Showing papers by "John J. McArdle published in 2004"

An experimental analysis of dynamic hypotheses about cognitive abilities and achievement from childhood to early adulthood.

Abstract: This study examined the dynamics of cognitive abilities and academic achievement from childhood to early adulthood. Predictions about time-dependent "coupling" relations between cognition and achievement based on R. B. Cattell's (1971, 1987) investment hypothesis were evaluated using linear dynamic models applied to longitudinal data (N=672). Contrary to Cattell's hypothesis, a first set of findings indicated that fluid and crystallized abilities, as defined by the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; R. W. Woodcock & M. B. Johnson, 1989-1990), were not dynamically coupled with each other over time. A second set of findings provided support for the original predictions and indicated that fluid ability was a leading indicator of changes in achievement measures (i.e., quantitative ability and general academic knowledge). The findings of this study suggest that the dynamics of cognitive abilities and academic achievement follow a more complex pattern than that specified by Cattell's investment hypothesis.

Ethnic and gender differences in science graduation at selective colleges with implications for admission policy and college choice

Abstract: Using Bowen and Bok's data from 23 selective colleges, we fit multilevel logit models to test two hypotheses with implications for affirmative action and group differences in attainment of science, math, or engineering (SME) degrees. Hypothesis 1, that differences in precollege academic preparation will explain later SME graduation disparities, was fully supported with respect to the outcome gap between Whites and underrepresented minorities, partially supported for that between Asians and underrepresented minorities, and between men and women. Hypothesis 2, that college selectivity, after accounting for student characteristics, will be positively associated with SME persistence, was not supported. We demonstrate that the significance of the selectivity effect is overestimated when unilevel models are used. Admission officials are advised to carefully consider the relative academic preparedness of science-interested students, and such students choosing among colleges are advised to compare their academic qualifications to those of successful science students at each institution.

Structural Modeling of Dynamic Changes in Memory and Brain Structure Using Longitudinal Data From the Normative Aging Study

Abstract: This is an application of new longitudinal structural equation modeling techniques to time-dependent associations of memory and brain structure measurements. There were 225 participants aged 30-80 years at baseline who were measured again after a 7-year interval on both the lateral ventricular size and Wechsler memory score. Multiple regression analyses show nonlinear associations with age but no relationships among longitudinal changes. Mixed-effects latent growth curve analyses and analyses based on latent difference scores indicate that longitudinal changes in both variables are reasonably well described by an exponential or dual change model. Bivariate dynamic structural equation modeling analyses indicate age-lagged changes operate in a coupled-over-time fashion, with the brain measure (lateral ventricular size) as a leading indicator in time of memory (Wechsler memory score) declines.

Modeling Latent Growth Curves With Incomplete Data Using Different Types of Structural Equation Modeling and Multilevel Software

Abstract: This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in the case of models of latent growth fitted to incomplete data. The general purpose of this article is to provide a demonstration that integrates programming features from different software. The most immediate goal is to help researchers implement these LGC models as a useful way to test hypotheses of growth.

Dynamic Structure of Emotions Among Individuals with Parkinson's Disease

Abstract: With few exceptions, the dynamics underlying the mood structures of individuals with Parkinson's Disease have consistently been overlooked Based on 12 participants' daily self-reports over 72 days, we identified 10 participants whose covariance matrices for positive and negative affect were similar enough to warrant pooling Dynamic factor models that included factor autoregression and cross-regressions were fitted to the pooled, lagged covariance matrix representing approximately 700 occasions of measurement Although results from the pooled data indicated that both positive and negative affect had a strong lag-1 autoregressive impact on current positive and negative affect, most individuals showed stronger autoregressive effects for positive than negative affect when examined individually There was also a weak cross-regression effect of positive affect on negative affect, but the reverse was not true Through model fitting, we demonstrated that failure to incorporate lagged relations among factors cou

Methods for Dynamic Change Hypotheses

Abstract: In this rcscarch wc describe longitudinal structural equaiion modeis useful for testing dynamic hypothesis. The Statistical modeis described here come from recent research on latent variable siruclural equaiion modeling (SEM) for longiiudinal data. The initial set of analyses arc hased on considerations about measurement modeis with changing scales over timc following modcls used by McArdle & Woodcock (1997). A second set of analyses are based directly on the latent growth curvc model of Meredith & Tisak (1990). A third set of analyses are based on latent difference score modcls of McArdle & Nesselroade (1994) and McArdle (2001). In a fourth and final set of analyses we present some new bivariate dynamic model across different variables at different ages from McArdle & Hamagami (2001). These SEM analyses permit a dynamk Interpretation of the developmental influences of onc variable upon another over timc and can bc used with many form of repeated mcasures longiiudinal data. This rcscarch paper emphasi/cs practica! aspects of testing dynamic hypolheses with SEM, but implications for turther experimcntal and developmental rcscarch arc also discussed.