Non-Experimental designs: Correlational and Quasi-experiments Psych 231: Research Methods in Psychology

Announcements Lab attendance is critical this week because group projects are being administered Attendance will be taken. Flash drives & copies Don’t forget Quiz 8 (chapters 9 & 10) due Friday night

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

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 Y X For this example, we have a linear relationship, it is positive, and fairly strong

Correlational designs Advantages: Does not require manipulation of variable Sometimes the variables of interest cannot be manipulated Allows for simple observations of variables in naturalistic settings (increasing external validity) Can look at a lot of variables at once

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

Misunderstood Correlational designs 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 lower grades than those [each and every child] who sit in the front.” Incorrect interpretation: Sitting in the back of the classroom causes lower grades. Better way to say it: “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 mindPsych you mind | PsyBlogPsyBlog

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

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 or have control over Flashbulb memories for traumatic events Program already being implemented in some schools Interested in subject variables high vs. low IQ, males vs. females Time is used as a variable age 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

Quasi-experimental designs Example: 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 College student isn’t as important as becoming a young adult For a nice reviews see, Zagorsky & Smith (2011) & Brown (2008)Zagorsky & Smith (2011) Brown (2008) Note: the original study was Hovell, Mewborn, Randle, & Fowler-Johnson (1985)Hovell, Mewborn, Randle, & Fowler-Johnson (1985)

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)

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

Quasi-experiments Program evaluation – Systematic research on programs that is conducted to evaluate their effectiveness and efficiency. – 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?

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

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

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

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

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

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 Data collected in: 1957, 1964, 1975, 1992, 2004, 2011

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)

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 Baby boomers Generation X Mellennials Generation Z

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

Developmental designs Cohort-sequential design Time of measurement 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

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

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

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, animal studies, expertise, etc.)

Small N designs In contrast to Large N-designs (comparing aggregated performance of large groups of participants) One or a few participants Data are typically not analyzed statistically; rather rely on visual interpretation of the data

Small N designs Observations begin in the absence of treatment (BASELINE) Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded Steady state (baseline) = observation Treatment introduced

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) Steady state (baseline) Transition steady state = observation Treatment introduced Reversibility Treatment removed

Small N designs  Before introducing treatment (IV), baseline needs to be stable  Measure level and trend 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? Unstable Stable

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.) Steady state (baseline)Transition steady stateReversibility There are other designs as well (e.g., ABAB see figure13.6 in your textbook)

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

Small N designs Disadvantages Difficult to determine how generalizable the effects are 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

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