Workshop on SPSS: Basic to Intermediate Level

SPSS: Basic to Intermediate
Hiram Ting & Ernest Cyril de Run
16-17 May 2015, Kuching
Organized by Sarawak Research Society

Acknowledgement
Gratitude to Prof Ernest Cyril de Run and
Prof Thurasamy Ramayah for providing useful information
during the preparation of the workshop slides.

Content
Installation of SPSS
Introduction to SPSS
Understanding of Analysis
Preliminary Decision
Data Entry
Data Cleaning
Frequency
Cross-tabulation
Normality Test
Reliability Test
Validity Test
Handling Qualitative Data
Test of Independence
Test for Goodness of Fit
Test of Difference
• T-test
• ANOVA
Test of Relationship
• Pearson Correlation
• Linear Regressions
• Multiple Regressions
Factor Analysis
Presentation of Findings
Syntax

Preparation
• Install SPSS.
• Download workshop materials folder.
• Open SPSS Workshop 16-17 May 2015 file in the folder.
• Open SPSS to check whether it works as a full version.

Preparation
Hands-on Exercise
• Install SPSS (set-up)
• Click ‘OK’ for every step.
• Copy and paste license number, or
• Copy and paste crack files in your program folder.

What is SPSS?
• Statistical Package for the Social Sciences (SPSS) is a widely used
program for statistical analysis in social sciences. It is used by
market researchers, health researchers, survey companies,
government, education researchers, marketing organizations, data
miners and others. It is regarded as the first generation technique.

What is SPSS?
Statistics included in the software:
• Descriptive statistics: Cross tabulation, Frequencies, Descriptives,
Explore, Descriptive Ratio Statistics.
• Bivariate statistics: Means, t-test, ANOVA, Correlation.
• Prediction for numerical outcomes: Linear regression.
• Prediction for identifying groups: Factor analysis, cluster
analysis, Discriminant analysis.
• Non-parametric tests and others.

When is SPSS Useful
SPSS is useful for:
• Data entry
• Data cleaning
• Descriptive analysis and output
• Parametric and non-parametric test – tests of relationship and
difference
• Data division based on factors and groups
• Quantitative research with observed variables
• Qualitative research with coded themes

Before using SPSS, it is important to understand some of the
fundamental things in research and data analysis techniques.
• Types of data
• Levels of measurement
• Types of variable
• Key terms in research
• Types of analysis
• Missing values

Types of data
• Numeric
• String (Categorical)
Levels of measurement
• Nominal
• Ordinal
• Interval
• Ratio
• Continuous

Types of variable
• Independent
• Dependent
• Moderating
• Mediating
• Control
• Endogenous, exogenous

A
B
C
D G
F
E
J1
J2
J3
J4 H I

Key terms in research
• Theory
• Concept
• Construct
• Variable, item/indicator
• Model, framework
• Operational definition

• A theory of systematically interrelated concepts, definitions, and
propositions that are advanced to explain and predict phenomena
(facts).
• A model is defined as a representation of a system that is
constructed to study some aspect of the system or the system as a
whole.
• Theory’s role is explanation whereas a model’s role is
representation.
• While theoretical framework is the theory on which the study is
based, conceptual framework is the operationalization of the theory.
It is the researcher’s own position on the problem and gives
direction to the study.

• A concept is a generally accepted collection of meanings or
characteristics associated with certain events, objects, conditions,
situations and behavior.
• A construct is an image or abstract idea specifically invented for a
given research and/or theory building purpose.
• A variable can be defined as any aspect of a theory that can vary or
change as part of the interaction within the theory.
• An operational definition is a definition stated in terms of specific
criteria for testing or measurement. Their characteristics and how
they are to be observed must be specified.

Types of Analysis
• Parametric
 Normal distribution is assumed
• Non-parametric
 Distribution free
• Types of variables involved
 Univariate, bivariate, multivariate

Handling blank responses/missing values
• Initial screening
 If the whole page is missing, discard the questionnaire.
 If the whole section is missing, discard the questionnaire.
 If important responses are missing (e.g. key questions using single
item), discard the questionnaire.
 If straight-lining or answering pattern is found, discard the
questionnaire.

Handling blank responses/missing values
• Data cleaning
 If > 25% missing, remove the observation.
Hair et al. (2014) advocate for less than15%.
 Using the midpoint of the scale.
 Replacing blank responses with a value.
 Mean of those responding or respondents.
 Using Expected Maximization (EM).

Instrument design
• Levels of measurement
• Types of scale
• Single or multiple items
• Positive or negative worded statements
• Structured, semi-structured or unstructured
• Wordings (e.g. double negatives, double-barrelled, culture-specific
terms, long complex questions)

Distribution and collection of data
 Sampling technique
 Paper questionnaire, mail or online
 Interview or self-administered questionnaire
 Response rate: distributed, collected and usable copies

In any report the first thing that is normally reported is the response
rates. When the response rate is low it raises question about the
representativeness of the sample. Another reason is the problem of non
response. Would the responses of those who have not responded be
different form those who responded?

Data analysis and interpretation
 Confidence level
 Significant level
 One-tailed or two-tailed
 Types of analytical method
 Hypothesis development and testing

Addressing Errors
 Random sampling errors
 Systematic errors/non-sampling errors
• Administrative errors: sample selection, administrator, data
processing
• Respondent errors: non-response, response bias - deliberate
falsification, unconscious misinterpretation
 Common method variance and social desirability

Pre-test
 The purpose is to ensure instrument is well-designed, hence the
statements/questions would be understood and responded to the
manner which they were developed for.
 Using pilot study.
 Using debriefing or protocol method.
 Issue with sample size.

Data Entry
An Overview
• SPSS Data Editor
• SPSS Viewer (Output)
• Variable View
Includes Name, Type, Width, Decimals, Label, Values, Missing,
Columns, Align, Measure
• Data View

Data Entry
Rules for naming of variables
• Variable names:
• Must be unique (i.e. each variable must have a different name)
• Must begin with a letter (not a number)
• Cannot include full stops, spaces or symbols (! , ? * “)
• Cannot include words used as commands by SPSS (all, ne, eq, to, le,
lt, by, or, gt, and, not, ge, with)
• Cannot exceed 64 characters.

Data Entry
Hands-on Exercise
• Open SPSS.
• Open Questionnaire Sample.
• Begin with ‘Variable View’, fill up the first row with information
provided in Data Entry Exercise.
• Continue with the second and third rows.
• Continue with the fourth to sixth rows.
• Move to ‘Data View’, fill up the blanks with responses of five
respondents.

Data Cleaning
Hands-on Exercise
• Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Frequencies’.
• Move every variable from left column to right column, click ‘OK’.
• Read the output and check.
• Addressing missing values using EM.

Useful Features
Hands-on Exercise
• Sort the data file
Go to ‘Data’, click ‘Sort Cases’, choose ‘Ascending’ or ‘Descending’
• Split the data file
Go to ‘Data’, click ‘Split File’ and ‘Compare Group’
• Select cases
Go to ‘Data’, click ‘Select Cases’, ‘If Condition is Satisfied’ and ‘If’. For
example, GEN = 1 to select only male respondents

Data Transformation
Reason for transformation
 to improve interpretation and compatibility with other data sets
 to enhance symmetry and stabilize spread
 improve linear relationship between the variables (Standardized
score)

Data Transformation
Hands-on Exercise
• Recode
 The purpose is to
redefine categories of
data.
 Go to ‘Transform’, click
‘Recode into Different
Variables’.

Data Transformation
Hands-on Exercise
• Compute
 The purpose is to
create a new variable.
 Go to ‘Transform’, click
‘Compute Variable’.

Descriptive Analysis
• The purpose is to describe the distribution of the variable of interest.
• It includes Frequencies and Cross-tabulation for nominal or
categorical data, and Descriptives (Mean and Standard Deviation) for
continuous data.

Frequencies
• The purpose is to provide frequency counts. It is useful in presenting
respondents profile and categorical findings.
Hands-on Exercise
• Open Data Analysis Exercise
• Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Frequencies’.
• Splitting dataset is useful when presenting findings based on
categories in separation. Go to ‘Data’, click ‘Split File’

Cross-tabulation
• The purpose is a joint frequency distribution of cases based on two or
more categorical variables.
• Chi-square will be explained in later slides.
Hands-on Exercise
• Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Crosstabs’. Select
the variables on for ‘Row’ and ‘Column’. In ‘Cell’, click ‘Percentages’.

Descriptives
• The purpose is to provide statistical summary of descriptive findings.
• ‘Kurtosis’ and ‘Skewness’ are useful to assess data distribution.
Hands-on Exercise
• Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Descriptives’.
• Click ‘Option’, check ‘Mean’ and ‘Std. Deviation’.

Normality Test
• Parametric test assumes data is normally distributed.
• Assessing normality using Q-Q Plots.
• Hands-on Exercise: Go to ‘Analyze’ and click Q-Q Plots.
• Assessing normality using Explore.
• Hands-on Exercise: Go to ‘Analyze’, and click ‘Explore’.
• Assessing outliers using Scatterplot.
• Hands-on Exercise: Go to ‘Graphs’, and click ‘Legacy Dialogs’ and
‘Scatter/Dot’.

Normality Test
• Skewness value provides an indication of the symmetry of the
distribution. Kurtosis, on the other hand, provides information about
the ‘peakedness’ of the distribution.
• If the distribution is perfectly normal, you would obtain a skewness
and kurtosis value of 0 (rather an uncommon occurrence in the social
sciences).
• With reasonably large samples, skewness will not ‘make a
substantive difference in the analysis’ (Tabachnick & Fidell 2007, p.
80). Kurtosis can result in an underestimate of the variance, but this
risk is also reduced with a large sample (200+ cases: see Tabachnick
& Fidell 2007, p. 80).

Normality Test
General guideline
• From 5% Trimmed Mean, compare the original mean and the new
trimmed mean to assess whether extreme scores are having a strong
influence on the mean.
• The values for asymmetry and kurtosis between -2 and +2 are
considered acceptable in order to prove normal univariate distribution
(George & Mallery, 2010).
• For sample more than 100, use Kolmogorov-Smirnoff test; for sample
less than 100, use Shapiro Wilk test. A non-significant result (Sig.
value of more than .05) indicates normality.
• Shape of histogram, Q-Q plots and boxplot.
• Outliers appear as little circles with a number attached. Outliers are
cases with scores that are quite different from the remainder of the
sample, either much higher or much lower.

Goodness of Measures
Reliability and Validity

Reliability
• Typically, in any research we use a number of questions (sometimes)
referred to as items to measure a particular variable.
• Cronbach's Alpha is a measure of how well each individual item in a
scale correlates with the sum of the remaining items. It measures
consistency among individual items in a scale.
• Reliability refers to the degree of consistency, as Kerlinger (1986)
puts it; if a scale possesses a high reliability the scale is
homogeneous. According to Nunnally (1978) alpha values equal to or
greater than 0.70 are considered to be a sufficient condition. Thus, it
can be concluded that these measures possess sufficient reliability.

• Go to ‘Analyze’, click ‘Scale’ and ‘Reliability Analysis’.

Several types of validity
• Content/Face validity
• Convergent validity
• Discriminant validity
• Criterion-related validity

Content validity
• Content validity refers to the extent to which an instrument covers the
meanings included in the concept (Babbie, 1992). Researchers,
rather than by statistical testing, subjectively judge content validity
(Chow and Lui, 2001). The content validity of the proposed instrument
is at least sufficient because the instrument is carefully refined from a
proven instrument with an exhaustive literature review process (Chow
and Lui, 2001). This can also be tested during the pre-test by using
subjects who are qualified (academicians and practitioners) to rate
whether the content of each factor was well represented by the
measurement items (Saraph et al., 1989). As Nunnally (1967) put it
content validity depends on how well the researchers created
measurement items to cover the domain of the variable being
measured.

Convergent validity
• According to Campbell and Fiske (1959) convergent validity refers to
all items measuring a construct actually loading on a single construct.
• The criteria used by Igbaria et al., 1995 to identify and interpret
factors were: each item should load 0.50 or greater on one factor and
0.35 or lower on the other factor.
• These results confirm that each of these constructs is unidimensional
and factorially distinct and that all items used to measure a particular
construct loaded on a single factor.

Discriminant validity
• Discriminant validity refers to the extent to which measures of 2
different constructs are relatively distinctive, that their correlation
values were neither an absolute value of 0 nor 1 (Campbell and Fiske,
1959). Correlation analysis is used. If all the factors are not perfectly
correlated where their correlation coefficients range between 0 or 1,
we can conclude that discriminant validity has been established.

Criterion-related validity
• Criterion related validity refers to the extent to which the factors
measured are related to pre-specified criteria (Saraph et al., 1986).
This is also called as nomological validity or external validity. We can
also do this by running a multiple regression analysis and looking at
the Multiple R value (correlation coefficient), the values we are looking
for are any values higher that 0.5.

• If data is collected using qualitative methods, such as interview and
focus group, coding process is required to quantify the themes before
using SPSS to perform any analysis.

Example: Beliefs about the use of Instagram
1. I enjoy using Instagram coz it is fun.
2. Taking and uploading pictures are what attracts me.
3. When I am bored, I play Instagram.
4. I am enthralled by its ease of use.
5. I find hashtag a useful function of Instagram.
6. It is easy to use, even my little brother is using it.
How many theme(s) can you identify?

• Chi-square test is used when you wish to explore the relationship
between 2 categorical variables with each having 2 or more
categories.
• It is also a statistical method assessing the goodness of fit between a
set of observed values and those expected hypothetically. It is used
when the parameter to be tested is proportion and there is no
assumption of normality.
• If the level of significance is set at 0.05, then p-value of less than 0.05
means rejection of null hypothesis.

Chi square test for independence
• Example: Is the proportion of male employees with high intention to
share information the same as the proportion of female with high
intention to share information?
 For Gender, we have (1= Male/2=Female) whereas for Level, we
have(1=Low/2=High)
 As such, we will have a (2 X 2) contingency table

Chi square test for goodness of fit
• Example: A researcher would like to test the association between
cigarette smoking and lung cancer. After randomly selecting smokers,
it is found that 25 out of 65 heavy smokers are at high risk of
developing lung cancer while for light smokers the figure is 20 out of
124.
• Ho: There is no association
Ha: There is association
• Findings: X2 = 11.66; p-value = 0.0016
• Decision: Reject null hypothesis
• Conclusion: Smoking and lung cancer risk are associated

Hands-on Exercise
Test for Independence/Relatedness
• Go to ‘Analyze’, click ‘Cross-tabulation’.
Test for Goodness of Fit
• Go to ‘Analyze’ , click ‘Non-parametric Test’ and ‘Dialog Legacy’.

Test of Difference
• Parametric Techniques: t-test; paired t-test, one-way ANOVA, two-
way ANOVA
• Non-Parametric Techniques: Mann-Whitney/Wilcoxon rank sum test;
Wilcoxon signed rank sum test, Kruskal Wallis; Friedman test

Test of Difference
Independent Sample T-test
• Comparing two populations/groups using Mean
Paired Samples T-test
• Comparing the Mean of two related populations/groups
One-way ANOVA
• Comparing the Mean of more than two populations/groups
Hands-on Exercise
• Go to ‘Analyze’, click ‘Compare Means’.

Test of Difference
How to interpret the findings.
 For Independent t-test, if the Levene test is significant (Sig. value is
less than 0.05), this indicates the variance of the two samples is
significantly different.
 For Paired t-test, the two variables correlate if Sig. value is less than
0.05.
 If the t-test is significant (Sig. 2-tailed value is less than 0.05), this
indicates the two samples are significantly different in the variable
under investigation.

Test of Difference
• Example: A one-way between-group ANOVA was conducted to test
whether intention to share differed by level of education.

Test of Difference
How to interpret the findings.
• There was a statistically significant differences at the p< 0.05 level in
intention scores for the 4 educational levels [F(3,188) = 2.728,
p=0.045]. Despite reaching statistical significance, the actual
difference in mean scores between the groups was quite small. The
effect size, calculated using the eta squared, was 0.04. Post-hoc
comparison using the Duncan’s range test indicated that the mean
score for Masters (M=3.51, SD=0.92) and First degree (M=3.82,
SD=0.62) was statistically different from PhD (M=4.40, SD=0.46).
Those with Diploma education (M=3.89, SD=0.47) did not differ
statistically from the PhD group.

Correlation
• Correlation is used to denote association between two quantitative
variables, assuming that the association is linear. It provides
information about the strength and direction of relationship.
• Strength: 0.10-0.29 (small), 0.30-0.49 (medium), 0.50 (large)
Hands-on Exercise
• Go to ‘Analyze’, click ‘Correlate’ and ‘Bivariate’.
• Select ‘Pearson’ (for continuous data) and ‘One-tailed’.
• If the Sig. value is less than 0.05, then the two variables are
significantly correlated. Only then the strength and direction of
relationship are looked at.

How to present the findings.
• There was a strong positive correlation between intention to share
and actual sharing [r=0.76, n=192, p<0.01] with high levels of
intention associated with high levels of actual sharing.

Regressions
• Simple linear regression is used when we would like to see the impact
of a single independent variable on a dependent variable.
• Multiple linear regression is used when we would like to see the
impact of more than one independent variable on a dependent
variable.
• Multiple regression analysis is a statistical technique that can be used
to analyze the relationship between a single dependent variable
(continuous) and several independent variables (continuous or even
nominal). In the case of nominal independent variables, dummy
variables are introduced.

• In standard multiple regression, all of the independent variables are
entered into the regression equation at the same time
• Multiple R and R² measure the strength of the relationship between
the set of independent variables and the dependent variable. An F
test is used to determine if the relationship can be generalized to the
population represented by the sample.
• A t-test is used to evaluate the individual relationship between each
independent variable and the dependent variable.

Things to consider:
• Strong Theory (conceptual or theoretical)
• Measurement Error
The degree to which the variable is an accurate and consistent
measure of the concept being studied. If the error is high than even
the best predictors may not be able to achieve sufficient predictive
accuracy.
• Specification error
Inclusion of irrelevant variables or the omission of relevant variables
from the set of independent variables.

Assumptions
• Normality
One of the basic assumptions is the normality which can be assessed
by plotting the histogram. If the histogram shows not much deviation
then we can assume the data follows a normal distribution.

• Normality of the error terms
The second assumption is that the
error term must be normally
distributed. This can be assessed by
looking at the normal P-P plot. The
idea is that the points should be as
close as possible to the diagonal
line. If they are then we can assume
that the error terms are normally
distributed.

• Linearity
The third assumption is the relationship between the independent
variables and the dependent variable must be linear. This is
assessed by looking at the partial plots. The idea is to see if we can
draw a straight line on the scatter plot that is generated.

• Constant Variance –
Homoscedasticity
The fourth assumption is that the
variance must be constant
(Homoscedasticity) as opposed to
not constant (Heterosedasciticity).
Heterosedasciticity is generally
observed when we see a
consistent pattern when we plot
the studentized residual (SRESID)
against the predicted value of Y
(ZPRED).

• Multicollinearity
The fifth assumption is the collinearity problem. This is a problem
when the independent variables are highly correlated among one
another, generally at r > 0.8 to 0.9 which is termed as
multicollinearoty. To assess this assumption we will look at two
indicators. The first one is the VIF and tolerance. A low tolerance
value of < 0.1 will result in a VIF value of > 10 as VIF is actually
1/Tolerance. If the value is more than 10 we can suspect there is a
problem of multicollinearity.

• Multicollinearity
The second value that we should look at is the conditional index. If
this value exceeds 30 we can also suspect the presence of
multicollinearity. When the value is more than 30 we should also
look across the variance proportions and see if we can spot any 2 or
more variables with a value of 0.9 and above excluding the
constant. If there are 2 or more than only we can conclude there is
multicollinearity.

• Independence of the error term - Autocorrelation
This is an assumption that is particularly a problem with time series
data and not for cross sectional data. We assume that each
predicted value is independent, which means that the predicted
value is not related to any other prediction; that is, they are not
sequenced by any variable such as time. This can be assessed by
looking at the Durbin Watson value. If the D-W value is between 1.5
– 2.5 then we can assume there is no problem.

• Outliers
These are values which are extremely large and influential that they
can influence the results of the regression. Usually the threshold is
set at ± 3 standard deviations. Although this is the default some
researchers may set a threshold of ± 2.5 to get better predictive
power. This assumption can be easily identified by looking at
whether there are casewise diagnostics.

Hands-on Exercise
• Go to ‘Analyze’, click
‘Regression’ and
‘Linear’
Descriptive Statistics
3.15 2.653 113
2.12 1.084 113
2.90 1.575 113
HOW OFTEN R ATTENDS
RELIGIOUS SERVICES
STRENGTH OF
AFFILIATION
HOW OFTEN DOES R
PRAY
Mean Std. Dev iation N
The minimum ratio of valid cases to independent variables for multiple
regression is 5 to 1. With 113 valid cases and 2 independent variables,
the ratio for this analysis is 56.5 to 1, which satisfies the minimum
requirement.
Different authors tend to give different guidelines concerning the
number of cases required for multiple regression. Stevens (1996, p.
72) recommends that ‘for social science research, about 15
participants per predictor are needed for a reliable equation’.

ANOVAb
374.757 2 187.379 49.824 .000a
413.685 110 3.761
788.442 112
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), HOW OFTEN DOES R PRAY, STRENGTH OF AFFILIATIONa.
Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESb.
The probability of the F statistic (49.824) for the
overall regression relationship is <0.001, less than or
equal to the level of significance of 0.05. We reject
the null hypothesis that there is no relationship
between the set of independent variables and the
dependent variable (R² = 0). We support the
research hypothesis that there is a statistically
significant relationship between the set of
independent variables and the dependent variable.

Model Summary
.689a
.475 .466 1.939
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), HOW OFTEN DOES R PRAY,
STRENGTH OF AFFILIATION
a.
Look in the Model Summary box and check the value given under the
heading R Square. This tells you how much of the variance in the
dependent variable is explained by the model. The rule of thumb: a
correlation less than or equal to 0.20 is characterized as very weak;
greater than 0.20 and less than or equal to 0.40 is weak; greater than 0.40
and less than or equal to 0.60 is moderate; greater than 0.60 and less
than or equal to 0.80 is strong; and greater than 0.80 is very strong.
You will notice an Adjusted R Square value in the output. When a small
sample is involved, the R square value in the sample tends to be a rather
optimistic overestimation of the true value in the population (see
Tabachnick & Fidell 2007). The Adjusted R square statistic ‘corrects’ this
value to provide a better estimate of the true population value.

Coefficientsa
7.167 .442 16.206 .000
-1.138 .194 -.465 -5.857 .000
-.554 .134 -.329 -4.145 .000
(Constant)
STRENGTH OF
AFFILIATION
HOW OFTEN
DOES R PRAY
Model
1
B Std. Error
Unstandardized
Coeff icients
Beta
Standardized
Coeff icients
t Sig.
Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESa.
For the independent variable strength of affiliation, the
probability of the t statistic (-5.857) for the b
coefficient is <0.001 which is less than or equal to the
level of significance of 0.05. We reject the null
hypothesis that the slope associated with strength of
affiliation is equal to zero (b = 0) and conclude that
there is a statistically significant relationship between
strength of affiliation and frequency of attendance at
religious services.

Coefficientsa
7.167 .442 16.206 .000
-1.138 .194 -.465 -5.857 .000
-.554 .134 -.329 -4.145 .000
(Constant)
STRENGTH OF
AFFILIATION
HOW OFTEN
DOES R PRAY
Model
1
B Std. Error
Unstandardized
Coeff icients
Beta
Standardized
Coeff icients
t Sig.
Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESa.
The beta coefficient associated with strength of affiliation is negative, indicating
an inverse relationship in which higher numeric values for strength of affiliation
are associated with lower numeric values for frequency of attendance at
religious services.
To compare the different variables it is important that you look at the
standardised coefficients, not the unstandardised ones. ‘Standardised’ means
that these values for each of the different variables have been converted to the
same scale so that you can compare them. If you were interested in
constructing a regression equation, you would use the unstandardised
coefficient values listed as B.

Factor Analysis
• The purpose is to define the underlying structure in a data matrix;
analyze the structure of interrelationships among a large number of
variables by defining a set of common underlying dimensions called
factors.
• Factor analysis in SPSS is exploratory in function. The analysis is
driven by data, rather than theory.
• Sample size: preferably >100 cases or the ratio of 20:1
(case/variable).

Factor Analysis
• Important decisions includes:
 Correlation matrix: KMO and Barlett’s test, Anti-image
 Methods of extracting factors: Principal component
 Latent root/eigenvalues criterion (>1)
 Apriori criterion on number to be extracted
 Percentage of variance explained (>50)
 Rotation: Varimax, Promax
 Loading significance (> 0.3 if 350 cases, > 0.5 if 120)

Factor Analysis
Hands-on Exercise
• Go to ‘Analyze’, click ‘Data Reduction’ and ‘Factor’.

Using Syntax
• Syntax in SPSS is the program language.
• If you need to repeat your analysis, you can save the command
language in a ‘Syntax’ file so that you can run an analysis at a later
date or to repeat various analyses.
• Whenever you run an analysis, you will notice that there is a Paste
button. When you click on the paste button, a syntax file will open with
the syntax for the analysis that you intended to do.
Hands-on Exercise
• Go to ‘File’, click ‘New/Open’ and ‘Syntax’.

Thank You
Next workshop
22 May : Advanced PLS-SEM
23-24 May : Advanced SPSS, Process
Join us at Sarawak Research Society on

Thank You
Hiram Ting, PhD
Email: hiramparousia@gmail.com
Ernest Cyril de Run, PhD
Email: drernest@feb.unimas.my

Workshop on SPSS: Basic to Intermediate Level

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