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Sampling (conclusion) & Experimental Research Design

Sampling (conclusion) & Experimental Research Design. Readings: Baxter and Babbie, 2004, Chapters 7 & 9. Issues in Non-probability sampling. Bias? Is the sample representative? Types of sampling problems: Alpha: find a trend in the sample that does not exist in the population

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Sampling (conclusion) & Experimental Research Design

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  1. Sampling (conclusion) & Experimental Research Design Readings: Baxter and Babbie, 2004, Chapters 7 & 9

  2. Issues in Non-probability sampling • Bias? • Is the sample representative? • Types of sampling problems: • Alpha: find a trend in the sample that does not exist in the population • Beta: do not find a trend in the sample that exists in the population

  3. Principles of Probability Sampling • each member of the population an equal chance of being chosen within specified parameters • Advantages • ideal for statistical purposes • Disadvantages • hard to achieve in practice • requires an accurate list (sampling frame or operational definition) of the whole population • expensive

  4. Types of Probability Sampling • 1. Simple Random Sample • With replacement • Without replacement: link • 2. Systematic Sample (every “n”th person) With Random Start • Urban studies example) • 3. Stratified Sampling: • Sampling Disproportionately and Weighting • 4. Cluster Sampling

  5. Examples of sampling issues & techniques • Survey about football (soccer) market • Rural poverty project and sampling issues

  6. Postpone: Techniques for Assessing Probability Sampling We will discuss these in connection with Chapter 11 material: • Standard deviation • Sampling error • Sampling distribution • Central limit theorem • Confidence intervals (margin of error)

  7. Introduction to Experimental Design • Recall discussion of experiments in lecture on Research Ethics • Milgram experiment (on obedience) • Stanford prison experiment about how prisons as institutions communicate roles and shape actions (still photo from video on right showing research subjects dressed as prison guard & prisoners)

  8. Trends in Experimental Social Research • types of subjects & reporting style (naming vs. anonymity) • deception & risk • debriefing

  9. Single & double Blind Experiments Neuman (2000: 239)

  10. Key Notions / Terms • Treatment, stimulus, manipulation (independent variable) • observable outcome (dependent variable) • Experimental Group • Control group • pretest (measurement before treatment) • posttest (measurement after treatment)

  11. Random Assignment Neuman (2000: 226)

  12. Comparison with Random Sampling Neuman (2000: 226)

  13. How to Randomly Assign Neuman (2000: 227)

  14. Experimental Design Notation • O= observation • X= treatment • R= random assignment

  15. Some Common Types of Design

  16. Three common types of experimental design: Classical pretest-post test – • Total population randomly divided into two samples; • control sample • experimental sample. • Only the experimental sample is exposed to the manipulated variable. • compares pretest results with the post test results for both samples. • divergence between the two samples is assumed to be a result of the experiment.

  17. Solomon four group design – • The population is randomly divided into four samples. • Two of the groups are experimental samples. • Two groups experience no experimental manipulation of variables. • Two groups receive a pretest and a post test. • Two groups receive only a post test. • improvement over the classical design because it controls for the effect of the pretest.

  18. Factorial design – • similar to a classical design except additional samples are used. • Each group is exposed to a different experimental manipulation.

  19. Factorial Design

  20. Validity Issues • internal validity: elimination of plausible alternative explanations • external validity: ability to generalize (outside the experiment)

  21. Internal Validity Threats • selection bias: groups not equivalent • history: unrelated event affects exp. • maturation: separate process causes effects • testing: ex. Pretest effects

  22. More Internal Validity Threats • instrumentation: measure changes • mortality/attrition • statistical regression : ex. Violent films • contamination • compensatory behaviour • experimenter expectancy

  23. External Validity Threats • realism • reactivity: • Hawthorne effect • novelty effect • placebo effect

  24. Laboratory vs. Field experiments • lab.- more control , higher internal validity • field- more natural, higher external validity

  25. Recall : New Ethical Norms • protection of subjects • debates about deception

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