Running head REFLECTION 1REFLECTIO.docx
- 1. Running head: REFLECTION 1 REFLECTION 3Reflection – Research Hypothesis QNT 351 10/23/14 The steps in testing a research hypothesis There are various steps that are involved in testing a research hypothesis. The first step is usually to make assumptions about the research one is going to conduct. Making assumptions is usually very important in the process of testing a research hypothesis. This is because once you make an assumption, it is very easy to derive hypotheses whether null or true hypothesis. Without the assumptions, it is very difficult to test the hypothesis in a research appropriately. The assumptions are usually made concerning the level of measurement of the variable (Martin, & Bridgmon, 2012). Secondly, statement of the research and the null hypothesis is what follows the making of the assumptions. In this step, the research hypothesis is usually the one that is in support of the research while the null hypothesis is the hypothesis that is usually in contrast to the topic of research. It is usually stated in negative terms. It is always null unless tested otherwise at the end of the research.
- 2. Selection of the distribution of sampling is what follows. It is important to select the sampling distribution because the hypothesis has to be applied on some statistics for them to be tested (Martin, & Bridgmon, 2012). After the selection, computation of the statistics in order to test the hypothesis deduced is what follows. In this step, the sampling distribution is used. Finally, the decision is made on whether the hypothesis was true of false. In this step, interpretations of the sampling distribution are done. Comparing the means of two or more groups There are two steps involved in the process of comparing the means of two or more groups. To begin with, the comparison means that there must have been two or more groups that were selected as the samples for distribution. This could have been may be a control group and the actual group. The first step is to carry out an ANOVA analysis. This is referred to as the analysis of variance. Once the variance is analyzed, it is possible to tell whether all the means to the groups are equal or whether they vary and if they do, by how much (Koop, 2000). Secondly, if the means of the groups are different, the test is done to show the significance of the difference that exists between the means of the groups. In comparing the means of the two groups, the honest significance difference is found to show the spread of the means of the two groups. Calculating the correlation between two variables
- 3. In calculating the correlation between two variables, the first step is to label the variables as either independent on the x axis and the depended on the y-axis. After labelling the variables, one creates a table on which the values of the two variables are filled (Koop, 2000). The total values of each variable are taken for instance; values for x are filled in column A while those of Y are filled in column C. The squares of X and Y are filled in column B and D consecutively. Once the totals have been arrived at, computation is done to arrive at the correlation between the two variables. The correlation prediction of whether a change in one variable is likely to affect the other variable to the extent of producing a proportional change on the variable. References Koop, G. (2000). Analysis of economic data. Chichester: John Wiley. Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and Statistical Research Methods: From Hypothesis to Results. Hoboken: John Ballard Integrated Managed Services, Inc., Part 2 QNT/351 Version 4 1 Ballard Integrated Managed Services, Inc., Part 2 The initial survey effort led by Debbie Horner, HR manager of Ballard Integrated Managed Services, Inc. (BIMS), did not produce useful findings. The survey had several flaws that made
- 4. the majority of the results questionable. Some items were biased. A few questions were worded awkwardly, likely affecting the response. Some of the information needed was not asked, further reducing the value of the effort. Additionally, the data entry typist and general office support person made a number of errors when keying the data into the spreadsheet, compounding the poor results. In hindsight, Debbie suggested that she should have pretested the sample instrument before issuing it to the workforce. Such a step would have likely revealed many of these problems. Further, to improve the 17.3% response rate, she should have taken different steps to encourage employee participation. Just inserting it into the payroll process did not inform employees sufficiently about the purpose and sponsor of the survey. Advance information to explain the need for gathering their views, as well as reassurances about confidentiality and anonymity, plus descriptions of how the information would be used are among the many steps that Debbie might have taken to increase the response rate. Knowing that Barbara Tucker, general manager of the BIMS operation at the Douglas Medical Center, and the rest of the top management team were disappointed in the findings, Debbie proposed that she create a second, improved survey effort that was better planned and marketed. Although somewhat reluctant to authorize the effort for fear of creating more damage, Barbara approved the request. She felt the need to understand the current employee dissatisfaction and increased turnover rate was urgent and thus merited the continued effort. Learning from the initial effort, Debbie designed another survey instrument. This time she circulated it among the senior management team, inviting each person to complete the survey, reading for comprehension and flow of the actual wording, as well as for completeness. A number of suggestions were made in terms of question phrasing as well as about adding new items. These ideas were incorporated into the survey design.
- 5. The revised instrument was again circulated among the same group of senior managers. The group’s consensus was that the revised instrument was complete and ready to administer. To ensure the instrument was easily understood from the employee perspective, Debbie solicited five craft workers to voluntarily pretest it as well. These five were all on noncritical medical leave, so they were able to comfortably conduct the review. Additionally, as they were currently on leave, none would be in the actual surveyed population when the study instrument was issued later that month. Each of the five had minor phrasing suggestions that Debbie incorporated. Finally, Debbie sent this last version to the senior management team for final review. It was approved unanimously (see Exhibit C for this second data collection instrument). Then, Debbie had a sudden thought. Why interview current employees about why they might quit and about their level of satisfaction? Perhaps she should be surveying those that had already left the organization. By asking them, “Why?” she might learn more about who would quit in the future. She might be able to develop a model for predicting voluntary terminations. This indeed would be an important contribution to the company. With this in mind, Debbie decided that her next study population would be those who voluntarily left their employment with BIMS. Given the higher than normal, and unfortunate, turnover rate, Debbie was certain that she would be able to collect the data over the next 2 to 3 months. She would ask those departing to complete the survey during their exit interview with her office. Usually the exit interview was conducted by the immediate supervisor, but given the nature of this effort, Debbie felt that her staff should assume that responsibility on a temporary basis—just for the few months that were required to accumulate 75 to 80 completed surveys. After that time, the task of conducting the exit interview would revert to the immediate supervisor.
- 6. Debbie’s goal was to use the data to create a regression statement that could be used to predict future resignations. She also intended to use the information to identify the areas of greatest concern to the resigning employees; therefore, both descriptive statistics and frequencies were to be calculated. As the goal was to reduce employee turnover and improve morale, these key areas would become the center of attention for future internal HR development programs. Once again, Barbara Tucker has asked your Learning Team to act as consultants who analyze and interpret this second set of data. As described by Debbie, the intent is to increase senior management’s understanding of the sources of employee dissatisfaction and to possibly create a model that predicts employee resignation. As before, Barbara asks that your team prepare a 1,050- to 1,750-word written report along with a 7- to 9-slide Microsoft® PowerPoint® presentation for the senior management team that presents your findings (see Exhibit D for the data set of this second survey). Exhibit C BIMS Exit Interview Survey Using the scale provided, record your answer by circling the number that is closest to your view where 5 is a very positive response (you strongly agree with the statement) and 1 is a very negative choice (you do not agree at all with the statement). Do Not Agree Neutral Strongly Agree 1. You are well trained for your work. 2. The company provided the needed training. 3. You were fairly paid for the work you did. 4. You were given as many hours that you desired. 5. Your supervisor treated you fairly. 6. Your manager treated your division fairly. 7. The company is good at communicating.
- 7. 8. Your job was secure. 9. You liked working at this location. 10. Getting to and from work was easy. 11. What was the PRIMARY reason that led you to decide to quit? (Select only one.) A. In which division did you work? B. How long have you worked for BIMS? C. What is your gender? 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 A. I do not like the work. B. I do not like my supervisor. C. I am not satisfied with the pay. D. I am not satisfied with my shift. E. Other: ____________________ Food: _ Housekeeping: _ Maintenance: _
- 8. Years: _____ Months: _____ Female: _____ Male: _____ Exhibit D Survey B Data Set No. Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 A B C 1 3 3 5 5 1 3 4 3 2 2 2 3 14 2
- 43. Read: Ballard Integrated Managed Services, Inc., Part 2. Your team acts as a consultant group that analyzes and interprets this second set of data. The intent is to increase senior management’s understanding of the sources of employee dissatisfaction and to create a model that predicts employee resignation. Combine your Week Two Learning Team paper and Week Three findings with Week Five findings and make a recommendation to BIMS. (Attached) Use the statistical tables given in the appendices of the textbook and a statistical analysis application: a Microsoft® Excel® spreadsheet, Minitab® statistical software, or SPSS™ software. Prepare a 1,050- to 1,750-word written report along with a 7- to 9-slide Microsoft® PowerPoint® presentation for the senior management team to present your findings (see Exhibit D for the data set of the second survey). Click the Assignment Files tab to submit your assignment.