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

Format:

Book

Author/Creator:

Samoilenko, Sergey, 1966- author.

Osei-Bryson, Kweku-Muata, author.

Language:

English

Subjects (All):

Quantitative research.

Data mining.

Physical Description:

1 online resource (311 pages)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : Routledge, 2022.

Summary:

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.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Preface: Possible Uses of this Book

Introduction

SECTION I: Development of the Methodological Modules

Chapter 1: Pre-Requisite General Questions

Impact of the Assumption of Homogeneity of the Sample on Research Questions

From a Basket of Apples to a Set of Systems (Decision-Making Units)

From Systems to Systems in Context

Chapter 2: Components of Multi-Method Methodologies

Cluster Analysis (CA)

Classification Decision Trees Induction (CDTI)

Neural Networks (NNs)

Association Rules Mining (ARM)

Data Envelopment Analysis (DEA)

Multiple Regression (MR)

Chapter 3: Framework for Methodological Modules

SECTION II: Description of the Methodological Modules

Chapter 4: A1: Homogeneous Sample - DEA and DTI

Phase 1: DEA

Phase 2: DTI

Examples of Application of DEA and DTI

Chapter 5: A2: Homogeneous Sample - DEA and ARM

Phase 2: ARM

Examples of Application of DEA and ARM

Chapter 6: B1: Heterogeneous Sample (Groupings Are Given) - DTI and ARM

Phase 1: DTI

Examples of Application of DTI and ARM

Chapter 7: B2: Heterogeneous Sample (Groupings Are Given) - DTI and MR

Option 1: DTI Using the Data Set Comprised of a Causal Model Only

Option 2: DTI Using the Data Set without Causal Model

Option 3: DTI Using the Complete Data Set

Phase 2: MR

Option 1: MR Using the Causal Model Only

Option 2: MR Using the Adapted Causal Model - Contextual Independent Variable

Option 3: Creating a New MR Using Contextual Independent Variables

Example of Application of DTI and MR

Chapter 8: B3: Heterogeneous Sample (Groupings Are Given) - DTI, DEA, and ARM

Option 1: The Data Set Is Comprised of the Variables of the DEAea MODel.

Option 2: The Data Set Contains Contextual Variables

Phase 2: DEA

Phase 3: ARM

Option 1: ARM to Generate "If (Level of the Top-Split Variable(s))"

Option 2: ARM to Generate "If (DEA Model's Inputs)"

Option 3: ARM to Generate "If (DEA Model' s Outputs)"

Option 4: ARM to Generate "If (Level of Averaged Relative Efficiency)"

Option 5: ARM to Generate "If (Received Categorization)"

Examples of Application of DTI, DEA, and ARM

Chapter 9: B4: Heterogeneous Sample (Groupings Are Given) - DTI, DEA, and NN

Phase 3: NN

Step 1: Generate NN Model of Transformative Capacity

Step 2: Generate Outputs of a Less Efficient Group Based on Transformative Capacity of a More Efficient Group

Step 3: Generate Outputs of a More Efficient Group Based on Transformative Capacity of a Less Efficient Group

Step 4: Compile the Generated Outputs in a New Data Set

Phase 4: DEA

Example of Application of DTI, DEA, and NN

Chapter 10: C1: Heterogeneous Sample (Groupings Are Not Known) - CA and DTI

Phase 1: CA

Examples of Application of CA and DTI

Chapter 11: C2: Heterogeneous Sample (Groupings Are Not Known) - CA and ARM

Option 1: ARM Using Only Intrinsic Variables

Option 2: ARM Using Only Contextual Variables

Option 3: ARM Using Intrinsic and Contextual Variables

Examples of Application of CA and ARM

Chapter 12: C3: Heterogeneous Sample (Groupings Are Not Known) - CA, DTI, and MR

Option 1: Data Set Is Limited to Variables of the MR Model

Option 2: Data Set Comprises Variables of the MR Model and Contextual Variables

Phase 3: MR

Example of Application of CA, DTI, and MR

Chapter 13: C4: Heterogeneous Sample (Groupings Are Not Known) - CA, DTI, and ARM

Phase 2: DTI.

Option 1: A Priori Target Variable

Option 2: CA-based Target Variable

Step 1

Step 2

Step 3

Step 4

Examples of Application of CA, DTI, and ARM

Chapter 14: C5: Heterogeneous Sample (Groupings Are Not Known) - CA and DEA

Option 1: CA based on the DEA Model

Option 2: CA based on the DEA Model and Contextual Variables

Examples of Application of CA and DEA

Chapter 15: C6: Heterogeneous Sample (Groupings Are Not Known) - CA, DEA, and ARM

Option 1: Complete Sample, # of Variables = the DEA Model

Option 2: Complete Sample, # of Variables = the DEA Model + Contextual Variables

Option 3: Sub-Sets of the Sample, # of Variables = the DEA Model

Option 4: Sub-sets of the Sample, # of Variables = the DEA Model + Contextual Variables

Examples of Application of CA, DEA, and ARM

Chapter 16: C7: Heterogeneous Sample (Groupings Are Not Known) - CA, DTI, and DEA

Phase 3: DEA

Examples of Application of CA, DTI, and DEA

Chapter 17: C8: Heterogeneous Sample (Groupings Not Known) - CA, DTI, DEA, and NN

Phase 4: NN

Step 1: Creating an NN Model of "Low-Level" Cluster

Step 2: Creating an NN Model of "High-Level" Cluster

Step 3: Simulation of the Outputs of "Low-Level" Cluster Using NN Model of "High-Level" Cluster

Step 4: Simulation of the Outputs of "High-Level" Cluster Using NN Model of "Low-Level" Cluster

Phase 5: DEA

Examples of Application of CA, DTI, DEA, and NN

SECTION III: Methodological Modules - Examples of Their Application

Chapter 18: A Hybrid DEA/DM-based DSS for Productivity-Driven Environments

Description of the DSS

Externally Oriented Functionality

Internally Oriented Functionality.

Architecture of the DSS

An Illustrative Application

Step 1: Is the Business Environment Homogeneous?

Step 2: What Are the Factors Responsible for Heterogeneity of the Business Environment?

Step 3: Do Groups of Competitors Differ in Terms of the Relative Efficiency?

Step 4: What Are some of the Factors Associated with the Differences in Relative Efficiency?

Step 5: Are There any Complementarities Between the Relevant Variables?

Step 6: What Is a Better Way to Improve Production of Outputs?

Conclusion

Acknowledgment

References

Chapter 19: Determining Sources of Relative Inefficiency in Heterogeneous Samples: Methodology Using Cluster Analysis, DEA, and Neural Networks

Description of the Methodology

Description of Steps 3-5 of the Methodology

Step 3: Generate a "Black Box" Model of Transformative Capacity of Each Cluster

Step 4: Generate Simulated Sets of the Outputs for Each Cluster

Step 5: Determine the Sources of the Relative Inefficiency of the DMUs in the Sample

Motivation for Steps 3 and 5 of the Methodology

Motivation for Step 3

Motivation for Step 5

Illustrative Example

Description of the Illustrative Data Set

Application of the Methodology on the Illustrative Data Set

Results of Step 1: Evaluate the Scale Heterogeneity Status of the Data Set

Results of Step 2: Determine the Relative Efficiency Status of Each DMU

Results of Steps 3 and 4: Generate Simulated Sets of the Outputs for Each Cluster Based on Black Box Models Transformative Capacity Processes

Results of Step 5

Discussion and Conclusion

Chapter 20: Exploring Context Specific Micro-Economic Impacts of ICT Capabilities

Theoretical Framework and the Research Model

The Methodology of the Study.

Phase 1: Application of Data Envelopment Analysis (DEA)

Phase 1, Step 1

Phase 1, Step 2

Phase 1, Step 3

Phase 2: Decision Tree-Based Analysis

Phase 2, Step 1

Phase 2, Step 2

Description of the Data

Results of the Data Analysis

Results from Phase 1: Application of Data Envelopment Analysis (DEA)

Results from Phase 2 - Decision Tree (DT) Based Analysis

Contributions to Theory

Contributions to Practice

Chapter 21: A Methodology for Identifying Sources of Disparities in the Socio-Economic Outcomes of ICT Capabilities in SSAs

Research Framework

Proposed Methodology

A New Methodology: Benefits and Justifications

Phase 1: Data Envelopment Analysis (DEA)

Phase 2: Decision Tree Induction (DTI)

Phase 3: Association Rule Mining (ARM)

Research Questions and Null Hypotheses of the Study

The Data

Phase 1: Data Envelopment Analysis

Phase 2: Decision Tree Induction

Phase 3: Association Rule Mining

Discussion of the Results

Chapter 22: Discovering Common Causal Structures that Describe Context-Diverse Heterogeneous Groups

A Conceptualization of the Benchmarking Problem

Research Problem and Research Questions of the Study

The Proposed Methodology

Justification &amp

Benefits of the Methodology

Illustrative Example - Application to Sub-Saharan Economies

Phase 1: Define the Transformation Framework

Phase 2: Partition the Set of Decision Making Units into Meaningful Groups

Phase 3: Data Envelopment Analysis

Phase 4: Decision Tree Induction (DTI)

Phase 5: Association Rule Mining

Acknowledgment.

References.

Notes:

Description based on print version record.

ISBN:

1-00-302414-9

1-000-43112-6

1-003-02414-9

1-000-43113-4

9781003024149

OCLC:

1259323164