primary data collection: experimentation chapter 7

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