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| cross sectional data analysis: Cross Section and Experimental Data Analysis Using EViews I. Gusti Ngurah Agung, 2011-02-15 A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews. This book is a reflection of the vast experience and knowledge of the author. It is a useful reference for students and practitioners dealing with cross sectional data analysis ... The strength of the book lies in its wealth of material and well structured guidelines ... Prof. Yohanes Eko Riyanto, Nanyang Technological University, Singapore This is superb and brilliant. Prof. Agung has skilfully transformed his best experiences into new knowledge ... creating a new way of understanding data analysis. Dr. I Putu Gede Ary Suta, The Ary Suta Center, Jakarta Basic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less experienced researchers. This book addresses this issue by providing a hands-on practical guide to conducting data analysis using EViews combined with a variety of illustrative models (and their extensions). Models having numerically dependent variables based on a cross-section data set (such as univariate, multivariate and nonlinear models as well as non-parametric regressions) are concentrated on. It is shown that a wide variety of hypotheses can easily be tested using EViews. Cross Section and Experimental Data Analysis Using EViews: Provides step-by-step directions on how to apply EViews to cross section data analysis - from multivariate analysis and nonlinear models to non-parametric regression Presents a method to test for all possible hypotheses based on each model Proposes a new method for data analysis based on a multifactorial design model Demonstrates that statistical summaries in the form of tabulations are invaluable inputs for strategic decision making Contains 200 examples with special notes and comments based on the author’s own empirical findings as well as over 400 illustrative outputs of regressions from EViews Techniques are illustrated through practical examples from real situations Comes with supplementary material, including work-files containing selected equation and system specifications that have been applied in the book This user-friendly introduction to EViews is ideal for Advanced undergraduate and graduate students taking finance, econometrics, population, or public policy courses, as well as applied policy researchers. |
| cross sectional data analysis: Econometric Analysis of Cross Section and Panel Data, second edition Jeffrey M. Wooldridge, 2010-10-01 The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of generalized instrumental variables (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the generalized estimating equation literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain obvious procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights. |
| cross sectional data analysis: Encyclopedia of Survey Research Methods Paul J. Lavrakas, 2008-09-12 To the uninformed, surveys appear to be an easy type of research to design and conduct, but when students and professionals delve deeper, they encounter the vast complexities that the range and practice of survey methods present. To complicate matters, technology has rapidly affected the way surveys can be conducted; today, surveys are conducted via cell phone, the Internet, email, interactive voice response, and other technology-based modes. Thus, students, researchers, and professionals need both a comprehensive understanding of these complexities and a revised set of tools to meet the challenges. In conjunction with top survey researchers around the world and with Nielsen Media Research serving as the corporate sponsor, the Encyclopedia of Survey Research Methods presents state-of-the-art information and methodological examples from the field of survey research. Although there are other how-to guides and references texts on survey research, none is as comprehensive as this Encyclopedia, and none presents the material in such a focused and approachable manner. With more than 600 entries, this resource uses a Total Survey Error perspective that considers all aspects of possible survey error from a cost-benefit standpoint. Key Features Covers all major facets of survey research methodology, from selecting the sample design and the sampling frame, designing and pretesting the questionnaire, data collection, and data coding, to the thorny issues surrounding diminishing response rates, confidentiality, privacy, informed consent and other ethical issues, data weighting, and data analyses Presents a Reader′s Guide to organize entries around themes or specific topics and easily guide users to areas of interest Offers cross-referenced terms, a brief listing of Further Readings, and stable Web site URLs following most entries The Encyclopedia of Survey Research Methods is specifically written to appeal to beginning, intermediate, and advanced students, practitioners, researchers, consultants, and consumers of survey-based information. |
| cross sectional data analysis: Planning Clinical Research Robert A. Parker, Nancy G. Berman, 2016-10-12 Planning clinical research requires many decisions. The authors of this book explain key decisions with examples showing what works and what does not. |
| cross sectional data analysis: Econometrics in Theory and Practice Panchanan Das, 2019-09-05 This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research. The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research. Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata 15.1, and assumes that the reader is somewhat familiar with the Strata software. The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis that economists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data. There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data. In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. It covers intensively both the univariate and multivariate time series econometric models and their applications with software programming in six chapters. Part IV takes care of panel data analysis in four chapters. Different aspects of fixed effects and random effects are discussed here. Panel data analysis has been extended by taking dynamic panel data models which are most suitable for macroeconomic research. The book is invaluable for students and researchers of social sciences, business, management, operations research, engineering, and applied mathematics. |
| cross sectional data analysis: Longitudinal Data Analysis Garrett Fitzmaurice, Marie Davidian, Geert Verbeke, Geert Molenberghs, 2008-08-11 Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory |
| cross sectional data analysis: Spatial Econometrics J. Paul Elhorst, 2013-09-30 This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted. |
| cross sectional data analysis: Advances in Cross-Section Data Methods in Applied Economic Research Nicholas Tsounis, Aspasia Vlachvei, 2020-02-24 This proceedings volume presents new methods and applications in applied economics with special interest in advanced cross-section data estimation methodology. Featuring select contributions from the 2019 International Conference on Applied Economics (ICOAE 2019) held in Milan, Italy, this book explores areas such as applied macroeconomics, applied microeconomics, applied financial economics, applied international economics, applied agricultural economics, applied marketing and applied managerial economics. International Conference on Applied Economics (ICOAE) is an annual conference that started in 2008, designed to bring together economists from different fields of applied economic research, in order to share methods and ideas. Applied economics is a rapidly growing field of economics that combines economic theory with econometrics, to analyze economic problems of the real world, usually with economic policy interest. In addition, there is growing interest in the field of applied economics for cross-section data estimation methods, tests and techniques. This volume makes a contribution in the field of applied economic research by presenting the most current research. Featuring country specific studies, this book is of interest to academics, students, researchers, practitioners, and policy makers in applied economics, econometrics and economic policy. |
| cross sectional data analysis: Longitudinal and Panel Data Edward W. Frees, 2004-08-16 An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers. |
| cross sectional data analysis: Longitudinal Qualitative Research Johnny Saldaña, 2003 Johnny Saldana outlines the basic elements of longitudinal qualitative data, focusing on micro-levels of change observed within individual cases and groups of participants. He draws upon his primary experience in theater education to examine time and change in longitudinal qualitative studies; contending that playwrights and qualitative researchers write for the same purpose: to create a unique, insightful, and engaging text about the human condition. Offering sixteen specific questions through which researchers may approach the analysis of longitudinal qualitative data, Professor Saldana presents a text intended as a primer for fellow newcomers to long term inquiry, based on traditional social science methods from traditional qualitative and quantitative paradigms, but enriched by an artist-educator's unconventional perspective. |
| cross sectional data analysis: Spatial Analysis for the Social Sciences David Darmofal, 2015-11-12 This book shows how to model the spatial interactions between actors that are at the heart of the social sciences. |
| cross sectional data analysis: A Practical Guide to Using Panel Data Simonetta Longhi, Alita Nandi, 2014-12-01 This timely, thoughtful book provides a clear introduction to using panel data in research. It describes the different types of panel datasets commonly used for empirical analysis, and how to use them for cross sectional, panel, and event history analysis. Longhi and Nandi then guide the reader through the data management and estimation process, including the interpretation of the results and the preparation of the final output tables. Using existing data sets and structured as hands-on exercises, each chapter engages with practical issues associated with using data in research. These include: Data cleaning Data preparation Computation of descriptive statistics Using sample weights Choosing and implementing the right estimator Interpreting results Preparing final output tables Graphical representation Written by experienced authors this exciting textbook provides the practical tools needed to use panel data in research. |
| cross sectional data analysis: The SAGE Handbook of Regression Analysis and Causal Inference Henning Best, Christof Wolf, 2013-12-20 ′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. |
| cross sectional data analysis: Mixed Effects Models for Complex Data Lang Wu, 2009-11-11 Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis. |
| cross sectional data analysis: Quantile Regression for Cross-Sectional and Time Series Data Jorge M. Uribe, Montserrat Guillen, 2020-03-30 This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R. |
| cross sectional data analysis: Methods and Applications of Longitudinal Data Analysis Xian Liu, 2015-09-01 Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: - descriptive methods for delineating trends over time - linear mixed regression models with both fixed and random effects - covariance pattern models on correlated errors - generalized estimating equations - nonlinear regression models for categorical repeated measurements - techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples. |
| cross sectional data analysis: Age-Period-Cohort Analysis Yang Yang, Kenneth C. Land, 2016-04-19 This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions. |
| cross sectional data analysis: Research Design in Business and Management Stefan Hunziker, Michael Blankenagel, 2021-11-09 The present book project on Research Design, which is planned in English, is intended to create an innovative textbook that can be used at university undergraduate and graduate levels in internationally oriented education in the German-speaking countries. This textbook shall provide comprehensive guidance for students when tackling their (applied) research papers. Instead of reiterating qualitative and quantitative methods it focuses on how to come up with an appropriate research design that allows the student to make the intended intellectual contribution. Starting from the desired (hypothetical) conclusion or statement the student will be guided through the process of finding the appropriate Research Question that will be answered by such a statement and the required Research Design consisting of data collection and data analysis, that allows for such a statement as the conclusion of the study. Common Research Designs in Business and Management, i.e. well beyond the standard Research Designs of Social Sciences and curtailed to the focus area, will be described with regard to their suitability to answer specific kinds of questions as well as the idiosyncrasies of the these Designs and their impact on the written research reports. Examples for each Research Design will be provided as well as guidance about how to write about such research. |
| cross sectional data analysis: Modern Epidemiology Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, 2008 The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology. |
| cross sectional data analysis: Student's Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data, second edition Jeffrey M. Wooldridge, 2011-06-24 This is the essential companion to the second edition of Jeffrey Wooldridge's widely used graduate econometrics text. The text provides an intuitive but rigorous treatment of two state-of-the-art methods used in contemporary microeconomic research. The numerous end-of-chapter exercises are an important component of the book, encouraging the student to use and extend the analytic methods presented in the book. This manual contains advice for answering selected problems, new examples, and supplementary materials designed by the author, which work together to enhance the benefits of the text. Users of the textbook will find the manual a necessary adjunct to the book. |
| cross sectional data analysis: Longitudinal Categorical Data Analysis Brajendra C. Sutradhar, 2014-10-30 This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics. |
| cross sectional data analysis: Quantile Regression I. Gusti Ngurah Agung, 2021-06-18 QUANTILE REGRESSION A thorough presentation of Quantile Regression designed to help readers obtain richer information from data analyses The conditional least-square or mean-regression (MR) analysis is the quantitative research method used to model and analyze the relationships between a dependent variable and one or more independent variables, where each equation estimation of a regression can give only a single regression function or fitted values variable. As an advanced mean regression analysis, each estimation equation of the mean-regression can be used directly to estimate the conditional quantile regression (QR), which can quickly present the statistical results of a set nine QR(τ)s for τ(tau)s from 0.1 up to 0.9 to predict detail distribution of the response or criterion variable. QR is an important analytical tool in many disciplines such as statistics, econometrics, ecology, healthcare, and engineering. Quantile Regression: Applications on Experimental and Cross Section Data Using EViews provides examples of statistical results of various QR analyses based on experimental and cross section data of a variety of regression models. The author covers the applications of one-way, two-way, and n-way ANOVA quantile regressions, QRs with multi numerical predictors, heterogeneous QRs, and latent variables QRs, amongst others. Throughout the text, readers learn how to develop the best possible quantile regressions and how to conduct more advanced analysis using methods such as the quantile process, the Wald test, the redundant variables test, residual analysis, the stability test, and the omitted variables test. This rigorous volume: Describes how QR can provide a more detailed picture of the relationships between independent variables and the quantiles of the criterion variable, by using the least-square regression Presents the applications of the test for any quantile of any numerical response or criterion variable Explores relationship of QR with heterogeneity: how an independent variable affects a dependent variable Offers expert guidance on forecasting and how to draw the best conclusions from the results obtained Provides a step-by-step estimation method and guide to enable readers to conduct QR analysis using their own data sets Includes a detailed comparison of conditional QR and conditional mean regression Quantile Regression: Applications on Experimental and Cross Section Data Using EViews is a highly useful resource for students and lecturers in statistics, data analysis, econometrics, engineering, ecology, and healthcare, particularly those specializing in regression and quantitative data analysis. |
| cross sectional data analysis: Analysis of Health Surveys Edward L. Korn, Barry I. Graubard, 2011-01-25 How to apply statistical methods to survey data--a guide toeffective analysis of health surveys. With large health surveys becoming increasingly available forpublic use, researchers with little experience in survey methodsare often faced with analyzing data from surveys to addressscientific and programmatic questions. This practical book providesstatistical techniques for use in survey analysis, making healthsurveys accessible to statisticians, biostatisticians,epidemiologists, and health researchers. The authors clearlyexplain the theory and methods of survey analysis along withreal-world applications. They draw on their work at the NationalInstitutes of Health as well as up-to-date information from acrossthe literature to present: * The sampling background necessary to understand health surveys. * The application of such techniques as t-tests, linear regression,logistic regression, and survival analysis to survey data. * The use of sample weights in survey data analysis. * Dealing with complications in variance estimation in large healthsurveys. * Applications involving cross-sectional, longitudinal, andmultiple cross-sectional surveys, and the use of surveys to performpopulation- based case-control analyses. * Guidance on the correct use of statistical methods found insoftware packages. * Extensive bibliography. |
| cross sectional data analysis: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| cross sectional data analysis: Pooled Cross-Sectional and Time Series Data Analysis Terry Dielman, 1988-09-29 A review of methods for estimating multivariate relationships of individual entities in a data base and for summarizing these relationships. Focuses on methodologies such as classical pooling, error components, analysis of covariance, seemingly unrelated regressions, and random coefficient regressio |
| cross sectional data analysis: Encyclopedia of Social Measurement Kimberly Kempf-Leonard, 2005 The Encyclopedia of Social Measurement captures the data, techniques, theories, designs, applications, histories, and implications of assigning numerical values to social phenomena. Responding to growing demands for transdisciplinary descriptions of quantitative and qualitative techniques, measurement, sampling, and statistical methods, it will increase the proficiency of everyone who gathers and analyzes data. Covering all core social science disciplines, the 300+ articles of the Encyclopedia of Social Measurement not only present a comprehensive summary of observational frameworks and mathematical models, but also offer tools, background information, qualitative methods, and guidelines for structuring the research process. Articles include examples and applications of research strategies and techniques, highlighting multidisciplinary options for observing social phenomena. The alphabetical arrangement of the articles, their glossaries and cross-references, and the volumes' detailed index will encourage exploration across the social sciences. Descriptions of important data sets and case studies will help readers understand resources they can often instantly access. Also available online via ScienceDirect - featuring extensive browsing, searching, and internal cross-referencing between articles in the work, plus dynamic linking to journal articles and abstract databases, making navigation flexible and easy. For more information, pricing options and availability visit www.info.sciencedirect.com. Introduces readers to the advantages and potential of specific techniques and suggests additional sources that readers can then consult to learn more Conveys a range of basic to complex research issues in sufficient detail to explain even the most complicated statistical technique. Readers are provided with references for further information Eleven substantive sections delineate social sciences and the research processes they follow to measure and provide new knowledge on a wide range of topics Authors are prominent scholars and methodologists from all social science fields Within each of the sections important components of quantitative and qualitative research methods are dissected and illustrated with examples from diverse fields of study Actual research experiences provide useful examples |
| cross sectional data analysis: Research Methodology in the Social, Behavioural and Life Sciences Herman J Ader, Gideon J Mellenbergh, 1999-12-07 This is an ideal text for advanced courses in research methods and experimental design. It argues that the methodology of quantitative research is a unified discipline with basic notions, procedures and ways of reasoning which can be applied across the social, behavioural and life sciences. Key designs, models and methods in research are covered by leading contributors in their field who seek to explain the fundamentals of the research process to enable the student to understand the broader implications and unifying themes. |
| cross sectional data analysis: The Aging Population in the Twenty-First Century National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Statistics for an Aging Population, 1988-02-01 It is not news that each of us grows old. What is relatively new, however, is that the average age of the American population is increasing. More and better information is required to assess, plan for, and meet the needs of a graying population. The Aging Population in the Twenty-First Century examines social, economic, and demographic changes among the aged, as well as many health-related topics: health promotion and disease prevention; quality of life; health care system financing and use; and the quality of careâ€especially long-term care. Recommendations for increasing and improving the data availableâ€as well as for ensuring timely access to themâ€are also included. |
| cross sectional data analysis: Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes Feng Qu, Chihwa Kao, 2020-08-24 This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field. |
| cross sectional data analysis: Analysis of Microdata Rainer Winkelmann, Stefan Boes, 2006-09-21 The availability of microdata has increased rapidly over the last decades, and standard statistical and econometric software packages for data analysis include ever more sophisticated modeling options. The goal of this book is to familiarize readers with a wide range of commonly used models, and thereby to enable them to become critical consumers of current empirical research, and to conduct their own empirical analyses. The focus of the book is on regression-type models in the context of large cross-section samples. In microdata applications, dependent variables often are qualitative and discrete, while in other cases, the sample is not randomly drawn from the population of interest and the dependent variable is censored or truncated. Hence, models and methods are required that go beyond the standard linear regression model and ordinary least squares. Maximum li- lihood estimation of conditional probability models and marginal probability e?ects are introduced here as the unifying principle for modeling, estimating and interpreting microdata relationships. We consider the limitation to m- imum likelihood sensible, from a pedagogical point of view if the book is to be used in a semester-long advanced undergraduate or graduate course, and from a practical point of view because maximum likelihood estimation is used in the overwhelming majority of current microdata research. In order to introduce and explain the models and methods, we refer to a number of illustrative applications. The main examples include the deter- nants of individual fertility, the intergenerational transmission of secondary schoolchoices,andthewageelasticityoffemalelaborsupply. |
| cross sectional data analysis: The Encyclopedia of Crime and Punishment Wesley G. Jennings, George E. Higgins, Mildred M. Maldonado-Molina, David N. Khey, 2016-01-19 The Encyclopedia of Crime and Punishment provides the most comprehensive reference for a vast number of topics relevant to crime and punishment with a unique focus on the multi/interdisciplinary and international aspects of these topics and historical perspectives on crime and punishment around the world. Named as one of Choice's Outstanding Academic Titles of 2016 Comprising nearly 300 entries, this invaluable reference resource serves as the most up-to-date and wide-ranging resource on crime and punishment Offers a global perspective from an international team of leading scholars, including coverage of the strong and rapidly growing body of work on criminology in Europe, Asia, and other areas Acknowledges the overlap of criminology and criminal justice with a number of disciplines such as sociology, psychology, epidemiology, history, economics, and public health, and law Entry topics are organized around 12 core substantive areas: international aspects, multi/interdisciplinary aspects, crime types, corrections, policing, law and justice, research methods, criminological theory, correlates of crime, organizations and institutions (U.S.), victimology, and special populations Organized, authored and Edited by leading scholars, all of whom come to the project with exemplary track records and international standing 3 Volumes www.crimeandpunishmentencyclopedia.com |
| cross sectional data analysis: Analyzing Social and Political Change Angela Dale, Richard B Davies, 1994-07-05 Understanding change over time is a central concern for research in sociology, political science, education, geography and related disciplines. It is also an issue which presents significant methodological problems, in response to which different techniques have been developed - for example, time series analysis, multilevel models, log-linear models and event history analysis. Outlining the nature of such techniques, this accessible collection covers: the respective values of cross-sectional and longitudinal data in the analysis of change; the variety of methods available for the analysis of change over time; the types of research objective to which various techniques are suited; the limitations and constraints of individual methods; and the different philosophies which underlie particular approaches. |
| cross sectional data analysis: Encyclopedia of Behavioral Medicine Marc D. Gellman, J. Rick Turner, |
| cross sectional data analysis: Econometrics Badi Hani Baltagi, 2002 As well as specification testing, Gauss-Newton regressions and regression diagnostics. In addition, the book features a set of empirical illustrations that demonstrate some of the basic results. The empirical exercises are solved using several econometric software packages. |
| cross sectional data analysis: Naked Statistics: Stripping the Dread from the Data Charles Wheelan, 2013-01-07 A New York Times bestseller Brilliant, funny…the best math teacher you never had. —San Francisco Chronicle Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called sexy. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life. |
| cross sectional data analysis: Practical Business Statistics Andrew F. Siegel, 1996-08 Practical Business Statistics, 5/e was written in response to instructors not wanting a formula driven, mathematically encyclopedic book. The use of computer applications means some topics no longer require coverage in detail. This allows future managers to know how to use and understand statistics. The text does this by using examples with real data that relate to the functional areas of business such as finance, accounting, and marketing. It de-emphasizes the theoretical, and presents the material in a well-written, easy style designed to motivate students. The emphasis is on understanding and applications as opposed to mathematical precision and formula detail. |
| cross sectional data analysis: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov) |
| cross sectional data analysis: Cloud Computing Systems and Applications in Healthcare Bhatt, Chintan M., Peddoju, S. K., 2016-08-30 The implementation of cloud technologies in healthcare is paving the way to more effective patient care and management for medical professionals around the world. As more facilities start to integrate cloud computing into their healthcare systems, it is imperative to examine the emergent trends and innovations in the field. Cloud Computing Systems and Applications in Healthcare features innovative research on the impact that cloud technology has on patient care, disease management, and the efficiency of various medical systems. Highlighting the challenges and difficulties in implementing cloud technology into the healthcare field, this publication is a critical reference source for academicians, technology designers, engineers, professionals, analysts, and graduate students. |
| cross sectional data analysis: Multi-Asset Risk Modeling Morton Glantz, Robert Kissell, 2013-12-03 Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which currently receives sparse attention in the literature. By giving coherent recommendations about which statistical models to use for which asset class, this book makes a real contribution to the sciences of portfolio management and risk management. - Covers all asset classes - Provides mathematical theoretical explanations of risk as well as practical examples with empirical data - Includes sections on equity risk modeling, futures and derivatives, credit markets, foreign exchange, and commodities |
| cross sectional data analysis: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
Jesus and the Cross - Biblical Archaeology Society
Jan 26, 2025 · The cross remains as you said, as a symbol of the degradation and suffering that Jesus submitted his body as a sacrifice for the sins of …
How Was Jesus Crucified? - Biblical Archaeology Society
Apr 16, 2025 · Gospel accounts of Jesus’s execution do not specify how exactly Jesus was secured to the cross. Yet in Christian tradition, Jesus had …
Roman Crucifixion Methods Reveal the History of Crucifixi…
Aug 17, 2024 · Nailing to a cross is “less severe” and “less humiliating” as the condemned dies within a day from loss of blood. Tying to a cross is the most …
The Staurogram - Biblical Archaeology Society
Sep 24, 2024 · But the cross had nothing to do with Jesus Christ. The New Catholic Encyclopedia explains: “The cross is found in both pre …
Ancient Crucifixion Images - Biblical Archaeology Society
Mar 15, 2025 · The cross is the ultimate symbol for the crucifixion of Christ. I give out pennies with the cross punched in them and tell people …
Jesus and the Cross - Biblical Archaeology Society
Jan 26, 2025 · The cross remains as you said, as a symbol of the degradation and suffering that Jesus submitted his body as a sacrifice for the sins of humanity. The cross with or without the …
How Was Jesus Crucified? - Biblical Archaeology Society
Apr 16, 2025 · Gospel accounts of Jesus’s execution do not specify how exactly Jesus was secured to the cross. Yet in Christian tradition, Jesus had his palms and feet pierced with …
Roman Crucifixion Methods Reveal the History of Crucifixion
Aug 17, 2024 · Nailing to a cross is “less severe” and “less humiliating” as the condemned dies within a day from loss of blood. Tying to a cross is the most severe form of punishment usually …
The Staurogram - Biblical Archaeology Society
Sep 24, 2024 · But the cross had nothing to do with Jesus Christ. The New Catholic Encyclopedia explains: “The cross is found in both pre-Christian and non-Christian cultures.” Jesus did not …
Ancient Crucifixion Images - Biblical Archaeology Society
Mar 15, 2025 · The cross is the ultimate symbol for the crucifixion of Christ. I give out pennies with the cross punched in them and tell people whether you are an atheist, Muslim, Moonie, etc. …
What is the difference between cross_validate and cross_val_score?
Mar 11, 2021 · Note: When the cv argument is an integer, cross_val_score uses the KFold or StratifiedKFold strategies by default, the latter being used if the estimator derives from …
A Tomb in Jerusalem Reveals the History of Crucifixion and Roman ...
Aug 6, 2024 · The second device added to the cross was the suppedaneum, or foot support. It was less painful than the sedile, but it also prolonged the victim’s agony. Ancient historians …
When to use cross-validation? - Data Science Stack Exchange
Jan 23, 2021 · Cross-validation. Hi, I'm deploying machine learning models in my MSc thesis using Weka. I have noticed that when I use 10-fold cross-validation in the training dataset I get …
Cross validation - Data Science Stack Exchange
Apr 17, 2024 · Then cross-validation is only applied to the training data as it is part of the training process. The other issue raised in the linked post do not seem to me specific to cross …
Nested-cross validation pipeline and confidence intervals
Nov 26, 2024 · However, I would like to point out that the "class imbalance problem" is not at all the big problem that it is sometimes made out to be. See the following two threads over at …
