QNT 351 Week 2 Team Assignment Data Collection Set 2 (1)
QNT 351 Week 2 Team Assignment Data Collection Set 2 (1)
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Date Created: 11/14/15
Data Collection Name QNT/351 Date Instructor Name Data Collection Data collection through surveys is a valuable tool that businesses can adopt to help increase productivity and grow their companies successfully. This paper will examine the presented BIMS situation, defining the problem, purpose, research questions and hypothesis. The instruments used for data collection will be described and the level of measurements will be identified. The procedures used to code, evaluate, and clean the data will also be examined. BIMS Ballard Integrated Managed Services, Inc. (BIMS) has undertaken an integrative study to gain a better understanding of the root cause of the employee turnover problem at the company. In the first study the process was plagued by data coding, entry problems, and problems with the construction of the questionnaire. This compromised the data integrity and yielded disappointing results. However, the quantitative analysis of the data from the first study also provided some useful lessons, which prepared the organization to undertake a more concrete quantitative analysis in a subsequent study. The second quantitative study involved a much improved data base using descriptive and inferential statistics to determine the relationships between antecedent variables, for purposes of developing a predictive model that would allow the organization to better understand cause and effect relationships with respect to the reasons why employees quit. Although the results derived from the second quantitative study were substantive, it was determined that it did not provide specific information to the management about what needed to be done to correct the turnover problem. So, as a part of a planned internal employee development program the company embarked on a third study based on a qualitative approach. This involved content analysis of data gathered from dissatisfied employees who were departing the organization, as well as selected employees currently employed with the company. Data Collection Debbie Horner, the HR manager of BIMS conducted an employee survey as an instrument of measure to determine possible reasons for the high employee turnover rate. “BIMS typically experiences an annual turnover rate of 5560%”(Ballard, 2012, p.12), which is common for this industry. However, recently BIMS had an increase in that turnover, reaching 64%, which brought cause for the survey. This survey included questions about ‘working conditions, shift hours, quality of training, level of compensation, fair treatment, internal company communications, and job security’ (Ballard, 2012). Demographics were involved to divide by divisions in an effort to cipher descriptive and frequency procedures and further evaluate correlations. The data collected within the survey instrument is considered qualitative. It is qualitative because it cannot be measured in a natural numerical scale. The questions in one survey form can be answered in paragraph form, but would not provide the company with the statistical data sought. So, the survey is posed in a question form that asked the surveyed to rate the question on a scale from one to five, having one represent very negative response and five representing a very positive response. In addition, the survey entailed four questions that asked specifics regarding the division, length of service, gender, and if the position was of authority. In the altered rating scale, the questions are still qualitative as opposed to quantitative because the responses are not naturally numerical. Levels of Measurement The survey asked questions about working conditions, quality of training, shift hours, level of compensation, fair treatment, internal company communications, and job security. These variables are classified using the ordinal level of measurement “one classification is “higher” or “better” than the next one” (Lind, Marchal, & Wathen, 2011, p.11). The employees select one through five to indicate their feelings about the questions, one corresponding to very negative and five corresponding to very positive. Ordinal data has relative values therefore the employee’s responses can be ranked so that the management can see how many people are unhappy with certain aspects of BIMS. Demographics were also collected so that the company could determine department, gender, length of employment, and level of employment. This data is the nominal level of measurement. The nominal level has two properties “the variable of interest is divided in to categories or outcomes and there is no natural order to the outcomes” (Lind, Marchal, & Wathen, 2011, p. 10). Therefore, responses can be categorized per department and if, for instance, the maintenance department has the most negative answers management can concentrate on how to improve that department first. Data Coding The data provided by Debbie Horner has been coded and is shown in exhibit B. The data was coded by breaking each question down and giving it a numerical identifier for questions one through ten. Each of these questions required a one to represent a very negative response and a five is a very positive response leaving two, three, and four in the middle to help with selecting the best answer for each individual. Questions A through D were broken down numerically as well. Question A gave numerical data by identifying food service as 1, housekeeping as 2, and maintenance as 3. Question B obtains the age of the employee by asking the year and month of their birth, resulting a numerical answer in years. Question C separates the gender of each employee by identifying the females with a 1 and the males with a 2. The final question D, asks if the employee is a manager or supervisor with yes equaling 1 and no equaling 2. This data has been collected and given a numerical identifier so that the collected data can be broken down easier and identified by its number. This type of data is considered qualitative in nature. The data showed: Questions 1 2 3 4 5 1. 16 21 15 13 13 2. 17 22 13 14 12 3. 16 21 15 13 13 4. 18 21 12 12 15 5. 14 22 14 14 14 6. 23 30 19 6 0 7. 15 21 15 13 14 8. 19 22 12 13 12 9. 17 32 24 5 0 10. 19 22 12 15 10 Total 178 234 151 118 103 Question A. In which division do you work? 23 worked in Food service. 36 worked in Housekeeping. 9 worked in Maintenance Question B. How long have you worked for BIMS? 011 months 19 1235months 22 3659 months 14 60119 months 16 120179 months 2 180239 months 2 240360 months 3 Question C. What is your Gender? 30 females and 48 males Question D. Are you a manager or supervisor? 14 are managers or supervisors 64 are nonmanagement. Data Scrub To present the BIMS leadership with precise results; it is necessary that a data scrub be performed to eliminate of input errors in the sample data. Known errors within the data are those who failed to provide a response to a survey question, therefore an internal decision was made to enter zero for any question left blank. There are several of these errors present in the sample data; 5 zeros are present in the demographic questions and 17 zeros are present in questions one through 10. Additionally, there is another known error within the data, the result of a keystroke error, which resulted in invalid value of ‘6’ present in questions one through 10. The appropriate survey response for questions one through 10 should reflect a value of 1, 2, 3, 4, or 5 with ‘1’ representing ‘Very Negative’ and ‘5’ representing ‘Very Positive’—there are six occurrences of this error (University of Phoenix, 2012, BIMS, Inc. Part I). Conclusion Based on the survey results, the data collected by BIMS to help solve their employee turnover problem showed that the majority of the employees surveyed were nonmanagement and had worked at the company for less than 35 months. This could be a potential reason for the high turnover as lowlevel employees are more likely to be transient and move from job to job. The results also showed that the majority of the sample was more dissatisfied with company operations rather than highly satisfied. The qualitative data showed more negative responses (1 and 2) versus positive or middle ground responses. Many employees feared losing their job and felt they were not being compensated fairly. Also, the majority of the employees surveyed did not enjoy their assigned shift nor did they feel they were well trained for their jobs. Finally, the majority of the sample also felt the company was not good at communicating effectively. All these would indicate reasons why the company would have such a high employee turnover rate. Therefore, the survey was effective in determining why employees were leaving so frequently, enabling the company to develop new policies and procedures that will help increase employee moral and happiness which could lead to reducing the high turnover rate. References Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economic (7th ed.). New York, NY: McGrawHill/Irwin. University of Phoenix. (2012). Week Two supplement: Ballard Integrated Managed Services, Inc. Part 1. Retrieved from https://portal.phoenix.edu/classroom/coursematerials/qnt_351/20121127/OSIRIS:43517012
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