Building the Research House

Imagine that you have been given a huge toychest filled with toy parts and pieces. Your goal is to build a toy house from these mix of pieces. As you will see, this is not all that different from certain aspects of the research process.

In much of research, our goal is to predict or explain one variable from a set of other variables using the multiple regression technique. The variable that you predict or explain is usually called the dependent variable (or response variable). The variables that do the predicting or explaining are usually called the independent variables (or predictor variables). So, in our example, the house is the dependent variable, and all of those pieces are the independent variables.

We may know ahead of time that certain pieces fit well with our conception of a house. For example, if our goal is a log-cabin, any toy piece made of wood might get automatically included in the mix. What about a toy necklace? Well, the beads might be used for decorations. But is it necessary to include them? On the other hand, why not just include every toy piece?

In multiple regression, we are faced with similar decisions to make. Suppose we are trying to predict student math test scores. An obvious predictor would be exposure to training (i.e., attending a math course with content related to that math test), which can be measured by attendance. We might also include time spent studying in hours. We could go a step further and include variables such as presence of a tutor/mentor and family socioeconomic status. Technically, we could go even further...adding every toy piece from the chest...because, in reality, almost everything is related to everything in some way, shape, or form. So, how do we handle this?

1. Keep the model as simple as possible. Do not include extraneous variables just for the sake of including them. Have a strong rationale for including every variable that you include in the model.

2. Keep the model as complex as necessary. In contrast, to the previous advice, do not omit variables simply because you decided to keep it simple. If you liked having three predictors, but ignoring a fourth predictor would lead to an incomplete "house", by all means...include the fourth predictor. What you want is a set of variables that represent the majority of what you are predicting; variables that have negligible impact can be omitted from the research.

3. Ensure that your proxy variables resemble reality as much as possible. For example, in educational research, we often measure socioeconomic status using the percent on free and/or reduced lunch programs as a proxy. And, this is currently argued to be the best proxy available.

4. Base all decisions on strong theory. Attempt to find literature to justify your choice to include or ignore variables.

5. Document anything that might help future researchers, and include this in your discussion. Did you ignore a particular variable because of lack of theory? Future researchers could develop a theory. Did you ignore a particular variable because of lack a decent proxy or available data? Future researchers might tackle that problem (or not be dealing with it).

6. Acknowledge that human behavior includes inherent randomness that will not be explained by any particular set of variables.

So, returning to the toychest, what we want is basic but requires much planning. We want a house that uses the minimum number of toy parts. However, we want as many toy parts as necessary to ensure that we have built something resembling a house. We connect our choices to preconceived notions such as wanting a particular style of house. We tell others that we didn't include certain pieces, and we tell them why since they might want to attempt this in the future. Finally, we accept that our final product will not be a perfect toy house; however, we made every attempt to create as perfect of one as possible.

For consultations, please send an e-mail to
Dr. Miller