NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 15 results Save | Export
Peer reviewed Peer reviewed
Freedman, D. A. – Journal of Educational Statistics, 1987
Presents a review of path analysis techniques using Hope's (1984) comparative study of schooling and social mobility in Scotland and the United States as a case study. Path analysis has been used in the social sciences to untangle complex cause and effect relationships. The limits of path analysis as a way to analyze complex social phenomena are…
Descriptors: Case Studies, Correlation, Models, Path Analysis
Peer reviewed Peer reviewed
Hope, Keith – Journal of Educational Statistics, 1987
Response to Freedman's critique of path analysis discusses appropriate ways to represent social processes in numerical form and the use of path analysis to achieve that goal. Discusses two main issues: the selection of variables and whether multiple regression can be used to model social processes. (RB)
Descriptors: Models, Path Analysis, Regression (Statistics), Social Science Research
Peer reviewed Peer reviewed
Achen, Christopher H. – Journal of Educational Statistics, 1987
Comments on the uses of statistical analysis in social sciences within the context of Freedman's "As Other's See Us: A Case Study in Path Analysis." Points out that there is an unacknowledged gap between statistical principles and the data analysis that social scientists use. (RB)
Descriptors: Data Analysis, Path Analysis, Regression (Statistics), Social Science Research
Peer reviewed Peer reviewed
Cliff, Norman – Journal of Educational Statistics, 1987
Suggests that improving the data collected for a study will lead to the development of better models to analyze that data. Also urges researchers to have a more critical attitude toward the literal interpretation of test results. (RB)
Descriptors: Data Analysis, Models, Path Analysis, Social Science Research
Peer reviewed Peer reviewed
Fox, John – Journal of Educational Statistics, 1987
Examines D. A. Freedman's criticism of path analysis, agreeing with Freedman's criticism of its application to nonexperimental data in the social sciences. Argues that Freedman's overall conclusions, however, are too pessimistic. (RB)
Descriptors: Data Analysis, Models, Path Analysis, Regression (Statistics)
Peer reviewed Peer reviewed
Karlin, Samuel – Journal of Educational Statistics, 1987
Discusses the application of path analysis in the context of genetic epidemiology. Examines the coherence of model specification, plausibility of modeling assumptions, the interpretability and usefulness of the model, and the validity of statistical procedures. (RB)
Descriptors: Epidemiology, Family Environment, Genetics, Models
Peer reviewed Peer reviewed
Muthen, Bengt O. – Journal of Educational Statistics, 1987
Argues that path analysis can clarify aspects of social processes but only if skilled practitioners use the models. The practitioners need to have better methodological training and statisticians need to contribute more to the training process. (RB)
Descriptors: Path Analysis, Research Methodology, Social Science Research, Statistical Analysis
Peer reviewed Peer reviewed
Rogosa, David – Journal of Educational Statistics, 1987
Makes a distinction in methodological work between building and applying statistical models for the processes that generate data for the social sciences and applying that data to available statistical methods. Agrees with Freeman that researchers need to think more about underlying social processes. (RB)
Descriptors: Correlation, Data Analysis, Models, Path Analysis
Peer reviewed Peer reviewed
Rothenberg, Thomas J. – Journal of Educational Statistics, 1987
Comment agrees with Freedman's criticism of the path analysis model for studying education and social stratification. The model makes assumptions about how data are generated that are generally false. (RB)
Descriptors: Models, Path Analysis, Research Methodology, Social Science Research
Peer reviewed Peer reviewed
Seneta, E. – Journal of Educational Statistics, 1987
This comment is intended to complement points made by Freedman in his critique of the path analysis method. The role of statistical analysis in social sciences is also discussed. (RB)
Descriptors: Models, Path Analysis, Research Methodology, Statistical Analysis
Peer reviewed Peer reviewed
Freedman, D. A. – Journal of Educational Statistics, 1987
Freedman responds to a series of articles commenting on his critique of the path analysis model. The focus of this rejoinder is the assumptions made by researchers who use the path analysis method, particularly the assumption that the equations in path analysis are structural. (RB)
Descriptors: Models, Path Analysis, Research Methodology, Social Science Research
Peer reviewed Peer reviewed
Wilson, Mark – Journal of Educational Statistics, 1989
An empirical sampling approach was used to assess the accuracy of a Taylor approximation for the estimation of sampling errors. The sampling errors were in the statistics involved in estimating a path model based on medium-sized samples gathered using five sample designs commonly used in educational research. (TJH)
Descriptors: Educational Research, Error of Measurement, Estimation (Mathematics), Mathematical Models
Peer reviewed Peer reviewed
Bentler, P. M. – Journal of Educational Statistics, 1987
Discusses the theoretical aspects of structural modeling rather than its applications. Considers the basic hypothesis in structural modeling and concludes that it can be an effective tool in some research contexts, though not all. (RB)
Descriptors: Models, Path Analysis, Scientific Methodology, Social Science Research
Peer reviewed Peer reviewed
Wold, Herman – Journal of Educational Statistics, 1987
Explains two methods of systems analysis: the Fix-Point (FP) and the Partial Least Squares (PLS). Argues against Freedman's rejection of the structural assumptions of path analysis models. (RB)
Descriptors: Models, Path Analysis, Research Methodology, Social Science Research
Peer reviewed Peer reviewed
Bentler, P. M.; Lee, Sik-Yum – Journal of Educational Statistics, 1983
A method for the estimation of covariance structure models under polynomial constraints (such as quadratic constraints) is presented. Estimation is on maximum likelihood principles, and the test statistics, parameter estimates, and standard errors are based on a statistical theory which takes the constraints into account. (Author/JKS)
Descriptors: Analysis of Covariance, Correlation, Estimation (Mathematics), Factor Analysis