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

2. Journal Summary 2 due this week
Announcements

3. Stages of survey research cont.
Stage 6) Designing the survey instrument
Question construction: How the questions are written is very important
Clearly identify the research objectives
Do your questions really target those research objectives?
Take care wording of the questions
Keep it simple, don’t ask two things at once, avoid loaded or biased questions, etc.
How should questions be answered?
Stages of survey research cont.

4. Survey Questions Question types
Open-ended (fill in the blank, short answer)
Can get a lot of information, but
Coding is time intensive and potentially ambiguous
Close-ended (pick best answer, pick all that apply)
Easier to code
Response alternatives are the same for everyone
Rating scales
Used for “how much” judgments
e.g., Likert scale – measures attitudes, agree/disagree
Take care with your labels
Range of scores, anchors
Survey Questions

5. 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
Stages of survey research cont.

6. Quasi-experiments What are quasi-experiments?
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

7. Quasi-experiments What are quasi-experiments?
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

8. Quasi-experiments What are quasi-experiments? 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

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

10. 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

11. 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

12. 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

13. 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
Quasi-experiments

14. 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

15. 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

16. 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

17. 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

18. 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 likely to reflect changes due to aging process
Changes in performance are compared on an individual basis and overall
Developmental designs

19. 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

20. 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

21. 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

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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. Statistics (Chapter 14)
Next time