1. Reliability a measure is reliable if it gives the same information every time it is used. reliability is assessed by a number – typically a correlation between two sets of scores

2. Reliability Measurement of human ability and knowledge is challenging because: ability is not directly observable – we infer ability from behavior all behaviors are influenced by many variables, only a few of which matter to us

3. Observed Scores O = T + eO = Observed score T = True score e = error

4. Reliability – the basics 1.A true score on a test does not change with repeated testing 2.A true score would be obtained if there were no error of measurement. 3.We assume that errors are random (equally likely to increase or decrease any test result).

5. Reliability – the basics Because errors are random, if we test one person many times, the errors will cancel each other out (Positive errors cancel negative errors) Mean of many observed scores for one person will be the person’s true score

6. Reliability – the basics Example: to measure Sarah’s spelling ability for English words. We can’t ask her to spell every word in the dictionary, so… Ask Sarah to spell a subset of English words % correct estimates her true English spelling skill But which words should be in our subset?

7. Estimating Sarah’s spelling ability… Suppose we choose 20 words randomly… Then, by chance, we may get a lot of very easy words – cat, tree, chair, stand… Or, by chance, we may get a lot of very difficult words – desiccate, arteriosclerosis, numismatics

8. Estimating Sarah’s spelling ability… Sarah’s observed score will vary with the difficulty of the random sets of words we choose But presumably her actual spelling ability remains constant.

9. Reliability – the basics Other things can produce error in our measurement E.g. on the first day that we test Sarah she’s tired but on the second day, she’s rested…

10. Estimating Sarah’s spelling ability… Conclusion: O = T + e But e 1 ≠ e 2 ≠ e 3 … The variation in Sarah’s scores is produced by measurement error. How can we measure such effects – how can we measure reliability?

11. Reliability – the basics In what follows, we consider various sources of error in measurement. Different ways of measuring reliability are sensitive to different sources of error.

12. How do we deal with sources of error? Error due to test itemsDomain sampling error

13. Domain Sampling error A knowledge base or skill set containing many items is to be tested.  E.g., chemical properties of foods. We can’t test the entire set of items.  So we sample items.  That produces sampling error, as in Sarah’s spelling test.

14. Domain Sampling error Smaller sets of items may not test entire knowledge base. A person’s score may vary depending upon what is included or excluded from test. Reliability increases with number of items on a test

15. Domain Sampling error Parallel Forms Reliability: Choose 2 different sets of test items. Across all people tested, if correlation between scores on 2 sets of words is low, then we probably have domain sampling error.

16. How do we deal with sources of error? Error due to test items Error due to testing occasions Time sampling error

17. Time Sampling error Test-retest Reliability  person taking test might be having a very good or very bad day – due to fatigue, emotional state, preparedness, etc. Give same test repeatedly & check correlations among scores High correlations indicate stability – less influence of bad or good days.

18. Time sampling error Advantage: easy to evaluate, using correlation Disadvantage: carryover & practice effects

19. How do we deal with sources of error? Error due to test items Error due to testing occasions Error due to testing multiple traits Internal consistency error

20. Internal consistency approach Suppose a test includes both (1) items on social psychology and (2) items requiring mental rotation of abstract visual shapes. Would you expect much correlation between scores on the two parts?  No – because the two ‘skills’ are unrelated.

21. Internal consistency approach A low correlation between scores on 2 halves of a test, suggests that the test is tapping two different abilities or traits. In such a case, the two halves of the test give information about two different, uncorrelated traits

22. Internal consistency approach So we assess internal consistency by dividing the test into 2 halves and computing the correlation between scores on those two halves for the people who took the test But how should we divide the test into halves to check the correlation?

23. Internal consistency approach Split-half method Kuder-Richardson formula Cronbach’s alpha All of these assess the extent to which items on a given test measure the same ability or trait.

24. Split-half Reliability After testing, divide test items into halves A & B that are scored separately. Compute correlation of results for A with results for B. Various ways of dividing test into two – randomly, first half vs. second half, odd- even…

25. Kuder-Richardson 20 Kuder & Richardson (1937): an internal- consistency measure that doesn’t require arbitrary splitting of test into 2 halves. KR-20 avoids problems associated with splitting by simultaneously considering all possible ways of splitting a test into 2 halves.

26. Internal Consistency – Cronbach’s α KR-20 can only be used with test items scored as 1 or 0 (e.g., right or wrong, true or false). Cronbach’s α (alpha) generalizes KR-20 to tests with multiple response categories. α is a more generally- useful measure of internal consistency than KR-20

27. Review: How do we deal with sources of error? ApproachMeasuresIssues Test-RetestStability of scoresCarryover Parallel FormsEquivalence & StabilityEffort Split-halfEquivalence & InternalShortened consistency test KR-20 & αEquivalence & InternalDifficult to consistencycalculate

28. Reliability in Observational Studies Some psychologists collect data by observing behavior rather than by testing. This approach requires time sampling, leading to sampling error Further error due to:  observer failures  inter-observer differences

29. Reliability in Observational Studies Deal with possibility of failure in the single- observer situation by having more than 1 observer. Deal with inter- observer differences using:  Inter-rater reliability  Kappa statistic

30. Validity We distinguish between the validity of a measure of some psychological process or state and the validity of a conclusion. Here, we focus on validity of measures. A subsequent lecture will consider the validity of conclusions.

31. Theory: A influences B Prediction: A  B Operationalization of A = a, B = b Measurement of b We’ll consider validity of these in a few weeks We’ll look at validity of these phases today

32. Validity a measure is valid if it measures what you think it measures. we traditionally distinguish between four types of validity:  face  content  construct  criterion

33. Four types of validity Face The test appears to measure what it is supposed to measure  not formally recognized as a type of validity

34. Four types of validity Face Construct The measure captures the theoretical construct it is supposed to measure

35. Four types of validity Face Construct Content The measure samples the range of behavior covered by the construct.

36. Four types of validity Face Construct Content Criterion Results relate closely to those produced by other measures of the same construct. Results do not relate to those produced by measures of other constructs

37. Review (last week & this week) We’re not really interested in things that stay the same. We’re interested in variation. But only systematic variation, not random variation  systematic variation can be explained  random variation can’t

38. Quick Review Some variation in performance is random and some is systematic The scientist’s tasks are to separate the systematic variation from the random, and then to build models of the systematic variation.

39. Quick Review We choose a measurement scale. We prefer either ratio or interval scales, when we can get them. We try to maximize both the reliability and the validity of our measurements using that scale.

40. Review questions Which would you expect to be easier to assess – reliability or validity? Why do we have tools and machines to measure some things for us (such as rulers, scales, and money)? What are some analogues for rulers and scales, used when we measure psychological constructs?