Advertisement
| cross sectional study vs time series: Design and Analysis of Time Series Experiments Richard McCleary, David McDowall, Bradley J. Bartos, 2017 Design and Analysis of Time Series Experiments develops methods and models for analysis and interpretation of time series experiments while also addressing recent developments in causal modeling. Unlike other time series texts, it integrates the statistical issues of design, estimation, and interpretation with foundational validity issues. Drawing on examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, this text addresses researchers and graduate students in a wide range of the behavioral, biomedical, and social sciences. |
| cross sectional study vs time series: 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 study vs time series: The Oxford Handbook of Political Methodology Janet M. Box-Steffensmeier, Henry E. Brady, David Collier, 2008 The Oxford Handbooks of Political Science are the essential guide to the state of political science today. With engaging contributions from major international scholars The Oxford Handbook of Political Methodology provides the key point of reference for anyone working throughout the discipline. |
| cross sectional study vs time series: 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 study vs time series: 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 study vs time series: Introduction to Modern Time Series Analysis Gebhard Kirchgässner, Jürgen Wolters, 2008-08-27 This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary. |
| cross sectional study vs time series: 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 study vs time series: 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 study vs time series: An Introduction to Epidemiology for Health Professionals Jørn Olsen, Kaare Christensen, Jeff Murray, Anders Ekbom, 2010-06-14 Today, the public worries about emerging diseases and rapid changes of the frequency of well known diseases like autism, diabetes and obesity making the word epidemic part of the general discussion. Epidemiology should therefore be a basic component of medical training, yet often it is undertaught or even neglected. Concise and readable while also rigorous and thorough, An Introduction to Epidemiology for Health Professionals goes beyond standard textbook content to ground the reader in scientific methods most relevant to the current health landscape and the evolution of evidence-based medicine—valuable keys to better understanding of disease process, effective prevention, and targeted treatment. |
| cross sectional study vs time series: Time Series and Panel Data Econometrics M. Hashem Pesaran, 2015 The book describes and illustrates many advances that have taken place in a number of areas in theoretical and applied econometrics over the past four decades. |
| cross sectional study vs time series: 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 study vs time series: 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 study vs time series: 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. |
| cross sectional study vs time series: 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 study vs time series: 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 study vs time series: Nonlinear Time Series Jiti Gao, 2007-03-22 Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully |
| cross sectional study vs time series: State-Space Methods for Time Series Analysis Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade, 2018-09-03 The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work. |
| cross sectional study vs time series: Econometric Analysis of Panel Data Badi Baltagi, 2008-06-30 Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models. |
| cross sectional study vs time series: Interrupted Time Series Analysis David McDowall, Richard McCleary, Bradley J. Bartos, 2019 Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments. |
| cross sectional study vs time series: Quantitative Momentum Wesley R. Gray, Jack R. Vogel, 2016-10-03 The individual investor's comprehensive guide to momentum investing Quantitative Momentum brings momentum investing out of Wall Street and into the hands of individual investors. In his last book, Quantitative Value, author Wes Gray brought systematic value strategy from the hedge funds to the masses; in this book, he does the same for momentum investing, the system that has been shown to beat the market and regularly enriches the coffers of Wall Street's most sophisticated investors. First, you'll learn what momentum investing is not: it's not 'growth' investing, nor is it an esoteric academic concept. You may have seen it used for asset allocation, but this book details the ways in which momentum stands on its own as a stock selection strategy, and gives you the expert insight you need to make it work for you. You'll dig into its behavioral psychology roots, and discover the key tactics that are bringing both institutional and individual investors flocking into the momentum fold. Systematic investment strategies always seem to look good on paper, but many fall down in practice. Momentum investing is one of the few systematic strategies with legs, withstanding the test of time and the rigor of academic investigation. This book provides invaluable guidance on constructing your own momentum strategy from the ground up. Learn what momentum is and is not Discover how momentum can beat the market Take momentum beyond asset allocation into stock selection Access the tools that ease DIY implementation The large Wall Street hedge funds tend to portray themselves as the sophisticated elite, but momentum investing allows you to 'borrow' one of their top strategies to enrich your own portfolio. Quantitative Momentum is the individual investor's guide to boosting market success with a robust momentum strategy. |
| cross sectional study vs time series: 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 study vs time series: Handbook for Clinical Research Flora Hammond, MD, James F. Malec, Todd Nick, Ralph Buschbacher, MD, 2014-08-26 With over 80 information-packed chapters, Handbook for Clinical Research delivers the practical insights and expert tips necessary for successful research design, analysis, and implementation. Using clear language and an accessible bullet point format, the authors present the knowledge and expertise developed over time and traditionally shared from mentor to mentee and colleague to colleague. Organized for quick access to key topics and replete with practical examples, the book describes a variety of research designs and statistical methods and explains how to choose the best design for a particular project. Research implementation, including regulatory issues and grant writing, is also covered. The book opens with a section on the basics of research design, discussing the many ways in which studies can be organized, executed, and evaluated. The second section is devoted to statistics and explains how to choose the correct statistical approach and reviews the varieties of data types, descriptive and inferential statistics, methods for demonstrating associations, hypothesis testing and prediction, specialized methods, and considerations in epidemiological studies and measure construction. The third section covers implementation, including how to develop a grant application step by step, the project budget, and the nuts and bolts of the timely and successful completion of a research project and documentation of findings: procedural manuals and case report forms collecting, managing and securing data operational structure and ongoing monitoring and evaluation and ethical and regulatory concerns in research with human subjects. With a concise presentation of the essentials for successful research, the Handbook for Clinical Research is a valuable addition to the library of any student, research professional, or clinician interested in expanding the knowledge base of his or her field. Key Features: Delivers the essential elements, practical insights, and trade secrets for ensuring successful research design, analysis, and implementation Presents the nuts and bolts of statistical analysis Organized for quick access to a wealth of information Replete with practical examples of successful research designs Û from single case designs to meta-analysis - and how to achieve them Addresses research implementation including regulatory issues and grant writing |
| cross sectional study vs time series: Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences Stephanie T. Lanza, Ashley N. Linden-Carmichael, 2021-05-06 This book is the first to introduce applied behavioral, social, and health sciences researchers to a new analytic method, the time-varying effect model (TVEM). It details how TVEM may be used to advance research on developmental and dynamic processes by examining how associations between variables change across time. The book describes how TVEM is a direct and intuitive extension of standard linear regression; whereas standard linear regression coefficients are static estimates that do not change with time, TVEM coefficients are allowed to change as continuous functions of real time, including developmental age, historical time, time of day, days since an event, and so forth. The book introduces readers to new research questions that can be addressed by applying TVEM in their research. Readers gain the practical skills necessary for specifying a wide variety of time-varying effect models, including those with continuous, binary, and count outcomes. The book presents technical details of TVEM estimation and three novel empirical studies focused on developmental questions using TVEM to estimate age-varying effects, historical shifts in behavior and attitudes, and real-time changes across days relative to an event. The volume provides a walkthrough of the process for conducting each of these studies, presenting decisions that were made, and offering sufficient detail so that readers may embark on similar studies in their own research. The book concludes with comments about additional uses of TVEM in applied research as well as software considerations and future directions. Throughout the book, proper interpretation of the output provided by TVEM is emphasized. Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences is an essential resource for researchers, clinicians/practitioners as well as graduate students in developmental psychology, public health, statistics and methodology for the social, behavioral, developmental, and public health sciences. |
| cross sectional study vs time series: 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 study vs time series: Wine and Tourism Marta Peris-Ortiz, María de la Cruz Del Río Rama, Carlos Rueda-Armengot, 2015-12-01 The aim of this book is to show how wine tourism can be used as a model for sustainable economic development, driving economic growth and social development in some locations. It will explore the interaction between tourism and viticulture in wine tourism destinations, while also explaining some of the repercussions of these activities. This book covers various topics including regional development, environmental management, sustainable viticulture, quality management in wineries and wine tourism routes among others. Wine tourism, which combines two important yet distinct economic activities (i.e., tourism and viticulture), has recently emerged as a new tourism product driven by tourists’ search for new experiences and wineries’ need to diversify their businesses and seek new revenue streams to boost sales. This new form of tourism, which typically takes place in rural areas and which combines wine production with tourist activities, is becoming important for such regions by providing a complementary income source. It provides a model for sustainable economic development for these regions, which for various reasons may otherwise struggle to develop. Featuring cases and business implications from various locations, this book provides an important source of knowledge—both theoretical and practical—suitable to academics, scholars, researchers, and practitioners in the tourism sector and the wine industry. |
| cross sectional study vs time series: 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 study vs time series: Priority Patterns and the Demand for Household Durable Goods , |
| cross sectional study vs time series: Introduction to Social Statistics Thomas Dietz, Linda Kalof, 2009-03-02 Introduction to Social Statistics is a basic statistics text with a focus on the use of models for thinking through statistical problems, an accessible and consistent structure with ongoing examples across chapters, and an emphasis on the tools most commonly used in contemporary research. Lively introductory textbook that uses three strategies to help students master statistics: use of models throughout; repetition with variation to underpin pedagogy; and emphasis on the tools most commonly used in contemporary research Demonstrates how more than one statistical method can be used to approach a research question Enhanced learning features include a ‘walk-through’ of statistical concepts, applications, features, advanced topics boxes, and a ‘What Have We Learned’ section at the end of each chapter Supported by a website containing instructor materials including chapter-by-chapter PowerPoint slides, answers to exercises, and an instructor guide Visit www.wiley.com/go/dietz for additional student and instructor resources. |
| cross sectional study vs time series: Research Methods for Criminology and Criminal Justice Mark L. Dantzker, Ronald D. Hunter, 2006 Research Methods for Criminology and Criminal Justice: A Primer, Second Edition provides students of criminology and criminal justice with a clear and simple approach to understanding social science research. Completely updated and redesigned, this text is written to engage students and make the complex subject of research methods easy for the would-be criminal justice practitioner to comprehend. In addition to covering current topics such as community policing, alternative sentencing for nonviolent offenders, and gang violence, each chapter starts with a case study demonstrating how research methods are used in practical applications within the field. Later, these issues are also addressed in exercises and questions found at the end of the chapter. This indispensable resource is accessible, understandable, and user-friendly, and is a must-read for students in any research methods course.Each chapter of this text begins with a case study illustrating how research methods, requirements, and processes are used in real-life applications. Research Methods for Criminology and Criminal Justice: A Primer uses important contemporary issues such as gangs, drugs, teen alcohol abuse, and alternative sentencing options for non-violent offenders, to illustrate role of research in developing policies and procedures. These illustrations are also addressed at the end of each chapter in exercises and review questions. Research Methods for Criminology and Criminal Justice: A Primer makes learning research methods easy, understandable, and applicable to the criminal justice topics students are most interested in.Research Methods for Criminology and Criminal Justice: A Primer will be available with instructor's resources including an Instructor's Manual, including lecture outlines and review question solutions, Microsoft PowerPoint(tm) presentations, and a test bank. |
| cross sectional study vs time series: R Cookbook Paul Teetor, 2011-03-03 With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time.—Jeffrey Ryan, software consultant and R package author |
| cross sectional study vs time series: Novel Approaches in Microbiome Analyses and Data Visualization Jessica Galloway-Peña, Michele Guindani, 2019-02-06 High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field. |
| cross sectional study vs time series: Big Data Analytics in Oncology with R Atanu Bhattacharjee, 2022-12-29 Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area. Features: Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics. |
| cross sectional study vs time series: CMT Level II 2016: Theory and Analysis Market Technician's Association, Mkt Tech Assoc, 2015-12-02 Everything you need to pass Level II of the CMT Program CMT Level II 2016: Theory and Analysis fully prepares you to demonstrate competency applying the principles covered in Level I, as well as the ability to apply more complex analytical techniques. Covered topics address theory and history, market indicators, construction, confirmation, cycles, selection and decision, system testing, statistical analysis, and ethics. The Level II exam emphasizes trend, chart, and pattern analysis, as well as risk management concepts. This cornerstone guidebook of the Chartered Market Technician® Program will provide every advantage to passing Level II. |
| cross sectional study vs time series: Smart Use of State Public Health Data for Health Disparity Assessment Ge Lin, Ming Qu, 2018-09-03 Health services are often fragmented along organizational lines with limited communication among the public health–related programs or organizations, such as mental health, social services, and public health services. This can result in disjointed decision making without necessary data and knowledge, organizational fragmentation, and disparate knowledge development across the full array of public health needs. When new questions or challenges arise that require collaboration, individual public health practitioners (e.g., surveillance specialists and epidemiologists) often do not have the time and energy to spend on them. Smart Use of State Public Health Data for Health Disparity Assessment promotes data integration to aid crosscutting program collaboration. It explains how to maximize the use of various datasets from state health departments for assessing health disparity and for disease prevention. The authors offer practical advice on state public health data use, their strengths and weaknesses, data management insight, and lessons learned. They propose a bottom-up approach for building an integrated public health data warehouse that includes localized public health data. The book is divided into three sections: Section I has seven chapters devoted to knowledge and skill preparations for recognizing disparity issues and integrating and analyzing local public health data. Section II provides a systematic surveillance effort by linking census tract poverty to other health disparity dimensions. Section III provides in-depth studies related to Sections I and II. All data used in the book have been geocoded to the census tract level, making it possible to go more local, even down to the neighborhood level. |
| cross sectional study vs time series: The Evaluation Of Research Methodology Dr. Neeraj Tiwari, Dr. Pushpa Dwivedi, 2023-01-18 Evaluation would be a specific kind of study that employs its own unique methodology. They allow one to assess deeds and deed-doings in light of one's own principles, objectives, and norms. Evaluating programs and policies is another method used to improve their efficiency in the general eye. A thorough explanation of what occurs and what has to be handled differently to attain different results is necessary for both improvement and judgment. The purpose of this book is to serve as an overarching framework for doing research in almost any field. The importance of research methodology is growing as the scope and volume of studies expand as well. In this light, it's striking how few high-quality books there are on the topic. This book tries to strike a middle ground between the one-size-fits-all approach to study and the many diverse research techniques that can be employed to complete the many tasks that may arise throughout the course of any given study. Research problems have been the primary focus of debates on the kind of research procedures that are suitable in different fields, with the recognition that a particular research work, such as comparing options, may need a combination of techniques from several fields. This book comprises almost every topic related to Research and Research Evolution. This book was written for the upcoming students and researchers. |
| cross sectional study vs time series: Student Study Guide With IBM® SPSS® Workbook for Research Methods for the Behavioral Sciences Gregory J. Privitera, 2019-01-24 This study guide for Gregory J. Privitera’s best-selling Research Methods for the Behavioral Sciences, Third Edition includes a review of chapter learning objectives, chapter summaries, and tips and cautions. To help students practice their skills, the guide offers quizzes and exercises accompanied by answers keys; SPSS in Focus exercises with general instructions complement those in Privitera’s main text. |
| cross sectional study vs time series: The Handbook of Occupational and Environmental Medicine Tee L. Guidotti, 2020-10-27 Provides health professionals with a single, accessible, and interesting source to prepare for the field of occupational and environmental medicine. The new edition is extensively updated and includes questions for review in preparation for taking exams. This set is designed to be a thorough introduction for physicians entering the occupational and environmental medicine field, whether preparing for specialty examinations or moving into the field from other medical specialties or from primary care. It also serves as a convenient guide and reference for nurses, health professionals, and those outside of health care who need a quick orientation. The set is written with a strong and coherent point of view about the value of occupational and environmental medicine and commitment to ethical, worker-centered practice. It is unusual in the depth of its coverage; its inclusion of important topics that are usually overlooked in textbooks of the field, such as risk science; its emphasis on good management of occupational health services; and its thorough integration of material that fits topics together rather than presenting them as if they were separate and unrelated. |
| cross sectional study vs time series: Air, the Environment and Public Health Anthony Kessel, 2006 Air, the Environment and Public Health traces the theme of air and health from ancient civilisations to the present day. The author explores the changing conceptions of air and health alongside historical developments in public health, and critically examines contemporary problems - conceptual, scientific, philosophical and ethical - in public health theory and practice. The first part surveys air and health in early civilisations, as well as the nineteenth century debates around miasma and evolution. The second part explores the history of smoke pollution and health. Part three examines philosophical issues around modern air pollution epidemiology, and part four looks at climate change and ethical frameworks in public health. The book is a unique blend of public health science, history of medicine, ethics and philosophy. It will be of interest to those working or studying in public health, environmental health, medicine, history of medicine, environmental philosophy, and medical ethics. |
| cross sectional study vs time series: A Tale of Two Cultures Gary Goertz, James Mahoney, 2012-09-09 Some in the social sciences argue that the same logic applies to both qualitative and quantitative methods. In A Tale of Two Cultures, Gary Goertz and James Mahoney demonstrate that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. They identify and discuss major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, Goertz and Mahoney also seek to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. This book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points. |
| cross sectional study vs time series: A Primer in Longitudinal Data Analysis Toon W Taris, 2000-05-25 `The author has done a remarkable job of writing a very accessible introduction to a broad literature. As such, he should be congratulated on achieving his objective to provide the ideal primer for this growing area of social research′ - Kwantitatieve Methoden This accessible introduction to the theory and practice of longitudinal research takes the reader through the strengths and weaknesses of this kind of research, making clear: how to design a longitudinal study; how to collect data most effectively; how to make the best use of statistical techniques; and how to interpret results. Although the book provides a broad overview of the field, the focus is always on the practical issues arising out of longitudinal research. This book supplies the student with all that they need to get started and acts as a manual for dealing with opportunities and pitfalls. It is the ideal primer for this growing area of social research. |
Analysing change over time: repeated cross sectional and …
Cross-sectional survey data are data for a single point in time. Repeated cross-sectional data are created where a survey is administered to a new sample of interviewees at successive time …
Lecture 15 Panel Data Models - Bauer College of Business
• With panel data we can study different issues:-Cross sectional variation(unobservable in time series data) vs. Time series variation (unobservable in cross sectional data) - Heterogeneity …
Comparing Cross-Section and Time-Series Factor Models
Factors in time-series asset pricing models are often motivated by evidence from Fama and MacBeth (FM 1973) cross-section regressions that average returns are related to asset …
Cross Sectional Time Series: The Normal Model and Panel …
Parks’s FGLS (Feasible Generalized Least Squares) is one way that was proposed to deal with CXTS data. One estimates with OLS, then uses the residuals to calculate autocorrelation and …
Explaining Fixed Effects: Random Effects modelling of Time …
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial …
Epidemiology: Study Designs - GitHub Pages
Dr. Wan Nor Arifin Epidemiology: Study Designs 30 Cross-sectional (analytical) Cross-section / snapshot of population at a single time point Comparison element (vs descriptive version of …
CHAPTER 7: CROSS-SECTIONAL DATA ANALYSIS AND …
We have explained and applied regression tools in the context of time-ordered data. The same tools are directly applicable to cross-sectional data. In one respect the cross-sectional …
Topic 8 - Cross-Sectional Studies - Texas A&M University
Cross-sectional studies are usually inexpensive and can be conducted relatively faster than time-series studies. Using cross-sectional data, analyses can be conducted outcomes at the same …
Time-series and cross-sectional momentum strategies under …
The study compares the performance of alternative implementations of both time-series (Moskowitz et al., 2012) and cross-sectional (Jegadeesh & Titman, 1993) momentum …
Cross-Sectional Vs. Time Series Benchmarking in
Small area single-stage time series benchmarking Basic idea: Fit a time series model to direct estimators in all the areas, incorporate benchmark constraints into model equations. Variances …
Pooling Cross Sections Across Time and Simple Panel Data …
The difference is that pooling cross sections means different elements are sampled in each period, whereas panel data follows the same elements through time. The objective is to …
The Relationship between Cross-Sectional and Time Series …
Cross-sectional studies examine geo-graphical variations in community mortality rates and air pollution levels and typically involve fitting the pa-rameters of proportional exposure -mortality …
Cross-Sectional Studies - CHEST
Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand …
Matching Methods for Causal Inference with Time-Series …
We propose a matching method fortime-series cross-sectional data 1 create amatched setfor each treated observation 2 re ne the matched set via any matching or weighting method
Choosing the right study design - European AIDS Clinical …
Cross-sectional vs. Longitudinal Cross-sectional study Patients are studied at a single time-point only (e.g. patients are surveyed on a single day, patients are interviewed at the start of …
Chapter 13 Pooling Cross Sections Across Time: Simple Panel …
Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods . Panel data looks at set of observations that have a cross sectional dimension and a time dimension. Two types …
A Survey of Current Statistical Methodology - JSTOR
cross-sectional and time series data. Like cross-sectional data, it describes each of a number of individ-uals. Like time series data, it describes each single indi-vidual through time. Pooled …
How to Make Causal Inferences with Time-Series Cross …
Repeated measurements of the same countries, people, or groups over time form the foundation of many elds of quantitative political science. These measurements, some-times called time …
Case Series, Descriptive, and Cross-Sectional Studies
Quick, conducted over short period of time, easy, inexpensive. Can study multiple exposures and disease outcomes simultaneously. Can assess only association but not a “causal association”. …
Cross-Sectional andTime-SeriesTests of Return Predictability
We compare the performance of time-series (TS) and cross-sectional (CS) strategies based on past returns. While CS strategies are zero-net investment long/short strategies, TS strategies …
Analysing change over time: repeated cross sectional and …
Cross-sectional survey data are data for a single point in time. Repeated cross-sectional data are created where a survey is administered to a new sample of interviewees at successive time …
Lecture 15 Panel Data Models - Bauer College of Business
• With panel data we can study different issues:-Cross sectional variation(unobservable in time series data) vs. Time series variation (unobservable in cross sectional data) - Heterogeneity …
Comparing Cross-Section and Time-Series Factor Models
Factors in time-series asset pricing models are often motivated by evidence from Fama and MacBeth (FM 1973) cross-section regressions that average returns are related to asset …
Cross Sectional Time Series: The Normal Model and Panel …
Parks’s FGLS (Feasible Generalized Least Squares) is one way that was proposed to deal with CXTS data. One estimates with OLS, then uses the residuals to calculate autocorrelation and …
Explaining Fixed Effects: Random Effects modelling of …
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial when …
Epidemiology: Study Designs - GitHub Pages
Dr. Wan Nor Arifin Epidemiology: Study Designs 30 Cross-sectional (analytical) Cross-section / snapshot of population at a single time point Comparison element (vs descriptive version of cross …
CHAPTER 7: CROSS-SECTIONAL DATA ANALYSIS AND …
We have explained and applied regression tools in the context of time-ordered data. The same tools are directly applicable to cross-sectional data. In one respect the cross-sectional regressions will …
Topic 8 - Cross-Sectional Studies - Texas A&M University
Cross-sectional studies are usually inexpensive and can be conducted relatively faster than time-series studies. Using cross-sectional data, analyses can be conducted outcomes at the same …
Time-series and cross-sectional momentum strategies under …
The study compares the performance of alternative implementations of both time-series (Moskowitz et al., 2012) and cross-sectional (Jegadeesh & Titman, 1993) momentum strategies …
Cross-Sectional Vs. Time Series Benchmarking in
Small area single-stage time series benchmarking Basic idea: Fit a time series model to direct estimators in all the areas, incorporate benchmark constraints into model equations. Variances …
Pooling Cross Sections Across Time and Simple Panel Data …
The difference is that pooling cross sections means different elements are sampled in each period, whereas panel data follows the same elements through time. The objective is to explore what …
The Relationship between Cross-Sectional and Time …
Cross-sectional studies examine geo-graphical variations in community mortality rates and air pollution levels and typically involve fitting the pa-rameters of proportional exposure -mortality …
Cross-Sectional Studies - CHEST
Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand …
Matching Methods for Causal Inference with Time-Series …
We propose a matching method fortime-series cross-sectional data 1 create amatched setfor each treated observation 2 re ne the matched set via any matching or weighting method
Choosing the right study design - European AIDS Clinical Society
Cross-sectional vs. Longitudinal Cross-sectional study Patients are studied at a single time-point only (e.g. patients are surveyed on a single day, patients are interviewed at the start of therapy) …
Chapter 13 Pooling Cross Sections Across Time: Simple …
Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods . Panel data looks at set of observations that have a cross sectional dimension and a time dimension. Two types of …
A Survey of Current Statistical Methodology - JSTOR
cross-sectional and time series data. Like cross-sectional data, it describes each of a number of individ-uals. Like time series data, it describes each single indi-vidual through time. Pooled data …
How to Make Causal Inferences with Time-Series Cross …
Repeated measurements of the same countries, people, or groups over time form the foundation of many elds of quantitative political science. These measurements, some-times called time-series …
Case Series, Descriptive, and Cross-Sectional Studies
Quick, conducted over short period of time, easy, inexpensive. Can study multiple exposures and disease outcomes simultaneously. Can assess only association but not a “causal association”. …
Cross-Sectional andTime-SeriesTests of Return …
We compare the performance of time-series (TS) and cross-sectional (CS) strategies based on past returns. While CS strategies are zero-net investment long/short strategies, TS strategies take on …
