Research Design, Mobile Data Collection and Data Analysis using NVIVO and R Course

Introduction

The Research Design, Mobile Data Collection, and Data Analysis using NVivo and R Course is a comprehensive, hands-on program designed to equip participants with the skills and tools required to conduct effective and efficient research in today’s data-driven environment. With the increasing demand for evidence-based decision-making across development, academic, and corporate sectors, this course bridges the gap between traditional research methods and modern digital tools that enhance data accuracy, speed, and analytical depth.

Participants will begin by mastering the principles of research design, including identifying research questions, choosing appropriate methodologies (qualitative, quantitative, or mixed methods), and developing sound sampling and data collection strategies. The course then explores mobile data collection tools, focusing on how to digitize surveys and gather field data efficiently using smartphones and tablets. Emphasis is placed on real-time data capture, validation, and syncing using platforms like KoboToolbox, ODK, or SurveyCTO.

The training advances into data analysis, introducing participants to powerful software tools: NVivo for qualitative and mixed-methods data analysis, and R for statistical and quantitative analysis. Participants will gain practical skills in coding, theme generation, content analysis, and visualization in NVivo, alongside data cleaning, statistical modeling, and visualization in R. The integration of these tools allows learners to manage, analyze, and interpret data holistically.

By the end of the course, participants will be equipped to conduct end-to-end research from designing robust methodologies and collecting field data to producing high-quality analyses and visual reports. Whether for academic research, M&E work, program evaluations, or policy formulation, this course empowers professionals to harness modern tools and methods for impactful research outcomes.

Duration

10 days

Who should Attend?

This course is ideal for:

·       Researchers and Research Assistants involved in academic, institutional, or policy-oriented research requiring robust design and data analysis.

·       Monitoring and Evaluation (M&E) Officers who collect, manage, and analyze data for program assessments and impact evaluations.

·       Data Analysts and Statisticians interested in applying advanced tools like NVivo and R for mixed-methods data interpretation.

·       Program and Project Managers overseeing data-driven development initiatives and needing reliable research outputs for decision-making.

·       NGO and Development Sector Professionals involved in field surveys, needs assessments, and donor reporting.

·       Academics conducting thesis or dissertation research using qualitative or quantitative methods.

·       Consultants and Independent Researchers working on evaluations, baseline surveys, and other evidence-based assignments.

·       Government and Public Sector Officers engaged in planning, policy formulation, or national statistical work involving data systems.

Course Objectives

By the end of this course the learners should be able to:

·       Understand the fundamentals of research design, including formulating research questions, selecting appropriate methodologies, and developing effective data collection frameworks for both qualitative and quantitative studies.

·       Develop skills in mobile-based data collection, including designing digital surveys, deploying tools such as KoboToolbox or ODK, and managing real-time data collection and synchronization using smartphones or tablets.

·       Apply best practices in sampling techniques, ethical considerations, and data quality assurance during the design and fieldwork phases of a research project.

·       Use NVivo software for qualitative and mixed-methods data analysis, including importing data, coding, theme development, content analysis, and generating visual outputs such as word trees and thematic maps.

·       Utilize R programming language for quantitative data analysis, including data cleaning, statistical modeling, descriptive and inferential statistics, data visualization, and reporting.

·       Integrate and manage multiple data sources, combining both qualitative and quantitative approaches to draw comprehensive insights and support evidence-based conclusions.

·       Interpret and present research findings effectively, using visual tools, charts, and written narratives tailored for various audiences such as funders, policymakers, or academic institutions.

·       Enhance digital literacy and technical competence in using open-source and proprietary tools for data collection and analysis in real-world research settings.

·       Design and implement end-to-end research projects, from conceptualization and data collection to analysis and dissemination of results.

Course Outline

Module 1: Foundations of Research and Statistics

• Introduction to research and its importance
• Basic statistical terms and concepts
• Types of data: qualitative, quantitative, mixed
• Descriptive and inferential statistics
• Ethics in research and informed consent
• Exercise: framing research questions and hypotheses

Module 2: Research Design and Methodology

• The role and purpose of research design
• Types of research designs (exploratory, descriptive, experimental, etc.)
• The research process step-by-step
• Mixed-methods approach
• Choosing the right method
• Exercise: Develop a complete research design for a selected topic

Module 3: Digital and Mobile Data Collection

• Introduction to mobile-based data collection
• Benefits and limitations of mobile tools
• Types of surveys and sampling techniques
• Data quality, validation, and device management
• Data aggregation and storage
• Common platforms: KoboToolbox, ODK, SurveyCTO, CommCare

Module 4: Form Design and Survey Authoring

• Questionnaire structure and data types
• Using ODK Build, KoboToolbox, PurcForms
• Manual form design using XLSForms
• Advanced survey logic: skip patterns, constraints, calculations
• Multimedia and geolocation questions
• Hands-on: Building and testing mobile surveys

Module 5: Mobile Device Configuration and Deployment

• Installing and configuring ODK Collect and similar apps
• Uploading forms, testing deployments
• Offline and online survey considerations
• Hosting surveys: Kobo, Ona.io, FormHub, Aggregate
• Configuring local servers and downloading data
• Hands-on: Form deployment and mobile data collection simulation

Module 6: Introduction to GIS and Geospatial Mapping

• Basics of GIS for researchers
• Integrating GPS data with QGIS
• Mapping survey responses
• Visualizing spatial data for analysis and decision-making

Module 7: Fundamentals of Qualitative Research

• Understanding qualitative vs. quantitative approaches
• Sources and types of qualitative data
• Introduction to NVivo and qualitative data management
• Importing and organizing textual and multimedia data
• NVivo workspace, ribbons, and tools

Module 8: Qualitative Data Coding and Analysis with NVivo

• Creating projects and working with nodes
• Data-driven and theory-driven coding
• Thematic, descriptive, and analytic coding
• Memos, annotations, links, and classifications
• Queries: Text, matrix, word frequency, and coding queries
• Building models and visualizations (e.g., cluster diagrams, treemaps)

Module 9: Advanced NVivo Analysis and Interpretation

• Grounded theory, content analysis, discourse, and narrative analysis
• Triangulating data sources
• Integrating qualitative and quantitative findings
• Drawing insights, conclusions, and lessons learned
• Visualizing NVivo outputs
• Hands-on: Coding and analyzing sample interview transcripts

Module 10: Introduction to R for Quantitative Analysis

• Introduction to R and RStudio environment
• Installing packages and reading datasets
• Data structures, importing/exporting, indexing
• Data cleaning and management
• Exploratory data analysis and data wrangling with tidyverse

Module 11: Applied Statistics with R

• Measures of central tendency and variability
• Confidence intervals, probability distributions
• Hypothesis testing: t-tests, ANOVA, non-parametrics
• Cross-tabulation, chi-square tests
• Visualizations using ggplot2

Module 12: Regression and Predictive Modeling with R

• Correlation and linear regression
• Logistic regression and Poisson models
• Multicollinearity and interaction terms
• Model validation and diagnostics
• Generalized linear models (GLM)
• Mixed effect models and ANCOVA

Module 13: Multivariate and Advanced Statistical Techniques

• Principal Component Analysis (PCA)
• Cluster Analysis and LDA
• Multivariate Analysis of Variance (MANOVA)
• NMDS and Correspondence Analysis
• Time series analysis and forecasting (optional advanced add-on)

Module 14: Integrating NVivo and R

• Exporting NVivo coded data for statistical analysis
• Mixed-method triangulation: merging narratives with numbers
• Joint display frameworks for integrated findings
• Communicating multi-dimensional insights effectively

Module 15: Data Use, Communication and Reporting

• Writing high-quality research and evaluation reports
• Data visualization best practices for reporting
• Designing charts, dashboards, and infographics
• Data dissemination and communication planning
• Demand generation and evidence-based decision-making
• Presentations and action planning sessions

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.