![]() First, let's take a look at these four assumptions: Even when your data fails certain assumptions, there is often a solution to overcome this. This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a dependent t-test when everything goes well! However, don't worry. In practice, checking for these four assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.īefore we introduce you to these four assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., is not met). You need to do this because it is only appropriate to use a dependent t-test if your data "passes" four assumptions that are required for a dependent t-test to give you a valid result. When you choose to analyse your data using a dependent t-test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a dependent t-test. ![]() ![]() However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a dependent t-test to give you a valid result. This "quick start" guide shows you how to carry out a dependent t-test using SPSS Statistics, as well as interpret and report the results from this test. If your dependent variable is dichotomous, you should instead use McNemar's test. For example, you could use a dependent t-test to understand whether there was a difference in smokers' daily cigarette consumption before and after a 6 week hypnotherapy programme (i.e., your dependent variable would be "daily cigarette consumption", and your two related groups would be the cigarette consumption values "before" and "after" the hypnotherapy programme). The dependent t-test (called the paired-samples t-test in SPSS Statistics) compares the means between two related groups on the same continuous, dependent variable. If there were multiple groups in the model (as in Example 12 in the AMOS 4 User's Guide), then you would multiply the number of moments per group (variances, covariances and means (if means are requested in model)) by the number of groups.Dependent T-Test using SPSS Statistics Introduction Add the 14 sample means and you have 105+14=119 sample moments. (There are 14*14=196 total elements in the covariance matrix, but the matrix is symmetric about the diagonal, so only 105 values are unique). For 14 observed variables, this equals 14 variances and 14*13/2 = 91 covariances for a total of 14+91=105 unique values in the sample covariance matrix. For K observed variables, the number of unique elements in the sample covariance matrix is K*(K+1)/2, comprised of K variances and K*(K-1)/2 covariances. In general the number of degrees of freedom equals:ĭF = Number of sample moments - Number of free parameters in the model.įrom your question, I understand that you have 14 observed variables and that you have requested a model with means and intercepts.
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