Hi,
I am trying to evalaute the impact of gender quotas on women's political engagement. I am using the world values survey data on different countries over the time period of range 1981-2009. I wish to do a country and time fixed regression of gender quotas on a variable while controlling for age. However age in the survey is divided into categories, how can i recode it for my regression. Should I use binning to control for age or should I use the mean values of the categories?
If age is an ordinal (ordered categorical) variable in your data set, you can simply add that variable as another predictor to your regression model.
Before you dive into recoding, you'll want to fully understand how age is grouped in your dataset. Typically, these categories might look something like 18-24, 25-34, etc. Make sure you know exactly what these ranges are and how they've been set up in the survey.
Options for Recoding Age Categories
Option 1: Using Midpoints of Age Categories
You can simplify each age category to a single value by using the midpoint of that range. For example, the category 18-24 would be represented by 21 (that’s just (18+24)/2). This approach treats age as a continuous variable, which can be great if you think the relationship between age and political engagement is pretty straightforward or linear.
- Pros: It keeps your model simple and clean, and you’re working with just one additional variable.
- Cons: It assumes that everyone in an age category is clustered around the middle, which isn't always true.
Option 2: Creating Dummy Variables for Age Categories
Alternatively, you can create a dummy variable for each age category. This means each range would have its own variable in the analysis, and you'd compare each to a reference category (usually the first or last category).
- Pros: This method doesn’t make any assumptions about the distribution within each age group and can capture more complex, non-linear relationships.
- Cons: It can make your model quite a bit bulkier, as you're adding several new variables. This can make your results a bit harder to interpret and might eat into your degrees of freedom.
If your main concern is capturing precise effects and you suspect that age influences political engagement in a nuanced way, go with the dummy variables. It’s a bit more work, but it can provide better insights. If you’re more into seeing the big picture and are okay with a rougher approximation of age’s effects, using midpoints could be enough and will keep your analysis straightforward.
How to Implement These in Your Regression
- Using Midpoints: Just calculate a new variable in your dataset for these midpoints and include it in your model as a continuous predictor.
- Using Dummies: Most stats software makes it easy to turn categorical data into dummies with just a few clicks or lines of code.
The route you choose depends on what your study aims to uncover and how detailed you need your analysis to be. It might even be worth running both methods to see if they significantly differ—this could provide some interesting insights into how age interacts with political engagement across your datasets.
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