Transcript Attachment error

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
Conducting Scientific Research

The Goal is Inference:


The procedures are public


Replicable
The conclusions are uncertain


Generalizability
“Statistics is never having to say you’re certain.”
Follow the rules of inference

We’ll learn these as we go
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
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(X1,X2)
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
Z is ANTECEDENT
Z
X
Y
Z
Y
Z is INTERVENING
X
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.
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
Y.
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.
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.
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
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
the relationships.
Example - Question, d.v., level, i.v.s, hypotheses



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
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?
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…
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?
Concepts



Part of our theories
Define as clearly and concretely as
possible
Link to Empirical phenomenon

Makes much easier to defend.
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.
1. Where it fits in the model
•Independent
•Dependent
•Intervening
•Antecedent
2. Is it observed?
• Latent
• Manifest.
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
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?
Measurement
Creating variables often requires creativity
Approximate concept that you wish to
measure.
How to measure abstract concepts?
- also depends on level of analysis.
Types of Operationalization

Non-orderable Discrete Categories




Orderable Discrete





“Qualitative variable”
Could fall into either of the above
Presence or absence of something
Interval



Ordered, but not precisely ordered
E.g., professor quality
Dummy, Dichotomous, 0/1


A.k.a. Nominal
Categories, names
E.g., gender
Consensus on differences between the units
E.g., temperature
Ratio Scale

Same as interval but with an absolute 0 point
Example of Levels of
Measurement

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?)
http://www.douglas.bc.ca/psychd/
handouts/measurement_scales.htm
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.
Variables are columns
V1
Case
1
Cases Case
Are
2
…
Rows
Case
n
V2
…
VK
Examples of Measurements
www.freedomhouse.org/research/freew
orld/2000/table1.htm
www.transparency.org/documents/
cpi/2001/cpi2001.html