New developments in categorical data analysis for the social and behavioral sciences
- 其他作者: Ark, L. Andries van der. , Croon, Marcel A. , Sijtsma, K.
- 出版: Mahwah, N.J. : L. Erlbaum Associates ©2005.
- 稽核項: 1 online resource (xii, 261 pages) :illustrations.
- 叢書名: Quantitative methodology series
- 標題: Sciences sociales , SOCIAL SCIENCE , Methodology. , Méthodes statistiques. , SOCIAL SCIENCE Methodology. , Social sciences Statistical methods. , Statistical methods. , Sciences sociales Méthodes statistiques. , Electronic books. , Social sciences , Sozialwissenschaften
- ISBN: 1410612023 , 9781410612021
- 試查全文@TNUA:
- 附註: Includes bibliographical references and indexes. Statistical models for categorical variables / L. Andries van der Ark, Marcel A. Croon, and Klaas Sijtsma -- Misclassification phenomena in categorical data analysis : regression toward the mean and tendency toward the mode / Jacques A. Hagenaars -- Factor analysis with categorical indicators : a comparison between traditional and latent class approaches / Jeroen K. Vermunt and Jay Magidson -- Bayesian computational methods for inequality constrained latent class analysis / Olav Laudy, Jan Boom, and Herbert Hoijtink -- Analyzing categorical data by marginal models / Wicher P. Bergsma and Marcel A. Croon -- Computational aspects of the E-M and Bayesian estimation in latent variable models / Irini Moustaki and Martin Knott -- Logistic models for single-subject time series / Peter W. van Rijn and Peter C.M. Molenaar -- The effect of missing data imputation on Mokken scale analysis / L. Andrew van der Ark and Klaas Sijtsma -- Building IRT models from scratch : graphical models, exchangeability, marginal freedom, scale types, and latent traits / Henk Kelderman -- The Nedlesky model for multiple-choice items / Timo M. Bechger [and others] -- Application of the polytomous saltus model to stage-like proportional reasoning data / Karen Draney and Mark Wilson -- Multilevel IRT model assessment / Jean-Paul Fox.
- 摘要: A collection of studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting datasets. A prominent breakthrough in categorical data analysis is the development and use of latent variable models.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=125950
- 系統號: 005314011
- 資料類型: 電子書
- 讀者標籤: 需登入
- 引用網址: 複製連結
Categorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. This volume concentrates on latent class analysis and item response theory. These methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification of a group of respondents according to their pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data and allows the estimation of factor structures and regression models conditional on the latent class structure. Item response theory leads to the identification of one or more ordinal or interval scales. In psychological and educational testing these scales are used for individual measurement of abilities and personality traits. The focus of this volume is applied. After a method is explained, the potential of the method for analyzing categorical data is illustrated by means of a real data example to show how it can be used effectively for solving a real data problem. These methods are accessible to researchers not trained explicitly in applied statistics. This volume appeals to researchers and advanced students in the social and behavioral sciences, including social, developmental, organizational, clinical and health psychologists, sociologists, educational and marketing researchers, and political scientists. In addition, it is of interest to those who collect data on categorical variables and are faced with the problem of how to analyze such variables--among themselves or in relation to metric variables.
來源: Google Book
來源: Google Book
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