EVAL 6970: Experimental and Quasi- Experimental Designs Dr. Chris L. S. Coryn Dr. Anne Cullen Spring 2012.
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EVAL 6970:Experimental and Quasi-
Experimental DesignsDr. Chris L. S. Coryn
Dr. Anne CullenSpring 2012
Agenda
• Basic design elements and notation• Quasi-experimental designs that
either lack a control group or lack pretest observations on the outcome
• Midterm examination• Case study
Questions to Consider
• What are the limitations of designs lacking either control groups and/or pretest observations?
• What simple strategies can be used to improve these types of designs?
• Why are such designs sometimes the only ones that can be used?
Basic Design Elements and Notation
Assignment
• Random assignment• Cutoff-based assignment• Other nonrandom assignment• Matching and stratifying• Masking
Measurement
• Posttest observations– Single posttests– Nonequivalent dependent variables– Multiple substantive posttests
• Pretest observations– Single pretest– Retrospective pretest– Proxy pretest– Repeated pretests over time– Pretests on independent samples
• Moderator variable with predicted interaction• Measuring threats to validity
Comparison Groups
• Single nonequivalent groups• Multiple nonequivalent groups• Cohorts• Internal versus external controls• Constructed contrasts– Regression extrapolation contrasts– Normed contrasts– Secondary data contrasts
Treatments
• Switching replications• Reversed treatments• Removed treatments• Repeated treatments
Notation
X = treatmentO = observationR = random assignmentNR = nonrandom assignmentX = removed treatmentX+ = treatment expected to produce an
effect in one directionX- = conceptually opposite treatment expected to reverse an effectC = cutting score- - - = non-randomly formed groups… = cohort
Logic of Quasi-Experimentation
Rationale
• Quasi-experiments are often a necessity given practical and logistical constraints– Greater emphasis on construct or external validity
rather than cause-effect associations (least common)– Funding, ethics, administration (somewhat common)– The intervention has already occurred (most common)
• Sometimes they are the best alternative, even if causal inferences are weaker than is possible with other designs
• Even so, great care must be taken when planning such studies as numerous threats that cannot be controlled are often operating
Central Principles
• Identification and study of plausible threats to internal validity– Careful scrutiny of plausible alternative
explanations for treatment-outcome covariation
• Primacy of control by design– Use carefully planned and implemented design
elements rather than statistical controls for anticipated confounds
• Coherent pattern matching– Complex (a priori) causal hypotheses that
reduce the plausibility of alternative explanations
Designs without Control Groups
One-Group Posttest Only Design
• Absence of pretest makes it difficult to know if change has occurred and absence of a control group makes it difficult to know what would have happened without treatment
X O1
One-Group Pretest-Posttest Design
• Adding a pretest provides weak information concerning what might have happened to participants had the treatment not occurred
O1 X O2
One-Group Pretest-Posttest Design with Double Pretest
• Adding multiple pretests reduces the plausibility of maturation and regression effects
• Additional pretests can confirm maturational trends
O1 O2 X O3
One-Group Pretest-Posttest Design Using a Nonequivalent Variable
• Measure A is expected to change because of treatment, B is not
• Both A and B are expected to respond to the same validity threats in the same way
{O1A , O1B} X {O2A , O2B}
• Lottery ticket sales in convenience stores after introduction of signs in store windows reading “did you buy your ticket?”
A = sale of lottery ticketsB = sale of alcoholC = sale of tobacco
A
A
B
B
CC
Removed-Treatment Design
• Demonstrates that outcomes rise and fall with the presence or absence of treatment
O1 X O2 O3 X O4
• Generally interpretable outcome pattern
O1
Outc
om
e
Wors
eB
ett
er
O2 O3 O4X X
Uninterpretableoutcome
Interpretableoutcome
Repeated-Treatment Design
• Few threats could explain a close relationship between treatment introductions and removals and parallel outcome changes
O1 X O2 X O3 X O4
• Mean narcotics use over multiple Methadone maintenance on/off conditions
off MM on MM #1
off MM on MM #2
off MM on MM #3
off MM on MM #4
off MM on MM #5
0%
10%
20%
30%
40%
50%
60%
70%
A-B Designs
• Multiple-baseline design (a class of single-subject designs), or collection of A-B designs, to assess the effects of an intervention across separate baselinesA = baselineB = treatment
• The intervention is introduced in a staggered manner and the baseline provides a predicted level of the dependent variable in absence of the treatment
• A-B-A designs are sometimes called removal designs (i.e., the treatment is removed)
Nu
mb
er
of
Accid
en
ts
Weeks
Baseline
Baseline
Baseline
Treatment
Treatment
Treatment
Sit
e 1
Sit
e 2
Sit
e 3
Effect
Effect
Effect
Designs that use a Control Group but no Pretest
Posttest-Only Design with Nonequivalent Control Group
• Unknown pretest group differences make it extremely difficult to separate treatment effects from selection effects
NR X O1
NR O1
Posttest-Only Design using an Independent Sample Pretest
• Assumes overlapping group membership• Useful when pretest measurements may
be reactive, cannot follow same groups over time, or when interested in studying intact communities whose members change over time
NR O1 X O2
NR O1 O2
Posttest-Only Design using Proxy Pretest
• Proxy measures should be conceptually related to and correlated with outcome
• Can be used for a variety of purposes including indexing selection bias and/or attrition
NR OA1 X OB2
NR OA1 OB2
Case Control Studies
• Predominant method for many forms of epidemiological research
• Used to identify factors that may contribute to a condition by comparing subjects who have that condition (i.e., 'cases') with those who do not have the condition but are otherwise similar (i.e., 'controls')
• Famously, the association between smoking and lung cancer
• Similar in many respects to Scriven’s GEM and MOM
Midterm Examination
Midterm Examination
• The examination will consist of 50-75 multiple-choice items, scored as 0 or 1
• You will have 2½ hours to complete the examination
• You may use one page of notes (front and back) on 8½” X 11’’ paper– You will be asked questions about
statistical power, but will not be required to calculate power
Case Study
Case Study Activity
• An aid agency implemented a project in Bangladesh with the objective of improving the nutritional and health status of men and women
• The intervention consisted of a package of services including: nutrition education, primary health care, and other activities
• To determine whether the intervention might be effective, the project was field-tested in a small rural community prior to large-scale implementation throughout the country
• A small monetary incentive was provided and slightly more than half of the community’s men and women participated in the study
• All men and women in the community were weighed prior to the intervention and then were measured for body mass index (BMI) six months after the intervention
• Those who did not participate were used as a control group and the evaluators found significant improvements in nutritional and health indicators for the treatment group contrasted with the control
Questions
• What is the design of the study?• What internal validity threats are
most plausible?• How might the design feasibly be
improved?
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