Primary Data Collection:
Experimentation
Chapter 7
What is an Experiment?Example of a magazine company printing
two cover designs and evaluation in the office
Example of the same magazine company printing two cover designs and measuring sales in two different cities
Maker of Grape Jelly trying various formulations
Laboratory Experiment
Field Experiment
Study in a realistic Situation –
Natural setting
Study in a controlled Situation –
outside the natural setting
Experiment
ExperimentsStudies in which conditions are controlled so
that one or more independent variable can be manipulated to test a hypothesis about a dependent variable. Randomization.
Manipulation of A treatment variable (x), followed by observation of response variable or dependent variable (y).
Goal is to obtain an experimental effect.Experiment must be designed to control for
other variables to establish causal relationship.
Causal relationship is keyManipulation of variable(s) to observe the
effect on another variableConditions for causality
Concomitant VariationTemporal orderSpurious factors
Correlation vs. Causation
Observing an association
If X, then Yand
If not X, then not Y
Non-spuriousWe say that a relationship between two variables
is spurious when it is actually due to changes in a third variable, so what appears to be a direct connection is in fact not one.
i.e. If we measure children’s shoe sizes and their academic knowledge, for example, we will find a positive association.
Does that mean that shoe size causes academic knowledge?
What about this?Do schools with better resources produce better
students?
Correlation vs. CausationCorrelation= degree of association between two
variableThey must vary together: when one goes up (or down)
the other must go up (or down)Linear relationshipThe correlation coefficient can range between +1 and
-1. Positive values indicate a relationship between X and
Y variables so that as X increases so does Y. Negative values mean the relationship between X and
Y is such that as values for X increase, values for Y decrease.
A value near zero means that there is a random, nonlinear relationship between the two variables
r- coefficient of correlation
Experimental Setting- IssuesNotationDesign and TreatmentExperimental EffectsControl groups vs. Experimental groups.
Basic IssuesControl Factors
RandomizationStatistical Control
Experimental ValidityInternal ValidityExternal Validity
Basic Symbols and NotationsO denotes a formal observation or measurement
X denotes exposure of test units participating in the study to the experimental manipulation of treatment
EG denotes an experimental group of test units that are exposed to the experimental treatment.
CG denotes a control group of test units participating in the experiment but not exposed to the experimental treatment.
R denotes random assignment of test units and experimental treatments to groups. Increases reliability
Experimental Designs
One Group, After-only Design
EG X O1
Two Group, After-only Design
EG X O1
- - - - - - - - - - - - - -
CG O2
Experimental Designs (Contd.)One-group Before-After Design
EG O1 X O2
Two-group, Before-after Design
EG O1 X O2
- - - - - - - - - - - - - -
- CG O3 O4
True-experimental DesignsTwo-group After-only Design
EG R X O1
- - - - - - - - - - - - - -
- CG R O2
True-experimental DesignsTwo-group Before-After Design
EG R O1 X O2
- - - - - - - - - - - - - -
- CG R O3 O4
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Internal ValidityThe degree to which plausible alternative
causes have been controlled forAre the observed effects on the D.V. a
cause of the treatment? Or could they have been caused by something else?
Internal validity
Threats to Validity
HistoryTreatmentMaturationInstrument VariationSelection BiasMortalityTesting EffectsRegression to the Mean
Threats to Internal ValidityHistory: Events external to the experiment that affect responses of the people involved in the experiment (weather, news reports, time of day)
-The “cohort effect”: members of one experimental condition experience historical situations different from othersExample: Linda McCartney’s death might have affected responses to breast cancer PSAs more for her age cohort; Members of the WW II generation are more responsive to calls for volunteerism and community activism
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Threats to Internal Validity
Treatment Effect: Awareness of being in the test causes subjects to act different than they otherwise would
Types of treatment effects:The Hawthorne Effect: special attention
received in experiment produces the resultDemand Effect: awareness of test produces
response desired by researchers
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Threats to Internal ValidityMaturation: Changes in respondents over
the time period of the experiment (maturing, getting hungry, getting tired)
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Threats to Internal ValidityTesting Effect: A before treatment
measurement sensitizes subjects to the treatment
Example: Colon Cancer PSA (phoning subjects for pre-test measurements may have sensitized subjects to ads that appeared on TV)
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Threats to Internal ValidityInstrumentation Effects: The measuring
instrument may change, different interviewers may be used, or an interviewer or confederate gets tiredA common case: order of presentation
produces an effect Example: consumers may prefer first product tasted if
they can’t tell the difference
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Threats to Internal ValidityMortality (or attrition): Some subjects drop
out of the experiment between measurements.
Those subjects who drop out may differ from those who stay
Example: testing a weight-loss program
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Threats to Internal Validity
Selection Bias: An experimental group is different from control groups
For convenience, many experimental studies have self-selected subjects
random assignment to treatments will solve this
Example: Latin students
External validity
Experimentation: Pros and ConsBest method to evaluate causationCostsSecurityImplementation Issues
Steps for starting a good design1. Select problem2. Determining dependent variables3. Determining independent variables4. Determining the number of levels of
independent variables5. Determining the possible combinations6. Determining the number of observations8. Randomization