Objectivity, realism, and psychometrics
•Examines the question of objectivity for latent variable modelling.•Focuses on conditional independence, circularity, and factor indeterminacy.•Proposes a conceptual framework to address problems for objectivity. The aim of this paper is raise and address questions regarding the status of objectivi...
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Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2019-10, Vol.145, p.292-299 |
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creator | Nowland, Trisha Beath, Alissa Boag, Simon |
description | •Examines the question of objectivity for latent variable modelling.•Focuses on conditional independence, circularity, and factor indeterminacy.•Proposes a conceptual framework to address problems for objectivity.
The aim of this paper is raise and address questions regarding the status of objectivity for the generalized latent variable model (GLVM) in psychometric research, given the conceptual, logical and mathematical problems of circularity, conditional independence, and factor indeterminacy, respectively. The question of objectivity for the model is examined with respect to measurement and realist perspectives. Drawing on insights from measurement and systems dynamics literature, a proposal for a conceptual framework is presented, that integrates: i) inference from the best systematisation; and ii) axiomatic set theory. This conceptual framework, which addresses the whole of a research project, invites specification of the expected relations, conditions, and assumptions which are relevant to the implementation of the GLVM. While this does not eliminate the problems for the GLVM, it provides future researchers with maximal objective information in standardized form, supporting minimization of definitional and instrumental uncertainty, in psychological modelling practices. |
doi_str_mv | 10.1016/j.measurement.2019.05.038 |
format | Article |
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The aim of this paper is raise and address questions regarding the status of objectivity for the generalized latent variable model (GLVM) in psychometric research, given the conceptual, logical and mathematical problems of circularity, conditional independence, and factor indeterminacy, respectively. The question of objectivity for the model is examined with respect to measurement and realist perspectives. Drawing on insights from measurement and systems dynamics literature, a proposal for a conceptual framework is presented, that integrates: i) inference from the best systematisation; and ii) axiomatic set theory. This conceptual framework, which addresses the whole of a research project, invites specification of the expected relations, conditions, and assumptions which are relevant to the implementation of the GLVM. While this does not eliminate the problems for the GLVM, it provides future researchers with maximal objective information in standardized form, supporting minimization of definitional and instrumental uncertainty, in psychological modelling practices.</description><identifier>ISSN: 0263-2241</identifier><identifier>EISSN: 1873-412X</identifier><identifier>DOI: 10.1016/j.measurement.2019.05.038</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Conceptual framework ; Generalised latent variable model ; Objectivity ; Psychometrics ; Quantitative psychology ; Questions ; Set theory ; Variables</subject><ispartof>Measurement : journal of the International Measurement Confederation, 2019-10, Vol.145, p.292-299</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Oct 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-af21cfe2fe7a88400f1e2d2331aa943383863383695d4e0f5de0462ea06c01633</citedby><cites>FETCH-LOGICAL-c349t-af21cfe2fe7a88400f1e2d2331aa943383863383695d4e0f5de0462ea06c01633</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.measurement.2019.05.038$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Nowland, Trisha</creatorcontrib><creatorcontrib>Beath, Alissa</creatorcontrib><creatorcontrib>Boag, Simon</creatorcontrib><title>Objectivity, realism, and psychometrics</title><title>Measurement : journal of the International Measurement Confederation</title><description>•Examines the question of objectivity for latent variable modelling.•Focuses on conditional independence, circularity, and factor indeterminacy.•Proposes a conceptual framework to address problems for objectivity.
The aim of this paper is raise and address questions regarding the status of objectivity for the generalized latent variable model (GLVM) in psychometric research, given the conceptual, logical and mathematical problems of circularity, conditional independence, and factor indeterminacy, respectively. The question of objectivity for the model is examined with respect to measurement and realist perspectives. Drawing on insights from measurement and systems dynamics literature, a proposal for a conceptual framework is presented, that integrates: i) inference from the best systematisation; and ii) axiomatic set theory. This conceptual framework, which addresses the whole of a research project, invites specification of the expected relations, conditions, and assumptions which are relevant to the implementation of the GLVM. While this does not eliminate the problems for the GLVM, it provides future researchers with maximal objective information in standardized form, supporting minimization of definitional and instrumental uncertainty, in psychological modelling practices.</description><subject>Conceptual framework</subject><subject>Generalised latent variable model</subject><subject>Objectivity</subject><subject>Psychometrics</subject><subject>Quantitative psychology</subject><subject>Questions</subject><subject>Set theory</subject><subject>Variables</subject><issn>0263-2241</issn><issn>1873-412X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqNkEtPwzAQhC0EEuXxH4o4cGnC2uu4zhFVvKRKvYDEzTLOWjhqkmKnlfrvcVUOHLnsXGZmNR9jNxxKDlzdt2VHNm0jddSPpQBel1CVgPqETbieYyG5-DhlExAKCyEkP2cXKbUAoLBWE3a3-mzJjWEXxv1sGsmuQ-pmU9s3003au6-hozEGl67YmbfrRNe_esnenx7fFi_FcvX8unhYFg5lPRbWC-48CU9zq7UE8JxEIxC5tbVE1KjV4aq6aiSBrxoCqQRZUC7PQbxkt8feTRy-t5RG0w7b2OeXRoi50sgBZXbVR5eLQ0qRvNnE0Nm4NxzMgYtpzR8u5sDFQGUyl5xdHLOUZ-wCRZNcoN5RE2ImYZoh_KPlBy53cIg</recordid><startdate>201910</startdate><enddate>201910</enddate><creator>Nowland, Trisha</creator><creator>Beath, Alissa</creator><creator>Boag, Simon</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201910</creationdate><title>Objectivity, realism, and psychometrics</title><author>Nowland, Trisha ; Beath, Alissa ; Boag, Simon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-af21cfe2fe7a88400f1e2d2331aa943383863383695d4e0f5de0462ea06c01633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Conceptual framework</topic><topic>Generalised latent variable model</topic><topic>Objectivity</topic><topic>Psychometrics</topic><topic>Quantitative psychology</topic><topic>Questions</topic><topic>Set theory</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nowland, Trisha</creatorcontrib><creatorcontrib>Beath, Alissa</creatorcontrib><creatorcontrib>Boag, Simon</creatorcontrib><collection>CrossRef</collection><jtitle>Measurement : journal of the International Measurement Confederation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nowland, Trisha</au><au>Beath, Alissa</au><au>Boag, Simon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Objectivity, realism, and psychometrics</atitle><jtitle>Measurement : journal of the International Measurement Confederation</jtitle><date>2019-10</date><risdate>2019</risdate><volume>145</volume><spage>292</spage><epage>299</epage><pages>292-299</pages><issn>0263-2241</issn><eissn>1873-412X</eissn><abstract>•Examines the question of objectivity for latent variable modelling.•Focuses on conditional independence, circularity, and factor indeterminacy.•Proposes a conceptual framework to address problems for objectivity.
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subjects | Conceptual framework Generalised latent variable model Objectivity Psychometrics Quantitative psychology Questions Set theory Variables |
title | Objectivity, realism, and psychometrics |
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