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Finally, parametric methods can be adversely affected by nonlinear but monotonic relationships (both in the population and the sample) Nonlinear but monotonic relationships tend to in ate parametric standard errors and lead to lower power (Birkes & Dodge, 1993, chap 1) Ordinal methods can perform much better with such relationships, due to the fact that the ordinal information is identical for monotonic and linear functions Suppose we take a linear bivariate relationship and induce a nonlinear but monotonic relationship via monotonic transformation The parametric standard error for the transformed relationship would tend to be larger than the original and the power of a parametric method would tend to be lower On the other hand, the standard error and power of an ordinal method would not change The functional relationship is continuously increasing or decreasing (barring ties) in both situations, and thus the ordinal information is the same.



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Ordinal Methods to Be Presented The methods of ordinal analysis to be presented fall into two categories: measures of correlation and measures for group comparison We rst consider the bivariate situation, and then show how the methods can be extended for use in more complex analyses We believe that the ordinal statistics presented in the following discussion have clear substantive interpretations, in contrast to traditional nonparametric methods These statistics should be considered as estimates of meaningful population counterparts and not simply as devices for testing null hypotheses Another distinction of our presentation from that of traditional nonparametric methods is an emphasis on inferential methods that apply in the non-null case Many traditional nonparametric inferential methods rely on distributional assumptions that only hold in the null case In contrast, we present inferential methods based on estimating.

The reader does not have to worry about the inverse reference maps in (2.64), since in the element-by-element procedure they are never evaluated explicitly. This will be explained in detail in Paragraph 2.4.9. Notice that for p = 1 relation (2.64) yields the hat functions (2.25). An example of a quadratic Lagrange nodal vertex basis function is shown in Figure 2.22.

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As Upton and Fingleton (1989) note in their authoritative chapter on circular statistics, numerous tests are available for researchers interested in testing for this property. As a class, the least restrictive are generally referred to as gap tests, because they concern the pattern of gaps (or angular separations) between adjacent variables on the circle. For example, if variables are perfectly uniform in circular spacing, then the gap between adjacent variables will be a constant equal to 360 / n, where n is the number of variables. Gap tests generally are used to determine the probability that the actual distribution departs from this ideal of uniform spacing. As an example of applications, Tracey and Rounds (1995) examined the distribution of vocational interest items around a RIASEC circle; using the Neaves-Selkirk gap test (Upton & Fingleton, 1989), they determined that the uniform distribution hypothesis could not be rejected, thereby suggesting that contrary to typology conceptions, vocational interests are not clustered at particular points on the circle. In another example, Gurtman (1997) looked at the distribution of personality items (Q-sort items) in three circumplexes based on combinations of the major personality factors (e.g., Hofstee et al., 1992). Using a test attributed to Rao (Upton & Fingleton, 1989), he found evidence for a uniform distribution in two of three domains. Gap tests generally require a relatively large number of variables (points on the circle) in order to effectively test the null hypothesis, especially when normal curve approximations are applied. For this reason, gap tests for six- or eightvariable circumplex models (such as the PGI) would usually be severely underpowered, and hence not practical.

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