Identifying person-fit latent classes, and explanation of categorical and continuous person misfit

J.M. Conijn, K. Sijtsma, W.H.M. Emons

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Latent class (LC) cluster analysis of a set of subscale lz person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students (N = 91,648) on the nine-subscale School Attitude Questionnaire Internet (SAQI). Inspection of the class-specific lz mean and variance structure combined with explanatory analysis of class membership showed that the data included a poor-fit class, a class showing good fit combined with social desirability bias, a good-fit class, and two classes that were more difficult to interpret. A comparison of multinomial logistic regression predicting class membership and multiple regression predicting continuous person fit showed that LC cluster analysis provided information about aberrant responding unattainable by means of linear multiple regression. It was concluded that LC person-fit analysis has added value to common approaches to explaining aberrant responding to multiscale measures.
Keywords: aberrant responding, explanatory person-fit analysis, latent class cluster analysis, person-fit statistic lz, response inconsistency, School Attitude Questionnaire Internet
Original languageEnglish
Pages (from-to)128-141
JournalApplied Psychological Measurement
Volume40
Issue number2
DOIs
Publication statusPublished - 2016

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human being
cluster analysis
class membership
Cluster Analysis
regression
statistics
Internet
social desirability
questionnaire
value added
school
Linear Models
Logistic Models
logistics
trend
student
Surveys and Questionnaires

Cite this

Conijn, J.M. ; Sijtsma, K. ; Emons, W.H.M. / Identifying person-fit latent classes, and explanation of categorical and continuous person misfit. In: Applied Psychological Measurement. 2016 ; Vol. 40, No. 2. pp. 128-141.
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Identifying person-fit latent classes, and explanation of categorical and continuous person misfit. / Conijn, J.M.; Sijtsma, K.; Emons, W.H.M.

In: Applied Psychological Measurement, Vol. 40, No. 2, 2016, p. 128-141.

Research output: Contribution to journalArticleScientificpeer-review

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AB - Latent class (LC) cluster analysis of a set of subscale lz person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students (N = 91,648) on the nine-subscale School Attitude Questionnaire Internet (SAQI). Inspection of the class-specific lz mean and variance structure combined with explanatory analysis of class membership showed that the data included a poor-fit class, a class showing good fit combined with social desirability bias, a good-fit class, and two classes that were more difficult to interpret. A comparison of multinomial logistic regression predicting class membership and multiple regression predicting continuous person fit showed that LC cluster analysis provided information about aberrant responding unattainable by means of linear multiple regression. It was concluded that LC person-fit analysis has added value to common approaches to explaining aberrant responding to multiscale measures.Keywords: aberrant responding, explanatory person-fit analysis, latent class cluster analysis, person-fit statistic lz, response inconsistency, School Attitude Questionnaire Internet

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