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INTRO TO EXPERIMENTAL RESEARCH, continued

INTRO TO EXPERIMENTAL RESEARCH, continued. Lawrence R. Gordon Psychology Research Methods I. “TRUE” EXPERIMENTS. Investigate the “effect(s) of X(s) on Y(s)” At least one IV is manipulated (X) with two or more “levels” All extraneous variables are controlled At least one DV is measured (Y)

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INTRO TO EXPERIMENTAL RESEARCH, continued

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  1. INTRO TO EXPERIMENTAL RESEARCH, continued Lawrence R. Gordon Psychology Research Methods I

  2. “TRUE” EXPERIMENTS • Investigate the “effect(s) of X(s) on Y(s)” • At least one IV is manipulated (X) with two or more “levels” • All extraneous variables are controlled • At least one DV is measured (Y) • SIMPLEST EXPERIMENT: one IV with two levels and one measured DV

  3. Independent Variables • Two or more “levels” (conditions, treatments) • Types: • Situational variables • Task variables • Instructional variables • “Control groups” - not all have one • Expression: • Manipulated variables (above) - assigned to participants • Subject variables - participants selected for (‘ex post facto’ study if none manipulated).

  4. Extraneous Variables • Variables NOT of interest to our research question • But if they covary with the IV, become a “confound” or an “alternative” or “rival” explanation for effect on DV • Control procedures are all about this -- next chapter • Goodwin p. 152 has excellent tables for this; try to “fix” the example shown!

  5. Dependent Variables • “...some characteristic of behavior or reported experience” (Woodworth, 1938) • Measured • Considerations • Operational definition of construct • Reliability and validity of measurement

  6. A Simple Experiment: “Time Flies” • EXAMPLE: “Time flies when you’re having fun” • Hypothesis: IF one is “having more fun”, THEN time will seem to pass more quickly • Design: • IV: 100 persons randomly assigned to two groups: • 1: “Having more fun” • 2: “Having less fun” • Procedure: manipulation of cartoon captions • DV: Estimate of a standard 10 minute interval

  7. A Simple Experiment, cont. • Results (cont.) • Group 1 = “More fun” • Mean = 8.60, SD = 2.72, N = 50 • Group 2 = “Less fun” • Mean = 12.48, SD = 3.35, N = 50 • Quickie summary of results: the “More fun” group gave shorter estimates of the 10-minute interval, on average, than the “Less fun” group. • Can you think of any possible confounds, or alternative explanations for this effect?

  8. Research Validity • How do we know we’re answering the question we asked? • Statistical conclusion validity • Construct validity • External validity • Internal validity

  9. External Validity • Generalization to • other populations? who • Other environments? where • Other occasions? When • Affects the scope of our inference; usually addressed in “discussion” of the research • Often the target when adding additional IVs

  10. Internal Validity • Does X indeed cause Y? • Analysis of possible confounds • There are special “threats” to internal validity that the “design” of research attempts to address • Affecting Pre-Post research • Concerning our participants

  11. Threats to Internal Validity • Affecting Pre-Post research • Pre X Y Post X • History and maturation • Regression to the mean • Testing and instrumentation • Major solutions • Eliminate Pre-X (“Posttest only design”) • Add control condition without Y: • Pre X Y Post X • Pre X Post X

  12. Threats to Internal Validity • Concerning our participants • Subject selection (are groups equivalent?) • Section by Other interactions (Sel  History) • Attrition (“mortality”)

  13. Wrap up: The Bower Experiment • IV(s)? • DV(s)? • EVs? • Results • Problems?

  14. Bower: Histograms (F’02)

  15. BOWER: Descriptives (F’02)

  16. BOWER: Inferential (a peek) (F’02) “Significant difference” if “Sig (2-tailed)” is <.05

  17. CORRELATION: The Problem • Are two variables related? • Does one increase as the other increases? • e. g. skills and income • Does one decrease as the other increases? • e. g. health problems and nutrition • How can we get a numerical measure of the degree of relationship? SPSS, for now...

  18. Correlation: A Quick Introduction • Descriptive: “Pearson product-moment correlation coefficient,” r • Values: -1 __________ 0 _________ +1

  19. Correlation Coefficient • A measure of degree of relationship. • Sign refers to direction. • Based on covariance • Measure of degree to which large scores go with large scores, and small scores with small scores

  20. Correlation: A Quick Introduction • Descriptive: “Pearson product-moment correlation coefficient,” r • Values: -1 __________ 0 _________ +1 • Visualization: SCATTERPLOTS

  21. {X = 6, Y = 11} N=19 data pairs

  22. What Does the Scatterplot Show? • As smoking increases, so does coronary heart disease mortality. • Relationship looks strong • Not all data points on line. • These are “residuals” or “errors of prediction”

  23. Correlation: A Quick Introduction • Descriptive: “Pearson product-moment correlation coefficient,” r • Values: -1 __________ 0 _________ +1 • Visualization: SCATTERPLOTS • Inferential -- is the computed r unlikely from a population with a correlation of 0? • EXAMPLES -- from Bower & Class Survey 

  24. Are Correct vs. Incorrect recall related? That is, do they predict one another “really”? YES - why?  Bower: Correct vs. Incorrect BOWER, revisited, F’02

  25. CLASS SURVEY 2002 CFC Scale by NC Scale • Are the scales measuring Need for Cognition and Concern for Future Consequences related? • That is, do they predict one another “really”? • YES - why? 

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