Variables cont.

1. Variables cont. Psych 231: Research Methods in Psychology

2. Download the class experiment results from the web page and bring to labs this week • Class experiment due dates: • First draft: in labs Oct 23 & 24 • Final draft: in class Nov. 19th (no labs that week) Announcements

3. blue, green, brown, Lrg, Small, Med, • Categorical variables • Nominal scale • Consists of a set of categories that have different names. • Ordinal scale • Consists of a set of categories that are organized in an ordered sequence. • Quantitative variables Scales of measurement

4. blue, green, brown, Lrg, Small, Med, • Categorical variables • Nominal scale • Consists of a set of categories that have different names. • Ordinal scale • Consists of a set of categories that are organized in an ordered sequence. • Quantitative variables • Interval scale • Ratio scale Scales of measurement

5. Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. • Example: Fahrenheit temperature scale • With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. • However, Ratios of magnitudes are not meaningful. 20º 40º 20º increase The amount of temperature increase is the same 60º 80º 20º increase 40º “Not Twice as hot” 20º Scales of measurement

6. Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Categories with order Ordered Categories of same size Scales of measurement

7. Ratios of numbers DO reflect ratios of magnitude. • It is easy to get ratio and interval scales confused • Example: Measuring your height with playing cards • Ratio scale: An interval scale with the additional feature of an absolute zero point. Scales of measurement

8. Ratio scale 8 cards high Scales of measurement

9. Interval scale 5 cards high Scales of measurement

10. Ratio scale Interval scale 8 cards high 5 cards high 0 cards high means ‘as tall as the table’ 0 cards high means ‘no height’ Scales of measurement

11. Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Categories with order Ordered Categories of same size Ordered Categories of same size with zero point “Best” Scale? • Given a choice, usually prefer highest level of measurement possible Scales of measurement

12. Independent variables • Dependent variables • Measurement • Scales of measurement • Errors in measurement • Extraneous variables • Control variables • Random variables • Confound variables Variables

13. Example: Measuring intelligence? • How do we measure the construct? • How good is our measure? • How does it compare to other measures of the construct? • Is it a self-consistent measure? Measuring the true score

14. In search of the “true score” • Reliability • Do you get the same value with multiple measurements? • Validity • Does your measure really measure the construct? • Is there bias in our measurement? (systematic error) Errors in measurement

15. Bull’s eye = the “true score” Dartboard analogy

16. Bull’s eye = the “true score” Validity = measuring what is intended Reliability = consistency of measurement unreliable invalid reliable invalid reliablevalid Dartboard analogy

17. True score + measurement error • A reliable measure will have a small amount of error • Many “kinds” of reliability Reliability

18. Test-restest reliability • Test the same participants more than once • Measurement from the same person at two different times • Should be consistent across different administrations Reliable Unreliable Reliability

19. Internal consistency reliability • Multiple items testing the same construct • Extent to which scores on the items of a measure correlate with each other • Cronbach’s alpha (α) • Split-half reliability • Correlation of score on one half of the measure with the other half (randomly determined) Reliability

20. Inter-rater reliability • At least 2 raters observe behavior • Extent to which raters agree in their observations • Are the raters consistent? • Requires some training in judgment 5:00 4:56 Reliability

21. VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT • Does your measure really measure what it is supposed to measure? Validity : many varieties

22. VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT • Does your measure really measure what it is supposed to measure? Validity: many varieties

23. At the surface level, does it look as if the measure is testing the construct? “This guy seems smart to me, and he got a high score on my IQ measure.” Face Validity

24. Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity

25. Did the change in the DV result from the changes in the IV or does it come from something else? • The precision of the results Internal Validity

26. History – an event happens the experiment • Maturation – participants get older (and other changes) • Selection – nonrandom selection may lead to biases • Mortality – participants drop out or can’t continue • Testing – being in the study actually influences how the participants respond Threats to internal validity

27. Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” External Validity

28. Variable representativeness • Relevant variables for the behavior studied along which the sample may vary • Subject representativeness • Characteristics of sample and target population along these relevant variables • Setting representativeness • Ecological validity - are the properties of the research setting similar to those outside the lab External Validity

29. Control variables • Holding things constant - Controls for excessive random variability • Random variables – may freely vary, to spread variability equally across all experimental conditions • Randomization • A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. • Confound variables • Variables that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) • Co-varys with both the dependent AND an independent variable Extraneous Variables

30. Pilot studies • A trial run through • Don’t plan to publish these results, just try out the methods • Manipulation checks • An attempt to directly measure whether the IV variable really affects the DV. • Look for correlations with other measures of the desired effects. “Debugging your study”