# Measurement: Reliability and Validity For a measure to be useful, it must be both reliable and valid Reliable = consistent in producing the same results.

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Measurement: Reliability and Validity For a measure to be useful, it must be both reliable and valid Reliable = consistent in producing the same results every time the measure is used Valid = measuring what it is supposed to measure

Reliability Observed Score = true score + systematic error + random error Observed Scores are the data gathered by the researcher True Scores are the actual unknown values that correspond to the construct of interest Systematic Error is variations that results from constructs of disinterest Random Error is nonsystematic variations in the observed scores

Sources of Variability Construct of interest –corresponds to the IV –“effect” Constructs of disinterest –“systematic error” Non-systematic variation –“random error” –“error variance”

Observed Score = True Score Systematic Error Random Error More Reliable: Less Reliable:

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How do we determine whether a measure is reliable? Types of reliability Test-retest Internal Consistency –Split-half –Cronbach’s alpha: average of all possible split- half reliabilities

Factors that increase reliability Number of items High variation among individuals being tested Clear instructions Optimal testing situation

How do we determine whether our measures are valid? Types of Validity Face Content Criterion –Concurrent –Predictive Construct –Convergent –Discriminant

Threats to reliability and validity Score = effect + systematic + random error Systematic error: threat to validity Random error: threat to reliability

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