Dear all,

For my research I have the following facts:

- I have two categorical IVs:

- treatment condition called competition, which has three conditions ("low", "medium" and "high")

- Another factor that is given to half of the group, called accountability (two options: "non-accountability" and "accountability")

Just to be clear, are you saying that half of the total sample size is in the accountability group and the other half is in the non-accountability group (and within each of those, they are split into one of the three categories for competition?)?

- Hence, I have a 3x2 experiment with 20 participants in each group.

- My DV is a scale variable

- I also have a set of control variables (both scale and categorical)

Would you clarify what you mean by a scale variable (I think I'm unfamiliar with that particular term).

Then:

- Hypothesis one is that if you go from low to medium to high, the DV will increase

- Hypothesis two is that if you introduce accountability, the differences between the low, medium and high group decrease.

The problem I encounter is that I have three groups for the competition condition (which are coded 0, 1 and 2). If I multiply it with accountability, they become 0, 2 and 4, which is a problem according to my supervisor. Therefore, a factorial ANOVA seems to be a suboptimal solution.

He recommended a regression analysis with dummy variables of the three competition scenarios. But then still I have no idea on how to test for the interaction effect between competition and accountability.

Assuming that the dependent variable is appropriate for an ANOVA/Regression, I agree with your supervisor that it would be better to fit a regression with the appropriate dummy variables.

First, you will need to determine the baseline/reference group. I recommend setting low competition and non-accountability as your baselines. In other words, for competition, you have three groups, so you will need 2 dummy variables (number of groups minus 1, and fit the intercept in the model). Create X1: 1 if medium competition, 0 if not; Create X2: 1 if high competition, 0 if not; this implicitly codes low as the reference level (0,0), so if your hypothesis #1 is true, you would see positive coefficients for X1 and X2 since they represent the difference of the group relative to low.

Create X3 for accountability: 1 if accountability, 0 if non-accountable

To test for interaction, include terms: X1*X3, X2*X3

You will have 5 coefficients aside from an intercept.

Conduct a global f-test and proceed if significant (also make sure assumptions are reasonably satisfied).

I would then do a nested f-test to test the 2 coefficients for interaction.

If you've followed up until this point, feel free to post some output with the full model and interaction subset test. If you're a little confused, feel free to ask some more questions!