":"Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data","internal_url":"https://www.academia.edu/82361235/Investigating_the_Normativity_of_Trait_Estimates_from_Multidimensional_Forced_Choice_Data","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":88096123,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88096123/thumbnails/1.jpg","file_name":"Frick_20et_20al._20Normativity_20of_20MFC_20SUPPLEMENT.pdf","download_url":"https://www.academia.edu/attachments/88096123/download_file","bulk_download_file_name":"Investigating_the_Normativity_of_Trait_E.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88096123/Frick_20et_20al._20Normativity_20of_20MFC_20SUPPLEMENT-libre.pdf?1656523020=\u0026response-content-disposition=attachment%3B+filename%3DInvestigating_the_Normativity_of_Trait_E.pdf\u0026Expires=1740454704\u0026Signature=eZr3zWkCGaBRPampq9ZNIyK2z8-Fa4BAcArVGIpplM3j6Yma03oWpRFlfxJjzANfUhg2KjMS1xTnDysReHka5u~wIozW6MrF9zxUHOCfsG6EpMZs5gpuDhz7B40PMTuKG-rKunw-EGavIKmZeLv81m5nvJfHioQtV~SwITfkOBkO88tuAsfYrNONnMdZ3PJve25Dp4NDt5M5IasmjN1wYmSBxoMo1ruUJ31qmP2Gi-L2q4CR4VPN1L1z0XRVxjg2TXy17Zkqu9oN-WWsjDSoT2L-uO4GGywa3RLrj0idQxCsgv210Rnsabq40ljShxTk5MSz1MTAIZFOO0hAdF5C0Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048891"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses"><img alt="Research paper thumbnail of Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses" class="work-thumbnail" src="https://attachments.academia-assets.com/84532090/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses">Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses</a></div><div class="wp-workCard_item"><span>Assessment</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) u...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) under answer honest and instructed faking conditions in a within-subjects design. We analyse these data with a novel application of trifactor modeling that models the two substantive factors measured by the BIDR-Self-Deceptive Enhancement (SDE) and Impression Management (IM), condition-related common factors and item specific factors. The model permits examination of invariance and change within subjects across conditions. Participants were able to significantly increase their SDE and IM in the instructed faking condition relative to the honest response condition. Mixture modeling confirmed the existence of a theoretical two-class solution comprised of approximately two thirds of 'compliers' and one third of 'non-compliers'. Factor scores had good determinacy and correlations with observed scores were near unity for continuous scoring, supporting observed score interpretations of BIDR scales in high stakes settings. Correlations were somewhat lower for the dichotomous scoring protocol. Overall, results show that the BIDR scales function similarly as measures of socially desirable functioning in low and high stakes conditions. We discuss conditions under which we expect these results will and will not generalise to other validity scales.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b86c686d4e65abfffab0ca241833b43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84532090,"asset_id":77048891,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84532090/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048891"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048891"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048891; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048891]").text(description); $(".js-view-count[data-work-id=77048891]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048891; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048891']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5b86c686d4e65abfffab0ca241833b43" } } $('.js-work-strip[data-work-id=77048891]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77048891,"title":"Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses","internal_url":"https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84532090,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/84532090/thumbnails/1.jpg","file_name":"BIDR_accepted.docx","download_url":"https://www.academia.edu/attachments/84532090/download_file","bulk_download_file_name":"Can_faking_be_measured_with_dedicated_va.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84532090/BIDR_accepted.docx?1738494355=\u0026response-content-disposition=attachment%3B+filename%3DCan_faking_be_measured_with_dedicated_va.docx\u0026Expires=1740454704\u0026Signature=AEtsEdZmrqJakh-vgfbkK5SSvouLPnsWLKkCdy56RllfEnOuBjeYCQJpM5gmVLt44CQvpQI4qW1Kfpjhk8z2l3ssxK4hcIawpaWDFAZMCR69xXwCxoIJn8~O8ZtSFUaqJ0ToFNSWvJOx5gOYNgZZlHdzhEs01k--RWM9G5GdvLKPMTkbthwb-fRtCu6EwYRz6S08qSMW9XXpy1siH~MdgqArFBpxTvlrVfdIiF2B5-GDLOfi9wBobvBHmNMn-N~ShLU6ao-nNUkKxU48e74CNrMay~xFlbluOZB8cMgHKTwnK7GXFUrVeJ5wL-7TeXCxoKcMy4SP36pS9s6upmxtwQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048721"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048721/Multidimensional_Forced_Choice_CAT_with_Dominance_Items_An_Empirical_Comparison_with_Optimal_Static_Testing_under_Different_Desirability_Matching"><img alt="Research paper thumbnail of Multidimensional Forced-Choice CAT with Dominance Items: An Empirical Comparison with Optimal Static Testing under Different Desirability Matching" class="work-thumbnail" src="https://attachments.academia-assets.com/84531961/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048721/Multidimensional_Forced_Choice_CAT_with_Dominance_Items_An_Empirical_Comparison_with_Optimal_Static_Testing_under_Different_Desirability_Matching">Multidimensional Forced-Choice CAT with Dominance Items: An Empirical Comparison with Optimal Static Testing under Different Desirability Matching</a></div><div class="wp-workCard_item"><span>Educational and Psychological Measurement</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by simulations and lacking in empirical deployment. This empirical study trialed a FC CAT with dominance items described by the Thurstonian Item Response Theory model with research participants. This study investigated important practical issues such as the implications of adaptive item selection and social desirability balancing criteria on score distributions, measurement accuracy and participant perceptions. Moreover, nonadaptive but optimal tests of similar design were trialed alongside the CATs to provide a baseline for comparison, helping to quantify the return on investment when converting an otherwise-optimized static assessment into an adaptive one. Although the benefit of adaptive item selection in improving measurement precision was confirmed, results also indicated that at shorter test lengths CAT had no notable advantage compared with optimal static tests. Taking a holistic view incorporating both psychometric and operational considerations, implications for the design and deployment of FC assessments in research and practice are discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c0c184e08f27e74fb26b0136175aced1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531961,"asset_id":77048721,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531961/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048721"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048721"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048721; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048721]").text(description); $(".js-view-count[data-work-id=77048721]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048721; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048721']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048409"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048409/Investigating_the_Normativity_of_Trait_Estimates_From_Multidimensional_Forced_Choice_Data"><img alt="Research paper thumbnail of Investigating the Normativity of Trait Estimates From Multidimensional Forced-Choice Data" class="work-thumbnail" src="https://attachments.academia-assets.com/84531681/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048409/Investigating_the_Normativity_of_Trait_Estimates_From_Multidimensional_Forced_Choice_Data">Investigating the Normativity of Trait Estimates From Multidimensional Forced-Choice Data</a></div><div class="wp-workCard_item"><span>Multivariate Behavioral Research</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Thurstonian item response model (Thurstonian IRT model) allows deriving normative trait estim...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Thurstonian item response model (Thurstonian IRT model) allows deriving normative trait estimates from multidimensional forced-choice (MFC) data. In the MFC format, persons must rank-order items that measure different attributes according to how well the items describe them. This study evaluated the normativity of Thurstonian IRT trait estimates both in a simulation and empirically. The simulation investigated normativity and compared Thurstonian IRT trait estimates to those using classical partially ipsative scoring, from dichotomous true-false (TF) data and rating scale data. The results showed that, with blocks of opposite-keyed items, Thurstonian IRT trait estimates were normative in contrast to classical partially ipsative estimates. Unbalanced numbers of items per trait, few oppositekeyed items, traits correlated positively or assessing fewer traits did not decrease measurement precision markedly. Measurement precision was lower than that of rating scale data. The empirical study investigated whether relative MFC responses provide a better differentiation of behaviors within persons than absolute TF responses. However, criterion validity was equal and construct validity (with constructs measured by rating scales) lower in MFC. Thus, Thurstonian IRT modeling of MFC data overcomes the drawbacks of classical scoring, but gains in validity may depend on eliminating common method biases from the comparison.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7f48c758761719473e18e7579c5f7a3a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531681,"asset_id":77048409,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531681/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048409"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048409"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048409; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048409]").text(description); $(".js-view-count[data-work-id=77048409]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048409; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048409']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048159"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048159/Intermittent_Faking_of_Personality_Profiles_in_High_Stakes_Assessments_A_Grade_of_Membership_Analysis"><img alt="Research paper thumbnail of Intermittent Faking of Personality Profiles in High-Stakes Assessments: A Grade of Membership Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/84531502/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048159/Intermittent_Faking_of_Personality_Profiles_in_High_Stakes_Assessments_A_Grade_of_Membership_Analysis">Intermittent Faking of Personality Profiles in High-Stakes Assessments: A Grade of Membership Analysis</a></div><div class="wp-workCard_item"><span>Psychological Methods</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In high stakes assessments of personality and similar attributes, test takers may engage in impre...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In high stakes assessments of personality and similar attributes, test takers may engage in impression management (aka faking). This paper proposes to consider responses of every test taker as a potential mixture of 'real' (or retrieved) answers to questions, and 'ideal' answers intended to create a desired impression, with each type of response characterized by its own distribution and factor structure. Depending on the particular mix of response types in the test taker profile, grades of membership in the 'real' and 'ideal' profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed Faking-as-Grade-of-Membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in 'real' and 'ideal' profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c56c439f425856a5690a7ced14d5aa04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531502,"asset_id":77048159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531502/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048159"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048159]").text(description); $(".js-view-count[data-work-id=77048159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923758/Does_multidimensional_forced_choice_prevent_faking_Comparing_the_susceptibility_of_the_multidimensional_forced_choice_format_and_the_rating_scale_format_to_faking"><img alt="Research paper thumbnail of Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking" class="work-thumbnail" src="https://attachments.academia-assets.com/84533276/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923758/Does_multidimensional_forced_choice_prevent_faking_Comparing_the_susceptibility_of_the_multidimensional_forced_choice_format_and_the_rating_scale_format_to_faking">Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking</a></div><div class="wp-workCard_item"><span>Psychological Assessment</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A common concern with self-reports of personality traits in selection contexts is faking. The mul...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A common concern with self-reports of personality traits in selection contexts is faking. The multidimensional forced-choice (MFC) format has been proposed as an alternative to rating scales (RS) that could prevent faking. The goal of this study was to compare the susceptibility of the MFC format and RS format to faking in a simulated high-stakes setting when using normative scoring for both formats. Participants were randomly assigned to three groups (total N = 1,867) and filled out the Big Five Triplets once under an honest instruction and once under a fake-good instruction. Latent mean differences between the honest and fake-good administrations indicated that the Big Five domains were faked in the expected direction. Faking effects for all traits were larger for RS compared to MFC. Faking effects were also larger for the MFC version with mixed triplets compared to the MFC version with triplets that were fully matched regarding their social desirability. The MFC format does not prevent faking completely, but it reduces faking substantially. Faking can be further reduced in the MFC format by matching the items presented in a block regarding their social desirability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a1f7e2b1271b6274553776d38b5732d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84533276,"asset_id":76923758,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84533276/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923758; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923758]").text(description); $(".js-view-count[data-work-id=76923758]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923758; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923758']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a1f7e2b1271b6274553776d38b5732d" } } $('.js-work-strip[data-work-id=76923758]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923758,"title":"Does multidimensional forced-choice prevent faking? 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Users ar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The version in the Kent Academic Repository may differ from the final published version. Users are advised to check for the status of the paper. Users should always cite the published version of record.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="53f7111001d81b583f578ee848bdfb68" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471215,"asset_id":76923757,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471215/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923757"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923757"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923757; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923757]").text(description); $(".js-view-count[data-work-id=76923757]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923757; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923757']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "53f7111001d81b583f578ee848bdfb68" } } $('.js-work-strip[data-work-id=76923757]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923757,"title":"Ordinal Factor Analysis of Graded-Preference Questionnaire Data","internal_url":"https://www.academia.edu/76923757/Ordinal_Factor_Analysis_of_Graded_Preference_Questionnaire_Data","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84471215,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84471215/thumbnails/1.jpg","file_name":"Thurstonian_20scaling_20of_20graded_20preference_20data_20R1_20final_20SELF-ARCHIVING.pdf","download_url":"https://www.academia.edu/attachments/84471215/download_file","bulk_download_file_name":"Ordinal_Factor_Analysis_of_Graded_Prefer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84471215/Thurstonian_20scaling_20of_20graded_20preference_20data_20R1_20final_20SELF-ARCHIVING-libre.pdf?1650379032=\u0026response-content-disposition=attachment%3B+filename%3DOrdinal_Factor_Analysis_of_Graded_Prefer.pdf\u0026Expires=1740454704\u0026Signature=TZmf-j0aUyY9Pa-ikV9VU31BrkkdUnADYftW-jdCcL5XPr2-JJkyP4roD2OP88ELX8I1VO~pOXphqTtj5akW7mKkWDJLd9w7ELuwsalNDXdN68siDPVgxkw4q6eF7SSUvFPZi4dKbgU5QQ2LhLfUpr4LyEcZSV7wRBvCCFm5OeSzoA6LHx72-UU4w~hz8lSY3pxUWmRKGPguZp7wtm9FWI8WOr6M6ykuB9mOaAE3Z-zLJAUmXbvYJV4LKJleABk04Dei2xYOmd2ZMzlOCv7jCNzdu7vYl7MxPWCPjEpxs-BVtPlw5l44Zj2T8AvfCdPFizjjogNK-RbA0xw1vOUOUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923755"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent"><img alt="Research paper thumbnail of How valid are 11‐plus tests? Evidence from Kent" class="work-thumbnail" src="https://attachments.academia-assets.com/84471214/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent">How valid are 11‐plus tests? Evidence from Kent</a></div><div class="wp-workCard_item"><span>British Educational Research Journal</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Despite profound influence of selection-by-ability on children's educational opportunities, empir...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Despite profound influence of selection-by-ability on children's educational opportunities, empirical evidence for validity of 11-plus tests is scarce. This study focused on secondary selection in Kent, the largest grammar school area in England. We analysed scores from the 'Kent Test' (the 11-plus test used in Kent), Cognitive Assessment Tests (CAT4), and Key Stage 2 Standardised Assessment Tests (KS2) using longitudinal data of two year cohorts (N1=95, N2=99) from one primary school. All the assessment batteries provided highly overlapping information, with the decisive effect of content area (e.g. verbal versus maths) over task type (e.g. knowledge-loaded versus knowledge-free). Thus, the value in differentiating 'pure' (i.e. knowledge-free) ability in 11-plus testing is questionable. KS2 and Kent Test aggregated scores overlapped very strongly, sharing nearly 80% of variance; moreover, KS2-based eligibility decisions had higher sensitivity than the Kent Test in predicting the actual admissions to grammar schools after Head Teacher Assessment (HTA) panels have taken place. This study provides preliminary evidence that national examinations could be a good basis for selection to grammar schools, and questions the value of 11-plus tests.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d824a33ff54fc63a1bd9e8dbbecec945" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471214,"asset_id":76923755,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471214/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923755"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923755"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923755; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923755]").text(description); $(".js-view-count[data-work-id=76923755]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923755; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923755']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d824a33ff54fc63a1bd9e8dbbecec945" } } $('.js-work-strip[data-work-id=76923755]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923755,"title":"How valid are 11‐plus tests? Evidence from Kent","internal_url":"https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84471214,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84471214/thumbnails/1.jpg","file_name":"CBER-2019-0029_20submitted_20manuscript_20and_20tables_20PRE-REFEREEING.pdf","download_url":"https://www.academia.edu/attachments/84471214/download_file","bulk_download_file_name":"How_valid_are_11_plus_tests_Evidence_fro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84471214/CBER-2019-0029_20submitted_20manuscript_20and_20tables_20PRE-REFEREEING-libre.pdf?1650379031=\u0026response-content-disposition=attachment%3B+filename%3DHow_valid_are_11_plus_tests_Evidence_fro.pdf\u0026Expires=1740454704\u0026Signature=e4wsJIdJdkbj-IIWqAK~v6DeUAoU3ajoQaRk6hCcw1dBV8i6htLtUdXJBF-BrSC76R57VGDtPtysYqR8rUcYfhh6RVXjaVI4MRW-pR60sJSnPFZw3FnL~8Hur03-vU5EfhYoZvYZr9Y2LdfmGKj1zDXfFD-U7x7mYb4kU~cRgx7cG-xRAy26OJsNxmvfNRLOiKzaCa-aMWLMuBnzIOCbZKZHE55sFcG23jfBWBIX0GZHbiYScXzob-o7jORF8oovBeitk3j4Cf0Zn4RfwQ1CUQH-G71IkepyFUmwqcGtDP2-WOjahcMmoKJdJ9ONws3ut2fKhnfWlsbzUpIB8y-Qxg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923754"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923754/Measuring_the_quality_of_life_of_family_carers_of_people_with_dementia_development_and_validation_of_C_DEMQOL"><img alt="Research paper thumbnail of Measuring the quality of life of family carers of people with dementia: development and validation of C-DEMQOL" class="work-thumbnail" src="https://attachments.academia-assets.com/84453772/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923754/Measuring_the_quality_of_life_of_family_carers_of_people_with_dementia_development_and_validation_of_C_DEMQOL">Measuring the quality of life of family carers of people with dementia: development and validation of C-DEMQOL</a></div><div class="wp-workCard_item"><span>Quality of Life Research</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose We aimed to address gaps identified in the evidence base and instruments available to mea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose We aimed to address gaps identified in the evidence base and instruments available to measure the quality of life (QOL) of family carers of people with dementia, and develop a new brief, reliable, condition-specific instrument. Methods We generated measurable domains and indicators of carer QOL from systematic literature reviews and qualitative interviews with 32 family carers and 9 support staff, and two focus groups with 6 carers and 5 staff. Statements with five tailored response options, presenting variation on the QOL continuum, were piloted (n = 25), pre-tested (n = 122) and fieldtested (n = 300) in individual interviews with family carers from North London and Sussex. The best 30 questions formed the C-DEMQOL questionnaire, which was evaluated for usability, face and construct validity, reliability and convergent/ discriminant validity using a range of validation measures. Results C-DEMQOL was received positively by the carers. Factor analysis confirmed that C-DEMQOL sum scores are reliable in measuring overall QOL (ω = 0.97) and its five subdomains: 'meeting personal needs' (ω = 0.95); 'carer wellbeing' (ω = 0.91); 'carer-patient relationship' (ω = 0.82); 'confidence in the future' (ω = 0.90) and 'feeling supported' (ω = 0.85). The overall QOL and domain scores show the expected pattern of convergent and discriminant relationships with established measures of carer mental health, activities and dementia severity and symptoms. Conclusions The robust psychometric properties support the use of C-DEMQOL in evaluation of overall and domain-specific carer QOL; replications in independent samples and studies of responsiveness would be of value.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a5e1cd42f5ba0ba954795d728f4d813" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84453772,"asset_id":76923754,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84453772/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923754"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923754"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923754; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923752"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923752/The_Narcissism_Epidemic_Is_Dead_Long_Live_the_Narcissism_Epidemic"><img alt="Research paper thumbnail of The Narcissism Epidemic Is Dead; Long Live the Narcissism Epidemic" class="work-thumbnail" src="https://attachments.academia-assets.com/84471216/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923752/The_Narcissism_Epidemic_Is_Dead_Long_Live_the_Narcissism_Epidemic">The Narcissism Epidemic Is Dead; Long Live the Narcissism Epidemic</a></div><div class="wp-workCard_item"><span>Psychological science</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Are recent cohorts of college students more narcissistic than their predecessors? To address deba...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Are recent cohorts of college students more narcissistic than their predecessors? To address debates about the so-called &quot;narcissism epidemic,&quot; we used data from three cohorts of students (1990s: N = 1,166; 2000s: N = 33,647; 2010s: N = 25,412) to test whether narcissism levels (overall and specific facets) have increased across generations. We also tested whether our measure, the Narcissistic Personality Inventory (NPI), showed measurement equivalence across the three cohorts, a critical analysis that had been overlooked in prior research. We found that several NPI items were not equivalent across cohorts. Models accounting for nonequivalence of these items indicated a small decline in overall narcissism levels from the 1990s to the 2010s ( d = -0.27). At the facet level, leadership ( d = -0.20), vanity ( d = -0.16), and entitlement ( d = -0.28) all showed decreases. Our results contradict the claim that recent cohorts of college students are more narcissistic than earlie...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15a091c597af8da6223490f2087ad2d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471216,"asset_id":76923752,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471216/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923752"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923752"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923752; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="93783" id="papers"><div class="js-work-strip profile--work_container" data-work-id="119870659"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/119870659/Mixed_Keying_or_Desirability_Matching_in_the_Construction_of_Forced_Choice_Measures_An_Empirical_Investigation_and_Practical_Recommendations"><img alt="Research paper thumbnail of Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? An Empirical Investigation and Practical Recommendations" class="work-thumbnail" src="https://attachments.academia-assets.com/115190109/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/119870659/Mixed_Keying_or_Desirability_Matching_in_the_Construction_of_Forced_Choice_Measures_An_Empirical_Investigation_and_Practical_Recommendations">Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? An Empirical Investigation and Practical Recommendations</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://kent.academia.edu/AnnaBrown">Anna Brown</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LiMT4">Li MT</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LingyueLi2">Lingyue Li</a></span></div><div class="wp-workCard_item"><span>Organizational Research Methods</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Forced-choice (FC) measures are becoming increasingly popular as an alternative to single-stateme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Forced-choice (FC) measures are becoming increasingly popular as an alternative to single-statement (SS) measures. However, to ensure the practical usefulness of an FC measure, it is crucial to address the tension between psychometric properties and faking resistance by balancing mixed keying and social desirability matching. It is currently unknown from an empirical perspective whether the two design criteria can be reconciled, and how they impact respondent reactions. By conducting a two-wave experimental design, we constructed four FC measures with varying degrees of mixed-keying and social desirability matching from the same statement pool and investigated their differences in terms of psychometric properties, faking resistance, and respondent reactions. Results showed that all FC measures demonstrated comparable reliability and induced similar respondent reactions. FC measures with stricter social desirability matching were more faking resistant, while FC measures with more mixed keyed blocks had higher convergent validity with the SS measure and displayed similar discriminant and criterion-related validity profiles as the SS benchmark. More importantly, we found that it is possible to strike a balance between social desirability matching and mixed keying, such that FC measures can have adequate psychometric properties and faking resistance. A 7-step recommendation and a tutorial based on the autoFC R package (Li et al., 2022) were provided to help readers construct their own FC measures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34be8b64c220151b47b97155f4a4d703" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115190109,"asset_id":119870659,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115190109/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="119870659"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119870659"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119870659; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119870659]").text(description); $(".js-view-count[data-work-id=119870659]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119870659; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119870659']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "34be8b64c220151b47b97155f4a4d703" } } $('.js-work-strip[data-work-id=119870659]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119870659,"title":"Mixed-Keying or Desirability-Matching in the Construction of Forced-Choice Measures? 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A response process model of faking on multidimensional forced-choice personality assessments" class="work-thumbnail" src="https://attachments.academia-assets.com/115189970/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/119870209/How_do_applicants_fake_A_response_process_model_of_faking_on_multidimensional_forced_choice_personality_assessments">How do applicants fake? A response process model of faking on multidimensional forced-choice personality assessments</a></div><div class="wp-workCard_item"><span>International Journal of Selection and Assessment</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Faking on personality assessments remains an unsolved issue, raising major concerns regarding the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Faking on personality assessments remains an unsolved issue, raising major<br />concerns regarding their validity and fairness. Although there is a large body of<br />quantitative research investigating the response process of faking on personality<br />assessments, for both rating scales (RS) and multidimensional forced choice (MFC), only a few studies have yet qualitatively investigated the faking cognitions when responding to MFC in a high‐stakes context (e.g., Sass et al., 2020). Yet, it could be argued that only when we have a process model that adequately describes the response decisions in high stakes, can we begin to extract valid and useful<br />information from assessments. Thus, this qualitative study investigated the faking<br />cognitions when responding to MFC personality assessment in a high‐stakes<br />context. Through cognitive interviews with N = 32 participants, we explored and<br />identified factors influencing the test‐takers' decisions regarding specific items and<br />blocks, and factors influencing the willingness to engage in faking in general. Based<br />on these findings, we propose a new response process model of faking forced-choice items, the Activate‐Rank‐Edit‐Submit (A‐R‐E‐S) model. We also make four<br />recommendations for practice of high‐stakes assessments using MFC.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="12499b7a0812019873f794d03f5c290f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115189970,"asset_id":119870209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115189970/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="119870209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119870209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119870209; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119870209]").text(description); $(".js-view-count[data-work-id=119870209]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119870209; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119870209']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "12499b7a0812019873f794d03f5c290f" } } $('.js-work-strip[data-work-id=119870209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119870209,"title":"How do applicants fake? A response process model of faking on multidimensional forced-choice personality assessments","internal_url":"https://www.academia.edu/119870209/How_do_applicants_fake_A_response_process_model_of_faking_on_multidimensional_forced_choice_personality_assessments","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":115189970,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115189970/thumbnails/1.jpg","file_name":"Int_J_Selection_Assessment_2022_Fuechtenhans_How_do_applicants_fake.pdf","download_url":"https://www.academia.edu/attachments/115189970/download_file","bulk_download_file_name":"How_do_applicants_fake_A_response_proces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115189970/Int_J_Selection_Assessment_2022_Fuechtenhans_How_do_applicants_fake-libre.pdf?1716465331=\u0026response-content-disposition=attachment%3B+filename%3DHow_do_applicants_fake_A_response_proces.pdf\u0026Expires=1740454704\u0026Signature=AuiDUS8BYJw~sUrgDNdFr~qQdoouqewGGziNBN9Ru7k3iRiJRfKRrn~Z8jaByL2ZjF~hlDOdMByWqu2go6e17rP8nQVThWQ7Wf8LVGQCuFbqDs~edsFK5zmQZdbXicGG9L1ejfjBPe8J4g~sQKvmDS12mcahXPfz3jmw6iU3qfhP7ZsRHRzyyth8MoslLx9GWjlDDdiCty9rwKU671gbB7aN5GFMr96m8Ct2umO6vj9zkUq8VNyOxdr4pV0RvNSuU75A6WwLSDEeYllAZoysnjwoaxA0UlMROYiENZFlmAwhLp95aNfVUROYRnoS76lcTGAtaOGHvg8jpLauXv1MUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="96221300"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design"><img alt="Research paper thumbnail of BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design">BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Risk behaviors such as substance use or deviance are often limited to the early stages of the lif...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Risk behaviors such as substance use or deviance are often limited to the early stages of the life course. Whereas the onset of risk behavior is well studied, less is currently known about the decline and timing of cessation of risk behaviors of different domains during young adulthood. Prevalence and longitudinal developmental patterning of alcohol use, drinking to the point of drunkenness, smoking, cannabis use, deviance, and HIV-related sexual risk behavior were compared in a Swiss community sample (N 2,843). Using a longitudinal cohort-sequential approach to link multiple assessments with 3 waves of data for each individual, the studied period spanned the ages of 16 to 29 years. Although smoking had a higher prevalence, both smoking and drinking up to the point of drunkenness followed an inverted U-shaped curve. Alcohol consumption was also best described by a quadratic model, though largely stable at a high level through the late 20s. Sexual risk behavior increased slowly from ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96221300"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96221300"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96221300; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=96221300]").text(description); $(".js-view-count[data-work-id=96221300]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 96221300; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='96221300']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=96221300]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":96221300,"title":"BRIEF REPORT Comparing Growth Trajectories of Risk Behaviors From Late Adolescence Through Young Adulthood: An Accelerated Design","internal_url":"https://www.academia.edu/96221300/BRIEF_REPORT_Comparing_Growth_Trajectories_of_Risk_Behaviors_From_Late_Adolescence_Through_Young_Adulthood_An_Accelerated_Design","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="96221270"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2"><img alt="Research paper thumbnail of Shorter Personality Questionnaires: A User’s Guide Part 2" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2">Shorter Personality Questionnaires: A User’s Guide Part 2</a></div><div class="wp-workCard_item"><span>Assessment and Development Matters</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The final part of this two-part series summarises some of the issues involved in determining the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The final part of this two-part series summarises some of the issues involved in determining the correct length of assessment in a personality questionnaire (PQ). Part 1 discussed general issues that face test designers; this article covers some more modern solutions and provides practical messages for practitioners.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96221270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96221270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96221270; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=96221270]").text(description); $(".js-view-count[data-work-id=96221270]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 96221270; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='96221270']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=96221270]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":96221270,"title":"Shorter Personality Questionnaires: A User’s Guide Part 2","internal_url":"https://www.academia.edu/96221270/Shorter_Personality_Questionnaires_A_User_s_Guide_Part_2","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="90285571"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire"><img alt="Research paper thumbnail of RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire">RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms of approach, avoid, or equivocate (i.e., neither approach nor avoid). While there are numerous theories of personality, temperament, and character, few seem to take advan-tage of parsimonious taxonomy. The present study sought to implement this taxonomy by creating a questionnaire based on a categorization of behavioral temperaments/tendencies first identified in Buddhist accounts over fifteen hundred years ago. Items were developed using historical and contemporary texts of the behavioral temperaments, described as “Greedy/Faithful”, “Aversive/Discerning”, and “Deluded/Speculative”. To both maintain this categorical typology and benefit from the advantageous properties of forced-choice response format (e.g., reduction of response biases), binary pairwise preferences for items were modeled using Latent Class Analysis (LCA). One sample (n1 = 394) was used to esti-mate the item parameters,...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="90285571"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90285571"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90285571; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90285571]").text(description); $(".js-view-count[data-work-id=90285571]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 90285571; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='90285571']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=90285571]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90285571,"title":"RESEARCH ARTICLE Development and Validation of the Behavioral Tendencies Questionnaire","internal_url":"https://www.academia.edu/90285571/RESEARCH_ARTICLE_Development_and_Validation_of_the_Behavioral_Tendencies_Questionnaire","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="90285567"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/90285567/Planning_a_Career_in_Psychological_Assessment"><img alt="Research paper thumbnail of Planning a Career in Psychological Assessment" class="work-thumbnail" src="https://attachments.academia-assets.com/93890227/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/90285567/Planning_a_Career_in_Psychological_Assessment">Planning a Career in Psychological Assessment</a></div><div class="wp-workCard_item"><span>European Journal of Psychological Assessment</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two aims that are explicitly formulated in the mission statement of the European Association of P...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two aims that are explicitly formulated in the mission statement of the European Association of Psychological Assessment (EAPA) are to "promote the interest of young professionals and scientists in the science of psychological assessment" and to "create opportunities for scientific exchange about psychological assessment" (EAPA, 2021). In an effort to address these goals, EAPA held its first-ever Winter School 1 bringing early career researchers (ECRs) and experts together. The school was hosted by the Martin Luther University Halle-Wittenberg on May 7, 2021 (online due to the pandemic situation). In this Editorial, we highlight some of the issues discussed, advice and concerns expressed about what to consider when planning a career in psychological assessment. While this cannot be a comprehensive guide, it should be seen as a starting point for a more extensive discussion aimed at helping emerging scholars in the field. Of note, the European Journal of Psychological Assessment, EJPA, as the flagship journal of EAPA, often receives submissions from ECRs (as first authors) and shares the EAPA's commitment toward supporting them through high-quality and swift review processes. We hope that this Editorial helps to serve as an additional building block in supporting young scholars at the beginning of their career. 1 Skepticism against holding a Winter School in May can be alleviated: The temperature in Halle was 6 degrees Celsius to provide the suitable ambience to rightfully call it Winter School.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d83d0da9d570a6b5588e27243cfa7eed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93890227,"asset_id":90285567,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93890227/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="90285567"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90285567"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90285567; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="90285502"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/90285502/IRT_model_for_recovering_latent_traits_from_forced_choice_personality_tests"><img alt="Research paper thumbnail of IRT model for recovering latent traits from forced-choice personality tests" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90285502/IRT_model_for_recovering_latent_traits_from_forced_choice_personality_tests">IRT model for recovering latent traits from forced-choice personality tests</a></div><div class="wp-workCard_item"><span>PsycEXTRA Dataset</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="90285502"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90285502"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90285502; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=90285502]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90285502,"title":"IRT model for recovering latent traits from forced-choice personality tests","internal_url":"https://www.academia.edu/90285502/IRT_model_for_recovering_latent_traits_from_forced_choice_personality_tests","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="85575023"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/85575023/Can_Faking_Be_Measured_With_Dedicated_Validity_Scales_Within_Subject_Trifactor_Mixture_Modeling_Applied_to_BIDR_Responses"><img alt="Research paper thumbnail of Can Faking Be Measured With Dedicated Validity Scales? Within-Subject Trifactor Mixture Modeling Applied to BIDR Responses" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/85575023/Can_Faking_Be_Measured_With_Dedicated_Validity_Scales_Within_Subject_Trifactor_Mixture_Modeling_Applied_to_BIDR_Responses">Can Faking Be Measured With Dedicated Validity Scales? Within-Subject Trifactor Mixture Modeling Applied to BIDR Responses</a></div><div class="wp-workCard_item"><span>Assessment</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) u...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) under answer honest and instructed faking conditions in a within-subjects design. We analyze these data with a novel application of trifactor modeling that models the two substantive factors measured by the BIDR—self-deceptive enhancement (SDE) and impression management (IM), condition-related common factors, and item-specific factors. The model permits examination of invariance and change within subjects across conditions. Participants were able to significantly increase their SDE and IM in the instructed faking condition relative to the honest response condition. Mixture modeling confirmed the existence of a theoretical two-class solution comprised of approximately two thirds of “compliers” and one third of “noncompliers.” Factor scores had good determinacy and correlations with observed scores were near unity for continuous scoring, supporting observed score interpretations of BIDR sca...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="85575023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="85575023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85575023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85575023]").text(description); $(".js-view-count[data-work-id=85575023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85575023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85575023']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=85575023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85575023,"title":"Can Faking Be Measured With Dedicated Validity Scales? 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Users ar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The version in the Kent Academic Repository may differ from the final published version. Users are advised to check <a href="http://kar.kent.ac.uk" rel="nofollow">http://kar.kent.ac.uk</a> for the status of the paper. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="85574970"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/85574970/Multidimensional_Forced_Choice_CAT_With_Dominance_Items_An_Empirical_Comparison_With_Optimal_Static_Testing_Under_Different_Desirability_Matching"><img alt="Research paper thumbnail of Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching" class="work-thumbnail" src="https://attachments.academia-assets.com/90231557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/85574970/Multidimensional_Forced_Choice_CAT_With_Dominance_Items_An_Empirical_Comparison_With_Optimal_Static_Testing_Under_Different_Desirability_Matching">Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching</a></div><div class="wp-workCard_item"><span>Educational and Psychological Measurement</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by simulations and lacking in empirical deployment. This empirical study trialed a FC CAT with dominance items described by the Thurstonian Item Response Theory model with research participants. This study investigated important practical issues such as the implications of adaptive item selection and social desirability balancing criteria on score distributions, measurement accuracy and participant perceptions. Moreover, nonadaptive but optimal tests of similar design were trialed alongside the CATs to provide a baseline for comparison, helping to quantify the return on investment when converting an otherwise-optimized static assessment into an adaptive...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4da697b49f3e341b0cd8d900d7269888" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90231557,"asset_id":85574970,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90231557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="85574970"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="85574970"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85574970; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="82361235"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/82361235/Investigating_the_Normativity_of_Trait_Estimates_from_Multidimensional_Forced_Choice_Data"><img alt="Research paper thumbnail of Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data" class="work-thumbnail" src="https://attachments.academia-assets.com/88096123/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/82361235/Investigating_the_Normativity_of_Trait_Estimates_from_Multidimensional_Forced_Choice_Data">Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data</a></div><div class="wp-workCard_item"><span>Multivariate Behavioral Research</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6023b734bf5619a3ea1292a9919cf54c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88096123,"asset_id":82361235,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88096123/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82361235"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82361235"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82361235; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82361235]").text(description); $(".js-view-count[data-work-id=82361235]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82361235; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82361235']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048891"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses"><img alt="Research paper thumbnail of Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses" class="work-thumbnail" src="https://attachments.academia-assets.com/84532090/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses">Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses</a></div><div class="wp-workCard_item"><span>Assessment</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) u...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A sample of 516 participants responded to the Balanced Inventory of Desirable Responding (BIDR) under answer honest and instructed faking conditions in a within-subjects design. We analyse these data with a novel application of trifactor modeling that models the two substantive factors measured by the BIDR-Self-Deceptive Enhancement (SDE) and Impression Management (IM), condition-related common factors and item specific factors. The model permits examination of invariance and change within subjects across conditions. Participants were able to significantly increase their SDE and IM in the instructed faking condition relative to the honest response condition. Mixture modeling confirmed the existence of a theoretical two-class solution comprised of approximately two thirds of 'compliers' and one third of 'non-compliers'. Factor scores had good determinacy and correlations with observed scores were near unity for continuous scoring, supporting observed score interpretations of BIDR scales in high stakes settings. Correlations were somewhat lower for the dichotomous scoring protocol. Overall, results show that the BIDR scales function similarly as measures of socially desirable functioning in low and high stakes conditions. We discuss conditions under which we expect these results will and will not generalise to other validity scales.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b86c686d4e65abfffab0ca241833b43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84532090,"asset_id":77048891,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84532090/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048891"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048891"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048891; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048891]").text(description); $(".js-view-count[data-work-id=77048891]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048891; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048891']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5b86c686d4e65abfffab0ca241833b43" } } $('.js-work-strip[data-work-id=77048891]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77048891,"title":"Can faking be measured with dedicated validity scales? Within Subject Trifactor Mixture Modeling applied to BIDR responses","internal_url":"https://www.academia.edu/77048891/Can_faking_be_measured_with_dedicated_validity_scales_Within_Subject_Trifactor_Mixture_Modeling_applied_to_BIDR_responses","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84532090,"title":"","file_type":"docx","scribd_thumbnail_url":"https://attachments.academia-assets.com/84532090/thumbnails/1.jpg","file_name":"BIDR_accepted.docx","download_url":"https://www.academia.edu/attachments/84532090/download_file","bulk_download_file_name":"Can_faking_be_measured_with_dedicated_va.docx","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84532090/BIDR_accepted.docx?1738494355=\u0026response-content-disposition=attachment%3B+filename%3DCan_faking_be_measured_with_dedicated_va.docx\u0026Expires=1740454704\u0026Signature=AEtsEdZmrqJakh-vgfbkK5SSvouLPnsWLKkCdy56RllfEnOuBjeYCQJpM5gmVLt44CQvpQI4qW1Kfpjhk8z2l3ssxK4hcIawpaWDFAZMCR69xXwCxoIJn8~O8ZtSFUaqJ0ToFNSWvJOx5gOYNgZZlHdzhEs01k--RWM9G5GdvLKPMTkbthwb-fRtCu6EwYRz6S08qSMW9XXpy1siH~MdgqArFBpxTvlrVfdIiF2B5-GDLOfi9wBobvBHmNMn-N~ShLU6ao-nNUkKxU48e74CNrMay~xFlbluOZB8cMgHKTwnK7GXFUrVeJ5wL-7TeXCxoKcMy4SP36pS9s6upmxtwQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048721"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048721/Multidimensional_Forced_Choice_CAT_with_Dominance_Items_An_Empirical_Comparison_with_Optimal_Static_Testing_under_Different_Desirability_Matching"><img alt="Research paper thumbnail of Multidimensional Forced-Choice CAT with Dominance Items: An Empirical Comparison with Optimal Static Testing under Different Desirability Matching" class="work-thumbnail" src="https://attachments.academia-assets.com/84531961/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048721/Multidimensional_Forced_Choice_CAT_with_Dominance_Items_An_Empirical_Comparison_with_Optimal_Static_Testing_under_Different_Desirability_Matching">Multidimensional Forced-Choice CAT with Dominance Items: An Empirical Comparison with Optimal Static Testing under Different Desirability Matching</a></div><div class="wp-workCard_item"><span>Educational and Psychological Measurement</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by simulations and lacking in empirical deployment. This empirical study trialed a FC CAT with dominance items described by the Thurstonian Item Response Theory model with research participants. This study investigated important practical issues such as the implications of adaptive item selection and social desirability balancing criteria on score distributions, measurement accuracy and participant perceptions. Moreover, nonadaptive but optimal tests of similar design were trialed alongside the CATs to provide a baseline for comparison, helping to quantify the return on investment when converting an otherwise-optimized static assessment into an adaptive one. Although the benefit of adaptive item selection in improving measurement precision was confirmed, results also indicated that at shorter test lengths CAT had no notable advantage compared with optimal static tests. Taking a holistic view incorporating both psychometric and operational considerations, implications for the design and deployment of FC assessments in research and practice are discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c0c184e08f27e74fb26b0136175aced1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531961,"asset_id":77048721,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531961/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048721"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048721"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048721; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048409"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048409/Investigating_the_Normativity_of_Trait_Estimates_From_Multidimensional_Forced_Choice_Data"><img alt="Research paper thumbnail of Investigating the Normativity of Trait Estimates From Multidimensional Forced-Choice Data" class="work-thumbnail" src="https://attachments.academia-assets.com/84531681/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048409/Investigating_the_Normativity_of_Trait_Estimates_From_Multidimensional_Forced_Choice_Data">Investigating the Normativity of Trait Estimates From Multidimensional Forced-Choice Data</a></div><div class="wp-workCard_item"><span>Multivariate Behavioral Research</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Thurstonian item response model (Thurstonian IRT model) allows deriving normative trait estim...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Thurstonian item response model (Thurstonian IRT model) allows deriving normative trait estimates from multidimensional forced-choice (MFC) data. In the MFC format, persons must rank-order items that measure different attributes according to how well the items describe them. This study evaluated the normativity of Thurstonian IRT trait estimates both in a simulation and empirically. The simulation investigated normativity and compared Thurstonian IRT trait estimates to those using classical partially ipsative scoring, from dichotomous true-false (TF) data and rating scale data. The results showed that, with blocks of opposite-keyed items, Thurstonian IRT trait estimates were normative in contrast to classical partially ipsative estimates. Unbalanced numbers of items per trait, few oppositekeyed items, traits correlated positively or assessing fewer traits did not decrease measurement precision markedly. Measurement precision was lower than that of rating scale data. The empirical study investigated whether relative MFC responses provide a better differentiation of behaviors within persons than absolute TF responses. However, criterion validity was equal and construct validity (with constructs measured by rating scales) lower in MFC. Thus, Thurstonian IRT modeling of MFC data overcomes the drawbacks of classical scoring, but gains in validity may depend on eliminating common method biases from the comparison.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7f48c758761719473e18e7579c5f7a3a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531681,"asset_id":77048409,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531681/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048409"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048409"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048409; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048409]").text(description); $(".js-view-count[data-work-id=77048409]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048409; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048409']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77048159"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77048159/Intermittent_Faking_of_Personality_Profiles_in_High_Stakes_Assessments_A_Grade_of_Membership_Analysis"><img alt="Research paper thumbnail of Intermittent Faking of Personality Profiles in High-Stakes Assessments: A Grade of Membership Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/84531502/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77048159/Intermittent_Faking_of_Personality_Profiles_in_High_Stakes_Assessments_A_Grade_of_Membership_Analysis">Intermittent Faking of Personality Profiles in High-Stakes Assessments: A Grade of Membership Analysis</a></div><div class="wp-workCard_item"><span>Psychological Methods</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In high stakes assessments of personality and similar attributes, test takers may engage in impre...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In high stakes assessments of personality and similar attributes, test takers may engage in impression management (aka faking). This paper proposes to consider responses of every test taker as a potential mixture of 'real' (or retrieved) answers to questions, and 'ideal' answers intended to create a desired impression, with each type of response characterized by its own distribution and factor structure. Depending on the particular mix of response types in the test taker profile, grades of membership in the 'real' and 'ideal' profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed Faking-as-Grade-of-Membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in 'real' and 'ideal' profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c56c439f425856a5690a7ced14d5aa04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84531502,"asset_id":77048159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84531502/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77048159"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77048159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77048159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77048159]").text(description); $(".js-view-count[data-work-id=77048159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77048159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77048159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923758/Does_multidimensional_forced_choice_prevent_faking_Comparing_the_susceptibility_of_the_multidimensional_forced_choice_format_and_the_rating_scale_format_to_faking"><img alt="Research paper thumbnail of Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking" class="work-thumbnail" src="https://attachments.academia-assets.com/84533276/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923758/Does_multidimensional_forced_choice_prevent_faking_Comparing_the_susceptibility_of_the_multidimensional_forced_choice_format_and_the_rating_scale_format_to_faking">Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking</a></div><div class="wp-workCard_item"><span>Psychological Assessment</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A common concern with self-reports of personality traits in selection contexts is faking. The mul...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A common concern with self-reports of personality traits in selection contexts is faking. The multidimensional forced-choice (MFC) format has been proposed as an alternative to rating scales (RS) that could prevent faking. The goal of this study was to compare the susceptibility of the MFC format and RS format to faking in a simulated high-stakes setting when using normative scoring for both formats. Participants were randomly assigned to three groups (total N = 1,867) and filled out the Big Five Triplets once under an honest instruction and once under a fake-good instruction. Latent mean differences between the honest and fake-good administrations indicated that the Big Five domains were faked in the expected direction. Faking effects for all traits were larger for RS compared to MFC. Faking effects were also larger for the MFC version with mixed triplets compared to the MFC version with triplets that were fully matched regarding their social desirability. The MFC format does not prevent faking completely, but it reduces faking substantially. Faking can be further reduced in the MFC format by matching the items presented in a block regarding their social desirability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a1f7e2b1271b6274553776d38b5732d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84533276,"asset_id":76923758,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84533276/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923758; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923758]").text(description); $(".js-view-count[data-work-id=76923758]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923758; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923758']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a1f7e2b1271b6274553776d38b5732d" } } $('.js-work-strip[data-work-id=76923758]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923758,"title":"Does multidimensional forced-choice prevent faking? 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Users ar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The version in the Kent Academic Repository may differ from the final published version. Users are advised to check for the status of the paper. Users should always cite the published version of record.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="53f7111001d81b583f578ee848bdfb68" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471215,"asset_id":76923757,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471215/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923757"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923757"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923757; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923757]").text(description); $(".js-view-count[data-work-id=76923757]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923757; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923757']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "53f7111001d81b583f578ee848bdfb68" } } $('.js-work-strip[data-work-id=76923757]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923757,"title":"Ordinal Factor Analysis of Graded-Preference Questionnaire Data","internal_url":"https://www.academia.edu/76923757/Ordinal_Factor_Analysis_of_Graded_Preference_Questionnaire_Data","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84471215,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84471215/thumbnails/1.jpg","file_name":"Thurstonian_20scaling_20of_20graded_20preference_20data_20R1_20final_20SELF-ARCHIVING.pdf","download_url":"https://www.academia.edu/attachments/84471215/download_file","bulk_download_file_name":"Ordinal_Factor_Analysis_of_Graded_Prefer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84471215/Thurstonian_20scaling_20of_20graded_20preference_20data_20R1_20final_20SELF-ARCHIVING-libre.pdf?1650379032=\u0026response-content-disposition=attachment%3B+filename%3DOrdinal_Factor_Analysis_of_Graded_Prefer.pdf\u0026Expires=1740454704\u0026Signature=TZmf-j0aUyY9Pa-ikV9VU31BrkkdUnADYftW-jdCcL5XPr2-JJkyP4roD2OP88ELX8I1VO~pOXphqTtj5akW7mKkWDJLd9w7ELuwsalNDXdN68siDPVgxkw4q6eF7SSUvFPZi4dKbgU5QQ2LhLfUpr4LyEcZSV7wRBvCCFm5OeSzoA6LHx72-UU4w~hz8lSY3pxUWmRKGPguZp7wtm9FWI8WOr6M6ykuB9mOaAE3Z-zLJAUmXbvYJV4LKJleABk04Dei2xYOmd2ZMzlOCv7jCNzdu7vYl7MxPWCPjEpxs-BVtPlw5l44Zj2T8AvfCdPFizjjogNK-RbA0xw1vOUOUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923755"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent"><img alt="Research paper thumbnail of How valid are 11‐plus tests? Evidence from Kent" class="work-thumbnail" src="https://attachments.academia-assets.com/84471214/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent">How valid are 11‐plus tests? Evidence from Kent</a></div><div class="wp-workCard_item"><span>British Educational Research Journal</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Despite profound influence of selection-by-ability on children's educational opportunities, empir...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Despite profound influence of selection-by-ability on children's educational opportunities, empirical evidence for validity of 11-plus tests is scarce. This study focused on secondary selection in Kent, the largest grammar school area in England. We analysed scores from the 'Kent Test' (the 11-plus test used in Kent), Cognitive Assessment Tests (CAT4), and Key Stage 2 Standardised Assessment Tests (KS2) using longitudinal data of two year cohorts (N1=95, N2=99) from one primary school. All the assessment batteries provided highly overlapping information, with the decisive effect of content area (e.g. verbal versus maths) over task type (e.g. knowledge-loaded versus knowledge-free). Thus, the value in differentiating 'pure' (i.e. knowledge-free) ability in 11-plus testing is questionable. KS2 and Kent Test aggregated scores overlapped very strongly, sharing nearly 80% of variance; moreover, KS2-based eligibility decisions had higher sensitivity than the Kent Test in predicting the actual admissions to grammar schools after Head Teacher Assessment (HTA) panels have taken place. This study provides preliminary evidence that national examinations could be a good basis for selection to grammar schools, and questions the value of 11-plus tests.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d824a33ff54fc63a1bd9e8dbbecec945" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471214,"asset_id":76923755,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471214/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923755"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923755"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923755; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76923755]").text(description); $(".js-view-count[data-work-id=76923755]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76923755; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76923755']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d824a33ff54fc63a1bd9e8dbbecec945" } } $('.js-work-strip[data-work-id=76923755]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76923755,"title":"How valid are 11‐plus tests? Evidence from Kent","internal_url":"https://www.academia.edu/76923755/How_valid_are_11_plus_tests_Evidence_from_Kent","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[{"id":84471214,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84471214/thumbnails/1.jpg","file_name":"CBER-2019-0029_20submitted_20manuscript_20and_20tables_20PRE-REFEREEING.pdf","download_url":"https://www.academia.edu/attachments/84471214/download_file","bulk_download_file_name":"How_valid_are_11_plus_tests_Evidence_fro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84471214/CBER-2019-0029_20submitted_20manuscript_20and_20tables_20PRE-REFEREEING-libre.pdf?1650379031=\u0026response-content-disposition=attachment%3B+filename%3DHow_valid_are_11_plus_tests_Evidence_fro.pdf\u0026Expires=1740454704\u0026Signature=e4wsJIdJdkbj-IIWqAK~v6DeUAoU3ajoQaRk6hCcw1dBV8i6htLtUdXJBF-BrSC76R57VGDtPtysYqR8rUcYfhh6RVXjaVI4MRW-pR60sJSnPFZw3FnL~8Hur03-vU5EfhYoZvYZr9Y2LdfmGKj1zDXfFD-U7x7mYb4kU~cRgx7cG-xRAy26OJsNxmvfNRLOiKzaCa-aMWLMuBnzIOCbZKZHE55sFcG23jfBWBIX0GZHbiYScXzob-o7jORF8oovBeitk3j4Cf0Zn4RfwQ1CUQH-G71IkepyFUmwqcGtDP2-WOjahcMmoKJdJ9ONws3ut2fKhnfWlsbzUpIB8y-Qxg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923754"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923754/Measuring_the_quality_of_life_of_family_carers_of_people_with_dementia_development_and_validation_of_C_DEMQOL"><img alt="Research paper thumbnail of Measuring the quality of life of family carers of people with dementia: development and validation of C-DEMQOL" class="work-thumbnail" src="https://attachments.academia-assets.com/84453772/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923754/Measuring_the_quality_of_life_of_family_carers_of_people_with_dementia_development_and_validation_of_C_DEMQOL">Measuring the quality of life of family carers of people with dementia: development and validation of C-DEMQOL</a></div><div class="wp-workCard_item"><span>Quality of Life Research</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose We aimed to address gaps identified in the evidence base and instruments available to mea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose We aimed to address gaps identified in the evidence base and instruments available to measure the quality of life (QOL) of family carers of people with dementia, and develop a new brief, reliable, condition-specific instrument. Methods We generated measurable domains and indicators of carer QOL from systematic literature reviews and qualitative interviews with 32 family carers and 9 support staff, and two focus groups with 6 carers and 5 staff. Statements with five tailored response options, presenting variation on the QOL continuum, were piloted (n = 25), pre-tested (n = 122) and fieldtested (n = 300) in individual interviews with family carers from North London and Sussex. The best 30 questions formed the C-DEMQOL questionnaire, which was evaluated for usability, face and construct validity, reliability and convergent/ discriminant validity using a range of validation measures. Results C-DEMQOL was received positively by the carers. Factor analysis confirmed that C-DEMQOL sum scores are reliable in measuring overall QOL (ω = 0.97) and its five subdomains: 'meeting personal needs' (ω = 0.95); 'carer wellbeing' (ω = 0.91); 'carer-patient relationship' (ω = 0.82); 'confidence in the future' (ω = 0.90) and 'feeling supported' (ω = 0.85). The overall QOL and domain scores show the expected pattern of convergent and discriminant relationships with established measures of carer mental health, activities and dementia severity and symptoms. Conclusions The robust psychometric properties support the use of C-DEMQOL in evaluation of overall and domain-specific carer QOL; replications in independent samples and studies of responsiveness would be of value.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a5e1cd42f5ba0ba954795d728f4d813" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84453772,"asset_id":76923754,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84453772/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923754"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923754"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923754; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76923752"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76923752/The_Narcissism_Epidemic_Is_Dead_Long_Live_the_Narcissism_Epidemic"><img alt="Research paper thumbnail of The Narcissism Epidemic Is Dead; Long Live the Narcissism Epidemic" class="work-thumbnail" src="https://attachments.academia-assets.com/84471216/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76923752/The_Narcissism_Epidemic_Is_Dead_Long_Live_the_Narcissism_Epidemic">The Narcissism Epidemic Is Dead; Long Live the Narcissism Epidemic</a></div><div class="wp-workCard_item"><span>Psychological science</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Are recent cohorts of college students more narcissistic than their predecessors? To address deba...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Are recent cohorts of college students more narcissistic than their predecessors? To address debates about the so-called &quot;narcissism epidemic,&quot; we used data from three cohorts of students (1990s: N = 1,166; 2000s: N = 33,647; 2010s: N = 25,412) to test whether narcissism levels (overall and specific facets) have increased across generations. We also tested whether our measure, the Narcissistic Personality Inventory (NPI), showed measurement equivalence across the three cohorts, a critical analysis that had been overlooked in prior research. We found that several NPI items were not equivalent across cohorts. Models accounting for nonequivalence of these items indicated a small decline in overall narcissism levels from the 1990s to the 2010s ( d = -0.27). At the facet level, leadership ( d = -0.20), vanity ( d = -0.16), and entitlement ( d = -0.28) all showed decreases. Our results contradict the claim that recent cohorts of college students are more narcissistic than earlie...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15a091c597af8da6223490f2087ad2d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471216,"asset_id":76923752,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471216/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76923752"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76923752"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76923752; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="7725960" id="bookchapters"><div class="js-work-strip profile--work_container" data-work-id="35232426"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/35232426/Response_biases"><img alt="Research paper thumbnail of Response biases" class="work-thumbnail" src="https://attachments.academia-assets.com/55091298/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/35232426/Response_biases">Response biases</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Response biases comprise a variety of systematic tendencies of responding to questionnaire items....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Response biases comprise a variety of systematic tendencies of responding to questionnaire items. Response biases exert an influence on item responses in addition to any constructs that the questionnaire is designed to measure and can therefore potentially bias the corresponding trait level estimates. In this chapter we address general response biases that are independent of item content including response styles (e.g., extreme response style, acquiescence) and rater biases (halo effect, leniency/severity bias) as well as response biases that are related to item content and depend strongly on the context (socially desirable responding). We describe methods of assessing these response biases including approaches based on observed variables and approaches based on latent variable modeling (confirmatory factor analysis and item response theory). We summarize research on correlates of response biases and research on inter-individual and cross-cultural differences in engaging in response styles and rater biases. We describe different methods that can be applied at the test construction stage to prevent or minimize the occurrence of response biases. These are related to the testing situation (e.g., low stakes versus high stakes assessments) and instrument characteristics (e.g., rating scales versus forced-choice). Finally, we depict methods developed for correcting for the effects of response biases when they have been detected in the data. The effects of response biases are a long-standing problem in psychological assessment. Despite a need for further methodological and substantive research on response biases, effective methods for pre-assessment control and post-hoc correction exist and can be used to achieve better assessments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39f4d48a9e32030e787f2139c5e5a5f6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55091298,"asset_id":35232426,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55091298/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="35232426"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="35232426"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35232426; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="11520524"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/11520524/Scoring_and_estimating_score_precision_using_multidimensional_IRT"><img alt="Research paper thumbnail of Scoring and estimating score precision using multidimensional IRT" class="work-thumbnail" src="https://attachments.academia-assets.com/37030135/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/11520524/Scoring_and_estimating_score_precision_using_multidimensional_IRT">Scoring and estimating score precision using multidimensional IRT</a></div><div class="wp-workCard_item"><span>In Reise, S. P. & Revicki, D. A. (Eds.). Handbook of Item Response Theory Modeling: Applications to Typical Performance Assessment </span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The ultimate goal of measurement is to produce a score by which individuals can be assessed and d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The ultimate goal of measurement is to produce a score by which individuals can be assessed and differentiated. Item response theory (IRT) modeling views responses to test items as indicators of a respondent’s standing on some underlying psychological attributes (van der Linden & Hambleton, 1997) – we often call them latent traits – and devises special algorithms for estimating this standing. This chapter gives an overview of methods for estimating person attribute scores using one-dimensional and multi-dimensional IRT models, focusing on those that are particularly useful with patient-reported outcome (PRO) measures. <br />To be useful in applications, a test score has to approximate the latent trait well, and importantly, the precision level must be known in order to produce information for decision-making purposes. Unlike classical test theory (CTT), which assumes the precision with which a test measures the same for all trait levels, IRT methods assess the precision with which a test measures at different trait levels. In the context of patient-reported outcomes measurement, this enables assessment of the measurement precision for an individual patient. Knowing error bands around the patient’s score is important for informing clinical judgments, such as deciding upon significance of any change, for instance in response to treatment etc. (Reise & Haviland, 2005). At the same time, summary indices are often needed to summarize the overall precision of measurement in a research sample, population group, or in the population as a whole. Much of this chapter is devoted to methods for estimating measurement precision, including the score-dependent standard error of measurement and appropriate sample-level or population-level marginal reliability coefficients. <br />Patient-reported outcome measures often capture several related constructs, the feature that may make the use of multi-dimensional IRT models appropriate and beneficial (Gibbons, Immekus & Bock, 2007). Several such models are described, including a model with multiple correlated constructs, a model where multiple constructs are underlain by a general common factor (second-order model), and a model where each item is influenced by one general and one group factor (bifactor model). To make the use of these models more easily accessible for applied researchers, we provide specialized formulae for computing test information, standard errors and reliability. We show how to translate a multitude of numbers and graphs conditioned on several dimensions into easy-to-use indices that can be understood by applied researchers and test users alike. All described methods and techniques are illustrated with a single data analysis example involving a popular PRO measure, the 28-item version of the General Health Questionnaire (GHQ28; Goldberg & Williams, 1988), completed in mid-life by a large community sample as a part of a major UK cohort study.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e0e595b251e26fbe2be37fc1c2e45f1d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37030135,"asset_id":11520524,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37030135/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="11520524"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="11520524"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11520524; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11520524]").text(description); $(".js-view-count[data-work-id=11520524]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11520524; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='11520524']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e0e595b251e26fbe2be37fc1c2e45f1d" } } $('.js-work-strip[data-work-id=11520524]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":11520524,"title":"Scoring and estimating score precision using multidimensional IRT","translated_title":"","metadata":{"abstract":"The ultimate goal of measurement is to produce a score by which individuals can be assessed and differentiated. Item response theory (IRT) modeling views responses to test items as indicators of a respondent’s standing on some underlying psychological attributes (van der Linden \u0026 Hambleton, 1997) – we often call them latent traits – and devises special algorithms for estimating this standing. This chapter gives an overview of methods for estimating person attribute scores using one-dimensional and multi-dimensional IRT models, focusing on those that are particularly useful with patient-reported outcome (PRO) measures. \r\nTo be useful in applications, a test score has to approximate the latent trait well, and importantly, the precision level must be known in order to produce information for decision-making purposes. Unlike classical test theory (CTT), which assumes the precision with which a test measures the same for all trait levels, IRT methods assess the precision with which a test measures at different trait levels. 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All described methods and techniques are illustrated with a single data analysis example involving a popular PRO measure, the 28-item version of the General Health Questionnaire (GHQ28; Goldberg \u0026 Williams, 1988), completed in mid-life by a large community sample as a part of a major UK cohort study. \r\n","internal_url":"https://www.academia.edu/11520524/Scoring_and_estimating_score_precision_using_multidimensional_IRT","translated_internal_url":"","created_at":"2015-03-19T01:43:43.850-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":519455,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":293475,"work_id":11520524,"tagging_user_id":519455,"tagged_user_id":472888,"co_author_invite_id":null,"email":"t***9@cam.ac.uk","affiliation":"University of Cambridge","display_order":null,"name":"Tim Croudace","title":"Scoring and estimating score precision using multidimensional IRT"}],"downloadable_attachments":[{"id":37030135,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/37030135/thumbnails/1.jpg","file_name":"Scoring_and_reliability_chapter_R1_for_sharing.pdf","download_url":"https://www.academia.edu/attachments/37030135/download_file","bulk_download_file_name":"Scoring_and_estimating_score_precision_u.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/37030135/Scoring_and_reliability_chapter_R1_for_sharing-libre.pdf?1426754967=\u0026response-content-disposition=attachment%3B+filename%3DScoring_and_estimating_score_precision_u.pdf\u0026Expires=1738738790\u0026Signature=YGeK5WDydRFXz~EF1CFATyC~f3hWRrwBR~JDjQwNeRdb15FQmNwjqI9nCbxQq1~1Or1J6H556BzsF290AFFY2MgkBge9LPi1SqobwCYgljBneKu7RsTim~WPGbdFdmYvK-ffCIuhUZ3ADD3mV9z0kvNAHMloPdAAdoSRk8ArEnJ7GQ76-0YjINq9OIc~kOyXUvhXseWLB9xgl~oXRiSSd7yH3b5fLjfJKhAosN1lRAE1VZeOozSz1sF4dt5LOYei-BbA7Hz5JRfbLZh0kbziI~NvlgqTwRfoM6nYSe9xnlRV2fErncGmBcYGdRTkVMIprxYdVEVYv9J31IA6S-rRQA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Scoring_and_estimating_score_precision_using_multidimensional_IRT","translated_slug":"","page_count":41,"language":"en","content_type":"Work","summary":"The ultimate goal of measurement is to produce a score by which individuals can be assessed and differentiated. Item response theory (IRT) modeling views responses to test items as indicators of a respondent’s standing on some underlying psychological attributes (van der Linden \u0026 Hambleton, 1997) – we often call them latent traits – and devises special algorithms for estimating this standing. This chapter gives an overview of methods for estimating person attribute scores using one-dimensional and multi-dimensional IRT models, focusing on those that are particularly useful with patient-reported outcome (PRO) measures. \r\nTo be useful in applications, a test score has to approximate the latent trait well, and importantly, the precision level must be known in order to produce information for decision-making purposes. Unlike classical test theory (CTT), which assumes the precision with which a test measures the same for all trait levels, IRT methods assess the precision with which a test measures at different trait levels. In the context of patient-reported outcomes measurement, this enables assessment of the measurement precision for an individual patient. Knowing error bands around the patient’s score is important for informing clinical judgments, such as deciding upon significance of any change, for instance in response to treatment etc. (Reise \u0026 Haviland, 2005). At the same time, summary indices are often needed to summarize the overall precision of measurement in a research sample, population group, or in the population as a whole. Much of this chapter is devoted to methods for estimating measurement precision, including the score-dependent standard error of measurement and appropriate sample-level or population-level marginal reliability coefficients.\r\nPatient-reported outcome measures often capture several related constructs, the feature that may make the use of multi-dimensional IRT models appropriate and beneficial (Gibbons, Immekus \u0026 Bock, 2007). Several such models are described, including a model with multiple correlated constructs, a model where multiple constructs are underlain by a general common factor (second-order model), and a model where each item is influenced by one general and one group factor (bifactor model). To make the use of these models more easily accessible for applied researchers, we provide specialized formulae for computing test information, standard errors and reliability. We show how to translate a multitude of numbers and graphs conditioned on several dimensions into easy-to-use indices that can be understood by applied researchers and test users alike. 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D. (Ed.), International Encyclopedia of the Social and Behavioural Sciences, 2nd Edition</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Instead of responding to questionnaire items one at a time, respondents may be forced to make a c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Instead of responding to questionnaire items one at a time, respondents may be forced to make a choice between two or more items measuring the same or different traits. The forced-choice format eliminates uniform response biases, although the research on its effectiveness in reducing the effects of impression management is inconclusive. Until recently, forced-choice questionnaires were scaled in relation to person means (ipsative data), providing information for intra-individual assessments only. Item response modeling enabled proper scaling of forced-choice data, so that inter-individual comparisons may be made. New forced-choice applications in personality assessment and directions for future research are discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3556a262010333f21c10887231ef7011" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37030183,"asset_id":11520560,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37030183/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="11520560"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="11520560"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11520560; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11520560]").text(description); $(".js-view-count[data-work-id=11520560]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11520560; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='11520560']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="11520592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/11520592/Modeling_forced_choice_response_formats"><img alt="Research paper thumbnail of Modeling forced-choice response formats" class="work-thumbnail" src="https://attachments.academia-assets.com/37030206/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/11520592/Modeling_forced_choice_response_formats">Modeling forced-choice response formats</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://kent.academia.edu/AnnaBrown">Anna Brown</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://sc.academia.edu/AlbertoMaydeuOlivares">Alberto Maydeu-Olivares</a></span></div><div class="wp-workCard_item"><span>In Irwing, P., Booth, T. & Hughes, D. (Eds.), The Wiley Handbook of Psychometric Testing.</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To counter response distortions associated with the use of rating scales in personality and simil...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To counter response distortions associated with the use of rating scales in personality and similar assessments, test items may be presented in so-called ‘forced-choice’ formats. Respondents may be asked to rank-order a number of items, or distribute a fixed number of points between several items – therefore they are forced to make a choice. Until recently, basic classical scoring methods were applied to such formats, leading to scores relative to the person’s mean (ipsative scores). While interpretable in intra-individual assessments, ipsative scores are problematic when used for inter-individual comparisons. Recent advances in estimation methods enabled rapid development of item response models for comparative data, including the Thurstonian IRT model (Brown & Maydeu-Olivares, 2011a), the Multi-Unidimensional Pairwise Preference model (Stark, Chernyshenko & Drasgow, 2005), and others. Appropriate item response modeling enables estimation of person scores that are directly interpretable for inter-individual comparisons, without the distortions and artifacts produced by ipsative scoring.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2350d766cd35be46bf9482949cadda67" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37030206,"asset_id":11520592,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37030206/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="11520592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="11520592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11520592; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="11520700"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/11520700/Item_Response_Theory_approaches_to_test_scoring_and_evaluating_the_score_accuracy"><img alt="Research paper thumbnail of Item Response Theory approaches to test scoring and evaluating the score accuracy" class="work-thumbnail" src="https://attachments.academia-assets.com/37030291/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/11520700/Item_Response_Theory_approaches_to_test_scoring_and_evaluating_the_score_accuracy">Item Response Theory approaches to test scoring and evaluating the score accuracy</a></div><div class="wp-workCard_item"><span>In Irwing, P., Booth, T. & Hughes, D. (Eds.), The Wiley Handbook of Psychometric Testing.</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The ultimate goal of psychometric testing is to produce a score by which people can be differenti...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The ultimate goal of psychometric testing is to produce a score by which people can be differentiated. Item Response Theory (IRT) devises methods for estimating person’s score on one or more psychological constructs (traits) from his/her responses to test items. This chapter gives an overview of scoring methods applicable to situations when the test items indicate one trait only; or a set of related traits but each item contributes to measurement of one trait; or when each item indicates multiple traits. We consider scoring methods based on item responses only, as well as Bayesian methods, which use prior knowledge of the trait distribution. Much of this chapter is devoted to methods for assessing measurement precision provided by individual items, the whole test, and the prior distribution. In IRT, this precision can be evaluated for each individual response pattern. All described methods are illustrated with a single empirical example.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="80bd49697d24cd8339b054e27b0c85e1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37030291,"asset_id":11520700,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37030291/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="11520700"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="11520700"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11520700; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="7725963" id="researchreports"><div class="js-work-strip profile--work_container" data-work-id="1751792"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1751792/How_Item_Response_Theory_can_solve_problems_of_ipsative_data"><img alt="Research paper thumbnail of How Item Response Theory can solve problems of ipsative data" class="work-thumbnail" src="https://attachments.academia-assets.com/21442674/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1751792/How_Item_Response_Theory_can_solve_problems_of_ipsative_data">How Item Response Theory can solve problems of ipsative data</a></div><div class="wp-workCard_item"><span>Doctoral dissertation at University of Barcelona</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Multidimensional forced-choice questionnaires can reduce the impact of numerous response biases t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Multidimensional forced-choice questionnaires can reduce the impact of numerous response biases typically associated with Likert scales. However, if scored with traditional methodology these instruments produce ipsative data, which has psychometric problems, such as constrained total test score and negative average scale inter-correlation. Ipsative scores distort scale relationships and reliability estimates, and make interpretation of scores problematic. This research demonstrates how Item Response Theory (IRT) modeling may be applied to overcome these problems. A multidimensional IRT model for forced-choice questionnaires is introduced, which is suitable for use with any forced-choice instrument composed of items fitting the dominance response model, with any number of measured traits, and any block sizes (i.e. pairs, triplets, quads etc.). The proposed model is based on Thurstone's framework for comparative data. Thurstonian IRT models are normal ogive models with structured factor loadings, structured uniquenesses, and structured local dependencies. These models can be straightforwardly estimated using structural equation modeling (SEM) software Mplus. Simulation studies show how the latent traits are recovered from the comparative binary data under different conditions. The Thurstonian IRT model is also tested with real participants in both research and occupational assessment settings. It is concluded that when the recommended design guidelines are met, scores estimated from forced-choice questionnaires with the proposed methodology reproduce the latent traits well.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="54073952c9b8d77501ede30fc9845128" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":21442674,"asset_id":1751792,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/21442674/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1751792"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1751792"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1751792; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5220222"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/5220222/Applied_Psychometrics_course_materials"><img alt="Research paper thumbnail of Applied Psychometrics - course materials" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/5220222/Applied_Psychometrics_course_materials">Applied Psychometrics - course materials</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Here I present materials developed under the ESRC Researcher Development Initiative grant “Advanc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Here I present materials developed under the ESRC Researcher Development Initiative grant “Advancing Quantitative Methods in Psychological Assessment” . The research outputs from the grant were series of training workshops and summer schools in Applied Psychometrics, developed and held at the University of Cambridge between June 2010 and April 2012.<br /><br />From the provided URL, you can access all of the materials from five short courses and two summer schools. These training courses take learners through the theory and applications of established and state-of-the-art psychometric methods in the social sciences. Each course comprises a combination of lectures, research examples, software demonstrations and practical computing sessions, using commercial and open source software packages (Mplus, R, and other freeware).<br /><br />The tutorials can be used for distant learning. Guidance provided includes an introduction to each tutorial that covers learning objectives and the minimum entry level of knowledge for learners.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5220222"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5220222"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5220222; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19397459"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/19397459/MPLUS_SYNTAX_BUILDER"><img alt="Research paper thumbnail of MPLUS SYNTAX BUILDER" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/19397459/MPLUS_SYNTAX_BUILDER">MPLUS SYNTAX BUILDER</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Educational and Psychological Measurement, 71, 460-502. doi: 10.1177/0013164410375112 Brown, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Educational and Psychological Measurement, 71, 460-502. doi: 10.1177/0013164410375112 Brown, A. &amp;amp; Maydeu-Olivares, A. (2012). Fitting a Thurstonian IRT model to forced-choice datausing Mplus. Behavior Research Methods. Muthén, LK &amp;amp; Muthén, BO (1998-2010). ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19397459"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19397459"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19397459; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19397459]").text(description); $(".js-view-count[data-work-id=19397459]").attr('title', description).tooltip(); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90830106]").text(description); $(".js-view-count[data-work-id=90830106]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 90830106; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='90830106']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="69926059"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/69926059/Response_distortions_in_self_reported_and_other_reported_measures_Is_there_light_at_the_end_of_the_tunnel"><img alt="Research paper thumbnail of Response distortions in self-reported and other-reported measures: Is there light at the end of the tunnel?" class="work-thumbnail" src="https://attachments.academia-assets.com/79836673/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/69926059/Response_distortions_in_self_reported_and_other_reported_measures_Is_there_light_at_the_end_of_the_tunnel">Response distortions in self-reported and other-reported measures: Is there light at the end of the tunnel?</a></div><div class="wp-workCard_item"><span>10th conference of International Test Commission, Vancouver, 2016</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Asking people to assess themselves or others on a set of psychological characteristics is by far ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Asking people to assess themselves or others on a set of psychological characteristics is by far the most popular method of gathering data in our field. We use this method either because it is the cheapest, or the best there currently exists for measuring the target characteristic. However, respondent-reported data are commonly affected by conscious and unconscious response distortions. Examples include individual styles in using rating options, inattention or cognitive difficulties in responding to reversed items, tendency to present self in positive light, halo effects, distortions driven by political pressures etc. The extent to which respondents engage in such behaviors varies, and if not controlled, the biases alter the true ordering of respondents on traits of interest. Response distortions, therefore, should concern everyone who uses respondent-reported measures. This talk provides an overview of research on biasing factors evoked by responding to questionnaire items with dif...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="459511b995d7a97b0c5736e694be6841" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79836673,"asset_id":69926059,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79836673/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="69926059"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69926059"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69926059; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19397451"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/19397451/Online_personality_and_motivation_testing_Is_unsupervised_administration_an_issue"><img alt="Research paper thumbnail of Online personality and motivation testing: Is unsupervised administration an issue" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/19397451/Online_personality_and_motivation_testing_Is_unsupervised_administration_an_issue">Online personality and motivation testing: Is unsupervised administration an issue</a></div><div class="wp-workCard_item"><span>20th annual SIOP …</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19397451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19397451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19397451; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19397451]").text(description); $(".js-view-count[data-work-id=19397451]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19397451; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19397451']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=19397451]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19397451,"title":"Online personality and motivation testing: Is unsupervised administration an issue","internal_url":"https://www.academia.edu/19397451/Online_personality_and_motivation_testing_Is_unsupervised_administration_an_issue","owner_id":519455,"coauthors_can_edit":true,"owner":{"id":519455,"first_name":"Anna","middle_initials":"","last_name":"Brown","page_name":"AnnaBrown","domain_name":"kent","created_at":"2011-06-28T22:12:30.582-07:00","display_name":"Anna Brown","url":"https://kent.academia.edu/AnnaBrown"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19397455"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19397455/Doing_less_but_getting_more_Improving_forced_choice_measures_with_IRT"><img alt="Research paper thumbnail of Doing less but getting more: Improving forced-choice measures with IRT" class="work-thumbnail" src="https://attachments.academia-assets.com/84532613/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19397455/Doing_less_but_getting_more_Improving_forced_choice_measures_with_IRT">Doing less but getting more: Improving forced-choice measures with IRT</a></div><div class="wp-workCard_item"><span>Society for Industrial and Organizational Psychology conference, 2-4 April 2009, New Orleans</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Using IRT we show how more efficient use can be made of information in forced-choice questionnair...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Using IRT we show how more efficient use can be made of information in forced-choice questionnaires. The approach described reduces the length of the instrument, and provides information on people"s absolute trait standing and the scales" relationships. Both of these are impossible to obtain from CTT-scored forced choice questionnaires. Multidimensional forced-choice (MFC) questionnaires typically show good validities and are resistant to impression management effects. However, they yield ipsative data, which distorts scale relationships and makes comparisons between people problematic. Depressed reliability estimates also led developers to create tests of potentially excessive length. We apply an IRT Preference Model to make more efficient use of information in existing MFC questionnaires. OPQ32i used for selection and assessment internationally is examined using this approach. The latent scores recovered from a much reduced number of MFC items are superior to the full test"s ipsative scores, and comparable to unbiased normative scores.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5190190f5e555221808df83329cde436" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84532613,"asset_id":19397455,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84532613/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19397455"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19397455"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19397455; 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(FREE) Textbook and data resource." class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/119871324/Psychometrics_in_exercises_using_R_and_RStudio_FREE_Textbook_and_data_resource">Psychometrics in exercises using R and RStudio. (FREE) Textbook and data resource.</a></div><div class="wp-workCard_item"><span>annabrown.name</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This FREE book was born from computing workshop exercises that I created for my students over the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This FREE book was born from computing workshop exercises that I created for my students over the past 13 years to practice psychometric techniques that they learnt in lectures. When preparing exercises for them, it quickly became apparent that while there are many good textbooks about psychometric theory (my absolute favourite is “Test Theory: A Unified Treatment” by the late Roderick McDonald), there aren’t any comprehensive sources of practical exercises that students can use to internalise and practice these techniques. Various tutorials have good illustrations with data, but they do not provide a systematic guide that an instructor can use to teach students, and they do not provide self-test questions that students can answer to test own understanding.<br /><br />This book is intended to fill this gap.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="119871324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119871324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119871324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119871324]").text(description); $(".js-view-count[data-work-id=119871324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119871324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119871324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=119871324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119871324,"title":"Psychometrics in exercises using R and RStudio. 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