Four assumptions of parametric statistics
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Further, non-parametric tests do not require that we meet some to the assumptions that are required for use of parametric tests (e.g., homogeneity of variance and normal distribution). The Chi Square test for independence is often used to determine if there is a relationship between two categorical variables.
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Note that if you double the difference (d) you want to detect, the sample size is cut by a factor of four. If you double the standard deviation (s), the sample size goes up by a factor of four. Streiner and Norman observe that plausibly small adjustments in the initial estimate can have big effects on the calculation!
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As the previous section demonstrates, whenever the outcome is a ranking, non-parametric statistics are appropriate. Whenever the outcome is categorical, sometimes called nominal, another suite of tests is appropriate. Some, but not all, researchers utilize the term nonparametric to describe these tests as well.
[DOC File]Sampling and Sample Size - Columbia University
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Lesson 7: Non-Parametric Techniques For the most non-parametric analyses, assumptions about the shape of the population are not required. For that reason, they are often used when small sample sizes are involved. Chi-square test for goodness of fit
[DOC File]We assume first that the relata of the type causal ...
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Prior to the main analyses, assumptions of parametric statistics were tested. The data on the covariate -math grades- were severely positively skewed. Therefore, an inverse transformation was performed on this variable. ... Once the four outliers were removed, the final sample consisted of 240 in the upper, 425 in the middle and 158 in the ...
[DOC File]Reliability Analysis
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module four, part one: sample selection in Economic Education research. William E. Becker and William H. Greene * Modules One and Two addressed an economic education empirical study involved with the assessment of student learning that occurs between the start of a program (as measured, for example, by a pretest) and the end of the program (posttest).
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For example, under the four assumptions listed, the causal graph in Figure 2 can be represented as A ( B ( C, and the causal Markov Assumption will still apply to this DAG and the joint population distribution as measured at a particular time. ... Under these parametric assumptions, the population distribution and the causal graph satisfy the ...
[DOC File]Topic 8: Transformations of the data [S&T Ch
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Statistics Resource Page. This is a collection of statistical resources for faculty, staff and students. ... Four Assumptions of Multiple Regression That Researchers Should Always Test. Multivariate Statistics: Concepts, Models, And Applications ... Regression is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
The Four Assumptions of Parametric Tests - Statology
The other alternative in this case is to analyze the data using non-parametric statistics. 8. 2. Transformations. If the evidence indicates that the assumptions for an analysis of variance cannot be satisfied, two courses of action are available.
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