What is it called when all the participants in an experiment are exposed to all of the different levels of the IV ie all of the participants are in all of the conditions )?
IntroductionAn ANOVA with repeated measures is used to compare three or more group means where the participants are the same in each group. This usually occurs in two situations: (1) when participants are measured multiple times to see changes to an intervention; or (2) when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared. Show
For example, you could use a repeated measures ANOVA to understand whether there is a difference in cigarette consumption amongst heavy smokers after a hypnotherapy programme (e.g., with three time points: cigarette consumption immediately before, 1 month after, and 6 months after the hypnotherapy programme). In this example, "cigarette consumption" is your dependent variable, whilst your independent variable is "time" (i.e., with three related groups, where each of the three time points is considered a "related group"). Alternately, you could use a repeated measures ANOVA to understand whether there was a difference in breaking speed in a car based on three different coloured tints of windscreen (e.g., breaking speed under four conditions: no tint, low tint, medium tint and dark tint). In this example, "breaking speed" is your dependent variable, whilst your independent variable is "condition" (i.e., with four related groups, where each of the four conditions is considered a "related group"). Note: Whilst the repeated measures ANOVA is used when you have just "one" independent variable, if you have "two" independent variables (e.g., you measured time and condition), you will need to use a two-way repeated measures ANOVA. This "quick start" guide shows you how to carry out a repeated measures ANOVA using SPSS Statistics, as well as interpret and report the results from this 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 repeated measures ANOVA to give you a valid result. We discuss these assumptions next. SPSS StatisticsAssumptionsWhen you choose to analyse your data using a repeated measures ANOVA, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a repeated measures ANOVA. You need to do this because it is only appropriate to use a repeated measures ANOVA if your data "passes" five assumptions that are required for a repeated measures ANOVA to give you a valid result. In practice, checking for these five 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. Before we introduce you to these five 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). This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a repeated measures ANOVA when everything goes well! However, don’t worry. Even when your data fails certain assumptions, there is often a solution to overcome this. First, let’s take a look at these five assumptions:
You can check assumptions #3, #4 and #5 using SPSS Statistics. Before doing this, you should make sure that your data meets assumptions #1 and #2, although you don't need SPSS Statistics to do this. Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running a repeated measures ANOVA might not be valid. This is why we dedicate a number of sections of our enhanced repeated measures ANOVA guide to help you get this right. You can find out about our enhanced content as a whole on our Features: Overview page, or more specifically, learn how we help with testing assumptions on our Features: Assumptions page. In the section, Test Procedure of SPSS Statistics, we illustrate the SPSS Statistics procedure to perform a repeated measures ANOVA assuming that no assumptions have been violated. First, we set out the example we use to explain the repeated measures ANOVA procedure in SPSS Statistics. SPSS Statistics ExampleA researcher wants to understand how exercise might reduce heart disease. The researcher wants to concentrate on a protein called C-Reactive Protein (CRP), which is a marker of chronic inflammation in the body and associated with heart disease: the greater the concentration of CRP, the greater the risk of heart disease. Regular exercise reduces the risk of heart disease. Therefore, the researcher would like to know whether exercise has an effect on CRP concentration because this might indicate that exercise has an anti-inflammatory effect. To test out this theory, the researcher recruits 10 subjects to undergo a 6-month exercise-training programme. CRP concentration is measured at three different stages within the 6-month exercise-training programme: (1) pre-intervention (i.e., pre); (2) midway through the intervention at 3 months (i.e., mid); and (3) immediately post-intervention (i.e., post). These three time points reflect the three levels of our within-subjects factor, time (i.e., a within-subjects factor that is measured on an ordinal scale, although a within-subjects factor can also be measured on a nominal scale when carrying out a one-way repeated measures ANOVA). The dependent variable is CRP, which is measured in mg/L (i.e., a dependent variable that is measured on a continuous scale). The CRP concentrations pre-intervention were recorded in the crp_pre variable, the CRP concentrations midway through the intervention in the crp_mid variable and the post-intervention CRP concentrations in the crp_post variable. The researcher would like to know whether there are changes in CRP concentration over time. In variable terms, the researcher would like to know if there are differences between the three variables: crp_pre, crp_mid and crp_post. In our enhanced repeated measures ANOVA guide, we show you how to correctly enter data in SPSS Statistics to run a repeated measures ANOVA. You can learn about our enhanced data setup content on our Features: Data Setup page. Alternatively, see our generic, "quick start" guide: Entering Data in SPSS Statistics. SPSS StatisticsTest Procedure in SPSS StatisticsThe General Linear Model > Repeated Measures... procedure below shows you how to analyse your data using a repeated measures ANOVA in SPSS Statistics when the five assumptions in the previous section, Assumptions, have not been violated. At the end of these 13 steps, we show you how to interpret the results from this test. If you are looking for help to make sure your data meets assumptions #3, #4 and #5, which are required when using a repeated measures ANOVA and can be tested using SPSS Statistics, you can learn more in our enhanced guides (see our Features: Overview page to learn more). Since some of the options in the General Linear Model > Repeated Measures... procedure changed in SPSS Statistics version 25, we show how to carry out a repeated measures ANOVA depending on whether you have SPSS Statistics versions 25, 26, 27 or 28 (or the subscription version of SPSS Statistics) or version 24 or an earlier version of SPSS Statistics. The latest versions of SPSS Statistics are version 28 and the subscription version. If you are unsure which version of SPSS Statistics you are using, see our guide: Identifying your version of SPSS Statistics. SPSS Statistics versions 25, 26, 27 and 28 |