Conducting Meta Analysis Using Sas

Author: Winfred Arthur, Jr.
Publisher: Psychology Press
ISBN: 1135643458
Size: 42.12 MB
Format: PDF, ePub, Docs
View: 6735
Download
Conducting Meta-Analysis Using SAS reviews the meta-analysis statistical procedure and shows the reader how to conduct one using SAS. It presents and illustrates the use of the PROC MEANS procedure in SAS to perform the data computations called for by the two most commonly used meta-analytic procedures, the Hunter & Schmidt and Glassian approaches. This book serves as both an operational guide and user's manual by describing and explaining the meta-analysis procedures and then presenting the appropriate SAS program code for computing the pertinent statistics. The practical, step-by-step instructions quickly prepare the reader to conduct a meta-analysis. Sample programs available on the Web further aid the reader in understanding the material. Intended for researchers, students, instructors, and practitioners interested in conducting a meta-analysis, the presentation of both formulas and their associated SAS program code keeps the reader and user in touch with technical aspects of the meta-analysis process. The book is also appropriate for advanced courses in meta-analysis psychology, education, management, and other applied social and health sciences departments.

Applied Data Analytic Techniques For Turning Points Research

Author: Patricia Cohen
Publisher: Routledge
ISBN: 113691076X
Size: 49.73 MB
Format: PDF, ePub, Docs
View: 5627
Download
This innovative volume demonstrates the use of a range of statistical approaches that examine "turning points" (a change in direction, magnitude, or meaning) in real data. Analytic techniques are illustrated with real longitudinal data from a variety of fields. As such the book will appeal to a variety of researchers including: Developmental researchers interested in identifying factors precipitating turning points at various life stages. Medical or substance abuse researchers looking for turning points in disease or recovery. Social researchers interested in estimating the effects of life experiences on subsequent behavioral changes. Interpersonal behavior researchers looking to identify turning points in relationships. Brain researchers needing to discriminate the onset of an experimentally produced process in a participant. The book opens with the goals and theoretical considerations in defining turning points. An overview of the methods presented in subsequent chapters is then provided. Chapter goals include discriminating "local" from long-term effects, identifying variables altering the connection between trajectories at different life stages, locating non-normative turning points, coping with practical distributional problems in trajectory analyses, and changes in the meaning and connections between variables in the transition to adulthood. From an applied perspective, the book explores such topics as antisocial/aggressive trajectories at different life stages, the impact of imprisonment on criminal behavior, family contact trajectories in the transition to adulthood, sustained effects of substance abuse, alternative models of bereavement, and identifying brain changes associated with the onset of a new brain process. Ideal for advanced students and researchers interested in identifying significant change in data in a variety of fields including psychology, medicine, education, political science, criminology, and sociology.

Longitudinal Data Analysis

Author: Jason Newsom
Publisher: Routledge
ISBN: 1136705473
Size: 24.59 MB
Format: PDF, ePub
View: 1296
Download
First Published in 2012. Routledge is an imprint of Taylor & Francis, an informa company.

Structural Equation Modeling With Mplus

Author: Barbara M. Byrne
Publisher: Routledge
ISBN: 1136663460
Size: 64.57 MB
Format: PDF
View: 3262
Download
"This text aims to provide readers with a nonmathematical introduction to the basic concepts associated with structural equation modeling, and to illustrate its basic applications using the Mplus program"--Provided by publisher.

A Paul Meehl Reader

Author: Niels G. Waller
Publisher: Routledge
ISBN: 1134812140
Size: 33.86 MB
Format: PDF, Kindle
View: 2757
Download
This new book introduces a new generation to the important insights of Paul Meehl. In addition to selected papers from the classic reader, Psychodiagnosis, this book features new material selected from Meehl's most influential writings. The resulting collection is a tour de force illustrating quantitative analysis of life science problems, an examination of the inadequacy of some methods of analysis, and a review of the application of taxometrics. A Paul Meehl Reader is organized into five content areas: theory building and appraisal - how we discover and test the true causal relations of psychological constructs; specific etiology - an examination of genetic, behavioral, and environmental etiology in psychopathology; diagnosis and prediction - a review of the appropriate use of base rates; taxometrics - a look at Meehl's development of the method he invented; thinking effectively about psychological questions - a critique of correlation research and the power of quantitative thinking in psychology. The Reader features section introductions to orient the reader and provide a context and structure for Paul Meehl's work. The section on diagnosis and prediction features problem sets with solutions to guide the reader through practical applications of the principles described. An accompanying DVD contains footage from Paul Meehl's engaging seminar on clinical versus statistical prediction. This book appeals to advanced students and professionals in psychology, sociology, law, education, human development, and philosophy.

Handbook Of Ethics In Quantitative Methodology

Author: Sonya K. Sterba
Publisher: Taylor & Francis
ISBN: 113688873X
Size: 25.49 MB
Format: PDF, ePub, Docs
View: 1588
Download
"Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process.

Longitudinal Structural Equation Modeling

Author: Jason T. Newsom
Publisher: Routledge
ISBN: 1317975359
Size: 77.80 MB
Format: PDF, Mobi
View: 882
Download
This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling options are available for which hypotheses. The author demonstrates how SEM is related to other longitudinal data techniques throughout. By exploring connections between models, readers gain a better understanding of when to choose one analysis over another. The book explores basic models to sophisticated ones including the statistical and conceptual underpinnings that are the building blocks of the analyses. Accessibly written, research examples from the behavioral and social sciences and results interpretations are provided throughout. The emphasis is on concepts and practical guidance for applied research rather than on mathematical proofs. New terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues. Each chapter also includes examples of each model type, comment sections that provide practical guidance, model extensions, and recommended readings. Highlights include: Covers the major SEM approaches to longitudinal analysis in one resource. Explores connections between longitudinal SEM models to enhance integration. Numerous examples that help readers match research questions to appropriate analyses and interpret results. Reviews practical issues related to model specification and estimation to reinforce connections. Analyzes continuous and discrete (binary and ordinal) variables throughout for breadth not found in other sources. Reviews key SEM concepts for those who need a refresher (Ch. 1). Emphasizes how to apply and interpret each model through realistic data examples. Provides the book’s data sets at www.longitudinalsem.com along with the Mplus and R-lavaan syntax used to generate the results. Introduces the LISREL notation system used throughout (Appendix A). The chapters can be read out of order but it is best to read chapters 1 – 4 first because most of the later chapters refer back to them. The book opens with a review of latent variables and analysis of binary and ordinal variables. Chapter 2 applies this information to assessing longitudinal measurement invariance. SEM tests of dependent means and proportions over time points are explored in Chapter 3, and stability and change, difference scores, and lagged regression are covered in Chapter 4. The remaining chapters are each devoted to one major type of longitudinal SEM -- repeated measures analysis models, full cross-lagged panel models and simplex models, modeling stability with state-trait models, linear and nonlinear growth curve models, latent difference score models, latent transition analysis, time series analysis, survival analysis, and attrition. Missing data is discussed in the context of many of the preceding models in Chapter 13. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this text also appeals to researchers in these fields. Intended for those without an extensive math background, prerequisites include familiarity with basic SEM. Matrix algebra is avoided in all but a few places.

Statistical Methods For Meta Analysis

Author: Larry V. Hedges
Publisher: Academic Press
ISBN: 0080570658
Size: 76.91 MB
Format: PDF, Kindle
View: 127
Download
The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Advances In Meta Analysis

Author: Terri Pigott
Publisher: Springer Science & Business Media
ISBN: 1461422779
Size: 21.71 MB
Format: PDF
View: 3848
Download
This book describes multivariate analyses for several indices commonly used in meta-analysis, outlines how to do power analysis for meta-analysis, and examines issues around research quality and research design and their roles in synthesis.

Integrating Results Through Meta Analytic Review Using Sas Software

Author: Morgan C. Wang
Publisher: SAS Institute
ISBN: 9781580252935
Size: 65.49 MB
Format: PDF, ePub, Mobi
View: 902
Download
Finally...a book addressing the various needs, concepts, and approaches forSAS users who work with meta-analytic procedures! Wang and Bushman introduce the reader to the important concepts in meta-analysis and how to use SAS software for this specific type of analysis. The authors thoroughly describe how meta-analysis can be used in data mining projects to discover meaningful relations among variables in a collection of studies. In addition, the following concepts are covered in detail: how to present your results in graphical format, how to combine effect-size estimates based on categorical and continuous data, how to use vote-counting procedures to show the statistical significance of results, how to combine effect-size estimates and vote counts, how to deal with fixed- and random-effects models, how to combine dependent or correlated effect-size estimates using multivariate procedures, and how to report the results and conduct the data analysis portion of a meta-analysis.