Designing a Productivity Study

The process of designing a study requires consideration of the way various variables interact and sway an outcome of interest. According to Gravetter and Forzano, the primary objective of designing a study is to elucidate the relationship between dependent and independent variables while minimizing or eliminating the effects of confounding variables (175). In a study, confounding variables distort findings because they influence both dependent and independent variables. Hence, the focus of the research design is to control for the effects of these confounding variables. In this case, the coursework examines a research design that elucidates how the productivity of employees, the outcome of interest, is subject to many factors, including management styles.

In the case study, findings reveal that management styles have a significant relationship, but they do not predict productivity among employees. The analysis of variables in the research design depicts the limitation of using correlation and regression analyses in determining the relationship and degree of impact, respectively. Since the correlation analysis shows that the two management styles studied have a significant covariance, they exhibit the limitation of collinearity. Harrell explains that collinearity occurs when predictors highly correlate, yet they predict the same criterion variable (78). The two leadership styles depicting collinearity constitute predictors of the productivity of employees in the study. To address the limitation of collinearity, the study requires the removal of one of the variables because they are redundant.

The presence of unmeasured factors that confound the relationship between management styles and the productivity of employees is another limitation. The existence of a significant covariance but an insignificant explanation of the overall impact reveals that unmeasured factors confound the actual relationships. Gravetter and Forzano assert that confounding variables mediate relationships by influencing independent and dependent variables, leading to the generation of invalid findings (176). Controlling for the effects of confounders is a strategy that can address the limitation of unmeasured factors. Randomization, restriction, matching, and stratification are some of the ways of creating a homogenous sample to reduce the effect of confounders and enhance the internal validity of research design (Montgomery 311). Moreover, the application of partial correlation, semi-partial correlation, and hierarchical regression control for the effect of confounders during analysis.

In addition to management styles, technical, financial, and personal factors influence the productivity of employees. The inclusion of these factors into the study would account for the effects of confounders and boost the validity of findings. The technical expertise of employees is an essential factor that determines their performance in the workplace. Remuneration is a financial factor that influences the motivation level of employees, and thus, their performance in their respective organizations. Given that talents vary from one individual to another, they constitute personal factors that determine the performance of workers. Therefore, the study should control for the effects of these factors to obtain the actual impact of management styles on the performance of employees.

Research design plays a central role in the generation of accurate and valid results. A research design focuses on the variables of interest and masks the effects of extraneous variables. A significant covariance in the management styles shows the limitation of collinearity, whereas an insignificant impact of independent variables on the productivity of employees indicates the limitation of confounders. The removal of one of the collinear variables and the control for confounders during design and analysis would address these limitations.

Works Cited

Gravetter, Frederick, and Lori-Ann Forzano. Research Methods for the Behavioral Sciences, 5th ed., Cengage Learning, 2015.

Harrell, Frank E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer, 2015.

Montgomery, Douglas C. Design and Analysis of Experiments. John Wiley & Sons, 2017.

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ChalkyPapers. "Designing a Productivity Study." August 3, 2022.