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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.

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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


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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


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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 l.jpg

Systematic Error

True Score

Random Error

Observed Score =

More Reliable:

Less Reliable:


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How many o’s?

Test 1

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Test 2

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How many o’s?

Test 1

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Test 2

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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


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Factors that increase reliability

  • Number of items

  • High variation among individuals being tested

  • Clear instructions

  • Optimal testing situation


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How do we determine whether our measures are valid?

Types of Validity

  • Face

  • Content

  • Criterion

    • Concurrent

    • Predictive

  • Construct

    • Convergent

    • Discriminant


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Threats to reliability and validity

Score = effect + systematic + random error

  • Systematic error:

    threat to validity

  • Random error:

    threat to reliability


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