Correlation and Regression, Second Edition, provides students with an accessible textbook on statistical theories in correlation and regression.
Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world." The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the
foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.Summary
Contents
Subject index
Chapter : APPLICATIONS OF PEARSON CORRELATION TO MEASUREMENT THEORY
APPLICATIONS OF PEARSON CORRELATION TO MEASUREMENT THEORY
CHAPTER OBJECTIVES
After reading this chapter, you should be able to:
- Explain the concept of reliability.
- Discuss and define the different types of reliability and the strengths and/or weaknesses of each.
- Explain the concept of validity.
- Explain why information abut a measure's reliability is not necessarily sufficient information to justify implementation of the ...
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Science strives to achieve safe knowledge, i.e. insights we can trust. To the extent that a piece of knowledge depends on a method, we therefore require that the method be precise, i.e. it reliably leads to a certain result. A scientific method is reliable to the extent that it produces the same results on repeated application. Reliability is, thus, a measure of the precision of a method and of the stability of its results.
In an empirical investigation, a researcher R applies a method M – possibly involving a (measuring) instrument I – to a set of data D. All of these components must be reliable:
- M is reliable with respect to R if its results do not depend on the identity of R. This aspect of reliability is called “inter-rater reliability” in psychology, but often separated out as objectivity.– For instance, if the medical diagnosis provided by a diagnostic method depends on the person of the physician, it is unreliable.
- M is reliable with respect to I if I produces ceteris paribus the same measurements for the same data.– For instance, if a scale produces different weight values on repeated measurements of the same object, it is unreliable.
- M is reliable with respect to D if it produces like results for like data, i.e. it is not the case that it only works for a particular set of data.– For instance, the method of getting grammaticality judgments from a group of informants is reliable to the extent that the same average grammaticality judgment is produced by the same group of informants on different occasions (and even by like groups of informants).
The reliability of a method may be ascertained as a value between 0 and 1. The value required depends on the goals of the investigation.
A method can be valid only if it is reliable; but it may be reliable without being valid. In other words, reliability is necessary, but not sufficient for validity.
SHORT ANSWER1.Explain why your textbook argues, “In fact, operationalizationsare one place where creativity comes intothe research process.”
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DIF:MediumREF:Ways to Measure Variables: Three Common Types of MeasuresOBJ: Learning Objective 1MSC: Understanding2.Imagine that you are trying to measure people’s stress. Provide an example of measuring stress using aself-report measure, a physiological measure, and a behavioral measure.
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DIF:MediumREF:Ways to Measure Variables: Three Common Types of MeasuresOBJ: Learning Objective 2MSC: Applying3.Imagine that you are trying to measure people’s stress. Provide three quantitative definitions of stress: onethat is ordinal, one that is interval, and one that is ratio.