Module 1: Foundations of Robust Research Design

·       Defining research questions, aims, and objectives

·       Reviewing fundamental quantitative, qualitative, and mixed methods approaches

·       Developing the Theory of Change and Logic Models

·       Literature review strategies and gap analysis

·       Ensuring internal and external validity in study design

·       Practical session: Drafting a concise research problem statement and objectives

Module 2: Conceptualizing the Research Framework

·       Operationalizing concepts and variables

·       Creating conceptual and analytical frameworks

·       Selecting appropriate data collection instruments (Surveys, KIIs, FGDs)

·       Designing and structuring data collection checklists

·       Pilot testing protocols and refining instruments

·       Practical session: Mapping variables to the research objectives and selecting appropriate metrics

Module 3: Sampling Strategies and Ethical Practice

·       Overview of probability and non-probability sampling methods (e.g., Random, Stratified, Purposive)

·       Calculating appropriate sample sizes for quantitative studies

·       Defining selection criteria for qualitative participants

·       Understanding the principles of research ethics (Informed Consent, Confidentiality)

·       Data protection and privacy compliance (e.g., GDPR principles)

·       Practical session: Calculating sample size using appropriate formulas and developing an Informed Consent form

Module 4: Introduction to Data Types and Management

·       Distinguishing between primary and secondary data sources

·       Understanding different data structures (Cross-sectional, Time-series, Panel)

·       Principles of clean data organization and file naming conventions

·       Data cleaning methodologies and identifying common errors (outliers, missing data)

·       Introduction to data security and storage protocols

·       Practical session: Structuring a raw dataset for analysis and defining variable labels

Module 5: Implementing Mobile Data Collection Tools

·       Comparative overview of popular tools (ODK, KoboToolbox, SurveyCTO)

·       Setting up the server, user accounts, and project creation

·       Designing forms using the tool's web interface or XLSForm

·       Offline vs. online data collection considerations

·       Managing user access and enumerator groups

·       Practical session: Setting up a server account (e.g., KoboToolbox) and creating the first simple project

Module 6: Advanced Survey Design for Mobile Platforms

·       Utilizing question types: select_one, select_multiple, integers, geo-points

·       Implementing skip logic and branching for complex questionnaires

·       Using calculated fields and pre-loading data

·       Incorporating multimedia (photos, audio) into the survey design

·       Best practices for creating user-friendly mobile forms

·       Practical session: Designing a survey form that incorporates complex skip logic and calculated fields using XLSForm

Module 7: Form Programming, Logic, and Deployment

·       Mastering XLSForm syntax for advanced form programming

·       Applying validation constraints (e.g., range, length, dependencies) to ensure data accuracy

·       Using relevant and constraint expressions to control the survey flow

·       Testing and debugging forms prior to deployment

·       Finalizing form submission settings and encryption

·       Practical session: Debugging a complex XLSForm for syntax errors and deploying the final version to enumerators

Module 8: Real-Time Data Quality Assurance

·       Setting up real-time data monitoring dashboards and reports

·       Identifying and addressing common data collection biases (e.g., interviewer effect)

·       Monitoring enumerator performance and submission rates

·       Implementing data cleaning procedures during collection

·       Conducting high-frequency checks (HFCs) and back checks

·       Practical session: Using server analytics features to monitor data submissions and flag potential outliers in real time

Module 9: Introduction to Qualitative Data Analysis and NVIVO

·       Understanding the nature of qualitative data (Text, Audio, Video)

·       Principles of systematic qualitative analysis (Thematic, Content, Grounded Theory)

·       Exploring the NVIVO interface, projects, and navigation

·       Creating a structured NVIVO project and importing source files

·       NVIVO workspace management and customizing settings

·       Practical session: Creating a new NVIVO project and importing various types of source files (e.g., interview transcripts, documents)

Module 10: NVIVO: Importing, Organizing, and Initial Coding

·       Importing transcripts, documents, and social media data

·       Managing and classifying demographic data using Cases and Case Classifications

·       Creating Nodes (Thematic and Descriptive Coding)

·       In-vivo coding and basic thematic identification

·       Memo writing and annotation techniques to capture emergent insights

·       Practical session: Applying initial descriptive and in-vivo coding to a sample interview transcript

Module 11: NVIVO: Advanced Thematic Analysis and Node Structures

·       Developing a hierarchical node structure (Parent and Child Nodes)

·       Coding at Nodes: Merging, renaming, and reviewing coding stripes

·       Structuring and grouping Nodes using Node Classifications

·       Using Relationships to map connections between concepts

·       Exploring patterns and connections across sources

·       Practical session: Refining the Node structure by creating a thematic hierarchy and merging redundant codes

Module 12: NVIVO: Queries, Visualizations, and Reporting

·       Running Text Search and Word Frequency queries to explore language patterns

·       Utilizing Matrix Coding Queries for cross-tabulation of themes and cases

·       Creating graphical representations (Maps, Charts, Cluster Analysis)

·       Exporting codebooks and visualizations for reporting

·       Integrating findings with quantitative data using Mixed Methods Query

·       Practical session: Performing a Matrix Coding Query to compare themes across different demographic groups (Cases)

Module 13: Introduction to Quantitative Analysis and the Stata Environment

·       Understanding Stata's command syntax and structure

·       Navigating the Stata interface (Command, Results, Variables, Review windows)

·       Creating and managing Do-files for reproducible analysis

·       Importing data from various formats (CSV, Excel, Mobile Data)

·       Saving and managing Stata data files (.dta)

·       Practical session: Setting up a Do-file, importing a mobile data file (CSV format), and saving it as a Stata data file

Module 14: Stata: Data Management, Cleaning, and Manipulation

·       Using essential commands: generate, replace, recode

·       Handling missing values and data inconsistencies

·       Creating and managing variable labels and value labels

·       Combining datasets using merge and append commands

·       Creating indicator (dummy) variables from categorical data

·       Practical session: Writing Stata Do-file commands to clean, recode, and label a raw dataset

Module 15: Stata: Descriptive Statistics and Advanced Data Visualization

·       Generating frequency tables and cross-tabulations (tabulate)

·       Calculating summary statistics (summarize, mean, sd, egen)

·       Creating high-quality graphs: histograms, box plots, scatter plots (graph twoway)

·       Customizing graph aesthetics (titles, labels, schemes)

·       Exploring data distributions and identifying normality

·       Practical session: Using Stata to generate presentation-ready descriptive tables and complex overlaid graphs

Module 16: Stata: Inferential Statistics - Comparison Tests

·       Review of hypothesis testing principles (null and alternative hypotheses)

·       Conducting t-tests for comparison of means (one-sample, two-sample)

·       Performing Analysis of Variance (ANOVA) for multiple group comparison

·       Non-parametric alternatives (Mann-Whitney U, Kruskal-Wallis)

·       Understanding and calculating statistical power

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588