The ultimate goal of descriptive statistics is to describe a set of data, identify patterns, and draw a conclusion, which enables an organization to make effective and informed decisions (McClave, Benson, & Sincich, 2011). The company, Ballard Integrated Managed Services (BIMS), a support services company will leverage statistics to gather information on the company’s employees to analyze and identify patterns. The goal of this research project is to determine the reason for the high employee turnover and low morale. The research team has developed a strategy that ensures that the management dilemma will be resolved in the most economical way, and with valid, reliable, stable, and practical information. Research Problem and Purpose
The most prolific management dilemma that businesses encounter is retaining employees. BIMS has experienced an increase in employee turnover, which puts the company’s long-term sustainability and client relationships at risk. It is the function of management to organize the resources, locations, and authority within the organization to ensure optimal use of the company’s resources. The purpose of the research study is to determine the source of the low employee morale, productivity, and engagement. Research Questions
Research questions are open-ended questions, with the objective of discovering future tasks, identifying variables, ethical considerations, and defining the hypothesis (Cooper & Schindler, 2011). This step is essential to ensuring that the research team solves the right management dilemma. Prior to implementation, the research team would develop a lot of research questions. These questions include:
1. Do you enjoy your assigned shift?
2. How many times have you called in sick in the past month?
3. Were you trained for the position you currently work in?
4. Does you manager communicate properly?
5. Do you feel you are being paid the proper salary for your position?
The Hypothesis and Variables
A hypothesis is an empirical statement that can be objectively tested and measured (Cooper & Schindler, 2011). The hypothesis for this research study would be, improving the supervisor’s competencies, and abilities will improve employee morale. The goal of a hypothesis is to establish a relationship between variables, which define an event, act, characteristics, trait or attribute that can be measured, valued, and observed (Cooper & Schindler, 2011). By clearly identifying the variables, it will assist the researchers to determine and limit the scope of the research study (Cooper & Schindler, 2011). Instrument
BIMS utilized a survey to collect the data, which enables the researcher to sample a group and record their responses (McClave, Benson, & Sincich, 2011). The survey implemented by BIMS included 14 questions and was given to all employees with their paychecks. The survey focused on a variety of topics to ensure that all relevant data was collected in the most economical way. Out of the 449 employees only 78 responses to the survey were received. The ultimate goal of the instrument is to collect mature instrument ensures the collection of valid, relevant, stable, unbiased, and practical data (McClave, Benson, & Sincich, 2011). Data Collected
BIMS used the survey to collect quantitative and qualitative data. The qualitative data gathered by BIMS within the employee survey is the division in which the employee works, the employee’s gender, and the employee’s position. The quantitative data collected by the survey includes how long the employee has worked for the company, and questions one through ten, which requests the employee to rank the responses. The data gathered will enable the company to answer a variety of research questions and resolve the hypothesis. Level of Measurements
Once the data has been collected it will be grouped into levels of measurements. The data collected by the survey falls into three levels of measurement. First, the qualitative data qualifies as nominal data. Second, questions one through ten qualify as ordinal data because of the relative rankings without consistent distances. Finally, the time worked for BIMS qualifies as ratio level data because it is easily ordered, consistent differences and zero is meaningful (McClave, Benson, & Sincich, 2011). Each level of data has unique characteristics, which dictate the way the information is calculated, summarized, and presented. Coding and Cleaning the Data
Coding the data is a systematic way in which data is condensed into smaller easily analyzed units (Lockyer, 2004). By coding the surveys received by BIMs it enables the company to analyze and interpret the data to draw an informed conclusion. The coding enables the research team to identify mistakes, and outliers, which improves the data’s validity (Lockyer, 2004). To code BIMS’ data, the surveys must be numbered, and all corresponding data must be manually entered into Microsoft Excel, which increases the cost, time, and risk. The data is at risk of being entered incorrectly. Next, the raw data must be cleaned to ensure validity, relevancy, and accuracy. After the data was imported into Excel, the data must be reviewed for mistakes. One error that was identified was that a six was entered into Excel even though a five was observed. By coding and cleaning the data, the researcher will proactively identify errors or outliers while enabling a computer to complete statistical analysis and graphing for final review and interpretation.
Once the data has been cleaned and coded, the data must be analyzed. Two ways the data can be descriptively analyzed to make inferences about the population is central tendency and variability. Central Tendency
Central Tendency utilizes a single value to describe qualitative data around a central position within a given set of data (McClave, Benson, & Sincich, 2011). The mean is sensitive to any outliers or skewed data (McClave, Benson, & Sincich, 2011). The median is useful in large data sets. The researcher can identify if the data is skewed by comparing the relationship between median and mean (McClave, Benson, & Sincich, 2011). When the data is skewed right or left, the mean will not equal mean, and the data is considered to have a weaker central tendency (McClave, Benson, & Sincich, 2011). The data is symmetric when the mean equals the median and will have a strong central tendency (McClave, Benson, & Sincich, 2011). Another way the central tendency can be analyzed is through the mode or the most frequent number. It enables the researcher to identify the most common opinion within the employees. The researcher will be able to identify the most accurate measurement, and recognize if the data is skewed with the correct data analysis.
Variability enables the researcher to identify if the data set is spread out or clustered the mean (McClave, Benson, & Sincich, 2011). First, the researcher will analyze the standard deviation of the data. A lower standard deviation, such as questions, 6 and 9, have lower variability and will be more reliable and repeatable (McClave, Benson, & Sincich, 2011). Higher standard deviations, such as question 4 and B, will be more volatile. Another way researchers can analyze the variability is the range. The smaller the range, the more clustered the data set. By analyzing the data set’s variability, the researchers will be able to visualize the data and see if the opinions of the employees are clustered around the mean. The more clustered and correlated the data set, the more meaningful the result.
Testing the Hypothesis
The final step in the research process is answering the research questions and testing the hypothesis. First, the researchers will focus on answering the research questions. The researchers did not have sufficient evidence to reject the null hypothesis and prove that the employees agreed that they were well trained, well paid for their position or that management communicated properly. Additionally, the employee’s assigned shift was not cited as a main for leaving BIMS. Research Questions
Do you enjoy your assigned shift?
Only 3 or 3.8% people cited the assigned shift as the reason for leaving How many times have you called in sick in the past month?
No longer answerable due to the revised survey questions
Were you trained for the position you currently work in?
Insufficient evidence to reject the null hypothesis
Does you manager communicate properly?
Insufficient evidence to reject the null hypothesis
Do you feel you are being paid the proper salary for your position? H0: u=3
Insufficient evidence to reject the null hypothesis
Secondly, a straight-line model was leveraged to evaluate the correlation between the company’s ability to communicate and the length of employment at BIMs. Using a probabilistic model, the researchers developed a formula to predict the value of y based upon the inputted value of x. The formula is y=-2.590x+24.257, which means there is a negative correlation and a high likelihood of random error. Next, the coefficient of correlation was calculated at -0.093, which means there is a weak negative correlation. The model will not be a good model to predict employee resignation. BIMS should do further research on the statistical outliers, which will reduce the error and improve the line’s usefulness. Finally, the researchers tested the hypothesis. The researchers determined there was insufficient evidence to reject the null hypothesis because the t-value is .816 is smaller than 1.675. There is insufficient evidence to prove that good communication enabled BIMs to retain employees longer. Conclusion
First, the data collected had to be verified for accuracy and free from human error. Once this was completed, the data could be analyzed. The data was analyzed in two ways central tendency, and variability. Central tendency enabled the researchers to compare the median and mean, which helped identify if the data was skewed. Next, variability was analyzed through the standard deviation and range. Finally, the data was analyzed to identify a linear relationship between company’s ability to communicate and the length of employment at BIMS. BIMS should do more research on the outliers, and continue to test other hypotheses as the hypothesis tested used by this research team was rejected by the data.
Cooper, D., & Schindler, P. (2011). Business research methods (11th ed.). New York, NY: McGraw-Hill/Irwin. McClave, J.T., Benson, P.G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.). Boston, MA: Pearson Education, Inc. Lind, D.A., Marchal, W.G., & Wathen, S.A. (2011). Basic Statistics for Business & Economics (7th ed.). New York, NY: The McGraw-Hill Irwin Companies, Inc. Lockyer, S. (2004). Coding Qualitative Data. Retrieved from The Sage Encyclopedia of Social Science Research Methods, v. 1, 137-138. Thousand Oaks, Calif.