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Varieties of Research Designs
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3x3 Structure for single-IV designs– (3) Design differences & causal interpretability– (3) Design differences & statistical models
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Operational Definitions & “kinds” of IVs
Varieties of Single-Factor Research DesignsCausal
Statistical Design
Interp.
BG
WG
MG
True-ExpQuasi-ExpNat. Grps
Varieties of Research Designs -- Causal Interpretability• True Experiment• Quasi - Experiment• Natural Groups Design
-- also called concomitant measurement
design, natural groups design,correlational design, etc.
Note:
Choice of ANOVA is not influenced by which of these types of designs is used -- only the causal
interpretability of results.
Basic properties of a …True Experiment• individual participants are randomly assigned to
conditions of the IV by the researcher beforemanipulation of the IV
- IV is manipulated by researcher• DV is measured by experimenter• try to maintain procedural control to minimize confounds of
on going equivalence
- field studies and longer-term studies make this more
difficult
Basic properties of a …Quasi - Experiment• intact groups are assigned to IV conditions (hopefully
randomly by the researcher – but some variability inthe definition!) before manipulation of
the IV
- IV is manipulated by the researcher (again some
variability in the definition!)
- DV is (sometimes) measured by experimenter• procedural control is usually limited or absent
- usually longer-term field studies• usually “intruding & manipulating” on some ongoing
process
Intact groups …an “intact group” is assembled by any process other than by
random assignment by the researcher Examples:•^
state, county, town, block where you live
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hospital, clinic, center
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school, class, section Why randomizing intact groups doesn’t produce initial
equivalence,
There is some “reason” folks are in the groups they are --not random or independent assignment
There is no reason to believe that different groups haveinitial equivalence relative to each other
So, randomly assignment groups doesn’t endure initialequivalence of individuals Often referred to “unit of assignment” (groups) not matching the
“unit of analysis” (individuals)
Candidates for Matching Variables• Subject/measured variables that are known or likely potential
causal influences on the DV (besides the IV)
- e.g., age, prior performance, SES, personality trait• if the groups are equivalent on a variable, by matching, it
can’t be a confounding variable
- a “pretest” on the DV is often a very good matching variable
- if the groups are equivalent on the DV before the
manipulation, then whatever “confounds” were operating onthat DV are expected to be continue operating equivalentlyduring the study
- often this is more available than other variables
- Procedural variables can also be included (formally called
“yoking”)
- e.g., “treatment deliverer,” location, number of exposures
Remember, you must:• have a good reason for using each matching variable
- the more matching variables the harder it is to make a match
- get good measures on the matching variable
- avoid “proxy” variables whenever you can
- have a large enough sample to form a useful number of good
matches •there’s a trade-off between the “exactness” of the matches
and the number of matches you can make
- get the matching variable measured “before hand” so you can
form the matches before time to manipulate the IV (or it be“naturally manipulated”)
Which ANOVA for which design?What we’ve called “Between Groups ANOVA” is more properlycalled “ANOVA for Independent Groups”
- different participants are in different conditions – so thescores in the different conditions are “independent” What we’ve called “Within-Groups ANOVA” is more properlycalled “ANOVA for Dependent Groups” - the same participants are in all conditions – so the scores inthe different conditions are “dependent” So, which ANOVA for Matched Groups ?? - different participants in different conditions, but they areassembled into matched groups, so… the scores in thedifferent conditions are “dependent”• Dependent Groups or Within-Groups ANOVA is used for
Matched Groups designs
Kinds of Independent Variables …Manipulated by the Experimenter
-- required for causal interpretability of the results-- not all IVs can be manipulated-- limited by technology, ethics, cost, ingenuity
Measured by the Experimenter
-- results are not be causally interpretable
Having the these two types of IVs means you have to paycareful attention to the operationalization of the IV &sometimes have to be specific about which variable is the IVand which is the DV (especially since Psychologists can bevery clever about finding ways to manipulate IVs)
e.g.,
Mood and Performance
Version #1 RH: Mood influences PerformanceUpon entering the lab, each subject completed a questionnairethat was used to assign them to either the “good mood” or the“poor mood” condition. Each subject then completed a batteryof complex concept formation tasks, from which a performancescore is determined.IV ??
Type ??
DV ??Causally Interpretable ??
Version #
RH: Mood influences Performance
Upon enter the lab, each subject was approached by aconfederate of the researcher who sat next to them and (basedupon the results of a coin-flip) either complimented their dressand appearance, etc., or “accidentally” knocked over their books,spilled a drink on them, etc. Each subject then completed abattery of complex concept formation tasks, from which aperformance score was determined.IV ??
Type ??
DV ??Causally Interpretable ?? Was “mood” operationalized the same in the two studies?Which version has better internal validity? … external validity?
