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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
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Quantitative Data Analysis_A Companion for Accounting and Information Systems Research.pdf
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