CINXE.COM
(PDF) A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency
<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="CAQqPMAPm3YidWZJ1ut34D_DkVUEOwbZsdwq9dAI32AwciVHHsiV88JIXWqUp6LbW5alZ2P_T7KTHaCSk7yHqw" /> <meta name="citation_title" content="A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency" /> <meta name="citation_publication_date" content="2010/01/01" /> <meta name="citation_journal_title" content="Revista Española de …" /> <meta name="citation_author" content="Rafael Muñoz de Bustillo Llorente" /> <meta name="citation_author" content="Kea Tijdens" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency" /> <meta name="twitter:title" content="A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency" /> <meta name="twitter:description" content="Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting" /> <meta name="twitter:image" content="https://0.academia-photos.com/34784877/15524205/16124750/s200_kea.tijdens.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency" /> <meta property="og:title" content="A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting" /> <meta property="article:author" content="https://uva.academia.edu/KeaTijdens" /> <meta property="article:author" content="https://usal.academia.edu/RafaelMu%C3%B1ozdeBustilloLlorente" /> <meta name="description" content="Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting" /> <title>(PDF) A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency</title> <link rel="canonical" href="https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '9744e839ffe2d813ef8b7eb988ae0a3341a6052d'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1743321144000); window.Aedu.timeDifference = new Date().getTime() - 1743321144000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting methodologies are ...","author":[{"@context":"https://schema.org","@type":"Person","name":"Kea Tijdens","url":"https://uva.academia.edu/KeaTijdens","image":"https://0.academia-photos.com/34784877/15524205/16124750/s200_kea.tijdens.jpg","sameAs":[]},{"@context":"https://schema.org","@type":"Person","name":"Rafael Muñoz de Bustillo Llorente","url":"https://usal.academia.edu/RafaelMu%C3%B1ozdeBustilloLlorente","sameAs":[]}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Rafael Muñoz de Bustillo Llorente","url":"https://usal.academia.edu/RafaelMu%C3%B1ozdeBustilloLlorente","sameAs":[]}],"dateCreated":"2015-11-11","dateModified":"2017-02-01","datePublished":"2010-01-01","headline":"A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency","image":"https://attachments.academia-assets.com/39892765/thumbnails/1.jpg","inLanguage":"en","keywords":["Reis"],"publication":"Revista Española de …","publisher":{"@context":"https://schema.org","@type":"Organization","name":"REIS"},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"uva"},{"@context":"https://schema.org","@type":"EducationalOrganization","name":"usal"}],"thumbnailUrl":"https://attachments.academia-assets.com/39892765/thumbnails/1.jpg","url":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 16px;--buttons-small-buttons-l-r-padding: 20px;--buttons-small-buttons-height: 48px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 48px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 32px;--buttons-large-buttons-height: 64px;--buttons-large-buttons-icon-only-width: 64px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 16px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #f4f7fc;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: DM Sans;--type-font-family-serif: Gupter;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 32px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 30px;--type-sans-serif-lg-line-height: 36px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 30px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 24px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 24px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 18px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 32px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 20px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 32px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 40px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 24px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 26px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 48px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 52px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 48px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 58px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 80px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 64px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}@media(min-width: 568px)and (max-width: 1279px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 16px;--buttons-small-buttons-l-r-padding: 20px;--buttons-small-buttons-height: 48px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 48px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 32px;--buttons-large-buttons-height: 64px;--buttons-large-buttons-icon-only-width: 64px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 16px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #f4f7fc;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: DM Sans;--type-font-family-serif: Gupter;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 42px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 32px;--type-sans-serif-lg-line-height: 36px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 34px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 28px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 25px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 20px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 30px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 24px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 40px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 48px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 28px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 32px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 58px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 68px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 74px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 82px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 104px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 80px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}@media(min-width: 1280px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 16px;--buttons-small-buttons-l-r-padding: 20px;--buttons-small-buttons-height: 48px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 48px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 32px;--buttons-large-buttons-height: 64px;--buttons-large-buttons-icon-only-width: 64px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 16px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #f4f7fc;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: DM Sans;--type-font-family-serif: Gupter;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 42px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 32px;--type-sans-serif-lg-line-height: 38px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 34px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 28px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 25px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 20px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 30px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 24px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 40px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 48px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 28px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 32px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 58px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 68px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 74px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 82px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 152px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 80px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}</style><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp_redesigns/safe_sidebar-c753697a42fde4fbbc3ee76250154260ac15a7b94c6e5bf20d0b973a291d4bbb.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/heading-95367dc03b794f6737f30123738a886cf53b7a65cdef98a922a98591d60063e3.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-170d1319f0e354621e81ca17054bb147da2856ec0702fe440a99af314a6338c5.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-8c9ae4b5c8a2531640c354d92a1f3579c8ff103277ef74913e34c8a76d4e6c00.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/text_button-d1941ab08e91e29ee143084c4749da4aaffa350a2ac6eec2306b1d7a352d911a.css" /><link crossorigin="" href="https://fonts.gstatic.com/" rel="preconnect" /><link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Gupter:wght@400;500;700&family=IBM+Plex+Mono:wght@300;400&family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20,400,0,0&display=swap" rel="stylesheet" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/common-57f9da13cef3fd4e2a8b655342c6488eded3e557e823fe67571f2ac77acd7b6f.css" /> </head> <body> <div id='react-modal'></div> <div class="js-upgrade-ie-banner" style="display: none; text-align: center; padding: 8px 0; background-color: #ebe480;"><p style="color: #000; font-size: 12px; margin: 0 0 4px;">Academia.edu no longer supports Internet Explorer.</p><p style="color: #000; font-size: 12px; margin: 0;">To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to <a href="https://www.academia.edu/upgrade-browser">upgrade your browser</a>.</p></div><script>// Show this banner for all versions of IE if (!!window.MSInputMethodContext || /(MSIE)/.test(navigator.userAgent)) { document.querySelector('.js-upgrade-ie-banner').style.display = 'block'; }</script> <div class="bootstrap login"><div class="modal fade login-modal" id="login-modal"><div class="login-modal-dialog modal-dialog"><div class="modal-content"><div class="modal-header"><button class="close close" data-dismiss="modal" type="button"><span aria-hidden="true">×</span><span class="sr-only">Close</span></button><h4 class="modal-title text-center"><strong>Log In</strong></h4></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><button class="btn btn-fb btn-lg btn-block btn-v-center-content" id="login-facebook-oauth-button"><svg style="float: left; width: 19px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="facebook-square" class="svg-inline--fa fa-facebook-square fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h137.25V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.27c-30.81 0-40.42 19.12-40.42 38.73V256h68.78l-11 71.69h-57.78V480H400a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48z"></path></svg><small><strong>Log in</strong> with <strong>Facebook</strong></small></button><br /><button class="btn btn-google btn-lg btn-block btn-v-center-content" id="login-google-oauth-button"><svg style="float: left; width: 22px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="google-plus" class="svg-inline--fa fa-google-plus fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256,8C119.1,8,8,119.1,8,256S119.1,504,256,504,504,392.9,504,256,392.9,8,256,8ZM185.3,380a124,124,0,0,1,0-248c31.3,0,60.1,11,83,32.3l-33.6,32.6c-13.2-12.9-31.3-19.1-49.4-19.1-42.9,0-77.2,35.5-77.2,78.1S142.3,334,185.3,334c32.6,0,64.9-19.1,70.1-53.3H185.3V238.1H302.2a109.2,109.2,0,0,1,1.9,20.7c0,70.8-47.5,121.2-118.8,121.2ZM415.5,273.8v35.5H380V273.8H344.5V238.3H380V202.8h35.5v35.5h35.2v35.5Z"></path></svg><small><strong>Log in</strong> with <strong>Google</strong></small></button><br /><style type="text/css">.sign-in-with-apple-button { width: 100%; height: 52px; border-radius: 3px; border: 1px solid black; cursor: pointer; } .sign-in-with-apple-button > div { margin: 0 auto; / This centers the Apple-rendered button horizontally }</style><script src="https://appleid.cdn-apple.com/appleauth/static/jsapi/appleid/1/en_US/appleid.auth.js" type="text/javascript"></script><div class="sign-in-with-apple-button" data-border="false" data-color="white" id="appleid-signin"><span ="Sign Up with Apple" class="u-fs11"></span></div><script>AppleID.auth.init({ clientId: 'edu.academia.applesignon', scope: 'name email', redirectURI: 'https://www.academia.edu/sessions', state: "d6d8646a24304339ac2724cb8d46ccf430b219e07e4af98774e157925fb636b2", });</script><script>// Hacky way of checking if on fast loswp if (window.loswp == null) { (function() { const Google = window?.Aedu?.Auth?.OauthButton?.Login?.Google; const Facebook = window?.Aedu?.Auth?.OauthButton?.Login?.Facebook; if (Google) { new Google({ el: '#login-google-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } if (Facebook) { new Facebook({ el: '#login-facebook-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } })(); }</script></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><div class="hr-heading login-hr-heading"><span class="hr-heading-text">or</span></div></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><form class="js-login-form" action="https://www.academia.edu/sessions" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="NjrQBtSz2RphxduX3QnXfaaxQm_n2E-22e8R2jSREH8OTN99CnTXn4H44LSfRQJGwuR2XYAcBt37Lpu9dyVItA" autocomplete="off" /><div class="form-group"><label class="control-label" for="login-modal-email-input" style="font-size: 14px;">Email</label><input class="form-control" id="login-modal-email-input" name="login" type="email" /></div><div class="form-group"><label class="control-label" for="login-modal-password-input" style="font-size: 14px;">Password</label><input class="form-control" id="login-modal-password-input" name="password" type="password" /></div><input type="hidden" name="post_login_redirect_url" id="post_login_redirect_url" value="https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency" autocomplete="off" /><div class="checkbox"><label><input type="checkbox" name="remember_me" id="remember_me" value="1" checked="checked" /><small style="font-size: 12px; margin-top: 2px; display: inline-block;">Remember me on this computer</small></label></div><br><input type="submit" name="commit" value="Log In" class="btn btn-primary btn-block btn-lg js-login-submit" data-disable-with="Log In" /></br></form><script>typeof window?.Aedu?.recaptchaManagedForm === 'function' && window.Aedu.recaptchaManagedForm( document.querySelector('.js-login-form'), document.querySelector('.js-login-submit') );</script><small style="font-size: 12px;"><br />or <a data-target="#login-modal-reset-password-container" data-toggle="collapse" href="javascript:void(0)">reset password</a></small><div class="collapse" id="login-modal-reset-password-container"><br /><div class="well margin-0x"><form class="js-password-reset-form" action="https://www.academia.edu/reset_password" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="QCYv1iP8hov3GkKEi7rTdHpwtCSflUPQkxpjJ-80SmJ4UCCt_TuIDhcneafJ9gZPHiWAFvhRCrux2-lArIASqQ" autocomplete="off" /><p>Enter the email address you signed up with and we'll email you a reset link.</p><div class="form-group"><input class="form-control" name="email" type="email" /></div><input class="btn btn-primary btn-block g-recaptcha js-password-reset-submit" data-sitekey="6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj" type="submit" value="Email me a link" /></form></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/collapse-45805421cf446ca5adf7aaa1935b08a3a8d1d9a6cc5d91a62a2a3a00b20b3e6a.js"], function() { // from javascript_helper.rb $("#login-modal-reset-password-container").on("shown.bs.collapse", function() { $(this).find("input[type=email]").focus(); }); }); </script> </div></div></div><div class="modal-footer"><div class="text-center"><small style="font-size: 12px;">Need an account? <a rel="nofollow" href="https://www.academia.edu/signup">Click here to sign up</a></small></div></div></div></div></div></div><script>// If we are on subdomain or non-bootstrapped page, redirect to login page instead of showing modal (function(){ if (typeof $ === 'undefined') return; var host = window.location.hostname; if ((host === $domain || host === "www."+$domain) && (typeof $().modal === 'function')) { $("#nav_log_in").click(function(e) { // Don't follow the link and open the modal e.preventDefault(); $("#login-modal").on('shown.bs.modal', function() { $(this).find("#login-modal-email-input").focus() }).modal('show'); }); } })()</script> <div id="fb-root"></div><script>window.fbAsyncInit = function() { FB.init({ appId: "2369844204", version: "v8.0", status: true, cookie: true, xfbml: true }); // Additional initialization code. if (window.InitFacebook) { // facebook.ts already loaded, set it up. window.InitFacebook(); } else { // Set a flag for facebook.ts to find when it loads. window.academiaAuthReadyFacebook = true; } };</script> <div id="google-root"></div><script>window.loadGoogle = function() { if (window.InitGoogle) { // google.ts already loaded, set it up. window.InitGoogle("331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"); } else { // Set a flag for google.ts to use when it loads. window.GoogleClientID = "331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"; } };</script> <div class="header--container" id="main-header-container"><div class="header--inner-container header--inner-container-ds2"><div class="header-ds2--left-wrapper"><div class="header-ds2--left-wrapper-inner"><a data-main-header-link-target="logo_home" href="https://www.academia.edu/"><img class="hide-on-desktop-redesign" style="height: 24px; width: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015-A.svg" width="24" height="24" /><img width="145.2" height="18" class="hide-on-mobile-redesign" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015.svg" /></a><div class="header--search-container header--search-container-ds2"><form class="js-SiteSearch-form select2-no-default-pills" action="https://www.academia.edu/search" accept-charset="UTF-8" method="get"><svg style="width: 14px; height: 14px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="search" class="header--search-icon svg-inline--fa fa-search fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M505 442.7L405.3 343c-4.5-4.5-10.6-7-17-7H372c27.6-35.3 44-79.7 44-128C416 93.1 322.9 0 208 0S0 93.1 0 208s93.1 208 208 208c48.3 0 92.7-16.4 128-44v16.3c0 6.4 2.5 12.5 7 17l99.7 99.7c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.6.1-34zM208 336c-70.7 0-128-57.2-128-128 0-70.7 57.2-128 128-128 70.7 0 128 57.2 128 128 0 70.7-57.2 128-128 128z"></path></svg><input class="header--search-input header--search-input-ds2 js-SiteSearch-form-input" data-main-header-click-target="search_input" name="q" placeholder="Search" type="text" /></form></div></div></div><nav class="header--nav-buttons header--nav-buttons-ds2 js-main-nav"><button class="ds2-5-button ds2-5-button--secondary js-header-login-url header-button-ds2 header-login-ds2 hide-on-mobile-redesign react-login-modal-opener" data-signup-modal="{"location":"login-button--header"}" rel="nofollow">Log In</button><button class="ds2-5-button ds2-5-button--secondary header-button-ds2 hide-on-mobile-redesign react-login-modal-opener" data-signup-modal="{"location":"signup-button--header"}" rel="nofollow">Sign Up</button><button class="header--hamburger-button header--hamburger-button-ds2 hide-on-desktop-redesign js-header-hamburger-button"><div class="icon-bar"></div><div class="icon-bar" style="margin-top: 4px;"></div><div class="icon-bar" style="margin-top: 4px;"></div></button></nav></div><ul class="header--dropdown-container js-header-dropdown"><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/login" rel="nofollow">Log In</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/signup" rel="nofollow">Sign Up</a></li><li class="header--dropdown-row js-header-dropdown-expand-button"><button class="header--dropdown-button">more<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="caret-down" class="header--dropdown-button-icon svg-inline--fa fa-caret-down fa-w-10" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 512"><path fill="currentColor" d="M31.3 192h257.3c17.8 0 26.7 21.5 14.1 34.1L174.1 354.8c-7.8 7.8-20.5 7.8-28.3 0L17.2 226.1C4.6 213.5 13.5 192 31.3 192z"></path></svg></button></li><li><ul class="header--expanded-dropdown-container"><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/about">About</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/press">Press</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/documents">Papers</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/terms">Terms</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/privacy">Privacy</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/copyright">Copyright</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://www.academia.edu/hiring"><svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="briefcase" class="header--dropdown-row-icon svg-inline--fa fa-briefcase fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg>We're Hiring!</a></li><li class="header--dropdown-row"><a class="header--dropdown-link" href="https://support.academia.edu/hc/en-us"><svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="question-circle" class="header--dropdown-row-icon svg-inline--fa fa-question-circle fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"></path></svg>Help Center</a></li><li class="header--dropdown-row js-header-dropdown-collapse-button"><button class="header--dropdown-button">less<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="caret-up" class="header--dropdown-button-icon svg-inline--fa fa-caret-up fa-w-10" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 512"><path fill="currentColor" d="M288.662 352H31.338c-17.818 0-26.741-21.543-14.142-34.142l128.662-128.662c7.81-7.81 20.474-7.81 28.284 0l128.662 128.662c12.6 12.599 3.676 34.142-14.142 34.142z"></path></svg></button></li></ul></li></ul></div> <script src="//a.academia-assets.com/assets/webpack_bundles/fast_loswp-bundle-9cea386162bbd6f1b6effa30e0b84b5628d426474957cb071b3dc763161b9250.js" defer="defer"></script><script>window.loswp = {}; window.loswp.author = 34784877; window.loswp.bulkDownloadFilterCounts = {}; window.loswp.hasDownloadableAttachment = true; window.loswp.hasViewableAttachments = true; // TODO: just use routes for this window.loswp.loginUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F18133279%2FA_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency%3Fauto%3Ddownload"; window.loswp.translateUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F18133279%2FA_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":39892765,"identifier":"Attachment_39892765","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":18133279,"created_at":"2015-11-11T00:37:21.735-08:00","from_world_paper_id":144203362,"updated_at":"2022-05-28T19:19:10.070-07:00","_data":{"abstract":"Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting methodologies are ...","publisher":"REIS","publication_date":"2010,,","publication_name":"Revista Española de …"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true,"seo_quality":null}}["work"]; window.loswp.workCoauthors = [32256258,34784877]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "safe_sidebar"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.showExperimentalOverhaulLoginForm = false window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="safe-loswp--grid-container"><div class="safe-work-card--container js-loswp-work-card"><div class="safe-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":39892765,"attachmentType":"pdf"}"><img alt="First page of “A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/39892765/mini_magick20190222-12668-1llcrsq.png?1550841414" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="safe-work-card--work-information"><h1 class="ds-work-card--work-title ds2-5-heading-sans-serif-lg">A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="32256258" href="https://usal.academia.edu/RafaelMu%C3%B1ozdeBustilloLlorente"><img alt="Profile image of Rafael Muñoz de Bustillo Llorente" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Rafael Muñoz de Bustillo Llorente</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="34784877" href="https://uva.academia.edu/KeaTijdens"><img alt="Profile image of Kea Tijdens" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/34784877/15524205/16124750/s65_kea.tijdens.jpg" />Kea Tijdens</a></div><p class="ds-work-card--detail ds2-5-body-sm">2010, Revista Española de …</p><div class="ds-work-card--button-container"><div class="primary-buttons"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":39892765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span>See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":39892765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency","location":"/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency","scheme":"https","host":"www.academia.edu","port":null,"pathname":"/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="CitationButton" data-props="{"entity_id":18133279,"citations":[{"name":"MLA","citation":"de Bustillo Llorente, Rafael Muñoz, and Kea Tijdens. “A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency.” Revista Española De …, REIS, 2010."},{"name":"APA","citation":"de Bustillo Llorente, R. M., \u0026 Tijdens, K. (2010). A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency. Revista Española De …."},{"name":"Chicago","citation":"Bustillo Llorente, Rafael Muñoz de, and Kea Tijdens. “A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency.” Revista Española De …, 2010."},{"name":"Vancouver","citation":"Bustillo Llorente RM de, Tijdens K. A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency. Revista Española de …. 2010; "},{"name":"Harvard","citation":"Bustillo Llorente, R. M. de and Tijdens, K. (2010) “A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency,” Revista Española de …. REIS."}],"source":"swp_citation_modal","buttonClassName":"ds-work-card--citation-button"}" data-trace="false" data-dom-id="CitationButton-react-component-c63c8401-4b00-402c-8723-9b4698b5bc0a"></div> <div id="CitationButton-react-component-c63c8401-4b00-402c-8723-9b4698b5bc0a"></div> </div><div class="share-and-save-buttons"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"bookmark-button--work-card","attachmentId":39892765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency"}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">bookmark</span><span class="ds2-5-text-link__content">Save to Library</span></button><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"share-button--work-card","attachmentId":39892765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency"}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">share</span><span class="ds2-5-text-link__content">Share</span></button></div></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="18133279" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. If the signup banner trigger (a 242px-high* invisible div underneath the 'See Full PDF' / 'Download PDF' buttons) // is already fully scrolled above the viewport, show the banner by default // 2. If the signup banner trigger is fully visible, show the banner // 3. If the signup banner trigger has even a few pixels scrolled below the viewport, hide the banner // // * 242px is the empirically determined height of the signup banner. It's better to be a bit taller than // necessary than too short, so it's fine that the mobile (small breakpoint) banner is shorter. // First check session storage for the signup banner's visibility state const signupBannerHidden = sessionStorage.getItem('ds-signup-banner-hidden'); if (signupBannerHidden === 'true') { return; } const signupBanner = document.querySelector('.ds-signup-banner'); const signupBannerTrigger = document.querySelector('.ds-signup-banner-trigger'); if (!signupBannerTrigger) { window.Sentry.captureMessage("Signup banner trigger not found"); return; } let footerShown = false; window.addEventListener('load', () => { const rect = signupBannerTrigger.getBoundingClientRect(); // If page loaded up already scrolled below the trigger (via scroll restoration), show the banner by default if (rect.bottom < 0) { footerShown = true; signupBanner.classList.add('ds-signup-banner-visible'); } }); // Wait for trigger to fully enter viewport before showing banner (ensures PDF CTAs are never covered by banner) const observer = new IntersectionObserver((entries) => { entries.forEach(entry => { if (entry.isIntersecting && !footerShown) { footerShown = true; signupBanner.classList.add('ds-signup-banner-visible'); } else if (!entry.isIntersecting && footerShown) { if (signupBannerTrigger.getBoundingClientRect().bottom > 0) { footerShown = false; signupBanner.classList.remove('ds-signup-banner-visible'); } } }); }); observer.observe(signupBannerTrigger); // Set up signup banner close button event handler: const signupBannerCloseButton = document.querySelector('#ds-signup-banner-close-button'); signupBannerCloseButton.addEventListener('click', () => { signupBanner.classList.remove('ds-signup-banner-visible'); observer.unobserve(signupBannerTrigger); // Store the signup banner's visibility state in session storage sessionStorage.setItem('ds-signup-banner-hidden', 'true'); }); })();</script></div></div><style>@media screen and (min-width: 568px) and (max-width: 621px) { .ds-work-card--button-container .primary-buttons .ds2-5-button, .ds-work-card--button-container .primary-buttons .ds-work-card--citation-button { font-size: var(--font-size-small); line-height: var(--line-height-small); padding: 0 var(--buttons-small-buttons-l-r-padding, 12px); height: var(--buttons-small-buttons-height, 44px); gap: var(--buttons-small-buttons-gap, 8px); border-radius: var(--buttons-small-buttons-corner-radius, 8px); }}</style><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="39892765" data-landing_url="https://www.academia.edu/18133279/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="safe-abstract--container"><h2 class="safe-abstract--heading ds2-5-heading-sans-serif-lg">Abstract</h2><div class="safe-abstract--body"><style type="text/css">.truncate-component { position: relative; overflow: hidden; display: -webkit-box; line-clamp: 16; -webkit-line-clamp: 16; -webkit-box-orient: vertical; } .toggle-truncation-button { display: none; position: absolute; bottom: 0; right: 0; }</style><div class="truncate-component js-truncate-component--abstract"><p class="ds2-5-body-md">Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «model-based» approach, two alternative data weighting methodologies are ...</p><div class="toggle-truncation-button js-toggle-truncation-button--abstract"><button class="safe-abstract--toggle-truncation-button"><span>... </span><span class="safe-abstract--toggle-truncation-button-text">Read more</span></button></div></div></div></div><div class="safe-right-rail--container"><div class="safe-right-rail--related-works"><h2 class="safe-related-content--heading">Related papers</h2><div class="safe-related-content--container"><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work " data-collection-position="0" data-entity-id="80167419" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/80167419/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency_Una_encuesta_voluntaria_y_continua_en_la_red_en_Espa%C3%B1a_sesgo_ponderaci%C3%B3n_y_eficiencia">A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency / Una encuesta voluntaria y continua en la red en España: sesgo, ponderación y eficiencia</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32256258" href="https://usal.academia.edu/RafaelMu%C3%B1ozdeBustilloLlorente">Rafael Muñoz de Bustillo Llorente</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «modelbased» approach, two alternative data weighting methodologies are implemented. Thirdly, in order to test whether weighting corrects the bias, thus making it possible to obtain conclusions applicable to the whole labour force, the efficiency of the weighting</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Spanish Continuous Volunteer Web Survey: Sample Bias, Weighting and Efficiency / Una encuesta voluntaria y continua en la red en España: sesgo, ponderación y eficiencia","attachmentId":86639628,"attachmentType":"pdf","work_url":"https://www.academia.edu/80167419/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency_Una_encuesta_voluntaria_y_continua_en_la_red_en_Espa%C3%B1a_sesgo_ponderaci%C3%B3n_y_eficiencia","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/80167419/A_Spanish_Continuous_Volunteer_Web_Survey_Sample_Bias_Weighting_and_Efficiency_Una_encuesta_voluntaria_y_continua_en_la_red_en_Espa%C3%B1a_sesgo_ponderaci%C3%B3n_y_eficiencia"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work " data-collection-position="1" data-entity-id="18133304" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/18133304/A_Spanish_Continuous_Voluntary_Web_Survey_Sample_Bias_Weights_and_Efficiency_of_Weights">A Spanish Continuous Voluntary Web Survey: Sample Bias, Weights and Efficiency of Weights</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34784877" href="https://uva.academia.edu/KeaTijdens">Kea Tijdens</a></div><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">Using micro data from a continuous volunteer web survey (CVWS), the WageIndicator (WI), this paper firstly analyses the type of bias that such a survey method produces. Secondly, following a «modelbased » approach, two alternative data weighting methodologies are implemented. Thirdly, in order to test whether weighting corrects the bias, thus making it possible to obtain conclusions applicable to the whole labour force, the efficiency of the weighting methodologies is evaluated. Since the WageIndicator is a labour market oriented survey, weighting efficiency is evaluated by calculating mean salaries, inequality indices and salary regressions before and after applying weights to WI data, and by comparing the results obtained with those achieved using the Structure of Earnings Survey (SES), a wage survey run by the Spanish National Statistics Institute. It is concluded that, after weighting, estimated statistics and parameters move in the right direction.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Spanish Continuous Voluntary Web Survey: Sample Bias, Weights and Efficiency of Weights","attachmentId":39892748,"attachmentType":"pdf","work_url":"https://www.academia.edu/18133304/A_Spanish_Continuous_Voluntary_Web_Survey_Sample_Bias_Weights_and_Efficiency_of_Weights","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/18133304/A_Spanish_Continuous_Voluntary_Web_Survey_Sample_Bias_Weights_and_Efficiency_of_Weights"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work " data-collection-position="2" data-entity-id="18133306" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/18133306/Sample_bias_weights_and_efficiency_of_weights_in_a_continuous_web_voluntary_survey">Sample bias, weights and efficiency of weights in a continuous web voluntary survey</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34784877" href="https://uva.academia.edu/KeaTijdens">Kea Tijdens</a></div><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">Using micro data from a continuous voluntary web survey, the Wage Indicator, the paper analyses the type of bias that such a sampling method produces and discusses a methodology to weight the data in order to correct such bias and make it possible to run analyses to obtain results and conclusions applicable to the whole population. In order to evaluate the efficiency of the weighting methodology to solve the potential sample bias of web surveys, the results are confronted with those obtained from an alternative standard labour survey dealing with the same issues. Since the Wage Indicator is a survey oriented to labour market issues, we considered that a labour market case study was most appropriate for the evaluation of the results. The method of evaluation followed is to calculate mean salaries, inequality indexes and salary regressions before and after implementing the weights using the Wage Indicator Survey data for Spain. The results are compared with those reached using the Str...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Sample bias, weights and efficiency of weights in a continuous web voluntary survey","attachmentId":39892751,"attachmentType":"pdf","work_url":"https://www.academia.edu/18133306/Sample_bias_weights_and_efficiency_of_weights_in_a_continuous_web_voluntary_survey","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/18133306/Sample_bias_weights_and_efficiency_of_weights_in_a_continuous_web_voluntary_survey"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work " data-collection-position="3" data-entity-id="18133280" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/18133280/Comparing_different_weighting_procedures_for_volunteer_web_surveys_lessons_to_be_learned_from_German_and_Dutch_WageIndicator_data">Comparing different weighting procedures for volunteer web surveys: lessons to be learned from German and Dutch WageIndicator data</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34784877" href="https://uva.academia.edu/KeaTijdens">Kea Tijdens</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009</p><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">The strengths and weaknesses of web surveys have been widely described in the literature. Of particular interest is the question to which degree the obtained results can be generalised for the whole population? To deal with this problem weighting adjustments, like post-...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparing different weighting procedures for volunteer web surveys: lessons to be learned from German and Dutch WageIndicator data","attachmentId":39892741,"attachmentType":"pdf","work_url":"https://www.academia.edu/18133280/Comparing_different_weighting_procedures_for_volunteer_web_surveys_lessons_to_be_learned_from_German_and_Dutch_WageIndicator_data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/18133280/Comparing_different_weighting_procedures_for_volunteer_web_surveys_lessons_to_be_learned_from_German_and_Dutch_WageIndicator_data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work " data-collection-position="4" data-entity-id="4393135" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4393135/WP_76_Comparing_different_weighting_procedures_for_volunteer_web_surveys">WP 76 - Comparing different weighting procedures for volunteer web surveys</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="273338" href="https://uva.academia.edu/StephanieSteinmetz">Stephanie Steinmetz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009</p><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">comments and suggestions for improvement which they have received from participants of the ESRA conference 2009,the 3 rd MESS Workshop, the AIAS Lunch seminar and the EUR-FSW Brown bag seminar.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"WP 76 - Comparing different weighting procedures for volunteer web surveys","attachmentId":31824151,"attachmentType":"pdf","work_url":"https://www.academia.edu/4393135/WP_76_Comparing_different_weighting_procedures_for_volunteer_web_surveys","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/4393135/WP_76_Comparing_different_weighting_procedures_for_volunteer_web_surveys"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work hidden" data-collection-position="5" data-entity-id="43778472" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43778472/Data_report_online_surveys_Wave_1">Data report: online surveys Wave 1</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="18329" href="https://uva.academia.edu/EvelynErsanilli">Evelyn Ersanilli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">The MOBILISE project examines why some people respond to discontent by protesting, others by migrating while yet others stay immobile. It focuses on four countries that have seen outmigration and protest in recent year (Ukraine, Poland, Morocco and Argentina) and migrants from these countries who live in Germany, the United Kingdom and Spain. Migrants were surveyed online and recruited into the sample through Facebook advertising. MOBILISE also conducted online surveys of the national populations of Argentina and Ukraine. This report explains why MOBILISE choose to recruit the sample through Facebook advertisements and provides detailed information on the set-up of the sampling. It also present an overview of the effectiveness of this method, in terms of costs, reach and bias, and of issues encountered. We find that sampling through Facebook advertisements is a cost-effective way to obtaining a large sample. The method seems particularly effective in reaching recent migrants and reaching migrants from small communities. There is some indication of a bias in gender, education and political interest. The paper ends with recommendations on the use of this approach for future surveys.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Data report: online surveys Wave 1","attachmentId":64092839,"attachmentType":"pdf","work_url":"https://www.academia.edu/43778472/Data_report_online_surveys_Wave_1","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/43778472/Data_report_online_surveys_Wave_1"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work hidden" data-collection-position="6" data-entity-id="108532059" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108532059/Comment_Struggles_with_Survey_Weighting_and_Regression_Modeling">Comment: Struggles with Survey Weighting and Regression Modeling</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="224832815" href="https://independent.academia.edu/DannyPfeffermann">Danny Pfeffermann</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Statistical Science, 2007</p><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">Andrew Gelman's article "Struggles with survey weighting and regression modeling" addresses the question of what approach analysts should use to produce estimates (and associated estimates of variability) based on sample survey data. Gelman starts by asserting that survey weighting is a "mess." While we agree that incorporation of the survey design for regression remains challenging, with important open questions, many recent contributions to the literature have greatly clarified the situation. Examples include relatively recent contributions by Pfeffermann and Sverchkov (1999), Graubard and Korn (2002) and Little (2004). Gelman's paper is a very welcome addition to that literature. There are some understandable reasons for the current lack of resolution. First, U.S. federal statistical agencies have been historically limited by their mission statements to producing statistical summaries, primarily means, percentages, ratios and cross-classified tables of counts. This is one explanation for why Cochran (1977) and Kish (1965) devote the great majority of their classical texts to these estimates. As a result, the job of using regression and other more complex models to learn about any causal structure underlying these summary statistics was generally left to sister policy agencies and outside users. However, things are changing. The federal statistical system (whether it likes it or not) is becoming more involved with complex modeling. This includes small-area estimation (e.g., unemployment estimates and census net undercoverage estimates) and research into models combining information from surveys with administrative data. (There will also likely be increased demands to use data mining procedures on federal statistical data.) This relatively new development has</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comment: Struggles with Survey Weighting and Regression Modeling","attachmentId":106888508,"attachmentType":"pdf","work_url":"https://www.academia.edu/108532059/Comment_Struggles_with_Survey_Weighting_and_Regression_Modeling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/108532059/Comment_Struggles_with_Survey_Weighting_and_Regression_Modeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work hidden" data-collection-position="7" data-entity-id="56347540" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56347540/Clarifying_Some_Issues_in_the_Regression_Analysis_of_Survey_Data">Clarifying Some Issues in the Regression Analysis of Survey Data</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="27299089" href="https://independent.academia.edu/PhillipKott">Phillip Kott</a></div><p class="ds2-5-body-sm ds-related-work--abstract hide-on-large">The literature offers two distinct reasons for incorporating sample weights into the estimation of linear regression coefficients from a model-based point of view. Either the sample selection is nonignorable or the model is incomplete. The traditional sample-weighted least-squares estimator can be improved upon when the sample selection is nonignorable, but not when the standard linear model fails and needs to be extended. Conceptually, it can be helpful to view the realized sample as the result of a two-phase process. In the first phase, the finite population is drawn from a hypothetical superpopulation via simple random (cluster) sampling. In the second phase, the actual sample is drawn from the finite population. In the extended model, the parameters of this superpopulation are vague. Mean-squared-error estimation can become problematic when the primary sampling units are drawn within strata using unequal probability sampling without replacement. This remains true even under the ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Clarifying Some Issues in the Regression Analysis of Survey Data","attachmentId":71777498,"attachmentType":"pdf","work_url":"https://www.academia.edu/56347540/Clarifying_Some_Issues_in_the_Regression_Analysis_of_Survey_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/56347540/Clarifying_Some_Issues_in_the_Regression_Analysis_of_Survey_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work hidden" data-collection-position="8" data-entity-id="86083751" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/86083751/The_representativeness_of_the_1999_Spanish_FADN_survey">The representativeness of the 1999 Spanish FADN survey</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="40400" href="https://uc3m.academia.edu/CarlosSanJuan">Carlos San Juan</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The representativeness of the 1999 Spanish FADN survey","attachmentId":90615425,"attachmentType":"pdf","work_url":"https://www.academia.edu/86083751/The_representativeness_of_the_1999_Spanish_FADN_survey","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/86083751/The_representativeness_of_the_1999_Spanish_FADN_survey"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card js-safe-related-work-related-work hidden" data-collection-position="9" data-entity-id="111506810" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/111506810/Survey_response_and_survey_characteristics_Micro_level_evidence_from_the_ECHP">Survey response and survey characteristics: Micro-level evidence from the ECHP</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37660400" href="https://york.academia.edu/ChetiNicoletti">Cheti Nicoletti</a></div><p class="ds-related-work--metadata ds2-5-body-xs">RePEc: Research Papers in Economics, 2004</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Survey response and survey characteristics: Micro-level evidence from the ECHP","attachmentId":109026569,"attachmentType":"pdf","work_url":"https://www.academia.edu/111506810/Survey_response_and_survey_characteristics_Micro_level_evidence_from_the_ECHP","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/111506810/Survey_response_and_survey_characteristics_Micro_level_evidence_from_the_ECHP"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><button class="ds2-5-text-link ds2-5-text-link--inline safe-related-works--view-more-button js-safe-related-works-related-work--view-more-button"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">keyboard_arrow_down</span><span class="ds2-5-text-link__content">View more papers</span></button></div></div></div><div class="safe-below-fold--container"><div class="safe-below-fold--related-works"><h2 class="safe-related-content--heading">Related papers</h2><div class="safe-related-content--container"><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="0" data-entity-id="213718" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/213718/Statistical_Tests_for_the_WageIndicator_Web_Survey_A_Preliminary_Skirmish">Statistical Tests for the WageIndicator Web-Survey: A Preliminary Skirmish</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="130308" href="https://uclouvain.academia.edu/IsabelleFerreras">Isabelle Ferreras</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Statistical Tests for the WageIndicator Web-Survey: A Preliminary Skirmish","attachmentId":699171,"attachmentType":"pdf","work_url":"https://www.academia.edu/213718/Statistical_Tests_for_the_WageIndicator_Web_Survey_A_Preliminary_Skirmish","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/213718/Statistical_Tests_for_the_WageIndicator_Web_Survey_A_Preliminary_Skirmish"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="1" data-entity-id="113205692" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/113205692/Assessing_bias_in_online_surveys_using_alternative_survey_modes">Assessing bias in online surveys using alternative survey modes</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="66675816" href="https://independent.academia.edu/DagSverreSyrdal">Dag Sverre Syrdal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Work Organisation, Labour &amp; Globalisation</p><p class="ds2-5-body-sm ds-related-work--abstract ">Due to concerns that respondents to online surveys are different from populations of interest, parallel offline surveys can be undertaken and results compared. In this article we create a set of principles to compare results from online surveys with those from surveys using other survey modes. Rather than just comparing estimates and confidence intervals from the different modes, these principles consider biases that each survey mode introduces and whether the results obtained are compatible with each other, given these different biases. Using the example of a survey of platform work, we demonstrate that this approach can be used effectively and be applied to a variety of social science studies that use online surveys.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Assessing bias in online surveys using alternative survey modes","attachmentId":110226639,"attachmentType":"pdf","work_url":"https://www.academia.edu/113205692/Assessing_bias_in_online_surveys_using_alternative_survey_modes","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/113205692/Assessing_bias_in_online_surveys_using_alternative_survey_modes"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="2" data-entity-id="99160617" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99160617/Web_surveys_Can_the_weighting_solve_the_problem">Web surveys: Can the weighting solve the problem</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6469282" href="https://independent.academia.edu/zenelb">Zenel Batagelj</a></div><p class="ds-related-work--metadata ds2-5-body-xs">1999</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Web surveys: Can the weighting solve the problem","attachmentId":100321175,"attachmentType":"pdf","work_url":"https://www.academia.edu/99160617/Web_surveys_Can_the_weighting_solve_the_problem","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/99160617/Web_surveys_Can_the_weighting_solve_the_problem"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="3" data-entity-id="14112839" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14112839/The_Use_of_Survey_Weights_in_Regression_Analysis">The Use of Survey Weights in Regression Analysis</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33115116" href="https://independent.academia.edu/ivanfaiella">ivan faiella</a></div><p class="ds-related-work--metadata ds2-5-body-xs">SSRN Electronic Journal, 2000</p><p class="ds2-5-body-sm ds-related-work--abstract ">While there is a wide consensus in using survey weights when estimating population parameters, it is not clear what to do when using survey data for analytic purposes (i.e. with the objective of making inference about model parameters). In the model-based framework (MB), under the hypothesis that the underlying model is correctly specified, using survey weights in regression analysis potentially involves a loss of efficiency. In a design-based perspective (DB), weighted estimates are both design consistent and can provide robustness to model mis-specification. In this paper, I suggest that the choice of using survey weights can be seen in a regression diagnostic set. The survey data analyst should check if the design information included in survey weights has some explanatory power in describing the model outcome. To accomplish this task a set of econometric tests is suggested, that could be supplemented by the analysis of model features under the two strategies.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The Use of Survey Weights in Regression Analysis","attachmentId":44591801,"attachmentType":"pdf","work_url":"https://www.academia.edu/14112839/The_Use_of_Survey_Weights_in_Regression_Analysis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14112839/The_Use_of_Survey_Weights_in_Regression_Analysis"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="4" data-entity-id="22427066" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22427066/The_potential_of_a_multi_mode_data_collection_design_to_reduce_non_response_bias_The_case_of_a_survey_of_employers">The potential of a multi-mode data collection design to reduce non response bias. The case of a survey of employers</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="15050" href="https://essex.academia.edu/PeterLynn">Peter Lynn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Quality & Quantity, 2009</p><p class="ds2-5-body-sm ds-related-work--abstract ">The aim of the paper is to compare two alternative survey designs in terms of resultant response rates, non response bias and cost. The first design is a simple postal survey with follow-up mailings; the second design is a two-phase multi-mode design, where the postal survey is followed at the second phase by a telephone survey of non-respondents. We present a case study based on a survey of employers. In this study we find evidence that the sample obtained using only postal methods is biased in important respects. Bias is not apparent in the demographic characteristics of the employees. But bias is observed in some of the employees' employment characteristics and some of the characteristics of the firms in which they work. The multi mode design seems, overall, to have reduced or removed the bias of the postal sample. Only in marginal respects was some further bias introduced. We also compare costs of the two designs, to enable a comparison of cost-effectiveness at bias reduction.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The potential of a multi-mode data collection design to reduce non response bias. The case of a survey of employers","attachmentId":43048738,"attachmentType":"pdf","work_url":"https://www.academia.edu/22427066/The_potential_of_a_multi_mode_data_collection_design_to_reduce_non_response_bias_The_case_of_a_survey_of_employers","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22427066/The_potential_of_a_multi_mode_data_collection_design_to_reduce_non_response_bias_The_case_of_a_survey_of_employers"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="5" data-entity-id="126125198" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/126125198/Influence_diagnostic_in_survey_sampling_Estimating_the_conditional_bias">Influence diagnostic in survey sampling: Estimating the conditional bias</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="291843009" href="https://independent.academia.edu/JuanMMu%C3%B1oz2">Juan M. Muñoz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Metrika, 2002</p><p class="ds2-5-body-sm ds-related-work--abstract ">The conditional bias has been proposed by Moreno Rebollo et al. (1999) as an in¯uence diagnostic in survey sampling, when the inference is based on the randomization distribution generated by a random sampling. The conditional bias is a population parameter. So, from an applied point of view, it must be estimated. In this paper, we propose an estimator of the conditional bias and we study conditions that guarantee its unbiasedness. The results are applied in a Simple Random Sampling and in a Proportional Probability Aggregated Size Sampling, when the ratio estimator is used.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Influence diagnostic in survey sampling: Estimating the conditional bias","attachmentId":120050693,"attachmentType":"pdf","work_url":"https://www.academia.edu/126125198/Influence_diagnostic_in_survey_sampling_Estimating_the_conditional_bias","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/126125198/Influence_diagnostic_in_survey_sampling_Estimating_the_conditional_bias"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="6" data-entity-id="126304636" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/126304636/Analysis_of_Survey_Data">Analysis of Survey Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="327630002" href="https://independent.academia.edu/EmiroMolina1">Emiro Molina</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Analysis of Survey Data, 2003</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysis of Survey Data","attachmentId":120202565,"attachmentType":"pdf","work_url":"https://www.academia.edu/126304636/Analysis_of_Survey_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/126304636/Analysis_of_Survey_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="7" data-entity-id="3071286" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3071286/Can_Weighting_Compensate_for_Sampling_Issues_in_Internet_Surveys">Can Weighting Compensate for Sampling Issues in Internet Surveys?</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="23110676" href="https://independent.academia.edu/MetteSijtsma">Mette Sijtsma</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37756476" href="https://independent.academia.edu/JayBeaman">Jay Beaman</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="17708984" href="https://colostate.academia.edu/JerryVaske">Jerry Vaske</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2529396" href="https://wur.academia.edu/MaartenJacoba">Maarten Jacobs</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34803633" href="https://colostate.academia.edu/JVaske">Jerry Vaske</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Human Dimensions of …, 2011</p><p class="ds2-5-body-sm ds-related-work--abstract ">While Internet surveys have increased in popularity, results may not be representative of target populations. Weighting is commonly used to compensate for sampling issues. This article compared two surveys conducted in the Netherlands-a random mail survey (n = 353) and a convenience Internet survey (n = 181). Demographic characteristics of the samples were weighted by three variables (sex, current residence, age) using Census data. Prior to weighting, the mail sample approximated the population in half of the joint distributions formed by the weighting variables. The Internet sample differed from the population on all 12 cell-by-cell comparisons and no respondents were over age 65. After weighting, the two samples yielded different estimates for non-weighting variables. The Internet sample overrepresented those in the highest education category and appears to have overrepresented those who are ambivalent toward wildlife. Caution is advised when generalizing results from open access Internet surveys.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Can Weighting Compensate for Sampling Issues in Internet Surveys?","attachmentId":50490967,"attachmentType":"pdf","work_url":"https://www.academia.edu/3071286/Can_Weighting_Compensate_for_Sampling_Issues_in_Internet_Surveys","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/3071286/Can_Weighting_Compensate_for_Sampling_Issues_in_Internet_Surveys"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="8" data-entity-id="48974587" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/48974587/An_Empirical_Investigation_of_Biased_Survey_Data">An Empirical Investigation of Biased Survey Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64900537" href="https://independent.academia.edu/JamesPrieger">James Prieger</a></div><p class="ds-related-work--metadata ds2-5-body-xs">SSRN Electronic Journal</p><p class="ds2-5-body-sm ds-related-work--abstract ">This paper investigates response bias in survey data on annual driving mileage and evaluates the performance of an econometric remedy proposed in the literature, Orbit. There are three contributions in this paper. I first discuss pseudo-precision bias, caused by asking respondents to quantify something they have never precisely measured, and estimate the bias in a self-report of annual mileage driven. The estimation of the bias accounts for the non-standard censoring of the data. Individuals systematically exaggerate their deviation from the sample average, and using the self-reported data leads to misleading estimates of the income elasticity of travel mileage. Second, I extend the Orbit procedure, which has been proposed to correct for reporting bias, to allow misreporting at the lower censoring point. This generalized version may be useful in other settings as well. Finally, I demonstrate that in this application Orbit does not improve the accuracy of the estimation and does not correctly uncover the direction of the pseudo-precision bias. The message for practitioners using biased data is therefore a cautionary one.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Empirical Investigation of Biased Survey Data","attachmentId":67365646,"attachmentType":"pdf","work_url":"https://www.academia.edu/48974587/An_Empirical_Investigation_of_Biased_Survey_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/48974587/An_Empirical_Investigation_of_Biased_Survey_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card js-safe-related-work-wsj " data-collection-position="9" data-entity-id="109709549" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/109709549/Survey_Mode_Effects_on_Objective_and_Subjective_Questions_Evidence_from_the_Labour_Force_Survey">Survey Mode Effects on Objective and Subjective Questions: Evidence from the Labour Force Survey</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="279746631" href="https://independent.academia.edu/CesareAntonioFabioRiillo">Cesare Antonio Fabio Riillo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Official Statistics, 2021</p><p class="ds2-5-body-sm ds-related-work--abstract ">Web questionnaires are increasingly used to complement traditional data collection in mixed mode surveys. However, the utilization of web data raises concerns whether web questionnaires lead to mode-specific measurement bias. We argue that the magnitude of measurement bias strongly depends on the content of a variable. Based on the Luxembourgish Labour Force Survey, we investigate differences between web and telephone data in terms of objective (i.e., Employment Status) and subjective (i.e., Wage Adequacy and Job Satisfaction) variables. To assess whether differences in outcome variables are caused by sample composition or mode-specific measurement bias, we apply a coarsened exact matching that approximates randomized experiments by reducing dissimilarities between web and telephone samples. We select matching variables with a combination of automatic variable selection via random forest and a literature-driven selection. The results show that objective variables are not affected by...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Survey Mode Effects on Objective and Subjective Questions: Evidence from the Labour Force Survey","attachmentId":107753220,"attachmentType":"pdf","work_url":"https://www.academia.edu/109709549/Survey_Mode_Effects_on_Objective_and_Subjective_Questions_Evidence_from_the_Labour_Force_Survey","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/109709549/Survey_Mode_Effects_on_Objective_and_Subjective_Questions_Evidence_from_the_Labour_Force_Survey"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="safe-sticky-ctas--container js-loswp-sticky-ctas hidden"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":39892765,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":39892765,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_39892765" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div></div></div><div class="footer--content"><ul class="footer--main-links hide-on-mobile"><li><a href="https://www.academia.edu/about">About</a></li><li><a href="https://www.academia.edu/press">Press</a></li><li><a href="https://www.academia.edu/documents">Papers</a></li><li><a href="https://www.academia.edu/topics">Topics</a></li><li><a href="https://www.academia.edu/hiring"><svg style="width: 13px; height: 13px; position: relative; bottom: -1px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="briefcase" class="svg-inline--fa fa-briefcase fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg> <strong>We're Hiring!</strong></a></li><li><a href="https://support.academia.edu/hc/en-us"><svg style="width: 12px; height: 12px; position: relative; bottom: -1px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="question-circle" class="svg-inline--fa fa-question-circle fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"></path></svg> <strong>Help Center</strong></a></li></ul><ul class="footer--research-interests"><li>Find new research papers in:</li><li><a href="https://www.academia.edu/Documents/in/Physics">Physics</a></li><li><a href="https://www.academia.edu/Documents/in/Chemistry">Chemistry</a></li><li><a href="https://www.academia.edu/Documents/in/Biology">Biology</a></li><li><a href="https://www.academia.edu/Documents/in/Health_Sciences">Health Sciences</a></li><li><a href="https://www.academia.edu/Documents/in/Ecology">Ecology</a></li><li><a href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a></li><li><a href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a></li><li><a href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a></li><li><a href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a></li></ul><ul class="footer--legal-links hide-on-mobile"><li><a href="https://www.academia.edu/terms">Terms</a></li><li><a href="https://www.academia.edu/privacy">Privacy</a></li><li><a href="https://www.academia.edu/copyright">Copyright</a></li><li>Academia ©2025</li></ul></div> </body> </html>