Using a Multivariate Multilevel Polytomous Item Response Theory Model to Study Parallel Processes of Change: The Dynamic Association Between Adolescents' Social Isolation and Engagement With Delinquent Peers in the National Youth Survey

The application of multidimensional item response theory models to repeated observations has demonstrated great promise in developmental research. It allows researchers to take into consideration both the characteristics of item response and measurement error in longitudinal trajectory analysis, which improves the reliability and validity of the latent growth curve (LGC) model. The purpose of this study is to demonstrate the potential of Bayesian methods and the utility of a comprehensive modeling framework, the one combining a measurement model (e.g., a multidimensional graded response model, MGRM) with a structural model (e.g., an associative latent growth curve analysis, ALGC). All analyses are implemented in WinBUGS 1.4.3 (Spiegelhalter, Thomas, Best, &; Lunn, 2003 Spiegelhalter, D. J., Thomas, A., Best, N. G. and Lunn, D. 2003. WinBUGS user manual Cambridge, , UK: MRC Biostatistics Unit, Institute of Public Health. Retrieved October 24, 2006, from http://www.mrc-bsu.cam.ac.uk/bugs [Google Scholar]), which allows researchers to use Markov chain Monte Carlo simulation methods to fit complex statistical models and circumvent intractable analytic or numerical integrations. The utility of this MGRM-ALGC modeling framework was investigated with both simulated and empirical data, and promising results were obtained. As the results indicate, being a flexible multivariate multilevel model, this MGRM-ALGC model not only produces item parameter estimates that are readily estimable and interpretable but also estimates the corresponding covariation in the developmental dimensions. In terms of substantive interpretation, as adolescents perceived themselves more socially isolated, the chance that they are engaged with delinquent peers becomes profoundly larger. Generally, boys have a higher initial exposure extent than girls. However, there is no gender difference associated with other latent growth parameters.     

What Carries a Mediation Process? Configural Analysis of Mediation

Mediation is a process that links a predictor and a criterion via a mediator variable. Mediation can be full or partial. This well-established definition operates at the level of variables even if they are categorical. In this article, two new approaches to the analysis of mediation are proposed. Both of these approaches focus on the analysis of categorical variables. The first involves mediation analysis at the level of configurations instead of variables. Thus, mediation can be incorporated into the arsenal of methods of analysis for person-oriented research. Second, it is proposed that Configural Frequency Analysis (CFA) can be used for both exploration and confirmation of mediation relationships among categorical variables. The implications of using CFA are first that mediation hypotheses can be tested at the level of individual configurations instead of variables. Second, this approach leaves the door open for different types of mediation processes to exist within the same set. Using a data example, it is illustrated that aggregate-level analysis can overlook mediation processes that operate at the level of individual configurations.     

Family Support and Mental Health in Pregnant Women Experiencing Interpersonal Partner Violence: An Analysis of Ethnic Differences

This study investigated the relationship between family support and mental health in a community sample of pregnant women experiencing interpersonal partner violence (IPV). This study also examined how family support may differentially affect mental health in Black and White women experiencing IPV. A total of 110 participants (32 Black and 78 White) were obtained from the first time period of a longitudinal study examining the effects of domestic violence on women. The model was supported (χ² = 142.55; df = 72, p > .001, RMSEA = .07). Black women had better mental health than White women. However, ethnicity was not a significant predictor of family support. Implications from this study highlight the need for further research on the role of family support in the lives of physically abused women as well as the need to incorporate cross cultural perspectives when examining this issue.     

The structure of childhood disruptive behaviors

Attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) frequently co-occur. Comorbidity of these 2 childhood disruptive behavior domains has not been satisfactorily explained at either a structural or etiological level. The current study evaluated a bifactor model, which allows for a "g" factor in addition to distinct component factors, in relation to other models to improve understanding of the structural relationship between ADHD and ODD. Participants were 548 children (321 boys, 227 girls) between the ages of 6 years and 18 years who participated in a comprehensive diagnostic assessment incorporating parent and teacher ratings of symptoms. Of these 548 children, 153 children were diagnosed with ADHD (without ODD), 114 children were diagnosed with ADHD + ODD, 26 children were diagnosed with ODD (without ADHD), and 239 children were classified as non-ADHD/ODD comparison children (including subthreshold cases). ADHD symptoms were assessed via parent report on a diagnostic interview and via parent and teacher report on the ADHD Rating Scale. ODD symptoms were assessed via teacher report. A bifactor model of disruptive behavior, comprising a "g" factor and the specific factors of ADHD and ODD, exhibited best fit, compared to 1-factor, 2-factor, 3-factor, and 2nd-order factor models of disruptive behaviors. It is concluded that a bifactor model of childhood disruptive behaviors is superior to existing models and may help explain common patterns of comorbidity between ADHD and ODD.     

Temporal patterns of variable relationships in person-oriented research: longitudinal models of configural frequency analysis

This article reviews the premises of configural frequency analysis (CFA), including methods of choosing significance tests and base models, as well as protecting alpha, and discusses why CFA is a useful approach when conducting longitudinal person-oriented research. CFA operates at the manifest variable level. Longitudinal CFA seeks to identify those temporal patterns that stand out as more frequent (CFA types) or less frequent (CFA antitypes) than expected with reference to a base model. A base model that has been used frequently in CFA applications, prediction CFA, and a new base model, auto-association CFA, are discussed for analysis of cross-classifications of longitudinal data. The former base model takes the associations among predictors and among criteria into account. The latter takes the auto-associations among repeatedly observed variables into account. Application examples of each are given using data from a longitudinal study of domestic violence. It is demonstrated that CFA results are not redundant with results from log-linear modeling or multinomial regression and that, of these approaches, CFA shows particular utility when conducting person-oriented research.     

External Validation of Bifactor Model of ADHD: Explaining Heterogeneity in Psychiatric Comorbidity,Cognitive Control,and Personality Trait Profiles Within DSM-IV ADHD

The current paper provides external validation of the bifactor model of ADHD by examining associations between ADHD latent factor/profile scores and external validation indices. 548 children (321 boys; 302 with ADHD), 6 to 18 years old, recruited from the community participated in a comprehensive diagnostic procedure. Mothers completed the Child Behavior Checklist, Early Adolescent Temperament Questionnaire, and California Q-Sort. Children completed the Stop and Trail-Making Task. Specific inattention was associated with depression/withdrawal, slower cognitive task performance, introversion, agreeableness, and high reactive control; specific hyperactivity-impulsivity was associated with rule-breaking/aggressive behavior, social problems, errors during set-shifting, extraversion, disagreeableness, and low reactive control. It is concluded that the bifactor model provides better explanation of heterogeneity within ADHD than DSM-IV ADHD symptom counts or subtypes.     

Direction of dependence in measurement error models

Methods to determine the direction of a regression line, that is, to determine the direction of dependence in reversible linear regression models (e.g., x→y vs. y→x), have experienced rapid development within the last decade. However, previous research largely rested on the assumption that the true predictor is measured without measurement error. The present paper extends the direction dependence principle to measurement error models. First, we discuss asymmetric representations of the reliability coefficient in terms of higher moments of variables and the attenuation of skewness and excess kurtosis due to measurement error. Second, we identify conditions where direction dependence decisions are biased due to measurement error and suggest method of moments (MOM) estimation as a remedy. Third, we address data situations in which the true outcome exhibits both regression and measurement error, and propose a sensitivity analysis approach to determining the robustness of direction dependence decisions against unreliably measured outcomes. Monte Carlo simulations were performed to assess the performance of MOM-based direction dependence measures and their robustness to violated measurement error assumptions (i.e., non-independence and non-normality). An empirical example from subjective well-being research is presented. The plausibility of model assumptions and links to modern causal inference methods for observational data are discussed.     

Instructional set and internet use by low-income adults.

This research examined the effects of instructional set on Internet use by low-income adults during a 16-month longitudinal study. Participants (n = 117) received instructions that focused on either the Internet's communication tools or its information tools. Internet use was continuously and automatically recorded. Survey measures of computer and Internet experiences, affect and attitudes were obtained to examine their mediational role in the relationship between instructional set and Internet use. Results indicated that instructions focused on the Internet's information tools led to greater Internet use than instructions focused on its communication tools or only basic instructions about how to use the Internet. Implications for reducing the digital divide are discussed.