This document provides an overview of key concepts for conducting scientific research, including the scientific method, research design, hypotheses, variables, levels of analysis, and data collection. It discusses developing a research question, theory, and model to outline hypotheses and relationships among variables. It emphasizes defining concepts and variables clearly and measuring them at appropriate levels of analysis. The goal of research is to make inferences and generalizations about relationships while recognizing conclusions are uncertain.

1. Review from Last Week
 Appropriate for all types of research, all 4
types of Scientific Method
 For any area of research
 Political Science, Physics, Economics…
 Basics of Research design
 Anthropology to Zoology
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2. Conducting Scientific Research
 The Goal is Inference:
 Generalizability
 The procedures are public
 Replicable
 The conclusions are uncertain
 “Statistics is never having to say you’re certain.”
 Follow the rules of inference
 We’ll learn these as we go
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3. Components of Research Design
The Basic Steps
A) The Research Question
B) The Theory
C) The Model
D) The Data
E) The Use of the Data
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4. A theory includes Hypotheses
Hypothesis: A Statement of What we
believe to be factual.
Independent Variable (X1)
Dependent
Variable (Y)
Independent Variable (X2)
Y=f(XX11,XX22))
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5. Good Hypothesis should:
 Have explanatory power
 State Expected Relationship & Direction if
Possible
 Be Testable
 Written as simply as possible
 Relate to general, not specific
phenomenon
 Be plausible
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6. Z is ANTECEDENT
Z X Y
Z is INTERVENING
X Z Y
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7. SPURIOUS RELATIONSHIPS
X
Y
?
We hypothesize that X leads to Y, but
the true relationship is that another
factor is causing both.
The only way we see this is by reasoning in our model and in
our theory. Just looking at the data, we cannot uncover the
causal relationships at work.
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8. Alternative Hypotheses and Null
Hypotheses
Two are compliments, not strictly opposites.
 HA and H0 are:
Mutually Exclusive & Exhaustive
 HA: X is true
H0 : X is not true.
 HA: X is related to Y
H0 : X is not related to Y
 HA: X is positively related to Y
H0 : X is negatively related or not related to
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9. Example: Average score on the stats exam is 70. Our class
has an average of 78. We can test the hypothesis that our
class average was higher just because of sampling error and
the hypothesis that our class average was higher because we
have smarter students
A hypothesis is a statement about a relationship between
variables. The null hypothesis H0 states there is no true
difference between scores in the population. The alternative
hypothesis Ha, is that the difference in our sample is truly
reflecting a real difference in the population, that the
difference is not due to sampling error.
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10. All hypothesis testing is done against the
null hypothesis
The Null Hypothesis
H0
is the result you could
get by chance.
The Alternative
Hypothesis Ha
is your research
hypothesis. It is what
you believe will
happen.
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11. Positive and Negative Relationships
Positive
 As X increases Y
increases Or
 As X decreases Y
decreases
 Two go in the same
direction
Negative (or inverse)
 As X increases, Y
decreases Or
 As X decreases, Y
increases
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12. www.StudsPlanet.com

13. The Model
 A basic summary of our theory, specifying
the relationships among all the relevant
factors
 Answers the research question by
explaining the Dependent Variable
 Is a representation of real world
 Outlines the hypotheses we believe and
will try to test
 DIAGRAM on the next slides should clarify
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14. Example - Question, d.v., level, i.v.s, hypotheses
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15.  Each circle is a variable: Independent
variables pointing to the dependent
variable
 Each arrow is a hypothesis about the
relationship between variables (causality)
 Overall, model represents part (or all) of
our theory
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16. Level of Analysis
(we implicitly make these decision when we
chose the Dependent variable)
 Choose:
 Level of Analysis
 Choose: Unit of Analysis
 Choose: Cases
 How do we do this?
 Begin by asking: What is our population?
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17. Building a Model II, Getting to Data
 Cases will all be at the same level
Bill, Susan, George, Henry...
81st
Congress, 82nd
Congress, 83rd
….
Canada, France, USA….
Bill, Susan, Suffolk County, Cuba, Bill last year…
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18. Getting to Data…
• What will your population be?
• Your sample of cases should be
representative of the population.
• When thinking about your cases be
obsessively specific!
• What will qualify as a case?
• What is the time frame?
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19. Concepts
 Part of our theories
 Define as clearly and concretely as
possible
 Link to Empirical phenomenon
 Makes much easier to defend.
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20. Variables
 Empirically observable characteristics
of some phenomenon
 Varies across cases
 3 ways to discuss a Variable:
 Where it fits in the model
 Whether or not it is observed
 How it is measured.
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21. 1. Where it fits in the model
•Independent
•Dependent
•Intervening
•Antecedent
2. Is it observed?
• Latent
• Manifest.
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22. 3. How it is measured
 OPERATIONALIZATION
 convert abstract theoretical notions into concrete
terms, thereby allowing measurement.
 OR…
 process of applying measuring instrument in order to
assign values to some characteristic or property of
the phenomenon being studied.
 OR…
 TURN CONCEPTS INTO VARABLES and then into
DATA
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23. Rules for Variables
 More possible values is usually better
 Mutually Exclusive - a case can hold only
one value
 You can’t be both tall and short
 Exhaustive - Every Case has a value
 If a case changes over time so that it
holds different values of a variable… you
should?
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24. Measurement
Creating variables often requires creativity
Approximate concept that you wish to
measure.
How to measure abstract concepts?
- also depends on level of analysis.
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25. Types of Operationalization
 Non-orderable Discrete Categories
 A.k.a. Nominal
 Categories, names
 E.g., gender
 Orderable Discrete
 Ordered, but not precisely ordered
 E.g., professor quality
 Dummy, Dichotomous, 0/1
 “Qualitative variable”
 Could fall into either of the above
 Presence or absence of something
 Interval
 Consensus on differences between the units
 E.g., temperature
 Ratio Scale
 Same as interval but with an absolute 0 point
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26. Example of Levels ofExample of Levels of
MeasurementMeasurement
 Suppose you wanted to measure
smoking.
• Ordinal: How often do you smoke?
 Never
 2-3 per day
 1 pack per day
 > 1 pack per day
• Interval: How many cigarettes do you
smoke each day?
• (What’s the level of analysis here? How would you define smoking for other levels of analysis?)
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27. http://www.douglas.bc.ca/psychd/
handouts/measurement_scales.htm
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28. DATA
Choose cases based on level
Represent population we want to generalize about
Collect facts about each of our variables for each of our
cases.
V 1 V 2 … V K
Case
1
Case
2
…
Case
n
Cases
Are
Rows
Variables are columns
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29. www.StudsPlanet.com

30. Examples of Measurements
www.freedomhouse.org/research/freew
orld/2000/table1.htm
www.transparency.org/documents/
cpi/2001/cpi2001.html
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