Robustness of Adaptive Measurement of Change to Item Parameter Estimation Error.

Adaptive measurement of change (AMC) is a psychometric method for measuring intra-individual change on one or more latent traits across testing occasions. Three hypothesis tests-a Z test, likelihood ratio test, and score ratio index-have demonstrated desirable statistical properties in this context, including low false positive rates and high true positive rates. However, the extant AMC research has assumed that the item parameter values in the simulated item banks were devoid of estimation error. This assumption is unrealistic for applied testing settings, where item parameters are estimated from a calibration sample before test administration. Using Monte Carlo simulation, this study evaluated the robustness of the common AMC hypothesis tests to the presence of item parameter estimation error when measuring omnibus change across four testing occasions. Results indicated that item parameter estimation error had at most a small effect on false positive rates and latent trait change recovery, and these effects were largely explained by the computerized adaptive testing item bank information functions. Differences in AMC performance as a function of item parameter estimation error and choice of hypothesis test were generally limited to simulees with particularly low or high latent trait values, where the item bank provided relatively lower information. These simulations highlight how AMC can accurately measure intra-individual change in the presence of item parameter estimation error when paired with an informative item bank. Limitations and future directions for AMC research are discussed.

A Multivariate Study of Human Mate Preferences: Findings from the California Twin Registry.

Much research examining the biological and social-cultural underpinnings of human mate preferences has focused on univariate or bivariate analyses of demographic variables and personality constructs. In this paper, we argue that a multivariate approach more effectively highlights the multifaceted structure and correlates of human mate preferences. To support this claim, we applied several multivariate techniques to data from a large adult sample to (1) examine the major dimensions underlying individual differences in mate preferences, and (2) elucidate how these preferences relate to individual differences in personality. An exploratory factor analysis of an omnibus mate preference questionnaire yielded a 14-factor solution with dimensions mirroring trends in evolutionary psychology and the Big Five personality framework. An inter-battery factor analysis of these dimensions paired with higher-order personality factors provided strong support for the "likes attract" model of partner preferences. Bootstrap confidence intervals for all factor loadings highlighted the robustness of our results.

Heywood you go away! Examining causes, effects, and treatments for Heywood cases in exploratory factor analysis.

Exploratory factor analysis (EFA) is a popular method for elucidating the latent structure of data. Unfortunately, EFA models can sometimes produce improper solutions with nonsensical results. For example, improper EFA solutions can include one or more Heywood cases, where common factors account for 100% or more of an observed variable's variance. To better understand these senseless estimates, we conducted four Monte Carlo studies that illuminate the (a) causes, (b) consequences, and (c) effective treatments for Heywood cases in EFA models. Studies 1 and 2 showed that numerous model and data characteristics are associated with Heywood cases, such as small sample sizes, poorly defined factors with low factor score determinacy values, and factor overextraction. In Study 3, we examined the consequences of Heywood cases for EFA model interpretation and found that Heywood cases increase factor loading variances and upwardly bias factor score determinacy values. Study 4 compared the model recovery of several EFA algorithms that were designed to avoid Heywood cases. Our results indicated that, among the algorithms compared, regularized common factor analysis (Jung & Takane, 2008) was the most reliable method for avoiding Heywood cases and producing EFA parameter estimates with small mean squared errors. We discuss best practices for conducting EFA with data sets that might yield Heywood cases. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effects of a two-generation human capital program on low-income parents' education, employment, and psychological wellbeing.

Two-generation human capital programs for families provide education and workforce training for parents simultaneously with education for children. This study uses a quasi-experimental design to examine the effects of a model two-generation program, CareerAdvance, which recruits parents of children enrolled in Head Start into a health care workforce training program. After 1 year, CareerAdvance parents demonstrated higher rates of certification and employment in the health care sector than did matched-comparison parents whose children were also in Head Start. More important, there was no effect on parents' short-term levels of income or employment across all sectors. CareerAdvance parents also experienced psychological benefits, reporting higher levels of self-efficacy and optimism, in addition to stronger career identity compared with the matched-comparison group. Notably, even as CareerAdvance parents juggled the demands of school, family, and employment, they did not report higher levels of material hardship or stress compared with the matched-comparison group. These findings are discussed in terms of the implications of a family perspective for human capital programs. (PsycINFO Database Record (c) 2019 APA, all rights reserved).