Run another two-sample t test to see if there is a statistically significant difference in mean GCSE score between respondents in the variable s1q1e , which concerns whether or not a respondent is enrolled in full time education. Before you run the test, use the Frequencies function to make sure there are no coded missing values in s1q1e. If there are, recode them. What are the results of your t test? Are they what you might expect? Univariate analysis Bivariate analysis Multivariate analysis: Linear.
Bivariate analysis. If you have a large sampleset test also the variance ratio Hartley's Fmax. If you still have a problem with the unequal variances "Maybe using Welch and Brown - Forsythe test and Games Howell test as post hoc? I can highly recommend the statistics book from andy field. ALthough it is about SPSS, it helps you with a lot of "basic" statistic problems and is written in an easy and understandable way. This is no advertisment, but the source of which my answer is based on.
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Learn more. Additionally, using a dichotomized variable created via a cut point generally reduces the power of the test compared to using a non-dichotomized variable.
The Confidence Interval Percentage box allows you to specify the confidence level for a confidence interval. Note that this setting does NOT affect the test statistic or p-value or standard error; it only affects the computed upper and lower bounds of the confidence interval.
You can enter any value between 1 and 99 in this box although in practice, it only makes sense to enter numbers between 90 and The Missing Values section allows you to choose if cases should be excluded "analysis by analysis" i. This setting is not relevant if you have only specified one dependent variable; it only matters if you are entering more than one dependent continuous numeric variable. In that case, excluding "analysis by analysis" will use all nonmissing values for a given variable.
If you exclude "listwise", it will only use the cases with nonmissing values for all of the variables entered. Depending on the amount of missing data you have, listwise deletion could greatly reduce your sample size. In our sample dataset, students reported their typical time to run a mile, and whether or not they were an athlete.
Suppose we want to know if the average time to run a mile is different for athletes versus non-athletes. This involves testing whether the sample means for mile time among athletes and non-athletes in your sample are statistically different and by extension, inferring whether the means for mile times in the population are significantly different between these two groups.
You can use an Independent Samples t Test to compare the mean mile time for athletes and non-athletes. In the sample data, we will use two variables: Athlete and MileMinDur. It will function as the independent variable in this T test. The variable MileMinDur is a numeric duration variable h:mm:ss , and it will function as the dependent variable. In SPSS, the first few rows of data look like this:. Before running the Independent Samples t Test, it is a good idea to look at descriptive statistics and graphs to get an idea of what to expect.
This corresponds to a variance of seconds for non-athletes, and a variance of seconds for athletes 1. If the variances were indeed equal, we would expect the total length of the boxplots to be about the same for both groups. However, from this boxplot, it is clear that the spread of observations for non-athletes is much greater than the spread of observations for athletes. Already, we can estimate that the variances for these two groups are quite different.
It should not come as a surprise if we run the Independent Samples t Test and see that Levene's Test is significant. The significance level is the threshold we use to decide whether a test result is significant. The first section, Group Statistics , provides basic information about the group comparisons, including the sample size n , mean, standard deviation, and standard error for mile times by group. In this example, there are athletes and non-athletes.
The mean mile time for athletes is 6 minutes 51 seconds, and the mean mile time for non-athletes is 9 minutes 6 seconds.
From left to right:. The p -value of Levene's test is printed as ". This tells us that we should look at the "Equal variances not assumed" row for the t test and corresponding confidence interval results.
Note that the mean difference is calculated by subtracting the mean of the second group from the mean of the first group. The sign of the mean difference corresponds to the sign of the t value. The positive t value in this example indicates that the mean mile time for the first group, non-athletes, is significantly greater than the mean for the second group, athletes.
The associated p value is printed as ". Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required. Home Support. It is not really clear what to do next. Here are some thoughts: This gives you strong evidence that the groups are not selected from identical populations. You haven't yet tested whether the means are distinct, but you already know that the variances are different.
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