# Chapter 5 notes

Chapter 5 – Measurement concepts

Reliability of measures

-Reliability : consistency or stability of a measure of behaviour

-A reliable measure does not fluctuate from one reading to the next. If the measure

does fluctuate, there is error in the measurement device

-Any measure you make comprises two components (1) a true score, which is the

real score of the variable, and (2) measurement error

-When doing research, you can measure each person only once; you can’t give the

measure 50 or 100 times to discover the true score

-studying behaviour using unreliable measures is a waste of time because the

results will be unstable and unreplicable.

-Reliability is most likely achieved when researchers use careful measurement

procedures

-We can’t directly observe the true score and error of an actual score on the

measure. But we can assess the stability of measures using correlation coefficients

-A correlation coefficient is a number that tells us how strongly two variables are

related to each other

-Most common correlation coefficient when discussing reliability is the Pearson

product-moment correlation coefficient. Pearson correlation coefficient

(symbolized as r) can range from 0.00 to +1.00 and 0.00 to -1.00. A correlation of

0.00 tells us that the two variables are not related at all. The closer a correlation is

to 1.00, either +1.00 or -1.00 the stronger the relationship

-When the correlation coefficient is positive there is a positive linear relationship –

high scores on one variable are associated with high scores on the second variable

-A negative linear relationship is indicated by a minus sign – high scores on one

variable are associated with low scores on the second variable

-To assess the reliability of a measure, we will need to obtain at least two scores on

the measure from many individuals. If the measure is reliable, the two scores

should be very similar; a Pearson correlation coefficient that relates the two scores

should be a high positive correlation

Test-Retest Reliability

-Test-retest reliability : assessed by measuring the same individuals at two points in

time

-we would have two scores for each person, and a correlation coefficient could be

calculated to determine the relationship between the first test score and the retest

score

-For most measures the reliability coefficient should probably be at least .80

-Given that test-retest reliability involves administering the same test twice, the

correlation might be high because the individual remembers how they responded

the first time. Alternate forms reliability is sometimes used to avoid this problem.

Alternate forms reliability involves administering two different forms of the same

test to the same individual at two points in time

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-Intelligence is a variable that can be expected to stay relatively constant over

time; thus, we expect the test-retest reliability for intelligence to be very high

Internal Consistency Reliability

-It is possible to assess reliability by measuring individuals at only one point in

time because most psychological measures are made up of a number of different

questions, called items

-Internal consistency reliability : the assessment of reliability using responses at

only one point in time

-Split-half reliability : correlation of an individual’s total score on one half of the

test with the total score on the other half. The two halves are created by randomly

dividing the items into two parts. Thus, the combined measure will have more

items and will be more reliable than either half by itself

-Split-half reliability is relatively straightforward and easy to calculate, even

without a computer

-One drawback is that it does not take into account each individual item’s role in a

measure’s reliability

-Another internal consistency indicator of reliability is called Cronbach’s alpha, is

based on the individual items. Here the researcher calculates the correlation of

each item with every other item (done with computer b/c a lot of correlations).

The value of alpha is the average of all the correlation coefficients

-It is possible to examine the correlation of each item score with the total score

based on all items. Such item-total correlations and Cronbach’s alpha are very

informative because they provide information about each individual item

-Items that do not correlate with the other items can be eliminated from the

measure to increase reliability

Interrater Reliability

-In some research, raters observe behaviours and make ratings or judgements

-You could have one rater make judgements about aggression, but the single

observations of one rater might be unreliable. The solution is to use at least two

raters who observe the same behaviour

-Interrater reliability : the extent to which raters agree in their observations

-A commonly used indicator of interrater reliability is called Cohen’s Kappa

Reliability and Accuracy of Measures

-Reliability tells us about measurement error but it doesn’t tell us about whether

we have a good measure of the variable of interest

Construct validity of measures

-If something is valid, it is “true” in the sense that it is supported by available

evidence

-Construct validity : refers to the adequacy of the operational definition of variables

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## Document Summary

A reliable measure does not fluctuate from one reading to the next. : consistency or stability of a measure of behaviour does fluctuate, there is error in the measurement device. Any measure you make comprises two components (1) a true score, which is the real score of the variable, and (2) measurement error. Reliability is most likely achieved when researchers use careful measurement procedures. We can"t directly observe the true score and error of an actual score on the measure. But we can assess the stability of measures using correlation coefficients. A correlation coefficient is a number that tells us how strongly two variables are related to each other. Most common correlation coefficient when discussing reliability is the pearson product-moment correlation coefficient. Pearson correlation coefficient (symbolized as r) can range from 0. 00 to +1. 00 and 0. 00 to -1. 00. 0. 00 tells us that the two variables are not related at all.