On modeling missing data of an incomplete design in the CFA framework

  • The paper reports an investigation on whether valid results can be achieved in analyzing the structure of datasets although a large percentage of data is missing without replacement. Two types of confirmatory factor analysis (CFA) models were employed for this purpose: the missing data CFA model with an additional latent variable for representing the missing data and the semi-hierarchical CFA model that also includes the additional latent variable and reflects the hierarchical structure assumed to underlie the data. Whereas, the missing data CFA model assumes that the model is equally valid for all participants, the semi-hierarchical CFA model is implicitly specified differently for subgroups of participants with and without omissions. The comparison of these models with the regular one-factor model in investigating simulated binary data revealed that the modeling of missing data prevented negative effects of missing data on model fit. The investigation of the accuracy in estimating the factor loadings yielded the best results for the semi-hierarchical CFA model. The average estimated factor loadings for items with and without omissions showed the expected equal sizes. But even this model tended to underestimate the expected values.

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Author:Karl Schweizer, Andreas GoldGND, Dorothea Krampen, Tengfei Wang
Parent Title (English):Frontiers in psychology
Publisher:Frontiers Media
Place of publication:Lausanne
Document Type:Article
Date of Publication (online):2020/12/03
Date of first Publication:2020/12/03
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2020/12/17
Tag:confirmatory factor analysis; incomplete design; missing data; planned missing data design; quantitative methods; structural investigation
Issue:Article 581709
Page Number:13
Institutes:Psychologie und Sportwissenschaften / Psychologie
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Licence (German):License LogoCreative Commons - Namensnennung 4.0