What factors influence the choice of variables in a model?

The choice of variables in a model is influenced by the research question, data availability, and the model's purpose.

The research question is the primary determinant of the variables to be included in a model. The variables should be directly related to the research question and should be able to provide answers to it. For instance, if the research question is about the factors affecting student performance, the variables could include hours of study, attendance, and teacher quality among others. The variables chosen should be able to provide a comprehensive understanding of the research question.

Data availability is another crucial factor. The choice of variables is limited to the data that is available for analysis. If certain data is not available, then those variables cannot be included in the model. For example, if we want to analyse the impact of socio-economic status on student performance, but we do not have data on the parents' income, then we cannot include this variable in our model. Therefore, the availability and quality of data can significantly influence the choice of variables.

The purpose of the model also plays a significant role in the choice of variables. If the model is being built for predictive purposes, then the variables chosen should be those that have a strong predictive power. On the other hand, if the model is being built for explanatory purposes, then the variables chosen should be those that can best explain the phenomenon under study. For instance, in a model built to predict house prices, variables like location, size, and age of the house might be included because they are known to strongly influence house prices.

Moreover, the choice of variables can also be influenced by the theoretical framework or previous research in the field. If previous research has identified certain variables as important, then these variables are likely to be included in the model. Similarly, the theoretical framework might suggest certain variables that should be included in the model.

Lastly, the choice of variables can be influenced by practical considerations such as the cost and time involved in collecting data. If collecting data on a certain variable is too costly or time-consuming, then that variable might be excluded from the model.