http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (xvii, 212 pages) : illustrations
An incomparably useful examination of statistical methods for comparisonThe nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not inde.
Front Matter -- Introduction -- Statistical Foundation for Comparison -- Comparison in Linear Models -- Nonparametric Comparison -- Comparison of Rates -- Comparison in Generalized Linear Models -- Additional Topics of Comparison in Generalized Linear Models -- Comparison in Structural Equation Modeling -- Comparison with Categorical Latent Variables -- Comparison in Multilevel Analysis -- References -- Index -- Wiley Series in Probability and Statistics.
Rationale for Statistical Comparison -- Comparative Research in the Social Sciences -- Statistical Foundation for Comparison -- A System for Statistical Comparison -- Test Statistics -- What to Compare? -- Comparison in Linear Models -- An Example -- Some Preliminary Considerations -- The Linear Model -- Comparing Two Means -- ANOVA -- Multiple Comparison Methods -- ANCOVA -- Multiple Linear Regression -- Regression Decomposition -- Which Linear Method to Use? -- Nonparametric Comparison -- Nonparametic Tests -- Resampling Methods -- Relative Distribution Methods -- Comparison of Rates -- The Data -- Standardization -- Decomposition -- Comparison in Generalized Linear Models -- Comparing Generalized Linear Models -- A Logit Model Example -- A Hazard Rate Model Example -- Data Used in Section 6.4 -- Additional Topics of Comparison in Generalized Linear Models -- GLM for Matched Case-Control Studies -- Dispersion Heterogeneity -- Bayesian Generalized Linear Models -- The Data for the n : m Design -- Comparison in Structural Equation Modeling -- Statistical Background -- Mean and Covariance Structures -- Group Comparison in SEM -- Examples of Computer Program Listings -- Comparison with Categorical Latent Variables -- Latent Class Models -- Latent Trait Models -- Latent Variable Models for Continuous Indicators -- Causal Models with Categorical Latent Variables -- Comparison with Categorical Latent Variables -- Software for Categorical Latent Variables -- Computer Program Listings for the Examples.
Includes bibliographical references (pages 199-206) and index.