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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Data, data, data. More data is available to us now than ever before. As work and private activities are increasingly facilitated and enacted by our digital devices, we leave traces that can be picked up and analyzed anytime and anywhere. Data collected through surveys, archives, and experiments also remain relevant, as digital traces do not necessarily reflect perceptions, attitudes, and intentions. What also has not changed is that data is meaningless until it is analyzed. That is what this book is about: analyzing data. More precisely: analyzing quantitative data. Numbers.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 2. Comparing Differences Across Groups

Abstract
Imagine you want to find out whether people who read statistics books are better at analyzing data than those who do not. You could study this question in at least three ways: (1) You set up an experiment with two groups, one of which you make read a statistics book (a real cruelty), and then you make both groups analyze the same data. (2) You run an experiment with one group only, test their analysis skills, make them read a statistics book, and then test their skills again. (3) You find some people who read statistics books and other people who do not and compare their analysis skills by, for example, studying the number of their quantitative research publications or their grades in statistics classes. All three ways would end up with one variable that tells you whether a person reads statistics books or not—a dichotomous variable that defines group membership—and one continuous variable that summarizes people’s analysis skills (or statistics performance). Answering your research question would require you to evaluate whether the analysis skills of the group that read the book are better than those of the other group. This form of group comparisons—comparing one variable score between two groups—is the simplest. This chapter starts from this simple example and adds complexity by adding more groups and variables of interest.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 3. Assessing (Innocuous) Relationships

Abstract
Have you ever submitted a research paper to an academic conference and had reviews come back? If so, you know that conference papers are scored on a range of criteria, including originality, clarity, significance, and methodology. Have you ever wondered which of these criteria really affects whether a paper is accepted or rejected for presentation at the conference?
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 4. Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling

Abstract
One of the best-known models in Information Systems research is the Technology Acceptance Model (TAM), which postulates that users will intend to use a system if they find it useful and easy to use, and that they will find a system useful if they find it is easy to use. This model has been studied over and over again, typically by surveying users (or even non-users) of some system with questions about the degree to which they find the system useful and/or easy to use and whether they intend to use it in the future.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 5. Nested Data and Multilevel Models: Hierarchical Linear Modeling

Abstract
Most of the people and cases that are subject to research in business and information systems are nested within hierarchies. A hierarchy attaches roles to certain levels and typically makes higher-level roles responsible for lower-level roles. At all levels of the organizational hierarchy, this approach translates into small clusters of managers and larger clusters of team members (which may include managers of lower-level teams). Such a hierarchy could range from a CEO and her team of executives to a line manager and his team of operators, but the hierarchy even continues beyond the organization, as organizations are nested within industries, industries within countries, and so on. Sometimes we want to study effects that cross these hierarchical layers. For example, we may be interested in the effect of managers’ behavior on their team members’ behavior, or the effect of remuneration policies at the level of the organization on individual performance and individual turnover intentions. In other words, we may want to study the effect of a variable that varies at the group level (i.e., between groups) on another variable that differs for every individual (i.e., it varies within groups). This kind of investigation calls for the use of hierarchical linear models.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 6. Analyzing Longitudinal and Panel Data

Abstract
Data often comes from observations made at multiple points in time. Obtaining repeated observations on the same units allows the researcher to access a richer information set about observed units than would be possible with single observations and to map the evolution of the phenomenon over multiple periods for both individual units and overall as a trend. (For example, relationships between two variables may strengthen, weaken, or even disappear over time.) Longitudinal data can be gathered via survey instruments or archival databases that offer repeated measures on the same variables at different times.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 7. Causality: Endogeneity Biases and Possible Remedies

Abstract
Many, if not all, studies in accounting and information systems address causal research questions. A key feature of such questions is that they seek to establish whether a variation in X (the treatment) leads to a state change in Y (the effect). These studies go beyond an association between two phenomena (i.e., a correlation between variables in the empirical model) to find a true cause-effect relationship. Moving from a simple association to a causal claims requires meeting a number of conditions.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 8. How to Start Analyzing, Test Assumptions and Deal with that Pesky p-Value

Abstract
This chapter discusses the steps to take before any of the analyses discussed in earlier chapters. Although it may seem counterintuitive to put this information in the last chapter, experience teaches us that these are things people do not want to read first when they embark on their analysis journey. We all start out with a big idea and full of courage, but all too often our courage is blown to bits because words and terms like “homoscedasticity,” “skewness,” and “multivariate normality” make our heads spin and our plans seem impossible. However, we hope that, after you have gotten a kick from seeing first results with the method of your choice, you are now ready to learn about all the things you should have done first—the things that make your results credible.
Willem Mertens, Amedeo Pugliese, Jan Recker

Chapter 9. Keeping Track and Staying Sane

Abstract
Here this books draws to a close, but your efforts in data analysis continue. As we are sure you know, conducting data analyses can be long, tedious, and confusing. Some of us enjoy digging around, trying new things, and looking at new software and new ways of collecting, treating, and analyzing data, and we get excited about results and what they might mean. On the other hand, the absence of good data, good findings, clear outcomes, and a clear understanding of what they mean can weigh heavily. Even so, rest assured that there are some tricks to get you going and keep you going.
Willem Mertens, Amedeo Pugliese, Jan Recker

Backmatter

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