Search

This study evaluated measurement invariance across males and females on the Neurobehavioral Symptom Inventory (NSI) in U.S. military veterans enrolled in the VA Million Veteran Program. Participants (N = 17,059; males: n = 15,450; females: n = 1,609) included Veterans who took part in the VA Traumatic Brain Injury (TBI) Screening and Evaluation Program and completed the NSI. Multiple-group confirmatory factor analyses investigated measurement invariance of the NSI 4-factor model. The configural (comparative fit index [CFI] = 0.948, root mean square error of approximation [RMSEA] = 0.060) and metric (CFI = 0.948, RMSEA = 0.058) invariance models showed acceptable fit. There was a minor violation of scalar invariance (Δχ2 = 232.50, p <.001); however, the degree of noninvariance was mild (ΔCFI = −0.002, Δ RMSEA = 0. 000). Our results demonstrate measurement invariance across sex, suggesting that the NSI 4-factor model can be used to accurately assess symptoms in males and females following TBI. Findings highlight the importance of considering validity of measurement across study groups to increase confidence that a measure is interpreted similarly by respondents from different subgroups. [ABSTRACT FROM AUTHOR]

Previous studies have detailed the consequence of ignoring a level of clustering in multilevel models with straightly hierarchical structures and have proposed methods to adjust for the fixed effect standard errors (SE s). However, in behavioral and social science research, there are usually two or more crossed clustering levels, such as when students are cross-classified by schools and neighborhoods, yet it is not uncommon that researchers focus only on one level of clustering. Using the generalized least squares framework, in this study, we derive the bias in the fixed effect SE estimators when one crossed random effect is omitted. We then showed, using data from the Scotland Neighborhood Study, how one can correct for the SE s and obtain corrected statistical inference when a misspecified two-level model was used in a primary study, which is useful when evaluating observational studies or cluster randomized trials that ignored a crossed random effects or when conducting meta-analyses. In addition, our analytic results provide theoretical insights on how one can quantify imbalance with cross-classified data by the strength of association between the two-crossed random effects in a contingency table and how the degree of imbalance relates to the correction factor for the fixed effect SE s. [ABSTRACT FROM AUTHOR]

Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural equation modeling (SEM) framework, namely, the reliability-adjusted product indicator (RAPI) method and the latent moderated structural equations (LMS) method, which can both flexibly take into account measurement errors. Results showed that both these methods generally produced unbiased estimates of the interaction effects. On the other hand, the path model—without considering measurement errors—led to substantial bias and a low confidence interval coverage rate of nonzero interaction effects. Other findings and implications for future studies are discussed. [ABSTRACT FROM AUTHOR]

Multilevel modeling techniques are becoming more popular in handling data with multilevel structure in educational and behavioral research. Recently, researchers have paid more attention to cross-classified data structure that naturally arises in educational settings. However, unlike traditional single-level research, methodological studies about multilevel effect size have been rare and those that have recently appeared had an emphasis on strictly hierarchical data structure. This article extends the work on multilevel standardized mean differences from strictly hierarchical structure to both fully and partially cross-classified structures. Analytically derived formulae for calculating effect sizes and the corresponding sampling variances (or standard errors) are presented, verified by simulation results, and illustrated with real data examples. Implications for primary research studies and meta-analyses are discussed. [ABSTRACT FROM PUBLISHER]

Recent research suggests human life history strategy (LHS) may be subsumed by multiple dimensions, including mating competition and Super-K, rather than one. In this study, we test whether a two-dimensional structure best fit data from a predominantly urban sample of young adults ages 18–24. We also test whether latent life history dimensions are associated with environmental harshness and unpredictability as predicted by life history theory. Results provide evidence that a two-dimensional model best fit the data. Furthermore, a moderate inverse residual correlation between mating competition and Super-K was found, consistent with a life history trade-off. Our findings suggest that parental socioeconomic status may enhance investment in mating competition, that harshness might persist into young adulthood as an important correlate of LHS, and that unpredictability may not have significant effects in young adulthood. These findings further support the contention that human LHS is multidimensional and environmental effects on LHS are more complex than previously suggested. The model presented provides a parsimonious explanation of an array of human behaviors and traits and can be used to inform public health initiatives, particularly with respect to the potential impact of environmental interventions. [ABSTRACT FROM AUTHOR]

This article attends to recent discussions of validity in psychometric research on human life history strategy (LHS), provides a constructive critique of the extant literature, and describes strategies for improving construct validity. To place the psychometric study of human LHS on more solid ground, our review indicates that researchers should (a) use approaches to psychometric modeling that are consistent with their philosophies of measurement, (b) confirm the dimensionality of life history indicators, and (c) establish measurement invariance for at least a subset of indicators. Because we see confirming the dimensionality of life history indicators as the next step toward placing the psychometrics of human LHS on more solid ground, we use nationally representative data and structural equation modeling to test the structure of middle adult life history indicators. We found statistically independent mating competition and Super-K dimensions and the effects of parental harshness and childhood unpredictability on Super-K were consistent with past research. However, childhood socioeconomic status had a moderate positive effect on mating competition and no effect on Super-K, while unpredictability did not predict mating competition. We conclude that human LHS is more complex than previously suggested—there does not seem to be a single dimension of human LHS among Western adults and the effects of environmental components seem to vary between mating competition and Super-K. [ABSTRACT FROM AUTHOR]