Multiple Regression in Practice
Walden University
RSCH 8210: Quantitative Reasoning and Analysis
14:08:16 GMT -05:00
, Multiple Regression in Practice
Multiple regression provides statistical analysis of the impact of multiple independent
variables on the dependent variable (Frankfort-Nachmias et al., 2021). The use of multiple
regression is beneficial to identify variables that have the most significant associations to the
dependent variable when multiple factors may be affecting it. Guidry et al.’s (2021) use multiple
regression analysis to determine people’s intent to engage in behaviors that prevent the spread of
COVID19. The authors asked two research questions to understand which demographic and
psychosocial factors predicted adherence to COVID19 preventive measures. In the first multiple
regression model, several demographic factors acted as the independent variables, and intent to
adhere to COVID19 preventative measures served as the dependent variable. In the second
multiple regression model, psychosocial factors were the independent variables, and the
dependent variable remained the same. Multiple regression analysis provided the authors with an
understanding of factors within the health belief model that would improve the intent to comply
with COVID19 precautions (Guidry et al., 2021). As a result, the authors could determine which
variable made up the most considerable variation in the prediction of intent to comply.
Do you think it’s the most appropriate choice? Why or Why not?
The use of multiple regression was the best choice for the statistical analysis because the
research questions required several independent variables to be examined (Frankfort-Nachmias
et al., 2021). Without completing the multiple regression analysis, the authors would not
determine what variable to hold as a constant. Furthermore, it is difficult to isolate a control
variable in social sciences because of the complex interplay of variables on human behavior.
Thus, it is essential to use multiple regression analysis to understand the variation and
association of multiple independent variables.
14:08:16 GMT -05:00
Walden University
RSCH 8210: Quantitative Reasoning and Analysis
14:08:16 GMT -05:00
, Multiple Regression in Practice
Multiple regression provides statistical analysis of the impact of multiple independent
variables on the dependent variable (Frankfort-Nachmias et al., 2021). The use of multiple
regression is beneficial to identify variables that have the most significant associations to the
dependent variable when multiple factors may be affecting it. Guidry et al.’s (2021) use multiple
regression analysis to determine people’s intent to engage in behaviors that prevent the spread of
COVID19. The authors asked two research questions to understand which demographic and
psychosocial factors predicted adherence to COVID19 preventive measures. In the first multiple
regression model, several demographic factors acted as the independent variables, and intent to
adhere to COVID19 preventative measures served as the dependent variable. In the second
multiple regression model, psychosocial factors were the independent variables, and the
dependent variable remained the same. Multiple regression analysis provided the authors with an
understanding of factors within the health belief model that would improve the intent to comply
with COVID19 precautions (Guidry et al., 2021). As a result, the authors could determine which
variable made up the most considerable variation in the prediction of intent to comply.
Do you think it’s the most appropriate choice? Why or Why not?
The use of multiple regression was the best choice for the statistical analysis because the
research questions required several independent variables to be examined (Frankfort-Nachmias
et al., 2021). Without completing the multiple regression analysis, the authors would not
determine what variable to hold as a constant. Furthermore, it is difficult to isolate a control
variable in social sciences because of the complex interplay of variables on human behavior.
Thus, it is essential to use multiple regression analysis to understand the variation and
association of multiple independent variables.
14:08:16 GMT -05:00
