Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis [electronic resource] / Andrew C. Porter and Maryellen McSweeney.
The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric tech...
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| Format: | Electronic eBook |
| Language: | English |
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[S.l.] :
Distributed by ERIC Clearinghouse,
1971.
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MARC
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| 100 | 1 | |a Porter, Andrew C. | |
| 245 | 1 | 0 | |a Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis |h [electronic resource] / |c Andrew C. Porter and Maryellen McSweeney. |
| 260 | |a [S.l.] : |b Distributed by ERIC Clearinghouse, |c 1971. | ||
| 300 | |a 35 p. | ||
| 500 | |a ERIC Document Number: ED048371. | ||
| 500 | |a ERIC Note: Paper presented at the Annual Meeting of the American Educational Research Association, New York, New York, February 1971. |5 ericd. | ||
| 520 | |a The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric techniques concerned are the Kruskal-Wallis test on data where experimental units have been randomly assigned to levels of the independent variable, Friedman's rank analysis of variance (ANOVA) on data in a randomized blocks design, and a nonparametric analysis of covariance (ANCOVA). The parametric counterparts are respectively; one way analysis of variance (ANOVA), two-way ANOVA, and parametric ANCOVA. Since the nonparametric tests are based on large sample approximations, the goodness of fit for small samples is also of concern. Statements of asymptotic relative efficiency and imprecision are reviewed as suggestive of small sample powers of the designs and statistics investigated. Results of a Monte Carlo investigation are provided to further illustrate small sample power and to provide data on goodness of fit. (Author/CK) | ||
| 650 | 1 | 7 | |a Analysis of Covariance. |2 ericd. |
| 650 | 1 | 7 | |a Analysis of Variance. |2 ericd. |
| 650 | 1 | 7 | |a Goodness of Fit. |2 ericd. |
| 650 | 0 | 7 | |a Hypothesis Testing. |2 ericd. |
| 650 | 1 | 7 | |a Nonparametric Statistics. |2 ericd. |
| 650 | 0 | 7 | |a Predictor Variables. |2 ericd. |
| 650 | 1 | 7 | |a Research Design. |2 ericd. |
| 650 | 0 | 7 | |a Sampling. |2 ericd. |
| 650 | 0 | 7 | |a Statistical Analysis. |2 ericd. |
| 700 | 1 | |a McSweeney, Maryellen. | |
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