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Validity, Reliability, & Sampling

Validity, Reliability, & Sampling. Psych 231: Research Methods in Psychology. Errors in measurement. Reliability If you measure the same thing twice do you get the same values? Validity Does your measure really measure what it is supposed to measure??. reliable valid. unreliable

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Validity, Reliability, & Sampling

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  1. Validity, Reliability, & Sampling Psych 231: Research Methods in Psychology

  2. Errors in measurement • Reliability • If you measure the same thing twice do you get the same values? • Validity • Does your measure really measure what it is supposed to measure?? reliablevalid unreliable invalid reliable invalid

  3. Reliability • True score + measurement error • A reliable measure will have a small amount of error • Multiple “kinds” of reliability • Test-retest • Internal consistency • Inter-rater

  4. Reliability • Test-restest reliability • Test the same participants more than once • Measurement from the same person at two different times • Should be consistent across different administrations Reliable Unreliable

  5. Reliability • Internal consistency reliability • Multiple items testing the same construct • Extent to which scores on the items of a measure correlate with each other • Cronbach’s alpha (α) • Split-half reliability • Correlation of score on one half of the measure with the other half (randomly determined)

  6. Reliability • At least 2 raters observe behavior • Inter-rater reliability • Extent to which raters agree in their observations • Are the raters consistent? • Requires some training in judgment

  7. Validity • Does your measure really measure what it is supposed to measure? • There are many “kinds” of validity

  8. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT

  9. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT

  10. Construct Validity • Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct

  11. Face Validity • At the surface level, does it look as if the measure is testing the construct? “This guy seems smart to me, and he got a high score on my IQ measure.”

  12. Internal Validity • Did the change in the DV result from the changes in the IV or does it come from something else? • The precision of the results

  13. Threats to internal validity • History – an event happens the experiment • Maturation – participants get older (and other changes) • Selection – nonrandom selection may lead to biases • Mortality – participants drop out or can’t continue • Testing – being in the study actually influences how the participants respond • The precision of the results

  14. External Validity • Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”

  15. External Validity • Variable representativeness • Relevant variables for the behavior studied along which the sample may vary • Setting representativeness • Are the properties of the research setting similar to those outside the lab (Ecological validity) • Subject representativeness • Characteristics of sample and target population along these relevant variables

  16. Sampling • Why do we do we use sampling methods? • Typically don’t have the resources to test everybody, so we test a subset

  17. Population Sampling Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample

  18. Sampling to make data collection manageable Inferential statistics used to generalize back Population Sample Sampling

  19. Sampling • Why do we do we use sampling methods? • Goals of “good” sampling: • Maximize Representativeness: • To what extent do the characteristics of those in the sample reflect those in the population • Reduce Bias: • A systematic difference between those in the sample and those in the population

  20. Have some element of random selection Susceptible to biased selection Sampling Methods • Probability sampling • Simple random sampling • Systematic sampling • Stratified sampling • Non-probability sampling • Convenience sampling • Quota sampling

  21. Simple random sampling • Every individual has a equal and independent chance of being selected from the population

  22. Systematic sampling • Selecting every nth person

  23. Stratified sampling • Step 1: Identify groups (strata) • Step 2: randomly select from each group

  24. Convenience sampling • Use the participants who are easy to get

  25. Quota sampling • Step 1: identify the specific subgroups • Step 2: take from each group until desired number of individuals

  26. Next time • Read: Chpt 8

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