On Modeling Missing Data in Structural Investigations Based on Tetrachoric Correlations With Free and Fixed Factor Loadings.

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.

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.

Effects of cognitive behavioral therapy with relaxation vs. imagery rescripting on test anxiety: A randomized controlled trial.

BACKGROUND: Test anxiety is a common condition in students, which may lead to impaired academic performance as well as to distress. The primary objective of this study was to evaluate the effectiveness of two cognitive-behavioral interventions designed to reduce test anxiety. Test anxiety in the participants was diagnosed as social or specific phobia according to DSM-IV. Subsequently subjects were randomized to three groups: a moderated self-help group, which served as a control group, and two treatment groups, where either relaxation techniques or imagery rescripting were applied. METHODS: Students suffering from test anxiety were recruited at two German universities (n=180). The randomized controlled design comprised three groups which received test anxiety treatment in weekly three-hour sessions over a period of five weeks. Treatment outcome was assessed with a test anxiety questionnaire, which was administered before and after treatment, as well as in a six-month follow-up. RESULTS: A repeated-measures ANOVA for participants with complete data (n=59) revealed a significant reduction of test anxiety from baseline to six-month follow-up in all three treatment groups (p<.001). LIMITATIONS: Participants were included if they had a clinical diagnosis of test anxiety. The sample may therefore represent only more severe forms of text anxiety . Moreover, the sample size in this study was small, the numbers of participants per group differed, and treatment results were based on self-report. Due to the length of the treatment, an implementation of the group treatments used in this study might not be feasible in all settings. CONCLUSIONS: Group treatments constitute an effective method of treating test anxiety, e.g. in university settings. Imagery rescripting may particularly contribute to treatment efficacy.

Do Adaptive Representations of the Item-Position Effect in APM Improve Model Fit? A Simulation Study.

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.

Reliability and validity of the Danish version of the Schema Mode Inventory (SMI).

Schema modes are conceptualized as different parts of oneself, related to specific emotions, cognitions, and behaviors. They are a central concept in schema therapy for personality pathology and other chronic psychiatric disorders. Prior research confirms the factorial validity of the Schema Mode Inventory (SMI), an instrument to assess schema modes. This study presents an evaluation of the Danish version of the SMI, including reliability and validity analyses. Psychometric properties, such as factor structure, internal reliability, as well as intercorrelations between subscales were assessed. This was done on the basis of a mixed Danish sample (N = 657; M age = 29.32 years; range = 18-66 years; 78.5% females) composed of 266 clinical and 391 nonclinical adult participants. Results indicated model fit for the 14-factor structure of the SMI and adequate to excellent internal reliabilities of the 14 subscales (Cronbach's α =.74-.96). Furthermore, the SMI scales differentiated between clinical and nonclinical participants as theoretically predicted. These findings imply that the SMI is a sound instrument to measure the schema mode model in a Danish setting, both for clinical and research purposes, and in particular for assessment prior to schema therapy.