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Evaluation Review

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Cappelleri, Joseph C.	1
Carlson, James E.	1
Spray, Judith A.	1
Trochim, William M. K.	1

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Random Measurement Error Does Not Bias the Treatment Effect Estimate in the Regression-Discontinuity Design. II. When an Interaction Effect Is Present.

Peer reviewed

Trochim, William M. K.; And Others – Evaluation Review, 1991

The regression-discontinuity design involving a treatment interaction effect (TIE), pretest-posttest functional form specification, and choice of point-of-estimation of the TIE are examined. Formulas for controlling the magnitude of TIE in simulations can be used for simulating the randomized experimental case where estimation is not at the…

Descriptors: Computer Simulation, Control Groups, Equations (Mathematics), Error of Measurement

Random Measurement Error Does Not Bias the Treatment Effect Estimate in the Regression-Discontinuity Design: I. The Case of No Interaction.

Peer reviewed

Cappelleri, Joseph C.; And Others – Evaluation Review, 1991

A conceptual approach and a set of computer simulations are presented to demonstrate that random measurement error in the pretest does not bias the estimate of the treatment effect in the regression-discontinuity design. Focus is on the case of no interaction between pretest and treatment on posttest. (SLD)

Descriptors: Analysis of Covariance, Computer Simulation, Equations (Mathematics), Error of Measurement

Analysis of Contingency Tables Involving Multiple-Response Data.

Carlson, James E.; Spray, Judith A. – 1986

This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…

Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement