The document outlines a comprehensive process for benchmarking testing on the Windows Operating System, specifically targeting performance metrics like graphics, CPU, memory, and file storage. It includes a detailed examination of benchmarking and its significance for IT managers, alongside a review of benchmark tools, statistical methods for data analysis, and experimental design strategies. Furthermore, it covers procedures for factor analysis, reliability assessments, and data preparation necessary for hypothesis testing in SPSS.

1. TOPIC: Bench-marking Testing
1. Windows operating system (Microsoft Windows 10 Pro
10.0.17763) in terms of what the literature says about the
efficiencies AND inefficiencies for each in terms of
Performance that you will measure (graphics, cpu, memory, file
storage). This section should be really detailed and contain
subheadings. Basically there are 4 sections.
2. Research what benchmarking is, its purpose, why its a
valuable tool for IT managers.
3. Research at least two benchmark tools that you can use in
your research (so free and downloadable). 2 for Windows
Describe what the benchmark tool is, who developed it, and find
a case study where its been used (if possible).
4. Discuss the data and visual reports that the tool will give you
so you can compare the results. Be specific here...this is critical
to success.
***You need at least 2 references PER fact. You must use APA
inline citations.
8. A 2 x 2 Experimental Design: - Quality and Economy (x1 and
x2 manipulation checks)
Dr. Boonghee Yoo
[email protected]
RMI Distinguished Professor in Business and
Professor of Marketing & International Business

2. Run factor analysis for x1 and x2 manipulation check questions.
2
x1 MC - Perceived service quality
x2 MC - Perceived contribution to local economy
Compute the composite variable for each x MC.
3
Create x1MC and x2MC.
Run t-test to check if the manipulation is well done.
Test variable (x1MC here):

3. Interval- or ratio-scaled
variable(s)
Grouping variable (x1 here):
A nominal-scaled variable:
Select two groups that
you want to compare.
Independent-samples t-test
Step 1.
See the sample mean of each group.
See if the mean difference is as expected (e.g., Hi > Low).
Step 2. Levene’s test (Ho: s2group1 = s2group2)
If p-value of Levene’s test > alpha, read the “Equal variances
assumed” line.
If p-value < alpha, read the “Equal variances NOT assumed”
line.
Step 3. t-test
Read the t-value, which is the test statistics.
And read p-value.
Levene’s test (Ho: s2group1 = s2group2)

4. The graph confirms a successful manipulation.
6
The service quality of the “High” scenario is perceived to be
higher than that of the “Low” scenario.
8. A 2 x 2 Experimental Design: - Quality and Economy (x1 and
x2 as independent variables)
Dr. Boonghee Yoo
[email protected]
RMI Distinguished Professor in Business and
Professor of Marketing & International Business
Make changes on the names, labels, and measure on the variable
view.

5. Check the measure.
Have the same keys between “Name” and “Label.”
Run factor analysis for ys (dependent variables).
Select “Principal axis factoring” from “Extraction.”
The two-factor solution seems the best as (1) they are over one

6. eigenvalue each and (2) the variance explained for is over 60%.
The new eigenvalues after the rotation.
The rotated factor matrix is clear.
But note that y3 and y1 are collapsed into one factor.
If not you should rerun factor analysis after removing the most
problematic item one at a time.
Repeat this procedure until the rotated factor pattern has
(1) no cross-loading,
(2) no weak factor loading (< 0.5), and
(3) an adequate number of items (not more than 5 items per
factor).
If a clear factor pattern is obtained, name the factors.
Attitude and purchase intention (y3 and y1)
Boycotting intention (y2)
Compute the reliability of the items of each factor

7. Make sure all responses were used.
Cronbach’s a (= Reliability a) must be greater than 0.70. Then,
you can create the composite variable out of the member items.
Means and STDs must be similar among the items.
No a here should be greater than Cronbach’s a. If not, you
should delete such item(s) to increase a.
Create the composite variable for each factor.
BI = mean (y2_1,y2_2,y2_3)
“PI” will be added to the data.
Go to the Variable View and change its “Name” and “Label.”
8. A 2 x 2 Experimental Design: - Quality and Economy (x1 and
x2 as independent variables)

8. Dr. Boonghee Yoo
[email protected]
RMI Distinguished Professor in Business and
Professor of Marketing & International Business
BLOCK 1. Title and introductory paragraph.
Title and introductory paragraph
Plus, background questions
BLOCK 2 to 5. Show one of four treatments randomly.
x1(hi), x2 (hi)
x1 (hi), x2 (low)
x1 (low), x2 (hi)
x1 (low), x2 (low)
BLOCK 6. Questions.
Manipulation check questions (multi-item scales)
y1, y2, and y3 (multi-item scales)
Socio-demographic questions
Write “Thank you for participation.”
The questionnaire (6 blocks)

9. A 2x2 between-sample design: SQ (Service quality and ECON
(Contribution to local economy)
Each of the four BLOCKs consist of:
The instruction: e.g., “Please read the following description of
company ABC carefully.”
The scenario: An image file or written statement
(No questions inside the scenario blocks)
Qualtrics Survey Flow (6 blocks)
Manipulation check questions y1,
y2, …, yn
Questions to verify that subjects were manipulated as intended.
For example, if the stimulus is dollar-amount price, the
manipulation check assesses “perceived price.” Typically, a
reliable (Cronbach’s alpha > 0.70) multi-item scale borrowed
from the literature is used for the check.
Perceived SQ (Service quality)

10. The likely quality of ABC’s services is extremely high.
The ABC services must be of very good quality.
Perceived ECON (Contribution to local economy)
ABC contributes a lot to the U.S. economy.
ABC builds a very strong economic relationship with the U.S.
ABC provides Americans with a great number of jobs.
(After data collection) Create x1 and x2 on the variable view
right below the 2 x 2 manipulation scenarios.
Type the code (1 or 2) into x1 and x2
based on the scenarios presented if xs are manipulated.

11. Sort by the scenarios to make it easy
to type codes into x1 and x2.
x1 and x2 now have codes (1 or 2).
Scenari
Check how many participants each scenario is exposed to
10

12. 7. Factor Analysis and Reliability
Dr. Boonghee Yoo
[email protected]
RMI Distinguished Professor in Business and
Professor of Marketing & International Business
Dependence vs. interdependence methods
Dependence Methods
A category of multivariate statistical techniques; dependence
methods explain or predict a dependent variable(s) on the basis
of two or more independent variables (e.g., regression, general
linear model, ANOVA, t-test)
2
Independence Methods
A category of multivariate statistical techniques;
interdependence methods give meaning to a set of variables or
seek to group things together (e.g., correlation, cluster analysis,

13. multidimensional scaling, factor analysis)
3
Factor analysis is a data-reduction technique that serves to
combine questions or variables (= manifest variables) to create
factors (= latent variables)
Factor is a latent variable or construct that is not directly
observable but needs to be inferred from the manifest variables
Purpose
To identify underlying constructs in the data
To reduce the number of variables to a more manageable set
(e.g., 20 questions can be reduced to 3 factors)
To use factors rather than the individual questions.
What is factor analysis?
Steps of factor analysis

14. multi-item scales
Obtain a “clear” factor pattern
Factor analysis
Naming &
Reliability
Composite variables
Statistics

15. X1
X2
X3
X4
X5
F2
F1
Survey questions
(Manifest variables)
Factors
(Latent variables)
Common (= exploratory) factor analysis
X1
X2

16. X3
X4
X5
F2
F1
One manifest variable is loaded on one factor only.
Confirmatory factor analysis
How many factors to retain?
Plus, A priori criterion:
The analyst decides
the number of factors
(1) Eigenvalue 1

17. Eigenvalue represents the amount of variance in the original
variables associated with a factor
(2) Scree Plot
Plot of the eigenvalues against the number of factors in order of
extraction
(3) Percentage of Variance
The number of factors extracted is determined so that the
cumulative percentage of variance extracted by the factors
reaches a satisfactory level (60% or higher)
Sum of the square of the factor loadings of each variable on a
factor represents the eigenvalue
Only factors with eigenvalues greater than 1.0 are retained
iscover the.
Find where the line changes the slope (called an elbow)

18. Factor analysis terms
Factor Scores
Values of each factor underlying the variables; To replace the
manifest variables
Factor Loadings
Correlations between the factors and the original variables; 0.30
is significant, 0.40 more significant, and 0.50 very significant
Communality
The amount of the variable variance that is explained by the
factors; Must be larger than 0.50

19. Factor Rotation
Factor analysis can generate several solutions for any data set.
Each solution is termed a particular factor rotation and is
generated by a particular factor rotation scheme.
A scale of department store image
Correlations of department store image items
11
Variance explained by each factor

20. A scree plot
A scree test shows the eigenvalues plotted against the number
of factors; Find where the line changes the slope, where the
“elbow” is.
13
Unrotated Factor Loading Matrix
14
Scatter diagram using correlations

21. 15
Scatter diagram after orthogonal rotation of axes (VARIMAX)
16
Factor Loading Matrix after Orthogonal Rotation
A clear factor pattern
F1 = Store attractiveness
F2 = Store convenience
Factor Analysis by SPSS (mobile shopping)
Run separately
Manipulation check questions
y1, y2, y3
All other Likert-scale questions

22. 18
DO NOT select Principal components.
When the principal axis factoring fails to produce factors, use
other red-marked ones.
Extraction method: Principal axis factoring

23. Communalities
The amount of the variable variance that is explained by the
selected factors.
Must be > 0.05.
PERCENTAGE OF VARIANCE
CRITERION
The first 4 factors account for over
60% of the total variance.
AFTER ROTATION
After the factors are
rotated, eigenvalues
change somewhat.
EIGENVALUE 1
CRITERION
The first 6 factors
exceed eigenvalue 1
each.

24. Eigenvalues and Total variances explained
Do you see the elbow at Factor 6?
Scree plot
Factor loadings are correlations of items with factors.
Weak loading < 0.60
Cross-loading: An item loaded on more than one factor with the
difference between the loadings < 0.2 (e.g., 0.49 vs 0.43).
A matrix of factor loadings
Achieve a clear factor matrix
Delete the “worst” item with cross-loading or weak loading.
Rerun the factor analysis.
Repeat 1. and 2. until there is no cross-loading or weak loading
any more.
Then, name each factor based on highest factor loading items
with the factor.

25. Final factor matrix
26
No weak loading
No cross-loading
Reliability
27
Reliability output
28
Cronbach’s alpha does not become better when any item is
eliminated.
So, keep all of them.

26. Create a measure and reliability table.
29
Factor loadings > 0.60
Reliability > 0.70
Create composite variables
30
COMPUTE PI=mean(Q26_12,Q26_13,Q26_14,Q26_15).
EXECUTE.
31
A composite variable = mean of the items of a factor
Give a label to the composite variable in the variable view

27. Things to consider in factor analysis
How many factors should be finally retained?
Do the member items of each factor carry the same concept
together?
Are the factors consistent with a priori theories?
What to do with cross-loaded items?
Have you obtained a clean factor matrix?
What is an appropriate name for each factor?
What is the reliability of each factor?
Have you created a proper summary table?
How to use the composite variables?
General linear model
Regression analysis
Factor analysis guideline for your data
Likert-scale survey questions are qualified.
Don’t include any single-item scale question.
Run factor analysis separately for
Manipulation check questions
y1, y2, y3
All other Likert-scale questions
Report the VARIMAX-rotated factor pattern extracted by
principal axis factoring.
Achieve a clear factor pattern.
Exclude cross-loaded and weakly-loaded items one at a time.
Name the factors.
Compute the reliability of each factor.

28. Create composite variables for the factors.
Note that the composite variables, not the individual items, will
be used in analysis.
Make a table called “Measures and reliability” and report factor
names, reliability, items, and factor loadings.
Table. Measures and Reliability
Items Factor loading
Satisfaction with the mobile shopping site/app (Reliability =
0.945)
Disgusted with:Contented with 0.825
Unhappy with this site (or app):Happy with this site (or app)
0.825
Did a poor job for me:Did a good job for me 0.824
Very dissatisfied with this site (or app):Very satisfied with this
site (or app) 0.824
This site (or app) displeased me:This site (or app) pleased me
0.818
Poor choice in buying from this site (or app):Wise choice in
buying from this site (or app) 0.783
Extremely unlikable:Extremely likable 0.686
Very udesirable:Very desirable 0.657
Very unattractive:Very attractive 0.621
Purchase intention from the mobile shopping site/app
(Reliability = 0.959)
P14. I expect to purchase through this site (or app) in the near
future. 0.903
P13. It is likely that I will purchase through this site (or app) in
the near future. 0.883
P12. I intend to purchase through this site (or app) in the near
future. 0.869

29. PI1. I will definitely buy products from this site (or app) in the
near future. 0.808
Mobile shopping site/app equity (Reliability = 0.883)
If there is another site (or app) as good as this site (or app), I
prefer to buy on this site (or app). 0.810
Even if another site (or app) has same features as this site (or
app), I would prefer to buy on this site (or app). 0.786
If another site (or app) is not different from this site (or app) in
any way, it seems smarter to purchase on this site (or app).
0.730
It makes sense to buy on this site (or app) instead of any other
site (or app), even if they are the same. 0.701
Perceived quality of the mobile shopping site/app (Reliability =
0.878)
The likely quality of this site (or app) is extremely high. 0.795
This site (or app) must be of very good quality. 0.733
This site (or app) is of high quality. 0.723
6. SPSS Data Preparation for Hypotheses Testing
Dr. Boonghee Yoo
[email protected]
RMI Distinguished Professor in Business and
Professor of Marketing & International Business

30. 2
Before data downloading the data,
check if the codes are correct in Qualtrics
An example of wrong codes
Recode the incorrect codes manually.
Click the setting button
left to the question.
Before downloading the data…
Write for the Question Labels in Qualtrics.
Give a very short variable label.

31. Download data into SPSS and open it in SPSS
Add a footer
6
VDI to use SPSS
PrideDesktop, a virtual desktop (VDI) application, can be run
on almost any device running macOS, Windows, ChromeOS,
iOS (iPhone, iPad), and Android OS. More information can be
found at www.hofstra.edu/PrideDesktop
Familiarize yourself by playing with SPSS.
Press “Help” buttons in SPSS, which are designed to work as a
manual.
Check out SPSS books from the library and eBrary.
Watch YouTube video tutorials.
See the SPSS tutor at Calkin’s Lab.
Ask the instructor, me.
SPSS Questions?
The SPSS helper at Hofstra
If you need a SPSS help, see Rose Tirotta at the Calkins Lab.
You first need to email her at [email protected] to set up an

32. appointment.
Name the variables (One word; Give no space)
Label the variables in a few words.
If that’s too long, change it in Qualtrics.
Values
Label the values of ordinal- or nominal-scaled variables.
Do not label the values of interval- or ratio-scaled variables.
Measures
Nominal, ordinal, or scale (= interval and ratio together)
SPSS Variable View: Define Variables
The Downloaded SPSS Data (Variable View)
Match the names and labels by retyping “Question numbers”

33. Define “Values” and check if the “Measure” is correct.
Delete invalid responses in rows in the SPSS data.
Were all questions answered?
Eliminate the rows (respondents) with too many non-responses
Was a reasonable time spent to complete the survey?
Eliminate the surveys completed in hurry. Time Spent = End
Time – Start Time
Were the answers consistent among themselves (consistency =
answering the similar-content questions in a similar way)?
Eliminate the surveys with contradictory answers to similar
questions.
Did the answers show a reasonable amount of variation for the
questions which are different in content to one another?
Eliminate the surveys showing too small overall standard
deviation of many and related questions: For example, compute
Stan_Dev = SD(v10 to v35).
After eliminating responses, is the sample size satisfactory?
If not, survey more.
Also, delete non-variables in columns.

34. Create x1 and x2
based on the scenarios presented if xs are manipulated.
Scen
Add x1 and x2 in the “variable view” in SPSS.
Sort by the scenarios to make it easy
to type codes (1 = low and 2 = high) into x1 and x2.
x1 and x2 now have codes (1 or 2).
Scenario 1 = x1 (hi

35. Scenario I 2 3 4
Hypotheses Testing
15
p-value is the probability that the test statistic takes place if Ho
is correct.
Ho typically asserts no relationship or no difference.
alpha (a) is Type I error to reject Ho if Ho is correct.
Reject Ho if p-value ≤ a
(i.e., Ha is supported)
Fail to reject Ho if p-value > a (i.e., Ha is not supported)
Measures (items of each measure retained by factor analysis)
Reliability of the measures
If fail, defend why and suggest how to redo the study.
Use the correct technique.
Know what to discuss about the procedure and the result in text.
Create the right tables summarizing the result.
Variables and their measures
p-value < alpha

36. Conclusion:
Hypotheses are supported or not
Statistical technique