1. Non-Experimental designs
Psych 231: Research Methods in Psychology

2. 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
Non-Experimental designs

3. 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

4. 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

5. 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

6. 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
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
Developmental designs

7. 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
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 20
Age 15
Developmental designs

8. Longitudinal 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

9. Developmental designs
Longitudinal design
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)
Developmental designs

10. 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

11. Developmental designs
Cohort-sequential design
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
Developmental designs

12. Developmental designs
Cohort-sequential design
Time of measurement
Cross-sectional component
1975
1985
1995
Age 5
Age 15
Age 25
Cohort A
1970s
Age 5
Age 15
Cohort B
1980s
Age 5
Cohort C
1990s
Longitudinal component
Developmental designs

13. Developmental designs
Cohort-sequential design
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
Developmental designs

14. 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
Non-Experimental designs

15. 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

16. 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

17. 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

18. 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

19. 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.)
ABA design

20. 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

21. 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

22. 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