Understanding and interpreting regression analysis

A nurse educator is interested in finding out the academic and non-academic predictors of success in nursing students. Given the complexity of educational and clinical learning environments, demographic, clinical and academic factors (age, gender, previous educational training, personal stressors, learning demands, motivation, assignment workload, etc) influencing nursing students’ success, she was able to list various potential factors contributing towards success relatively easily. Nevertheless, not all of the identified factors will be plausible predictors of increased success. Therefore, she could use a powerful statistical procedure called regression analysis to identify whether the likelihood of increased success is influenced by factors such as age, stressors, learning demands, motivation and education. Regression analysis allows for investigating the relationship between variables.1 Usually, the variables are labelled as dependent or independent. An independent variable is an input, driver or factor that has an impact on a dependent variable (which can also be called an outcome). For example, if we were to say age affects academic performance of students, what will be the independent and dependent variables here? Well here age is an independent variable, and it has the potential to impact on outcome/dependent variable—in this case, academic performance. Similarly, in the nurse educator's example, critical thinking is a dependent variable and age, experience and training are independent variables. Regression analysis has four primary purposes: description, estimation, …