Our situation: There has been an increased interest in regression techniques within pharmacy education to allow researchers to determine variables that may predict a specific outcome (e.g., predicting student scores on the Pharmacy Curriculum Outcomes Assessment). This article has been tailored for individuals who are interested in learning more about multiple linear regression as a data analysis tool and its potential utility in pharmacy education research.
Methodological literature review: Within this section, the basic steps of regression are outlined, starting with correlational analysis before progressing to simple linear regression and multiple regression. Key terms needed to understand and interpret regressions are also discussed.
Our recommendations and their applications: Nine practical recommendations are provided to help researchers better understand and implement regression analyses in their studies.
Potential impact: Regression analyses could be helpful in advancing pharmacy educational scholarship by enabling scholars to better understand variables that may predict specific outcomes such as student achievement or program retention.
Keywords: Biostatistics; Modeling; Quantitative research; Regression; Statistics.
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