Showing papers by "John J. McArdle published in 2010"

Longitudinal Modeling of Developmental Changes in Psychological Research

Abstract: In this article we provide a review of recent advances in longitudinal models for multivariate change. We first claim the need for dynamic modeling approaches as a way to evaluate psychological theories. We then describe one such approach, latent change score (LCS) models, and illustrate their utility with a summary of research findings in various areas of psychological science. We then highlight the most prominent features of LCS models. We conclude the article with suggestions for future research on multivariate models of change that can enhance our understanding of psychological science.

Financial Decision Making and Cognition in a Family Context

Abstract: In this paper, we studied the association of cognitive traits and in particular numeracy of both spouses on financial outcomes of the family. We found significant effects, particularly for numeracy for financial and non-financial respondents alike, but much larger effects for the financial decision maker in the family. We also examined who makes these financial decisions in the family and why. Once again, cognitive traits such as numeracy were an important component of that decision with larger effects of numeracy for husbands compared to wives.

Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data

Abstract: This paper describes a set of applications of one class of longitudinal growth analysis - latent curve (LCM) and latent change score (LCS) analysis using structural equation modeling (SEM) techniques. These techniques are organized in five sections based on Baltes & Nesselroade (1979). (1) Describing the observed and unobserved longitudinal data. (2) Characterizing the developmental shape of both individuals and groups. (3) Examining the predictors of individual and group differences in developmental shapes. (4) Studying dynamic determinants among variables over time. (5) Studying group differences in dynamic determinants among variables over time. To illustrate all steps, we present SEM analyses of a relatively large set of data from the National Longitudinal Survey of Youth (NLSY). The inclusion of all five aspects of latent curve modeling is not often used in longitudinal analyses, so we discuss why more efforts to include all five are needed in developmental research.

A structural analysis of health outcomes after spinal cord injury.

Abstract: Objective: To develop and validate a latent model of health outcomes among persons with spinal cord injury.Methods: Survey data were collected at a large specialty hospital in the southeastern USA from 1,388 adult participants with traumatic spinal cord injury of at least 1 year's duration. Multiple indicators of health outcomes were used, including general health ratings, days adversely affected by poor health and poor mental health, treatments and hospitalizations, depressive symptoms, symptoms of illness or infection (eg, sweats, chills, fever), and multiple individual conditions (eg, pressure ulcers, subsequent injuries, fractures, contractures).Results: We performed exploratory factor analysis on half of the sample and confirmatory factor analysis on the other. A 6-factor solution was the best overall solution, because there was an excellent fit with the exploratory factor analysis (root mean square error of approximation = 0.042) and acceptable fit with the confirmatory factor analysis (root...

Prediction of somatic and non-somatic depressive symptoms between inpatient rehabilitation and follow-up.

Abstract: Longitudinal. We identified changes in the association of somatic and non-somatic symptoms (as measured by the Patient Health Questionnaire-9, PHQ-9) between inpatient rehabilitation after spinal cord injury (SCI) and 1 year after discharge. A specialty hospital in the Southeastern USA. A total of 584 adults with traumatic SCI were administered the PHQ-9 during inpatient rehabilitation. Of them, 227 completed the PHQ-9 by survey at 1-year follow-up. We performed time-lagged regression between times of measurement for somatic and non-somatic factors of the PHQ-9. The non-somatic factor at baseline was significantly predictive of the non-somatic (r=0.67, P=0.002) and somatic factors at follow-up (r=0.53, P=0.019). The somatic factor did not significantly predict either the somatic (r=0.10, n.s.) or non-somatic factors at follow-up (r=−0.01, NS). Factor analysis also indicated changing factor structure between inpatient rehabilitation and follow-up. Our results question the interpretation of somatic items during inpatient rehabilitation, as they are not predictive of either somatic or non-somatic symptoms at follow-up.

Contemporary Modeling of Gene × Environment Effects in Randomized Multivariate Longitudinal Studies

Abstract: There is a great deal of interest in the analysis of Genotype × Environment interactions (G×E). There are some limitations in the typical models for the analysis of G×E, including well-known statistical problems in identifying interactions and unobserved heterogeneity of persons across groups. The impact of a treatment may depend on the level of an unobserved variable, and this variation may dampen the estimated impact of treatment. Some researchers have noted that genetic variation may sometimes account for unobserved, and hence unaccounted for, heterogeneity. The statistical power associated with the G×E design has been studied in many different ways, and most results show that the small effects expected require relatively large or nonrepresentative samples (i.e., extreme groups). In this article, we describe some alternative approaches, such as randomized designs with multiple measures, multiple groups, multiple occasions, and analyses, to identify latent (unobserved) classes of people. These approaches are illustrated with data from the Aging, Demographics, and Memory Study (part of the Health and Retirement Study) examining the relations among episodic memory (based on word recall), APOE4 genotype, and educational attainment (as a proxy for an environmental exposure). Randomized clinical trials (RCTs) and randomized field trials (RFTs) have multiple strengths in the estimation of causal influences, and we discuss how measured genotypes can be incorporated into these designs. Use of these contemporary modeling techniques often requires different kinds of data be collected and encourages the formation of parsimonious models with fewer overall parameters, allowing specific G×E hypotheses to be investigated with a reasonable statistical foundation.

What Life‐Span Data Do We Really Need?

Abstract: This chapter reviews various methodological innovations in life-span research that have come as a direct result of advances in dealing with incomplete data using structural equation models (SEMs). As with most topics in methodology, the newest approaches are not radically new at all, and much of what we present here is based on classical considerations from the analysis of variance (ANOVA). The broad methodological topics include statistical power, multivariate scale and item measurement, and longitudinal and dynamic measurements. This presentation emphasizes the use of available longitudinal data to examine the statistical approximations that would result if less data were actually available—a technique used by Bell (1954). This same technique is used to deal with approximations resulting from having less people, less scales, less items, less occasions, and less dynamic information. Recent examples from the Berkley-Bradway longitudinal data are presented to illustrate the life-span data we really do need. Keywords: structural equation modeling; incomplete data; life-span data; convergence; acceleration; anova designs; adaptive testing; selection effects; dynamic effects