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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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