There are four tests for cases involving two independent samples. These tests are: The Mann-Whitney U test The Wald-Wolfowitz Runs test The Kolmogorov-Smirnov Z test The Moses Extreme Reactions test The Mann-Whitney U test in the tests for two independent samples is an alternative form of the t-test.  It is widely used to test whether or not the two independent samples are significantly different.  In SPSS, the Mann-Whitney U test in the tests…

There are some significance tests for two dependent samples, and these are namely the McNemar test, the Marginal homogeneity test, the sign test, and the Wilcoxon test. The McNemar test assesses the significant difference between the two types of dependent samples in cases where the variables of the interest is dichotomous.  The McNemar test is basically used by the researcher during the pre and the post studies in order to test for an experimen…

Significance tests for two dependent samples are a study of correlated samples.  This includes the before-after effect and matched paired study. McNemar’s, Marginal Homogeneity, Sign and the Wilcoxon test are non parametric tests that are used for two dependent samples.  The McNemar test is the best test for dichotomous variables with two dependent sample studies.  When a category of the sample is more, then the two marginal homogeneity te…

There are three significance tests for cases involving more than two dependent samples.  These are the Friedman Test, the Kendall’s W test, and the Cochran’s Q test. The Friedman test in the significance test for more than two dependent samples is also known as the Friedman two-way analysis of variance.  The Friedman Test in the significance tests for more than two dependent samples is used to test the null hypothesis.  In other word…

onsidered.  ANOVA is a way to control these types of undesirable variables. Testing of the Assumptions These assumptions can be tested using statistical software.  The assumption of homogeneity of variance can be tested using tests such as Levene’s test or the Brown-Forsythe Test.  Normality of the distribution of the population can be tested using plots, the values of Skeweness and Kurtosis, or using tests such as Shpiro-Wilk or Kolmogorov-Smirn…

Statistical formula can be defined as the group of statistical symbols used to make a statistical statement.  Here we will discuss popular formulas and what they stand for. Population Mean The term population mean, which is the parameter of a given population, is represented by: μ = ( Σ Xi ) / N The symbol ‘μ’ represents the population mean.  The symbol ‘Σ Xi’ represents the overall sum of all variables present in the population (say, in this ca…

The Wilcoxon Sign test is a statistical comparison of the average of two dependent samples. The Wilcoxon sign test is a sibling of the t-tests, in fact it is a non-parametric alternative to the dependent samples t-test.  Thus the test is used in similar situations as the Mann-Whitney U-test.  The main difference is that the Mann-Whitney U-test tests two independent samples, whereas the Wilcoxon sign test tests two dependent samples. The Wilcoxon…

assumptions about distribution, particularly about normal distribution.  When the parametric test meets assumptions, then the parametric test is more powerful than the non-parametric test.  The following are common parametric tests: Binomial one-sample test of significance of dichotomous distributions T-test of the difference of means Normal curve Z-tests of the differences of means and proportions Non-parametric test: These tests do not assume…

Coefficient Kruskal-Wallis Test Latent Class Analysis LISREL Logistic Regression Logistic Regression Assumptions Mann-Whitney U Test MANOVA Mathematical Expectation McNemar’s Test McNemar, Marginal Homogeneity, Sign, Wilcoxon Tests Measures of Association Meta Analysis Moderator Variable Multiple Regression Multivariate Analysis of Covariance (MANCOVA) Multivariate GLM, MANOVA, and MANCOVA Nominal Variable Association Nonlinear Regression Normali…

ce structure models: Power analysis and null hypotheses. Psychological Methods, 11(1), 19-35. Murphy, K. R., & Myors, B. (2004). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (2nd ed.).Mahwah, NJ: Lawrence Erlbaum Associates. View Murphy, K. R., Myors, B., & Wolach, A. (2008). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (3rd ed.).Ma…