Have been around statistical analysis for nearly 75 years, and a wide Therefore, we fail to reject the null hypothesis and conclude that interaction between does not have a statistically significant effect on the History test score.Repeated Measures Analysis of Variance Using R Permutation Tests forįactorial ANOVA Designs David C. History test score:įinally, The p-value is 0.201 for the History test. So, we fail to reject the null hypothesis and conclude that the interaction between gender and training does not have a statistically significant effect on Math test scores. Secondly, The P-value of the Math variable is 0.682. So, we fail to reject the null hypothesis and conclude that interaction between gender and training does not have a statistically significant effect on English test scores. English test score:įirstly, the p-value is 0.592 for the English test scores. If p < 0.05, the interaction between gender and training has a statistically significant effect on a test score. Moreover, we should look at the p-value in row Gender*Training. Tests of between-subjects effects show how the dependent variables (English test score, Math test score, History test score) differ for the interaction between Gender and Training.
#TWO WAY ANOVA SPSS CODE HOW TO#
How to Report CTests of between-subjects Table in SPSS output? In addition, If we look at the p-value for Wilks’ Lambda in Gender row (p = 0.636), we fail to reject the null hypothesis and conclude that there is no effect of gender on test scores (English, Maths, History).įinally, If we look p-value for Wilks’ Lambda in Training row (p = 0.010), we must reject the null hypothesis and conclude that there is the effect of training on test scores (English, Maths, History). So, we fail to reject the null hypothesis and conclude that there is no statistically significant effect of the interaction of gender and training on test scores (English, Maths, History). Therefore, there is a statistically significant effect of the interaction of gender and training on test scores (English, Maths, History).įor example, our p-value is 0.512. Secondly, if our p-value is lower than 0.05. Therefore, there is no statistically significant effect of the interaction of gender and training on test scores (English, Maths, History). So, we should look at the results of Wilk’s Lambda test in the row for the interaction between Gender and Training.įirstly, If our p-value is greater than 0.05. Multivariate Tests show the results of Wilk’s Lambda test. How to Report Multivariate Tests Table SPSS Output? There is an effect of interaction between the two independent categorical variables on the two or more continuous dependent variables.ĭo you need to help with how to run the Two-way MANOVA test in SPSS? There is no effect of interaction between the two independent categorical variables on the two or more continuous dependent variables. Therefore, we have two independent categorical variables: Gender (male and female) and training (1 or 2 or 3 months) and three continuous dependent variables (English test score, Math test score, and History test score). We want to examine whether there is an interaction effect of gender and training on English test scores, Math test scores, and History test scores.
#TWO WAY ANOVA SPSS CODE SOFTWARE#
This guide will explain, step by step, how to run the Two-way MANOVA test in SPSS statistical software by using an example.
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