Three basic rules for selecting variables to add to a model are:Select variables that can answer the theoretical purpose (accepting/rejecting the null hypothesis), with sensible control variables and mediating variablesAdd enough variables for a good predictive powerKeep the model simpleThe explanatory variables should be highly correlated to the response variable but not to each other. Software can test and select explanatory variables. Possible strategies are backward elimination, forward selection and stepwise regression. In backward elimination all possible variables are added, tested for their P-value and then only the significant variables are selected. Forward selection starts from scratch, adding variables with the lowest P-value. Another version of this is stepwise regression, this method removes redundant variables when new variables are added.Software helps but it's up to the researcher to think and make choices. It also matters whether research is explanatory, starting with a theoretical model with known variables, or whether research is exploratory, openly looking for explanations of a phenomenon.Several criteria are indications of a good model. To find a model with big power but without an overabundance of variables, the adjusted...