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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]

In this article, we introduce and demonstrate the application of a newly developedRpackageRAMpathfor tracing path diagrams and conducting structural longitudinal data analysis.RAMpathwas developed to preserve the essential features of the classic DOS version of the RAMpath program (McArdle & Boker, 1990) and ease data analysis done through structural equation modeling (SEM). The applicability ofRAMpathis demonstrated through a mediation model, a MIMIC model, several latent growth curve models, a univariate latent change score model, and a bivariate latent change score model. In addition to performing regular SEM analysis,RAMpathhas unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore,RAMpathcan fit dynamic system models automatically based on latent change scores and generate vector field plots based on results obtained from a bivariate dynamic system.RAMpathis provided as an open-sourceRpackage. [ABSTRACT FROM AUTHOR]

This study illustrates the modeling of mutual dynamical changes to assess the comparative effectiveness of intervention effects of substance use prevention programs. Youth Action Research for Prevention aimed to improve self-efficacy and strengthen drug prevention skills among African American, Caribbean and Latino youth from high-risk US neighborhoods. Applying the Latent Change Scores dynamical modeling to a four-wave longitudinal data set, we demonstrate that the intervention has spurred a strong dynamic coupling effect between Internal Locus of Control (ILC) and Intent to Use Substances (IUS) in the intervention group ( nI = 113), as well as new mutual dynamic links - i.e. changes in ILC causing subsequent IUS changes, and vice versa; these were not manifested in the comparison group ( nC = 202). The dynamic models explain well the wave-like pattern of mutual changes uncovered in the intervention group. Our findings carry strong methodological implications for policy makers, practitioners and addiction researchers alike: dynamic modeling can be critical in uncovering the mechanisms involved in derailing the development of addictions. [ABSTRACT FROM AUTHOR]

The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this article we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai‘i (following McArdle, 2008; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply 3 recently available forms of longitudinal data analysis: (a)Dealing with incomplete data—We apply these methods to cohort-sequential data with relatively large blocks of data that are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (b)Ordinal measurement models(Muthén & Muthén, 2006)—We use a variety of statistical and psychometric measurement models, including ordinal measurement models, to help clarify the strongest patterns of influence. (c)Dynamic structural equation models(DSEMs; McArdle, 2008). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study. [ABSTRACT FROM AUTHOR]