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Experiments
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Correlational Studies
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Correlational
Studies
Analyses co-variables
relationship
Cause + effect inferred
Observations or self-report data to generate ordinal data
only one does
RMD
Spearman RHO
Identifies 4 correlation types: positive (direct proportion), negative (inverse), curvilinear + zero
Correlation coefficient displays correlations' strength/direction on scatter graph (+1 to -1)
3 Interpretations: cause + effect, 3rd variable (other cause), or chance (not related)
Natural variables, little manipulation; less contrived, more valid + realistic
Investigate situations experiment can't; indicate strength + relationship type
Indicate relationship, not cause + effect, less valuable
At least 1 measured variable has to be operationalised; artificial + contrived, may obscure other factors (validity issue)
Correlational Studies Evaluation
Strengths:
Little
variable manipulation
Occur naturally
Measured from existing situations
Less contrived
Less control is needed
More valid and realistic
Allow researchers to investigate situations that
experiment
can't
Also indicate the strength and type of relationship
Weakness:
Indicate relationship, not cause and effect
Scientific knowledge needs
causal connections
Cause is more valuable
Validity issues
At least one measured variable has to be
operationalised
Often quite artificial and contrived
May obscure other factors
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