Comparison of Three Common Experimental Designs to Improve Statistical Power When Data Violate Parametric Assumptions [electronic resource] / Andrew C. Porter and Maryellen McSweeney.
A Monte Carlo technique was used to investigate the small sample goodness of fit and statistical power of several nonparametric tests and their parametric analogues when applied to data which violate parametric assumptions. The motivation was to facilitate choice among three designs, simple random a...
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| Format: | Electronic eBook |
| Language: | English |
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1974.
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| Summary: | A Monte Carlo technique was used to investigate the small sample goodness of fit and statistical power of several nonparametric tests and their parametric analogues when applied to data which violate parametric assumptions. The motivation was to facilitate choice among three designs, simple random assignment with and without a concomitant variable and randomized blocks, and between nonparametric or parametric tests. The criteria for choice were power and robustness. The parameters of the Monte Carlo investigation were strength of relationship between the concomitant and dependent variables, number of levels of the independent variable, sample size, and location parameter. (Author) |
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| Item Description: | ERIC Document Number: ED091413. ERIC Note: Paper presented at the Annual Meeting of the American Educational Research Association (Chicago, Illinois, April, 1974). |
| Physical Description: | 40 p. |