Best Practices In Quantitative Methods - Osborne Jason W. (Curatore) | Libro Sage Publications Ltd 01/2008 - HOEPLI.it


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BEST PRACTICES IN QUANTITATIVE METHODS




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Genere:Libro
Lingua: Inglese
Pubblicazione: 01/2008





Sommario

Introduction - Jason W. Osborne
Part I: Best Practices in Measurement - Jason Osborne
Chapter 1: The New Stats: Attitudes for the Twenty-First Century - Fiona Fidler & Geoff Cumming
Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues - Thomas Kellow & Victor Willson
Chapter 3: Best Practices in Inter-rater Reliability: Assumptions and Implications of three common approaches - Steve Stemler
Chapter 4: An Introduction to Rasch Measurement - Cherdsak Iramaneerat, Everett V. Smith, Jr., & Richard M. Smith
Chapter 5: Applications of the Multi-Faceted Rasch Model - Edward W. Wolfe & Lidia Dobria
Chapter 6: Best Practices in Exploratory Factor Analysis - Jason W. Osborne, Anna B. Costello, & J. Thomas Kellow
Part II: Selected Best Practices in Research Design - Jason W. Osborne
Chapter 7: A Rational Foundation for Scientific Decisions: The Case for the Probability of Replication Statistic - Peter R. Killeen
Chapter 8: Best Practices in Mixed Methods Research - Jessica T. DeCuir-Gunby
Chapter 9: Designing a Rigorous Small Sample Study - Naomi Jeffery Petersen
Chapter10: Replication in Field Studies - William D. Schafer
Chapter 11: Best practices in ANCOVA may mean not using ANCOVA: Why paired subjects designs are a better choice - Elizabeth A. Stuart & Donald B. Rubin
Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis - Spyros Konstantopoulos
Part III: Best Practices in Data Cleaning and the Basics of Data Analysis - Jason W. Osborne
Chapter 13: Best Practices in Data Transformations: The Overlooked Effect of Minimum Values - Jason W. Osborne
Chapter 14: Best Practices in Data Cleaning: How Outliers can increase error rates and decrease the quality and precision of your results - Jason W. Osborne & Amy Overbay
Chapter 15: How to Deal With Missing Data - Jason C. Cole
Chapter16: Is Disattenuation of Effects a Best Practice? - Jason W. Osborne
Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, & Confidence Intervals for Effect Sizes - Bruce Thompson
Chapter 18: Robust Methods for Detecting Associations - Rand R. Wilcox
Part IV: Best Practices of Quantitative Methods - Jason W. Osborne
Chapter 19: Resampling: A Conceptual and Procedural Introduction - Chong Ho Yu
Chapter 20: Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals around Predictions - Jason W. Osborne
Chapter 21: Using Poisson Regression to Analyze Count Data - E. Michael Nussbaum, Sherif Elsadat, & Ahmed H. Khago
Chapter 22: Testing the Assumptions of Analysis of Variance - Yanyan Sheng
Chapter 23: Best Practices in ANOVA - David Howell
Chapter24: Logistic Regression in the Social Sciences - Jason E. King
Chapter 25: Bringing balance and accuracy to odds ratios - Jason W. Osborne
Chapter 26: Advanced Topics in Logistic Regression: Polytomous Response Variables - Carolyn J. Anderson & Leslie Rutkowski
Chapter 27: Enhancing Accuracy in Research Using Regression Mixture Analysis - Cody S. Ding
Chapter 28: Mediation, Moderation, and the Study of Individual Differences - A. Alexander Beaujean
Part V: Best Advanced Practices in Quantitative Methods - Jason W. Osborne
Chapter 29: Hierarchical Linear Modeling: What it is and when Researchers should use it - Jason W. Osborne
Chapter 30: Analysis of longitudinal data: Advantages of Hierarchical Linear Modeling and growth curve analysis over repeated measures ANOVA - Frans E.S. Tan
Chapter 31: Analysis of Moderator Effects in Meta-Analysis - Wolfgang Viechtbauer
Chapter 32: Best Practices in Structural Equation Modeling - Ralph O. Mueller & Gregory R. Hancock
Chapter 33: Introduction to Bayesian Modeling for Social Sciences - Gianluca Baio & Marta Blangiardo
Chapter 34: Using R for Data Analysis: A Best Practice for Research - Ken Kelley, Keke Lai, & Po-Ju Wu
Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference - Elizabeth A. Stuart & Donald B. Rubin








Trama

Best Practices in Quantitative Methods follows the tradition of 'handbooks' in that it calls on the top researchers in the field to share with us what they know. In this case, the focus of the chapters is on best practices for the vast field of quantitative methods. The volume provides readers with the most effective, evidence-based ways to use and analyze quantitative methods and quantitative data across the social and behavioral sciences and education .The text is divided into three main sections:

Basics of Best Practices, in which a comprehensive review of basic statistic and methodological practices is covered, including core statistical methods and critical data analysis issues such as power, effect sizes, and assumptions;

Advanced Best Practices, leading with logistic regression, and moving through IRT, Rasch Measurement, HLM, Meta-Analysis, and the inimitable area of Sampling; and

The Implications of Best Practices, including a discussion of the ethical implications of quantitative analysis.

Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-rangning examples along with any empirical evidence to show why certain techniques are better. The book encourages best practices in three very distinct ways: 1) Some chapters will describe important implicit knowledge to readers. For example, one of the most common data transformations is the square root transformation. Statistics and quantitative methods are filled with examples of these seemingly mundane aspects of research life that makes a substantial difference. Chapters in this book gather the important details, make them accessible to readers, and demonstrate why it is important to pay attention to these details. 2) Other chapters compare and contrast analytic techniques to give readers information they need to decide the best way to analyze particular data. For example, exploratory factor analysis has up to eight extraction methods, several rotation options, multiple ways to decide how many factors you have, and it is often the case that the options are not clearly described or discussed. Some of the chapters will examine instances where there are multiple options for doing things, and make recommendations as to what the ôbestö choice (or choices, as what is best often depends on the circumstances) are. 3) Finally, there are always new procedures being developed and disseminated. Many times (not all) newer procedures represent improvements over old procedures. Some chapters will present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use.This book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource to go to for practical and sound advice from leading experts in quantitative methods.








Altre Informazioni

ISBN:

9781412940658

Condizione: Nuovo
Dimensioni: 10.0000 x 7.0000 in
Formato: Hardback
Pagine Arabe: 608






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