Discussion 6
Paris04
2 years ago
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files (3)PPT9NonparametricsHow-toLecture.ppt
Discussion6NSU.docx
nonparametricsoverview.ppt
PPT9NonparametricsHow-toLecture.ppt
Discussion6NSU.docx
Discussion 6: Choosing the Correct Statistical Procedure!!
Let’s decide which statistical procedure is most appropriate to answer your research questions. Based on the information reviewed in the various PowerPoint presentations in this class, draft an analysis plan for each of your quantitative research questions. Please list your research question, your IV(s), DV(s), level of measurement for each of your variables, and the planned analysis to answer the research question. Please do this for EACH of your quantitative research questions.
nonparametricsoverview.ppt
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- Nonparametric, or distribution free tests are so-called because the assumptions underlying their use are “fewer and weaker than those associated with parametric tests” (Siegel& Castellan, 1988, p. 34).
- To put it another way, nonparametric tests require few if any assumptions about the shapes of the underlying population distributions
- For this reason, they are often used in place of parametric tests if/when one feels that the assumptions of the parametric test have been too grossly violated (e.g., if the distributions are too severely skewed).
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- Nonparametric statistical tests have much less restrictive assumptions concerning the distributions of the variables and the variances of comparison groups.
- If all assumptions are met, use Parametric techniques
- Use Nonparametric
When the dependant variable is either categorical or ordinal
If the distribution of the dependant variable is skewed
When the assumptions are not met, specifically:
Normality
Homogeneity of variance
Nonparametric tests do have at least two major disadvantages in comparison to parametric tests:
- First, nonparametric tests are less powerful. Why? Because parametric tests use more of the information available in a set of numbers.
- Parametric tests make use of information consistent with interval scale measurement, whereas parametric tests typically make use of ordinal information only. As Siegel and Castellan (1988) put it, “nonparametric statistical tests are wasteful.”
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- Second, parametric tests are much more flexible, and allow you to test a greater range of hypotheses. For example, factorial ANOVA designs allow you to test for interactions between variables in a way that is not possible with nonparametric alternatives.
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- There are nonparametric techniques to test for certain kinds of interactions under certain circumstances, but these are much more limited than the corresponding parametric techniques.
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Parametric and Nonparametric Counterparts
