This study investigated the effect the number of observed variables (p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.

This article compares two missing data procedures, full information maximum likelihood (FIML) and multiple imputation (MI), to investigate their relative performances in relation to the results from analyses of the original complete data or the hypothetical data available before missingness occurred. By expressing the FIML estimator as a special MI estimator, we predicted the expected patterns of discrepancy between the two estimators. Via Monte Carlo simulation studies where we have access to the original complete data, we compare the performance of FIML and MI estimators to that of the complete data maximum likelihood (ML) estimator under a wide range of conditions, including differences in sample size, percent of missingness, and degrees of model misfit. Our study confirmed well-known knowledge that the two estimators tend to yield essentially equivalent results to each other and to those from analysis of complete data when the postulated model is correctly specified. However, some noteworthy patterns of discrepancies were found between the FIML and MI estimators when the hypothesized model does not hold exactly in the population: MI-based parameter estimates, comparative fit index (CFI), and the Tucker Lewis index (TLI) tend to be closer to the counterparts of the complete data ML estimates, whereas FIML-based chi-squares and root mean square error of approximation (RMSEA) tend to be closer to the counterparts of the complete data ML estimates. We explained the observed patterns of discrepancy between the two estimators as a function of the interplay between the parsimony and accuracy of the imputation model. We concluded by discussing practical and methodological implications and issues for further research.

This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a wide range of conditions, including number of response categories, sample size, percent of missingness, and degree of model misfit. Results indicate that both PD and MI yield parameter estimates similar to those from analysis of complete data under conditions where the data are missing completely at random (MCAR). When the data are missing at random (MAR), PD parameter estimates are shown to be severely biased across parameter combinations in the study. When the percentage of missingness is less than 50%, MI yields parameter estimates that are similar to results from complete data. However, the fit indices (i.e., χ2, RMSEA, and WRMR) yield estimates that suggested a worse fit than results observed in complete data. We recommend that applied researchers use MI when fitting ordinal factor models with missing data. We further recommend interpreting model fit based on the TLI and CFI incremental fit indices.

Background: The purpose of this study is to examine the factor structure of the Korean version of the 20-Item Toronto Alexithymia Scale. The TAS-20 (source of the TAS-20K) has been supported the three-factor correlated model. However, some factor structure studies of the TAS-20 rejected the three-factor correlated model and adopted alternative models. Methods: In study 1, we conducted a comparison study of the alternative measurement models by using CFA. In study 2, we examined scale reliability and gender measurement invariance of the factor structure. To examine the alternative models and scale reliability, we using the bifactor model reliability indices. Results: As a result, the DIF and DDF factors have a close relationship but the EOT factor has some differences with DIF and DDF. So we adopted a two-factor correlated model with group factor. And the adopted factor structure has partial measurement invariance. Therefore we can compare gender differences of the TAS-20K. Conclusions: This study has significance that examining TAS-20K's factor structure and examining measurement invariance in gender.

Background: The present study aimed to investigate the number of latent groups that can be identified on the basis on the level of difficulties identifying feelings (DIF), difficulties in describing feelings (DDF), and externally oriented thinking (EOT).Methods: DIF, DDF, and EOT are the sub-factors of the 20-item Toronto Alexithymia Scale. Latent profile analysis was performed to identify the subgroups and investigate their properties. A total of 237 Korean university and graduate students were included in the study, and alexithymia subtypes were classified into 5 latent groups.Results: The groups were classified according to the DIF and DDF scores. Furthermore, it was observed that the EOT did not play a role in classifying the groups. The higher the DIF and DDF scores, the higher were the levels of depression and anxiety. The type 2 latent group, which had a unique profile with the highest DIF level and an average DDF level, showed high levels of depression and anxiety.Conclusions: These results suggest that the DIF significantly affects psychological adaptation, thus warranting the consideration of this parameter in counseling and psychotherapy.