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With increased workforce diversity, human service organizations are recognizing the need to create inclusive workplaces; yet little is known about how leaders can enhance workplace inclusion. We collected data at three time points in 6-month intervals from a public child welfare organization (n = 363). Using latent change score models, we analyzed whether leader-member exchange influenced how inclusion changed over time. Results indicate that favorable perceptions of leader-member exchange are associated with increased feelings of inclusion 6 and 12 months later. Findings highlight the importance of improving leadership interactions with their employees to increase workplace inclusion. [ABSTRACT FROM PUBLISHER]

Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. It is our aim to compare and contrast these methods for uncovering sample heterogeneity. We illustrate the use of these methods with longitudinal reading achievement data collected as part of the Early Childhood Longitudinal Study–Kindergarten Cohort. We present the use of structural equation model trees as an alternative framework that does not assume the classes are latent and uses observed covariates to derive their structure. We consider these methods as complementary and discuss their respective strengths and limitations for creating homogeneous groups. [ABSTRACT FROM PUBLISHER]

This research focuses on the problem of model selection between the latent change score (LCS) model and the autoregressive cross-lagged (ARCL) model when the goal is to infer the longitudinal relationship between variables. We conducted a large-scale simulation study to (a) investigate the conditions under which these models return statistically (and substantively) different results concerning the presence of bivariate longitudinal relationships, and (b) ascertain the relative performance of an array of model selection procedures when such different results arise. The simulation results show that the primary sources of differences in parameter estimates across models are model parameters related to the slope factor scores in the LCS model (specifically, the correlation between the intercept factor and the slope factor scores) as well as the size of the data (specifically, the number of time points and sample size). Among several model selection procedures, correct selection rates were higher when using model fit indexes (i.e., comparative fit index, root mean square error of approximation) than when using a likelihood ratio test or any of several information criteria (i.e., Akaike’s information criterion, Bayesian information criterion, consistent AIC, and sample-size-adjusted BIC). [ABSTRACT FROM AUTHOR]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0297.2010.02394.x Byline: James P. Smith (1), John J. McArdle (2), Robert Willis (3) Abstract: In this article, we study the association of cognitive traits and, in particular, numeracy of both spouses on financial outcomes of the family. We find significant effects, particularly for numeracy for financial and non-financial respondents alike, but much larger effects for the financial decision maker in the family. We also examine who makes these financial decisions in the family and why. Once again, cognitive traits such as numeracy are an important component of that decision with larger effects of numeracy for husbands compared to wives. Author Affiliation: (1)RAND Corporation (2)University of Southern California (3)University of Michigan Article note: (*) Corresponding author: James P. Smith, The RAND Corporation, 1776 Main Street, PO Box 2138, Santa Monica, CA 90407-2138, USA. Email: smith@rand.org.