Research question: “What are the effects of a leadership style on performance and job satisfaction of employees?”
This research question is the most appropriate for the statistical test. The question has both dependent and independent variables. Correlation can be used to test the association between the variables, particularly on how the variables affect each other. Regression can also be applied in the question to determine the extent of how one action causes the other. The association between the variables and the extent to which they influence each other will be analyzed statistically to come up with a concrete answer.
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In the research question, there are 3 variables, one independent variable, and two dependent variables. The independent variable in the question leadership styles while the dependent variables are satisfaction and performance. Leadership styles are attributes associated with leaders. Due to this, they can be measured using qualitative methods, such as interviews and observations, which makes this variable qualitative. In addition, this variable cannot be measured numerically, and if it does, and interpretation of raw qualitative data would be required. This makes the variable nominal since the data cannot be measured using numerical statistical methods and can only be measured subjectively. Performance and satisfaction can also be classified as qualitative since qualitative methods can be used, although the employees' feedback could be further interpreted to numerical figures using the Likert Scale or rating scales. When the measurement is done in terms of rating scales, the variables are quantitative. When satisfaction and performance are rated in terms of rating scales, the data becomes discrete as it can only be represented as an integer, and if the data is collected qualitatively, then the scale of measurement is ordinal as descriptional qualities are maintained.
The variable fits the statistical tests as they can be tested statistically to determine their relationship and the extent to which one causes the other. If this test were chosen, the correlation would be positive since the dependent and independent variables are directly proportional. Better leadership styles increase performance and job satisfaction and vice versa. This means that if a correlation is found, it would be predicted that inclusive leadership styles promote employee performance and job satisfaction.
Hypothesis
Null: Leadership styles do not influence job performance and satisfaction.
Alternative: Inclusive leadership styles improve employee satisfaction ad performance.
The null hypothesis would prove that there is no association between the variables, while the alternative would prove that there exists an association between inclusive leadership, job satisfaction, and performance. This would be possible if there were no errors. According to Bewick, Cheek, & Ball (2003), one error that could occur in this research would be sampling error, where information is gathered from a different population other than the intended.
Research Proposal
Introduction
The research is intended to examine the correlation between leadership techniques, job performance, and satisfaction. The question is: “what are the effects of a leadership style on performance and job satisfaction of employees?” The statistical test technique to be applied in this research is correlation and regression. This method is efficient in testing the relationship between variables and the extent to which they are related. Using this method will help identify how styles of leadership impact performance and satisfaction and the extent to which they are related.
The statistical notation for research will be;
X=Sample; P=Performance; S=satisfaction; and I=Inclusive leadership
A simple formula will be formulated to create a relation between the variables. The relationship is;
I=k (PxS), where k is a constant.
The null and the alternative hypothesis will be;
Null Hypothesis : Leadership styles do not affect employee performance and satisfaction.
Alternative Hypothesis : Inclusive leadership styles improve performance and satisfaction.
The null hypothesis will prove that leadership styles do not influence how employees perform, while the alternative hypothesis will prove that employee performance depends on the leader styles used.
Methods
The research will involve 100 participants from five different companies that deploy different leadership styles. Leadership styles will be classified into two, inclusive and non-inclusive. Inclusive styles involve the styles where employees are given a chance to voice their opinion before a change is made while employees are not involved in decision making in the organization. Each company will provide 20 employees who will be from both genders. The population sample will comprise of employees who have worked in the selected companies for about five years. This research effort will be made to ensure gender balance. The age of the population will be between 27-45 years, which will help engage an audience that has worked in the company for long. Selection will be made randomly to prevent positive or negative bias.
Procedures
The variables in the study are leadership styles, performance, and satisfaction. Leadership style represents the question’s independent variable. The dependent variables in the question are performance and satisfaction. The independent variable is qualitative, and a nominal scale of measurement will be used. Job satisfaction and performance will be collected qualitatively, but the answers rated on a rating scale to obtain numerical values. This makes the data discrete while the measurement scale will be ordinal.
Leadership style will be determined by the MLQ, which examines styles of leadership and the associated outcomes (Van'Jaarsveld et al., 2019). Employee satisfaction will be determined by the use of Single Global Rating, where the participants will be asked to rate their satisfaction, and a score used to determine the satisfaction (Kamper & Maher, 2009) . A graphic Rating Scale will be used to measure the performance of employees, where numbers will be used to determine the employees’ relative performance.
Results
After data is obtained, correlation and regression will be used to test the data. The correlation method will be employed to examine the association between styles of leadership, performance, and individuals’ satisfaction. Regression analysis will be used to determine the extent to which leadership affects performance and satisfaction. The assumptions during the test will be;
Both dependent and independent variables are random for correlation, and only one variable will be random in regression.
The observations made will be independent.
The information from the collected data will show the performance and satisfaction of employees under different leadership styles. Scatter diagrams will be drawn for and performance and satisfaction determined on different leadership styles. The confidence level is expected to be above 85% to increase the reliability of the results (Sim & Reid, 1999) . From the prior examination, the p-value in the research is small, which shows that more evidence favors the alternative hypothesis.
Discussion
The research is expected to be successful but may experience some limitations. Statistical bias is most likely in this research due to systematic errors during the sampling, testing, and sample selection. This may be due to the assumption that the variables are random and that the employees will give honest feedback. After overcoming the assumptions, the result can be used to conclude that job satisfaction and performance depends on leadership. Correlation cannot determine the extent of the relationship between the variables. Successful completion of the research will help business owners determine the best leadership approaches that will increase employee performance and satisfaction.
References
Bewick, V., Cheek, L., & Ball, J. (2003). Statistics review 7: Correlation and regression. Critical Care , 451-459. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC374386/
Kamper, S. J., & Maher, C. G. (2009). Global Rating of Change Scales: A Review of Strengths and Weaknesses and Considerations for Design. Journal of Manual and Manipulative Therapy , 163-170. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762832/
Sim, J., & Reid, N. (1999). Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization. Physical Therapy , 186-195. doi:10.1093/ptj/79.2.186
Van'Jaarsveld, L., Mentz, P. J., & Ellis, S. (2019). Implementing the Multifactor Leadership Questionnaire (MLQ) in a Challenging Context: Results from a Large-Scale Quantitative Study. International Journal of Educational Management , 604-613.
