Norman Cliff – författare

This book provides an alternative method for measuring individual differences in psychological, educational, and other behavioral sciences studies. It is based on the assumptions of ordinal statistics as explained in Norman Cliff's 1996 Ordinal Methods for Behavioral Data Analysis. It provides the necessary background on ordinal measurement to permit its use to assess psychological and psychophysical tests and scales and interpret the data obtained. The authors believe that some of the behavioral measurement models used today do not fit the data or are inherently self-contradictory. Applications of these models can therefore lead to unwarranted inferences regarding the status of the derived variables. These methods can also be difficult to apply, particularly in small-sample contexts without making additional, unrealistic assumptions. Ordinal methods more closely reflect the original data, are simple to apply, and can be used in samples of any size. The book's approach is in essence a return to simple empiricism in psychological measurement.Ordinal Measurement in the Behavioral Sciences provides:*methods for analyzing test responses;*extensive discussions of ordinal approaches to analyzing data that are judgments of stimuli;*methods for treating psychological data in ways consistent with its ordinal nature so as to stimulate new developments in this area; and*ordinal test theory and the unfolding methods that are applicable to cross-cultural studies.Advanced students, researchers, and practitioners concerned with psychological measurement should find this book relevant. Measurement professionals will find it provides useful and simple methods that stimulate thought about measurement's real issues.

This book was written with the belief that ordinal statistical methods--sometimes discussed under the title of "nonparametric statistics"--deserve much more serious attention as research tools than they have traditionally had. There are three classes of reasons for this: *Many behavioral variables constitute only ordinal scales, not interval measurements that are required for traditional statistics. *Various research issues that are of primary interest in behavioral research are themselves questions about order: Which group scores higher? Is the order on this variable similar to the order on that? *Inferences from ordinal statistics are less subject to distributional peculiarities of the data than are those from traditional statistics. Taking an innovative approach, this book treats ordinal methods in an integrated way rather than as a compendium of unrelated methods, and emphasizes that the ordinal quantities are highly meaningful in their own right, not just as stand-ins for more traditional correlations or analyses of variance. In fact, since the ordinal statistics have desirable descriptive properties of their own, the book treats them parametrically, rather then nonparametrically. The author discusses how ordinal statistics can be applied in a much wider set of research situations than has usually been thought, and that they can often come closer to answering the researcher's primary questions than traditional ones can. And he includes some extensions of ordinal methods in order to accomplish that end.

This book provides an alternative method for measuring individual differences in psychological, educational, and other behavioral sciences studies. It is based on the assumptions of ordinal statistics as explained in Norman Cliff's 1996 Ordinal Methods for Behavioral Data Analysis. It provides the necessary background on ordinal measurement to permit its use to assess psychological and psychophysical tests and scales and interpret the data obtained. The authors believe that some of the behavioral measurement models used today do not fit the data or are inherently self-contradictory. Applications of these models can therefore lead to unwarranted inferences regarding the status of the derived variables. These methods can also be difficult to apply, particularly in small-sample contexts without making additional, unrealistic assumptions. Ordinal methods more closely reflect the original data, are simple to apply, and can be used in samples of any size. The book's approach is in essence a return to simple empiricism in psychological measurement.Ordinal Measurement in the Behavioral Sciences provides:*methods for analyzing test responses;*extensive discussions of ordinal approaches to analyzing data that are judgments of stimuli;*methods for treating psychological data in ways consistent with its ordinal nature so as to stimulate new developments in this area; and*ordinal test theory and the unfolding methods that are applicable to cross-cultural studies.Advanced students, researchers, and practitioners concerned with psychological measurement should find this book relevant. Measurement professionals will find it provides useful and simple methods that stimulate thought about measurement's real issues.

This book was written with the belief that ordinal statistical methods--sometimes discussed under the title of "nonparametric statistics"--deserve much more serious attention as research tools than they have traditionally had. There are three classes of reasons for this: *Many behavioral variables constitute only ordinal scales, not interval measurements that are required for traditional statistics. *Various research issues that are of primary interest in behavioral research are themselves questions about order: Which group scores higher? Is the order on this variable similar to the order on that? *Inferences from ordinal statistics are less subject to distributional peculiarities of the data than are those from traditional statistics. Taking an innovative approach, this book treats ordinal methods in an integrated way rather than as a compendium of unrelated methods, and emphasizes that the ordinal quantities are highly meaningful in their own right, not just as stand-ins for more traditional correlations or analyses of variance. In fact, since the ordinal statistics have desirable descriptive properties of their own, the book treats them parametrically, rather then nonparametrically. The author discusses how ordinal statistics can be applied in a much wider set of research situations than has usually been thought, and that they can often come closer to answering the researcher's primary questions than traditional ones can. And he includes some extensions of ordinal methods in order to accomplish that end.