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:

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

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)

A B A

Steady state (baseline) | Transition steady state | Reversibility

– 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