1. Nonequivalent Control Group
Langer and Rodin (1976) Lack of opportunity to make personal decision contributes to psychological debilitation amongst nursing home residents.
Comparison Groups
(1) Treatment group:
communication stressing independent decision making
were also given a small plant as a gift (if they decided to accept it)
living on one floor of the nursing home
(2) Nonequivalent control group:
communication stressing staff responsibility for them
also received a plant as a gift (whether they chose to have one or not)
living on another floor of the nursing home
Questionnaires about control and happiness
One week before treatment
Three weeks after treatment

2. Nonequivalent Control Group
Suppose group differences are observed at a posttest.
Rule out threats to internal validity:
By adding a comparison group, researchers can rule out threats due to history, maturation, testing, instrumentation, and regression.
We assume that these threats happen the same to both groups, therefore, these threats can’t be used to explain posttest differences.

3. Nonequivalent Control Group
What threats are not ruled out?
Selection:
Because individuals are not randomly assigned to conditions, the two groups are not likely to be equivalent before the intervention (hence, “nonequivalent control”).
These preexisting differences may account for group differences in the outcome at the end of the experiment.

4. Nonequivalent Control Group
Additive Effects with Selection
selection- history effect: may have different experiences
selection- maturation effect: may mature at different rates
selection-instrumentation effect: be measured more or less sensitively by the instruments
differential subject attrition: may drop out of the study at different rates
differential regression: may differ in terms of regression to the mean

5. Nonequivalent Control Group
Langer and Rodin (1976)
Residents in the two groups did not differ significantly on the pretest measures
selection-maturation effect
When treatment group is selected not randomly assigned
The natural growth rate “rate of change” of two groups from different populations might be different see figure 10.4
Early ages and older ages most vulnerable
When two groups are from different populations
residents on different floors of a nursing facility
Assigned to rooms randomly
selection- history effect
event affecting the residents’ happiness and alertness occurred on one floor of the nursing home but not on the other

7. Nonequivalent Control Group
Langer and Rodin (1976)
selection-instrumentation effect
changes in a measuring instrument across floors
Floor or ceiling effects differ across floors
differential regression
regression toward the mean is to be expected when individuals are selected on the basis of extreme scores
pretest scores were more extreme across floors
Expectancy Effects, Contamination, and Novelty Effects
Expectancy: all the observers were kept unaware of the research hypothesis
Contamination: “there was not a great deal of communication between floors”
Novelty or Hawthorne effect: Individuals living on each floor were given equal attention and staff expressed caring about them

8. The issue of external validity
How much do the findings from Langer and Rodin generalized to other elderly residents
Characteristics of the nursing home where the research was completed
Quality of the nursing home
Characteristics of the residence within the nursing home
Amount of independence
Socioeconomic status
Replication of a study can help demonstrate external validity

9. Quasi-Experimental Designs
Simple Interrupted Time-Series Design
Observe a dependent variable for some time before and after a treatment is introduced
Could be following individuals
Could be from populations using archival data
Maybe difficult to get continuous baseline data
O1 O2 O3 O4 X O5 O6 O7 O8
Look for clear discontinuity in the time-series graph for evidence of treatment effectiveness.
Study Natural treatments such as a terrorist attack of September 11, 2001
Self-reported character strengths see figure 10.5

10. obvious discontinuity in the time series around the 9/11 event indicated an effect on self-reported character strengths

11. Quasi-Experimental Designs
Simple Interrupted Time-Series Design
Discontinuity in the time series is the major evidence of effective treatment
Usually is a gradual change
Impact of clean-air ordinance to ban smoking in Bowling Green
Hospital admissions due to smoking related diseases over a six-year period
Three years before and three years after the smoking ban
See figure 10.6

12. Hospitalizations decreased gradually after implementation of the smoking ban

13. Simple Interrupted Time-Series Design
Suppose a discontinuity is observed when treatment (X) is introduced.
Rule out threats to internal validity:
history threats are the most troublesome in this design,
Seasonal variation is also a possible problem for interpretation
Can compare to another year at the same season
instrumentation threats also are likely in some studies.

14. Simple Interrupted Time-Series Design
What threats are more easily ruled out?
Maturation: We assume maturational changes are gradual, not abrupt discontinuities.
Testing: If testing influences responses, these effects are likely to show up in the initial observations (i.e., before the intervention). Also, testing effects are less likely with archival data.
Regression: If scores regress to the mean, they will do so in the initial observations.

15. Quasi-Experimental Designs
Time Series with Nonequivalent Control Group Design
Add a comparison group to the simple interrupted time series design:
O1 O2 O3 O4 X O5 O6 O7 O8
O1 O2 O3 O O5 O6 O7 O8

16. Include data from hospitalization records from Kent Ohio similar population based on age and gender distribution but without a smoking ban

17. Example: Study Habits
Suppose a nonequivalent control group is added — these students don’t participate in the study habits course.
Who should be in the comparison group?
What threats are you able to rule out?

18. The Impact of Television: A Natural Experiment in Three Communities
Notel – no television
Unitel – one TV station
Multitel – four TV stations
They studied the towns twice:
once before TV came to Notel (Phase 1)
again after it had had TV for two years (Phase 2).
Changes in sex roles, cognitive scores, aggressive behavior, leisure activities all larger in Notel then in other towns.

19. Program Evaluation
Goal: To provide feedback to administrators of human service organizations in order to help them decide what services and to whom they will provide, and how to provide them most effectively and efficiently.
This is a big growth area — particularly in the field of mental health (managed health care).
Program evaluators assess needs, process, outcomes, and efficiency of social services.

20. Four Questions of Program Evaluation
Needs: Is an agency or organization meeting the needs of the people it serves? (survey designs)
Process: How is a program being implemented (is it going as planned)? (observational designs)
Outcome: Has a program been effective in meeting its stated goals? (experimental, quasi-experimental designs; archival data)
Efficiency: Is a program cost-efficient relative to alternative programs? (experimental, quasi-experimental designs; archival data)

21. Basic Research and Applied Research
Program evaluation is the most extreme case of applied research — the goal of program evaluation is practical, not theoretical.
The relationship between basic research and applied research is reciprocal:
Basic research provides scientifically based principles about behavior and mental processes.
These principles are applied in the complex, real world.
New complexities are recognized (e.g., the scientific principles may not always apply in real-world settings) and new hypotheses must be tested in the lab using basic research.

23. The social and political realities of society’s experiments
Which health care programs work
Medicare has health insurance for people over 65
Health quality partners program evaluated
Sending a trained nurse for regular home visits
Check patient’s health and medications, answered questions
Reduced hospitalizations by 33%
Cut Medicare costs by 22 %
Medicare decided to drop the program
Decided that the program was not scalable
Did not fit with current model of providing care
because of political and economic realities