Quasi Experimental

DesignsChapters 4 & 5

Som Nwegbu and Binh LeH615: Advanced Evaluation and Research Design

October 18, 2013

A Few Helpful Definitions

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)

Quasi-Experimental Designs

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

Logic of Quasi Experiments

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 validityPrimary of control by designCoherent pattern matching

Quasi Experimental Designs w/o Control Groups

One-Group Posttest-Only X O1

Examples…

Weaknesses…

One-Group Posttest-Only with Multiple Posttests

X1 {O1A O1B…O1N}Examples…

Weaknesses…

Quasi Experimental Designs w/o Control Groups

One-Group Pretest-PosttestO1 X O2

Examples…

Weaknesses…

One-Group Pretest-Posttest Using Double PretestO1 O2 X O3

Examples…

Weaknesses…

One-Group Pretest-Posttest Using Nonequivalent Dependent Variable

{O1A , O1B} X {O2A , O2B}

Quasi Experimental Designs w/o Control Groups

Removed-TreatmentO1 X O2 O3 χ O4

Examples…

Weaknesses…

Repeated-TreatmentO1 X O2 χ O3 X

O4

Examples…

Weaknesses…

Quasi-Experimental Designs w/Control Groups but no

PretestPosttest-Only Design with Nonequivalent

Groups

NR X O1NR O2

Posttest-Only Design Independent Pretest Sample

NR O1 | X O2NR O1 | O2

Posttest-Only Design Proxy Pretests

NR OA1 X OB2NR OA1 OB2

Improving the Posttest-Only DesignUsing Matching or Stratifying

Internal Controls

Multiple Control Groups

Predicted Interaction

Constructing Contrasts other than with

Independent GroupsRegression Extrapolation ContrastsCompares obtained posttest score of the treatment with the score predicted from other information

Normed Comparison ContrastsTreatment group at pretest and posttest compared with published norms

Secondary Source ContrastsConstruct opportunistic contrasts from secondary sources

Case Control Design

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

Case Control Design

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 difficultRandomly sampled controls are ideal but when not feasible, matching is the next optionMatching controls can still differ from cases in unobserved ways

Threats to ValidityReading on the field(5):

One-sided reference biasPositive results biasHot stuff bias

Specifying and selecting the study sample(22):

Diagnostic access biasUnacceptable disease biasMembership bias

Executing the experimental maneuvers (5):Contamination biasWithdrawal bias

Threats to Validity

Measuring Exposures and Outcomes (13):Underlying cause biasExpectation biasAttention bias

Analyzing the Data (5):Scale degradation biasTidying-up bias

Interpreting the Analysis (6):Magnitude biasSignificance biasCorrelation bias

Quasi-experimental Designs that Use

Both Control Groups and Pretests

Benefits of a PretestAddresses the issue of bias resulting from non-random 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)

Limitations of a Pretest

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.

Key: NR = Non-random assignment 01 = pre-test 02 = second pre-test (if any) 03 = post-test X = Test/intervention

X+ } = Reversed treatmentX-

Untreated Control Group Design with Dependent Pre-test & Post-

test samples NR O1 X O2 NR O1 O2

Most common and most basic usedOthers (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?

Limitations and/or weaknesses

aka ‘Threats to internal validity’• 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-instrumentationDetectable 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 versus plausible threat

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)

Answer: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 directionNo change in control groupInitial pretest difference (in favor of the treatment group) but then diminishes over timeInitial pretest difference (in favor of control group) but then diminishes over timeOutcomes that crossover (you wish!)

Point to noteIn 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 O1 O2 X O3

NR O1 O2 O3

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.

(2)SWITCHING REPLICATIONS

NR O1 X O2 O3

NR O1 O2 X O3

• 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)

(3) REVERSED TREATMENT-CONTROL GROUP

NR O1 X+ O2

NR O1 X─ O2

• 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.

(4) DIRECT MEASUREMENT OF THREAT

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

Demerits?

Merits?

MATCHING USING COHORTS AS CONTROLS

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]

It gets even better. . .

(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.

Moving on from threats to Internal validity to:

Improving Statistical conclusion validityHDFS 532 (or equivalent) highly recommended

Topics such as, SEM and Selection Bias Modeling covered in detail.

Further comments or contributions?

To wrap upWe 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