A large insurance company employs many clerical workers for routine computer-based tasks. The flow of tasks is enormous and never ending, so worker efficacy is crucial to the company’s smooth operat

A large insurance company employs many clerical workers for routine computer-based tasks. The flow of tasks is enormous and never ending, so worker efficacy is crucial to the company’s smooth operation. The director of human resources feels that new working conditions could improve productivity by a few percentage points, more than enough to cover the cost of the changes. What isn’t clear is which new conditions would have the best effect on productivity. An experiment was carried out to test the effects.

The director of human resources has written you a memo asking you to analyze the results (see attached data Module5_ANOVA_Data.xlsx. The relevant part of the memo says:

We took three divisions: (1) claims, (2) data processing, and (3) investments. The way it turned out, we could get 33 clerical people in each division to participate. We picked the people randomly from personnel lists and – even though there were a few refusals so we had to pick other people—we think that the groups are reasonably representative. Then, we had everybody draw numbers from a hat. Number 1 meant that the person went to a flextime schedule, 2 meant a four-day work week, and 3 meant a regular week in an enhance work environment. We ignored the data for the first two weeks while people were getting used to the new setup. Then we measured percentage efficiency gains for everybody over the next four weeks. The efficiency measures are good enough that we think they will be accurate for our purposes. We think we did the study pretty well.

We can’t figure out the results. The averages for the three conditions are patricianly the same, so it seems like it did not make much difference. But I’ve got some supervisors who think that one or another of the new conditions is doing great things for efficiency, and other supervisors who think some conditions is actually hurting efficiency. Are they nuts, or is there something in the results that we haven’t noticed?

Your task: Reanalyze the data and see what you can find about average efficacy gains. Write a memo to the director and explain your finding.

Post your memo by August 5th. You will be able to read classmates posting after you submit yours. 

Read classmates memo and respond at least one of your classmates posting by August 8th. Your reaction should be carefully reviewing memo first and answering following questions.

What is the difference between your memo and a classmate, if any? Any suggestion to improve a classmate report or any parts of your report to revise.