Ethnic Differences in Grade Trajectories During the Transition to Junior High

The transition from elementary to junior high school is difficult for some children, as indicated by a drop in grades in the new school setting from the beginning to end of the year. Finer-grain analysis of grade trajectories in the first year of junior high may reflect important differences among groups. In the present study, variables predicting linear and quadratic grade trajectories over the seventh grade were examined using a structural equation model (SEM, AMOS-4) and curve estimation procedures. Participants were 214 boys and 259 girls entering junior high (52% Anglo, 36% Hispanics, primarily of Mexican descent, and 12% Blacks). Three trajectory patterns were observed: “sliders,” students who showed a fairly steady grade decline over the year (characteristic of Anglos), “steadies,” students who varied little over the year (characteristic of Blacks), and “rebounders,” students whose grades dropped to a minimum in the fourth 6-week period, then showed limited recovery (characteristic of Hispanics). In the SEM, a lower intercept (representing the average of sixth grade grades) was associated with minority ethnic/racial status (Hispanic or Black), using more emotional discharge to cope, having a lower percentage of adults in the support network, poorer family functioning, and greater depression. Being Black was associated with a positive path coefficient to the linear slope of the grade trajectory, while a negative path coefficient was associated with using more emotional discharge in coping. The quadratic element (drop in grades with some recovery) was more pronounced for Hispanic participants, less pronounced for Black participants, and more pronounced when poorer family functioning was reported. Curve estimation procedures confirmed these ethnic/racial group differences. Reasons for such differences and their implications for schools and families are discussed.     

潜变量交互效应分析方法

简要回顾了分析显变量交互效应的常用方法。详细讨论了目前分析潜变量交互效应的主要方法,包括用潜变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并比较和评价了这些方法的优缺点。最后归纳了潜变量交互效应分析方法的研究趋势,并介绍了新近进展(包括LMS方法和GAPI方法)。     

Learning Disabilities, Gender, Sources of Efficacy, Self-Efficacy Beliefs, and Academic Achievement in High School Students

This study examined the impact of gender, learning disability (LD) status, and sources of efficacy on self-efficacy beliefs and academic achievement in the concept of Bandura's self-efficacy theory. Two hundred and seventy-eight high school students participated in the study. Structural equation modeling was used. The results revealed that LD status had indirect influence on self-efficacy via the source variable; gender did not have direct or indirect influences on self-efficacy; sources of efficacy had direct impact on self-efficacy, which in turn affected academic performance. The structural model fit the data well and explained 55% of the variance in academic achievement.     

Psychological Variables Associated with Pain Perceptions Among Individuals with Chronic Spinal Cord Injury Pain

Structural equation modeling was used to examine the relationships between selected psychological variables and pain perceptions in 103 individuals experiencing chronic pain following traumatic spinal cord injury (SCI). Previous studies have suggested strong relationships between psychological variables and chronic SCI pain, but further delineation of such relationships is needed in order ultimately to develop more effective pain management strategies for individuals afflicted with such pain. Anger was found to be significantly related to perceptions of pain (p < .05), but neither guilt nor anger suppression was significantly associated with perceived pain. Internal health locus of control was associated with decreased pain perceptions (p < .05), but there was no significant relationship between internal health locus of control and anger. Punishing responses from significant others to pain complaints were related to feelings of guilt (p < .05) and perceived pain (p < .05), but this relationship was not mediated by guilt.     

A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

Accounting for Parcel-Allocation Variability in Practice: Combining Sources of Uncertainty and Choosing the Number of Allocations

Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in structural equation models? Second, on what basis can we choose the number of repeated item-to-parcel allocations within sample? Third, how can we diagnose and report proportions of total variability per estimate arising due to parcel-allocation variability versus sampling variability? This article addresses these three methodological issues. Developments are illustrated using simulated and empirical examples, and software for implementing them is provided.     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

The two roads of passionate goal pursuit: links with appraisal,coping, and academic achievement

Background and Objectives: In this research, we tested the role of cognitive appraisals in explaining why harmonious and obsessive passion dimensions are related to distinct forms of coping and explored if performance was impacted by these appraisal and coping processes. Design: Undergraduate students (N = 489) participated in a longitudinal study and completed three surveys throughout the course of an academic year. Methods: Participants completed assessments of both passion dimensions (Time 1), reported how they were appraising and coping with the mid-year examination period (Time 2), and provided consent to obtain their final grade in Introductory Psychology (Time 3). The hypothesized model was tested using structural equation modeling. Results: Harmonious and obsessive passion dimensions were linked with approach and avoidant coping responses, respectively. Cognitive appraisals, particularly appraisals of challenge and uncontrollability, played an indirect role in these relationships. In addition, both appraisals and coping responses had an indirect effect in the relationship between passion dimensions and final grade. Conclusions: These results identify cognitive appraisal as a reason why passion dimensions are linked with distinct coping tendencies and demonstrate the role of appraisal and coping processes in the journey to passionate goal attainment.