MAN 6316 HRM MetricsIndividual Analysis Project Fall 2014G.docx

MAN 6316: HRM Metrics
Individual Analysis Project: Fall 2014
Gender
Age
Customer Service Perf.
Intent to Stay
Conscientiousness
Pay Satisfaction
Age
Correlation: -.092
Direction: Negative
Statistical Sig? (Yes/No):
No
Practical Sig:
Not useful
This box is blank because this would simply be a correlation of
“1”—the correlation between Age and Age
Customer Service
Performance
Correlation:
Direction:
Practical Sig:

Correlation:
Direction:
Practical Sig:
“1”
Intent to Stay
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
“1”

Conscientiousness
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
“1”
Pay
Satisfaction

Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:
Correlation:
Direction:
Practical Sig:

“1”
Feel free to copy and paste this table into your Executive
Summary to help organize your answers for Step 2 (I have
completed the first correlation between Age and Gender as an
example). You can delete the text in red—that information is
just there to help explain the blank cells.
Individual Analysis Project SPSS Correlation Output: Fall 2014
Individual Analysis Project: SPSS Correlation Output
This document contains the output that you will need to
complete “Step 2” of the Analysis Project:
The results from the SPSS correlation analysis of the dataset are
provided below. SPSS is a powerful software tool, and is
often used by consultants and analysts to conduct advanced
statistical data analysis. The results below are in their “rough
form,” just as they would be seen after the analysis was
conducted in SPSS. As you can see, this table looks quite
different
from the correlation table that we worked in for In-Class
Assignment #2. Using the in-class assignment as a guide, please
create your own, more stream-lined correlation table using the

information from the table below.
To assist you in interpreting the above correlation output, here
is some helpful information about the terms used in the table.
Pearson correlation: This is the correlation between the two
relationships (“Pearson” simply refers Karl Pearson, who is the
person who developed the method of correlation). For instance,
the value .151 refers to the correlation between Pay
Satisfaction and Gender.
Sig. (2-tailed): This refers to the actual significance level of the
correlation. This number is more generally what researchers
would call the “p-value.” For instance, the p-value for the Pay
Satisfaction and Gender correlation is .023. Because this p-
value of .023 is less than .05 (the arbitrary threshold that
indicates statistical significance), the correlation between Pay
Satisfaction and Gender is considered statistically significant.
The statistical significance of this correlation is further
indicated by the star (*) that appears next to it (e.g., .151*).
Rather than reporting the actual p-values of a correlation,
researchers will often simply flag their significant correlations
with asterisks/stars (*). This is an easy way for researchers to
show that which results are statistically significant.

N: This number refers to the number of cases used in the
analysis. The term “case” refers to the individuals surveyed or
polled
in a study. In this instance, data from 225 individuals was used
in the analysis (notice in the Excel dataset that there are 225
rows of actual data), thus we see the number 225 in the
correlation output. Letting your audience know how many cases
were
used in an analysis is important, as it can influence the
statistical and practical significance of your findings.
*Names and data contained within this assignment are purely
fictitious—any similarity to an actual retailer is purely
coincidental
Individual Analysis Project:
Background Information & Instructions
You have been hired as an external consultant to examine the
employee engagement at Focus Financial, a regional financial

advising firm located in the Northeastern United States. They
have been in business for about 20 years, and they now have 30
locations and 4000 employees. They face heavy competition
from other financial advising firms, and they have realized that
one of
the best ways to remain competitive in the market is to set
themselves apart by employing exceptional staff who provide
excellent
customer service. Given this focus on customer service, Focus
Financial knows that retaining their employees is crucial, as
experienced employees can often provide the best service. In an
effort to determine what factors may influence an employee’s
decision to remain with the company, the company has decided
to collect data on several variables.
Although Focus Financial has an in-house HR department, they
are mid-size compared to other regional financial advising
companies, and they do not have HR personnel who specialize
in the area of HR metrics or analytics. Due to their limited
experience, they hired a survey company to collect some data
from a sample of their employees (225 employees at two of
their
locations). The survey company collected the data and provided
them with a dataset and some rough correlation analyses, but
Focus Financial does not know how to analyze the data or

interpret the results. Given your knowledge of HR metrics, they
would
like you to examine this data and make some recommendations.
In particular, they would like you to complete this project in
two parts. The first is an initial “worksheet” (Part 1), which
includes
Step 1 below. The second is the full written report (Part 2),
which summarizes and reports the findings from Steps 1-3. The
final
written report should be 3-5 double-spaced pages (not including
tables/figures).
Here are the steps you need to complete for Part 1:
Step 1: Descriptive Statistics.
l
Analysis Project Dataset” file):
o Calculate the means, medians, modes, and standard deviations
for each of the variables using Excel (Descriptions
of each of the variables are provided on the following page).
Enter these values into the appropriate area of the
Part I Worksheet.
o Describe the characteristics of the sample by answering the
appropriate questions on the Part 1 Worksheet. Here
is some additional information about the company as a whole:

-wide; all 30 locations):
38 years old
-wide, all 30 locations)
who are female: 23%
(Note: Completing the Part 1 Worksheet is all you need to do to
successfully complete Part 1 of the project. You will do
additional work with this data for Part 2 of the project).
***Submit the Part 1 Worksheet via Blackboard by 11:59pm on
November 1
st
***
For Part 2 of the project, please complete Steps 2 and 3 below.
Step 2: Summarizing the Correlation Table
rrelation output provided by the survey
company (this information is provided on Blackboard in the
“Individual Analysis Project Correlations” file).
o Create a new, more stream-lined correlation table to include
in your final report. Use the correlation table from
In-Class Assignment #2 as a guide. Some general questions to
consider include the following:
could be omitted?

it more “user friendly” or easier to read?
a “complete” correlation table (e.g., means,
standard deviations, correlations, etc.)
o Examine the correlation table you just created, and summarize
the correlational relationships. (hint: organizing
this information in a table format can be helpful—a blank table
is provided in the “Individual Analysis Project”
Folder for your convenience. Please feel free to copy and paste
it into your executive summary).
*Names and data contained within this assignment are purely
fictitious—any similarity to an actual retailer is purely
coincidental
Step 3: Interpretations, Recommendations, and
Limitations/Next Steps
-2 and interpret
these findings. Specifically, make written recommendations to
the
company regarding the following:

a. How are Intentions to Stay related to Pay Satisfaction and
being Conscientiousness? For instance, are people who are
Conscientious more likely to have intentions to stay? How does
pay satisfaction influence whether an employee
intends to stay with the company?
b. Does age or gender share a statistically significant
relationship with any of the variables? If so, what do those
relationships mean?
c. Focus Financial believes that their best customer service
providers are the employees who are most likely to leave the
company. Does the data support this hypothesis? How is
Customer Service Performance related to each of the
variables studied?
d. Based on these results, what changes would you suggest
Focus Financial make to help its employee retention? In
other words, where should they focus their efforts to prompt the
most change? (note: you can be creative with your
suggestions, as long as they make sense based on the correlation
results).
e. Focus Financial had access to limited data during this
investigation, and they would like to conduct another study in
the near future. Think about what you have learned during this
class. What kinds of data do you think the company
should collect in the future to better assess their employee
retention concerns? Are there any changes to their data
collection methods that you would suggest? (Note: You can be

creative, but be sure to support your suggestions with
rationale/evidence from the course material.
The final written report should be 3-5 double-spaced pages (not
including any tables/figures). The final written report should
contain the following sections:
., what is the purpose of the report?)
***Submit Part 2 (the complete project) via Blackboard and in
paper form by 8:30am on November 22nd***
Due Dates:
PART 1 (Worksheet, Step 1 only): Saturday, November 1
st
by 11:59pm (via Blackboard)
PART 2 (complete project: Steps 1-3): Saturday, November 22
nd
by 8:30am (via Blackboard and via paper copy)

PLEASE NOTE: Project submissions will only be accepted until
the deadlines indicated above. Blackboard will automatically
close
the submission system at this time. Any reports submitted after
this timeframe will not be accepted.
Here is a brief description of each of the variables in the
dataset, as well as their associated scale reliabilities:
EmployeeID: Unique employee ID number given to every
survey respondent
Gender: Employee gender (0 = male, 1 = female); Scale
reliability: 0.95
Age: Employee age in years; Scale reliability: 0.91
Customer Service Performance: Customer service performance
rating of employee; made by their supervisor (1= below
expectations, 5=above expectations); Scale reliability: 0.54
Intent to Stay: The degree to which the employee agreed with
the statement “I intend to stay in this job for the next six
months.”

(1=strongly disagree, 5=strongly agree); Scale reliability:
0.75
Conscientiousness: The degree to which the employee is
conscientious. Survey question used: "I get tasks done on time."
(1=strongly disagree, 5=strongly agree); Scale reliability:
0.85
Pay Satisfaction: The degree to which the employee is satisfied
with the pay they receive. Survey question used: "I am satisfied
with the pay I receive from my employer." (1=strongly disagree,
5=strongly agree); Scale reliability: 0.81

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MAN 6316 HRM MetricsIndividual Analysis Project Fall 2014G.docx