The first column is an employee ID $($or what is often designated a participant ID$).$ This is simply a piece of nominal data that is a reference value to the participant$$s record. The employee ID does not have any use for you in terms of analysis, but can be used for a number of business purposes, such as follow on interviews or training.

Columns B and C are designations for protected sex and minority status. They are filled with what are known in programming as Boolean operators, where $1=$ true and $0=$ false. For column B$,$ for example, a value of $1$ indicates that the participant has a protected sex$/$gender status. A zero indicates not having that status. Similarly, in column C a $1$ indicates the participant has a protected minority status and a $0$ indicates non$-$minority.

Column D indicates the training team that evaluated the performance test for the participant. There are three teams assigned to certain geographic areas indicated on the map posted on Blackboard.

Column E indicates the participant score on an objective, multiple choice test that was required of all candidates.

Column F indicates the participant score on the performance test, observed by the training team.

Step $1$:

Your task is essentially to evaluate if any statistically significant differences exist between the various subgroups across protected sex and minority status on either the objective or performance tests for the company. Assume an alpha level of $0\mathrm{.}05.$

This will require you manipulate the data set to separate the dataset by sex and then by minority status. $($Excel$$s Sort function may come in very handy here.$)$ NOTE: Do not in this first step break down groups further by training team. You may need to do so later, but not in this step.

A word of caution, it is highly advisable to never work on the raw dataset. Always save the data in a new file before manipulating. That way, if you make a mistake, you can always revert to the original data file.

Step $2$:

After sorting, you will be able to easily break up the list into two distinct groups $($protected sex vs$.$ non$-$protected and later minority and non$-$minority$).$ You will then be able to compare these subgroups using tools explored in previous weeks. Remember that disparate outcomes are not necessarily evidence of disparate treatment. A significant effect only indicates that a difference exists. It doesn't tell you why it exists.

Hopefully, you will find no significant differences between these groups on either the objective or performance tests. However, if you do$...$

Step $3$:

One possible difference, if the difference is on the performance test, could be a difference in the training team. If you find a difference, you might need to dig down further by comparing the differences in scores across each of the three training teams. This would mean comparing Team $1$ and Team $2,$ Team $2$ and Team $3,$ and Team $1$ and Team $3.$

$($Incidentally$,$ there is another statistical tool called ANOVA that can perform this same analysis in one step, but we will not cover that in this course.$)$

Step $4$:

Once you$$ve concluded where the data suggests differences exist $($if any$),$ write up what remedies you would propose. From there you will have access to part two of the assignment.

So$,$ although this might seem a bit complicated. You will only need to perform several iterations of the same statistical tool. So I hope you won$$t find it as difficult as it seems at first glance.

Employee ID Protected Sex$/$ Gender Identity Minority Status Field Evaluation Team Objective Test Score Performance Test Aggregate Score

$1000050121216$

$1000340021418$

$1000780022018$

$1001050131511$

$1001180122017$

$1001710011114$

$1003500021415$

$1004140012421$

$1004280131613$

$1004541022218$

$1004611121519$

$1004630121014$

$1005250122114$

$1005380022219$

$1006000011519$

$1006160131014$

$1006330031418$

$1006710011920$

$1007500021813$

$1008320011415$

$1011351132015$

$1015000121215$

$1015021022219$

$1015280131914$

$1015600012018$

$1015910131413$

$1017700111521$

$101856012129$

$1022570132217$

$1025300122419$

$1026590132017$

$1027721132216$

$1028760032517$

$1029041032215$

$1031830122418$

$1032070011114$

$1034911022419$

$1036871131916$

$1038690121116$

$1039020031316$

$1040710022120$

$1040841011418$

$1040920011416$

$1040940132017$

$1041120131417$

$1041330011418$

$1041770131416$

$1041911012218$

$1042430112320$

$1042901012317$

$1043010132520$

$1043180131915$

$1043951132116$

$1044310011816$

$1044380022321$

$1044580012420$

$1044611111514$

$1044900011517$

$1044911121819$

$1044951021217$

$1045580012319$

$1045830011520$

$1045850131114$

$1045860121420$

$1046000012323$

$1046010032321$

$1046220011621$

$1046541112218$

$1046650132218$

$1046691011215$

$1046721132016$

$1047450121216$