Soc. 2155 Week 3
Causation and Experiments
I. Causation
– Relationships between variables
– Types of association
– Criteria for causality
II. Experiments – testing cause and effect
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Explanatory research
True experimental designs
Quasi-experimental designs
Internal validity
External validity
Ethical issues
Strengths and weaknesses
Association = relationship
• Does not have to be causal.
• Positive association = as X increases, Y
increases.
• Negative association = as X increases, Y
decreases.
• Qualitative variables: presence of X
predicts presence or absence of Y.
Which associations could be
causal?
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Years work experience/ income
# churches / # bars in a town
Cigarette smoking/ lung cancer
# firefighters called to fire/ $ amount of
damage
• Race/ poverty
Spurious association = apparent association caused by a third factor
Cause = necessary and sufficient
condition
Necessary: X must be present in order for Y
to follow.
(ex: to get an “A” it is necessary to complete all
assignments).
Sufficient: If X occurs, Y must follow.
(ex: if you get 100% on every assignment, you will get an
“A” in the class.)
3 criteria for causality
X causes Y if:
• X precedes Y in time
• X and Y are statistically associated
• All other potential causes of Y have been
ruled out.
Additional Criteria
• Mechanism – connection between “cause”
and “effect” – how the cause operates to
produce the effect.
• Context – situations, groups, places,
conditions, etc. In which the cause
produces the effect.
Determinants/ partial causes
Most sociological phenomena have multiple
causes. “Determinant” = partial cause or
predictor. Not a complete cause.
Example: Some determinants of income:
Education
Occupation
Marital status
Skill
Gender
Talent
Training
Race
Personality
Experience
Geographic area
Job duties
Intelligence
Industry
Type of company
Types of Causes
Nomothetic Cause – General explanation of
a class of phenomena. (e.g., causes of
terrorism, crime)
Idiographic Cause – Specific event or
sequence of events. (e.g., causes of 9/11
attacks, sudden rise in crime rates) May
be historical in focus.
Multivariate Relationships
Z
X
Y
X
Z
Y
Z intervenes B/T X and Y OR Z
“explains” relationship B/T X and Y
Z as spurious cause of X and Y
Z
X
Y
Z
X
Y
Direct and indirect effects
Multiple causes (determinants) of Y
Experiments
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Explanatory research
True experiments
Experimental designs
Quasi-experimental designs
Internal validity
External validity
Ethical issues
Strengths and weaknesses
Explanatory Research
• Purpose: to explain, to determine
cause/effect
• What is explained?
Variation in the dependent variable
• What can be studied in an experiment?
Limited, narrow causal relationships
Variables that can be studied in lab
Topics for which theory has been developed
True experiment includes
• Two groups (experimental and control)
• Random assignment to groups
• Variation in independent variable
(manipulated by researcher)
• Measurement of dependent variable
The groups
• Experimental group – is exposed to
independent variable (I.V.)
• Control group - is not exposed to I.V.
• I.V. is the only difference between the
groups
• Any differences in dependent variable
(D.V.) must be due to I.V.
Assignment to groups
• Randomization
– Easy to carry out
– Can control for unmeasured or uncontrolled
factors
• Matching
– Specific characteristics matched in both groups
– May be very precise
– Requires knowledge of relevant characteristics
– May not control for omitted factors
Pretesting
• Measures D.V. before experiment
• Establishes comparability of experimental
and control groups
• Provides baseline for comparison with
posttest
• May teach or “clue in” subjects (pretest
effect)
• Costs extra
Experimental Designs
Classic Pretest-Posttest-Control-Group
Groups
Pretest
I.V.
Uncontrolled Posttest
factors
Change
Exper.
O1
X
X
O3
O3-O1
Control
O2
X
O4
O4-O2
Effect of I.V. = (O3-O1) – (O4-O2)
Experimental Designs
Posttest-Only
Groups
Pretest
I.V.
Uncontrolled Posttest
factors
Change
Exper.
N/A
X
X
O1
N/A
Control
N/A
X
O2
N/A
Effect of I.V. = (O1-O2)
Eliminates effect of pretest
Experimental Designs
Solomon four-group
Groups
Pretest I.V.
Uncontrolled
factors
Posttest
Change
Exper. 1
O1
X
O3
O3-O1
Control 1
O2
X
O4
O4-O2
X
O5
X
O6
Exper. 2
Control 2
X
X
Effect of I.V. = (O3-O1) – (O4-O2) or (O5-O6)
Effect of pretest = (O3-O5) or (O4-O6)
Quasi-Experimental Designs
• May be used when true experiment isn’t
possible
• Usually involve fewer controls
– No control group
– Approximately equivalent control group
– May take place in the field
– May be “ex post facto:” designed after the
“treatment”
Internal Validity
Source of Invalidity
Solution
History – outside events
Control group
Maturation – changes in subjects
Control group
Testing – subject may learn
Control group
Instrumentation - measurement
Control group
Statistical regression - moderation
Control group
Selection bias- groups not comparable
Randomization
Mortality – dropping out
Randomization
Contamination (competition, demoralization)
Randomization
Treatment misidentification (experimenter
expectations, placebo effect, Hawthorne effect)
Randomization, double
blind, process analysis
External Validity
• Generalization to “real world”
• Often a problem in experiments
• 2 main issues
– Would sample subjects behave same way
outside lab?
– Cross-population generalizability: would
findings hold for different groups, times,
places?
Ethical Issues
• Deception (misleading subjects about
purpose of experiment)
• Selective distribution of benefits (also
risks, harm)
Experiments’ Strengths and
Weaknesses
Strengths
Isolation of cause/effect
High internal validity
Easy to replicate
Best used for explanatory
studies (testing of
hypotheses)
Weaknesses
External validity may be low
or undetermined
Ethical issues
High cost per subject