1. Non-Experimental designs Psych 231: Research Methods in Psychology

2. Exam 2 Mean = 76 Max = 94 Min = 48

3. Exam 2 Most common area of errors between/within manipulations interactions/main effects validity/reliability If you’d like to go over your exam, contact me and we’ll set up a time to go through it

4. Non-Experimental designs Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each

5. Correlational designs Looking for a co-occurrence relationship between two (or more) variables Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is. This suggests that there is a relationship between study time and test performance. We call this relationship a correlation. 3 properties: form, direction, strength

6. Strength r = 1.0 “perfect positive corr.” r = -1.0 “perfect negative corr.” r = 0.0 “no relationship” 0.0+1.0 The farther from zero, the stronger the relationship

7. Correlational designs Looking for a co-occurrence relationship between two (or more) variables Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is Explanatory variables (Predictor variables) Response variables (Outcome variables) For our example, which variable is explanatory and which is response? And why? It depends on your theory of the causal relationship between the variables

8. Scatterplot Y X 1 2 3 4 5 6 123456 Response (outcome) variable Explanatory (predictor) variable For descriptive case, it doesn’t matter which variable goes where Correlational analysis For predictive cases, put the response variable on the Y axis Regression analysis

9. Correlational designs Advantages: Doesn’t require manipulation of variable Sometimes the variables of interest can’t be manipulated Allows for simple observations of variables in naturalistic settings (increasing external validity) Can look at a lot of variables at once

10. Disadvantages: Don’t make casual claims Third variable problem Temporal precedence Coincidence (random co-occurence) Correlational designs Correlational results are often misinterpreted

11. Misunderstood Correlational designs Disadvantages: Example 2: Suppose that you notice that kids who sit in the front of class typically get higher grades. This suggests that there is a relationship between where you sit in class and grades. Daily Gazzett Children who sit in the back of the classroom receive lower grades than those who sit in the front. Possibly implied: “[All] Children who sit in the back of the classroom [always] receive worse grades than [each and every child] who sits in the front.” Better: “Researchers X and Y found that children who sat in the back of the classroom were more likely to receive lower grades than those who sat in the front.” Example from Owen Emlen (2006)

12. Non-Experimental designs Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each

13. Quasi-experiments What are they? Almost “true” experiments, but with an inherent confounding variable General types An event occurs that the experimenter doesn’t manipulate Something not under the experimenter’s control (e.g., flashbulb memories for traumatic events) Interested in subject variables high vs. low IQ, males vs. females Time is used as a variable

14. Quasi-experiments Program evaluation – Research on programs that is implemented to achieve some positive effect on a group of individuals. – e.g., does abstinence from sex program work in schools – Steps in program evaluation – Needs assessment - is there a problem? – Program theory assessment - does program address the needs? – Process evaluation - does it reach the target population? Is it being run correctly? – Outcome evaluation - are the intended outcomes being realized? – Efficiency assessment- was it “worth” it? The the benefits worth the costs?

15. Quasi-experiments Nonequivalent control group designs with pretest and posttest (most common) (think back to the second control lecture) participants Experimental group Control group Measure Non-Random Assignment Independent Variable Dependent Variable Measure Dependent Variable – But remember that the results may be compromised because of the nonequivalent control group (review threats to internal validity)

16. Quasi-experiments Advantages Allows applied research when experiments not possible Threats to internal validity can be assessed (sometimes) Disadvantages Threats to internal validity may exist Designs are more complex than traditional experiments Statistical analysis can be difficult Most statistical analyses assume randomness

17. Non-Experimental designs Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each

18. Developmental designs Used to study changes in behavior that occur as a function of age changes Age typically serves as a quasi-independent variable Three major types Cross-sectional Longitudinal Cohort-sequential

19. Developmental designs Cross-sectional design Groups are pre-defined on the basis of a pre- existing variable Study groups of individuals of different ages at the same time Use age to assign participants to group Age is subject variable treated as a between-subjects variable Age 4 Age 7 Age 11

20. Cross-sectional design Developmental designs Advantages: Can gather data about different groups (i.e., ages) at the same time Participants are not required to commit for an extended period of time

21. Cross-sectional design Developmental designs Disavantages: Individuals are not followed over time Cohort (or generation) effect: individuals of different ages may be inherently different due to factors in the environment Are 5 year old different from 15 year olds just because of age, or can factors present in their environment contribute to the differences? Imagine a 15yr old saying “back when I was 5 I didn’t have a Wii, my own cell phone, or a netbook” Does not reveal development of any particular individuals Cannot infer causality due to lack of control

22. Longitudinal design Developmental designs Follow the same individual or group over time Age is treated as a within-subjects variable Rather than comparing groups, the same individuals are compared to themselves at different times Changes in dependent variable likely to reflect changes due to aging process Changes in performance are compared on an individual basis and overall Age 11 time Age 20Age 15

23. Longitudinal Designs Example Wisconsin Longitudinal Study (WLS) Wisconsin Longitudinal Study Began in 1957 and is still on-going (50 years) 10,317 men and women who graduated from Wisconsin high schools in 1957 Originally studied plans for college after graduation Now it can be used as a test of aging and maturation

24. Longitudinal design Developmental designs Advantages: Can see developmental changes clearly Can measure differences within individuals Avoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging)

25. Longitudinal design Developmental designs Disadvantages Can be very time-consuming Can have cross-generational effects: Conclusions based on members of one generation may not apply to other generations Numerous threats to internal validity: Attrition/mortality History Practice effects Improved performance over multiple tests may be due to practice taking the test Cannot determine causality

26. Developmental designs Measure groups of participants as they age Example: measure a group of 5 year olds, then the same group 10 years later, as well as another group of 5 year olds Age is both between and within subjects variable Combines elements of cross-sectional and longitudinal designs Addresses some of the concerns raised by other designs For example, allows to evaluate the contribution of cohort effects Cohort-sequential design

27. Developmental designs Cohort-sequential design Time of measurement 197519851995 Cohort A Cohort B Cohort C Cross-sectional component 1970s 1980s 1990s Age 5 Age 15Age 25 Age 5 Age 15 Age 5 Longitudinal component

28. Developmental designs Advantages: Get more information Can track developmental changes to individuals Can compare different ages at a single time Can measure generation effect Less time-consuming than longitudinal (maybe) Disadvantages: Still time-consuming Need lots of groups of participants Still cannot make causal claims Cohort-sequential design

29. Small N designs What are they? Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes Even today, in some sub-areas, using small N designs is common place (e.g., psychophysics, clinical settings, expertise, etc.)

30. Small N designs One or a few participants Data are typically not analyzed statistically; rather rely on visual interpretation of the data Observations begin in the absence of treatment (BASELINE) Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded

31. Small N designs Baseline experiments – the basic idea is to show: 1. when the IV occurs, you get the effect 2. when the IV doesn’t occur, you don’t get the effect (reversibility)  Before introducing treatment (IV), baseline needs to be stable  Measure level and trend

32. Small N designs Level – how frequent (how intense) is behavior? Are all the data points high or low? Trend – does behavior seem to increase (or decrease) Are data points “flat” or on a slope?

33. ABA design ABA design (baseline, treatment, baseline) – The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.)

34. Small N designs Advantages Focus on individual performance, not fooled by group averaging effects Focus is on big effects (small effects typically can’t be seen without using large groups) Avoid some ethical problems – e.g., with non- treatments Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more observations on fewer subjects

35. Small N designs Disadvantages Effects may be small relative to variability of situation so NEED more observation Some effects are by definition between subjects Treatment leads to a lasting change, so you don’t get reversals Difficult to determine how generalizable the effects are

36. Small N designs Some researchers have argued that Small N designs are the best way to go. The goal of psychology is to describe behavior of an individual Looking at data collapsed over groups “looks” in the wrong place Need to look at the data at the level of the individual