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 you’re certain.”

Follow the rules of inference We’ll 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 HypothesesHypothesis: A Statement of What we

believe to be factual.

Independent Variable (X1)

Dependent Variable (Y)

Independent Variable (X2)

Y=f(XX11,XX22))www.StudsPlanet.com

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 X Y

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. www.StudsPlanet.com

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 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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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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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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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 levelBill, Susan, George, Henry...

81st Congress, 82nd Congress, 83rd….

Canada, France, USA….

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 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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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 Same as interval but with an absolute 0 point

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Example of Levels of Example 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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http://www.douglas.bc.ca/psychd/handouts/measurement_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.

V 1 V 2 … V K

Case 1

Case 2

…

Case n

Cases

Are

Rows

Variables are columns

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Examples of Measurements

www.freedomhouse.org/research/freeworld/2000/table1.htm

www.transparency.org/documents/cpi/2001/cpi2001.html

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