Over this 8 week period, back pain is measured at three time points, which represents the three groups of the "within-subjects" factor, "time" (i.e., back pain is measured "at the beginning of the programme", "midway through the programme" and "at the end of the programme" ).Īt the end of the experiment, the researcher uses a mixed ANOVA to determine whether any change in back pain (i.e., the dependent variable) is the result of the interaction between the type of treatment (i.e., the massage programme or acupuncture programme that is, the "conditions", which is the "between-subjects" factor) and "time" (i.e., the within-subjects factor, consisting of three time points). Of these 60 participants, 30 are randomly assigned to undergo treatment A (the massage programme) and the other 30 receive treatment B (the acupuncture programme). In total, 60 participants take part in the experiment.
These two treatments reflect the two groups of the "between-subjects" factor. More specifically, the two different treatments, which are known as "conditions", are a "massage programme" (treatment A) and "acupuncture programme" (treatment B). Therefore, the dependent variable is "back pain", whilst the within-subjects factor is "time" and the between-subjects factor is "conditions". The researcher wants to find out whether one of two different treatments is more effective at reducing pain levels over time. Imagine that a health researcher wants to help suffers of chronic back pain reduce their pain levels. Your between-subjects factor consists of conditions (also known as treatments). Before discussing this further, take a look at the examples below, which illustrate the three more common types of study design where a mixed ANOVA is used: The primary purpose of a mixed ANOVA is to understand if there is an interaction between these two factors on the dependent variable.
These groups form your "between-subjects" factor.
For example, a mixed ANOVA is often used in studies where you have measured a dependent variable (e.g., "back pain" or "salary") over two or more time points or when all subjects have undergone two or more conditions (i.e., where "time" or "conditions" are your "within-subjects" factor), but also when your subjects have been assigned into two or more separate groups (e.g., based on some characteristic, such as subjects' "gender" or "educational level", or when they have undergone different interventions). Mixed ANOVA using SPSS Statistics IntroductionĪ mixed ANOVA compares the mean differences between groups that have been split on two "factors" (also known as independent variables), where one factor is a "within-subjects" factor and the other factor is a "between-subjects" factor.
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