Best Practices in Quantitative Methods

Jason W. Osborne - Clemson University, USA
Best Practices in Quantitative Methods
November 2007 | 608 pages | Sage US
Create Flyer

Purchase

eBook
ISBN: 9781483333052
Available from January 0001
Hardcover
ISBN: 9781412940658
Available from January 0001

Description

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically.  Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences.

The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. 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-ranging examples along with any empirical evidence to show why certain techniques are better.

Key Features: 

  • Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details.
  • Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances.
  • Offers new procedures to update and explicate traditional techniques: The featured scholars 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.

Intended Audience:  Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Contents

Introduction

  • Chapter 1: The New Stats: Attitudes for the Twenty-First Century

Part I: Best Practices in Measurement

  • Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues

Part I: Best Practices in Measurement

  • Chapter 3: Estimating Inter-Rater Reliability: Assumptions and Implications of Three Common Approaches
  • Chapter 4: An Introduction to Rasch Measurement
  • Chapter 5: Applications of the Multi-Faceted Rasch Model
  • Chapter 6: Best Practices in Exploratory Factor Analysis

Part II: Selected Best Practices in Research Design

  • Chapter 7: Replication Statistics
  • Chapter 8: Mixed Methods Research in the Social Sciences
  • Chapter 9: Designing a Rigorous Small Sample Study
  • Chapter 11: Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference
  • Chapter 12: An Introduction to Meta-Analysis
  • Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis

Part III: Best Practices in Data Cleaning and the Basics of Data Analysis

  • Chapter 14: Best Practices in Data Cleaning: How Outliers and "Fringeliers" Can Increase Error Rates and Decrease the Quality and Precision of Your Results
  • Chapter 15: How to Deal with Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods
  • Chapter 16: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual and Technical Issues
  • Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes
  • Chapter 18: Robust Methods for Detecting and Describing Associations

Part IV: Best Practices of Quantitative Methods

  • Chapter 19: Resampling: A Conceptual and Procedural Introduction
  • Chapter 21: Advanced Topics in Power Analysis
  • Chapter 21: Best Practices in Analyzing Count Data: Poisson Regression
  • Chapter 22: Testing the Assumptions of Analysis of Variance
  • Chapter 23: Best Practices in the Analysis of Variance
  • Chapter 24: Binary Logistic Regression
  • Chapter 26: Multinomial Logistic Regression
  • Chapter 27: Mediation, Moderation, and the Study of Individual Differences
  • Chapter 28: Mediation, Moderation, and the Study of Individual Differences

Part V: Best Advanced Practices in Quantitative Methods

  • Chapter 30: Hierarchical Linear Modeling: What It is and When Researchers Should Use It
  • Chapter 30: Best Practices in Analysis of Longitudinal Data: A Multilevel Approach
  • Chapter 32: Best Practices in Structural Equation Modeling
  • Chapter 33: Introduction to Bayesian Modeling for the Social Sciences
  • Chapter 35: Measuring Accuracy in Psychological Research
  • Chapter 37: Ethical Implications for Best Practices in Quantitative Methods
  • (Dropped) Chapter 4: Best Practices in Graphically Displaying Data
  • (Dropped) Chapter 7: Choosing a Demoninator
  • (Dropped) Chapter 9: Four Assumptions of Multiple Regression You Should ALWAYS Check
  • (Dropped) Chapter 28: An Introduction to Item Response Theory
  • (Dropped) A Framework for Model Building in Social Science Research

Resources

Additional materials

Description

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically.  Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences.

The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. 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-ranging examples along with any empirical evidence to show why certain techniques are better.

Key Features: 

  • Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details.
  • Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances.
  • Offers new procedures to update and explicate traditional techniques: The featured scholars 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.

Intended Audience:  Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Contents

Introduction

  • Chapter 1: The New Stats: Attitudes for the Twenty-First Century

Part I: Best Practices in Measurement

  • Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues

Part I: Best Practices in Measurement

  • Chapter 3: Estimating Inter-Rater Reliability: Assumptions and Implications of Three Common Approaches
  • Chapter 4: An Introduction to Rasch Measurement
  • Chapter 5: Applications of the Multi-Faceted Rasch Model
  • Chapter 6: Best Practices in Exploratory Factor Analysis

Part II: Selected Best Practices in Research Design

  • Chapter 7: Replication Statistics
  • Chapter 8: Mixed Methods Research in the Social Sciences
  • Chapter 9: Designing a Rigorous Small Sample Study
  • Chapter 11: Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference
  • Chapter 12: An Introduction to Meta-Analysis
  • Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis

Part III: Best Practices in Data Cleaning and the Basics of Data Analysis

  • Chapter 14: Best Practices in Data Cleaning: How Outliers and "Fringeliers" Can Increase Error Rates and Decrease the Quality and Precision of Your Results
  • Chapter 15: How to Deal with Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods
  • Chapter 16: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual and Technical Issues
  • Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes
  • Chapter 18: Robust Methods for Detecting and Describing Associations

Part IV: Best Practices of Quantitative Methods

  • Chapter 19: Resampling: A Conceptual and Procedural Introduction
  • Chapter 21: Advanced Topics in Power Analysis
  • Chapter 21: Best Practices in Analyzing Count Data: Poisson Regression
  • Chapter 22: Testing the Assumptions of Analysis of Variance
  • Chapter 23: Best Practices in the Analysis of Variance
  • Chapter 24: Binary Logistic Regression
  • Chapter 26: Multinomial Logistic Regression
  • Chapter 27: Mediation, Moderation, and the Study of Individual Differences
  • Chapter 28: Mediation, Moderation, and the Study of Individual Differences

Part V: Best Advanced Practices in Quantitative Methods

  • Chapter 30: Hierarchical Linear Modeling: What It is and When Researchers Should Use It
  • Chapter 30: Best Practices in Analysis of Longitudinal Data: A Multilevel Approach
  • Chapter 32: Best Practices in Structural Equation Modeling
  • Chapter 33: Introduction to Bayesian Modeling for the Social Sciences
  • Chapter 35: Measuring Accuracy in Psychological Research
  • Chapter 37: Ethical Implications for Best Practices in Quantitative Methods
  • (Dropped) Chapter 4: Best Practices in Graphically Displaying Data
  • (Dropped) Chapter 7: Choosing a Demoninator
  • (Dropped) Chapter 9: Four Assumptions of Multiple Regression You Should ALWAYS Check
  • (Dropped) Chapter 28: An Introduction to Item Response Theory
  • (Dropped) A Framework for Model Building in Social Science Research

Resources

Additional materials

SAGE Publishing Logo

Best Practices in Quantitative Methods

Jason W. Osborne
Cancel Print

November 2007 | 608 pages | Sage US

Format Published Date ISBN Price
Hardcover 11/10/2019 9781412940658 $185.00
180 Day Ebook 25/10/2022 9781483333052 $102.00
Lifetime 25/10/2022 9781483333052 $148.00

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically.  Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences.

The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. 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-ranging examples along with any empirical evidence to show why certain techniques are better.

Key Features: 

  • Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details.
  • Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances.
  • Offers new procedures to update and explicate traditional techniques: The featured scholars 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.

Intended Audience:  Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.


Table Of Contents:

  • Introduction
  • Chapter 1: The New Stats: Attitudes for the Twenty-First Century
  • Part I: Best Practices in Measurement
  • Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues
  • Part I: Best Practices in Measurement
  • Chapter 3: Estimating Inter-Rater Reliability: Assumptions and Implications of Three Common Approaches
  • Chapter 4: An Introduction to Rasch Measurement
  • Chapter 5: Applications of the Multi-Faceted Rasch Model
  • Chapter 6: Best Practices in Exploratory Factor Analysis
  • Part II: Selected Best Practices in Research Design
  • Chapter 7: Replication Statistics
  • Chapter 8: Mixed Methods Research in the Social Sciences
  • Chapter 9: Designing a Rigorous Small Sample Study
  • Chapter 11: Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference
  • Chapter 12: An Introduction to Meta-Analysis
  • Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis
  • Part III: Best Practices in Data Cleaning and the Basics of Data Analysis
  • Chapter 14: Best Practices in Data Cleaning: How Outliers and "Fringeliers" Can Increase Error Rates and Decrease the Quality and Precision of Your Results
  • Chapter 15: How to Deal with Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods
  • Chapter 16: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual and Technical Issues
  • Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes
  • Chapter 18: Robust Methods for Detecting and Describing Associations
  • Part IV: Best Practices of Quantitative Methods
  • Chapter 19: Resampling: A Conceptual and Procedural Introduction
  • Chapter 21: Advanced Topics in Power Analysis
  • Chapter 21: Best Practices in Analyzing Count Data: Poisson Regression
  • Chapter 22: Testing the Assumptions of Analysis of Variance
  • Chapter 23: Best Practices in the Analysis of Variance
  • Chapter 24: Binary Logistic Regression
  • Chapter 26: Multinomial Logistic Regression
  • Chapter 27: Mediation, Moderation, and the Study of Individual Differences
  • Chapter 28: Mediation, Moderation, and the Study of Individual Differences
  • Part V: Best Advanced Practices in Quantitative Methods
  • Chapter 30: Hierarchical Linear Modeling: What It is and When Researchers Should Use It
  • Chapter 30: Best Practices in Analysis of Longitudinal Data: A Multilevel Approach
  • Chapter 32: Best Practices in Structural Equation Modeling
  • Chapter 33: Introduction to Bayesian Modeling for the Social Sciences
  • Chapter 35: Measuring Accuracy in Psychological Research
  • Chapter 37: Ethical Implications for Best Practices in Quantitative Methods
  • (Dropped) Chapter 4: Best Practices in Graphically Displaying Data
  • (Dropped) Chapter 7: Choosing a Demoninator
  • (Dropped) Chapter 9: Four Assumptions of Multiple Regression You Should ALWAYS Check
  • (Dropped) Chapter 28: An Introduction to Item Response Theory
  • (Dropped) A Framework for Model Building in Social Science Research
  • Chapter 14: Best Practices in ANCOVA May Mean Not Using ANCOVA: Why Paired Subjects Designs are a Better Choice

Recent Product Reviews:

Recommendations