The Philosophy of Quantitative Methods: Understanding Statistics
The Philosophy of Quantitative Methods focuses on the conceptual foundations of research methods within the behavioral sciences. In particular, it undertakes a close philosophical examination of a variety of quantitative research methods that are prominent in (or relevant for) the conduct of research in these fields. By doing so, the deep structure of these methods is examined in order to overcome the non-critical approaches typically found in the existing literature today.

In this book, Brian D. Haig focuses on the more well-known research methods such as exploratory data analysis, statistical significant testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. These methods are then examined with a philosophy consistent of scientific realism. In addition, each chapter provides a helpful Further Reading section in order to better assist the reader in extending their own thinking and research methods specific to their needs.
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The Philosophy of Quantitative Methods: Understanding Statistics
The Philosophy of Quantitative Methods focuses on the conceptual foundations of research methods within the behavioral sciences. In particular, it undertakes a close philosophical examination of a variety of quantitative research methods that are prominent in (or relevant for) the conduct of research in these fields. By doing so, the deep structure of these methods is examined in order to overcome the non-critical approaches typically found in the existing literature today.

In this book, Brian D. Haig focuses on the more well-known research methods such as exploratory data analysis, statistical significant testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. These methods are then examined with a philosophy consistent of scientific realism. In addition, each chapter provides a helpful Further Reading section in order to better assist the reader in extending their own thinking and research methods specific to their needs.
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The Philosophy of Quantitative Methods: Understanding Statistics

The Philosophy of Quantitative Methods: Understanding Statistics

by Brian D. Haig
The Philosophy of Quantitative Methods: Understanding Statistics

The Philosophy of Quantitative Methods: Understanding Statistics

by Brian D. Haig

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Overview

The Philosophy of Quantitative Methods focuses on the conceptual foundations of research methods within the behavioral sciences. In particular, it undertakes a close philosophical examination of a variety of quantitative research methods that are prominent in (or relevant for) the conduct of research in these fields. By doing so, the deep structure of these methods is examined in order to overcome the non-critical approaches typically found in the existing literature today.

In this book, Brian D. Haig focuses on the more well-known research methods such as exploratory data analysis, statistical significant testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. These methods are then examined with a philosophy consistent of scientific realism. In addition, each chapter provides a helpful Further Reading section in order to better assist the reader in extending their own thinking and research methods specific to their needs.

Product Details

ISBN-13: 9780190222055
Publisher: Oxford University Press
Publication date: 02/01/2018
Series: Understanding Statistics
Pages: 168
Product dimensions: 8.30(w) x 5.40(h) x 0.50(d)

About the Author

Brian D. Haig is a Professor in the Department of Psychology at the University of Canterbury. He has published extensively on the conceptual foundations of behavioral research methods. He is the author of Investigating the Psychological World (MIT Press, 2014) and, with Colin W. Evers, Realist Inquiry in Social Science (Sage, 2016). He is a Fellow of the Association for Psychological Science and the New Zealand Psychological Society.

Table of Contents

Chapter 1: Introduction

Chapter 2: Exploratory Data Analysis

Chapter 3: Tests of Statistical Significance

Chapter 4: Bayesianism

Chapter 5: Meta-Analysis

Chapter 6: Exploratory Factor Analysis

Chapter 7: Conclusions
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