Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of problematic measurement scales. We offer a theoretically-grounded alternative to modeling survey data called an autoregressive confirmatory factor analysis (AR-CFA), which is motivated by recognizing that responding to survey items is a sequential process that may create temporal dependencies among scale items. We compare an AR-CFA to other common approaches using a sample of 8,569 people measured along five common personality factors, showing how the AR-CFA can improve model fit and offer evidence of increased construct validity. We then introduce methods for testing AR-CFA hypotheses, including cross-level moderation effects using latent interactions among stable factors and time-varying residuals. We recommend considering the AR-CFA as a useful complement to other existing approaches and treat AR-CFA limitations.

Income, personality, and subjective financial well-being: the role of gender in their genetic and environmental relationships.

Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

Conducting multilevel analyses in medical education.

A significant body of education literature has begun using multilevel statistical models to examine data that reside at multiple levels of analysis. In order to provide a primer for medical education researchers, the current work gives a brief overview of some issues associated with multilevel statistical modeling. To provide an example of this technique, we then present a multilevel analysis examining the relationship between two individual-level variables and the "cross-level" interaction between this relationship and a school-level variable. In offering this discussion and example of multilevel modeling, we hope to provide medical educators with a basic introduction to multilevel statistics, including the advantages of utilizing these techniques.

The affective underpinnings of job perceptions and attitudes: a meta-analytic review and integration.

Using psychometric meta-analysis, the authors present a quantitative and qualitative review (k = 205, total pairwise N = 62,527) of the literature relating trait and state positive affect (PA) and negative affect (NA) to job-related attitudes, including job satisfaction, organizational commitment, turnover intentions, and dimensions of job burnout. Results indicated substantial correlations, ranging in absolute value from -.17 (PA and turnover intentions; NA and personal accomplishment) to.54 (NA and emotional exhaustion). Correlational results largely were consistent across hypothesized and exploratory moderator conditions. Meta-analytic multiple regression results generally supported the unique contribution of each affect to each attitude variable of interest. Implications and suggestions for future research on emotion-related aspects of job attitudes are discussed.