Basic Scientific Research Course

Lesson 1 • Research Ethics and Integrity

Covers honesty, transparency, and responsible conduct as non-negotiable research standards. Connects ethical behaviour to reproducibility and public trust in science.

Lesson 2 • Overview of the Research Process

Maps the end-to-end research cycle from question formation to dissemination. Provides a navigational framework for the entire course.

Lesson 3 • History of Scientific Inquiry

Traces key paradigm shifts from ancient natural philosophy to modern science. Contextualises current methods within their historical development.

Lesson 4 • Core Principles of Scientific Thinking

Introduces logical reasoning, scepticism, and evidence evaluation as cognitive tools. These principles underpin every methodological decision made in research.

Lesson 1 • Identifying Research Problems

Teaches gap analysis and problem identification from literature and practice. A clear problem statement is the prerequisite for every subsequent design decision.

Lesson 2 • Defining Study Objectives and Aims

Translates research questions into specific, measurable objectives using SMART criteria. Objectives serve as benchmarks for evaluating study success.

Lesson 3 • Crafting Research Questions

Applies PICO, FINER, and similar frameworks to construct focused, answerable questions. Well-formed questions directly determine appropriate methodology.

Lesson 4 • Conducting a Preliminary Literature Review

Introduces rapid scanning of existing literature to map the research landscape. Prevents duplication and informs hypothesis direction before full review.

Lesson 1 • Evaluating Source Credibility

Applies CRAAP and similar criteria to assess currency, relevance, authority, and accuracy. Source quality directly affects the validity of research conclusions.

Lesson 2 • Academic Database Navigation

Covers Boolean operators, MeSH terms, and filters across major scholarly databases. Efficient searching reduces retrieval bias and saves time.

Lesson 3 • Synthesising Literature

Moves from summarising individual articles to integrating themes, conflicts, and gaps. Synthesis is the intellectual core of a literature review.

Lesson 4 • Critical Appraisal of Research Articles

Teaches structured appraisal of methodology, results, and conclusions in published studies. Critical reading prevents uncritical acceptance of flawed evidence.

Lesson 1 • Experimental Design Principles

Covers randomisation, control groups, blinding, and replication as validity safeguards. These principles minimise bias and support causal inference.

Lesson 2 • Taxonomy of Research Designs

Maps the landscape of experimental, quasi-experimental, and observational designs. Understanding design categories enables informed selection before data collection.

Lesson 3 • Sampling Strategies

Distinguishes probability and non-probability sampling and their effects on generalisability. Sampling decisions determine to whom conclusions can be applied.

Lesson 4 • Ethical Approval and Regulatory Compliance

Outlines institutional review processes, risk-benefit assessment, and participant protections. Ethical clearance is a prerequisite for data collection in human research.

Lesson 5 • Variables and Measurement

Defines independent, dependent, and confounding variables and links them to measurement scales. Precise variable definition is essential for valid data collection.

Lesson 1 • Pilot Testing and Instrument Refinement

Applies cognitive interviewing and small-scale pilots to detect instrument flaws before full deployment. Pilot testing prevents costly errors in the main study.

Lesson 2 • Physiological and Sensor-Based Measurement

Covers instrumentation calibration, signal acquisition, and measurement error in physical data. Accurate sensor data requires rigorous equipment validation.

Lesson 3 • Structured Observation Methods

Introduces systematic observation protocols, coding schemes, and inter-rater reliability. Structured observation captures behavioural data without self-report bias.

Lesson 4 • Secondary Data and Existing Datasets

Teaches identification, access, and quality assessment of publicly available datasets. Secondary data reduces cost but requires careful provenance evaluation.

Lesson 1 • Qualitative Sampling and Recruitment

Covers purposive, snowball, and theoretical sampling for participant selection. Qualitative sampling prioritises information richness over statistical representativeness.

Lesson 2 • Foundations of Qualitative Inquiry

Contrasts qualitative and quantitative paradigms and introduces major traditions such as phenomenology and grounded theory. Paradigm choice shapes every methodological decision.

Lesson 3 • In-Depth Interviews

Teaches semi-structured interview guide development, probing techniques, and active listening. Skilled interviewing elicits rich, nuanced participant narratives.

Lesson 4 • Qualitative Data Analysis

Applies thematic analysis, coding, and constant comparison to derive meaning from text. Systematic analysis transforms raw transcripts into interpretable findings.

Lesson 5 • Focus Groups and Group Methods

Covers moderator skills, group dynamics management, and data capture in focus group settings. Group interaction generates data unavailable through individual interviews.

Lesson 1 • Common Inferential Tests

Applies t-tests, ANOVA, chi-square, and correlation to appropriate data types. Correct test selection depends on variable type, sample size, and study design.

Lesson 2 • Descriptive Statistics and Data Exploration

Covers measures of central tendency, dispersion, and graphical data exploration. Descriptive analysis reveals data structure before inferential testing begins.

Lesson 3 • Data Cleaning and Quality Control

Addresses missing data, outliers, and data transformation before analysis. Clean data is the prerequisite for trustworthy statistical conclusions.

Lesson 4 • Probability and Statistical Inference

Introduces probability distributions, sampling distributions, and the logic of hypothesis testing. Statistical inference generalises sample findings to the broader population.

Lesson 5 • Effect Size, Power, and Sample Size

Teaches effect size calculation, statistical power, and a priori sample size estimation. Underpowered studies waste resources and produce unreliable conclusions.

Lesson 1 • Reporting Standards and Guidelines

Introduces CONSORT, STROBE, PRISMA, and equivalent reporting checklists by study type. Adherence to reporting standards ensures transparency and reproducibility.

Lesson 2 • Measuring and Maximising Research Impact

Introduces citation metrics, altmetrics, and knowledge translation strategies. Impact measurement informs career development and funding applications.

Lesson 3 • Data Visualisation for Publication

Covers figure design, table construction, and chart selection for scientific audiences. Effective visuals communicate findings faster and more accurately than text alone.

Lesson 4 • Research Dissemination Beyond Journals

Covers conference presentations, policy briefs, and public engagement as dissemination channels. Broad dissemination amplifies research impact beyond academic audiences.

Lesson 5 • Peer Review and Journal Submission

Explains the submission process, peer review types, and responding to reviewer feedback. Understanding peer review helps researchers navigate publication strategically.