Author von Eye, Alexander
Title Configural Frequency Analysis : Methods, Models, and Applications
Imprint Mahwah : Taylor & Francis Group, 2002
©2002
book jacket
Edition 1st ed
Descript 1 online resource (468 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Note 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
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
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
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
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
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
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