What are the steps to choosing the best statistical model for your data?

2 Choose the model family

The next step is to choose the model family, which is the general class of statistical models that share some common features. For example, some common model families are linear models, logistic models, survival models, and mixed models. The model family depends on the type and distribution of your outcome variable, as well as the nature of the relationship between the outcome and the explanatory variables. For example, if your outcome variable is continuous and normally distributed, and you expect a linear relationship with the explanatory variables, you can choose a linear model. If your outcome variable is binary, and you expect a nonlinear relationship with the explanatory variables, you can choose a logistic model.

3 Specify the model form

The third step is to specify the model form, which is the specific equation that describes how the outcome variable depends on the explanatory variables and other parameters. The model form includes the choice of the explanatory variables, the interaction terms, the transformation terms, the error terms, and the coefficients. The model form depends on the research question, the model family, and the data characteristics. For example, if you choose a linear model, you need to specify which explanatory variables to include, whether to add any interaction or transformation terms, and how to account for the error term.

5 Evaluate the model fit

The final step is to evaluate the model fit, which means to assess how well the model captures the relationship between the outcome and the explanatory variables in the data. There are several ways to evaluate the model fit, such as using goodness-of-fit measures, checking the model assumptions, comparing different models, and validating the model predictions. The choice of the evaluation method depends on the model family, the research question, and the data quality.