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Showing 1 to 15 of 37 results Save | Export
Gelman, Andrew; Imbens, Guido – National Bureau of Economic Research, 2014
It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…
Descriptors: Regression (Statistics), Mathematical Models, Causal Models, Research Methodology
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
Peer reviewed Peer reviewed
Williams, John D. – Multiple Linear Regression Viewpoints, 1977
The problems of two way analysis of variance designs with unequal and disproportionate cell sizes are discussed. A variety of solutions are discussed and a new solution is presented. (JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Stoker, Howard W.; And Others – Evaluation Review, 1981
The use of analysis of variance was examined under the assumption that the treatment had been randomly assigned to students, when in fact, the class had been the unit. Data support the idea that if one can randomly assign treatments to intact classes, consideration should certainly be given to doing so. (Author/GK)
Descriptors: Analysis of Variance, Control Groups, Experimental Groups, Mathematical Models
Peer reviewed Peer reviewed
Marcoulides, George A.; Goldstein, Zvi – Educational and Psychological Measurement, 1990
A methodology for determining the optimal number of observations to use in a measurement design when resource constraints are imposed is presented. Two- and three-facet designs are outlined. Parallel closed form formulae can easily be determined for other designs. (TJH)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Generalizability Theory, Mathematical Models
Peer reviewed Peer reviewed
Hopkins, Kenneth D. – American Educational Research Journal, 1982
The recommendation to use group means when there may be nonindependence among observational units is unduly restrictive. When random factors are properly identified and included in the analysis, the results are identical in balanced analysis of variance designs, irrespective of whether group means or individual observations are employed.…
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Mathematical Models
Peer reviewed Peer reviewed
Magidson, Jay; Sorbom, Dag – Educational Evaluation and Policy Analysis, 1982
LISREL V computer program is applied to a weak quasi-experimental design involving the Head Start program, as a multiple analysis attempt to assure that differences between nonequivalent control groups do not confound interpretation of a posteriori differences. (PN)
Descriptors: Achievement Gains, Early Childhood Education, Mathematical Models, Program Evaluation
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Meyer, Donald L. – 1971
Bayesian statistical methodology and its possible uses in the behavioral sciences are discussed in relation to the solution of problems in both the use and teaching of fundamental statistical methods, including confidence intervals, significance tests, and sampling. The Bayesian model explains these statistical methods and offers a consistent…
Descriptors: Bayesian Statistics, Data Analysis, Decision Making, Mathematical Models
Peer reviewed Peer reviewed
Blair, R. Clifford; Higgings, J. J. – American Educational Research Journal, 1978
Kaufman and Sweet's article on the regression analysis of unbalanced factorial designs (EJ 111 767) is reviewed. A number of errors are noted, and relevant literature is cited. (GDC)
Descriptors: Least Squares Statistics, Mathematical Models, Multiple Regression Analysis, Research Design
Peer reviewed Peer reviewed
Werts, Charles E.; Linn, Robert L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Mathematical Models
Peer reviewed Peer reviewed
Maxwell, Scott E.; Howard, George S. – Educational and Psychological Measurement, 1981
This paper delineates conditions under which the use of change scores will not produce misleading results, and may perhaps be preferable to other methods of analysis. The validity of change scores in randomized pretest-posttest designs is discussed along with situations where analysis of change scores should be used. (Author/GK)
Descriptors: Analysis of Covariance, Analysis of Variance, Mathematical Models, Pretests Posttests
Peer reviewed Peer reviewed
Marcoulides, George A.; Goldstein, Zvi – Educational and Psychological Measurement, 1991
A method is presented for determining the optimal number of conditions to use in measurement designs when resource constraints are imposed. The method is illustrated using a multivariate two-facet design, and extensions to other designs are discussed. (SLD)
Descriptors: Budgeting, Data Collection, Efficiency, Equations (Mathematics)
Williams, John D.; Newman, Isadore – 1982
Problems associated with the analysis of data collected using the Solomon Four Group Design are discussed. The design includes an experimental group and a control group that have been pretested and posttested, and an experimental and a control group that have been posttested only. A sample problem is approached in three different ways. First, the…
Descriptors: Control Groups, Experimental Groups, Hypothesis Testing, Mathematical Models
Peer reviewed Peer reviewed
Gleser, Leon Jay – Intelligence, 1985
The present study points out problems in the model, indices of familiality, and design used by Benbow, Zonderman, and Stanley in a study of precocious children and their parents. (Author/LMO)
Descriptors: Cognitive Ability, Elementary Secondary Education, Genetics, High Achievement
Yap, Kim Onn; And Others – 1979
The effects of using different data analysis methods on estimates of treatment effects of educational programs were investigated. Various regression models, such as those recommended for Title I program evaluations, were studied. The first effect studied was the amount of bias that might be expected to occur in the various settings. Results…
Descriptors: Bias, Compensatory Education, Evaluation Methods, Mathematical Models
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