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Different approaches to bilingual education have been contrasted with almost exclusive regard for educational or peda-gogical issues. In so doing, the effects of …

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Types of Statistical Tests - University of Phoenix

Non-parametric Tests Non-parametric Test. Parametric Equivalent. Mann-Whitney (Wilcoxon) Independent samples t test. Wilcoxon Signed Rank . Paired samples . t. test. Kruskal-Wallis. One-way ANOVA. Friedman. Repeated measures ANOVA. Examples from Walden Locating • Library – Dissertations & Theses @ Walden University

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THE SHAPIRO-WILK AND RELATED TESTS FOR NORMALITY

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Checking normality in R - University of Sheffield

Although non-parametric tests require fewer assumptions and can be used on a wider range of data types, parametric tests are preferred because they are more sensitive at detecting differences between samples or an effect of the independent variable on the dependent variable.

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Sample Size Calculation - University of North Dakota

Non-parametric data: does not fit a normal distribution; alternative and less powerful tests available Paired (dependent) data: categories are related to one another (often result of before/after situations)

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Tips and Tricks for Analyzing Non-Normal Data

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Two-Sample T-Tests Allowing Unequal Variance

Two-Sample T-Tests Allowing Unequal Variance Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when no assumption of equal variances for the two population is made. This is commonly known as the Aspin- Welch test, Welch’s t-test (Welch, 1937), or the Satterthwaite method .

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R Language Definition

1 1 Introduction R is a system for statistical computation and graphics. It provides, among other things, a pro-gramming language, high level graphics, interfaces to other languages and debugging facilities.

Spearman's Rank-order Correlation -- Analysis of the ...

Spearman's Rank-order Correlation -- Analysis of the Relationship Between Two Quantitative Variables Application: To test for a rank order relationship between two quantitative variables when concerned that one or both variables is ordinal (rather than interval) and/or not normally distributed or when the sample size is small.