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non-parametric designs

by: Alejandra D. Rocha

non-parametric designs PSYC 4317

Alejandra D. Rocha
GPA 3.1
Advanced Statistics
Anthony Blum

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About this Document

a basic table for Non-parametric designs
Advanced Statistics
Anthony Blum
75 ?




Popular in Advanced Statistics

Popular in Psychlogy

This 1 page Bundle was uploaded by Alejandra D. Rocha on Sunday November 1, 2015. The Bundle belongs to PSYC 4317 at University of Texas at El Paso taught by Anthony Blum in Summer 2015. Since its upload, it has received 44 views. For similar materials see Advanced Statistics in Psychlogy at University of Texas at El Paso.


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Date Created: 11/01/15
Nonparametric test Parametric version Null hypothesis Logic of ranked scores MannWhitney test Independent sample Ttest Between subjects one factor two levels Ho the medians for the levels of the between subjects factor are the same We rank order all scores from highest to lowest if all or most of the high ranks are in one group and low ranks in the other we reject null hypothesis Wilcoxon signed ranks test Paired samples Ttest Repeated measures one factor two levels Ho there is no difference in the medians between and after conditions Compute subtraction of the first level scores minus the second level scores for each subjects KruskallWallis test One Way ANOVA univariate Between subjects one factor multiple level Ho mean ranks of the groups are the same Take all the scores rank order from lowest to highest 15t2 d3rd Put the ranked scores back in the levels Compare the groups in terms of ranks If all the high levels are in one group and the low levels in other group there is an effect If there s an equal distribution of ranks it means that there s no difference between the groups Friedman test One way repeated measures ANOVA Repeated measures one factor multiple level Ho the medians for all the conditions are equal For each subject ran order their scores from lowest to highest Then we compare the rankings for each condition If all most of the high ranks are in one conditions then expect to have a difference If all conditions had an equal number of high or low ranks we don t expect to find a difference


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