Download presentation

Presentation is loading. Please wait.

Published byJohanna Kiss Modified over 2 years ago

1
How good are our measurements? The last three lectures were concerned with some basics of psychological measurement: What does it mean to quantify a psychological variable? How do we operationally define both observable and latent variables? The next important issue concerns the quality of our measurements –How can we help make our measurements precise? –How can we determine whether we’re measuring what we think we’re measuring?

2
Reliability Reliability: the extent to which measurements are free of random errors Random error: nonsystematic mistakes in measurement –misreading a questionnaire item –observer looks away when coding behavior –nonsystematic misinterpretations of a behavior

3
Precision Precise measurements are free of random errors. A precise measurement is one that is highly reliable.

4
Reliability What are the implications of random measurement errors for the quality of our measurements?

5
Reliability O = T + E + S O = a measured score (e.g., performance on an exam) T = true score (e.g., the value we want) E = random error S = systematic error O = T + E (we’ll ignore S for now, but we’ll return to it later)

6
Reliability O = T + E The error becomes a part of what we’re measuring This is a problem if we’re operationally defining our variables using equivalence definitions because part of our measurement is based on the true value that we want and part is based on error. Once we’ve taken a measurement, we have an equation with two unknowns. We can’t separate the relative contribution of T and E. 10 = T + E

7
Reliability: Do random errors accumulate? Question: If we sum or average multiple observations, will random errors accumulate?

8
Reliability: Do random errors accumulate? Answer: No. If E is truly random, we are just as likely to overestimate T as we are to underestimate T.

9
Reliability: Do random errors accumulate? Note: The average of the seven O’s is equal to T

10
Reliability: Implications These demonstrations suggest that one important way to help eliminate the influence of random errors of measurement is to use multiple measurements. –operationally define latent variables via multiple indicators –use more than one observer when quantifying behaviors

11
Reliability: Estimating reliability Question: How can we estimate the reliability of our measurements? Answer: Two common ways: (a) test-retest reliability (b) internal consistency reliability

12
Reliability: Estimating reliability Test-retest reliability: Reliability assessed by measuring something at least twice at different time points. The logic is as follows: If the errors of measurement are truly random, then the same errors are unlikely to be made more than once. Thus, to the degree that two measurements of the same thing agree, it is unlikely that those measurements contain random error.

14
Reliability: Estimating reliability Internal consistency: Reliability assessed by measuring something at least twice within the same broad slice of time. Split-half: based on an arbitrary split (e.g, comparing odd and even, first half and second half) Cronbach’s alpha ( ): based on the average of all possible split-halves

15
Less errorMore error Item A Item B Item C Item D Item E Item F 4 5 6 5 4 5 3 5 7 5 3 5 Items D, E, & F yield an average scores of (5, 4, 5)/3 = 4.6. Items A, B, & C yield an average score of (3+5+7)/3 = 5. Items A, B, & C yield an average score of (4+5+6)/3 = 5. These two estimates are off by only.4 of a point. Items D, E, & F yield an average scores of (5, 3, 5)/3 = 4.3. These two estimates are off by.7 of a point.

16
Reliability: Final notes An important implication: As you increase the number of indicators, the amount of random error in the averaged measurement decreases. An important assumption: The entity being measured is not changing. An important note: Common indices of reliability range from 0 to 1; higher numbers indicate better reliability (i.e., less random error).

Similar presentations

OK

Reliability, the Properties of Random Errors, and Composite Scores Week 7, Psych 350 - R. Chris Fraley

Reliability, the Properties of Random Errors, and Composite Scores Week 7, Psych 350 - R. Chris Fraley

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power grid synchronization Ppt on computer science projects Ppt on hepatitis c virus Ppt on trade fair 2016 Ppt on ozone depletion and global warming A ppt on loch ness monster sightings Ppt on central limit theorem youtube Ppt on different types of computer softwares list Ppt on career in economics in india Price levels and the exchange rate in the long run ppt on tv