Repeated Measures Single Factor Experiment
A repeated measures single factor experiment is an experiment where both:
- One factor of two or more levels has been manipulated. For example, the experiment may be investigating the effect of different levels of price, or different flavors, or different advertisements. (Where two or more factors are manipulated, such as both price and flavor being varied, it is then a Multifactor Experiment and not a single factor experiment.)
- Each respondent in the survey has been shown all of the factors (e.g., if the experiment is comparing ten new products then each respondent rates all 10 of the products). (Incomplete block experiments, where respondents are only shown a subset of the levels, are analyzed in Q as Ranking Experiments and Multifactor Experiments).
How to set up a repeated measures randomized single factor experiment in Q
- Set the Question Type to either:
- Select the cells on the table and press
This example uses a Pick One - Multi question in the Cola.sav project, which is by default installed on your computer at C:\Program Files\Q\Examples. A NET of WEEKLY+ has been created and its cells are selected. Thus, the repeated measures test is comparing the proportions of people to consumer each of these brands once a week or more. Particular aspects to note about the output are:
- An overall test is conducted. In this case it is Cochran's Q.
- Multiple comparisons have also been conducted between the categories. (Additionally, Column Comparisons can be added directly to the table.)
Non-parametric and limited dependent variable repeated measures models
When the question is set as a Number - Multi Q will, by default, model it as Repeated Measures ANOVA with Greenhouse & Geisser Epsilon Correction (i.e., a two-way ANOVA where the blocking variable is the first component). That is, the model tests for differences in the means of the data.
If the dependent variable is Ordered Categorical you can instead change the Question Type to Ranking and the test will look for differences in the relative order of preferences (see Ranking Experiments).