Quantitative methodologies using multi-methods : models for social science and information technology research / Sergey Samoilenko & Kweku-Muata Osei-Bryson.

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Section I: Development of the Methodological Modules : Chapter 1. Pre Requisite General Questions ; Chapter 2. Components of Multi-Method Methodologies ; Chapter 3. Framework for Methodological Modules ; Section II: Description of the Methodological Modules ; Chapter 4. A1: Homogeneous Sample -- DEA and DTI ; Chapter 5. A2: Homogeneous Sample -- DEA and ARM ; Chapter 6. B1: Heterogeneous Sample (Groupings Are Given) DTI and ARM ; Chapter 7. B2: Heterogeneous Sample (Groupings Are Given) -- DTI and MR ; Chapter 8. B3: Heterogeneous Sample (Groupings Are Given) -- DTI, DEA, and ARM ; Chapter 9. B4: Heterogeneous Sample (Groupings Are Given) -- DTI, DEA, and NN ; Section III: Methodological Modules -- Examples of their Application : Chapter 18. A Hybrid DEA/DM-based DSS for Productivity-Driven Environments ; Chapter 19. Determining Sources of Relative Inefficiency in Heterogeneous Samples ; Chapter 20. Exploring Context Specific Micro-Economic Impacts of ITC Capabilities ; Chapter 21. A Methodology for Identifying Sources of Disparities in the Socio-econimic Outcomes of ITC Capabilities in SSA's ; Chapter 25. Contributing Factors to Information Technology Investment Utilization in Transition Economies ; Chapter 26. Increasing the Discriminatory Power of DEA in the Presence of the Sample Heterogeneity with Cluster Analysis and Decision Trees ; Chapter 27. An Exploration of the Intrinsic Negative Socio-economic Implications of ICT Interventions.

Quantitative Methodologies using Multi-Methods is a multifaceted book written to help researchers. It is a user-friendly introduction to the popular methods of data mining and data analysis. The book avoids getting involved into details that are more suitable for more advanced users; it is written for readers who have, at most, a surface-level knowledge of the methods presented in the book. The book also serves as an introductory guide to the subject of complementarity of the tools and techniques of data analysis. It shows how methods could be used in synergy to offer insights into the issues that could not be dissected by any single method alone. This text can also be used as a set of templates, where, given a set of research questions, the investigator could identify a set of methodological modules for answering the research questions of interest. This is not entirely unlike the relationship between theanalysis and design phases of the systems development life cycle--where the What of the analysis phase has to be translated into the How of the design phase. The book can guide the identification of modules (the How) that are suitable for answering research questions (the What). It can aid in transitioning a conceptual domain of the research questions into a scaffolding of data analytic and data mining methods. The book is also a guide to exploring what data under investigation holds. For example, an investigator may use the methodological modules presented in this book to generate a set of preliminary questions which, after a careful consideration and a requisite culling, could be formulated into a set of questions consistent within a selected theory or a framework. Finally, the book can be used as a generator of new research questions. Applying every method in each of the book's modules opens a new dimension ripe with follow-up questions such as, Why is this so? The answers to this question may provide new insight and lead to the development of a new theory. Contraportada.