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Vaske, Jerry J. – Sagamore-Venture, 2019
Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…
Descriptors: Surveys, Theories, Hypothesis Testing, Effect Size
Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling

Bechtoldt, Harold P. – Psychometrika, 1974
Procedures developed by Joreskog for studying similarities and differences in factor structures between different groups were applied to data from a study by Thurstoen to investigate the sampling stability of a hypothesized isolated configuration. The hypothesis of an isolated configuration was rejected but not by much. (Author/RC)
Descriptors: Comparative Analysis, Factor Analysis, Factor Structure, Hypothesis Testing

Montanelli, Richard G., Jr. – Educational and Psychological Measurement, 1974
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Reliability

McDonald, Roderick P. – Psychometrika, 1975
Descriptors: Analysis of Covariance, Factor Analysis, Hypothesis Testing, Matrices
Nitko, Anthony J.; Feldt, Leonard S. – Amer Educ Res J, 1969
Descriptors: Difficulty Level, Factor Analysis, Hypothesis Testing, Item Analysis
Broadbooks, Wendy J.; Elmore, Patricia B. – 1983
This study developed and investigated an empirical sampling distribution of the congruence coefficient. The effects of sample size, number of variables, and population value of the congruence coefficient on the sampling distribution of the congruence coefficient were examined. Sample data were generated on the basis of the common factor model and…
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Research Methodology
Joreskog, Karl G. – 1970
This paper is concerned with the study of similarities and differences in factor structures between different groups. A common situation is when a battery of tests has been administered to samples of examinees from several populations. A very general model is presented, in which any parameter in the factor analysis models (factor loadings, factor…
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Hypothesis Testing

Zwick, William R.; Velicer, Wayne F. – 1984
A common problem in the behavioral sciences is to determine if a set of observed variables can be more parsimoniously represented by a smaller set of derived variables. To address this problem, the performance of five methods for determining the number of components to retain (Horn's parallel analysis, Velicer's Minimum Average Partial (MAP),…
Descriptors: Behavioral Science Research, Comparative Analysis, Correlation, Data Interpretation
Lipson, Kay – Mathematics Education Research Journal, 2003
Many statistics educators believe that few students develop the level of conceptual understanding essential for them to apply correctly the statistical techniques at their disposal and to interpret their outcomes appropriately. It is also commonly believed that the sampling distribution plays an important role in developing this understanding.…
Descriptors: Statistical Inference, Learning Strategies, Sampling, Statistics
Penfield, Douglas A. – 1972
Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)
Descriptors: Analysis of Variance, Bayesian Statistics, Behavioral Science Research, Computers