Nisha C. Gottfredson

Does Diversity at Undergraduate Institutions Influence Student Outcomes

Using two separate samples, this study establishes and replicates a model of the influence of two types of educational diversity on student outcomes. Study 1, using survey data regarding undergraduate experiences from a volunteer sample of 1,963 incoming law students, confirms measurement models for diversity and outcome constructs and tests models predicting student outcomes from Classroom Diversity and Contact Diversity. Study 2 utilizes data from a nationally representative sample of 6,100 incoming law students to replicate results from Study 1. Both studies find a positive relationship between diversity and educational outcomes. Results suggest that institutions of higher education should support informal interactions between students of diverse backgrounds and should encourage students to enroll in courses dealing with diversity.

The Effects of Educational Diversity in a National Sample of Law Students: Fitting Multilevel Latent Variable Models in Data With Categorical Indicators

Controversy surrounding the use of race-conscious admissions can be partially resolved with improved empirical knowledge of the effects of racial diversity in educational settings. We use a national sample of law students nested in 64 law schools to test the complex and largely untested theory regarding the effects of educational diversity on student outcomes. Social scientists who study these outcomes frequently encounter both latent variables and nested data within a single analysis. Yet, until recently, an appropriate modeling technique has been computationally infeasible, and consequently few applied researchers have estimated appropriate models to test their theories, sometimes limiting the scope of their research question. Our results, based on disaggregated multilevel structural equation models, show that racial diversity is related to a reduction in prejudiced attitudes and increased perceived exposure to diverse ideas and that these effects are mediated by more frequent interpersonal contact with diverse peers. These findings provide support for the idea that administrative manipulation of educational diversity may lead to improved student outcomes. Admitting a racially/ethnically diverse student body provides an educational experience that encourages increased exposure to diverse ideas and belief systems.

Sample Size Considerations in Prevention Research Applications of Multilevel Modeling and Structural Equation Modeling

When the goal of prevention research is to capture in statistical models some measure of the dynamic complexity in structures and processes implicated in problem behavior and its prevention, approaches such as multilevel modeling (MLM) and structural equation modeling (SEM) are indicated. Yet the assumptions that must be satisfied if these approaches are to be used responsibly raise concerns regarding their use in prevention research involving smaller samples. In this article, we discuss in nontechnical terms the role of sample size in MLM and SEM and present findings from the latest simulation work on the performance of each approach at sample sizes typical of prevention research. For each statistical approach, we draw from extant simulation studies to establish lower bounds for sample size (e.g., MLM can be applied with as few as ten groups comprising ten members with normally distributed data, restricted maximum likelihood estimation, and a focus on fixed effects; sample sizes as small as N = 50 can produce reliable SEM results with normally distributed data and at least three reliable indicators per factor) and suggest strategies for making the best use of the modeling approach when N is near the lower bound.

Three-Year Trajectory of Teachers’ Fidelity to a Drug Prevention Curriculum

Little is known about the trajectories over time of classroom teachers’ fidelity to drug prevention curricula. Using the “Concerns-Based Adoption Model” (C-BAM) as a theoretical framework, we hypothesized that teachers’ fidelity would improve with repetition. Participants comprised 23 middle school teachers who videotaped their administration of three entire iterations of the All Stars curriculum. Investigators coded two key curriculum lessons, specifically assessing the proportion of activities of each lesson teachers attempted and whether they omitted, added, or changed prescribed content, or delivered it using new methods. Study findings provided only partial support for the C-BAM model. Considerable variability in teachers’ performance over time was noted, suggesting that their progression over time may be nonlinear and dynamic, and quite possibly a function of their classroom and school contexts. There was also evidence that, by their third iteration of All Stars, teachers tended to regress toward the baseline mean. That is, the implementation quality of those that started out with high levels of fidelity tended to degrade, while those that started out with very low fidelity to the curriculum tended to improve. Study findings suggest the need for ongoing training and technical assistance, as well as “just in time” messages delivered electronically; but it is also possible that some prevention curricula may impose unrealistic expectations or burdens on teachers’ abilities and classroom time.

An Item Factor Analysis and Item Response Theory-Based Revision of the Everyday Discrimination Scale

The Everyday Discrimination Scale (EDS), a widely used measure of daily perceived discrimination, is purported to be unidimensional, to function well among African Americans, and to have adequate construct validity. Two separate studies and data sources were used to examine and cross-validate the psychometric properties of the EDS. In Study 1, an exploratory factor analysis was conducted on a sample of African American law students (N = 589), providing strong evidence of local dependence, or nuisance multidimensionality within the EDS. In Study 2, a separate nationally representative community sample (N = 3,527) was used to model the identified local dependence in an item factor analysis (i.e., bifactor model). Next, item response theory (IRT) calibrations were conducted to obtain item parameters. A five-item, revised-EDS was then tested for gender differential item functioning (in an IRT framework). Based on these analyses, a summed score to IRT-scaled score translation table is provided for the revised-EDS. Our results indicate that the revised-EDS is unidimensional, with minimal differential item functioning, and retains predictive validity consistent with the original scale.

Parental involvement protects against self-medication behaviors during the high school transition

We examined how drinking patterns change as adolescents transition to high school, particularly as a function of parental involvement. Stress associated with the transition to high school may deplete psychological resources for coping with negative daily emotions in an environment when opportunities to drink are more common. A cohort of elevated-risk middle school students completed daily negative affect (sadness, worry, anger, and stress) and alcohol use assessments before and after the transition to high school, resulting in a measurement burst design. Adolescents who reported less parental involvement were at higher risk for drinking on any given day. After (but not before) the transition to high school, daily within-person fluctuations of sadness predicted an increased probability of same-day alcohol use for adolescents who reported that their parents were minimally involved in their lives. The other negative affect indicators were not predictive of use. Our results suggest that the transition to high school may represent an important intervention leverage point, particularly for adolescents who lack adequate parental support to help them cope with day-to-day changes in sadness.

An Examination of the Parent Report Version of the Inventory of Callous-Unemotional Traits in a Community Sample of First-Grade Children

Background. The Inventory of Callous-Unemotional Traits is a self- and other report questionnaire of callous-unemotional behaviors that is increasingly widely used in research and clinical settings. Nonetheless, questions about the factor structure and validity of scales remain. Method. This study provided the first large-scale (N = 1,078) investigation of the parent report version of the Inventory of Callous-Unemotional Traits in a community sample of school-age (first-grade) children. Results. Confirmatory factor analysis indicated that a two-factor model that distinguished empathic-prosocial (EP) from callous-unemotional (CU) behaviors provided the best fit to the data. EP and CU were moderately to strongly correlated with each other (ϕ = −.67, p < .001) and with oppositional defiant disorder and conduct disorder (ODD/CD) behaviors (ϕODD/CD, EP = −.55; ϕODD/CD, CU = .71, ps < .001). Individual differences in EP and CU behaviors explained unique variation, beyond that attributable to ODD/CD behaviors, in peer-, teacher-, and parent relationship quality. Moreover, whereas EP moderated the effects of ODD/CD in the prediction of student–teacher relationship quality, CU moderated the effects of ODD/CD in the prediction of peer and parent relationship quality. Conclusions. Results are discussed with respect to the use of the ICU with school-age children.

Analyzing repeated measures data on individuals nested within groups: Accounting for dynamic group effects

Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression.

Diagnostic Procedures for Detecting Nonlinear Relationships Between Latent Variables

Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can be used to visualize and detect nonlinear relationships between latent variables. The first procedure involves fitting a linear structural equation model and then inspecting plots of factor score estimates for evidence of nonlinearity. The second procedure is to use a mixture of linear structural equation models to approximate the underlying, potentially nonlinear function. Targeted simulations indicate that the first procedure is more efficient, but that the second procedure is less biased. The mixture modeling approach is recommended, particularly with medium to large samples.

Modeling Change in the Presence of Nonrandomly Missing Data: Evaluating a Shared Parameter Mixture Model

In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficient-dependent missingness, is statistically nonignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This article describes a shared parameter mixture model (SPMM) for testing the sensitivity of growth model parameter estimates to a random coefficient-dependent missingness mechanism. Simulations show that the SPMM recovers trajectory estimates as well as or better than a standard growth model across a range of missing data conditions. The article concludes with practical advice for longitudinal data analysts.