OL 325 Milestone Three Guidelines and Rubric Section

OL 325 Milestone Three Guidelines and Rubric
Section 2: External Competitiveness
Section 2 shifts your focus outside the company to compare pay
rates of positions inside the firm with similar positions in the
external market place. The shift to
outside the company will move you away from the previous
focus on e-sonic’s internal consistency to external
competitiveness. Conducting an analysis of
external market data will support your decisions about
appropriate pay-policy mixes for job structures in the company.
In section 2 of Milestone Three, you will be introduced to tools
compensation professionals use to allocate total compensation
within job structures. Total
compensation includes base pay, benefits, and varied incentives
used to attract and retain employees. During the simulation you
will use some of these tools to
develop pay policies for each e-sonic job structure.
In order to conduct your external market survey you will use
web-based salary sites developed by the US Bureau of Labor
Statistics and Glassdoor.com. These
websites develop salary information based off of actual pay data
from professionals working in specific jobs and potentially
represent the most current pay for
the job titles at e-sonic. Follow the steps outlined below:
Section 2 Outline:

Executive Summary Findings
1. Determine Appropriate Pay-Policy Levels for E-sonic Jobs
2. External Market Review
a) Research market competitiveness using the free salary
websites listed above, which provide salary data by title and
region.
b) Research trends about cost of living adjustments in e-sonic
locations. Apply some discussion around leading, lagging or
matching the market to
the salary data you found in your market salary research.
Assume that the salary research you are using is similar to
benchmark jobs. Also,
discuss whether jobs you researched would match the
benchmark jobs or require more or less experience and talent
than the benchmark job.
c) Update salary data for inflation using CPI-U.
3. Implement Salary Survey Results
a) Create pay grades and ranges by integrating external market
data with internal pay grades.
b) Evaluate and summarize decisions made for each job
structure.
The External Competitiveness section is fully described in the
MyManagementLab Building Strategic Compensation Systems
casebook for faculty and students,
linked in the course menu. Follow the explanations and outline
to complete this milestone. Section 2: External Marketplace is
due at the end of Module Six.

Rubric
Requirements of submission: Each section of the final project
must follow these formatti ng guidelines: 5–7 pages, double
spacing, 12-point Times New Roman
font, one-inch margins, and discipline-appropriate citations.
https://www.bls.gov/bls/blswage.htm
https://www.glassdoor.com/Salaries/index.htm
Critical Elements Exemplary (100%) Proficient (85%) Needs
Improvement (55%) Not Evident (0%) Value
Section 2: External
Competitiveness
Provides in-depth market
competitiveness report with all
of the elements of the outline
provided in Section 2 of the
Building Strategic
Compensation Project
documentation
Provides market
competitiveness report with
most of the elements of the
outline provided in Section 2 of
the Building Strategic
documentation

Provides market
some of the elements of the
outline provided in Section 2 of
the Building Strategic
documentation
Does not provide market
elements of the outline provided
in Section 2 of the Building
Strategic Compensation Project
documentation
50
Integration and
Application
All of the course concepts are
correctly applied
Most of the course concepts
are correctly applied
Some of the course concepts
are correctly applied
Does not correctly apply any of
the course concepts
20
Critical Thinking Draws insightful conclusions
that are thoroughly defended

with evidence and examples
Draws informed conclusions
that are justified with evidence
Draws logical conclusions, but
does not defend with evidence
Does not draw logical conclusions
20
Writing
(Mechanics/
Citations)
No errors related to
organization, grammar and
style, and APA citations
Minor errors related to
Some errors related to
Major errors related to
organization, grammar and style,
and APA citations

10
Total 100%
Review of Demonstrate Material from Week 9
Please view the output file for SPSS. This is the .spv files
(open with SPSS) to see the solution for each Demonstrate
activity. You can find these in the Week 6 folder.
Select TRANSFORM.
Click on RECODE and select INTO DIFFERENT
VARIABLES…
Click on recom and move it to NUMERIC VARIABLE OUTPUT
VARIABLE box.
Type “rrecom” in OUTPUT VARIABLE NAME box.
Type “loyalty” in OUTPUT VARIABLE LABEL box.
Click OLD AND NEW VAULES box.
Under OLD VALUES on the left, click RANGE. Type 0 and 6 in
the range boxes. Under NEW VALUES on the right, click
VALUE and type 1 in the value box. Click ADD.Video
Recoding Variables with
SPSShttps://www.youtube.com/watch?app=desktop&v=4xXpyi7
Dya0

Recode the Likelihood To Recommend variable in the Avery
Fitness Data Set. Create a pie chart of the distribution of the
recoded variable showing the percent loyal and not loyal. Use
the snipping tool or print screen to show the image on a Word
Document. Recode one other quantitative variable in the data
set of your choice to a dichotomous variable (two categories).
Create a pie charts showing the distribution of the newly
recoded variable.
Please upload demonstrate in one file in Assignments for Week
9. Use the print screen or snipping tool to show your output.
*
SPSS: Recoding Likelihood To Recommend
Under OLD VALUES on the left, click RANGE. Type 7 and 10
in the range boxes. Under NEW VALUES on the right, click
VALUE and type 2 in the value box. Click ADD.
Click CONTINUE.
Click CHANGE.
Click OK.
Depending on the SPSS version, you must then go to the data
view to create value labels for the recoded variable.
For the new variable rrecom code 1 can be labeled “not loyal”
and code 2 can be labeled “loyal” as your recoding put lower
ratings 0-6 in code 1 and higher ratings 7-10 in code 2. To
understand how to edit your data file to create a data labels for
this newly created variable see this video. The likelihood to
recommend variable in research is a variable to understand
customer loyalty.
Creating Data Labels with SPSS
https://www.youtube.com/watch?v=sSoOY99XqZ4

*
Recode the Likelihood To Recommend variable in the Avery
Fitness Data Set. Create a pie chart of the distribution of the
recoded variable showing the percent loyal and not loyal. Use
the snipping tool or print screen to show the image on a Word
Document. Recode one other quantitative variable in the data
set of your choice to a dichotomous variable (two categories).
Create a pie charts showing the distribution of the newly
recoded variable.
*
Please watch the video on the course site to learn how to
calculate Descriptive Statistics using Excel. The video is posted
below as well. The spreadsheet with data is on the course site.
Using the video as a guide, add the formulas and calculate the
descriptive statistics using EXCEL. Recreate the spreadsheet as
described in the video and show a print screen or use the

snipping tool to show your work.
Descriptive Statistics with Excel
http://link.brightcove.com/services/player/bcpid790261335001?
bckey=AQ~~,AAAAPmbRRLk~,C5G7jhYNtifB7aWTdZf87KOT
82XYugjP&bctid=2277365305001
*
Ho: There is no difference in use of services based on a doctor’s
recommendation
H1: There is a difference in use of services based on a doctor’s
recommendation
The null hypothesis can be rejected for therapy pool use and
classes. Significantly more members use those services if they
came to the center upon a doctor’s recommendation. See the
output file .spv for details. The results are strong.
*
Research Question:
Is there a difference is usage of any other services at the Avery

Fitness Center based on doctor’s recommendation?
State the null hypothesis Ho and the alternative hypothesis Ha.
Is there any area where the null hypothesis can be rejected?
Null Hypothesis: e.g. no effect, no difference between groups.
Hope to reject the null: Ho
Alternative Hypothesis: e.g. there is a difference between
groups. Hope to accept the alternative: HA
*
*
Analysis & Interpretation:Multivariate Analysis/ Inferential
Statistics
Independent Samples T-Test and Analysis of Variance
*
*

Relationship Among the Stages in
the Research Process
Formulate Problem
Design Data Collection
Method and Forms
Determine Research Design
Design Sample and Collect Data
Analyze and Interpret the Data
Prepare the Research Report
Why Conduct Multivariate Analysis?
*Multivariate analyses allow researchers a closer look at their
data than is possible with univariate analysesUnivariate
analyses provide insights about the data while multivariate
analyses can often provide further illumination of those insights
*
Descriptive versus Multivariate/Inferential Statistics
Inferential Statistics. Here we are trying to reach conclusions
that extend beyond the immediate data alone. For instance, we
use inferential statistics to try to infer from the sample data
what the population might think.
Or, we use inferential statistics to make judgments of the
probability that an observed difference between variables or a
relationship between variables is a dependable one or one that

might have happened by chance in this study.
Thus, we use inferential statistics to make inferences from our
data to more general conditions; we use descriptive statistics
simply to describe what's going on in our data.
*
Choosing a Statistical Test
Number of
Variables
Univariate
Analysis/Descriptive Stats
Multivariate
Analysis/Inferential
Stats
One
Two
or More
Example: Frequencies, Measures of Central Tendency and
Variability
The are many hypothesis tests to evaluate significant
differences associated with your research questions. To select
the correct question, your data must have the assumptions
needed for the test. Let’s define the Independent Samples T-
Tests.
Key Concepts

Type I Error: wrongly reject the null hypothesis: Saying there
is a difference when there is not.
Type II Error: wrongly do not reject the null: Saying there is
no difference when there is.
Hypothesis Testing
Watch this video to better understand the concept of hypothesis
testing
https://www.youtube.com/watch?v=d0eVIUyt_Uc
There are various multivariate/inferential statistics to use for
the hypothesis testing. This week we will analyze data with the
Independent Samples T-Test and One-Way ANOVA. We will
discuss the N-Way ANOVA as hypothesis tests as well. First,
the Independent Samples T-Test.
Analysis & Interpretation:Multivariate Analysis/ Inferential
Statistics
Independent Samples T-Test
*

Number of
Variables
Univariate
Multivariate
Stats
One
Two
or More
Variability
needed for the test. Let’s define the Independent Samples T-
Test and why it is appropriate for the hypothesis test associated
with the research question do visits to the AFC differ by gender.
Do recommendations for the AFC differ by gender?
One of the most used tools in statistical testing in Marketing
Research
What is it?A tool to explain the confidence one has about a
result
Explains the likelihood that the result is not due to chance
Tells us whether we have a numerical difference or a statistical
difference

p-values are compared to α to determine significance
When the p-value is equal to or less than α, we conclude that
there is a significant difference
Low probability of rejected a null hypothesis that is true—
saying there is significance when there is not.
When the p-value is greater than α, we conclude that there is
not a significant difference
*
One of the most used tools in statistical testing in Marketing
Research
Application?Comparing the results from independent samples.
Research Questions?Is GPA different between athletes and non
atheletes in the university?
Are sales higher in the test market versus the control market?
Is there a difference in purchase intent for the brand by gender?
Video Tutorial:
Independent Sample T Test
*
Types of Hypothesis Tests
What is the research question?

State:
Null Hypothesis Ho—No difference, no effect, no relationship
Alternative Hypothesis Ha—There is a difference, there is an
effect
there is a relationship
Test are evaluated by the p value. If the p is low the Null must
goTestDV Scale of MeasurementIV Scale of
MeasurementIndependent Sample t-testInterval or ratioNominal
or ordinal (binary)
The independent samples t-test is used where the dependent
variables are quantitative and the independent variables are
qualitative and binary—have two groups or two independent
samples.
The test statistic as with all hypothesis tests are evaluated by
their probability value—p value.
Video Tutorial on the Independent Samples T-Test
https://www.youtube.com/watch?v=8alv3kZt8Ug
*
ProbabilityProbability: the likelihood that a particular event

will occur, expressed as a proportion, ranging from .00
(impossible to occur) to 1.00 (will definitely occur)Example:
When flipping a fair coin, the probability of heads of .5
As with all hypothesis tests, the result will be evaluated by the
p-value. A probability value.
To identify why data typically appears in the various shapes it
appears in, we first need to explore the concept of probability.
Probability, broadly, refers to how likely a specific event is to
occur. It is typically expressed as a proportion and ranges from
.00 (a specific event will never occur) to 1.00 (a specific event
will always occur). One of the major purposes of statistics is to
accurately assess probabilities associated with data.
In some cases, probability is very easy to compute. For
example, consider a coin – one side is heads; the other is tails.
Any time you toss the coin in the air and let it fall, it will land
either heads-up or tails-up. If there’s nothing strange about the
coin, it will fall heads-up about half of the time and tails-up the
other half of the time. Thus, the probability of heads is .50. The
probability of tails is also .50.
*
ProbabilityChance: variation that occurs at random, i.e. luck
As we add more possible outcomes, the probabilities become
more complex. Next consider a ten-sided die with the numbers
0, 1, 2, 3, 4, 5, 6, 7, 8 and 9 on each side. The probability that
any particular number will land facing up when rolling that die
is 1/10, which we can express as a probability as .10. Thus, the

probability of a 0 is .10, 1 is .10, 2 is .10, 3 is .10, 4 is .10, 5 is
.10, 6 is .10, 7 is .10, 8 is .10 and 9 is .10. All together, these
numbers add up to 1.00, since with any given roll of the die,
one of these numbers will appear 100% of the time.
In organizational research, probability becomes even more
complicated, because we typically don’t know the total number
of possibilities. In our case study, how likely is it that an
employee will sell ten cars in any particular month? What about
6 or 30? Should we consider 100? Although some values may be
unrealistic, there are theoretically no boundaries for what this
value could be. Even if we did come up with boundaries, there’s
no way for us to know beforehand how probable any particular
value is.
Without a list of every possibility, we cannot compute a
specific, precise probability that any of these events will occur.
Fortunately, data typically take one of several common shapes,
and we can compute the probability of data occurring within any
of these shapes. The next sections will explore what these
shapes look like and the relative probabilities of the data they
contain.
*
Conducting a Statistical Testp-value: the probability that the
given sample was drawn from the population described by a
given null hypothesisRange from .00 – 1.00p-values are
compared to α (this is the risk level generally set to .05) to
determine significanceWhen the p-value is less than or equal to
α (.05 typically), we conclude that there is a significant
differenceWhen the p-value is greater than α (.05), we conclude
that there is not a significant differenceThe risk level is set
depending on the problem definition of the study. How much
risk is permissible for the action standard for the decision to be

made? Generally, in market research if the p-value is less than
or equal to α (.05), where we are 95% confident, we have a
significant result. In other words, we conclude that there is a
significant difference or relationship depending on our research
question.
As such, each hypothesis test is evaluated for significance with
its associated p value. This week we will focus on the
Independent Samples T-Test and the Analysis of Variance—One
Way and discuss N-Way Analysis of Variance
*
Conducting a Statistical Test
-p-value: the probability that the given sample was drawn from
the population described by a given null hypothesis. What is the
probability of rejecting a null hypothesis that is true?Range
from .00 – 1.00p-values are compared to α to determine
significanceWhen the p-value is equal to or less than α, we
conclude that there is a significant differenceWhen the p-value
is greater than α, we conclude that there is not a significant
difference
*

The p value that will be .05 for the 95% confidence level is
typical for marketing research.
If a research wants to be 99% confident that would be a p value
of .01. For most issues, this amount of conservatism is not
needed but again this will be driven by the problem definition.
Region of Rejection
Research questions can require a two tailed statistical test.
Research questions can require a one tailed statistical test
Examples on one-tailed and two-tailed research questions are
the slide that follows.
*
One tailed—the researcher is looking in one direction.
Two-tailed the researcher is looking for
differences/relationships in either directions.
This depends on your problem definition to include your
research questions.
More detailed on one tailed versus two tailed tests can be

explored in the link below:
https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-
are-the-differences-between-one-tailed-and-two-tailed-tests/
Based on your research question—to conduct your hypothesis
test, state the null hypothesis Ho and the alternative hypothesis
Ha.
Avery Fitness Center Project
This is an example of a descriptive study that we will use
throughout the semester. The case, the survey and the
associated SPSS file are on the course site. The decision
problem for the case is how to grow membership at the center.
The research problem to determine who their current customers
are and what are their attitudes and behavior around fitness
center activity. The survey and data collection effort support the
problem as defined. Read the case, review the survey and
associated data file after watching the video overviews of SPSS.
*
Avery Overview
Avery Questionnaire and Code book
Click the files in normal mode to open, they are also on the
course site with the SPSS file
We will use the Avery Fitness Center Case, Survey and Data Set
to illustrate statistical concepts. The case, survey and SPSS data
set are on the course site. We will use the Avery Fitness Center
Data Set to review these concepts.

There is a SPSS manual on the course site for your use as well.
*
19115.pdf
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AVERY FITNESS CENTER SURVEY

Thank you for taking time to provide important feedback about
Avery Fitness Center (AFC). Please answer the following
questions. Your candid
responses will help us provide better services in the future. No
one at AFC will see your specific responses, so please be
honest.
(I) Which of the following AFC services have you utilized at
least once in the last 30 days? (Please check all that apply)
o Weight Training 0 Exercise Circuit 0 Therapy Pool
o Classes 0 Circulation Station
(2) Within the past 30 days, approximately how many times
have you visited AFC to exercise?
___ Times in the last 30 days
(3) During what part ofthe day have you normally visited AFC?
(Please check only ene)
o morning 0 afternoon 0 evening
(4) How did you learn about AFC? (Please check all that apply)
o Recommendation from Doctor 0 Drove by location
o Recommendation from Friend or Acquaintance 0Article in
Paper
o Advertising (including Yellow Pages) 0 Other
o Heard AFC director speak
(5) How important to you personally is each of the following
reasons for participating in AFC
programs? (Circle a number on each scale)
not at all

important
very
important
General Health and Fitness 2 3 4 5
Social Aspects 2 3 4 5
Physical Enjoyment 2 3 4 5
Specific Medical Concerns 2 3 4 5
(6) How likely is it that you would recommend AFC to a friend
or colleague?
not at all extremely
likely neutral likely
o 1'- 6 7 8 9 103 4 ' 5
(7) What was the original event that caused you to begin using
services from AFC?
(8) Current Age _
(9) Gender o Male o Female
(10) Highest Level of Education Achieved:
o Less than High School 0 Some College
o High School Degree, 0 Associates Degree
o Four-year College Degree
o Advanced Degree
(II) What is your approximate annual household income from all

sources, before taxes?
. (Please check the appropriate category & employment status)
0$0-15,000 0 $6Q,001-75,000
0$15,001-30,000 0 $75,001-90,000
0$30,001-45,000 0 $90,001-105,000
o $45,001-60,000 0 $105,001-120,000o more than $120,000
r----------I
:0 Employed :
10 Retired I~---------~
THANKYOUI
© 2012 (engage Learning
Variable Name
10
WEIGHT
CLASSES
CIRCUIT
STATION
POOL
VISITS
DAYPART

DOCTOR
WOM
ADVERT
SPEAKER
LOCATION
ARTICLE
OTHER
FITNESS
SOCIAL
ENJOY
MEDICAL
RECOM
EVENT
AGE
GENDER
EDUCAT
INCOME
STATUS

REVENUE
102012 (engage Learning
Description Response Options
143
Questionnaire identification number
Utilized weight training in previous 30 days?
Utilized classes in previous 30 days?
Utilized exercise circuit in previous 30 days?
Utilized circulation station in previous 30 days?
Utilized therapy pool in previous 30 days?
Number of visits to AFC in previous 30 days?
Normal time to visit AFC?
O=nol=yes
0= no I = yes
0= no I = yes
0= no I = yes
0= no I = yes
(record number)

I == morning
2 = afternoon
3 = evening
How learned about AFC? Doctor Rec. O=nol=yes
0= no I = yes
0= no I = yes
O=nol=yes
0= no I = yes
0= no I = yes
0= no I = yes
(1-5, "not at all important - very important")
How learned aboutAFC? Friend Rec.
How learned about AFC? Advertising
How learned aboutAFC?Heard director speak
How learned aboutAFC? Drove by location
How learned about AFC? Article in newspaper
How learned about AFC? Other
Importance for participation: General Health and Fitness

Social Aspects
Physical Enjoyment
Specific Medical Concerns
How likely to recommend?
What original event caused you to begin
AFC? (open ended)
SAME
SAME
SAME _.
(1-10, "not at aillikely-extremely likely")
I = general health / exercise
2 = pool/facilities
3 = rehab / specific medical needs
4 = social considerations
5 = transfer from another center
6 = other
(record number)
I = male
2 = female
I = less than high school
2 = high school degree
3 = some college
4 = associates degree
5 = four-year college degree

6 = advanced degree
Current Age
Gender
Highest level of education achieved?
Annual household income before taxes I = $0 - 15,000
2 = $15,00 I - 30,000
3 = $30,00 I - 45,000
4 = $45,00 I - 60,000
5 = $60,00 I - 75,000
6 = $75,00 I - 90,000
7 = $90,00 I - 105,000
8 = $105,00 I - 120,000
9 = more than $120,000
I = employed 2 = retired
($$$ from secondary records)
Work Status
One-year Revenue from Respondent
MISSING = BLANK
CHAPTER 11: DATA PREPARATION FOR ANALYSIS
The Avery Fitness Center Project
The Avery Fitness Center is located in a mid-size city in the
southeastern United States; the company offers a variety of
exercise programs to its member under the supervision of

personal trainers. The company was founded 10 years ago and
operates from a single location in an old shopping center near a
large university. AFC primarily targets older men and women.
Some of the members are struggling with health issues. Many
customers are attracted to the large indoor therapy pool that
allows exercise using water resistance, which is much easier on
bones and joints than traditional exercise options. Individuals
become members of the fitness center by paying a monthl y fee;
they pay additional fees for special classes, use of personal
trainers, etc. Although business had been steady, AFC
managers believe that the company could grow substantially
without adding additional facilities. As a result, AFC managers
are interested in better understanding the kinds of individuals
that are attracted to AFC an how best to recruit more of these
kinds of people. More specifically, the AFC researchers are
addressing two research problems (1) Determine member
demographics and usage patterns and (2) investigate how
members learn about AFC.
To address these research problems, researchers decided to
conduct an online survey of AFCs customer base. Customer
was defined as any individual in the company’s member
database who had visited AFC at least once in the previous 12
months. Surveys were sent to 400 members drawn using a
simple random sample; respondents completed and returned 231
usable surveys for a response rate of 58%. Survey respondents
were then matched with total fees paid over the next 12 months.
After editing, coding and cleaning the data, the researchers were
ready to begin data analysis.
Hypothesis Testing Using an Independent-Samples t-test: Avery
Fitness Center Project
*

For the research question here, an independent samples T-Test
is the statistical technique to use to answer the questions. Why
is this a job for the independent samples t-test to be able to
reject the Null Hypothesis?
It meets the assumptions for the test where the dependent
variables are quantitative, and the independent variables are
qualitative and binary—have two groups or two independent
samples. Specifically, in this example the independent variable
is on a nominal scale and binary in this data set…gender (males
versus female); the dependent variable is on an interval scale.
(likelihood to recommend and # of times visiting the center in
the past 30 days, importance ratings)
Video tutorial: https://www.youtube.com/watch?v=8alv3kZt8Ug
*
Independent Samples T-Test: Drawing ConclusionsFor an
Independent Samples T-Test your conclusions should include:A
formal statement about retaining the null or rejecting the null
and accepting the alternative.A formal statement about the
statistical significance of the finding.A sentence interpreting the
results in terms of the research question.Interpretation of any
supplemental analyses.
SPSS Sequence:
Analyze> COMPARE MEANS and the Independent Samples T-
Test move “quantitative variable” to the Dependent List Box.
Move “ binary qualitative variable” to the Grouping Variable,
input coding for the two groups of the independent
variable>Click OK.
Independent Samples T-Test Tutorial:
*

SPSS
Analyze>Compare Means>Independent T Test, Input Grouping
Variable based on coding of gender in this case
*
Independent Sample T-Test
Avery Fitness Center
Likelihood to Recommend by Gender
Variable based on coding of gender in this case
SPSS Menu Sequence
There is a gender difference in the likelihood to recommend the
center: p value of .051
is significant at the 95% confidence interval.
Recreate this analysis in Demonstrate:
variable>Click OK.

The group statistics box shows that females are more likely to
recommend the center versus males. The test provides a p value
of .05 (.051 does not round the p value to .06). We can reject
the null hypothesis. In other words, there is a significant
difference in the likelihood to recommend the AFC to a friend
or family member.
P value is .051 which rounds to the 95% level of confidence on
the T Stat (the F Stat yields same information)…the confidence
interval provides the range on the upper and lower . There is a
difference between gender and the likelihood to recommend the
center.
Potential Marketing Implication: AFC management may want
to provide incentives to recommend the center to friends. There
is evidence as indicated by the test that efforts to increase male
member recommendations could be helpful to AFC decision
problem to increase membership at the center.
Independent Sample T-Test
Visits by Gender
Variable based on coding of gender in this case, male = 1 and
female = 2
SPSS Menu Sequence
There is no difference between gender and number of visits: p
value of .520 is not significant at the 95% confidence interval

variable>Click OK.
The group statistics box shows that females are slightly more
likely to visit the center versus males. The test provides a p
value of .520. (only a 49.8% confidence 1-.502).
We accept the null hypothesis. In other words, there is no
significant difference between men and women in terms of
visits to the center.
In other words, there is no difference in likelihood to visit by
gender, we cannot reject the null hypothesis.
Marketing implication: No need to create actions to increase
visits to the AFC by gender.
Analysis & Interpretation: Hypothesis Testing
Multivariate Analysis
*
One Way ANOVA
*

One-Way Analysis of VarianceAnalysis of variance (ANOVA)
is used as a test of means for two or more populations. The null
hypothesis, typically, is that all means are equal. ANOVA
compares the means on the dependent variable.Analysis of
variance must have a dependent variable that is metric
(measured using an interval or ratio scale).There must also be
one or more independent variables that are all categorical
(nonmetric). Categorical independent variables are also called
factors.
*
One-Way Analysis of VarianceA particular combination of
factor levels, or categories, is called a treatment.One-way
analysis of variance involves only one categorical variable, or a
single factor. In one-way analysis of variance, a treatment is the
same as a factor level.
*
One-Way Analysis of Variance
Marketing researchers are often interested in examining the
differences in the mean values of the dependent variable for
several categories of a single independent variable or factor.
For example:Do the various segments differ in terms of their
volume of product consumption?Do the brand evaluations of
groups exposed to different commercials vary?What is the effect
of consumers' familiarity with the store (measured as high,

medium, and low) on preference for the store?
One Way Anova
https://www.youtube.com/watch?v=_btBuD3LIsM
*
Conducting a Statistical Testp-value: the probability that the
given sample was drawn from the population described by a
given null hypothesisRange from .00 – 1.00p-values are
compared to α (this is the risk level generally set to .05) to
determine significanceWhen the p-value is less than or equal to
α (.05 typically), we conclude that there is a significant
differenceWhen the p-value is greater than α (.05), we conclude
that there is not a significant differenceThe risk level is set
depending on the problem definition of the study. How much
risk is permissible for the action standard for the decision to be
made? Generally, in market research if the p-value is less than
or equal to α (.05), where we are 95% confident, we have a
significant result. In other words, we conclude that there is a
significant difference or relationship depending on our research
question.
As such, each hypothesis test is evaluated for significance with
its associated p value. This week we will focus on the
Independent Samples T-Test and the Analysis of Variance—One
Way and discuss N-Way Analysis of Variance
*
Key Concepts

Type I Error: wrongly reject the null hypothesis: Saying there
is a difference when there is not.
Type II Error: wrongly do not reject the null: Saying there is
no difference when there is.
Hypothesis Testing
Watch this video to better understand the concept of hypothesis
testing
https://www.youtube.com/watch?v=d0eVIUyt_Uc
There are various multivariate/inferential statistics to use for
the hypothesis testing. This week we will analyze data with the
Independent Samples T-Test and One-Way ANOVA. We will
discuss the N-Way ANOVA as hypothesis tests as well. Now,
the One Way ANOVA.
Number of
Variables
Univariate
Multivariate
Stats
One
Two

or More
Variability
needed for the test. Let’s define One Way ANOVA and why it
is appropriate for the hypothesis test associated with the
research question does spending at the AFC (revenue variable)
differ by member income. As with all hypothesis tests, we will
reject or accept the null hypothesis based on the p value
associated with the test.
Conducting a Statistical Test
-p-value: the probability that the given sample was drawn from
the population described by a given null hypothesis. What is the
probability of rejecting a null hypothesis that is true?Range
from .00 – 1.00p-values are compared to α to determine
significanceWhen the p-value is equal to or less than α, we
conclude that there is a significant differenceWhen the p-value
is greater than α, we conclude that there is not a significant
difference

*
Conducting a Statistical TestThere are many possible statistical
tests that can be used, depending on your question, your data,
and other factors.
To explore differences of spending at the AFC (revenue) by
income level, we
will use the One-Way Analysis of Variance
*
State:
effect
goTestDV Scale of MeasurementIV Scale of MeasurementOne
Way AnovaInterval or ratioNominal or ordinal (factorial)

Factorial—more than 2 levels of the independent variable.
One Way Anova
MR/Brown & Suter
*
Relationship Amongst Commonly Used Stat Tests: T-Test and
Analysis of Variance
One Independent

Metric Dependent Variable
Independent Samples
T-Test
Categorical
Binary (e.g. gender 1
male 2 female)
Independent Variable

One-Way Analysis
of Variance
One Factor
N-Way Analysis
of Variance
More than
One Factor
Analysis of
Variance

Categorical:
Factorial
Analysis of
Covariance
Categorical
and Interval
Correlation
As the diagram illustrates, the Independent Samples T-Test
compares 2 groups whereas the One-Way ANOVA compares
more than 2 groups (this is called a factorial variable)
One Way Anova

Now let’s look at the application of the One Way Analysis of
Variance with the AFC case.
SPSS
Analyze> COMPARE MEANS and the One-Way ANOVA move
Revenue to the Dependent List Box. Move Income to the
FACTOR box>Click OPTIONS>Click DESCRIPTIVE>Click
CONTINUE>Click OK.
*
*
One-Way ANOVA
CONTINUE>Click OK. We maintain the null!
SPSS Menu Sequence
Ho There is no relationships between member income and
how much they spend at AFC.
HA There is a relationship between member income and how
much they spend at AFC.

This is a demonstration of one way ANOVA based on the AFC
case.
In this example we accept the null hypothesis (Ho) because the
p-value is .958 much larger than a significant p value of .05 or
less. No need for any alternate pricing strategies at the AFC.
Let’s now look at an example where we have a significant
result. The case follows on the next slide and the associated
data set is on the course site.
WHICH OF THESE WEBSITES IS THE BEST CHOICE?
*
One-Way ANOVA
Web Design
SPSS Menu Sequence
Ho There is no difference in web design performance.
HA There is a difference in web design performance.
There is a difference in web design performance. We reject the
null hypothesis with a p value of .000. When a significant result

is presented in a One Way ANOVA, a post hoc test must be
completed. The Scheffé is a popular choice. See the next slide
for a definition.
*
Post Hoc Tests
Once you have a significant result—go back to your One Way
ANOVA procedure and select Post Hoc and Scheffe.
SPSS
CONTINUE>CLICK POST HOC>CLICK SCHEFFE>CLICK
CONTINUE>Click OK.
*
*
One-Way ANOVA
Web Design

SPSS Menu Sequence
Ho There is no difference in web design performance.
HA There is a difference in web design performance.
Since Design B had the highest mean we can check that Design
B is significantly different from the other web design options.
As we compare the p values of web design B to other web
designs (A, C and D), we see that web design B is significantly
different (and higher) than all others.
Marketing Implication: Colleen should move forward with Web
Design B for her business.
One-way ANOVA: Drawing ConclusionsFor the one-way
ANOVA, your conclusions should include:A formal statement
about retaining the null or rejecting the null and accepting the
alternative.A formal statement about the statistical significance
of the finding.A sentence interpreting the results in terms of the
research question.Interpretation of any supplemental analyses.
SPSS Sequence:
“variable” to the Dependent List Box. Move “variable” to the
CONTINUE>Click OK.
*

One Independent

Independent Samples
T-Test
Categorical
male 2 female)
One-Way Analysis

of Variance
One Factor
N-Way Analysis
of Variance
More than
One Factor
Analysis of
Variance
Categorical:
Factorial
Analysis of
Covariance

Categorical
and Interval
Correlation
One Way Anova
As the diagram illustrates, the Independent Samples T-Test
compares 2 groups whereas the One-Way ANOVA compares
more than 2 groups (this is called a factorial variable).
In experimental design in particular, if there is more than one
factorial independent variable, a N-Way Analysis of Variance
can be used. The slide on the next page diagrams the procedure
in comparison to the Independent Samples T-Test and the One
Way ANOVA. An example of its use is presented and a video
tutorial is presented where you have the opportunity to further
EXPLORE this technique in the content for this week.

One Independent

Independent Samples
T-Test
Categorical
male 2 female
One-Way Analysis
of Variance

One Factor
N-Way Analysis
of Variance
More than
One Factor
Analysis of
Variance
Categorical:
Factorial
Analysis of
Covariance

Categorical
and Interval
Correlation
If two or more factors are involved, the analysis is termed n-
way analysis of variance.
In marketing research, one is often concerned with the effect of
more than one factor simultaneously. For example:
How do advertising levels (high, medium, and low) interact
with price levels (high, medium, and low) to influence a brand's
sale?
Do educational levels (less than high school, high school
graduate, some college, and college graduate) and age (less than
35, 35-55, more than 55) affect consumption of a brand?
What is the effect of consumers' familiarity with a department
store (high, medium, and low) and store image (positive,
neutral, and negative) on preference for the store?
What is the null hypothesis, Ho what is the alternative
hypothesis Ha
Can you reject or accept the null hypothesis?

N Way Analysis of Variance
https://www.youtube.com/watch?v=3uB3Asly4PI
State:
effect
goTestDV Scale of MeasurementIV Scale of
MeasurementIndependent Sample t-testInterval or ratioNominal
or ordinal (binary)One Way Anova and N-Way AnovaInterval or
ratioNominal or ordinal (factorial)Chi Square Test of
IndependenceNominal or ordinalNominal ordinal
*

Formulas
MR/Brown & Suter
*
Chi-Squared Tests: Conducting the Statistical TestCalculate the
chi-squared tests using the following formula:
MR/Brown & Suter
*
Chi-Squared Tests: Conducting Supplemental AnalysesCalculate
an effect size using the formula:
MR/Brown & Suter
*
Independent-Samples t-test: Conducting the Statistical
TestCalculate an independent-samples t-test using the formula:
MR/Brown & Suter
*

Independent-Samples t-test: Conducting the Statistical
TestCalculate the pooled variance using the formula:
MR/Brown & Suter
*
One-way ANOVA TermsF: the ratio of between-group
variability to within-group variability used in ANOVAk: the
number of groups being compared in ANOVAdfB: between-
groups degrees of freedom, calculated as k - 1dfW: within-
groups degrees of freedom, calculated as N - kSS: sum of
squares; shorthand for “sum of the squared deviations”
MR/Brown & Suter
*
One-way ANOVA: Conducting the Statistical TestCalculate the
one-way ANOVA using the following formulas:
MR/Brown & Suter
*

One-way ANOVA: Conducting Supplemental AnalysesIf we
found statistical significance, compute an effect size and a post-
hoc test.If we did not find statistical significance, no further
analyses needed.
MR/Brown & Suter
*
Region of RejectionTwo-tailed test: a hypothesis test in which
the region of rejection falls in both tailsRepresented with a ≠ in
the alternative hypothesis and = in the null hypothesis
MR/Brown & Suter
*
Region of RejectionOne-tailed test: a hypothesis test in which
the region of rejection falls in either the upper or lower
tailRepresented with a < or > in the alternative hypothesis and ≤
or ≥ in the null hypothesis
MR/Brown & Suter
*

Chi-Squared Tests: Conducting the Statistical TestCalculate the
chi-squared tests using the following formula:
MR/Brown & Suter
*
Chi-Squared Tests: Conducting Supplemental AnalysesCalculate
an effect size using the formula:
MR/Brown & Suter
*
Chi-Squared Test of Independence: Critical Values and
Decision RulesThe critical value for the chi-squared test of
independence depends on alpha and the degrees of freedom for
the testExample: If α = .05, k1 = 4, and k2 = 3, χ2crit(6) =
12.592
*

Independent Samples Tests: Critical Values and Decision
RulesThe critical value for any t-test depends on alpha, the
degrees of freedom for the test, and whether the test is one-
tailed or two tailedExample: For a two-tailed t-test where α =
.05 and n = 23, tcrit(21) = 2.080
MR/Brown & Suter
*
One-way ANOVA: Critical Values and Decision RulesThe
critical value for the one-way ANOVA depends on alpha and the
degrees of freedom for the testExample: If α = .05, k = 3, and
N = 21, Fcrit(2, 18) = 3.16
MR/Brown & Suter
*

Independent Samples Tests: Critical Values and Decision
RulesThe critical value for any t-test depends on alpha, the
degrees of freedom for the test, and whether the test is one-
tailed or two tailedExample: For a one-tailed t-test where α =
.05 and n = 23, tcrit(21) = 1.721
MR/Brown & Suter
*
Significance LevelSignificance level: the probability set as
acceptable by the researcher that the null hypothesis is rejected
when it is in fact trueRepresented by α (alpha)Example: If α =
.05 or less, there is less than a 5% probability that we have
rejected a true null hypothesis. In most research situations, this
is a permissible amount of risk.
All Hypothesis Tests are evaluated by their p value—the
probability value indicating whether or not a significant result
exists in the context of your research question.
*
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