Showing papers by "Stephen G. West published in 2016"

Propensity score analysis with missing data.

Abstract: Propensity score analysis is a method that equates treatment and control groups on a comprehensive set of measured confounders in observational (nonrandomized) studies. A successful propensity score analysis reduces bias in the estimate of the average treatment effect in a nonrandomized study, making the estimate more comparable with that obtained from a randomized experiment. This article reviews and discusses an important practical issue in propensity analysis, in which the baseline covariates (potential confounders) and the outcome have missing values (incompletely observed). We review the statistical theory of propensity score analysis and estimation methods for propensity scores with incompletely observed covariates. Traditional logistic regression and modern machine learning methods (e.g., random forests, generalized boosted modeling) as estimation methods for incompletely observed covariates are reviewed. Balance diagnostics and equating methods for incompletely observed covariates are briefly described. Using an empirical example, the propensity score estimation methods for incompletely observed covariates are illustrated and compared. (PsycINFO Database Record

Effect of Trajectories of Friends' and Parents' School Involvement on Adolescents' Engagement and Achievement

Abstract: In a sample of 527 academically at-risk youth, we investigated trajectories of friends' and parents' school involvement across ages 12-14 and the joint contributions of these trajectories to adolescents' age 15 school engagement and academic achievement. Girls reported higher levels of friends' and parents' school involvement than boys. Both parents' and friends' school involvement declined across ages 12-14. Combined latent growth models and structural equation models showed effects of the trajectories of friends' and parents' school involvement on adolescents' age 15 school engagement and academic achievement, over and above adolescents' prior performance. These effects were additive rather than interactive. Strategies for enhancing parent involvement in school and students' affiliation with peers who are positively engaged in school are discussed.

Weekly Cycles in Daily Report Data: An Overlooked Issue

Abstract: Announcement The Jeffrey S. Tanaka Occasional Papers in Quantitative Methods for Personality With the publication of this issue's lead article, “Weekly Cycles in Daily Report Data: An Overlooked Issue,” the Journal of Personality continues an ongoing series devoted to the memory of Jeffrey S. Tanaka. The goal of this series, exemplified by the Liu and West article, is to continue the spirit of Jeff Tanaka's work by illustrating how innovative methodological and statistical approaches can improve our understanding of personality phenomena. Proposals for the Occasional Papers series are welcomed and should follow the guidelines in the solicitation that appeared in the December, 1993 (Volume 61, Number 4) issue of the Journal. Howard Tennen, Editor Daily diaries and other everyday experience methods are increasingly used to study relationships between two time-varying variables X and Y. Although daily data potentially often have weekly cyclical patterns (e.g., stress may be higher on weekdays and lower on weekends), the majority of daily diary studies have ignored this possibility. In this study, we investigated the effect of ignoring existing weekly cycles. We reanalyzed an empirical dataset (stress and alcohol consumption) and performed Monte Carlo simulations to investigate the impact of omitting weekly cycles. In the empirical dataset, ignoring cycles led to the inference of a significant within-person X–Y relation whereas modeling cycles suggested that this relationship did not exist. Simulation results indicated that ignoring cycles that existed in both X and Y led to bias in the estimated within-person X–Y relationship. The amount and direction of bias depended on the magnitude of the cycles, magnitude of the true within-person X–Y relation, and synchronization of the cycles. We encourage researchers conducting daily diary studies to address potential weekly cycles in their data. We provide guidelines for detecting and modeling cycles to remove their influence and discuss challenges of causal inference in daily experience studies.

Testing Statistical Moderation in Research on Home–School Partnerships: Establishing the Boundary Conditions

Abstract: In this chapter, we provide a tutorial on how to test for moderated effects of home–school relationships using different statistical approaches. We first provide a brief review of current research on moderated effects of home–school relationships to illustrate the theoretical and practical value of statistical testing of moderated effects. Next, with a detailed multilevel data example, we demonstrate different statistical methods, including: moderation models with composite scores, latent factors, and the reliability-adjusted composite scores as well as the use of the latent moderated structural equations (LMS) to test the moderating effect of a child characteristic (i.e., ADHD symptoms) on home–school relationship. We have used both SPSS Mixed and Mplus to analyze the models and included the corresponding annotated syntax of both programs in this chapter. We also discuss some related issues on testing moderation effects such as centering variables and handling missing data to provide guidance to researchers.

Alternatives to Randomized

Abstract: Randomized experiments are preferred for making inferences about causality when they can be im plemented and their assumptions are met. Yet assumptions can fail (e.g., attrition, treatment noncompliance) or randomization may be unethical or infeasibh. I describe alternative design and statistical approaches that permit testing causal hypotheses and present current empirical evidence related to alternative designs. Alternative designs permit a wider range of research questions to be answered and permit more direct generalization of causal effects; however, when using such designs, estimates of the mag nitude of the causal effect may be more uncertain.

Analyzing Complex Treatment Effects in Nonrandomized Observational Studies: The Case of Retention of Students in Grade.

Abstract: Should low-achieving students be promoted to the next grade or be retained (held back) in the prior grade? This special section presents a discussion of the application of marginal structural models to the challenging problem of estimating the effect of promotion versus retention in grade on math scores in elementary school. Vandecandelaere, De Fraine, Van Damme, and Vansteelandt provide a didactic presentation of the marginal structural modeling approach, noting retention is a time-varying treatment because promoted low-achieving students may be retained in a subsequent grade. Steiner, Park, and Kim's commentary presents a detailed analysis of the treatment effects being estimated in same-age versus same-grade comparisons from the perspective of the potential outcomes model. Reshetnyak, Cham, and Kim's commentary clarifies the conditions under which same-age versus same-grade comparisons might be preferred; they also identify methods of further improving the estimation of retention effects. In th...

Nesselroade and Molenaar's Measurement Concerns and Proposals: An Introduction

Abstract: In this special section Nesselroade and Molenaar (N & M) propose a provocative new approach to measurement invariance. When measures are collected repeatedly over time (e.g., daily diary studies), a potentially unique measurement model relating the items to the underlying construct can be created for each individual. If hypothesized causal paths specified between constructs (e.g., frustration → aggression) can be constrained to be equal across the individuals, a model with idiographic measurement of the constructs, but with nomothetic structural relationships can be specified. Three commentaries react to N & M's proposal. Revelle and Wilt challenge the priority given by N & M to unique individual measurement structures, arguing that between subjects differences in structural relationships are empirically important and meaningful. Markus's uses David Hume's framework to raise philosophy of science challenges for N & M's approach. Maydeu-Olivares challenges the incremental validity of N & M's approa...