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RESEARCH METHODS
How do we know when we know
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Outline
What is Research Measurement Method Types Statistical Reasoning Issues in Human Factors
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What is Research
PurposeTo learn somethingTo base reasoning on evidence instead of
merely our own assumptions Scientific vs. Nonscientific Research
How one gathers evidenceEvidence in:
○ History○ Math○ Chemistry
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Measurement: General Definition: to put a number on an
observation e.g.: thermometer, IQ Why?
Allows easier comparisonThe inherent ambiguity of language
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Characteristics of Good Measurement Reliability
Consistency in measurementTake repeated measures, get same value
ValidityMeasure what think measure.
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Validity Types
EcologicalMatch to situation
Internal:The study is well designedThe conclusions regarding theory can be
made External:
The results apply to the desired populationImportant in Human Factors Research
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Example: Lighting Study
100 fC Illumination
10,000 fC Illumination
1000 fC Illumination
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Method Types
Descriptive Correlational Experimental
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Descriptive
Why Use?e.g. Anthropometric data
Archival Data Observational Methods
Interobserver Agreement
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Correlational
Measure patterns of relationship Prediction Laws Correlation does not imply causation
Why?
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Experimental
Manipulation Independent Variable Dependent Variable Causation
Requirements:○ Temporal Order○ Co-variation (Correlation)○ Rule out All Alternatives
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Statistical Reasoning
Elements Variation in Data
ErrorPossible influence of IV
Question:Is variation in data due to error?Is variation in data due to error and IV?Sound familiar? Signal Detection Theory
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Statistics and Signal Detection Theory Alpha = criterion Type I error: probability of concluding
there is an effect when there is not one = False AlarmUse Alpha to set this probability
Type II Error: Probability of not concluding there is an effect when there is one = Miss
Effect Size = d’
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Statistical Hypotheses
These are what are tested by stats – not theories
H0: Null Hypothesis: only error is making data vary
Ha: Alternative Hypothesis: error and IV are making data vary
Stats give you p value or sig value = p(H0) is true
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Proper uses of Stats
Are they necessary with large effect sizes (d’)?
What do you do if p > alpha? What do you do if p < alpha? What does it mean to Reject H0? Do you ever accept H0? If you reject H0 have you analyzed your
data?