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

2. Reminders Quiz 8 is due on Oct. 30 th at midnight Running your group projects in labs this week. Please be there, your participation is important!

3. Exam 2 results Mean = 75.4 Median = 78 Max = 94 Min = 35 Most common errors Between vs. within designs What “2x3 design” means Reliability vs. validity Confounds vs. extraneous random variables Main effects vs. interactions

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 studies 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. 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 studies Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each Finish up Start

6. 11 Stages of survey research Stage 1) Identify the focus of the study and select your research method Stage 2) Determining the research schedule and budget Stage 3) Establishing an information base Stage 4) Identify the sampling frame Stage 5) Determining the sample method and sampling size Review Probability and Non-Probability methods Voluntary response method Importance of sample size

7. 11 Stages of survey research Stage 6) Designing the survey instrument Question construction: How the questions are written is very important Clearly identify the research objectives; what constructs are you trying to measure? Do your questions really target those research objectives (think Internal and External Validity)? Take care wording of the questions Keep it simple, don’t ask two things at once, avoid loaded or biased questions, etc. How should questions be answered (question type)?

8. Good and poor questions GoodPoor Was the FDC negligent by ignoring the warnings about Vioxx during testing and approving it for sale? a)Yes b)No c)Unsure Do you favor eliminating the wasteful excess in the public school budget? a)Yes b)No c)Unsure If the FDC knew that Vioxx caused serious side effects during testing, what should it have done? a)Ban it from ever being sold b)Require more testing before approving it c)Unsure Do you favor reducing the public school budget? a)Yes b)No c)Unsure Problem: emotionally charged words Problem: emotionally charged words

9. Good and poor questions GoodPoor Should senior citizens be given more money for recreation centers and food assistance programs? a)Yes b)No c)Unsure Should senior citizens be given more money for recreation centers? a)Yes b)No c)Unsure Should senior citizens be given more money for food assistance programs? a)Yes b)No c)Unsure Problem: asks two different questions Problem: asks two different questions

10. Good and poor questions GoodPoor Are you against same sex marriage and in favor of a constitutional amendment to ban it? a)Yes b)No c)Unsure What is your view on same sex marriage? a)I think marriage is a matter of personal choice b)I’m against it but don’t want a constitutional amendment c)I want a constitutional amendment banning it Problem: Biased in more than one direction Problem: Biased in more than one direction Problem: Asks two questions Problem: Asks two questions

11. Survey Questions Question types Open-ended ( fill in the blank, short answer ) Can get a lot of information, but Coding is time intensive and potentially ambiguous Close-ended ( pick best answer, pick all that apply ) Easier to code Same response alternatives for everyone Take care with your labels Decide what kind of sca le Decide number/label of response alternatives What is the best thing about ISU? (choose one) 1. Location 2. Academics 3. Dorm food 4. People who sell things between Milner and the Bone What is the best thing about ISU?

12. Decide what kind of rating scalesrating scales Rating: e.g., Likert scaleLikert scale Survey Questions: Close-ended PSY 231 is an important course in the major. 1 2 3 4 5 Strongly Agree Neutral Disagree Strongly Agree Disagree Semantic differential: Rate how you feel about PSY 231 on these dimensions Important _____: _____: _____: _____: _____: Unimportant Boring _____: _____: _____: _____: _____: Interesting Nonverbal scale for children : Point to the face that shows how you feel about the toy.

13. Decide number/label of response alternatives Use odd number (mid point and equal # of responses above and below the mid point) Questions should be uni-dimensional (each concerned with only one thing) Labels should be clear Survey Questions: Close-ended

14. 11 Stages of survey research Stage 7) Pre-testing the survey instrument Fix what doesn’t seem to be working Stage 8) Selecting and training interviewers For telephone and in-person surveys Need to avoid interviewer bias Stage 9) Implementing the survey Stage 10) Coding and entering the data Stage 11) Analyzing the data and preparing a final report

15. 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

16. Correlational designs Looking for a co-occurrence relationship between two (or more) variables Used for Descriptive research do behaviors co-occur? Predictive research is one behavior predictive of another? Reliability and Validity Does your measure correlate with others (and itself)? Evaluating theories Look for co-occurrence posited by the theory.

17. 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 At a descriptive level this suggests that there is a relationship between study time and test performance.

18. Scatterplot Hours study X Exam perf. Y 66 12 56 34 32 Y X 1 2 3 4 5 6 123456 For this example, we have a linear relationship, it is positive, and fairly strong

19. 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

20. Correlational designs Looking for a co-occurrence relationship between two (or more) variables We call this relationship a correlation. 3 properties: form, direction, strength Y X 1 2 3 4 5 6 123456 For this example, we have a linear relationship, it is positive, and fairly strong

21. Form Non-linearLinear Y X Y X Y X Y X

22. Direction Positive X & Y vary in the same direction Y X Negative X & Y vary in opposite directions Y X

23. 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

24. 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 Example 2: The Freshman 15 (CBS story) (Vidette story)The Freshman 15 (CBS story)Vidette story Is it true that the average freshman gains 15 pounds? Recent research says ‘no’ – closer to 2.5 – 3 lbs Looked at lots of variables, sex, smoking, drinking, etc. Also compared to similar aged, non college students For a nice review see Brown (2008)Brown (2008) Zagorsky (2011)

25. Disadvantages: Don’t make casual claims Third variable problem Temporal precedence Coincidence (random co-occurrence) r=0.52 correlation between the number of republicans in US senate and number of sunspots From Fun with correlationsFun with correlations Correlational designs Correlational results are often misinterpreted Correlation is not causation blog posts: Internet’s favorite phrase Why we keep saying it

26. Misunderstood Correlational designs Example 3: 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) Other examples: Psych you mind | PsyBlogPsyBlog

27. 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

28. Quasi-experiments What are they? Almost “true” experiments, but with an inherent confounding variable General types 1) An event occurs that the experimenter doesn’t manipulate Something not under the experimenter’s control (e.g., flashbulb memories for traumatic events) 2) Interested in subject variables – high vs. low IQ, males vs. females 3) Time is used as a variable Relatively accessible article: Harris et al (2006). The use and interpretation of Quasi- Experimental studies in medical informaticsHarris et al (2006). The use and interpretation of Quasi- Experimental studies in medical informatics

29. 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

30. 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?

31. 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)

32. Quasi-experiments Interrupted & Non-interrupted time series designs Observe a single group multiple times prior to and after a treatment Obs Obs Obs Obs Treatment Obs Obs Obs Obs Look for an instantaneous, permanent change Interrupted – when treatment was not introduced by researcher, for example some historical event Variations of basic time series design Addition of a nonequivalent no-treatment control group time series O O O T O O O & O O O _ O O O Interrupted time series with removed treatment If treatment effect is reversible

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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)

42. 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

43. 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

44. 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

45. 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

46. 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.)

47. 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

48. 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

49. 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?

50. 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.)

51. 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

52. 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

53. 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