Analyzing and Interpreting Data

Analyzing and Interpreting Data

Team D has performed a second set of analysis for Ballard Integrated Managed Services, Inc (BIMS). These undertakings were the outcome of a developing tendency of attrition and employee dissatisfaction inside their organization. The original actions taken, involved data collection that was presented in the shape of an internal employee survey. The data collection analysis exposed our hypothesis and we set out to prove that the increase in employee turnover was due to low employee morale and poor employee performance.

The initial survey leads us to a modest response rate of only 17.3%--we did not achieve our goal of attaining the feedback of the vast majority of BIMS employees. By proceeding with our initial findings we analyzed, displayed, and interpreted, the outcome shows that BIMS was experiencing high turnover due to low pay and lack of communication within the organization. This knowledge provided to be promising from the standpoint that we were narrowing down to the fundamental problems within BIMS; the data was not pertinent enough to project or decide a future course of action.

Data Collection & Data Type

The data collected was performed through a written survey. As McClave, Benson, and Sincich (2011) state: "a survey [is where] questions are asked and recorded..." (p. 15). This survey distributed 10 questions, which were responded through a Likert Scale system of one to five, where one is very negative and five is very positive. At the end of the survey there were four supplementary questions coded under A, B, C, and D. Numerous employees see surveys as a waste of time, and BIMS employees are no different with only 78 employees responding, 449 employees were handed surveys. This survey totaled a response rate of only 17.3 % of the employees that took the time to fill out the survey. Additionally, the questions used within the survey to calculate the cause of the latest higher turnover rate were unclear. Therefore, Team D has evaluated whether these questions should be considered or be removed.

The type of data collected from the survey is of both qualitative and quantitative data measures. For example, the concluding two questions coded under C and D is of qualitative data because they ask questions of gender and have a yes or no format. Most of the questions from the survey comprise information of a qualitative view, but because a scale of one to five was used, this generates a way to gauge a response rate of the employees; consequently quantitative data is produced.

Analyzing and Interpreting Data - BIMS, Inc.

Consulting Group - Team D has performed a series of analysis on behalf of the top management of Ballard Integrated Managed Services, Inc. (BIMS). These tasks were the result of an emerging trend of attrition and employee dissatisfaction within their organization. The initial actions taken involved data collected that was presented in the form of an internal employee survey. The data collection analysis revealed our hypothesis and we set out to prove that the increase in employee turnover was due to low employee morale and poor employee performance.

The initial survey leads us to a very low response rate of 17.3%--we did not achieve our goal of obtaining the feedback of the vast majority of BIMS employees. By proceeding with our initial findings we analyzed, displayed, and interpreted, the outcome shows that BIMS was experiencing high turnover due to low pay and lack of communication within the organization. This information provided to be promising from the perspective that we were narrowing down to the core issues within BIMS; it just was not relevant enough for management to determine an effective course of action or forecasting.

The inferences made through our descriptive analysis approach made use of all three levels of measurement and dispersion were used and allowed us to rank the nominal feedback on scale of one through five, convert the ordinal and ratio feedback into a numerical value, where necessary. The demographic based questions were significant collected data based on years of service, division, gender and role and facilitated in our manipulation of the survey data. In combination, we were able to scratch the surface on a pattern of data that ranked very negatively and that also met the condition of our hypothesis--so all was not lost in our initial attempt.

At this point, we have been revisited by BIMS management to analyze and interpret a second set of data that has been re-engineered to utilize the exit interview as a means of gaining further insight on the initial patterns of data surrounding the attrition of BIMS employees. Their mind set is that if they can better understand the rationale behind employee dissatisfaction, then they could possibly create an intentional method for predicting when an employee reveals a pattern or behavior that leads to their untimely resignation.

Data Coding and Evaluation

The BIMS employee exit survey data is coded numerically on the ordinal, ratio, and nominal levels. Debbie's office provided exhibit D, which was the completed survey questions coded from exhibit C that were the survey questions. There were 78 employees that participated in the voluntary in house exit survey. Since the first set of survey questions were seen, as somewhat flawed based on the wording and comprehension, there was a second set of questions created and circulated through senior management to be reviewed and edited to agreement. The information consisted of ten survey questions about the employees working conditions and the coding summarized the data and made it a bit easier to understand. The code was set up numerically to give a qualitative question and quantitative answer. Each question got the code "Q" followed by the question number and

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