Biomedical subjects

Robert Cudeck

Publications and source records attributed to Robert Cudeck.

4 recordsLinked to original sources

PubMed2007

Analysis of nonlinear patterns of change with random coefficient models.

Nonlinear patterns of change arise frequently in the analysis of repeated measures from longitudinal studies in psychology. The main feature of nonlinear development is that change is more rapid in some periods than in others. There generally also are strong individual differences, so although there is a general similarity of patterns for different persons over time, individuals exhibit substantial heterogeneity in their particular response. To describe data of this kind, researchers have extended the random coefficient model to accommodate nonlinear trajectories of change. It can often produce a statistically satisfying account of subject-specific development. In this review we describe and illustrate the main ideas of the nonlinear random coefficient model with concrete examples.

Analysis of Variance↗

PubMed2006

Teasing apart a multiple component approach to adolescent alcohol prevention: what worked in Project Northland?

This paper presents the results of a post hoc component analysis designed to tease apart the effects of different intervention strategies used in Project Northland, a group-randomized, community-wide, multi-level intervention trial originally conducted in the 1990's to prevent and reduce alcohol use among a cohort of mainly White students in rural Minnesota. This study focuses on Phase I, when students were in 6th-8th grade. The intervention during this phase included five components: classroom curricula, peer leadership, youth-driven/led extra-curricular activities, parent involvement programs, and community activism. Student exposure to/participation in these components was followed over time using reliable process measures. These measures were used as time-varying covariates in growth curve analyses to estimate the effects of the intervention components over time. Multi-item scales from annually-administered student surveys were used to measure relevant outcome variables, like alcohol use. The impact of the components appears to have been differential. The strongest effects were documented for the planners of extra-curricular activities and parent program components. The classroom curricula proved moderately effective, but no effects were associated with differential levels of community activism. The interactions tested here did not provide support for synergistic effects between selected intervention components. Care must be taken when selecting and combining intervention strategies meant to reduce adolescent alcohol use.

Adolescent↗

PubMed2003

A realistic perspective on pattern representation in growth data: comment on Bauer and Curran (2003).

D. J. Bauer and P. J. Curran (2003) cautioned that results obtained from growth mixture models may sometimes be inaccurate. The problem they addressed occurs when a growth mixture model is applied to a single, general population of individuals but findings incorrectly support the conclusion that there are 2 subpopulations. In an artificial sampling experiment, they showed that this can occur when the variables in the population have a nonnormal distribution. A realistic perspective is that although a healthy skepticism to complex statistical results is appropriate, there are no true models to discover. Consequently, the issue of model misspecification is irrelevant in practical terms. The purpose of a mathematical model is to summarize data, to formalize the dynamics of a behavioral process, and to make predictions. All of this is scientifically valuable and can be accomplished with a carefully developed model, even though the model is false.

Body Height↗

PubMed2002

Multiphase mixed-effects models for repeated measures data.

Behavior that develops in phases may exhibit distinctively different rates of change in one time period than in others. In this article, a mixed-effects model for a response that displays identifiable regimes is reviewed. An interesting component of the model is the change point. In substantive terms, the change point is the time when development switches from one phase to another. In a mixed-effects model, the change point can be a random coefficient. This possibility allows individuals to make the transition from one phase to another at different ages or after different lengths of time in treatment. Two examples are reviewed in detail, both of which can be estimated with software that is widely available.

Humans↗