Research Methods Training

Lesson 1 • Paradigms and Worldviews

Introduces positivism, interpretivism, and pragmatism as philosophical stances shaping research design. Connects paradigm choice to methodology selection.

Lesson 2 • Nature and Purpose of Research

Defines research as disciplined inquiry and contrasts it with opinion or anecdote. Establishes why rigorous methodology matters for credible knowledge production.

Lesson 3 • The Research Process Overview

Maps the end-to-end research cycle from problem identification to dissemination. Orients learners to the sequence of decisions covered in subsequent chapters.

Lesson 1 • Identifying a Research Problem

Teaches how to locate gaps in knowledge through literature scanning and professional observation. A clear problem statement anchors the entire study.

Lesson 2 • Developing Hypotheses and Objectives

Explains when hypotheses are appropriate and how to state them in testable form. Connects objectives to questions for coherent study design.

Lesson 3 • Scoping and Feasibility Assessment

Evaluates time, resource, access, and ethical constraints before committing to a study. Prevents scope creep and ensures realistic project planning.

Lesson 1 • Searching the Literature Systematically

Covers database selection, Boolean operators, and search strategy documentation. Systematic searching ensures comprehensive and reproducible coverage of sources.

Lesson 2 • Evaluating Source Quality

Applies credibility criteria such as peer review, recency, and methodological rigor to sources. Critical evaluation prevents reliance on weak or biased evidence.

Lesson 3 • Synthesizing and Writing the Review

Transforms annotated sources into a thematic, argument-driven narrative rather than a summary list. Synthesis reveals patterns, contradictions, and gaps in the field.

Lesson 1 • Sampling Strategies and Sample Size

Distinguishes probability and purposive sampling and matches each to design type. Addresses power analysis for quantitative studies and saturation for qualitative ones.

Lesson 2 • Experimental and Quasi-Experimental Designs

Covers randomised controlled trials, pre-post designs, and control group logic for causal inference. Identifies threats to internal validity and mitigation strategies.

Lesson 3 • Non-Experimental Quantitative Designs

Surveys cross-sectional, longitudinal, and correlational designs for descriptive and relational inquiry. Clarifies what causal claims are and are not supported.

Lesson 1 • Structured Observation and Existing Data

Applies systematic observation protocols and secondary data extraction for quantitative studies. Reduces reliance on self-report and expands data source options.

Lesson 2 • Measurement, Validity, and Reliability

Defines construct, content, and criterion validity alongside internal consistency and test-retest reliability. Establishes standards for evaluating and reporting instrument quality.

Lesson 3 • Data Management and Cleaning

Establishes procedures for data entry, storage, missing value treatment, and outlier detection. Clean, well-documented datasets are prerequisites for valid analysis.

Lesson 1 • Observation and Field Methods

Introduces participant and non-participant observation, field notes, and reflexivity in naturalistic settings. Observational data captures behaviour that self-report cannot access.

Lesson 2 • Document and Artifact Analysis

Applies systematic analysis to texts, images, and organisational records as qualitative data sources. Documents provide unobtrusive evidence of practices and meanings.

Lesson 3 • Designing and Conducting Interviews

Develops semi-structured and in-depth interview guides and covers probing and active listening techniques. Interview quality directly shapes the depth of qualitative findings.

Lesson 1 • Inferential Statistics Fundamentals

Covers hypothesis testing logic, p-values, confidence intervals, and Type I and II errors. These concepts underpin interpretation of all inferential test results.

Lesson 2 • Regression and Multivariate Analysis

Introduces simple and multiple regression, logistic regression, and effect size reporting. Multivariate models control confounders and test complex theoretical relationships.

Lesson 3 • Descriptive Statistics and Data Visualization

Applies measures of central tendency, dispersion, and frequency distributions to summarise data. Visualisation choices communicate patterns clearly to diverse audiences.

Lesson 1 • Preparing and Organizing Qualitative Data

Covers transcription standards, data organisation, and qualitative software setup before analysis begins. Organised data enables efficient and auditable coding.

Lesson 2 • Thematic and Framework Analysis

Applies Braun and Clarke's thematic analysis phases and framework analysis matrices to structured data. These methods produce clear, evidence-linked themes for reporting.

Lesson 3 • Trustworthiness and Rigour in Qualitative Research

Applies credibility, transferability, dependability, and confirmability criteria to evaluate qualitative quality. Strategies include member checking, thick description, and peer debriefing.