Showing papers by "Stephen G. West published in 1991"

Multiple Regression: Testing and Interpreting Interactions

Abstract: Introduction Interactions between Continuous Predictors in Multiple Regression The Effects of Predictor Scaling on Coefficients of Regression Equations Testing and Probing Three-Way Interactions Structuring Regression Equations to Reflect Higher Order Relationships Model and Effect Testing with Higher Order Terms Interactions between Categorical and Continuous Variables Reliability and Statistical Power Conclusion Some Contrasts Between ANOVA and MR in Practice

Statistical issues in the study of temporal data: daily experiences.

Abstract: This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.

The use of structural equation modeling in generative research: toward the design of a preventive intervention for bereaved children.

Abstract: Describes a generative study of processes which may lead to symptomatology in children who have experienced the death of a parent. Based on existing literature, four putative mediating variables were identified: parental demoralization, family warmth, negative family events, and positive stable family events. Structural equation modeling techniques were used to compare several potential causal models involving these variables. The results were most consistent with a model in which bereavement was not directly related to the child symptomatology, but rather its effects were transmitted through these four mediational mechanisms. The implications of the results of the structural modeling for the design and evaluation of preventive interventions are discussed briefly.

Central and peripheral nervous system demyelination after infection with Mycoplasma pneumonia: evidence of an autoimmune process.

Abstract: We have reported a unique case of multiple central and peripheral nervous system abnormalities after a serologically documented infection due to Mycoplasma pneumoniae. The evidence suggests that this organism is capable of causing demyelination, probably through an autoimmune process. This case may help to provide further insight into the pathogenetic mechanisms involved in other demyelinating diseases in which the triggering exogenous agent is unknown. Certainly this case demonstrated that both central and peripheral nervous system demyelination can occur and that patients with M pneumoniae infections should be observed closely for possible development of neurologic symptoms.