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Things You Didn’t Know about MANOVA SPSS Output Interpretation

  • The one-way multivariate analysis of variance (MANOVA) provides the differences between more than one continuous dependent variable and independent groups. For an instance, one-way MANOVA helps in understanding the variations in the opinions, intelligence and attractiveness of drug users in movies.

    In this post, we will learn how to run manova in spss using the four main tables. Through this, understand the manova spss output interpretation. You can also check  interpretation of anova results spss and get understanding chi square, regression and anova outputs from spss.

    manova spss output interpretation


    Two or More Dependent Variables

    This first assumption is measured through the ratio level or interval. Variables like time, intelligence, weight, exam performance is included on this variable. Varieties of one- way MANOVA analysis are produces by SPSS. But in this post, we discuss the main tables necessary on your understanding of the one-way MANOVA results. Carrying and interpreting this MANOVA in SPSS requires the nine assumptions to check your data. These nine assumptions include scatterplot matrix, relevant boxplots, Mahalanobis distance test results, Pearson’s correlation coefficients, Shapiro-Wilk test for normality, Levene’s test of homogeneity of variance, and equality of covariance Box’s M test.

    Independent Variable

    The second assumption consists of more than two categorical independent groups. Included on this assumption are ethnicity, physical activity level, profession, and the likes.

    Independence of Observations

    The third assumption, which is the independence of observations show no relationship between groups and observations.

    Adequate Sample Size

    In this assumption, a larger sample size is advisable. Compared to the dependent variables you are studying, more cases should be in each group.

    Descriptive Statistics

    The first main table to understand MANOVA in SPSS is the Descriptive statistics. This provides necessary data for two different variables like the mean and standard deviation. Moreover, the Descriptive statistics table provides “Total” rows, which allows standard deviations and means for groups.

    Multivariate Tests

    Another table to interpret MANOVA SPSS is the Multivariate tests table, which provides the one-way MANOVA actual results. The important data in this table are the “School”, Wilks Lambda row found in the second Effect. Check the “Sig.” to identify the statistically significance of the one-way MANOVA.

    Univariate ANOVA’s

    Another table to identify the difference between dependent variable and independent variable is the Tests of Between- Subjects Effects table. For running multiple ANOVA’s use the alpha correction like the Bonferroni correction.

    Multiple Comparisons

    Through the Multiple Comparisons table, we can follow up important ANOVA’s.

    No Multicollinearity

    If the correlation of your dependent variable is too high, use the multicollinearity assumption.

    Above are the nine assumptions you can use in analysing your data using the MANOVA SPSS. Remember that some assumptions are not met, but still guarantee solutions to overcome the violations.

    Generally, one -way MANOVA is an omnibus test statistic, which everyone should understand. Moreover, it doesn’t tell which groups are different, thus providing at least two different groups. But instances happen that you have more than three groups on your research study that needs to be differentiated. You can work on this using the post- hoc test.

    You are now ready to report the MANOVA SPSS output!

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