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Wing, Coady; Bello-Gomez, Ricardo A. – American Journal of Evaluation, 2018
Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…
Descriptors: Regression (Statistics), Research Design, Validity, Evaluation Methods
Mark W. Lipsey; Christina Weiland; Hirokazu Yoshikawa; Sandra Jo Wilson; Kerry G. Hofer – Educational Evaluation and Policy Analysis, 2015
Much of the currently available evidence on the causal effects of public prekindergarten programs on school readiness outcomes comes from studies that use a regression-discontinuity design (RDD) with the age cutoff to enter a program in a given year as the basis for assignment to treatment and control conditions. Because the RDD has high internal…
Descriptors: Preschool Education, Preschool Children, School Readiness, School Entrance Age
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Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin – Society for Research on Educational Effectiveness, 2015
Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…
Descriptors: Pretests Posttests, Statistical Bias, Accuracy, Regression (Statistics)
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Faden, Vivian; Bobko, Philip – Educational and Psychological Measurement, 1982
Ridge regression offers advantages over ordinary least squares estimation when a validity shrinkage criterion is considered. Comparisons of cross-validated multiple correlations indicate that ridge estimation is superior when the predictors are multicollinear, the number of predictors is large relative to sample size, and the population multiple…
Descriptors: Correlation, Least Squares Statistics, Predictor Variables, Regression (Statistics)
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Gross, Alan L. – Educational and Psychological Measurement, 1982
It is generally believed that the correction formula will yield exact correlational values only when the regression of z on x is both linear and homoscedastic. The formula is shown to hold for nonlinear heteroscedastic relationships. A simple sufficient condition for formula validity and estimation predictions is demonstrated in a numerical…
Descriptors: Correlation, Data Analysis, Mathematical Formulas, Predictor Variables
Beasley, T. Mark; Leitner, Dennis W. – 1994
The use of stepwise regression has been criticized for both interpretive misunderstandings and statistical aberrations. A major statistical problem with stepwise regression and other procedures that involve multiple significance tests is the inflation of the Type I error rate. General approaches to control the family-wise error rate such as the…
Descriptors: Algorithms, Computer Simulation, Correlation, Error of Measurement
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Beck, E. M.; Tolnay, Stewart E. – Historical Methods, 1995
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
Descriptors: Data Interpretation, Evaluation Criteria, Higher Education, Multivariate Analysis
Sandler, Andrew B. – 1987
Statistical significance is misused in educational and psychological research when it is applied as a method to establish the reliability of research results. Other techniques have been developed which can be correctly utilized to establish the generalizability of findings. Methods that do provide such estimates are known as invariance or…
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Discriminant Analysis