Quantitative data analysis : a companion for accounting and information systems research / Willem Mertens, Amedeo Pugliese, Jan Recker.
Mertens, Willem, author.; Pugliese, Amedeo, author.; Recker, Jan, author.
2017
HD30.2
Available Online

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Title Quantitative data analysis : a companion for accounting and information systems research / Willem Mertens, Amedeo Pugliese, Jan Recker.
Author Mertens, Willem, author.
ISBN 9783319427003
3319427008
9783319426990
3319426990
DOI https://doi.org/10.1007/978-3-319-42700-3
Published Cham : Springer International Publishing : Imprint : Springer, 2017.
Language English
Description 1 online resource (x, 164 pages) : illustrations.
Item Number 10.1007/978-3-319-42700-3 doi
Call Number HD30.2
Dewey Decimal Classification 658.4038
Summary 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.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Added Author Pugliese, Amedeo, author.
Recker, Jan, author.
Available in Other Form Print version: 9783319426990
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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 models
Analyzing longitudinal and panel data
Causality: Endogeneity biases and possible remedies
How to start analyzing, test assumptions, and deal with that pesky p-value
Keeping track and staying sane.

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