A new measure of regulatory focus: Preventing measurement error by promoting best validation practices.

OBJECTIVE AND BACKGROUND: The goals of this project were to improve our understanding of chronic regulatory focus constructs and to provide researchers with a measure that adequately assesses the constructs, can distinguish individual differences effectively across the range of the constructs, and is appropriate for use in diverse populations. METHOD: Employing best practices in construct validation, we developed the International Personality Item Pool Regulatory Focus Scale (IPIP-RFS). Utilizing 14 samples (N = 4867), we established substantive (via expert ratings and regulatory focus literature), structural (via factor analysis, item response theory, and measurement invariance), and external (via convergent, discriminant, and predictive associations) validity. RESULTS: The IPIP-RFS adequately assesses the constructs of chronic promotion focus and prevention focus, can accurately assess individuals along the continua of the constructs, and is suitable for use among populations that vary in gender, race, and age. Individual differences in promotion focus reflect self-regulation and goal pursuit related to cognitive and behavioral exploration and flexibility (i.e., plasticity), whereas individual differences in prevention focus reflect self-regulation and goal pursuit related to motivational and interpersonal steadiness (i.e., stability). CONCLUSIONS: Promotion and prevention focus are important elements of personality with broad implications for functioning and outcomes in health and other important domains.

Comparing Two Algorithms for Calibrating the Restricted Non-Compensatory Multidimensional IRT Model.

The non-compensatory class of multidimensional item response theory (MIRT) models frequently represents the cognitive processes underlying a series of test items better than the compensatory class of MIRT models. Nevertheless, few researchers have used non-compensatory MIRT in modeling psychological data. One reason for this lack of use is because non-compensatory MIRT item parameters are notoriously difficult to accurately estimate. In this article, we propose methods to improve the estimability of a specific non-compensatory model. To initiate the discussion, we address the non-identifiability of the explored non-compensatory MIRT model by suggesting that practitioners use an item-dimension constraint matrix (namely, a Q-matrix) that results in model identifiability. We then compare two promising algorithms for high-dimensional model calibration, Markov chain Monte Carlo (MCMC) and Metropolis-Hastings Robbins-Monro (MH-RM), and discuss, via analytical demonstrations, the challenges in estimating model parameters. Based on simulation studies, we show that when the dimensions are not highly correlated, and when the Q-matrix displays appropriate structure, the non-compensatory MIRT model can be accurately calibrated (using the aforementioned methods) with as few as 1,000 people. Based on the simulations, we conclude that the MCMC algorithm is better able to estimate model parameters across a variety of conditions, whereas the MH-RM algorithm should be used with caution when a test displays complex structure and when the latent dimensions are highly correlated.