Som Nwegbu and Binh Le
H615: Advanced Evaluation and Research Design
October 18, 2013

Quasi – “seemingly; apparently but not really.”
Synonyms: supposedly, seemingly, apparently, allegedly, ostensibly, on the face of it, on the surface, to all intents and purposes, outwardly, superficially, purportedly, nominally”
Experiment – “A test under controlled conditions that is made to demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy of something previously untried.”
Quasi Experiment – Why is it ‘quasi’ ?
It’s “…an experiment in which units are not assigned to conditions randomly.” (SCC)

Lack a Control Group or Lack Pretest Observations on
Outcome
Why use designs?
Devote more resources to construct validity and external validity
Necessities imposed by funding, ethics, administrators, or logistical constraints
Sometimes the best design for the study, even if causal inference might be weaker

Causal inference must meet requirements: That cause precede effect, that cause covary with effect, and alternative explanations unlikely.
• Randomized and quasi-experiments manipulate treatment to force it to occur before the effect
• Covariation between cause and effect accomplished during statistical analysis
• Alternative explanations implausible by ensuring random distribution
Identification and study of plausible threats to internal validity
Primary of control by design
Coherent pattern matching

Examples…
Weaknesses…
One-Group Posttest-Only with Multiple Posttests
X
1
{O
1A
O
1B
…O
1N
}
Examples…
Weaknesses…
One-Group Posttest-Only
X O
1

One-Group Pretest-Posttest
O
1
X O
2
Examples…
Weaknesses…
One-Group Pretest-Posttest Using Double Pretest
O
1
O
2
X O
3
Examples…
Weaknesses…
One-Group Pretest-Posttest Using Nonequivalent Dependent Variable
{O
1A
, O
1B
} X {O
2A
, O
2B
}

O
1
Examples…
Weaknesses…
X
Removed-Treatment
O
2
O
3
χ O
4
X
Repeated-Treatment
O
2
χ O
3
X O
4
O
1
Examples…
Weaknesses…

Posttest-Only Design with Nonequivalent Groups
NR
NR
X O
1
O
2
Posttest-Only Design Independent Pretest Sample
NR
NR
O
1
O
1
|
| X O
2
O
2
Posttest-Only Design Proxy Pretests
NR
NR
O
A1
O
A1
X O
B2
O
B2

Using Matching or Stratifying
Internal Controls
Multiple Control Groups
Predicted Interaction

Regression Extrapolation Contrasts
Compares obtained posttest score of the treatment with the score predicted from other information
Normed Comparison Contrasts
Treatment group at pretest and posttest compared with published norms
Secondary Source Contrasts
Construct opportunistic contrasts from secondary sources

Also called case-referent, case-comparative, case-history, or retrospective design
One group of cases with outcome of interest and another group of controls without outcome
Typically dichotomous outcome
Generating hypotheses about causal connections
More feasible than experiments in cases, logistically easier to conduct, decrease risk of participants, and easy examination of multiple causes

Methodological problems
Decision on what counts as the presence or absence of an outcome
Disagreement about the decision, if they do, assessing the outcome may be unreliable or low validity
Selection of control cases is difficult
Randomly sampled controls are ideal but when not feasible, matching is the next option
Matching controls can still differ from cases in unobserved ways

Reading on the field(5):
One-sided reference bias
Positive results bias
Hot stuff bias
Specifying and selecting the study sample(22):
Diagnostic access bias
Unacceptable disease bias
Membership bias
Executing the experimental maneuvers (5):
Contamination bias
Withdrawal bias

Measuring Exposures and Outcomes (13):
Underlying cause bias
Expectation bias
Attention bias
Analyzing the Data (5):
Scale degradation bias
Tidying-up bias
Interpreting the Analysis (6):
Magnitude bias
Significance bias
Correlation bias

Addresses the issue of bias resulting from nonrandom selection
NB: However, ‘no difference’ between intervention and control groups at pretest does not guarantee zero selection bias.
Gives us a baseline to compare against (statistical analysis)

We cannot assume that any covariates unaccounted for, but present at pretest, are unrelated to outcome
In a randomized experiment, this would have been controlled for by random selection.

NR = Non-random assignment
X+
01 = pre-test
02 = second pre-test (if any)
03 = post-test
X = Test/intervention
} = Reversed treatment
X-

Untreated Control Group Design with
Dependent Pre-test & Post-test samples
NR O1 X O2
NR O1 O2
Most common and most basic used
Others (to come) are an attempt to improve internal validity and vary depending on context and resources available to the researcher.
Why do you think this is the most commonly used?

• Selection-maturation (various subtypes)
Pretest difference b/w intervention and con groups increases when intervention leads to improvement in group that was better to begin with
• Selection-instrumentation
Detectable pretest difference between intervention and control groups pretest started at different points e.g., one group starts at Q50 and another at
Q1
• Selection-regression??? Pg 139***
• Selection History
Events occurring midway b/w pre and posttest affect one group more than the other, thus widening/narrowing the observed pretest difference.

Possible – might have occurred, but highly unlikely.
Plausible – most probably did occur
We want to be able to, as much as we can, rule out all the
possibles and pursue the plausibles
Question:
How do we know which cases to worry about (plausibles) and which we can safely ignore (possibles)? (discuss)

Analyze results in context
Outside of your study, what do you already know about the threats?
What is the observed pattern of outcomes:
Groups grow apart in the same direction
No change in control group
Initial pretest difference (in favor of the treatment group) but then diminishes over time
Initial pretest difference (in favor of control group) but then diminishes over time
Outcomes that crossover (you wish!)

In view of the sub-types of sub-maturation threat, one must be prepared to present and justify a study’s assumptions about maturational differences (if using the basic quasi-experimental design).

Ways to improve on the internal validity of inferences made using the basic design:
(1) DOUBLE PRETEST
NR O 1 O 2 X O 3
NR O
1
O
2
O
3
Exposes selection maturation where present
Helps reveal regression effects if present
Helps statistical analysis by establishing more precise correlation between observations. Put simply, it gives us a baseline to compare against.

NR O
1
X O
2
NR O 1 O 2 X
O
O 3
3
• Treatment is administered at a later time point for the group that initially served as a control.
• May be employed where it would be unethical to withhold treatment/intervention (particularly if the treatment has been proven to be beneficial).
• Helps test both internal and external validity. (Discuss)

NR O
1
X + O
2
NR O 1 X ─ O 2
• Advantageous, particularly in terms of potential for improved construct validity. (Discuss)
• Allows for ruling out potential of Hawthorne effect.
• Assumptions/weakness – This design depends on the assumption that there are no historical or other extrinsic behavior modifying events occurring while the study is ongoing.

Researcher tries to conceive of every possible threat to validity and then put in checks to reduce such threats.
Demerits?
Merits?

Cohort (in this context) –
“…a group of subjects who have shared a particular event together during a particular time span e.g., people born in Europe between 1918 and 1939.”
Benefits?
Cost
Convenience
Allows one to make good use of well kept records where available
Others as outlined in SCC page 149 [paragraph 1]

(Don’t you mean more complicated?)
DESIGNS THAT COMBINE MANY DESIGN ELEMENTS
• Depending on context and need, one may use combinations of the above examples, or further variations, to improve internal validity and enable causal inference (or something close).
• Be prepared to explain and defend it though.

Improving Statistical conclusion validity
HDFS 532 (or equivalent) highly recommended
Topics such as, SEM and Selection Bias Modeling covered in detail.
Further comments or contributions?

We can never be 100% certain of our claims when using quasi-experimental designs and we must be prepared to own it and state our limitations upfront.
Still, there are numerous ways to strengthen the validity of our claims and these can be applied at different stages of the research study:
Assignment
Measurement
Use of comparison groups
Treatment
Statistical Analysis