The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

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Missing data: Our view of the state of the art.

A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.

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A comparison of methods to test mediation and other intervening variable effects.

"With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website offers data and program syntax files for most of the research examples, as well as links to CFA-related resources. New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages"--

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Confirmatory Factor Analysis for Applied Research

About the Author Series Editor's Introduction 1. Introduction 2. Linear Fixed Effects Models: Basics 3. Fixed Effects Logistic Models 4. Fixed Effects Models for Count Data 5. Fixed Effects Models for Events History Data 6. Structural Equation Models With Fixed Effects Appendix 1 Appendix 2 References Author Index Subject Index

Fixed Effects Regression Models

Survival analysis using the SAS system : a practical guide

From the Publisher:
If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, this book is for you! Informal and nontechnical, this book both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using SAS. Several social science real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis with the PHREG procedure, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. 
Supports releases 6.12 and higher of SAS software.
Author Biography: Paul D. Allison Paul D. Allison is a Professor of Sociology and Epidemiology at the University of Pennsylvaniawhere he teaches graduate courses in survival analysis and categorical data analysis. Every summer he teaches a five-day workshop about logistic regression that is attended by researchers from around the United States and Canada. Besides his numerous statistical papers, he has also published extensively on the subject of scientists' careers.

Logistic Regression Using the SAS System : Theory and Application

Introduction A Discrete-Time Method Parametric Methods for Continuous-Time Data Proportional Hazards and Partial Likelihood Multiple Kinds of Events Repeated Events Change of States Conclusion

Event History Analysis : Regression for Longitudinal Event Data

The history of an individual or group can always be characterized as a sequence of events People finish school, enter the labor force, marry, give birth, get promoted, change employers, retire, and ultimately die Formal organizations merge, adopt innovations, and go bankrupt Nations experience wars, revolutions, and peaceful changes of government It is surely the business of sociology to explain and predict the occurrence of such events Why is it, for example, that some individuals try marijuana while others do not? Why do some people marry early while others marry late? Do educational

https://www.jstor.org/stable/pdfplus/270718.pdf

Paul D. Allison

Papers

Fixed Effects Regression Models

Survival analysis using the SAS system : a practical guide

Logistic Regression Using the SAS System : Theory and Application

Event History Analysis : Regression for Longitudinal Event Data

Discrete-Time Methods for the Analysis of Event Histories