Promoting Statistical Practice and Collaboration in Developing Countries

✍ Scribed by O. Olawale Awe, Kim Love, Eric A. Vance

• Year: 2022
• Tongue: English
• Leaves: 635
• Edition: 1
• Category: Library

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Editors
Reviewers
Contributors
Part 1 Statistics Collaboration and Practice in Developing Countries: Experiences, Challenges, and Opportunities
1. Statistics and Data Science Collaboration Laboratories: Engines for Development
1.1 Introduction: Why Statistics and Data Science Have Extraordinary Potential for Data-Driven Development
1.2 Collaborative Statisticians and Data Scientists
1.3 Statistics and Data Science Collaboration Laboratories (“Stat Labs”)
1.3.1 The Purpose of Stat Labs
1.3.2 What Stat Labs Do
1.3.2.1 Supporting Domain Experts
1.3.2.2 Creating New Knowledge
1.3.2.3 Transforming Evidence into Action (TEA)
1.3.2.4 Training the Next Generation of Collaborative Statisticians and Data Scientists
1.3.3 Stat Labs Produce Collaborative Statisticians and Data Scientists and Data-Capable Development Actors
1.4 Exemplar Stat Labs
1.4.1 The Laboratory for Interdisciplinary Statistical Analysis (LISA) at Virginia Tech
1.4.2 LISA at the University of Colorado Boulder
1.4.3 The University of Ibadan LISA (UI-LISA)
1.5 Stat Labs Can Become Engines for Development
1.5.1 Theory of Change
1.5.2 Benefits and Impacts of a Stat Lab
1.6 Seven Steps for Creating a Stat Lab
1.7 What Makes a Stat Lab Strong and Sustainable
1.8 Conclusion
References
2. LISA 2020: Promoting Statistical Practice and Collaboration in Developing Countries
2.1 Introduction
2.2 The Origin and History of the LISA 2020 Program
2.2.1 The Beginning of LISA 2020
2.2.2 The Origins of LISA 2020
2.2.3 The Timeline of Key Events for LISA 2020
2.3 The Current State of the LISA 2020 Network
2.4 The Purpose of LISA 2020
2.4.1 Why LISA 2020 Pursues These Objectives
2.4.2 How LISA 2020 Pursues These Objectives
2.5 LISA 2020 as a Big Tent for Collaborative Statistics and Data Science
2.6 Conclusion
References
3. LISA 2020 Network Survey on Challenges and Opportunities for Statistical Practice and Collaboration in Developing Countries
3.1 Introduction
3.2 Education, Training, and Experience
3.3 Ways to Improve Statistical Practice
3.4 Ways to Improve Statistical Collaboration
3.4.1 Areas for Improvement/Development in Order to Advance Statistical Practice and Collaboration
3.5 Statistical Practice and Collaboration within Universities
3.6 Discussion
3.7 Conclusion
References
4. Exploring the Need for a Statistical Collaboration Laboratory in a Kenyan University: Experiences, Challenges, and Opportunities
4.1 Introduction
4.1.1 Context of Maseno University
4.1.2 What Consultancies Are Required in Kenya?
4.2 The Setbacks to Having a Stat Lab and Human Resources for It
4.2.1 Minimal Time for Lecturers for Consultations and Collaboration
4.2.2 Overly Theoretical Nature of Our Programs
4.2.3 Limited Statistical Computing Skills in Lectures
4.2.4 University Setup Challenges
4.2.5 The Challenge of Running Costs and Consistent Funding
4.2.6 The Challenge of Collaboration
4.2.7 Data Quality
4.2.8 University Process for Accrediting a Course
4.2.9 Pandemic
4.3 MU-LISA’s Strategies for Success
4.3.1 Administrative Strategies
4.3.2 Attending Workshops
4.3.3 Continuous Collaboration
4.3.4 Attachment and Internship Opportunities
4.3.5 Online Career Talks
4.4 Lessons Learned
References
5. Barriers, Challenges and Opportunities to Statistical Collaboration
5.1 Barriers in Data Quality in Developing Countries
5.2 Opportunities and Challenges Encountered by Stat Labs: The Case of Department of Statistics, Hawassa University, Ethiopia
5.2.1 Opportunities
5.2.2 Challenges
References
6. Challenges of Statistics Education That Leads to High Dropout Rate among Undergraduate Statistics Students in Developing Countries
6.1 Introduction
6.2 Challenges Peculiar to Statistics Education
6.3 Opportunities for Statisticians
References
7. Statistics Education and Practice in Secondary Schools in Nigeria: Experiences, Challenges and Opportunities
7.1 Introduction
7.2 Sample and Sampling Techniques
7.3 Instrumentation
7.4 Data Analysis
7.5 Discussion and Conclusion
7.6 Recommendations
References
Part 2 Building Capacity in Statistical Consulting and Collaboration Techniques through the Creation of Stat Labs
8. Promoting and Sustaining a Virile Statistical Laboratory in Nigeria’s Premier University: Lesson from UI-LISA Experience
8.1 Introduction
8.2 The Birth of UI-LISA
8.3 UI-LISA Setup and Programmes
8.3.1 One Hour with a Statistician
8.3.2 Mobile Statistical Clinic
8.4 Short Courses and Collaborative Training Workshops
8.4.1 Collaborative Projects
8.5 Conclusion
Appendix
References
9. The Complementary Role of UI-LISA in Statistical Training and Capacity Building at the University of Ibadan, Nigeria
9.1 Introduction
9.2 Benchmark Minimum Academic Standard (BMAS) for Statistics Programme
9.3 Internship Training at the University of Ibadan
9.4 Internship Training and UI-LISA Complementary Role
9.5 Conclusion
References
10. Statistical Consulting and Collaboration Techniques
10.1 Introduction
10.2 Consulting Comes in Many Colours
10.2.1 Types of Consulting
10.2.2 A Four-Circle Consulting Services
10.2.3 Important Skills Required for Providing Statistical Consulting
10.3 Collaboration Technique
10.4 Activities of LISA Statistical Capacity Building
10.5 Benefits of Statistical Consulting and Collaboration Techniques
10.6 Need to Improve Statistical Consulting and Collaboration
References
11. Statistical Consulting and Collaboration Practises: The Experience of NSUK-LISA Stat Lab
11.1 General Introduction
11.2 Challenges of Statistical Development in North Central Nigeria
11.3 The Birth of NSUK-LISA Stat Lab
11.4 Role and Potentials of NSUK-LISA Stat Lab
11.4.1 Workshop 1: Basic Statistical Methods for Physical Sciences with Examples in R
11.4.2 Workshop 2: Basic Statistical Methods for Physical, Management and Social Sciences with Examples in MINITAB
11.4.3 Workshop 3: Macroeconometric Analysis with EViews
11.4.4 Workshop and Webinar: Capacity Building on Consulting Best Practices Technique for Women in Statistics and Data Science in Nigeria amidst the COVID-19
11.4.5 Statistical Consulting Practices of NSUK-LISA Stat Lab
11.4.6 Some Progress from NSUK-LISA Stat Lab
11.5 Conclusion
References
12. Statistics: The Practice of Data Surgeon
12.1 Introduction
12.2 Abuses/Misuse: Data Handling
12.2.1 Data Misclassification and Ignorance
12.2.2 Data Dredging
12.2.3 Data Manipulation
12.2.4 Non-reproducible Statistical Analysis
12.3 Data Anatomy
12.4 Data Surgeon and Scientist
12.5 Way Forward and Conclusion
References
13. The ABC of Successful Statistical Collaborations: Adapting the ASCCR Frame in Developing Countries
13.1 Introduction
13.2 Attitude
13.3 Bonding
13.4 Communication
13.5 Concluding Remarks
Acknowledgment
References
Part 3 Statistics Education and Women’s Empowerment
14. Statistics and Women’s Empowerment: Challenges and Opportunities
14.1 Introduction
14.2 Types of Women’s Empowerment
14.2.1 Economic Empowerment
14.2.2 Socio-Cultural
14.2.3 Interpersonal
14.2.4 Legal
14.2.5 Political
14.2.6 Psychological
14.3 Statistical Measurement of Women’s Empowerment
14.3.1 Gender Development Index
14.3.2 Gender Inequality Index
14.4 Statistics for Improving the Ranking of Countries
14.5 Evolution of Policy for Women in India
14.6 Women in Agriculture
14.7 Success Story of a Farm Woman Entrepreneur
14.7.1 Rajkarni as Farm Woman Producing Field Crops
14.7.2 Rajkarni as a Farm Woman Producing Horticultural Crops
14.7.3 Rajkarni as a Farm Woman Processing Farm Produce
14.7.4 Rajkarni as a Farm Woman and Group Leader
14.7.5 Rajkarni as a Farm Woman Entrepreneur
14.7.6 Rajkarni as a Farm Woman and Environmentalist
14.7.7 Rajkarni as a Recognized Farm Woman Entrepreneur
14.8 Use of Statistics for Implementing Policies for Women
14.9 National Policy for the Empowerment of Women
14.9.1 Umbrella Integrated Child Development Services (ICDS)
14.9.2 Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)
14.9.3 National Rural Livelihood Mission (NRLM)
14.9.4 The Indira Awaas Yojana (IAY)
14.9.5 e-Trading of Products by Women
14.9.6 National Mission on Agricultural Extension and Technology (NMAET)
14.10 Constraints in Maintaining Statistics on Women in India
14.11 Conclusion
References
15. Women in STEM, Progress and Prospects: The Case of a Ghanaian University
15.1 Introduction
15.2 The Problem
15.3 Results
15.4 Linear Trend Model for Female Enrollment
15.5 Profile Analysis
15.6 Conclusion
References
16. Cross-Sectional Data Analysis of Female Labor Force Participation Using Factor Analysis: A Case Study of Bihar State, India
16.1 Introduction
16.2 The Current Status of the Economy of Bihar
16.3 Literature Review
16.4 The Employment Scenario in Bihar
16.5 Female Labor Force Participation in Bihar: A District-Level Analysis
16.6 Conclusion and Ways Forward
References
17. Determining Variation in Women’s Labor Force Participation in a Fragile Region of Sub-Saharan Africa Using Cochran’s Q Statistic
17.1 Introduction
17.1.1 The Cochran’s Q Statistic
17.1.2 Conflict and Women Participation in the Labor Force
17.1.3 Women in Post-conflict Development
17.2 Theoretical Framework
17.2.1 The Study Area
17.3 Data for the Study
17.3.1 Sampling Frame and Sample Size
17.3.2 Data Collection Technique
17.3.3 Data Needs
17.3.4 Analysis of Data
17.4 Findings
17.4.1 Sociodemographic Characteristics of the Women in Post-conflict Communities of Southwestern Nigeria
17.4.2 Sociodemographic Characteristics of Respondents about LFP in Post-conflict Communities of Southwestern Nigeria
17.4.3 Women LFP in Different Economic Activities by Their Sociodemographic Characteristics before the Conflicts in Southwestern Nigeria
17.4.4 Variation in LFP before, during, and after Conflicts in Southwestern Nigeria
17.4.5 The Difference in Poverty Status of Respondents by LFP Status before, during, and after Conflicts in Southwestern Nigeria
17.4.6 Occupational Status of Women Whose Spouses Were Lost to Conflicts  in Southwestern Nigeria
17.4.7 Occupational Change of Economically Active Women as a Result of Conflicts in Southwestern Nigeria
17.4.8 Effect of Conflicts on the Income Level of Women Whose Spouses Were Lost to the Conflict in Southwestern Nigeria
17.4.9 Determinants of Women’s LFP during and after Conflicts in Southwestern Nigeria
17.5 Discussion and Conclusion
References
Part 4 Statistical Literacy and Methods across Disciplines
18. Understanding Uncertainty in Real-Life
Scenarios through the
Concepts of Probability and Bayesian Statistics
18.1 Introduction
18.2 Basic Definitions and Terminologies
18.3 Probability
18.3.1 Counting Rule
18.3.2 Axiomatic Definition of Probability
18.3.3 Subjective Probability
18.3.4 Conditional Probability
18.3.5 Probability of Independent Events
18.3.6 Some Basic Results on Probability
18.4 Bayes’ Theorem
18.5 Bayes’ Theorem for Random Variables and the Parameter Concept
18.6 Why Is θ Random?
18.7 Choosing a Prior
18.8 Subjective Approach
18.9 Objective Approach
18.10 Conjugate Prior
18.11 Bayesian Interval Estimation
18.12 Predictive Posterior Density
18.13 Application of Bayesian Statistics to Real-Life
Data
18.14 Conclusion
References
19. Generalized Dual to Exponential Ratio Type Estimator for the Finite Population Mean in the Presence of Nonresponse
19.1 Introduction
19.2 The Proposed Estimator
19.3 Some Special Cases of the Generalized Estimator
19.4 Efficiency Comparison
19.5 Proposed Estimator
19.6 Empirical Study
19.7 Simulation Study
19.8 Results and Conclusion
References
20. A Comprehensive Tutorial on Factor Analysis with R: Empirical Insights from an Educational Perspective
20.1 Introduction
20.2 Exploratory Factor Analysis
20.2.1 Steps Involved in Conducting EFA
20.2.1.1 Determine Whether the Data Are Suitable for Factor Analysis
20.2.1.2 Extract Initial Factors
20.2.1.3 Determine the Exact Number of Factors to Retain
20.2.1.4 Rotate (Spread Variability) Factors
20.2.1.5 Interpreting and Naming the Factors
20.3 Confirmatory Factor Analysis
20.3.1 Steps in Conducting a Confirmatory Factor Analysis
20.3.1.1 Defining the Individual Constructs
20.3.1.2 Designing the Overall Measurement Model Theory
20.3.1.3 Specifying the Model (Structural Equation Model)
20.3.1.4 Assessing the Measurement Model Validity
20.4 Illustrative Example and Implementation with R
20.5 Description of Data for Exploratory Factor Analysis
20.6 Data Analysis and Results of EFA
20.7 Data and Results of Confirmatory Factor Analysis
20.8 Concluding Remark
References
21. Retrieval of Unstructured Datasets and R Implementation of Text Analytics in the Climate Change Domain
21.1 Introduction
21.2 Social Media as a Veritable Source of Data
21.3 Web Crawling
21.4 Web Crawler Architectures
21.5 Vector-Based Model of IR
21.6 Document Indexing
21.6.1 Indexing Using LUCENE
21.7 Local Weights
21.8 Global Weights
21.9 Methods of Accessing Datasets Online
21.9.1 Using Open-Source and Proprietary Software
21.9.2 Retrieving Data from Twitter Using API
21.10 Retrieving Data from a Given Web Page
21.11 Retrieving a Local CSV file
21.12 Data Preparation
21.12.1 Data Cleaning
21.13 Frequent Terms and #climatechange Discourse
21.14 Sentiment Analysis
21.15 The Trajectory of Climate Change Discourse
21.16 Conclusion
References
22. Control Charts and Capability Analysis for Statistical Process Control
22.1 Introduction
22.2 Basics of Control Chart
22.2.1 Justification of Three-Sigma
Limits
22.3 Control Charts for Statistical Process Control
22.4 Types of Data and Control Charts
22.4.1 Variables Control Charts
22.4.2 Data Structure for x̄ Control Chart
22.4.3 Three-Sigma Control Limits for the x̄[sub(i)] Control Chart
22.4.4 Illustrations of R and x̄ Control Charts
22.4.5 The R Chart
22.4.6 The x̄ Chart
22.4.7 Control Chart for Standard Deviation
22.4.8 Control Charts for Attributes
22.4.9 Case of Variable Sample Sizes
22.4.10 Percentage Chart
22.4.11 np or d Chart (No. of Defective Chart)
22.4.12 Illustration of p Chart
22.4.13 Control Charts for Nonconformities (Defects) or c Chart
22.4.14 Statistical Theory of c Chart
22.4.15 Control Chart for Average Number of Nonconformities per Unit or u Chart
22.4.16 Procedures with Variable Sample Size
22.4.17 Illustration of c Chart
22.5 Capability Analysis
22.5.1 Estimating the Process Capability
22.5.2 Special Cases
22.5.2.1 Case I
22.5.2.2 Case II
22.5.3 Capability Indices
22.5.4 Limitations of C[sub(p)]
22.5.5 CPL, CPU, and C[sub(pk)]
22.5.6 Illustration of Capability Analysis
22.6 Scenarios in Developing Countries
Acknowledgments
References
23. Determination of Sample Size and Errors
23.1 Introduction
23.2 Sample Size Determination Using Formulas
23.3 Sample Size Calculation Based on Confidence Intervals
23.3.1 Sample Size Determination for Estimating a Single Population Mean
23.3.1.1 Practical Considerations in Sample Size Determination for a Single Population Mean
23.3.2 Sample Size Determination for Estimating a Single Population Proportion
23.3.2.1 Finite Population Correction for Proportions
23.3.2.2 Practical Considerations in Sample Size Determination for a Single Population Proportion
23.3.3 Sample Size Determination for Estimating the Difference between Two Population Means
23.3.4 Sample Size Determination for Comparing Two Means from Paired Samples
23.3.5 Sample Size Determination for Estimation of the Difference between Two Population Proportions
23.3.6 Sample Size Calculation Based on Hypothesis Testing
23.3.7 Sample Size Determination for One Sample, Continuous Outcome
23.3.8 Sample Size Determination for One Sample, Dichotomous Outcome
23.3.9 Sample Size Determination for Two Independent Samples, Continuous Outcome
23.3.10 Sample Size Determination for Comparing Two Means from Paired Samples
23.3.11 Sample Size Determination for Two Independent Samples, Dichotomous Outcomes
23.3.12 Sample Size Determination for Comparing Two Proportions from Paired Samples
23.4 Estimates of Sampling Errors
23.5 Conclusion
References
Part 5 New Approaches to Statistical Learning in Developing Countries
24. Active and Agnostic: A Multidisciplinary Approach to Statistical Learning
24.1 Introduction
24.2 An Active and Agnostic Approach to Multidisciplinary SL
24.2.1 Active versus Passive Learning
24.2.2 IT-agnostic Learning
24.2.3 Student Segmentation
24.3 A Pilot Project in Brazil
24.4 Conclusion
References
25. Modernizing the Curricula of Statistics Courses through Statistical Learning
25.1 Introduction
25.2 Relation between Introduction to Big Data Modeling and ASA Guidelines
25.2.1 Increased Importance of Data Science
25.2.2 Real Applications
25.2.3 More Diverse Models and Approaches
25.2.4 Ability to Communicate
25.3 Case Study
25.4 Final Remarks
References
26. The Ten Most Similar Players: How to Use Statistics to Find the Best Soccer Players for Your Team
26.1 Introduction
26.2 Material and Methods
26.2.1 The dataset
26.2.2 Methods
26.3 Results
26.3.1 Exploratory Data Analysis
26.3.2 Web App
26.4 Final Considerations
References
27. Teaching and Learning Statistics in Nigeria with the Aid of Computing and Survey Data Sets from International Organizations
27.1 Introduction
27.2 Background of Statistics Education in Nigeria
27.3 Challenges of Statistical Teaching and Learning in Nigeria
27.4 Challenges and Cost of Statistical Software for Statistical Analysis
27.5 Teaching and Learning Statistics Using the Framework Called the TEAM
27.6 Overview of R Statistical Software and Some Free Statistical Software
27.7 Sources of Survey Data Sets
27.8 Conclusion and Policy Recommendations
Bibliography
Part 6 Importance of Statistics in Urban Planning and Development
28. Assessment of Indicators of Urban Housing Quality in Owerri Municipal, Nigeria: A Factor Analysis Approach
28.1 Introduction
28.2 Research Aim and Objectives
28.3 Review of the Literature
28.4 Conceptual Framework Residential Quality Model
28.5 Methodology Area of Study
28.6 Research Design
28.7 Sampling Procedure
28.8 Data Analysis
28.9 Presentation of Results
28.9.1 Summary of the Socioeconomic Data of Respondents
28.10 Factor Structure of Housing Quality
28.11 Classification for the Study Area Based on Housing Quality
28.12 Conclusions
28.13 Recommendations
References
29. Role of Gender Disaggregate Statistics in Urban Planning and Development in Pakistan
29.1 Introduction
29.2 Literature Review
29.3 Data and Methods
29.3.1 Data Sources
29.3.2 Variables
29.4 Blinder–Oaxaca Decomposition
29.5 Results
29.6 Discussion
29.7 Conclusion
References
Appendix A
Appendix B
30. Youth Empowerment and Sustainable Urban Development
30.1 Introduction
30.2 Youth Empowerment
30.3 Strengthening Skill Development
30.4 Indian Population Scenarios
30.5 Urbanization and Urban Development
30.6 Sustainable Urban Development
30.7 Monitoring of Youth Empowerment and Sustainable Urban Development
Acknowledgments
References
31. A Statistical Approach to Urbanization: The Case of Turkey
31.1 Introduction
31.2 Literature Review
31.3 Methodology
31.4 Data and Findings
31.5 Conclusion
References
Part 7 Statistical Literacy in the Wider Society
32. Statistical Literacy and Domain Experts: Evidence from UI-LISA
32.1 Introduction
32.2 Statistical Literacy
32.3 Domain Experts and Statistical Skills
32.3.1 Reports from the University of Ibadan Compendium of Abstracts (2005–2008)
32.4 UI-LISA Consultancy Services
32.4.1 Overview of UI-LISA Clients
32.4.2 Some Common Statistical Challenges
32.4.2.1 Writing a Good Research Proposal
32.4.2.2 Constructing Good Research Design
32.4.2.3 Challenges with Data Analysis
32.4.3 General Guidelines for Solving Common Statistical Challenges Writing a Strong Research Proposal
32.4.3.1 Choosing an Appropriate Research Design
32.4.3.2 Statistical Consideration for Choosing Research Topics and Objectives
32.4.3.3 Statistical Standards for Data Analysis
32.5 Conclusion
32.6 Recommendation
References
33. Media Presentation of Statistical Reports: How Adequate and Accurate?
33.1 Introduction
33.2 Statistical Literacy: A Tool in Media Literacy
33.3 The Role of Media in the National Statistical System: Nigeria as a Case Study
33.3.1 Presentation of Election Result: How Skillful Is the Media?
33.3.2 Presentation of Consumer Price Index: How Accurate Is the Methodology?
33.4 Analyses of Some Review Cases in Nigeria
33.4.1 Analysis of Reports of Media on the Nigeria’s Election Results
33.4.2 Analysis of Reports of Media on Index Number
33.5 Conclusion
References
34. The Challenges of Effective Planning in Developing Countries: Appraisal of Statistics Literacy
34.1 Introduction
34.2 Statistical Literacy as a Device for National Development
34.3 Nigeria National Statistical System (NSS) – Recent Trends and Challenges: The Evolution and Recent Growth of Nigeria National Statistical System
34.3.1 The Prospect of Nigerian National Statistical System
34.3.2 The Challenges of Nigerian National Statistical System
34.4 Federal School of Statistics (FSS) Ibadan, Nigeria – A Statistical Resource Center
34.4.1 The Role of International Organizations
34.5 The Donor Agencies and Nigeria Development
34.5.1 The Challenges of Donor Agencies in Developing Countries
34.6 Conclusion and Recommendation
References
35. Role of Statistics in Policymaking for National Development
35.1 Introduction
35.2 Literature Review Conceptual Review
35.2.1 Empirical Review
35.3 Benefits of Statistical Literacy in Society
35.3.1 Finance
35.3.2 Education
35.3.3 Business
35.4 Conclusion
References
36. Building Biostatistics Capacity in Developing Countries
36.1 Introduction
36.2 Why Is the Discipline of Biostatistics Important?
36.3 Biostatistics Training
36.4 Biostatistical Methods Used in Life Sciences
36.5 The Concept of Population and Sample
36.6 Sampling Process
36.7 Data Management and Biostatistics
36.8 Data Analysis
36.8.1 Types of Data Analysis
36.9 Biostatistics and Health Data Science
36.10 Contributions of Biostatistics to Other Disciplines
36.11 The Biostatistician and Biostatistical Consulting
36.12 Overview of the Differences between Biostatistics and Medical Statistics
36.13 Career Opportunities around Biostatistics
36.14 Biostatistical Resources
36.15 Strategies for Developing Biostatisticians in Developing Countries
36.15.1 Training of Experts in Biostatistics
36.15.2 Creating a Conducive and Attractive Environment for Biostatisticians
36.16 Challenges of Biostatistical Capacity Building in Developing Countries
36.17 Recommendations
36.17.1 Establishing Departments of Biostatistics in Developing Countries
36.17.2 Developing and Instituting Guidelines for Biostatisticians
36.17.3 Improving the Teaching of Biostatistics in Tertiary Institutions
36.17.4 Encouraging Biostatistical Consulting in the Universities
36.17.5 Developing and Implementing Strategies to Improve Biostatistical Resources
36.18 Conclusion
References
37. Technology and Multimedia in Statistical Education and Collaboration
37.1 Introduction
37.1.1 Collaboration and Teamwork
37.2 Types of Teamwork
37.2.1 Cooperative Learning
37.2.1.1 Cooperative Learning in Practice
37.2.2 Cooperative Faculty Group
37.2.2.1 Cooperative Faculty Group in Practice
37.3 Types of Collaborations
37.3.1 Collaboration within Academic Institutions
37.3.1.1 Opportunities for Collaboration within Academic Institutions
37.3.1.2 Motivations for Collaboration within Academic Institutions
37.3.1.3 Challenges of Collaboration within Academic Institutions
37.3.1.4 Elements of Successful Collaboration within Academic Institutions
37.3.1.5 Deliverables in Collaboration within Academic Institutions
37.3.1.6 Findings on Collaboration within Academic Institutions in Practice
37.3.2 Collaboration between Academic Institutions
37.3.2.1 Opportunities for Collaboration between Academic Institutions
37.3.2.2 The Motivation for Collaboration between Academic Institutions
37.3.2.3 Challenges of Collaboration between Academic Institutions
37.3.2.4 Elements of Successful Collaboration between Academic Institutions
37.3.2.5 Deliverables of Successful Collaboration between Academic Institutions
37.3.2.6 Findings on Collaboration between Academic Institutions in Practice
37.3.3 Collaboration between Academic Institutions and Government Agencies
37.3.3.1 Opportunities for Collaboration between Academic Institutions and Government Agencies/Departments
37.3.3.2 The Motivation for Collaboration between Academic Institutions and Government Agencies/Departments
37.3.3.3 Challenges of Collaboration between Academic Institutions and Government Agencies/Departments
37.3.3.4 Elements of Successful Collaboration between Academic Institutions and Government Agencies/Departments
37.3.3.5 Deliverables in Collaboration between Academic Institutions and Government Agencies/Departments
37.3.3.6 Findings on Collaboration between Academic Institutions and Government Agencies/Departments in Practice
37.3.4 Collaboration between Academic Institutions and Private Industry
37.3.4.1 Opportunities for Collaboration between Academic Institutions and Private Industry
37.3.4.2 The Motivation for Collaboration between Academic Institutions and Private Industry
37.3.4.3 Challenges of Collaboration between Academic Institutions and Private Industry
37.3.4.4 Elements of Successful Collaboration between Academic Institutions and Private Industry
37.3.4.5 Deliverables in Collaboration between Academic Institutions and Private Industry
37.3.4.6 Findings on Collaboration between Academic Institutions and Private Industry
37.3.5 International Research Collaboration
37.3.5.1 Opportunities for Domestic and International Research Collaboration
37.3.5.2 The Motivation for International Research Collaboration
37.3.5.3 Challenges of International Research Collaboration
37.3.5.4 Elements of Successful International Research Collaboration
37.3.5.5 Deliverables in International Research Collaboration
37.3.5.6 Findings from International Research Collaboration in Practice
37.4 Technology as an Important Tool for Communication in the 21st century
37.4.1 What is Communication?
37.4.2 The Importance of Communication in Learning and Collaboration
37.4.3 Communication for Learning and Collaboration Communication for Learning
37.4.3.1 Long Distance Learning (Online Courses and Degrees)
37.4.3.2 Interactive Classes
37.4.3.3 Fast and Time Saving
37.4.3.4 Expertise at Your Fingertips
37.4.3.5 Encourages Instant Practice and Creativity
37.4.3.6 Lowers the Cost Involved in Learning and Providing Knowledge
37.4.4 Importance of Technology and Multimedia in Communication for Collaboration
37.4.4.1 Ease of Communication of Ideas
37.4.4.2 Time Constraints and Difference Removed
37.4.4.3 Improves Productivity
37.4.4.4 Encourages the Involvement of More People
37.4.4.5 Easy Access to Learning Materials and Research Data
37.4.4.6 Reduces the Cost of Hosting Team Meetings
37.5 Collaboration in Statistics Using Technology and Multimedia
37.5.1 Collaboration Technologies
37.5.1.1 Communication Technology
37.5.1.2 Conferencing Technology
37.5.1.3 Coordination Technology
37.5.1.4 Computational Infrastructure
37.5.2 Collaboration in Statistics: Possible Options Available Using Multimedia and Technology
37.5.2.1 Academia
37.5.2.2 ResearchGate
37.5.2.3 Mendeley
37.5.2.4 Zotero
37.5.2.5 MethodSpace
37.5.2.6 Quora
37.5.2.7 Stack Overflow
37.5.3 Guidelines for Effective Statistical Collaboration through Technology and Multimedia
37.5.3.1 Establish Partnership
37.5.3.2 Execute the Project
37.5.3.3 Evaluate the Project
37.6 Conclusion
References
38. Statistical Approaches to Infectious Diseases Modelling in Developing Countries: A Case of COVID-19
38.1 Introduction
38.1.1 Early Transmission of COVID-19
in the Low and Lower
Middle-Income
Countries (L-LMICs)
38.1.2 Responses To and Challenges of COVID-19
in L-LMICs
38.1.2.1 India
38.1.2.2 The Republic of Mauritius
38.1.2.3 Nigeria
38.1.3 Different Mathematical Models Used in L-LMICs
38.1.3.1 Compartmental Models
38.1.3.2 Statistical Modelling
38.1.3.3 Model Complexity and Parameter Sensitivity
38.1.3.4 Disease Mapping
38.1.4 Economic Responses to COVID-19
in L-LMICs
38.1.4.1 Challenges to Economic Policies and Public Finances in L-LMIC
38.1.4.2 Labour Market and Household Income
38.1.4.3 Migration Flows and Remittances
38.1.5 International Collaboration on COVID-19
38.2 Conclusion
References
Bonus Chapter: Systematically Improving Your Collaboration Practice in the 21st Century
Index