VIII. THE PAST, PRESENT, AND FUTURE OF DEVELOPMENTAL METHODOLOGY.

This chapter selectively reviews the evolution of quantitative practices in the field of developmental methodology. The chapter begins with an overview of the past in developmental methodology, discussing the implementation and dissemination of latent variable modeling and, in particular, longitudinal structural equation modeling. It then turns to the present state of developmental methodology, highlighting current methodological advances in the field. Additionally, this section summarizes ample quantitative resources, ranging from key quantitative methods journal articles to the various quantitative methods training programs and institutes. The chapter concludes with the future of developmental methodology and puts forth seven future innovations in the field. The innovations discussed span the topics of measurement, modeling, temporal design, and planned missing data designs. Lastly, the chapter closes with a brief overview of advanced modeling techniques such as continuous time models, state space models, and the application of Bayesian estimation in the field of developmental methodology.

A predictor model for discharge destination in inpatient rehabilitation patients.

OBJECTIVE: The aims of this study were to conduct an exploratory factor analysis on admission data, identify key variables that may predict discharge to home, and create and test a predictor model using confirmatory factor analysis and structured equation modeling. DESIGN: A secondary data analysis was conducted using a data set of 176,419 cases. Statistical analyses used included an exploratory factor analysis, confirmatory factor analysis, and structural equation modeling using variables collected upon admission. RESULTS: The hypothesis model included ten items that loaded on a latent factor of physical performance and five items that loaded on a latent factor of cognitive performance. The final predictor model resulted in three physical performance items (grooming, toileting, and chair transfers) and four cognitive performance items (comprehension, expression, problem solving, and memory) with results of χ(2) (df) of 44,708.630 (11), root mean square error of approximation of 0.152, and comparative fit index/Tucker-Lewis index of 0.957/0.918. CONCLUSIONS: Four factors (admit cognitive scores, admit physical scores, age, and diagnosis category) were identified and tested. The latent factors admit cognitive performance scores and admit physical performance scores were shown to be strong predictors for discharge to home, whereas diagnosis categories and age were weak predictors in this model.

The relation of student behavior, peer status, race, and gender to decisions about school discipline using CHAID decision trees and regression modeling.

Peer nominations and demographic information were collected from a diverse sample of 1493 elementary school participants to examine behavior (overt and relational aggression, impulsivity, and prosociality), context (peer status), and demographic characteristics (race and gender) as predictors of teacher and administrator decisions about discipline. Exploratory results using classification tree analyses indicated students nominated as average or highly overtly aggressive were more likely to be disciplined than others. Among these students, race was the most significant predictor, with African American students more likely to be disciplined than Caucasians, Hispanics, or Others. Among the students nominated as low in overt aggression, a lack of prosocial behavior was the most significant predictor. Confirmatory analysis using hierarchical logistic regression supported the exploratory results. Similarities with other biased referral patterns, proactive classroom management strategies, and culturally sensitive recommendations are discussed.