COMM 88 Lecture Notes - Lecture 5: Internal Validity

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17 May 2018
School
Department
Course
Professor
4/17/18
Casual relationships between variables
X influences/affects/changes y
Different methods for testing different relationships
survey/observational research (researcher A)
Tests associations (just relationships/correlations)
measure/observe some attitudes/behaviors & correlate them or compare existing
groups of people on some measure
Great for external validity - ability to generalize results to other people
(if use representative sample) and to normal life settings (if observe or
ask people about normal behavior, etc)
Poor for causality
Experimental research (researcher B)
Tests casual connections: manipulate variables, separate people into groups and
give different “treatment” to each group; control everything else and measure
effects
Great for internal validity - ability to estimate that x causes y
Not just connection between variables but also estimate time order
(which variable came first)
Rules our extraneous (3rd) variables/causes
Poor for generalizability
Defining concepts and Variables
Independent Variable (IV)
In experiments: a “causal” variable (the cause in cause/effect relationship),
manipulated by researcher
In surveys/observational studies: a “predictor” variable (predict does NOT mean
“cause”)
Dependent variable (DV)
In experiments: an “effect” or outcome, the variable affected/changed by the IV
In survey/observational studies: a variable being predicted by the IV
Conceptualizing your variables
Defining what the concepts mean for purposes of investigation
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Document Summary

Measure/observe some attitudes/behaviors & correlate them or compare existing groups of people on some measure. Great for external validity - ability to generalize results to other people (if use representative sample) and to normal life settings (if observe or ask people about normal behavior, etc) Tests casual connections: manipulate variables, separate people into groups and give different treatment to each group; control everything else and measure effects. Great for internal validity - ability to estimate that x causes y. Not just connection between variables but also estimate time order (which variable came first) In experiments: a causal variable (the cause in cause/effect relationship), manipulated by researcher. In surveys/observational studies: a predictor variable (predict does not mean. In experiments: an effect or outcome, the variable affected/changed by the iv. In survey/observational studies: a variable being predicted by the iv. Defining what the concepts mean for purposes of investigation. Deciding exactly how the concepts will be measured (or manipulated) in a study.

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