Descriptor
| Analysis of Covariance | 145 |
| Mathematical Models | 145 |
| Statistical Analysis | 50 |
| Analysis of Variance | 43 |
| Correlation | 37 |
| Factor Analysis | 34 |
| Research Methodology | 31 |
| Comparative Analysis | 27 |
| Hypothesis Testing | 27 |
| Error of Measurement | 26 |
| Research Design | 25 |
| More ▼ | |
Source
Author
| Werts, Charles E. | 11 |
| Linn, Robert L. | 6 |
| Bentler, Peter M. | 4 |
| Maxwell, Scott E. | 4 |
| Porter, Andrew C. | 4 |
| Thompson, Bruce | 4 |
| Bentler, P. M. | 3 |
| Joreskog, Karl G. | 3 |
| Blair, R. Clifford | 2 |
| Bulcock, J. W. | 2 |
| Burstein, Leigh | 2 |
| More ▼ | |
Publication Type
| Reports - Research | 62 |
| Journal Articles | 51 |
| Speeches/Meeting Papers | 33 |
| Reports - Evaluative | 31 |
| Reports - Descriptive | 4 |
| Information Analyses | 2 |
| Books | 1 |
| Collected Works - General | 1 |
| Opinion Papers | 1 |
Education Level
Audience
| Researchers | 7 |
Location
| Canada | 1 |
| Finland | 1 |
| Netherlands | 1 |
| West Germany | 1 |
Laws, Policies, & Programs
| Elementary and Secondary… | 2 |
Assessments and Surveys
| California Achievement Tests | 1 |
| Illinois Test of… | 1 |
| Metropolitan Readiness Tests | 1 |
What Works Clearinghouse Rating
Peer reviewedHancock, Gregory R.; Kuo, Wen-Ling; Lawrence, Frank R. – Structural Equation Modeling, 2001
Using higher order factor models, this article illustrates latent curve analysis for the purpose of modeling longitudinal change directly in a latent construct. Provides examples with simultaneous estimation of covariance and mean structures for a single-group and two-group structure. (SLD)
Descriptors: Analysis of Covariance, Factor Analysis, Mathematical Models
Peer reviewedRovine, Michael J.; Molenaar, Peter C. M. – Structural Equation Modeling, 1998
Presents a LISREL model for the estimation of the repeated measures analysis of variance (ANOVA) with a patterned covariance matrix. The model is demonstrated for a 5 x 2 (Time x Group) ANOVA in which the data are assumed to be serially correlated. Similarities with the Statistical Analysis System PROC MIXED model are discussed. (SLD)
Descriptors: Analysis of Covariance, Correlation, Estimation (Mathematics), Mathematical Models
Keesling, J. Ward – 1973
In many circumstances it is appropriate to use the school as the unit of analysis. The variables measured on students must be aggregated to form a mean for each school. However, the means derived from the students sampled in a school will tend to fluctuate around the true mean for the school in a way determined by the within-school correlations…
Descriptors: Analysis of Covariance, Data Analysis, Males, Mathematical Models
Peer reviewedCeurvorst, Robert, W.; Stock, William A. – Multivariate Behavioral Research, 1978
The univariate and multivariate models for the analysis of covariance are compared for the case where an experimental design contains between and within subject factors, one dependent variable, and one observation per subject. (Author/JKS)
Descriptors: Analysis of Covariance, Analysis of Variance, Mathematical Models, Statistical Analysis
Peer reviewedMcDonald, Roderick P. – Psychometrika, 1982
Typically, nonlinear models such as those used in the analysis of covariance structures, are not globally identifiable. Investigations of local identifiability must either yield a mapping onto the entire parameter space, or be confined to points of special interest such as the maximum likelihood point. (Author/JKS)
Descriptors: Analysis of Covariance, Mathematical Models, Maximum Likelihood Statistics, Statistical Analysis
Peer reviewedSilvia, E. Suyapa M.; MacCallum, Robert C. – Multivariate Behavioral Research, 1988
The effects of several specification search strategies used with Covariance Structure Modeling to obtain more parsimonious models are examined. The initial models vary in their degree of "correctness." Restricting modifications to those justified by prior theoretical knowledge improves the success of a specification search. (TJH)
Descriptors: Analysis of Covariance, Mathematical Models, Research Methodology, Search Strategies
Blair, R. Clifford; Sawilowsky, Shlomo S. – 1991
Analysis of covariance (ANCOVA) is a data analysis method that is often used to control extraneous sources of variation in non-equivalent group designs. It is commonly believed that as long as the covariate is highly correlated with the dependent variable there is nothing to lose in using ANCOVA, even in non-randomized studies. This paper examines…
Descriptors: Analysis of Covariance, Equations (Mathematics), Mathematical Models, Research Design
Loftin, Lynn – 1990
Although analysis of covariance (ANCOVA) is used fairly infrequently in published research, the method is used much more frequently in dissertations and in evaluation research. This paper reviews the assumptions that must be met for ANCOVA to yield useful results, and argues that ANCOVA will yield distorted and inaccurate results when these…
Descriptors: Analysis of Covariance, Mathematical Models, Regression (Statistics), Research Methodology
Porter, Andrew C. – 1972
The basic design for the national evaluation of the Follow Through program is presented, and some of the related issues of analysis are considered. The design, as it now stands, presents many difficulties for analysis. These analysis issues are seen to include the following: (1) What should be the unit of analysis?; (2) How is the effect of a…
Descriptors: Analysis of Covariance, Evaluation Methods, Mathematical Models, Program Evaluation
Peer reviewedSorbom, Dag – Psychometrika, 1978
A general statistical model for simultaneous analysis of data from several groups is described. The model is primarily designed to be used for the analysis of covariance. The model can handle any number of covariates and criterion variables, and any number of treatment groups. (Author/JKS)
Descriptors: Analysis of Covariance, Hypothesis Testing, Mathematical Models, Research Design
Peer reviewedHuberty, Carl J.; Curry, Allen R. – Multivariate Behavioral Research, 1978
Classification is a procedure through which individuals are classified as being members of a particular group based on a variety of independent variables. Two methods of makin such classifications are discussed; the quadratic method is seen to be superior to the linear under certain constraints. (JKS)
Descriptors: Analysis of Covariance, Classification, Discriminant Analysis, Groups
Peer reviewedGorsuch, Richard L. – Educational and Psychological Measurement, 1973
Descriptors: Analysis of Covariance, Factor Analysis, Mathematical Models, Statistical Significance
Peer reviewedWerts, Charles E.; Linn, Robert L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Covariance, Analysis of Variance, Mathematical Models, Multiple Regression Analysis
Peer reviewedHollingsworth, Holly H. – Educational and Psychological Measurement, 1980
If heterogeneous regression slopes are present in analysis of covariance (ANCOVA), the likelihood of committing a Type I error is greater than what had been prespecified. The power of the ANCOVA test of hypothesis for all possible differences of treatment effects is not maximized. (Author/RL)
Descriptors: Analysis of Covariance, Hypothesis Testing, Mathematical Models, Power (Statistics)
Blumberg, Carol Joyce; Porter, Andrew C. – 1981
Analysis strategies are discussed for the nonequivalent control group design when three models of continuous natural growth are known. For Model I type natural growth it was shown that the fan spread hypothesis always holds, and Analysis of Covariance (ANCOVA), Analysis of Variance (ANOVA) of Residualized Gain Scores, and ANOVA of Standardized…
Descriptors: Analysis of Covariance, Analysis of Variance, Mathematical Models, Pretests Posttests


