I am trying to understand what factors affect debate participation in parliament and how this has changed over time. I want to run a regression in R, with a count variable as the dependent variable (the number of times each member has participated in debate in a specific policy area per year) and 4 explanatory variables - sex of the member (dummy variable), political party of the member (dummy variables for 3 main parties), whether or not they hold a leadership position in their party (dummy variable) and the number of years since they were elected (categorical variable).

I want to run a regression analysis over 11 years (2000 - 2010) to understand if changes have taken place over time.

I haven't worked with regressions over periods of time before. I have another categorical variable for the years. I was going to do a negative binomial regression, but I am not sure how to analyse how the debate participation has changed over time. Can I just include my year variable as an explanatory variable? Or should I do a two step regression?

  • $\begingroup$ Presumably you have multiple counts for the same MP, so look into repeated measurements models. There are many options in R! $\endgroup$ Commented Aug 9, 2020 at 15:04

1 Answer 1


You can include year as a fixed effect, either as a factor or numeric. If numeric then you will obtain and estimate of the linear trend over time. You might want to include non linear terms or spines. Coding it as a factor to begin with will as ascertain whether a linear trend is plausible.

You will need to account for repeated measures within subjects so provided you have sufficient of them, you can fit random intercepts for them.

This would be a longitudinal mixed effects model. You can extend the model with random slopes for fixed effects that can plausibly vary by subject. In these models time is often fitted as a random slope too.

  • $\begingroup$ Does this answer your question ? If so, please consider marking it as the accepted answer, and if not please let us know why. $\endgroup$ Commented Aug 21, 2020 at 12:06

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