Step 4: Developing a Coding Framework

This step is EXTREMELY important when conducting a meta-analysis. The coding framework determines what data you will and will not collect from each study that you read. It is always better to collect more information (rather than less) from each study in order to try and prevent the potential tragedy of completing your data collection, only to discover you have to re-read every study and extract some other piece of information you previously missed. To begin creating the coding framework, you will want to create a spreadsheet (google docs work well for this so multiple people can code simultaneously). Click HERE for tips on making your spreadsheet the best it can be. In this spreadsheet, the columns will correspond to your moderators--anything that could potentially affect your variable of interest--and each row will become a study (also known as an effect).

It is helpful to go ahead and enter your column titles in an SPSS-friendly way. Thus, every potential space between words should be replaced by an underscore. Some essential columns that every coding framework should include are:

1. LastName_firstauthor

2. year_published

3. sample_size

4. Number_effects (the importance of this column will be further explained in Step 5-Code)

5. A column for your variable of interest as well as one for its standard deviation and one for its standard error.

   • If your variable of interest is a pre-/post- measurement or changes over time, you will need to create the columns included in #5 for both the before and after values.

The remainder of your columns should be created based on your variable of interest and potential factors that could moderate this variable. For example, if you are studying something involving humans, BMI, age, gender, race, and disease/health status are all essential factors to add to the coding framework with a column for standard deviation and standard error when applicable. Other questions to ask yourself and potentially code for are:

1. Is some sort of treatment being administered?

   1. Does this treatment vary between studies?

   2. What information is necessary to describe the treatment?

2. Are characteristics changing over time?

   1. Do we need a BMI pre- and BMI post- column?

3. Is the variable of interest measured in multiple different ways?

   1. Are there disagreements on methodology?

   2. What information is necessary to distinguish one measurement technique from another?

4. ​What is the study design?

   1. If you are only looking at randomized controls, you will need columns describing the control group.

5. Are there potential psychological or sociological factors to take into consideration regarding your variable of interest?

It is helpful to create a column for both the raw data and a code. Codes are numbers greater than zero arbitrarily assigned to various characteristics that will become important later on in the analysis phase. For example, you could create a code for BMI in which normal weight = 1, overweight = 2, obese = 3, and not reported = 4. The thing to keep in mind when assigning codes is everyone working on your meta-analysis MUST use the numbers in the code to represent the same values. Once you have established your coding framework and feel confident that you have included columns for any/all potential moderators, you may BEGIN CODING!