Review from Last Week

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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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Conducting Scientific Research

• The Goal is Inference
• Generalizability
• The procedures are public
• Replicable
• The conclusions are uncertain
• Statistics is never having to say youre
  certain.
• Follow the rules of inference
• Well learn these as we go

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Components of Research DesignThe 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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A theory includes Hypotheses

• Hypothesis A Statement of What we believe to be
  factual.
• Independent Variable (X1)
• Dependent
  Variable (Y)
• Independent Variable (X2)

Yf(X1,X2)
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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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Z is ANTECEDENT
Z
Y
X
Z is INTERVENING
X
Z
Y
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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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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.

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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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All hypothesis testing is done against the null
hypothesis
The Alternative Hypothesis Ha is your research
hypothesis. It is what you believe will happen.
The Null Hypothesis H0 is the result you could
get by chance.
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Positive and Negative Relationships

• Negative (or inverse)
• As X increases, Y decreases Or
• As X decreases, Y increases

• Positive
• As X increases Y increases Or
• As X decreases Y decreases
• Two go in the same direction

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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.

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Example - Question, d.v., level, i.v.s, hypotheses
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• 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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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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Building a Model II, Getting to Data

• Cases will all be at the same level

• Or

Bill, Susan, George, Henry...

• Right

• Or

81st Congress, 82nd Congress, 83rd.
Canada, France, USA.

• Wrong!

Bill, Susan, Suffolk County, Cuba, Bill last year
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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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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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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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• 1. Where it fits in the model
• Independent
• Dependent
• Intervening
• Antecedent

• 2. Is it observed?
• Latent
• Manifest.

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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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Rules for Variables

• More possible values is usually better
• Mutually Exclusive - a case can hold only one
  value
• You cant 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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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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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

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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
• gt 1 pack per day
• Interval How many cigarettes do you smoke each
  day?
• (Whats the level of analysis here? How would you
  define smoking for other levels of analysis?)

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http//www.douglas.bc.ca/psychd/ handouts/measurem
ent_scales.htm
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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
V 1 V 2 V K
Case 1
Case 2

Case n
Cases Are Rows
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Examples of Measurements

• www.freedomhouse.org/research/freeworld/2000/table
  1.htm
• www.transparency.org/documents/cpi/2001/cpi2001.ht
  ml