The document is a presentation on basic quantitative data analysis using SPSS, covering topics such as quantitative research, sampling techniques, data entry, and statistical testing. It outlines the functions of SPSS and the importance of quantitative research for examining relationships and predictions. Additionally, it explains various measurement scales, data organization, and preliminary steps for data entry along with inferential analysis methods.

1. Basic Level Quantitative Data Analysis
Using SPSS
By
Dr. Imran Ghaffar Sulehri
Senior Librarian
Pakistan Institute of Fashion and Design (PIFD)
Email: imran.ghaffar@pifd.edu.pk

2. Acknowledgement
This presentation has been prepared
using various sources, I acknowledge
all the sources.

3. Contents to be Covered
What is Quantitative Research
Brief Intro to SPSS
What We Can Do With SPSS
Why Quantitative Research
Basic Concepts in Quantitative Research
Sampling and Sampling Techniques
Quantitative Data Analysis
Basics of Quantitative Data Analysis
Preliminary Steps Before Data Entry in SPSS
Data Entry in SPSS
Tests for Inferential Data Analysis

4. What is Quantitative Research
Quantitative research methods are
concerned with collecting and analyzing
data numerically
Findings generated from quantitative
research uncover behaviors and trends

5. Brief Intro to SPSS
Statistical Package for Social Sciences (SPSS)
It was developed by Norman H. Nie and C Hadlai Hull
in 1968 who worked under SPSS Inc.
In 2009 IBM acquired it against $ 1.2 Billion and now
its IBM SPSS (Statistical Product for Service Solutions)
Compatible with Windows, Linux, Unix Mac
A well know and popular software for simple and
highly complex data analysis

6. What We Can Do With SPSS
Using SPSS we are able to;
Get data related tables
Obtain graphs/ charts
Compare Mean
Perform statistical test for results’ extraction
Factors Analysis (for tool development)
We can manage our data using SPSS

7. Why Quantitative Research
Best approach to examine the relationship between
two or more variables, explore the cause and effect
To explore differences in variables/ behaviours/ opinion
For theory testing
For predictions
To generalize your results

8. Basic Concepts in Quantitative Research:
Variables and Hypothesis
Types of Variable:
Two types are commonly known in quantitative research
1- Independent Variable
Which has impact on other variable
2- Dependent Variable
Which changes with the change of independent variable
Famous Types of Hypothesis
1- Directional Hypothesis
2- Non Directional Hypothesis
3- Null and Alternative Hypothesis

9. Basic Concepts in Quantitative Research:
Scales for Measurement of Variables
There are some set standards for the
measurement of the responses which are of
many types and known as scale.
Nominal Scale:
Responses are clearly categorized (Gender, where do you live)
Ordinal Scale:
Priorities are asked (Which dress you like Pent, Trouser etc).
Interval/ Likert Scale
We divide the responses into equal intervals (SA To SDA)
Dichotomous Scale:
Only two responses are given (Yes / No)

10. Basic Concepts in Quantitative Research
Cross sectional Study:
Type of quantitative in which data is collected one time.
Longitudinal Study:
Type of quantitative in which data is collected in multiple
times. It has further three types
1- Trend Study 2- Panel Study 3- Cohort Analysis
Population:
Subject, object, unit or field etc under study. Or Total of
the individuals who have certain characteristics and of
researcher’s interest.
Sample:
Group of representatives or individuals from the population
having same characteristics as the population have.

11. Sampling and Sampling Techniques
Sampling:
Targeted audience from which data will be gathered
Margin of Error:
Chances of being frailty.
Confidence Interval:
How you are confident about your sample
Two major types of sampling techniques
1- Probability Sampling
2- Non-Probability Sampling

12. Non-Probability Sampling
Convenience Sampling:
Select those entities which are easily accessible for
data gathering
Purposive Sampling:
Select according to purpose or need (it might be a
specific group)
Quota Sampling:
Each group or segment is fixed (Numbers or percentage)
Snowball Sampling:
A chain sampling based on references.

13. Probability Sampling
Random Sampling:
Each member of population has an equal chance of
being selected.
Systematic Random:
We select the sample from an ordered sampling
frame though the nth number. Nth = P/ S.Size
Stratified Sampling:
We divide population into different meaningful
group (If studding on University, its faculties can be strata)
Cluster Sampling:
Population consists of with different clusters (you can
choose any cluster: Area wise)

14. Quantitative Data Analysis
There are two ways to analyze the data.
1- Descriptive Analysis (Frequency distribution)
 Frequency distribution numeric calculations with
percentages response to a question.
 It provide general picture of your data and responses,
minimum maximum, Mean, Median, Mode, Range,
Dispersion etc.
Tables
Graphs
2- Inferential Statistical Analysis
 Often followed where sample is taken randomly.
 Usage of parametric/ Nonparametric statistical tests
 Using the statistical terminologies with their values

15. Basics of Quantitative Data Analysis
Central Tendency
How much people or responses are like minded. To
measure C.T. Often following are used
Mean
The average value of the data
Exp. 2, 3, 5, 4, 2, 4, 5, 1, 8, 6 = 40
40/10 = 4

16. Basic Concepts
Median
The middle value of the data
Exp. 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13,14, 15
1, 2, 3, 4, 5, 6, 7, 8, 9,10
Mode
The most frequent or repeated value of data
Exp. 2, 4, 7, 3, 2, 6, 7, 2, 5, 1, 2, 7, 5, 4, 1, 3, 6, 2

17. Basic Concepts
Standard Deviation
How the responses are spread out
Range
The difference between smallest and biggest value
Minimum/ Maximum
The lowest and highest values in data
P-Value/ Beta/ Coefficient
Probability value/ effect size

18. Preliminary Steps Before Data Entry in SPSS
Organization of Data/ Data preparation
We collect raw data and transform into numeric data
Coding 1: Questionnaires
It is first step in which we allot numbers to responses
Coding 2: Variables/ Questions
We assign Abbreviations to variables/ items
Coding 3: Options/ responses
We assign a number to each response of option

19. Data Entry in SPSS
There are two ways of entering data into SPSS
1- Direct Entry
 Open SPSS software and manually enter your
data (It is recommended to get help of a person while
entering data).
 Give each item (variable) a valid name as per
code (No space, any punctuation mark).
 Each variable name must be unique.
 Always remember, First Row for Variable and
Second for Responses.
2- Export/ Copy Data
You can copy and paste data from an excel sheet

20. Data Cleaning and Identification of Missing
Values Before Getting Results
Before to operationalize your variables for
results, It is strongly recommended to check the
normality of your data.
To check data, check Frequencies of each item
(variable).
Manage the missing or incorrect data.
Then, Obtain results by operationalizing the
required type of statistics.

21. Tests for Inferential Data Analysis
Reliability Test
To check the reliability of scale, Cronbach's alpha
needed >.7
One Sample T-Test
To compare a single sample with a threshold value
Independent Samples T-Test
To compare Mean score of two samples (Differences)
Paired Samples T-Test
For pre and post analysis

22. Tests for Inferential Data Analysis
ANOVA
To see differences in Mean Scores of more than two
samples
Correlations
To explore relationship between constructs
Effect/ Impact
Where hypothesis are developed/ Cause and effect
Mediation/ Moderation
To see indirect influences of intervening variables

23. Thanks
???

24. Practical Session
Let’s explore SPSS practically for data
entry and Basic Analysis