PsyB01 Chapter 5&6.doc

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University of Toronto Scarborough
Connie Boudens

Chapter 5 Measurement Concepts Reliability of measures - Reliability refers to the 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 that you can make can be thought of as comprising two components: o True score, which is the real score on the variable o Measurement error - An unreliable measure of intelligence contains considerable measurement error and so does not provide an accurate indication of an individuals true intelligence - A reliable measure of intelligence one that contains little measurement error will yield an identical (or nearly identical) intelligence score each time the same individual is measured - The measurement error in an unreliable test is revealed in the greater variability of its results of the unreliable test - It is important to use a reliable measure since researchers only measure each person only once - Trying to study the behaviour using unreliable measures is a waste of time because the results will be unstable and unable to be replicated - Reliability is most likely to be achieved when researchers use careful measurement procedures - In many areas, reliability can be increased by making multiple measures; this is most commonly seen when assessing personality traits and cognitive abilities (e.g. a survey with more items [questions] is more reliable) - We can assess the stability of measures using correlation coefficients, which is a number that tells us how strongly two variables are related to each other - Pearson product-moment correlation coefficient (symbolized as r) can range from 0.00 to +1.00 and 0.00 to -0.00 o A correlation of 0.00 tells us that the two variables are not related at all; the closer a correlation is to either +1.00 or -1.00, the stronger the relationship o The positive and negative signs provide information about the direction of 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 when 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 (e.g. a Pearson correlation coefficient that relates the two scores should be a high positive correlation) Test-Retest Reliability - Test-retest reliability is assessed by measuring the same individuals at two points in time; having two scores for each person would allow the researcher to calculate the correlation coefficient and determine the relationship between the first test score and the retest score - If many people have very similar scores, we conclude that the measure reflects true scores rather than measurement error - 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 artificially high because the individuals remember how they responded the first time o To solve this problem, alternate forms reliability is used, which involves administering two different forms of the same tests to the same individuals at two points in time Internal Consistency Reliability - Internal consistency reliability is the assessment of reliability using responses at only one point in time; because all items (questions) measure the same variable, they should yield similar or consistent results - Split-half reliability is the correlation of an individuals total score on one half of the test with the total score on the other half o One drawback with this method is that it does not take into account each individual items role in a measures reliability - Cronbachs alpha is based on the individual items; here the researcher calculates the correlation of each item with every other item o The value of alpha is based on the average of all the interitem correlation coefficients and the number of items in the measure; more items indicate higher reliability - Item-total correlations is the examination of the correlation of each item score with the total score based on all items - Item-total correlations and Cronbachs 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 - Interrater reliability is the extent to which raters agree in their observations; high interrater reliability is obtained when most of the observations result in the same judgment Reliability and Accuracy of Measures - Reliability tells us about measurement error but it does not 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
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