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Validity. Notes Chapter 4. Overview. Validity and reliability are independent of each other Validity = Accuracy Reliability = Precision Marksman example Validity and reliability are continuous Errors in measurement are always either the result of

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Notes Chapter 4

  • Validity and reliability are independent of each other
    • Validity = Accuracy
    • Reliability = Precision
  • Marksman example
  • Validity and reliability are continuous
  • Errors in measurement are always either the result of
    • Systematic biasing of your scale (validity)
    • Random error introduced by your scale (reliability)
  • Four major types of experimental validity (Cook & Campbell, 1979)
    • Statistical conclusion validity
    • Internal validity
    • External validity
    • Construct validity
      • Most concerned with when creating a scale
      • Extent to which the measurements taken in a study properly represent the underlying theoretical construct
construct validity
Construct Validity
  • Measures the match between a variable representing a “true” measure of the construct and the scale responses
  • Applies only when attempting to relate a scale to a theoretical construct
  • May have validity for one purpose but not for another
criterion validity continued
Criterion Validity (continued)
  • When there is an objectively correct way to measure an underlying construct your scale was designed to represent
  • Demonstrate your scale is related to the correct measure
  • When there is no objective measurement there is no single procedure to measure validity
    • Build an argument for how to interpret the scale by demonstrating that measurements are consistent with the theoretical variable motivating the responses
face validity
Face Validity
  • Items composing the scale are logically related to the underlying construct
  • Scale “looks” appropriate
convergent validity
Convergent Validity
  • Most important to demonstrate
  • Shows that the responses to your scale are related to other measurements that are supposed to be affected by the same variable
  • Assess it numerous ways
    • Each time you demonstrate consistency with the underlying construct makes a more convincing argument that your scale provides an accurate representation of that construct
divergent validity
Divergent Validity
  • Demonstrating that your scale is not related to measurements that represent different variables
  • Shows that your scale is measuring a new concept
  • Assess it at the same time as convergent validity
  • Unclear whether the relation does not exist or your study lacked enough power to detect it
    • If you show significant relations in your study, it makes the argument that the nonsignificant findings in your divergent validity are not due to faults in your study
unique utility
Unique Utility
  • Demonstrate that your scale does something beyond similar measures that already exist
  • Shows that your scale can explain unique portions of the variance
  • Can cause people to use or not use your scale
final points on validity
Final Points on Validity
  • Validity and reliability are independent concepts but are related in important ways
    • Difficult to determine validity of a highly unreliable scale
    • Involve showing statistically significant relations between the scale and other measures
  • Validity measures how successfully your scale matches onto the theory you propose
    • Failure to validate the scale does not mean there is something wrong with your scale
    • May indicate there is something wrong with the theory underlying the validation