数据统计

• One way MANOVA： one-way multivariate analysis of variance
• used to determine whether there are any differences between independent groups on more than one continuous dependent variable.
• cannot tell you which specific groups were significantly different from each other; it only tells you that at least two groups were different.
• Need to use a post-hoc test to determine which of these groups differ from each other.

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Assumptions

• two or more dependent variables should be measured at the interval or ratio level (i.e., they are continuous).
• independent variable should consist of two or more categorical, independent groups.
• independence of observations.
• An adequate sample size. Although the larger your sample size, the better; for MANOVA, you need to have more cases in each group than the number of dependent variables you are analysing.
• no univariate or multivariate outliers.
• There is multivariate normality. You can test for this using the Shapiro-Wilk test of normality, which is easily tested for using SPSS Statistics.
• There is a linear relationship between each pair of dependent variables for each group of the independent variable.
• There is a homogeneity of variance-covariance matrices. You can test this assumption in SPSS Statistics using Box’s M test of equality of covariance. If your data fails this assumption, you may also need to use SPSS Statistics to carry out Levene’s test of homogeneity of variance to determine where the problem may lie.
• There is no multicollinearity. Ideally, you want your dependent variables to be moderately correlated with each other. If the correlations are low, you might be better off running separate one-way ANOVAs, and if the correlation(s) are too high (greater than 0.9), you could have multicollinearity.

Reference: https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php