5. Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing DataSociological Methodology - Tập 30 Số 1 - Trang 165-200 - 2000
Ke‐Hai Yuan, Peter M. Bentler
Survey and longitudinal studies in the social and behavioral sciences generally contain missing data. Mean and covariance structure models play an important role in analyzing such data. Two promising methods for dealing with missing data are a direct maximum-likelihood and a two-stage approach based on the unstructured mean and covariance estimates obtained by the EM-algorithm. Typical as...... hiện toàn bộ
Cluster AnalysisSociological Methodology - Tập 6 - Trang 59 - 1975
Kenneth D. Bailey
A General Framework for Comparing Predictions and Marginal Effects across ModelsSociological Methodology - Tập 49 Số 1 - Trang 152-189 - 2019
Trenton D. Mize, Long Doan, J. Scott Long
Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifyi...... hiện toàn bộ
Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and ProbitSociological Methodology - Tập 42 Số 1 - Trang 286-313 - 2012
Kristian Bernt Karlson, Anders Holm, Richard Breen
Logit and probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot...... hiện toàn bộ
Assessing Differences between Nested and Cross-Classified Hierarchical ModelsSociological Methodology - Tập 49 Số 1 - Trang 220-257 - 2019
David Melamed, Mike Vuolo
In multilevel data, cross-classified data structures are common. For example, this occurs when individuals move to different regions in longitudinal data or students go to different secondary schools than their primary school peers. In both cases, the data structure is no longer fully nested. Estimating cross-classified multilevel models is computationally intensive, so researchers have u...... hiện toàn bộ