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After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias. "The goal of Regression Models for Categorical Dependent Variables Using Stata, Third Edition is to make it easier to carry out the computations necessary to fully interpret regression models for categorical outcomes by using Stata's margins command. Because the models are nonlinear, they are more complex to interpret. Most software packages that fit these models do not provide options that make it simple to compute the quantities useful for interpretation. In this book, the authors briefly describe the statistical issues involved in interpretation, and then they show how you can use Stata to perform these computations."--Back cover.
Programming --- Quantitative methods in social research --- Mathematical statistics --- Regression analysis. --- Social sciences --- Stata (logiciel) --- Analyse de régression --- Sciences sociales --- Statistical methods --- Data processing. --- Méthodes statistiques --- Stata. --- Regression Analysis --- Data processing --- Stata --- Analyse de régression. --- Social sciences - Statistical methods - Data processing
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"The goal of Regression Models for Categorical Dependent Variables Using Stata, Third Edition is to make it easier to carry out the computations necessary to fully interpret regression models for categorical outcomes by using Stata's margins command. Because the models are nonlinear, they are more complex to interpret. Most software packages that fit these models do not provide options that make it simple to compute the quantities useful for interpretation. In this book, the authors briefly describe the statistical issues involved in interpretation, and then they show how you can use Stata to perform these computations."--Back cover.
Programming --- Quantitative methods in social research --- Mathematical statistics --- Regression Analysis --- Social sciences --- Statistical methods --- Data processing --- Stata --- Regression analysis. --- Data processing. --- #SBIB:303H4 --- 519.246 --- 301.085 --- 311 --- 681.333 --- 681.33 --- Regression analysis --- -519.536 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Informatica in de sociale wetenschappen --- Kwantitatieve sociologische onderzoeksmethoden --- Statistiekwetenschap --- Softwarepakket --- Programmering --- -Data processing --- Stata. --- 519.536 --- Statistical methods&delete& --- Social sciences - Statistical methods - Data processing
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Quantitative methods in social research --- Statistical science --- Programming
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Regression analysis --- Social sciences --- Statistical methods --- Data processing --- Stata.
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"Social science practitioners have recently witnessed numerous episodes of influential research that fell apart upon close scrutiny. These instances have spurred suspicions that other published results may contain errors or may at least be less robust than they appear. In response, an influential movement has emerged across the social sciences for greater research transparency, openness, and reproducibility. Transparent and Reproducible Social Science Research crystallizes the new insights, practices, and methods of this rising interdisciplinary field"--Provided by publisher.
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Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.
Reproducible research. --- Social sciences --- Research. --- consequences. --- erroneous published results. --- false positives. --- health. --- influential research. --- invalid research. --- law enforcement. --- new approaches. --- new generation of students. --- new insights. --- new practices. --- non reproducible findings. --- professionals. --- public policy. --- research practices. --- rigorous scrutiny. --- scholars. --- social science research. --- social science. --- sound research. --- summarize. --- synthesize.
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