Non-Experimental designs: Developmental designs & Small-N designs Psych 231: Research Methods in Psychology

Announcements Journal Summary 2 due date moved back a week (instead of this week, they are due next week)

Exam 2

Error in survey research Sampling error - Are there differences in your sample compared to the population as a whole? Response rate What proportion of the sample actually responded to the survey? Hidden costs here - what can you do to increase response rates Non-response error (bias) Is there something special about the data that you’re missing? From the people who didn’t respond Measurement error (validity and reliability) Are your questions really measuring what you want them to?

Quasi-experiments What are they? Almost “true” experiments, but with an inherent confounding variable Design includes a quasi-independent variable Examples An event occurs that the experimenter doesn’t manipulate Interested in subject variables Time is used as a variable

Quasi-experiments What are they? Almost “true” experiments, but with an inherent confounding variable Advantages Allows applied research when experiments not possible Threats to internal validity can (sometimes) be assessed

Quasi-experiments What are they? Disadvantages Almost “true” experiments, but with an inherent confounding variable Disadvantages Threats to internal validity may exist (which can not be addressed) Be careful when making causal claims Statistical analysis can be difficult Most statistical analyses assume randomness

Quasi-experiments What are they? Common types Almost “true” experiments, but with an inherent confounding variable Common types Nonequivalent control group designs Program evaluation Interrupted time series designs

Quasi-experiments Nonequivalent control group designs With pretest and posttest (most common) participants Experimental group Control Measure Non-Random Assignment Independent Variable Dependent Variable But remember that the results may be compromised because of the nonequivalent control group

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 Time series designs treatment Basic method: Observe a single group multiple times prior to and after a treatment treatment Obs Obs Obs Obs Obs Obs The pretest observations allow the researcher to look for pre-existing trends The posttest observations allow the researcher to look for changes in the trends Is it a temporary change, does it last, etc.?

Quasi-experiments Time series designs treatment A variation of basic time series design Addition of a nonequivalent no-treatment control group time series Obs treatment Obs

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

Developmental designs Cross-sectional design 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

Developmental designs Cross-sectional design 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 Example: are 5 year old different from 13 year olds just because of age, or can factors present in their environment contribute to the differences? Cannot infer causality due to lack of control

Developmental designs Longitudinal design 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 Repeated measurements over extended period of time Changes in dependent variable reflect changes due to aging process Changes in performance are compared on an individual basis and overall

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

Developmental designs Longitudinal design 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

Developmental designs Cohort-sequential design Measure groups of participants as they age Example: measure a group of 5 year olds, then the same group 5 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 generation effects

Developmental designs Cohort-sequential design Advantages: Can measure generation effect Less time-consuming than longitudinal Disadvantages: Still time-consuming Still cannot make causal claims

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.)

Small N designs One or a few participants Data are 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?

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.)

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 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

Next time Statistics (Chapter 14)