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distance, the transformed distance increases as the values of xh and xi increase Therefore, the meaning of any xed original distance is not maintained along the transformed scale Monotonic Transformation and Data Analysis A monotonic transformation of a scale maintains the property of order but not that of distance This fact has important implications when using monotonic transformations with highly parametric methods because such methods make use of the distances of the variables (see the section in this chapter entitled Pearson s Correlation Coef cient ) When the distances of the variables are changed through monotonic transformations, the results of the parametric methods (including test statistics) will be different Mild changes in distance produce small changes in the values of the parametric methods, whereas extreme changes can result in very different values.

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Basic research on survey methods has shown that responses change with the reference periods.

It is important for researchers to be aware of these facts because monotonic transformations are fundamental in two important applications: transforming to meet statistical assumptions, and item response theory (IRT; Rasch scaling is a special case of IRT) With careful thought and application, monotonic transformations can be very useful in clarifying relationships and interpreting variables (see Emerson & Stoto, 1983) A more arbitrary use of monotonic transformations is to induce sample data to be consistent with parametric assumptions (eg, normality) The arbitrariness arises from the fact that a transformation is often determined by the sample data without regard for replication or comparison with other analyses (Games, 1984) Consider results of research using reaction time measured in milliseconds (ms) It could be the case that the transformation f (ms) = ms(.

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835) is used to induce normality in one data set, whereas the very different transformation f (ms) = ms( 5) is used to induce normality in another data set Comparing parametric results based on these two transformations is dif cult The two transformations produce different distances between scale values and lead to different parametric results In contrast, the ordinal information is the same regardless the monotonic transformation, so that results based on ordinal methods are comparable Furthermore, ordinal methods make it unnecessary to use monotonic transformations to meet statistical assumptions because results will be the same whether raw or transformed scores are the basis of analysis Monotonic relationships and monotonic transformations are also important in the stochastic item response models (IRT) that underlie most of modern test theory A fundamental premise of IRT is that manifest and latent variables have a nonlinear but monotonic functional relationship (usually ogival)..

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A FEW LOOSE ENDS Recommendations for Quick and Dirty Analyses In this chapter, we have said that we do not recommend pairwise deletion or mean substitution, even for quick and dirty analyses. So what is the option As we said earlier, if you do not lose too many cases to missing data, complete cases analysis is a quite reasonable basis for quick and dirty analyses. However, what does one do if complete cases analysis is not an option Perhaps the best of the quick and dirty analyses is to run EM in NORM (or SAS) and to perform the analysis directly from the EM covariance matrix. With NORM, this can be done by making use of the ALPHNORM utility previously described. With SAS, the EM covariance matrix may be used directly by certain other procedures (PROC REG,

learning and moral-reasoning perspectives, and by Schwartz (1977), who relies most heavily on norms, especially personal norms Amalgamated models make three useful contributions First, they remind us of the complexity of prosocial behavior and thereby caution against simplistic explanations Second, they sketch a causal ordering of the various psychological processes assumed to be operating although these orderings are rarely tested Third, they provide a mnemonic for relevant psychological processes and theoretical perspectives Amalgamated models have potential liabilities too First, the desire to be comprehensive exerts pressure toward proliferation of boxes and arrows As more intervening steps are added and more arrows are drawn, multiple paths connect postulated antecedent and consequent variables This makes achieving clear causal prediction increasingly dif cult The models become less explanatory and more purely descriptive.

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