ONLY FOR MAESTRO

6. SPSS Data Preparation for Hypotheses Testing

Dr. Boonghee Yoo

mktbzy@hofstra.edu

RMI Distinguished Professor in Business and

Professor of Marketing & International Business

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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.

Download data into SPSS and open it in SPSS
Add a footer
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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 EdTech@Hofstra.edu to set up an 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”

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.

Create x1 and x2
based on the scenarios presented if xs are manipulated.
Scenario 1 = x1 (hi), x2 (hi)  x1 = 2, x2 = 2
Scenario 2 = x1 (hi), x2 (low)  x1 = 2, x2 = 1
Scenario 3 = x1 (low), x2 (hi)  x1 = 1, x2 = 2
Scenario 4 = x1 (low), x2 (low)  x1 = 1, x2 = 1
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), x2 (hi)  x1 = 2, x2 = 2
Scenario 2 = x1 (hi), x2 (low)  x1 = 2, x2 = 1
Scenario 3 = x1 (low), x2 (hi)  x1 = 1, x2 = 2
Scenario 4 = x1 (low), x2 (low)  x1 = 1, x2 = 1
Scenario I 2 3 4

Hypotheses Testing
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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 Conclusion: Hypotheses are supported or not Statistical technique

7. Factor Analysis and Reliability

Dr. Boonghee Yoo

mktbzy@hofstra.edu

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)
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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, multidimensional scaling, factor analysis)

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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
multi-item scales
Obtain a “clear” factor pattern

Factor analysis

Naming &
Reliability

Composite variables

Statistics

X1
X2
X3
X4
X5
F2
F1

Survey questions
(Manifest variables)
Factors
(Latent variables)
Common (= exploratory) factor analysis

X1
X2
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

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)

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

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

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Variance explained by each factor

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.

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Unrotated Factor Loading Matrix

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Scatter diagram using correlations

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Scatter diagram after orthogonal rotation of axes (VARIMAX)

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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

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

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.
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. 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. Create a measure and reliability table. 29 Factor loadings > 0.60
Reliability > 0.70

Create composite variables
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COMPUTE PI=mean(Q26_12,Q26_13,Q26_14,Q26_15).
EXECUTE.

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A composite variable = mean of the items of a factor
Give a label to the composite variable in the variable view

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.
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
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

8. A 2 x 2 Experimental Design: – Quality and Economy (x1 and x2 as independent variables)

Dr. Boonghee Yoo

mktbzy@hofstra.edu

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)

A 2×2 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)
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.
Scenario 1 = x1 (hi), x2 (hi)  x1 = 2, x2 = 2
Scenario 1I = x1 (hi), x2 (low)  x1 = 2, x2 = 1
Scenario 1II = x1 (low), x2 (hi)  x1 = 1, x2 = 2
Scenario 1V = x1 (low), x2 (low)  x1 = 1, x2 = 1

Sort by the scenarios to make it easy
to type codes into x1 and x2.

x1 and x2 now have codes (1 or 2).
Scenario 1 = x1 (hi), x2 (hi)  x1 = 2, x2 = 2
Scenario 1I = x1 (hi), x2 (low)  x1 = 2, x2 = 1
Scenario 1II = x1 (low), x2 (hi)  x1 = 1, x2 = 2
Scenario 1V = x1 (low), x2 (low)  x1 = 1, x2 = 1

Check how many participants each scenario is exposed to
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8. A 2 x 2 Experimental Design: – Quality and Economy (x1 and x2 as independent variables)

Dr. Boonghee Yoo

mktbzy@hofstra.edu

RMI Distinguished Professor in Business and

Professor of Marketing & International Business

Make changes on the names, labels, and measure on the variable view.
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 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 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 manipulation checks)

Dr. Boonghee Yoo

mktbzy@hofstra.edu

RMI Distinguished Professor in Business and

Professor of Marketing & International Business

Run factor analysis for x1 and x2 manipulation check questions.
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x1 MC – Perceived service quality
x2 MC – Perceived contribution to local economy

Compute the composite variable for each x MC.
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Create x1MC and x2MC.

Run t-test to check if the manipulation is well done.

Test variable (x1MC here):
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) 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.