TY  - JOUR
A1  - Schweizer, Karl
A1  - Gold, Andreas
A1  - Krampen, Dorothea
A1  - Wang, Tengfei
T1  - On modeling missing data of an incomplete design in the CFA framework
T2  - Frontiers in psychology
N2  - 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.
KW  - missing data
KW  - incomplete design
KW  - structural investigation
KW  - confirmatory factor analysis
KW  - quantitative methods
KW  - planned missing data design
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/57009
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-570095
SN  - 1664-1078
VL  - 11
IS  - Article 581709
PB  - Frontiers Media
CY  - Lausanne
ER  -