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Evaluation Review | 9 |
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Trochim, William M. K. | 2 |
Cappelleri, Joseph C. | 1 |
Carifio, James | 1 |
Davis, James E. | 1 |
Freedman, David A. | 1 |
Gardiner, Richard C. | 1 |
Haveman, Robert H. | 1 |
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Stanley, T. D. | 1 |
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Reports - Evaluative | 5 |
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Carifio, James – Evaluation Review, 1992
To solve an applied research problem with bipolar data, a set of equations was developed that combined all plus and minus data combinations into unique values and scale points. The equations were tested through computer simulations and empirical tests. Resulting index scores were approximately interval and linear and easy to use and interpret.…
Descriptors: Computer Simulation, Equations (Mathematics), Indexing, Mathematical Logic

Rasmussen, Jeffrey Lee – Evaluation Review, 1985
A recent study (Blair and Higgins, 1980) indicated a power advantage for the Wilcoxon W Test over student's t-test when calculated from a common mixed-normal sample. Results of the present study indicate that the t-test corrected for outliers shows a superior power curve to the Wilcoxon W.
Descriptors: Computer Simulation, Error of Measurement, Hypothesis Testing, Power (Statistics)

Trochim, William M. K.; Davis, James E. – Evaluation Review, 1986
Microcomputer simulations in evaluation research are useful for (1) improving student understanding of research principles and analytic techniques; (2) investigating problems arising in research implementations; and (3) exploring the accuracy and utility of novel analytic techniques. This article describes these simulation uses for the context of…
Descriptors: Computer Assisted Instruction, Computer Simulation, Computer Software, Evaluation Methods

Stanley, T. D. – Evaluation Review, 1991
W. M. K. Trochim and others defend the record of the regression-discontinuity (RD) design and blur the statistical tests for treatment effect. Their Monte Carlo results show the problematic nature of RD and its potential bias. New testing strategies and restrictions for the application of RD are proposed. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Error of Measurement, Estimation (Mathematics)

Haveman, Robert H. – Evaluation Review, 1986
This article describes the method and the development of microdata simulation modeling over the past two decades. After tracing a brief history of this evaluation method, its problems and prospects are assessed. The effects of this research method on the development of the social sciences are examined. (JAZ)
Descriptors: Computer Science, Computer Simulation, Economic Research, Government (Administrative Body)

Gardiner, Richard C. – Evaluation Review, 1994
Statistical identification of employees with unusual sick leave patterns can result in false-positive identifications. A computer simulation model was used to evaluate alternate methods of unusual sick leave detection for the New York State Department of Health. Results were used to refine identification parameters and modify supervisory…
Descriptors: Computer Simulation, Employees, Employment Practices, Evaluation Methods

Freedman, David A.; And Others – Evaluation Review, 1993
Techniques for adjusting census figures are discussed, with a focus on sampling error, uncertainty of estimates resulting from the luck of sample choice. Computer simulations illustrate the ways in which the smoothing algorithm may make adjustments less, rather than more, accurate. (SLD)
Descriptors: Algorithms, Census Figures, Computer Simulation, Error of Measurement

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

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