This document outlines key concepts and methods in social science research. It discusses three steps to social science: selecting concepts of interest, positing relationships between concepts, and empirically testing suggestions. It also describes quantitative and qualitative research approaches. Additional sections define concepts, constructs, and variables, and describe four main research methods: surveys, secondary data analysis, field research, and experiments. Guidelines are provided for properly conducting each type of research method.

1. Methods of Social Science
John Bradford, Ph.D.

2. Three Simple Steps to Social Science
(easier said than done)
STEP 1: Select some concepts of interest
(variables)
STEP 2: Posit (suggest) some relationship
between these concepts (Hypothesis)
STEP 3: Test these suggestions empirically to
see if they are right.

3. Quantitative and Qualitative Research
1. Quantitative Research
– Gathers data that are easily expressed in numbers
– Emile Durkheim felt that the goal of sociology was to
discover the laws that govern social behavior
2. Qualitative Research
– Focuses not only on objective nature of behavior but
also on its meaning
– Max Weber thought that sociology had to be an
interpretive science- it must take into account the
social meanings/reasons attached to behaviors.

4. Concepts and Constructs
• Concept: a label that is applied to things with
similar characteristics or attributes; a
generalization, or category.
• Constructs: an abstract concept; words used
to describe things that exist analytically (in our
minds) but are not directly observable or
perceivable.
– Examples: Racism, love, economic
depression, loyalty, etc.

5. Research Methods
• All research begins with a ‘Literature Review’!
– A ‘lit review’ is a review of the existing literature on
the topic
• Types of Research
1. Surveys (including questionnaires and interviews)
2. Secondary Data Analysis (aka statistical or
correlational analysis)
3. Field Research (aka Observation)
4. Experiments

6. I. Survey Examples
Close-ended Survey Questions: respondents
are provided with list of possible answers
– Examples.
1. Are you: _______ male _______ female?
2. What is your present marital status?
– ______ _never married
– _______married
– _______ separated
– _______ divorced
– _______ widowed
3. Are you presently employed? ______ no ______ yes

7. Survey Examples
Open-ended Survey Questions: Respondents
answer questions in their own words.
Examples:
– What is the most important thing you have
learned so far in this class?
– What is the thing that you like most about your
sociology class? The thing you like least?

8. Surveys
6 Guidelines for Crafting Survey Questions
1. Adapt phrasing of questions to the educational level
of your respondents.
2. Avoid double negatives in a question
3. Avoid ‘marathon’ questions.
4. Don’t ask ‘double-barreled’ questions: ask only one
question at a time!
5. Don’t ask ‘leading’ or ‘loaded’ questions
6. Don’t ask questions that your respondent cannot
answer
– Inaccessible information, or illogical questions.

9. II. Secondary Data Analysis
• What is it? Researchers use existing material
and analyze data that were collected by
others.
– Usually involves statistical or correlational
analysis.
– Also includes content analysis:

10. Secondary Data Analysis
WARNING:
Correlation does not prove causation!

11. Directionality
• Variables that vary in the same
direction have a positive
relationship.
• Variables that vary in the
opposite direction have a
negative relationship.

12. Directionality
• Example: a decrease in
employment is associated with
a decrease in income:
– Even though both variables go
down, they vary in the same
direction. This is a positive
relationship! (-1 * -1 = + 1)

13. What are Variables?
• Variable: something of interest to a social
researcher.
• Variables have two characteristics:
1. It is thought to influence or be influenced by
another thing.
2. A variable is some attribute of a category of things
that has more than one possible ‘value’
– A ‘variable’ is not a constant! Not all observed cases
are identical with respect to this value.
– Variables imply differences and hence comparability.

14. Variables:
Independent (X) vs Dependent (Y)
• Independent variable (X) = the cause.
Variable that influences.
• Dependent variable (Y) = the effect. Variable
that is influenced by the cause; it is dependent
on the cause.
• INCA: the INdependent variable is the CAuse.

15. Variables: Cause and Effect
• Below are some examples of how two variables may relate
causally. We usually designate with an ‘X’ the variable we
think is ‘causing’ (‘influencing’, ‘effecting’ etc.) the other
variable, which we designate as Y.
• X Y
• Examples:
– Gender (X) is thought to influence occupation (Y)
– Religious affiliation (Y) is thought to be influenced by income.
– Educational attainment (X) is thought to influence income (Y).
– Age (X) is thought to influence attitudes towards using
computers (Y)
– Income (Y) is thought to be influenced by race (X)

16. Sampling
• A Sample is a portion of the larger population
that you will study to make inferences about
the larger population.
• General rule: the more diverse a population
is, the larger the sample needs to be!
• Samples should be random: every element in
the population has the same probability of
being in the sample.

17. III. Field Research
• What is it? Observing people and carefully
recording measurements of their behavior.
• This includes:
– Ethnography: method of attempting to
understand a group from the inside (i.e. from their
point of view)
– Case Studies
– Unstructured Interviews

18. Field Research
• Strategies of Field Research:
– Complete participant
– Complete observer
– Participant Observer
Hawthorne Effect: observation by a researcher can
influence the subjects who are being observed.
NO TOTAL
PARTICIPATION PARTICIPATION
Complete Participant Complete
Observer Observer participant

19. IV. Experiment
• An experiment involves manipulating the
independent variable (X) and observing the effect on
the dependent variable (Y)
• Experiments are the only means by which we can
explore causal relationships; only way we can know
for sure if changes to X cause changes in Y.
• Experimenter needs two dependent variable (Y)
groups of Y:
1. Experimental group- receives ‘treatment’ of independent
variable (X)
2. Control group- does not receive treatment; is left alone.

20. Experiment
• Imagine a scientist testing the
effect that some drug, X, has on
growth of rats, Y.
• To see how the drug effects rat
growth, the experimenter will
compare growth in two groups
of rats: Y₁ , the group of rats
that gets the drug (X) and a
group of rates Y₂ that will not.
• Y₁ is the experimental
group, and Y₂ is the control
group.

21. Experiment
• One assumes separation or isolation
between the setting where X is
applied and the control, where X isn’t
applied.
• It is important that rats which receive
the drug and rats which do not be
alike in all relevant characteristics and
conditions, so that any observed
differences between rats which
receive the drug (the experimental
group) and those that do not (the
control group) can be attributed only
to the drug (X), and not to something
else.

22. Experiment
• Random Assignment to condition-
is the process whereby all
participants have an equal chance
of taking part in any condition of
the experiment.
• The purpose is to ensure that any
potentially relevant differences
between the experimental and
control groups are distributed
evenly and therefore won’t affect
the outcome (i.e. will cancel each
other out)

23. Experiment
• A counter-factual refers to something
that did not happen, but could have or
would have occurred.
• We use the ‘control group’ to make a
counterfactual argument, which says that:
“in the absence of X, this is how Y₁ would
have behaved.” We assume that Y₁ would
have behaved like Y₂, the control.
• Why? Because they are alike in all
relevant characteristics so any difference
we observe must be a result of the
independent variable, X.

24. Experiment
5 Rules for Doing True Experiments
1. Have at least two groups (control and experiment)
2. Randomly assign people to groups
3. Treat the experimental group by manipulating the
independent variable
4. Observe the effect of the treatment on the dependent
variable in the experimental group
5. Compare the dependent variable differences (the
outcome of treatment) in the experimental and
control groups