Analysis of nominal data, Issue 7 - download pdf or read online

By Henry T. Reynolds

ISBN-10: 0803906536

ISBN-13: 9780803906532

The up to date moment variation bargains multiplied discussions of the chi sq. attempt of value and the aptitude measures of organization to be had to be used with categoric facts. Reviewing uncomplicated suggestions in research of nominal info, this paper employs survey learn facts on occasion id and ideologies to point which measures and assessments are superb for specific theoretical matters. This ebook serves as a great primer for quantity 20, Log-Linear versions.

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Extra resources for Analysis of nominal data, Issue 7

Example text

This practice is understandable: Cross-classifications containing numerous zeros do not seem very reliable or impressive. Nevertheless, collapsing or combining categories to increase cell frequencies undoubtedly creates as many problems as it solves. There are two reasons for this. First, the variation in a nominal variable depends in part on the number of its categories: the greater the number of classes, the greater the variation, other things being equal. Here, "variation" refers to the measured differences among individuals.

In spite of the equivalence in the relationships (at least as measured by percentages), many measures do not give the same value for both tables. A researcher who computes an index for the second set of data might report a strong relationship, while someone analyzing the first table might find a much weaker association, even though they both use the same statistic. TABLE 11 Categorical Data with Different Marginal Distributions but the Same Inherent Relationshipab X Totals X Totals 60%20%10%60%20%10%(60)(200)(10)270(180)(120)(30)330Y 30 60 30 Y 30 60 30 (30)(600)(30)660(90)(360)(90)540 10 20 60 10 20 60 (10)(200)(60)270(30)(120)(180)330Totals100%100%100%Totals100%100%100%(100)(1000)(100)1200(300)(600)(300)1200NOTE: Numbers in parentheses are the number of cases.

Three considerations guide the choice of a measure: whether it is symmetric or asymmetric, its interpretation, and its sensitivity to confounding influences. Symmetric Versus Asymmetric Measures If a theory or common sense indicate that one variable causes another, then it is usually necessary to predict values of the dependent variable from knowledge of the causal or independent variable. In this case the most appropriate measure would be asymmetric. The calculation and interpretation of asymmetric measures depend on which variable is considered dependent.

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Analysis of nominal data, Issue 7 by Henry T. Reynolds

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