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Interpreting and Comparing Effects in Logistic, Probit and Logit Regression shows applied researchers how to compare coefficient estimates from regression models for categorical dependent variables in typical research situations. It presents a practical, unified treatment of these problems, and considers the advantages and disadvantages of each approach, and when to use them.
Log-linear, logit and logistic regression models are the most common ways of analyzing data when (at least) the dependent variable is categorical. This volume shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one equation, (ii) between identical equations estimated in different subgroups, and (iii) between nested equations. Each of these three kinds of comparisons brings along its own particular form of comparison problems. Further, in all three areas, the precise nature of comparison problems in logistic regression depends on how the logistic regression model is looked at and how the effects of the independent variables are computed. This volume presents a practical, unified treatment of these problems, and considers the advantages and disadvantages of each approach, and when to use them, so that applied researchers can make the best choice related to their research problem. The techniques are illustrated with data from simulation experiments and from publicly available surveys. The datasets, along with Stata syntax, are available on a companion website at: https://study.sagepub.com/researchmethods/qass/hagenaars-interpreting-effects.
What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.
Many problems in the social sciences are amenable to analysis using the analytical tools of logit and probit models. This book explains what ordered and multinomial models are and also shows how to apply them to analysing issues in the social sciences.
After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.
Interpreting Effects in Logistic Regression and Logit Models shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one model, (ii) between identical models estimated in different subgroups, and (iii) between nested models. Additionally, this volume presents a practical, unified treatment of comparison problems and considers the advantages and disadvantages of each approach and when to use them.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Evaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.