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

2. Announcements CITI ethics training due this week

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

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

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

6. Sampling Population Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample μ = 71 X = 68 Sampling error

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

8. 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

9. Sampling Methods Probability sampling Simple random sampling Cluster sampling Stratified sampling Non-probability sampling Convenience sampling Quota sampling Have some element of random selection Random element is removed. Susceptible to biased selection There are advantages and disadvantages to each of these methods I recommend that you check out table 6.1 in the textbook pp 117-118

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

11. Cluster sampling Step 1: Identify clusters Step 2: randomly select some clusters Step 3: randomly select from each selected cluster

12. Stratified sampling Step 1: Identify distribution of subgroups (strata) in population Step 2: randomly select from each group so that your sample distribution matches the population distribution 8/40 = 20%20/40 = 50%12/40 = 30%

13. Convenience sampling Use the participants who are easy to get (e.g., volunteer sign-up sheets, using a group that you already have access to, etc.)

14. Quota sampling Step 1: identify the specific subgroups (strata) Step 2: take from each group until desired number of individuals (not using random selection)

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

16. 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

17. 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?

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

19. 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?

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

21. 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.

22. 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

23. 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

24. 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 Majors, class level, seating in classroom,… 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

25. Experimental Control Our goal: To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. Control is used to: Minimize excessive variability To reduce the potential of confounds (systematic variability not part of the research design)

26. Experimental Control Our goal: To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. T = NR exp + NR other + R NR exp : Manipulated independent variables (IV) NR other : extraneous variables (EV) which covary with IV Random (R) Variability Nonrandom (NR) Variability Imprecision in measurement (DV) Randomly varying extraneous variables (EV) Condfounds Our hypothesis: the IV will result in changes in the DV

27. Experimental Control: Weight analogy Variability in a simple experiment: R NR other Treatment group Control group T = NR exp + NR other + R R NR exp NR other Absence of the treatment ( NR exp = 0 ) “perfect experiment” - no confounds ( NR other = 0 )

28. Experimental Control: Weight analogy Variability in a simple experiment: R NR exp R Treatment group Control group T = NR exp + NR other + R Difference Detector Our experiment is a “difference detector”

29. Experimental Control: Weight analogy If there is an effect of the treatment then NR exp will ≠ 0 R NR exp R Treatment group Control group Difference Detector Our experiment can detect the effect of the treatment Our experiment can detect the effect of the treatment

30. Things making detection difficult Potential Problems Confounding Excessive random variability Difference Detector

31. Potential Problems Confound If an EV co-varies with IV, then NR other component of data will be present, and may lead to misattribution of effect to IV IV DV EV Co-vary together

32. Confounding R NR exp NR other R Difference Detector Experiment can detect an effect, but can’t tell where it is from Experiment can detect an effect, but can’t tell where it is from Confound Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but could be due to the NR other

33. Confounding R NR other R Difference Detector Confound Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but could be due to the NR other R NR exp NR other R Difference Detector These two situations look the same These two situations look the same There is not an effect of the IV There is an effect of the IV

34. Potential Problems Excessive random variability If experimental control procedures are not applied Then R component of data will be excessively large, and may make NR exp undetectable

35. Excessive random variability R NR exp R Difference Detector Experiment can’t detect the effect of the treatment Experiment can’t detect the effect of the treatment If R is large relative to NR exp then detecting a difference may be difficult

36. Reduced random variability But if we reduce the size of NR other and R relative to NR exp then detecting gets easier RR NR exp Difference Detector Our experiment can detect the effect of the treatment Our experiment can detect the effect of the treatment So try to minimize this by using good measures of DV, good manipulations of IV, etc.

37. Controlling Variability How do we introduce control? Methods of Experimental Control Constancy/Randomization Comparison Production

38. Methods of Controlling Variability Constancy/Randomization If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate Control variable: hold it constant Random variable: let it vary randomly across all of the experimental conditions

39. Methods of Controlling Variability Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Training group No training (Control) group Without control groups if is harder to see what is really happening in the experiment It is easier to be swayed by plausibility or inappropriate comparisons Useful for eliminating potential confounds

40. Methods of Controlling Variability Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment 1 week of Training group 2 weeks of Training group Sometimes there are a range of values of the IV 3 weeks of Training group

41. Methods of Controlling Variability Production The experimenter selects the specific values of the Independent Variables 1 week of Training group 2 weeks of Training group 3 weeks of Training group Need to do this carefully Suppose that you don’t find a difference in the DV across your different groups Is this because the IV and DV aren’t related? Or is it because your levels of IV weren’t different enough

42. Experimental designs So far we’ve covered a lot of the about details experiments generally Now let’s consider some specific experimental designs. Some bad (but common) designs Some good designs 1 Factor, two levels 1 Factor, multi-levels Between & within factors Factorial (more than 1 factor)

43. Poorly designed experiments Bad design example 1: Does standing close to somebody cause them to move? “hmm… that’s an empirical question. Let’s see what happens if …” So you stand closely to people and see how long before they move Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”)

44. Poorly designed experiments Bad design example 2: Testing the effectiveness of a stop smoking relaxation program The participants choose which group (relaxation or no program) to be in

45. Poorly designed experiments Non-equivalent control groups participants Training group No training (Control) group Measure Self Assignment Independent Variable Dependent Variable Random Assignment Problem: selection bias for the two groups, need to do random assignment to groups Problem: selection bias for the two groups, need to do random assignment to groups Bad design example 2:

46. Poorly designed experiments Bad design example 3: Does a relaxation program decrease the urge to smoke? Pretest desire level – give relaxation program – posttest desire to smoke

47. Poorly designed experiments One group pretest-posttest design participantsPre-test Training group Post-test Measure Independent Variable Dependent Variable Problems include: history, maturation, testing, and more Pre-test No Training group Post-test Measure Add another factor Bad design example 3:

48. 1 factor - 2 levels Good design example How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough 1 Factor (Independent variable), two levels Basically you want to compare two treatments (conditions) The statistics are pretty easy, a t-test

49. 1 factor - 2 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable Good design example How does anxiety level affect test performance?

50. anxiety low moderate 8060 lowmoderate test performance anxiety One factor Two levels Use a t-test to see if these points are statistically different T-test = Observed difference between conditions Difference expected by chance Good design example How does anxiety level affect test performance? 1 factor - 2 levels

51. Advantages: Simple, relatively easy to interpret the results Is the independent variable worth studying? If no effect, then usually don’t bother with a more complex design Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern 1 factor - 2 levels

52. low moderate test performance anxiety What happens within of the ranges that you test? Interpolation Disadvantages: “True” shape of the function is hard to see Interpolation and Extrapolation are not a good idea 1 factor - 2 levels

53. Extrapolation lowmoderate test performance anxiety What happens outside of the ranges that you test? Disadvantages: “True” shape of the function is hard to see Interpolation and Extrapolation are not a good idea 1 factor - 2 levels high

54. 1 Factor - multilevel experiments For more complex theories you will typically need more complex designs (more than two levels of one IV) 1 factor - more than two levels Basically you want to compare more than two conditions The statistics are a little more difficult, an ANOVA (Analysis of Variance)

55. Good design example (similar to earlier ex.) How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough 1 Factor - multilevel experiments Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course

56. 1 factor - 3 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable High Test

57. 1 Factor - multilevel experiments anxiety low mod high 8060 lowmod test performance anxiety high

58. 1 Factor - multilevel experiments Advantages Gives a better picture of the relationship (function) Generally, the more levels you have, the less you have to worry about your range of the independent variable

59. Relationship between Anxiety and Performance lowmoderate test performance anxiety 2 levels highlowmod test performance anxiety 3 levels

60. 1 Factor - multilevel experiments Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons)

61. Pair-wise comparisons The ANOVA just tells you that not all of the groups are equal. If this is your conclusion (you get a “significant ANOVA”) then you should do further tests to see where the differences are High vs. Low High vs. Moderate Low vs. Moderate