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020    9781410606570|q(electronic bk.) 
020    |z9780805843231 
035    (MiAaPQ)EBC474649 
035    (Au-PeEL)EBL474649 
035    (CaPaEBR)ebr10358725 
035    (CaONFJC)MIL237893 
035    (OCoLC)52251060 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 BF39 -- .E94 2002eb 
082 0  150 
100 1  von Eye, Alexander 
245 10 Configural Frequency Analysis :|bMethods, Models, and 
       Applications 
250    1st ed 
264  1 Mahwah :|bTaylor & Francis Group,|c2002 
264  4 |c©2002 
300    1 online resource (468 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
505 0  Cover -- Configural Frequency Analysis: Methods, Models, 
       and Applications -- Copyright -- List of contents -- 
       Preface -- Part 1: Concepts and Methods of CFA -- 1. 
       Introduction: The Goals and Steps of Configural Frequency 
       Analysis -- 1.1 Questions that can be answered with CFA --
       1.2 CFA and the person perspective -- 1.3 The five steps 
       of CFA -- 1.4 A first complete CFA data example -- 2. Log-
       linear Base Models for CFA -- 2.1 Sample CFA base models 
       and their design matrices -- 2.2 Admissibility of log-
       linear models as CFA base models -- 2.3 Sampling schemes 
       and admissibility of CFA base models -- 2.3.1 Multinomial 
       sampling -- 2.3.2 Product-multinomial sampling -- 2.3.3 
       Sampling schemes and their implications for CFA -- 2.4 A 
       grouping of CFA base models -- 2.5 The four steps of 
       selecting a CFA base model -- 3. Statistical Testing in 
       Global CFA -- 3.1 The null hypothesis in CFA -- 3.2 The 
       binomial test -- 3.3 Three approximations of the binomial 
       test -- 3.3.1 Approximation of the binomial test using 
       Stirling's formula -- 3.3.2 Approximation of the binomial 
       test using the DeMoivre- Laplace limit theorem -- 3.3.3 
       Standard normal approximation of the binomial test -- 
       3.3.4 Other approximations of the binomial test -- 3.4 The
       X2 test and its normal approximation -- 3.5 Anscombe's 
       normal approximation -- 3.6 Hypergeometric tests and 
       approximations -- 3.6.1 Lehmacher's asymptotic 
       hypergeometric test -- 3.6.2 Küchenhoff's continuity 
       correction for Lehmacher's test -- 3.7 Issues of power and
       the selection of CFA tests -- 3.7.1 Naud's power 
       investigations -- 3.7.2 Applications of CFA tests -- 
       3.7.2.1 CFA of a sparse table -- 3.7.2.2 CFA tests in a 
       table with large frequencies -- 3.8 Selecting significance
       tests for global CFA -- 3.9 Finding types and antitypes: 
       Issues of differential power -- 3.10 Methods of protecting
       α 
505 8  3.10.1 The Bonferroni α protection (SS) -- 3.10.2 Holm's 
       procedure for α protection (SD) -- 3.10.3 Hochberg's 
       procedure for α protection (SU) -- 3.10.4 Holland and 
       Copenhaver's procedure for α protection (SD) -- 3.10.5 
       Hommel, Lehmacher, and Perli's modifications of Holm's 
       procedure for protection of the multiple level α (SD) -- 
       3.10.6 Illustrating the procedures for protecting the test
       -wise α -- 4. Descriptive Measures in Global CFA -- 4.1 
       The relative risk ratio, RR -- 4.2 The measure log P -- 
       4.3 Comparing the X2 component with the relative risk 
       ratio and log P -- Part II: Models and Applications of CFA
       -- 5. Global Models of CFA -- 5.1 Zero order global CFA --
       5.2 First order global CFA -- 5.2.1 Data example I: First 
       order CFA of social network data -- 5.2.2 Data example II:
       First order CFA of Finkelstein's Tanner data, Waves 2 and 
       3 -- 5.3 Second order global CFA -- 5.4 Third order global
       CFA -- 6. Regional models of CFA -- 6.1 Interaction 
       Structure Analysis (ISA) -- 6.1.1 ISA of two groups of 
       variables -- 6.1.2 ISA of three or more groups of 
       variables -- 6.2 Prediction CFA -- 6.2.1 Base models for 
       Prediction CFA -- 6.2.2 More P-CFA models and approaches -
       - 6.2.2.1 Conditional P-CFA: Stratifying on a variable -- 
       6.2.2.2 Biprediction CFA -- 6.2.2.3 Prediction 
       coefficients -- 7. Comparing k Samples -- 7.1 Two-sample 
       CFA I: The original approach -- 7.2 Two-sample CFA II: 
       Alternative methods -- 7.2.1 Gonzáles-Debén's π* -- 7.2.2 
       Goodman's three elementary views of non-independence -- 
       7.2.3 Measuring effect strength in two-sample CFA -- 7.3 
       Comparing three or more samples -- 7.4 Three groups of 
       variables: ISA plus k-sample CFA -- Part III: Methods of 
       Longitudinal CFA -- 8. CFA of Differences -- 8.1 A review 
       of methods of differences -- 8.2 The method of differences
       in CFA -- 8.2.1 Depicting the shape of curves by 
       differences: An example 
505 8  8.2.2 Transformations and the size of the table under 
       study -- 8.2.3 Estimating expected cell frequencies for 
       CFA of differences -- 8.2.3.1 Calculating a priori 
       probabilities: Three examples -- 8.2.3.2 Three data 
       examples -- 8.2.4 CFA of second differences -- 9. CFA of 
       Level, Variability, and Shape of Series of Observations --
       9.1 CFA of shifts in location -- 9.2 CFA of variability in
       a series of measures -- 9.3 Considering both level and 
       trend in the analysis of series of measures -- 9.3.1 
       Estimation and CFA of polynomial parameters for 
       equidistant points on X -- 9.3.1.1 Orthogonal polynomials 
       -- 9.3.1.2 Configural analysis of polynomial coefficients 
       -- 9.3.2 Estimation and CFA of polynomial parameters for 
       non-equidistant points on X -- 9.4 CFA of series that 
       differ in length -- an example of Confirmatory CFA -- 9.5 
       Examining treatment effects using CFA -- more confirmatory
       CFA -- 9.5.1 Treatment effects in pre-post designs (no 
       control group) -- 9.5.2 Treatment effects in control group
       designs -- 9.6 CFA of patterns of correlation or 
       multivariate distance sequences -- 9.6.1 CFA of 
       autocorrelations -- 9.6.2 CFA of autodistances -- 9.7 
       Unidimensional CFA -- 9.8 Within-individual CFA -- Part IV
       : The CFA Specialty File and Alternative Approaches to CFA
       -- 10. More facets of CFA -- 10.1 CFA of cross-
       classifications with structural zeros -- 10.2 The 
       parsimony of CFA base models -- 10.3 CFA of groups of 
       cells: Searching for patterns of types and antitypes -- 
       10.4 CFA and the exploration of causality -- 10.4.1 
       Exploring the concept of the wedge using CFA -- 10.4.2 
       Exploring the concept of the fork using CFA -- 10.4.3 
       Exploring the concept of reciprocal causation using CFA --
       10.5 Covariates in CFA -- 10.5.1 Categorical covariates: 
       stratification variables -- 10.5.2 Continuous covariates -
       - 10.6 CFA of ordinal variables 
505 8  10.7 Graphical displays of CFA results -- 10.7.1 
       Displaying the patterns of types and antitypes based on 
       test statistics or frequencies -- 10.7.2 Mosaic displays -
       - 10.8 Aggregating results from CFA -- 10.9 Employing CFA 
       in tandem with other methods of analysis -- 10.9.1 CFA and
       cluster analysis -- 10.9.2 CFA and discriminant analysis -
       - 11. Alternative approaches to CFA -- 11.1 Kieser and 
       Victor's quasi-independence model of CFA -- 11.2 Bayesian 
       CFA -- 11.2.1 The prior and posterior distributions -- 
       11.2.2 Types and antitypes in Bayesian CFA -- 11.2.3 
       Patterns of types and antitypes and protecting α -- 11.2.4
       Data examples -- Part V: Computational Issues -- 12. Using
       General Purpose Software to Perform CFA -- 12.1 Using 
       SYSTAT to perform CFA -- 12.1.1 SYSTAT's two-way cross-
       tabulation module -- 12.1.2 SYSTAT's log-linear modeling 
       module -- 12.2 Using S-plus to perform Bayesian CFA -- 
       12.3 Using CFA 2002 to perform frequentist CFA -- 12.3.1 
       Program description -- 12.3.2 Sample applications -- 
       12.3.2.1 First order CFA -- keyboard input of frequency 
       table -- 12.3.2.2 Two-Sample CFA with Two Predictors -- 
       Keyboard Input -- 12.3.2.3 Second Order CFA -- frequency 
       table input via file -- 12.3.2.4 CFA with covariates -- 
       input via file (frequencies) and keyboard (covariate) -- 
       Part VI: References, Appendices, and Indices -- References
       -- Appendix A: A brief introduction to log-linear modeling
       -- Appendix B: Table of α*-levels for the Bonferroni and 
       Holm adjustments -- Author Index -- Subject Index 
520    Configural Frequency Analysis (CFA) provides an up-to-the-
       minute comprehensive introduction to its techniques, 
       models, and applications. Written in a formal yet 
       accessible style, actual empirical data examples are used 
       to illustrate key concepts. Step-by-step program sequences
       are used to show readers how to employ CFA methods using 
       commercial software packages, such as SAS, SPSS, SYSTAT, S
       -Plus, or those written specifically to perform CFA. CFA 
       is an important method for analyzing results involved with
       categorical and longitudinal data. It allows one to answer
       the question of whether individual cells or groups of 
       cells of cross-classifications differ significantly from 
       expectations. The expectations are calculated using 
       methods employed in log-linear modeling or a priori 
       information. It is the only statistical method that allows
       one to make statements about empty areas in the data 
       space. Applied and or person-oriented researchers, 
       statisticians, and advanced students interested in CFA and
       categorical and longitudinal data will find this book to 
       be a valuable resource. Developed since 1969, this method 
       is now used by a large number of researchers around the 
       world in a variety of disciplines, including psychology, 
       education, medicine, and sociology. Configural Frequency 
       Analysis will serve as an excellent text for courses on 
       configural frequency analysis, categorical variable 
       analysis, or analysis of contingency tables. Prerequisites
       include an understanding of descriptive statistics, 
       hypothesis testing, statistical model fitting, and some 
       understanding of categorical data analysis and matrix 
       algebra 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Discriminant analysis.;Psychometrics 
655  4 Electronic books 
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       Analysis : Methods, Models, and Applications|dMahwah : 
       Taylor & Francis Group,c2002|z9780805843231 
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