Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) have contributed to test development and validation in counseling psychology, but additional applications have not been fully realized. The author presents an overview of the goals, terminology, and procedures of factor analysis; reviews best practices for extracting, retaining, and rotating factors in EFA; describes issues related to CFA, including potential hypotheses, how parameters are estimated, and current guidelines regarding sample size and the use of fit indices; and reviews more sophisticated applications of CFA, including multiple-groups analysis and testing of hierarchical models. Finally, the author recommends how to increase the application of factor analysis to counseling psychologists’ work in research, training, and practice settings.

No abstract

No abstract

The Linguistic Inquiry and Word Count (LIWC) text analysis program often is used as a measure of emotion expression, yet the construct validity of its use for this purpose has not been examined. Three experimental studies assessed whether the LIWC counts of emotion processes words are sensitive to verbal expression of sadness and amusement. Experiment 1 determined that sad and amusing written autobiographical memories differed in LIWC emotion counts in expected ways. Experiment 2 revealed that reactions to emotionally provocative film clips designed to manipulate the momentary experience of sadness and amusement differed in LIWC counts. Experiment 3 replicated the findings of Experiment 2 and found generally weak relations between LIWC emotion counts and individual differences in emotional reactivity, dispositional expressivity, and personality. The LIWC therefore appears to be a valid method for measuring verbal expression of emotion.

This cross-sectional study investigated predictors of research productivity and science-related career goals in a sample of 267 doctoral students (representing a response rate of 5S%) from 15 randomly selected APA-accredited counseling psychology doctoral programs. A structural equation modeling procedure revealed that career goals and research productivity could be predicted by Holland personality type, perceptions of the research training environment, interest in research, and research self-efficacy. Students' gender and year in the doctoral program also contributed to this causal model as additional predictor variables, providing a very good fit to the data The present findings contribute to theories of research training by presenting a comprehensive examination of the major factors previously investigated in the literature as predictors of research productivity and science-related career goals within the context of a structural equation model