Methodology
The craft of research, from the research process and study design to data collection, qualitative analysis, validity, ethics and scholarly writing. Concise topics you can scan and expand inline.
147 / 147 topics
The Research Process
14
- What Is Scientific Research?Systematic, empirical, replicable inquiry
- The Research ProcessFrom problem to dissemination
- The Research ProblemWhat makes a researchable problem
- Research QuestionsFocused, answerable questions
- Aims and ObjectivesThe overall aim and concrete objectives
- The Literature ReviewMapping what is known and finding the gap
- Theoretical FrameworkAnchoring a study in theory
- Conceptual FrameworkA map of concepts and expected relationships
- Types of HypothesesNull/alternative, directional, research/statistical
- Variables in ResearchDependent, independent, mediator, moderator, control
- Conceptualization and OperationalizationFrom abstract concept to measurable indicator
- Unit of AnalysisWho or what is being studied
- The Research ProposalPlanning and justifying the study
- Deductive, Inductive and Abductive ReasoningThree modes of reasoning in research
Research Approaches
7
- Quantitative ResearchMeasuring, testing and generalizing with numbers
- Qualitative ResearchMeaning, context and depth
- Mixed Methods ResearchCombining quantitative and qualitative
- Research Paradigms in PracticeOntology, epistemology and method
- Basic vs Applied ResearchKnowledge for its own sake vs solving problems
- Exploratory, Descriptive and Explanatory ResearchThe three purposes of research
- Cross-sectional vs Longitudinal ResearchA snapshot vs following over time
Research Designs
23
- What Is a Research Design?The blueprint linking question to evidence
- Experimental DesignManipulation, control, randomization
- Randomized Controlled TrialsThe gold standard for causal evidence
- Quasi-experimental DesignCausal inference without randomization
- Pre-experimental DesignsWeakly controlled, exploratory designs
- Between-subjects vs Within-subjectsDifferent people vs the same people
- Factorial DesignsTwo+ independent variables and interactions
- Repeated-measures and Crossover DesignsFollowing the same subjects across conditions
- Randomized Block and Latin Square DesignsControlling nuisance variation by blocking
- Correlational ResearchMeasuring association without manipulation
- Survey ResearchSystematically gathering data from a sample
- Case Study ResearchIn-depth study of a case in its context
- Cohort StudiesFollowing groups forward over time
- Case-control StudiesLooking back from outcome to exposure
- EthnographyProlonged immersion in a culture
- Grounded TheoryBuilding theory from the data
- PhenomenologyUnderstanding the essence of lived experience
- Narrative ResearchStudying the stories people tell
- Action ResearchImproving practice through cycles of action
- Design Science ResearchBuilding and evaluating artifacts
- Systematic ReviewA protocol-driven, replicable synthesis
- Meta-analysis as a MethodPooling effects across studies
- The Delphi MethodExpert consensus through anonymous rounds
Data Collection
10
- Primary vs Secondary DataNewly collected vs existing data
- Data Collection Methods: An OverviewMatching method to question and design
- Questionnaires and SurveysA standardized self-report instrument
- InterviewsStructured, semi-structured and unstructured
- Focus GroupsGenerating data through group interaction
- Observation MethodsWatching behaviour in its setting
- Experiments as Data CollectionGenerating data by controlled manipulation
- Document and Archival AnalysisUsing existing texts and records as data
- Pilot Studies and PretestingTesting instruments before the main study
- Secondary and Big DataAdministrative records, open data, digital traces
Measurement & Scaling
9
- Measurement in ResearchAssigning numbers or labels by rule
- Likert ScalesSummated rating statements
- Semantic Differential and Rating ScalesBipolar adjectives and other ratings
- Guttman and Thurstone ScalesCumulative and equal-interval scaling
- Index and Scale ConstructionCombining indicators into a composite measure
- Validity of MeasurementContent, criterion and construct validity
- Reliability of MeasurementConsistency and repeatability
- Questionnaire Design PrinciplesRules for writing good questions
- The Scale Development ProcessFrom construct definition to validation
Qualitative Analysis
12
- Qualitative Data Analysis: An OverviewMaking sense of non-numerical data
- Coding in Qualitative ResearchLabelling data with meaningful tags
- Thematic AnalysisIdentifying patterns as themes
- Content AnalysisSystematically categorizing text
- Grounded Theory AnalysisBuilding theory by constant comparison
- Discourse AnalysisStudying language in use and power
- Narrative AnalysisAnalysing the structure and meaning of stories
- Framework AnalysisMatrix-based systematic qualitative analysis
- TriangulationStrengthening findings with multiple sources
- Trustworthiness in Qualitative ResearchCriteria for rigour
- Reflexivity and PositionalityMaking the researcher's influence visible
- Saturation in Qualitative ResearchWhen new data add nothing new
Validity & Bias
11
- Internal ValidityThe soundness of a causal claim
- External Validity and GeneralizabilityExtending results beyond the study
- Construct Validity in ResearchStudying the construct you intend to
- Statistical Conclusion ValidityCorrect inference about covariation
- Threats to Internal ValidityFactors that confound causal claims
- Confounding VariablesThird variables that create spurious links
- Selection and Sampling BiasWhen the sample misrepresents the population
- Measurement and Response BiasSystematic distortion of the data
- Publication BiasThe over-representation of positive results
- Cognitive Biases in ResearchTendencies that distort the researcher's judgment
- Controlling Bias: Blinding and RandomizationDesigning bias out of a study
Research Ethics
12
- Principles of Research EthicsRespect, beneficence, justice
- Informed ConsentVoluntary, informed, competent participation
- Confidentiality and AnonymityProtecting identity and data
- Ethics Review Boards (IRB)Independent ethical approval
- Research MisconductFabrication, falsification, plagiarism
- Plagiarism and Academic IntegrityUsing others' work properly
- Conflict of InterestInterests that may bias judgment
- Authorship and Publication EthicsCrediting contributions fairly and honestly
- Data Management and FAIR PrinciplesMaking data findable and reusable
- Ethics with Human and Animal SubjectsThe Declaration of Helsinki and the 3Rs
- Questionable Research Practicesp-hacking, HARKing, selective reporting
- Privacy and Data Protection in ResearchSafeguarding personal and sensitive data
Scientific Writing & Communication
16
- Structure of a Research Paper (IMRaD)Introduction, Methods, Results, Discussion
- Writing the AbstractThe study's concise showcase
- Writing the IntroductionFrom context to gap to aim
- Writing the Methods SectionEnough detail to reproduce the study
- Reporting ResultsPresenting findings clearly, without interpretation
- Writing the Discussion and ConclusionInterpreting findings and owning limitations
- Writing a Literature ReviewSynthesis, not summary
- Citation and Referencing StylesAPA, MLA, Chicago, IEEE, Vancouver
- Avoiding Plagiarism in WritingQuoting, paraphrasing and citing well
- The Peer Review ProcessExpert scrutiny before publication
- Choosing a Journal and Impact MetricsScope fit and impact indicators
- Predatory JournalsRecognizing fake scholarly publishing
- Open Access and PreprintsMaking research freely available
- Reporting GuidelinesCONSORT, PRISMA, STROBE, COREQ
- Presenting ResearchConference talks, posters, slides
- Reproducibility and Open Science PracticesData/code sharing, preregistration, registered reports
Evidence-Synthesis Literacy
6
- Effect Sizes in Meta-AnalysisPutting studies on a common scale
- Fixed-effect vs Random-effects ModelsTwo assumptions for pooling
- Heterogeneity and I-squaredHow inconsistent the studies are
- Reading Forest PlotsThe visual summary of a meta-analysis
- Funnel Plots and Publication BiasDetecting missing studies graphically
- Subgroup and Sensitivity Analysis in ReviewsExploring heterogeneity and testing robustness
Causal-Inference Literacy
5
- Confounders, Colliders, and MediatorsWhich variables to adjust for
- Directed Acyclic Graphs (DAGs)Drawing causal assumptions explicitly
- The Potential Outcomes FrameworkCausal effect as a counterfactual
- Randomized vs Observational EvidenceWhy randomization identifies causes
- Causal Identification StrategiesRecovering causes from observational data
Scholarship Skills
6
- Literature Search StrategiesSearching databases systematically
- Reference Management ToolsOrganizing citations and PDFs
- Grey Literature and Searching Beyond DatabasesUnpublished and hard-to-find sources
- How to Read a Scientific PaperAn efficient, critical reading strategy
- Critical Appraisal of ResearchJudging whether a study is trustworthy
- Building a Synthesis MatrixTurning reading into a literature review
Frameworks & Standards
16
- CRISP-DMThe standard process for data mining
- The KDD ProcessKnowledge discovery in databases
- SEMMASAS data-mining process
- The OSEMN Data Science ProcessThe five steps of a data-science workflow
- The Team Data Science Process (TDSP)An agile, enterprise data-science lifecycle
- PICO and Question FrameworksStructuring an answerable research question
- The Research OnionPlanning methodology layer by layer
- Levels of EvidenceThe evidence hierarchy and pyramid
- The GRADE ApproachRating the certainty of evidence
- The Bradford Hill CriteriaFrom association to causation
- Risk of Bias AssessmentCritically appraising study quality
- The Research Data LifecycleFrom planning data to reusing it
- The DIKW PyramidData, information, knowledge, wisdom
- Logic Models and Theory of ChangeMapping programs from inputs to impact
- SWOT and PESTLE AnalysisFrameworks for strategic situation analysis
- Gantt Charts and Research Project ManagementPlanning and tracking a research project
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