1. Experiment Basics: Variables Psych 231: Research Methods in Psychology

2. Reminders Print out the Class experiment exercise (from the Lab web page) and bring it to labs this weekClass experiment Lab web page Group project introduction sections due this week Don’t forget to take the on-line quizzes quiz 5, chapter 4, was due yesterday quiz 6, chapter 6, is due Oct 1 (Tuesday) Journal Summary #1 is due in labs next week Journal Summary #1

3. Exam 1 Mean: 79.95% Median: 80% Range: 56-98 Results If you want to go over your exam set up a time to see me

4. Exam 1 Common errors: Four Cannons of scientific method (& pg 7- 10 of the textbook)

5. Exam 1 Common errors: A researcher examined the relationship between music and mood. He presented two groups of participants the same video clips but the two groups received different musical soundtracks. Following the presentation of the videos, participants completed a questionnaire designed to measure their current mood. (1)Identify the Research Design used in the study (2)Identify a major advantage of using this research design for this study (3)Identify a major disadvantage/limitation of using this research design for this study. Experiment: manipulated the music (IV) and measured the effects on mood (DV). Major advantage: ability to make causal claims Major disadvantage: (several possible answers, generalizability)

6. Many kinds of Variables Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables

7. Many kinds of Variables Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables

8. Identifying potential problems These are things that you want to try to avoid by careful selection of the levels of your IV (may be issues for your DV as well). Demand characteristics Experimenter bias Reactivity Floor and ceiling effects (range effects)

9. Demand characteristics Characteristics of the study that may give away the purpose of the experiment May influence how the participants behave in the study Examples: Experiment title: The effects of horror movies on mood Obvious manipulation: Having participants see lists of words and pictures and then later testing to see if pictures or words are remembered better Biased or leading questions: Don’t you think it’s bad to murder unborn children?

10. Experimenter Bias Experimenter bias (expectancy effects)expectancy effects The experimenter may influence the results (intentionally and unintentionally) E.g., Clever HansClever Hans One solution is to keep the experimenter (as well as the participants) “blind” as to what conditions are being tested

11. Knowing that you are being measured Just being in an experimental setting, people don’t always respond the way that they “normally” would. Cooperative Defensive Non-cooperative Reactivity

12. Range effects Floor: A value below which a response cannot be made Floor As a result the effects of your IV (if there are indeed any) can’t be seen. Imagine a task that is so difficult, that none of your participants can do it. Ceiling: When the dependent variable reaches a level that cannot be exceeded Ceiling So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out” Imagine a task that is so easy, that everybody scores a 100% To avoid floor and ceiling effects you want to pick levels of your IV that result in middle level performance in your DV

13. Variables Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables

14. 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: Free 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 Other ways Ordered recall Recognition Relearning Priming Cued recall fMRI, EEG Eye- movements

15. 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’sIntrospection Rating scales – strongly agree - agree - undecided - disagree - strongly disagreeRating scales Is the dependent variable directly observable? Choice/decision Is the dependent variable indirectly observable? Physiological measures (e.g. GSR, heart rate) Behavioral measures (e.g. speed, accuracy)

16. Measuring your dependent variables Scales of measurement Errors in measurement

17. Measuring your dependent variables Scales of measurement Errors in measurement

18. Measuring your dependent variables Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring Scales of measurement The scale that you use will (partially) determine what kinds of statistical analyses you can perform

19. Scales of measurement Categorical variables (qualitative) Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale

20. 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.

21. Scales of measurement Categorical variables (qualitative) Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories

22. 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.

23. Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order

24. 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 20º40º“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. 20º40º The amount of temperature increase is the same 60º80º 20º increase

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

26. 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.

27. Scales of measurement Ratio scale 8 cards high

28. Scales of measurement Interval scale 5 cards high

29. Scales of measurement Interval scaleRatio scale 8 cards high5 cards high 0 cards high means ‘no height’ 0 cards high means ‘as tall as the table ’

30. 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?

31. Measuring your dependent variables Scales of measurement Errors in measurement Reliability & Validity

32. 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?

33. 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)

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

35. Dartboard analogy Bull’s eye = the “true score” Reliability = consistency Validity = measuring what is intended reliable valid reliable invalid unreliable invalid Measurement error Estimate of true score biased

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

37. 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 ReliableUnreliable

38. 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)

39. 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

40. Validity Does your measure really measure what it is supposed to measure? There are many “kinds” of validity

41. VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Many kinds of Validity

42. VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Many kinds of Validity

43. 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.”

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

45. 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

46. Threats to internal validity Experimenter bias & reactivity History – an event happens the experiment Maturation – participants get older (and other changes) Selection – nonrandom selection may lead to biases Mortality (attrition) – participants drop out or can’t continue Regression to the mean – extreme performance is often followed by performance closer to the mean The SI cover jinx

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

48. 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

49. Measuring your dependent variables Scales of measurement Errors in measurement Reliability & Validity Sampling error

50. Sampling Population Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample Errors in measurement Sampling error

51. Sampling Sample Inferential statistics used to generalize back Sampling to make data collection manageable Population Allows us to quantify the Sampling error

52. Sampling Goals of “good” sampling: –Maximize Representativeness: –To what extent do the characteristics of those in the sample reflect those in the population –Reduce Bias: –A systematic difference between those in the sample and those in the population Key tool: Random selection

53. Sampling Methods Probability sampling Simple random sampling Systematic sampling Stratified sampling Non-probability sampling Convenience sampling Quota sampling Have some element of random selection Susceptible to biased selection

54. Simple random sampling Every individual has a equal and independent chance of being selected from the population

55. Systematic sampling Selecting every n th person

56. Cluster sampling Step 1: Identify groups (clusters) Step 2: randomly select from each group

57. Convenience sampling Use the participants who are easy to get

58. Quota sampling Step 1: identify the specific subgroups Step 2: take from each group until desired number of individuals

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

60. 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

61. 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?

62. Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1

63. 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?

64. Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2

65. 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.

66. Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2 Men Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1 Women MatchedMis-Matched

67. 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

68. 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

69. “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.