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

2. Announcements
Journal Summary 2 due this week
No names, SSN only

3. Stages of survey research cont.
Stage 7) Pre-testing the survey instrument
Fix what doesn’t seem to be working
Stage 8) Selecting and training interviewers
For telephone and in-person surveys
Need to avoid interviewer bias
Stage 9) Implementing the survey
Stage 10) Coding and entering the data
Stage 11) Analyzing the data and preparing a final report

4. 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?

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

6. Quasi-experiments Advantages Disadvantages
Allows applied research when experiments not possible
Threats to internal validity can (sometimes) be assessed
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

7. Quasi-experiments Common types Program evaluation
Nonequivalent control group designs
Interrupted time series designs

8. 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?

9. 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

10. Quasi-experiments Time series designs
Observe a single group multiple times prior to and after a treatment
Obs
Obs
Obs
Treatment
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.?

11. Quasi-experiments Time series designs
Variations of basic time series design
Addition of a nonequivalent no-treatment control group time series
O O O T O O O & O O O _ O O O
Interrupted time series
When the treatment is not created or manipulated by the researcher

12. Developmental designs
Used to study changes in behavior that occur as a function of age changes
Non-experimental or quasi-experimental
Age typically serves as a quasi-independent variable
Three major types
Cross-sectional
Longitudinal
Cohort-sequential

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

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

21. 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.)

22. 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

23. 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

24. 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?

25. 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.)

26. 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

27. 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

28. 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

29. Next time
Statistics (Chapter 14)