Experiment basics
- 1. Experiment Basics: Variables Psych 231: Research Methods in Psychology
- 2. Variables Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables
- 3. Dependent Variables The variables that are measured by the experimenter They are “dependent” on the independent variables (if there is a relationship between the IV and DV as the hypothesis predicts). Consider our class experiment Conceptual level: Memory Operational level: Recall test Present list of words, participants make a judgment for each word 15 sec. of filler (counting backwards by 3’s) Measure the accuracy of recall
- 4. Choosing your dependent variable How to measure your your construct: Can the participant provide self-report? • Introspection – specially trained observers of their own thought processes, method fell out of favor in early 1900’s • Rating scales – strongly agree-agree-undecided-disagree- strongly disagree Is the dependent variable directly observable? • Choice/decision (sometimes timed) Is the dependent variable indirectly observable? • Physiological measures (e.g. GSR, heart rate) • Behavioral measures (e.g. speed, accuracy)
- 5. Measuring your dependent variables Scales of measurement Errors in measurement
- 6. Measuring your dependent variables Scales of measurement Errors in measurement
- 7. Measuring your dependent variables Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring The scale that you use will (partially) determine what kinds of statistical analyses you can perform
- 8. Scales of measurement Categorical variables (qualitative) Quantitative variables Nominal scale
- 9. Scales of measurement Label and categorize observations, Do not make any quantitative distinctions between observations. Example: • Eye color: blue, green, brown, hazel Nominal Scale: Consists of a set of categories that have different names.
- 10. Scales of measurement Categorical variables (qualitative) Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories
- 11. Scales of measurement Rank observations in terms of size or magnitude. Example: • T-shirt size: Small, Med, Lrg, XL, XXL Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence.
- 12. Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order
- 13. Scales of measurement Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. Example: Fahrenheit temperature scale 20º 40º “Not Twice as hot” 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 The amount of temperature increase is the same 60º 80º 20º increase 20º increase
- 14. Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order Ordered Categories of same size
- 15. Scales of measurement 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.
- 16. Scales of measurement Ratio scale 8 cards high
- 17. Scales of measurement Interval scale 5 cards high
- 18. Scales of measurement Interval scale Ratio scale 8 cards high 5 cards high 0 cards high means ‘no height’ 0 cards high means ‘as tall as the table’
- 19. Scales of measurement 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 • Given a choice, usually prefer highest level of measurement possible “Best” Scale?
- 20. Measuring your dependent variables Scales of measurement Errors in measurement Reliability & Validity
- 21. Example: Measuring intelligence? Measuring the true score 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?
- 22. Errors in measurement 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)
- 23. Dartboard analogy Bull’s eye = the “true score”
- 24. Dartboard analogy Bull’s eye = the “true score” Reliability = consistency Validity = measuring what is intended reliable valid reliable invalid unreliable invalid
- 25. Reliability True score + measurement error A reliable measure will have a small amount of error Multiple “kinds” of reliability
- 26. Reliability 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
- 27. Reliability 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)
- 28. Reliability 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
- 29. Validity Does your measure really measure what it is supposed to measure? There are many “kinds” of validity
- 30. VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENT PREDICTIVE CONCURRENT FACE INTERNAL EXTERNAL Many kinds of Validity
- 31. VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENT PREDICTIVE CONCURRENT FACE INTERNAL EXTERNAL Many kinds of Validity
- 32. Face Validity 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.”
- 33. Construct Validity Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct
- 34. Internal Validity 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
- 35. Threats to internal validity 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
- 36. External Validity Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”
- 37. External Validity 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
- 38. Variables Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables
- 39. Extraneous Variables 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
- 40. Colors and words Divide into two groups: men women Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Women first. Men please close your eyes. Okay ready?
- 42. Okay, now it is the men’s turn. Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Okay ready?
- 44. Our results So why the difference between the results for men versus women? Is this support for a theory that proposes: “Women are good color identifiers, men are not” Why or why not? Let’s look at the two lists.
- 46. What resulted in the performance difference? Our manipulated independent variable (men vs. women) The other variable match/mis-match? Because the two variables are perfectly correlated we can’t tell This is the problem with confounds Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green IV DV Confound Co-vary together
- 47. What DIDN’T result in the performance difference? Extraneous variables Control • # of words on the list • The actual words that were printed Random • Age of the men and women in the groups These are not confounds, because they don’t co-vary with the IV Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green
- 48. “Debugging your study” 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.