Measures of teacher-student relationship quality (TSRQ), effortful engagement, and achievement in reading and math were collected once each year for 3 consecutive years, beginning when participants were in 1st grade, for a sample of 671 (53.1% male) academically at-risk children attending 1 of 3 school districts in Texas. In separate latent variable structural equation models, the authors tested the hypothesized model, in which Year 2 effortful engagement mediated the association between Year 1 TSRQ and Year 3 reading and math skills. Conduct engagement was entered as a covariate in these analyses to disentangle the effects of effortful engagement and conduct engagement. Reciprocal effects of effortful engagement on TSRQ and of achievement on effortful engagement were also modeled. Results generally supported the hypothesized model. Year 1 variables had a direct effect on Year 3 variables, above year-to-year stability. Findings suggest that achievement, effortful engagement, and TSRQ form part of a dynamic system of influences in the early grades, such that intervening at any point in this nexus may alter children's school trajectories.

The use and quality of longitudinal research designs has increased over the past two decades, and new approaches for analyzing longitudinal data, including multi-level modeling (MLM) and latent growth modeling (LGM), have been developed. The purpose of this paper is to demonstrate the use of MLM and its advantages in analyzing longitudinal data. Data from a sample of individuals with intra-articular fractures of the lower extremity from the University of Alabama at Birmingham's Injury Control Research Center is analyzed using both SAS PROC MIXED and SPSS MIXED. We start our presentation with a discussion of data preparation for MLM analyses. We then provide example analyses of different growth models, including a simple linear growth model and a model with a time-invariant covariate, with interpretation for all the parameters in the models. More complicated growth models with different between-and within-individual covariance structures and nonlinear models are discussed. Finally, information related to MLM analysis such as online resources is provided at the end of the paper. Publisher's Disclaimer:The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at http://www.apa.org/journals/rep NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptLongitudinal designs have recently received more attention in a variety of different disciplines of psychology including clinical, developmental, personality and health psychology . In some areas, such as developmental psychology and personality psychology, a substantial number of recently published studies have been longitudinal Khoo, West, Wu, & Kwok, 2006). For example, Khoo et al. (2006) found that slightly more than one third of articles published in Developmental Psychology in 2002 included at least one longitudinal study, defined as having at least two measurement occasions. This proportion is double the proportion of longitudinal studies published in the same journal in 1990. Furthermore, more than 70% of the longitudinal studies published in Developmental Psychology in 2002 included three or more measurement waves.In this paper, we will focus on the analyses of multiwave longitudinal data, where multiwave is defined as more than two waves.With the growing use of longitudinal research, a number of methodological and statistical sources on the analysis of multiwave longitudinal data have appeared in the past decade (e.g., Bollen & Curran, 2006;Collins & Sayer, 2001; Singer & Willet, 2003), including discussions of traditional approaches such as repeated-measures Univariate Analysis ...

Nef of primate lentiviruses is critical for high levels of viremia and the progression to AIDS. Nef associates with and activates a serine/threonine kinase (Nef-associated kinase [NAK]) via the small GTPases Rac1 and Cdc42. We identified the protooncogene and guanine nucleotide exchange factor Vav as the specific binding partner of Nef proteins from HIV-1 and SIV. The interaction between Nef and Vav led to increased activity of Vav and its downstream effectors. Both cytoskeletal changes and the activation of c-Jun N-terminal kinase (JNK) were observed. Furthermore, a dominant-negative Vav protein inhibited NAK activation and viral replication. Thus, the interaction between Nef and Vav initiates a signaling cascade that changes structural and physiological parameters in the infected cell.

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Cross-classified random-effects models (CCREMs) are used for modeling nonhierarchical multilevel data. Misspecifying CCREMs as hierarchical linear models (i.e., treating the cross-classified data as strictly hierarchical by ignoring one of the crossed factors) causes biases in the variance component estimates, which in turn, results in biased estimation in the standard errors of the regression coefficients. Analytical studies were conducted to provide closed-form expressions for the biases. With balanced design data structure, ignoring a crossed factor causes overestimation of the variance components of adjacent levels and underestimation of the variance component of the remaining crossed factor. Moreover, ignoring a crossed factor at the kth level causes underestimation of the standard error of the regression coefficient of the predictor associated with the ignored factor and overestimation of the standard error of the regression coefficient of the predictor at the (k-1)th level. Simulation studies were also conducted to examine the effect of different structures of cross-classification on the biases. In general, the direction and magnitude of the biases depend on the level of the ignored crossed factor, the level with which the predictor is associated at, the magnitude of the variance component of the ignored crossed factor, the variance components of the predictors, the sample sizes, and the structure of cross-classification. The results were further illustrated using the Early Childhood Longitudinal Study-Kindergarten Cohort data.