Search Results: longitudinal-research-quantitative-applications-in-the-social-sciences

Event History Analysis

Regression for Longitudinal Event Data

Author: Paul D. Allison

Publisher: SAGE

ISBN: 9780803920552

Category: Social Science

Page: 87

View: 4141

Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

Longitudinal Research

Author: Scott Menard

Publisher: SAGE

ISBN: 9780761922094

Category: Reference

Page: 93

View: 5907

"Since ... writing the first edition of this monograph in 1990, ... the 1990s have seen an increasing focus on more sophisticated approaches to dealing with missing data in both cross-sectional and longitudinal research. Software applicable to longitudinal research has also improved, and more evidence for the rapid pace of change in longitudinal analysis can be found in the dozen or so books written and edited about longitudinal research design and data analysis published in the 1990s and early in the present millennium. The organization of this monograph remains the same as in the first edition. ... There is much less said about the application of traditional methods of analysis to longitudinal data, and more focus on analytical methods specifically designed for longitudinal data, including time series analysis, linear panel analyis, multilevel and latent growth curve modeling, and event history analysis."--Preface.

Handbook of Longitudinal Research

Design, Measurement, and Analysis

Author: Scott Menard

Publisher: Elsevier

ISBN: 9780080554228

Category: Education

Page: 680

View: 592

Longitudinal research is a broad field in which substantial advances have been made over the past decade. Unlike many of the existing books that only address the analysis of information. The Handbook of Longitudinal Research covers design and measurement as well as the data analysis. Designed for use by a wide-ranging audience, this Handbook not only includes perspective on the methodological and data analysis problems in longitudinal research but it also includes contributors' data sets that enable readers who lack sophisticated statistics skills to move from theories about longitudinal data into practice. As the comprehensive reference, this Handbook has no direct competition as most books in this subject area are more narrowly specialized and are pitched at a high mathematical level. Contributors and subject areas are interdisciplinary to reach the broadest possible audience (i.e., psychology, epidemiology, and economics research fields) Summary material will be included for less sohisticated readers Extensive coverage is provided of traditional advanced topics

Applied Logistic Regression Analysis

Author: Scott Menard

Publisher: SAGE

ISBN: 9780761922087

Category: Mathematics

Page: 111

View: 9294

The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition: · More detailed consideration of grouped as opposed to casewise data throughout the book · Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency · Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data · Updated coverage of unordered and ordered polytomous logistic regression models.

Translating Questionnaires and Other Research Instruments

Problems and Solutions

Author: Orlando Behling,Kenneth S. Law

Publisher: SAGE

ISBN: 9780761918240

Category: Reference

Page: 70

View: 6192

This book covers the essential information needed to understand the problems involved in translating existing questionnaires and other paper and pencil instruments from one language to another as well as to apply methods for dealing with them. It shows researchers how to identify the problems (comparison of parameters, nomological nets, and semantic problems) with an existing instrument; how to solve each of these problems with step-by-step guidelines; and techniques for reconstructing an instrument or designing an original one to use with different cultural groups. This book will provide researchers with a guide for construction of cross-national survey instruments.

Interaction Effects in Multiple Regression

Author: James Jaccard,Jim Jaccard,Robert Turrisi

Publisher: SAGE

ISBN: 9780761927426

Category: Mathematics

Page: 92

View: 8697

This is a practical introduction to conducting analyses of interaction effects in the context of multiple regression. This new edition expands coverage on the analysis of three-way interactions in multiple regression analysis.

Fixed Effects Regression Models

Author: Paul D. Allison

Publisher: SAGE Publications

ISBN: 1483389278

Category: Social Science

Page: 136

View: 9418

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here

Latent Growth Curve Modeling

Author: Kristopher J. Preacher,Aaron L. Wichman,Robert C. MacCallum,Nancy E. Briggs

Publisher: SAGE Publications

ISBN: 1506333052

Category: Social Science

Page: 112

View: 2028

Latent growth curve modeling (LGM)—a special case of confirmatory factor analysis designed to model change over time—is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features · Provides easy-to-follow, didactic examples of several common growth modeling approaches · Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit · Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data · Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models Learn more about "The Little Green Book" - QASS Series! Click Here

Regression with Dummy Variables

Author: Melissa A. Hardy

Publisher: SAGE

ISBN: 9780803951280

Category: Social Science

Page: 90

View: 3211

It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.

Longitudinal and Panel Data

Analysis and Applications in the Social Sciences

Author: Edward W. Frees

Publisher: Cambridge University Press

ISBN: 9780521535380

Category: Business & Economics

Page: 467

View: 4952

An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

Interaction Effects in Logistic Regression

Author: James Jaccard,Jim Jaccard

Publisher: SAGE

ISBN: 9780761922070

Category: Mathematics

Page: 70

View: 1369

This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range of situations encountered in the research literature. The volume is oriented toward the applied researcher with a rudimentary background in multiple regression and logistic regression and does not include complex formulas that could be intimidating to the applied researcher.

Multilevel Modeling

Author: Douglas A. Luke

Publisher: SAGE

ISBN: 9780761928799

Category: Mathematics

Page: 79

View: 5072

A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.

Intensive Longitudinal Methods

An Introduction to Diary and Experience Sampling Research

Author: Niall Bolger,Jean-Philippe Laurenceau

Publisher: Guilford Press

ISBN: 1462506925

Category: Psychology

Page: 256

View: 783

This book offers a complete, practical guide to doing an intensive longitudinal study with individuals, dyads, or groups. It provides the tools for studying social, psychological, and physiological processes in everyday contexts, using methods such as diary and experience sampling. A range of engaging, worked-through research examples with datasets are featured. Coverage includes how to: select the best intensive longitudinal design for a particular research question, apply multilevel models to within-subject designs, model within-subject change processes for continuous and categorical outcomes, assess the reliability of within-subject changes, assure sufficient statistical power, and more. Several end-of-chapter write-ups illustrate effective ways to present study findings for publication. Datasets and output in SPSS, SAS, Mplus, HLM, MLwiN, and R for the examples are available on the companion website (www.intensivelongitudinal.com).

What is Quantitative Longitudinal Data Analysis?

Author: Vernon Gayle,Paul Lambert

Publisher: Bloomsbury Publishing

ISBN: 1472515412

Category: Social Science

Page: 168

View: 7181

Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution. The book is designed for postgraduates and researchers across the social sciences considering the use of quantitative longitudinal data resources in their research. It will also be an excellent text for undergraduate and postgraduate courses on advanced quantitative methods.

What is Qualitative Longitudinal Research?

Author: Bren Neale

Publisher: Bloomsbury Publishing

ISBN: 1472530810

Category: Social Science

Page: 176

View: 2239

This volume offers a new introduction to an evolving research method in the social sciences. Qualitative Longitudinal (QL) research is conducted through time. In its qualitative dimensions it opens up the potential to 'think dynamically' in creative, flexible and innovative ways. QL enquiry is rooted in a long-established tradition of qualitative temporal research, spanning the fields of social anthropology, sociological re-studies and biographical research. But over the past two decades, a growing body of scholarship has begun to document this approach and explore its theoretical underpinnings. This in turn has fuelled a growing interest in and rapid uptake of QL methodology across the disciplines and in international context. This practical volume will be a first port of call for students and researchers wishing to use QL research in their own projects. The chapters follow a logical development, from conceptual and methodological foundations, to research practice and ethics, to the generation and analysis of data. Each chapter offers practical examples drawn from the research field to illustrate key themes and the rich possibilities for new applications.

Hierarchical Linear Models

Applications and Data Analysis Methods

Author: Stephen W. Raudenbush,Anthony S. Bryk

Publisher: SAGE

ISBN: 9780761919049

Category: Mathematics

Page: 485

View: 2891

Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.

The SAGE Handbook of Quantitative Methodology for the Social Sciences

Author: David Kaplan

Publisher: SAGE

ISBN: 0761923594

Category: Social Science

Page: 511

View: 6278

The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

Multilevel Analysis for Applied Research

It's Just Regression!

Author: Robert Bickel

Publisher: Guilford Press

ISBN: 1609181069

Category: Psychology

Page: 355

View: 2392

This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual-level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, Robert Bickel emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps.

Missing Data

Author: Paul D. Allison

Publisher: SAGE Publications

ISBN: 1452207909

Category: Social Science

Page: 104

View: 5752

Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Latent Class and Latent Transition Analysis

With Applications in the Social, Behavioral, and Health Sciences

Author: Linda M. Collins,Stephanie T. Lanza

Publisher: John Wiley & Sons

ISBN: 111821076X

Category: Mathematics

Page: 330

View: 3022

A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.

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