Why did the authors use multiple regression?
The author used multiple regression because they wanted to comprehend the role that independent or individual predictor variables played in the regression (Maxwell, 2000). As such, it becomes easier for the author to understand the relationship that exists between several predictor or independent variables and the criterion or dependent variables. Thus, it was possible to build the regression line and construct a graph, which clear depicts the nature and relationship of the involved variables.
Do you think it’s the most appropriate choice? Why or why not?
Multiple regression was the most appropriate choice. This is because multiple regression helped the author to answer the general question “which is the best predictor of …?” As such, the author had an opportunity to comprehend how the dependent variable was related to multiple independent variables. The outcome of this is making an accurate prediction of the existence of things as they are displayed.
Did the authors display the data?
The author has displayed the data in an effective manner. The data is displayed in both tabular and graphical means. As such, the reader of the article is able to associate with the formulated hypothesis and develop an analytical and critical thinking of the displayed data. The display of the data also helps in understanding why the authors had to conduct the study.
Do the results stand alone? Why or why not?
The results stand alone. This is because these results add to existing statistical knowledge and insights on multiple regression. The article provides methods, which can be effectively used in the entire process of calculating the effect sizes in multiple regression. The results also indicate how the exchangeability structure can be achieved among the predictor variables. Thus, interested personnel in the field of statistics will find this article very resourceful.
Maxwell, S. (2000). Sample size and multiple regression analysis. Psychological methods. Vol.
5, No. 4, 434-458.