The reason that it is possible to have the same subjects in each group is because each subject has been measured on two occasions on the same dependent variable. "Related groups" indicates that the same subjects are present in both groups. Assumption #2: Your independent variable should consist of two categorical, "related groups" or "matched pairs".You can learn more about ordinal and continuous variables in our article: Types of Variable. Examples of continuous variables (i.e., interval or ratio variables) include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. Examples of ordinal variables include Likert items (e.g., a 7-point item from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 5-point item explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot"). Assumption #1: Your dependent variable should be measured at the ordinal or continuous level.These three assumptions as briefly explained below: The third assumption reflects the nature of your data and is the one assumption you test using SPSS Statistics. The first two assumptions relate to your study design and the types of variables you measured. You need to do this because it is only appropriate to use a Wilcoxon signed-rank test if your data "passes" three assumptions that are required for a Wilcoxon signed-rank test to give you a valid result. When you choose to analyse your data using a Wilcoxon signed-rank test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a Wilcoxon signed-rank 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 Wilcoxon signed-rank test to give you a valid result.
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This "quick start" guide shows you how to carry out a Wilcoxon signed-rank test using SPSS Statistics, as well as interpret and report the results from this test. You could also use a Wilcoxon signed-rank test to understand whether there was a difference in reaction times under two different lighting conditions (i.e., your dependent variable would be "reaction time", measured in milliseconds, and your two related groups would be reaction times in a room using "blue light" versus "red light"). This can occur when we wish to investigate any change in scores from one time point to another, or when individuals are subjected to more than one condition.įor example, you could use a Wilcoxon signed-rank 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). It is used to compare two sets of scores that come from the same participants.
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As the Wilcoxon signed-rank test does not assume normality in the data, it can be used when this assumption has been violated and the use of the dependent t-test is inappropriate. The Wilcoxon signed-rank test is the nonparametric test equivalent to the dependent t-test. Wilcoxon Signed-Rank Test using SPSS Statistics Introduction