Quantitative Data Analysis
A Companion for Accounting and Information Systems Research

Authors: Willem Mertens, Amedeo Pugliese, Jan Recker



A guide through the essential steps required in quantitative data analysis, from relatively simple (t-test and ANOVA) to more sophisticated techniques (structural equation modeling and panel data analysis)

Helps in choosing the right method before starting the data collection process, based on the questions to be answered rather than the technique that needs to be learned

Statistics with the math! This book is written in words, not equations

Includes guidance on how to report results in scientific articles and thesis

Offers numerous examples from various diciplines in accounting and information systems to help readers understand the real-life use of each method

No need to invest in expensive and complex software packages - any suite can be used,or no suite at all

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.

Table of contents

Introduction

Comparing Differences Across Groups

Assessing (Innocuous) Relationships

Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling

Nested Data and Multilevel Models: Hierarchical Linear Modeling

Analyzing Longitudinal and Panel Data

Causality: Endogeneity Biases and Possible Remedies

How to Start Analyzing, Test Assumptions and Deal with that Pesky

Keeping Track and Staying Sane