Transcript 5_sampling2

Sampling (conclusion) &
Experimental Research Design
Readings: Baxter and Babbie, 2004,
Chapters 7 & 9
Issues in Non-probability sampling
Bias?
 Is the sample representative?
 Types of sampling problems:
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 Alpha:
find a trend in the sample that does not
exist in the population
 Beta: do not find a trend in the sample that
exists in the population
Principles of Probability Sampling
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each member of the population an equal chance of
being chosen within specified parameters
Advantages
 ideal
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for statistical purposes
Disadvantages
 hard
to achieve in practice
 requires an accurate list (sampling frame or operational
definition) of the whole population
 expensive
Types of Probability Sampling
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1. Simple Random Sample
 With
replacement
 Without replacement: link
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2. Systematic Sample (every “n”th person) With Random Start
 Urban
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studies example)
3. Stratified Sampling:
 Sampling
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Disproportionately and Weighting
4. Cluster Sampling
Examples of sampling issues &
techniques
Survey about football (soccer) market
 Rural poverty project and sampling issues
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Postpone: Techniques for
Assessing Probability Sampling
We will discuss these in connection with
Chapter 11 material:
 Standard deviation
 Sampling error
 Sampling distribution
 Central limit theorem
 Confidence intervals (margin of error)
Introduction to Experimental Design
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Recall discussion of
experiments in lecture on
Research Ethics
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Milgram experiment (on
obedience)
Stanford prison experiment
about how prisons as
institutions communicate
roles and shape actions
(still photo from video on
right showing research
subjects dressed as prison
guard & prisoners)
Trends in Experimental Social
Research
types of subjects & reporting style (naming
vs. anonymity)
 deception & risk
 debriefing
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Single & double Blind Experiments
Neuman (2000: 239)
Key Notions / Terms
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Treatment, stimulus, manipulation (independent
variable)
observable outcome (dependent variable)
Experimental Group
Control group
pretest (measurement before treatment)
posttest (measurement after treatment)
Random Assignment
Neuman (2000: 226)
Comparison with Random Sampling
Neuman (2000: 226)
How to Randomly Assign
Neuman (2000: 227)
Experimental Design Notation
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O= observation
X= treatment
R= random
assignment
Some Common Types of Design
Three common types of
experimental design: Classical
pretest-post test –
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Total population randomly divided into
two samples;
 control
sample
 experimental sample.
Only the experimental sample is exposed
to the manipulated variable.
 compares pretest results with the post test
results for both samples.
 divergence between the two samples is
assumed to be a result of the experiment.
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Solomon four group design –
The population is randomly divided into four
samples.
 Two of the groups are experimental samples.
 Two groups experience no experimental
manipulation of variables.
 Two groups receive a pretest and a post test.
 Two groups receive only a post test.
 improvement over the classical design because it
controls for the effect of the pretest.
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Factorial design –
similar to a classical design except
additional samples are used.
 Each group is exposed to a different
experimental manipulation.
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Factorial
Design
Validity Issues
internal validity: elimination of plausible
alternative explanations
 external validity: ability to generalize
(outside the experiment)
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Internal Validity Threats
selection bias: groups not equivalent
 history: unrelated event affects exp.
 maturation: separate process causes effects
 testing: ex. Pretest effects
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More Internal Validity Threats
instrumentation: measure changes
 mortality/attrition
 statistical regression : ex. Violent films
 contamination
 compensatory behaviour
 experimenter expectancy
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External Validity Threats
realism
 reactivity:
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 Hawthorne
effect
 novelty effect
 placebo effect
Laboratory vs. Field experiments
lab.- more control , higher internal validity
 field- more natural, higher external validity
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Recall : New Ethical Norms
protection of subjects
 debates about deception
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