Multiple Regression Analysis
Name
Institution
Why did the authors use multiple regression?
The author sought to investigate whether the teacher’s knowledge indices are significant predictors of secondary school student’s academic achievement. Thus the key aspects that were being assessed included teacher’s depth of subject content and pedagogical knowledge. The application of the multiple regression, therefore, investigated student performance based on the two independent variables. Multiple regression analysis is conducted when there are two or more independent variables with one dependent variable (Darlington & Hayes, 2016).
Do you think it’s the most appropriate choice? Why or why not?
The use of regression analysis is the most appropriate based on the research issue that was being investigated. Understanding the use of regression analysis requires an understanding of the underlying assumptions which must be thoroughly evaluated to attain the needed outcomes. In conducting a regression analysis, there must be independent and dependent variables that are clearly defined (Schroeder et al., 2016). The dependent variable must be a continuous variable that is measured on either ratio or ordinal scale. The independent variables must two or more. The independent variables can either be categorical or continuous. Therefore when assessing the variables that have been considered in this case, the dependent variable is student performance, which is a continuous variable that is measured on a ratio scale. The independent variable includes teachers’ depth of subject content and pedagogical knowledge. These are continuous variables that are measured on a ratio scale. Thus the use of multiple regression was appropriate (Darlington & Hayes, 2016).
Did the authors display the data?
The authors have not displayed the data or provided a link to the data repository. However, the analysis tables have been revealed, which makes it possible to follow the analysis and decide on the findings. It is possible to relate the conclusions of the tables that have been conducted. However, since there is no data displayed, it is difficult to determine whether the results were effectively conducted through multiple regression analysis (Olasehinde-Williams et al., 2018). The authors have demonstrated all the multiple regression tables, which include a model summary, analysis of variance table, and coefficient table, which provide an understanding of independent predictors of the variables included in the report.
Do the results stand alone? Why or why not?
The results have shown that only pedagogical knowledge is a statistically significant predictor of student performance based on the data included in the analysis. The findings do not stand alone, considering that there are studies that have been conducted in the past, highlighting how the level of knowledge influences student performance (Tella, 2017). different factors predict student performance (Agustiani et al., 2016). the level of engagement with teachers positively through sharing of course content plays a significant role in defining the level of performance and ability to integrate different measures that help in determining improved outcomes (Marshik et al., 2017).
Did the authors report effect size? If yes, is this meaningful?
Based on the results that have been provided, the researchers did not provide an effect size despite the model being statistically significant. The researchers reported coefficient of determination (r2) = 0.107. This shows that the independent variables that were included in the analysis explain 10.7% of the student performance. This means that other factors explain 89.3% of student performance that has not been included in the model (Olasehinde-Williams et al., 2018).
References
Agustiani, H., Cahyad, S., & Musa, M. (2016). Self-efficacy and self-regulated learning as predictors of students’ academic performance. The Open Psychology Journal, 9(1).
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publications.
Marshik, T., Ashton, P. T., & Algina, J. (2017). Teachers’ and students’ needs for autonomy, competence, and relatedness as predictors of students’ achievement. Social Psychology of Education, 20(1), 39-67.
Olasehinde-Williams, F., Yahaya, L., & Owolabi, H. (2018). Teachers’ Knowledge Indices as Predictors of Secondary School Students’ Academic Achievement in Kwara State, Nigeria. IAFOR Journal of Education, 6(1), 73-90.
Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (2016). Understanding regression analysis: An introductory guide (Vol. 57). Sage Publications.
Tella, A. (2017). Teacher variables as predictors of academic achievement of primary school pupils mathematics. International Electronic Journal of Elementary Education, 1(1), 16-33.