Teaching slides for DSG Webinar - Introduction to SPSS (Basics) by Vignes Gopal

1. Introduction to SPSS(Basics)
Vignes Gopal Krishna
Fast track PhD student
University of Malaya

2. SPSS
• Tool for pure quantitative and social quantification
• Classical to Evolutionary
• EA(D) + CA(AS,IN,DI)
• Statistical Packages/Products for Social
Sciences(Economics, Sociology, Population Studies,
and etc)- Subjects – People/Society
• Statistical Packages/Products for Sciences(SPS)
(Health Sciences, Neurosciences, Medical Sciences,
Economics, Sociology and etc)-Subjects –
People/Society/Patients/Animals/Neurons

3. • SPSS- Rows X Columns X Cells (RCC)
Rows – Subjects, Columns – Variables, Cells –
Values/Statements
SPSS = Main Inputs (DV-views) X Outputs (Results)
Additional inputs (Scripts & Syntax)
Advantages
• Deals with the process of quantifying qualitative data
• Numerical presentation of qualitative data (Descriptive and
Inferential Statistics)
• Deals with both parametric, non-parametric ,and semi-
parametric approaches
• Deals with Cross Sectional Data, Time Series Data,Panel
Data, Longitudinal Data, Pooled Data.

4. Disadvantages
• Doesn’t deal with advanced mode of modeling and
quantitative techniques (Not possible by menus)
• Doesn’t deal with the advanced techniques of data
type.(Not possible by menus)
Common measurement
(a)Categorical variable (CAV)-Nominal & Ordinal
(b)Continuous variable (COV)-Scale(Ratio & Interval)
(c) String – Qualitative statements (Not important in
SPSS)-Nvivo, QDA-Miner, Dedoose, Atlas-TI, and etc

5. SPSS Layout
Rows
Cells
Columns
Icons
Menus
SPSS –Multi-dimensional Matrix
Will you be able to find the number of rows
and columns?
Data View
Variable View

6. Classification variable = is a partial element of
categorical variable.
Classification variable-variable that is used to classify
qualitative arguments/statements – variable by
categories (Categorical variable) + variable by
statements (Non-Categorical variable)
Categorical variable
(a)Dichotomous variable (Binomial) – 2 values – NO /
OR – Independent & Dependent samples
(b)Polychotomous variables (Multinomial)- >2 values
– NO/OR –Independent & Dependent samples

7. Flow of PQ and SQ
Data Screening
Validity
Reliability
Data Cleaning
MV
Outliers
Processed Data
EA
CA

8. Types of Variables
(a) Bi + nary variable = 2 groups of variables (0 and 1) Examples: Gender(0=Male, 1=Female), Case and
Control(0=Healthy, 1=Disease), Fluctuations(0=Increase, 1=Decrease.
(b) Dichotomous variable = 2 groups of variables(can be any 2 values) Examples:Gender(2=Male,3=Female), Case
and control(0=Before Treatment,1=Present Treatment)
(c) Independent variable = stand alone variable-Corx1,x2,x3 = 0 – Predictor/Regressor/Indicator
(d) Dependent variable = relying on factors –Cory,x1,x2 !=0)-Predictand/Regressand/Outcome
(e) Confounding variable = distorts the effects of one variable on another. -expansion of matching – reduces the
effects of confounding.
(f) Control variable –controls the effects of IV on DV.
(g) Controlled variable – another term of Dependent Variable
(h) Instrumental variable –variable that has zero correlation with residuals/error terms, but, has correlation with
dependent variable
(i) Criterion variable – a variable that has presumed effect –Non-experimental research
(j) Discrete variable – a variable that takes up distinct values
(k) Dummy variable – similar as binary variable –classification variable
(l) Endogeneous variable – inside the system-influenced by variables that are entering into the system.
(m) Exogeneous variable – outside the system- entering the systm-influencing the endogeneous variable
(n) Interval variable – a form of scale variable
(o) Ratio variable – a form of scale variable
(p) Intervening variable – intervene the association between the main variables. –moderating and mediating
variables
(q) Mediating variable – Indirect effect on the association between the main variables
(r) Moderating variable – indirect effect through interaction effects between related variables

9. (s)Polychotomous variables – take up more than 2
values/groups
(t)Manifest variable – indicator variable that can indicate the
presence of latent variable
(u)Latent variable –variable that cannot be measured directly
– it has to depend on manifest variables.
(v)Manipulated variable – Similar as IV
(w)Outcome variable – Similar as DV-presumed effect
(x)Predictor variable – Similar as IV-presumed cause
(y) Nominal variable – takes up any value – doesn’t follow
orders/ranks
(z) Ordinal variable –takes up values based on orders/ranks.
* Treatment variable – Similar as IV

10. Types of Quantitative Data
(a)Time Series Data –data follows the series of timing – single
country/industry/activity/firm/organization/stock
market/society and etc – multiple sampling periods
(b) Cross Sectional Data – data follows the cross evaluations of
various forms of
subjects(countries/industries/activities/firms)-single point of
time
(c) Panel Data – Time Series Data + Cross Sectional Data – with
different characteristics
(d) Pooled Data – Combined version of data – with similar
characteristics
(e) Longitudinal Data – Wider scope of data – variation of
timing

11. Types of Qualitative Data
(a)Factual Data – Demographical Data(Marital
Status, Level of Education, Age, Position and etc)-
(Experimental and Non-experimental Data) –
Yes/No versus Yes/No/Don’t know
True or False
(b)Positive and Normative Data – Actual versus
predicted, Agreement to Disagreement, Likes to
Dislikes
(c) Logical Arguments – True or False
(d) Boolean Statements – AND, OR, NOT
Which one is more
preferable?

12. Likert Scale(LS) and Scale(S)
LS != S
For example:-
5 Levels of Likert Scale
1=Strongly Agree
2=Agree
3=Neither Agree nor Disagree
4=Disagree
5=Strongly Disagree
In a normal case, Scale refers
to ratio or interval?

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www.dsgportal.org/site/webinar/