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

Announcements No labs this week; work on group project data Journal Summary 2 due in lecture Wed Please write your GA’s name on the checklist No names, SSN only

Developmental designs Non-experimental or quasi-experimental Used to study changes in behavior that occur as a function of age changes

Developmental designs Age serves as a quasi-independent variable Three major types Cross-sectional Longitudinal Cohort-sequential

Developmental designs Cross-sectional design Study groups of individuals of different ages at the same time Group means are then compared

Developmental designs Cross-sectional design Groups are pre-defined on the basis of a pre-existing variable 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 Disadvantages: 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 Repeated measurements over extended period of time Age is treated as a within-subjects variable Changes in dependent variable reflect changes due to aging process

Developmental designs Longitudinal design Rather than comparing groups, the same individuals are compared to themselves at different times 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 Example: are individuals who grew up during WWII the same or different from individuals who grew up after?

Developmental designs Longitudinal design Disadvantages Numerous threats to internal validity: Attrition/mortality History Practice effects Improved performance over multiple tests may be due to practice taking the test Absence of control Cannot determine causality

Developmental designs Cohort-sequential design 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 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

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) Journal summary due Wed in lecture Put your SSN on checklist & the name of your GA No labs this week