This document provides an overview of quantitative research methods in the social sciences. It discusses: 1. The key steps in quantitative research including selecting a problem, reviewing literature, formulating research questions and hypotheses, identifying and defining variables, designing instruments, collecting and analyzing data. 2. Common quantitative research designs like surveys, correlational research, experimental and quasi-experimental methods. 3. Important quantitative research concepts like populations, samples, levels of measurement, validity and reliability, descriptive and inferential statistics, and hypothesis testing.

1. Doing
Quantitative Research
in Social Sciences
Khalid Mahmood, PhD
Professor
University of the Punjab
1

2.  Professor of Information Management at University
of the Punjab, Pakistan
 Post-doctoral research fellow at University of
California, Los Angeles, USA
 160+ publications
 Supervised many doctoral, M.Phil. and master
theses
 Worked for many research journals as editor,
reviewer and editorial board member
 Conducted many trainings on research methods
About me
2

3. Acknowledgment
 I have prepared this presentation
with the help of many books,
presentations and Websites.
 I pay my sincere gratitude to all
authors, professors and experts for
their efforts and contributions.
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4. Introduction
 Systematic scientific investigation of
data and their relationships
 Quantity versus quality
 Numbers and statistics allow precise and
exact comparisons
 Generalization of findings
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5. Philosophy of quantitative
research
 Positivism
 Absolute reality; Empiricism; Objectivity; Replication;
Realism (Social reality is knowable)
 Post-positivism
 Researchers cannot be entirely neutral; Critical
realism (Social reality is knowable only in an
imperfect and probabilistic manner)
 Inductive and deductive reasoning
 Purposes of research
 Exploratory; Descriptive; Explanatory
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6. Steps in quantitative research
1. Selecting a problem
2. Reviewing the literature
3. Formulating research
question
4. Constructing hypotheses
5. Identifying and labeling
variables
6. Constructing operational
definitions of variables
7. Constructing a research
design
8. Identifying population
and sample
9. Designing instruments
10. Selecting statistical test
for testing the
hypothesis
11. Collecting data
12. Analyzing data
13. Writing the report
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7. Quantitative designs
 Survey
 Cross-sectional
 Data are collected from selected individuals at a single point in
time
 Longitudinal
 Trend survey examines changes over time in a particular
population defined by some particular trait
 Cohort survey involves one population selected at a particular
time period but multiple samples taken and surveyed at different
points in time
 Panel survey involves a sample in which the same individuals
are studied over time
 Follow-up survey addresses development or change in a
previously studied population, some time after the original survey
was given 7

8. Quantitative designs
 Correlational
 Causal-comparative
 Cause already exists and is not manipulated
 Experimental
 Manipulation of independent variable
 Quasi-experimental
 Lacking randomization or control
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9. Populations and samples
 Units of analysis
 Probability sampling methods
 Simple random, stratified random,
cluster, systematic
 Sample size
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10. Quantitative measurement
 Concept
 Abstract thinking to distinguish it from other
elements
 Construct
 Theoretical definition of a concept; must be
observable or measurable
 Variable
 Presented in research questions and hypotheses
 Types: Independent, dependent, intervening/
mediating, moderating, confounding/ extraneous
 Operationalization
 Specifically how the variable is observed or measured
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11. Quantitative measurement
 Levels of measurement
 Nominal, ordinal, interval, ratio
 Instrument development
 Validity and reliability
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12. Data analysis
 Descriptive statistics
 Measures of central tendency
 Measures of variability or dispersion
 Inferential statistics
 Hypothesis testing
 Hypothesis
 Parametric and non-parametric tests
 Statistical significance
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Best of luck for your
research endeavors!