# Analyzing clustered (repeated measures) data with very few clusters

What is the most appropriate method for analyzing clustered data (i.e., repeated measurements nested within subjects) when there are very few clusters (e.g., 3 subjects)?

Here's a brief description of the study:

• There are 3 subjects, each experiencing 5 conditions.
• Within each condition, there are 12-15 repeated measures.
• The measures are: the number of responses on two alternatives (B1 and B2) and number of reinforcers earned for each type of response (R1 and R2).

The question is how subjects allocate responses to the two alternatives (B1 and B2) based on the number of reinforcers earned under those alternatives (R1 and R2).

The image below provides examples of some possibilities.

• I don't understand your research question. Please can you explain the study design and describe all of the variables. What is the outcome/response/dependent variable, and what are the independent variables, and how are they all measured ? Nov 13, 2020 at 20:10
• The dependent variable is the number of responses (B1=number of responses on option 1, B2=number of responses on option 2). The independent variable is the number of reinforcers delivered for each response option. In the five conditions, reinforcement (food) is concurrently available on the two options, but the schedule of reinforcement for each response differs across conditions. In the control condition, the schedule of reinforcement is the same for both options. In the other conditions, reinforcement is available more frequently for one option (e.g., every 12s for B1 vs. every 60s for B2). Nov 13, 2020 at 20:37
• How are B1 and B2 related ? Are these seperate outcomes ? If you had more subjects what kind of model would you fit ? Nov 13, 2020 at 20:41
• B1 and B2 are separate outcomes. B1=moving to one location and B2=moving to a second location. I think a linear mixed-effects model might be suitable if there were more subjects. Nov 13, 2020 at 20:55