Edition 
1st ed 
Descript 
1 online resource (468 pages) 

text txt rdacontent 

computer c rdamedia 

online resource cr rdacarrier 
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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. Loglinear Base Models for CFA  2.1 Sample CFA base models and their design matrices  2.2 Admissibility of loglinear models as CFA base models  2.3 Sampling schemes and admissibility of CFA base models  2.3.1 Multinomial sampling  2.3.2 Productmultinomial 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 

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 testwise 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 PCFA models and approaches  6.2.2.1 Conditional PCFA: Stratifying on a variable  6.2.2.2 Biprediction CFA  6.2.2.3 Prediction coefficients  7. Comparing k Samples  7.1 Twosample CFA I: The original approach  7.2 Twosample CFA II: Alternative methods  7.2.1 GonzálesDebén's π*  7.2.2 Goodman's three elementary views of nonindependence  7.2.3 Measuring effect strength in twosample CFA  7.3 Comparing three or more samples  7.4 Three groups of variables: ISA plus ksample 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 

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 nonequidistant 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 prepost 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 Withinindividual CFA  Part IV: The CFA Specialty File and Alternative Approaches to CFA  10. More facets of CFA  10.1 CFA of crossclassifications 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 

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 quasiindependence 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 twoway crosstabulation module  12.1.2 SYSTAT's loglinear modeling module  12.2 Using Splus 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 TwoSample 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 loglinear modeling  Appendix B: Table of α*levels for the Bonferroni and Holm adjustments  Author Index  Subject Index 

Configural Frequency Analysis (CFA) provides an uptotheminute 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. Stepbystep program sequences are used to show readers how to employ CFA methods using commercial software packages, such as SAS, SPSS, SYSTAT, SPlus, 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 crossclassifications differ significantly from expectations. The expectations are calculated using methods employed in loglinear 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 personoriented 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 

Description based on publisher supplied metadata and other sources 

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries 
Link 
Print version: von Eye, Alexander Configural Frequency Analysis : Methods, Models, and Applications
Mahwah : Taylor & Francis Group,c2002 9780805843231

Subject 
Discriminant analysis.;Psychometrics


Electronic books

