Quantitative Data Analysis

1. Front Matter

   Pages i-x

   PDF

2. Introduction

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 1-6

3. Comparing Differences Across Groups

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 7-19

4. Assessing (Innocuous) Relationships

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 21-36

5. Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 37-59

6. Nested Data and Multilevel Models: Hierarchical Linear Modeling

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 61-72

7. Analyzing Longitudinal and Panel Data

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 73-98

8. Causality: Endogeneity Biases and Possible Remedies

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 99-134

9. How to Start Analyzing, Test Assumptions and Deal with that Pesky p-Value

   • Willem Mertens, Amedeo Pugliese, Jan Recker

   Pages 135-156

10. Keeping Track and Staying Sane

    • Willem Mertens, Amedeo Pugliese, Jan Recker

    Pages 157-159

11. Back Matter

    Pages 161-164

    PDF

This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.

• quantitative data analysis
• ANOVA
• regression
• nested models
• causality
• quantitative data analysis method
• building data analysis skills

• QUT Business School, Queensland University of Technology, Brisbane, Australia

  Willem Mertens

• Dept. of Economics and Management, University of Padova, Padova, Italy

  Amedeo Pugliese

• School of Accountancy, Queensland University of Technology, Brisbane, Australia

  Jan Recker

Willem Mertens is a Postdoctoral Research Fellow at Queensland University of Technology, Brisbane, Australia, and a Research Fellow of Vlerick Business School, Belgium. His main research interests lie in the areas of innovation, positive deviance and organizational behavior in general.

Amedeo Pugliese (PhD, University of Naples, Federico II) is currently Associate Professor of Financial Accounting and Governance at the University of Padova and Colin Brain Research Fellow in Corporate Governance and Ethics at Queensland University of Technology. His research interests span across boards of directors and the role of financial information and corporate disclosure on capital markets. Specifically he is studying how information risk faced by board members and its effects on the decision-making quality and monitoring in the boardroom.

Jan Recker is Alexander-von-Humboldt Fellow and tenured Full Professor of Information Systems at Queensland University of Technology. His research focuses on process-oriented systems analysis, Green Information Systems and IT-enabled innovation. He has written a textbook on scientific research in Information Systems that is used in many doctoral programs all over the world. He is Editor-in-Chief of the Communications of the Association for Information Systems, and Associate Editor for the MIS Quarterly.

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