Search

In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of switching between models with free and fixed factor loadings. In a simulation study, confirmatory factor analysis (CFA) models with and without a missing data latent variable were used for investigating the structure of data with and without missing data. In addition, the numbers of columns of data sets with missing data and the amount of missing data were varied. The root mean square error of approximation (RMSEA) results revealed that an additional missing data latent variable recovered the degree-of-model fit characterizing complete data when tetrachoric correlations served as input while comparative fit index (CFI) results showed overestimation of this degree-of-model fit. Whereas the results for fixed factor loadings were in line with the assumptions of modeling missing data, the other results showed only partial agreement. Therefore, modeling missing data with fixed factor loadings is recommended.

Conceptualizing two-variable disturbances preventing good model fit in confirmatory factor analysis as item-level method effects instead of correlated residuals avoids violating the principle that residual variation is unique for each item. The possibility of representing such a disturbance by a method factor of a bifactor measurement model was investigated with respect to model identification. It turned out that a suitable way of realizing the method factor is its integration into a fixed-links, parallel-measurement or tau-equivalent measurement submodel that is part of the bifactor model. A simulation study comparing these submodels revealed similar degrees of efficiency in controlling the influence of two-variable disturbances on model fit. Perfect correspondence characterized the fit results of the model assuming correlated residuals and the fixed-links model, and virtually also the tau-equivalent model. [ABSTRACT FROM AUTHOR]

The item-position effect describes how an item's position within a test, that is, the number of previous completed items, affects the response to this item. Previously, this effect was represented by constraints reflecting simple courses, for example, a linear increase. Due to the inflexibility of these representations our aim was to examine whether adapted representations are more appropriate than the existing ones. Models of confirmatory factor analysis were used for testing the different representations. Analyses were conducted by means of simulated data that followed the covariance pattern of Raven's Advanced Progressive Matrices (APM) items. Since the item-position effect has been demonstrated repeatedly for the APM, it is a very suitable measure for our investigations. Results revealed no remarkable improvement by using an adapted representation. Possible reasons causing these results are discussed.