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Review from Last Week

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: Generalizability

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Review from Last Week

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  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

  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

  3. 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

  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(X1,X2)

  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

  6. Z is ANTECEDENT Z Y X Z is INTERVENING X Z Y

  7. SPURIOUSRELATIONSHIPS 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.

  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 Y.

  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.

  10. 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.

  11. 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 Positive and Negative Relationships

  12. 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.

  13. Example - Question, d.v., level, i.v.s, hypotheses

  14. 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

  15. 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?

  16. 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…

  17. 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?

  18. Concepts • Part of our theories • Define as clearly and concretely as possible • Link to Empirical phenomenon • Makes much easier to defend.

  19. 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.

  20. 1. Where it fits in the model • Independent • Dependent • Intervening • Antecedent 2. Is it observed? • Latent • Manifest.

  21. 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

  22. 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?

  23. Measurement Creating variables often requires creativity Approximate concept that you wish to measure. How to measure abstract concepts? - also depends on level of analysis.

  24. 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

  25. 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?)

  26. http://www.douglas.bc.ca/psychd/ handouts/measurement_scales.htm

  27. 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 Cases Are Rows

  28. Examples of Measurements www.freedomhouse.org/research/freeworld/2000/table1.htm www.transparency.org/documents/cpi/2001/cpi2001.html

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