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    Maths and Stats Support

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    Differences between two independent groups
    The choice of test when comparing two groups differs by the data type of your dependent (outcome) variable. This page is for continuous and ordinal dependent variables. If you have non-ordered categories (nominal), use aChi-squared test of association.

    Parametric: Independent T-Test

    Use: Comparing the means of two different groups of subjects (independent groups)

    Dependent (outcome) variable: Continuous
    Independent (predictor) variable: Two categories

    Example: testing for a difference in the means hours of housework for males and females

    Summary statistics/graphs:Use means with standard deviations of each group. Can use mean bar chart with error bars or boxplot

     

    Non-parametric: Mann-Whitney/ Wilcoxon rank sum

    Use: Comparing the distributions of two different groups of subjects (independent groups) when the assumptions of the t-test are not met or the dependent is one ordinal question. The test is called Mann-Whitney in SPSS or Jamovi and the Wilcoxon rank sum in R or SAS..

    Dependent (Outcome): Ordinal or skewed data       
    Independent (predictor): Two categories

    Example:Comparing hours of housework for males and females when housework is very skewed or comparing the responses to one strongly disagree - strongly agree style question for two independent groups.

    Summary statistics/graphs:Use medians, Interquartile range and boxplot or for ordinal sometimes %'s and bar charts explain differences more clearly.

    Note: If you have taken the mean or sum of several ordered questions (scale mean), use parametric tests such as t-tests. Some disciplines use parametric tests for individual ordinal questions but a wider range of responses (ideally 7+) is needed    

     

    Resources by software

          The following resources show you how to carry out, interpret and report tests using SPSS.

      There are a set of videos taking you through all the steps including discussing key concepts, data summary and choosing between t-tests and non-parametric equivalents. There are also quick guides.

      The resources guide you through the r code and interpretation of the relevant summary statistics and test for comparing two different groups of subjects.  The script files with all the code can be adapted to run each technique on your own data.

         These resources contain the SAS code needed for the independent t-test and non-parametric equivalent, the Wilcoxon rank sum, if the assumptions have not been met

     These resources show the calculations for the specified techniques

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