Non-Experimental designs

Non-Experimental designs Psych 231: Research Methods in Psychology

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

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 Non-Experimental 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 Correlational designs

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

Looking for a co-occurrence relationship between two (or more) variables • Explanatory variables (Predictor variables) • Response variables (Outcome variables) • Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is • 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 Correlational designs

Y 6 5 4 3 2 1 1 2 3 4 5 6 X Response (outcome) 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 Explanatory (predictor) variable Scatterplot

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 Correlational designs

Disadvantages: • Don’t make casual claims • Third variable problem • Temporal precedence • Coincidence (random co-occurence) • Correlational results are often misinterpreted 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.” Misunderstood Correlational designs Example from Owen Emlen (2006)

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 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? Quasi-experiments

Independent Variable Dependent Variable Dependent Variable Non-Random Assignment Experimental group Measure Measure participants Control group Measure Measure • Nonequivalent control group designs • with pretest and posttest (most common) (think back to the second control lecture) • 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

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 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 • Cross-sectional 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 • Longitudinal design time Age 11 Age 15 Age 20 Developmental designs

Example • Wisconsin Longitudinal Study(WLS) • 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 Longitudinal 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 • Longitudinal design 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 1975 1985 1995 Cohort A 1970s Age 5 Age 5 Age 5 Cross-sectional component Cohort B 1980s Age 15 Age 15 Cohort C 1990s Age 25 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 Developmental 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.) 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 Small N designs

Baseline experiments – the basic idea is to show: • when the IV occurs, you get the effect • 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 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? Small N designs

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.) ABA design

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 • 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 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 Small N designs

Non-Experimental designs