Handbook of Statistical Modeling for the Social and Behavioral SciencesG. Arminger, Clifford C. Clogg, M.E. Sobel Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems. |
Contents
| 1 | |
| 10 | |
Missing Data | 39 |
4 | 54 |
Regression Models | 77 |
Nicholas T Longford | 176 |
Specification and Estimation of Mean and CovarianceStructure Models | 185 |
The Analysis of Contingency Tables | 251 |
1 | 373 |
Panel Analysis for Qualitative Variables | 401 |
2 | 404 |
Analysis of Event Histories | 453 |
4 | 538 |
5 | 547 |
7 | 557 |
| 579 | |
Other editions - View all
Handbook of Statistical Modeling for the Social and Behavioral Sciences G. Arminger,Clifford C. Clogg,M. E. Sobel No preview available - 2014 |
Handbook of Statistical Modeling for the Social and Behavioral Sciences G. Arminger,Clifford C. Clogg,M.E. Sobel No preview available - 2013 |
Handbook of Statistical Modeling for the Social and Behavioral Sciences G. Arminger,Clifford C. Clogg,M.E. Sobel No preview available - 1995 |
Common terms and phrases
algorithm approach Arminger assumed assumption asymptotic covariance matrix causal Clogg column computed conditional considered consistent estimator contingency table correlation corresponding covariance matrix covariance structure defined denoted density dependent variable derivatives diagonal discrepancy function discussed Econometrics elements error term example exogenous expected value explanatory variables exponential family formulation given æ independent individual inference iteration Journal Latent Class Models latent variable linear models log-likelihood function log-linear model logit model maximum likelihood estimation mean and covariance mean structure measure methods missing data ML estimation multinomial multivariate normal nonlinear nonresponse normal distribution observed obtained odds ratios panel data parameter estimates parameter vector parameterization Poisson prediction probability probit model procedure random coefficient models random effects random variable regression coefficients regression model residuals restrictions Rubin sample scale scores Section Sobel specification test statistic univariate variance y₁ θο