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Chapter 5: Measurement Concepts

by: Erin Kaufman

Chapter 5: Measurement Concepts PSY

Marketplace > Mississippi State University > 3314 > PSY > Chapter 5 Measurement Concepts
Erin Kaufman

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About this Document

Covers the notes discussed in class as well as material from the book from week 9 of class.
Experimental Psychology
Dr. Bradshaw
Class Notes
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This 2 page Class Notes was uploaded by Erin Kaufman on Tuesday October 18, 2016. The Class Notes belongs to PSY at Mississippi State University taught by Dr. Bradshaw in Fall 2016. Since its upload, it has received 9 views. For similar materials see Experimental Psychology in 3314 at Mississippi State University.


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Date Created: 10/18/16
Chapter Five: Measurement concepts Reliability:​ The consistency or stability of a measure of behavior. Can also be used in the real world. A friend or teacher that arrives to something scheduled is considered reliable. A friend or teacher that is never on time and never responds to messages is considered unreliable. ● True score: ​ the real score on a variable ● Measurement error:​ the degree to which a measurement deviates from the true value score. How do we test reliability? ● Reliability coefficient: Is the measure producing steady results? You can discover this using correlation coefficients. The most commonly used one is the Pearson product-moment correlation coefficient. ● Test-retest reliability: measuring the same individuals at two different points in time. If the measure produces the same results both times, then the measure is reliable. ○ Alternate forms reliability: a form of test-retest. Two different forms of the same test to the same individuals at different times. ● Internal consistency reliability: assessment of reliability using responses at only one point in time. Uses multiple items, or responses. A real world example would be a test in a class. ○ Split-half reliability: the correlation of the total score on one half of the test with the total score on the other half. The two halves are created randomly. ● Interrater reliability: the extent to which two separate raters agree with their observations. If they are in agreement then the measure is good. ● Reliability tells us about the measurement of error, but it does not tell us about whether or not the measure is good. Does it measure what it’s supposed to? Types of Construct Validity: -Concurrent validity: The construct validity of a measure is assessed by examining whether groups of people differ on the measure in expected ways -Predictive validity: The construct validity of a measure is assessed by examining the ability of the measure to predict a future behavior -Face validity: On the surface, is it measuring what it needs to measure? The content of the measure appears to reflect the construct being measured. -Content validity: The content of the measure is linked to the universe of content that defines that construct. (For example, when creating a measure for depression, the researchers find the domains, or categories, that depression falls under.) -Concurrent validity: Different measures running at the same time. -Convergent validity: Used on the iPiP. Used an established test and a free test to see if they get the same results. Correlation good. -Discriminant validity: Determining if a measure is the same as another. It’s bad if the measures are the same. Reactivity:​ Does awareness of being observed change behavior? Hawthorne effect: People are watching, I don’t want to be fired, so I’m going to be more productive. People do not generally act as they naturally are if they know people are observing them. Nonreactive or unobtrusive measure: a measure that indirectly records the variable one is studying in an experiment. Scales of measurement: -Nominal: categorical. All you can do with this is count frequencies. Has no numerical or quantitative properties. The categories differ. An example is gender or sex of a person. All one does is assign names to different categories. -Ordinal: Things are in order, but it’s a weird kind of order. The intervals between these things are not equal Most common example, ranking runners in a race. All it is is a ranking scale. -Ratio: Equal intervals, but it has a true zero. Zero degrees Kelvin. There is no heat. Because we have a true zero, we can compare or compute ratios. A true zero is when the absence of the variable can be recorded. Time is an example of this. -Interval: Equal intervals, but it has no zero or an artificial zero. Zero degrees on the fahrenheit scale is not actually a true zero. 20 degrees F is not twice as hot as 10 degrees F. IQ is another scale. Intervals are the same, but there is no true zero. Ratio and interval scales allow us to have means and standard deviations.


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