Maximum Likelihood For Social Science
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Maximum Likelihood For Social Science
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Author : Michael D. Ward
language : en
Publisher: Cambridge University Press
Release Date : 2018-11-22
Maximum Likelihood For Social Science written by Michael D. Ward and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-22 with Political Science categories.
Practical, example-driven introduction to maximum likelihood for the social sciences. Emphasizes computation in R, model selection and interpretation.
Maximum Likelihood Estimation
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Author : Scott R. Eliason
language : en
Publisher: SAGE
Release Date : 1993
Maximum Likelihood Estimation written by Scott R. Eliason and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Mathematics categories.
"Maximum Likelihood Estimation. . . provides a useful introduction. . . it is clear and easy to follow with applications and graphs. . . . I consider this a very useful book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Eliason reveals to the reader the underlying logic and practice of maximum likelihood (ML) estimation by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
The Sage Encyclopedia Of Social Science Research Methods
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Author : Michael Lewis-Beck
language : en
Publisher: SAGE
Release Date : 2004
The Sage Encyclopedia Of Social Science Research Methods written by Michael Lewis-Beck and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Reference categories.
"This defining work will be valuable to readers and researchers in social sciences and humanities at all academic levels. As a teaching resource it will be useful to instructors and students alike and will become a standard reference source. Essential for general and academic collections."--CHOICE"Appreciative users of this volume will be students, faculty, and researchers in academic, special, and large public libraries, for whom it is recommended."--LIBRARY JOURNALSAGE Reference is proud to announce The SAGE Encyclopedia of Social Science Research Methods, a three-volume resource that is a first of its kind, developed by the leading publisher of social science research methods books and journals. This unique multi-volume reference set offers readers an all-encompassing education in the ways of social science researchers. Written to be accessible to general readers, entries do not require any advanced knowledge or experience to understand the purposes and basic principles of any of the methods. The Encyclopedia features two major types of entries: definitions, consisting of a paragraph or two, provide a quick explanation of a methodological term; and topical treatments or essays discussing the nature, history, application/example and implication of using a certain method. Also included are suggested readings and references for future study. To help provide a more complete explanation than is often achieved within the scope of a single article, key terms and concepts appear in SMALL CAPITAL LETTERSto refer readers to related terms explained elsewhere. In addition to epistemological issues that influence the nature of research questions and assumptions, The SAGE Encyclopedia of Social Science Research Methods tackles topics not normally viewed as part of social science research methodology, from philosophical issues such as poststructuralismto advanced statistical techniques. In covering the full range of qualitative and quantitative data analyses, this key reference offers an integrated approach that allows the reader to choose the most appropriate and robust techniques to apply to each situation. Many entries treat traditional topics in a novel way, stimulating both interest and new perspectives. One example is the entry Econometrics, by Professor DamodarGujarati. Following a process which many educators preach but seldom practice, Gujarati walks the reader twice through the research process from economic theory to data and models to analysis, once in principle and a second time with an example. In using the ordinary process of economic research to achieve an extraordinary impact, he leaves the reader thinking not only about methods and models but also the fundamental purpose of econometrics. Topics Covered Analysis of Variance Association and Correlation Basic Qualitative Research Basic Statistics Causal Modeling (Structural Equations) Discourse/Conversation Analysis Econometrics Epistemology Ethnography Evaluation Event History Analysis Experimental Design Factor Analysis & Related Techniques Feminist Methodology Generalized Linear Models Historical/Comparative Interviewing in Qualitative Research Latent Variable Model Life History/Biography LoglinearModels (Categorical Dependent Variables) Longitudinal Analysis Mathematics and Formal Models Measurement Level Measurement Testing & Classification Multiple Regression Multilevel Analysis Qualitative Data Analysis Sampling in Surveys Sampling in Qualitative Research Scaling Significance Testing Simple Regression Survey Design Time Series Key Features Over 900 entries arranged A to Z Each entry is written by a leading authority in the field, covering both quantitative and qualitative methods Covers all disciplines within the social sciences Contains both concise definitions and in-depth essays Three volumes and more than 1500 pages
The Sage Handbook Of Quantitative Methodology For The Social Sciences
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Author : David Kaplan
language : en
Publisher: SAGE Publications
Release Date : 2004-06-21
The Sage Handbook Of Quantitative Methodology For The Social Sciences written by David Kaplan and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-21 with Social Science categories.
The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.
Mathematics For Social Scientists
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Author : Jonathan Kropko
language : en
Publisher: SAGE Publications
Release Date : 2015-09-09
Mathematics For Social Scientists written by Jonathan Kropko and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-09 with Social Science categories.
Written for social science students who will be working with or conducting research, Mathematics for Social Scientists offers a non-intimidating approach to learning or reviewing math skills essential in quantitative research methods. The text is designed to build students’ confidence by presenting material in a conversational tone and using a wealth of clear and applied examples. Author Jonathan Kropko argues that mastering these concepts will break students’ reliance on using basic models in statistical software, allowing them to engage with research data beyond simple software calculations.
Maximum Likelihood Estimation For Sample Surveys
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Author : Raymond L. Chambers
language : en
Publisher: CRC Press
Release Date : 2012-05-02
Maximum Likelihood Estimation For Sample Surveys written by Raymond L. Chambers and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-02 with Mathematics categories.
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.
Econometric Modelling With Time Series
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Author : Vance Martin
language : en
Publisher: Cambridge University Press
Release Date : 2013
Econometric Modelling With Time Series written by Vance Martin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Business & Economics categories.
"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn"-- publisher.
Probability And Social Science
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Author : Daniel Courgeau
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-23
Probability And Social Science written by Daniel Courgeau and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-23 with Social Science categories.
This work examines in depth the methodological relationships that probability and statistics have maintained with the social sciences from their emergence. It covers both the history of thought and current methods. First it examines in detail the history of the different paradigms and axioms for probability, from their emergence in the seventeenth century up to the most recent developments of the three major concepts: objective, subjective and logicist probability. It shows the statistical inference they permit, different applications to social sciences and the main problems they encounter. On the other side, from social sciences—particularly population sciences—to probability, it shows the different uses they made of probabilistic concepts during their history, from the seventeenth century, according to their paradigms: cross-sectional, longitudinal, hierarchical, contextual and multilevel approaches. While the ties may have seemed loose at times, they have more often been very close: some advances in probability were driven by the search for answers to questions raised by the social sciences; conversely, the latter have made progress thanks to advances in probability. This dual approach sheds new light on the historical development of the social sciences and probability, and on the enduring relevance of their links. It permits also to solve a number of methodological problems encountered all along their history.
Statistical Modeling And Inference For Social Science
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Author : Sean Gailmard
language : en
Publisher: Cambridge University Press
Release Date : 2014-06-09
Statistical Modeling And Inference For Social Science written by Sean Gailmard and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-09 with Business & Economics categories.
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.
Handbook Of Computational Social Science Volume 2
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Author : Uwe Engel
language : en
Publisher: Routledge
Release Date : 2021-11-10
Handbook Of Computational Social Science Volume 2 written by Uwe Engel and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-10 with Computers categories.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.