research_methods_info.json

{
  "methods": [
    {
      "method": "Phenomenology",
      "paradigm": "Constructivist",
      "use_when": "To explore lived experiences and subjective meaning-making.",
      "data_collection": [
        "In-depth interviews",
        "Reflective journals"
      ],
      "analysis": [
        "Interpretative Phenomenological Analysis (IPA)",
        "Thematic coding"
      ],
      "example_questions": [
        "What is the lived experience of first-generation college students?",
        "How do teachers experience inclusion in post‑COVID classrooms?",
        "How does living with a chronic illness shape daily life?",
        "What are students’ perceptions of remote learning?"
      ],
      "strengths": [
        "Provides rich, contextual insights",
        "Captures subjective realities"
      ],
      "limitations": [
        "Limited generalizability",
        "Requires interpretive sensitivity"
      ],
      "references": [
        "Moustakas, C. (1994). Phenomenological Research Methods. SAGE.",
        "Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative Phenomenological Analysis. SAGE."
      ]
    },
    {
      "method": "Ethnography",
      "paradigm": "Interpretivist / Cultural Relativism",
      "use_when": "To study cultures, rituals, and social interactions in real‑world settings.",
      "data_collection": [
        "Participant observation",
        "Field notes",
        "Interviews",
        "Artifact analysis"
      ],
      "analysis": [
        "Thematic coding",
        "Narrative construction",
        "Cultural interpretation"
      ],
      "example_questions": [
        "How do teacher–student interactions shape classroom culture in a rural school?",
        "What rituals define corporate culture in a tech startup?",
        "How do youth subcultures emerge in an urban community center?",
        "How do rural and urban schools differ in pedagogical practices?"
      ],
      "strengths": [
        "Deep cultural insight",
        "Naturalistic context"
      ],
      "limitations": [
        "Time‑consuming",
        "Risk of observer bias"
      ],
      "references": [
        "Geertz, C. (1973). The Interpretation of Cultures. Basic Books.",
        "Hammersley, M., & Atkinson, P. (2007). Ethnography: Principles in Practice. Routledge."
      ]
    },
    {
      "method": "Action Research",
      "paradigm": "Critical / Emancipatory",
      "use_when": "To collaboratively solve practical problems and improve practice.",
      "data_collection": [
        "Observations",
        "Journals",
        "Interviews",
        "Surveys"
      ],
      "analysis": [
        "Reflective analysis",
        "Thematic categorization"
      ],
      "example_questions": [
        "How can teachers co‑design a strategy to improve student engagement?",
        "What changes improve communication in a nursing team?",
        "Which interventions boost math participation in middle school?",
        "How can community programs increase youth involvement?"
      ],
      "strengths": [
        "Immediate applicability",
        "Practitioner engagement"
      ],
      "limitations": [
        "Limited generalizability",
        "Role conflict between researcher and practitioner"
      ],
      "references": [
        "Kemmis, S., & McTaggart, R. (1988). The Action Research Planner. Deakin University.",
        "Stringer, E. T. (2013). Action Research (4th ed.). SAGE."
      ]
    },
    {
      "method": "Grounded Theory",
      "paradigm": "Constructivist / Pragmatist",
      "use_when": "To develop theory grounded in systematic qualitative data collection.",
      "data_collection": [
        "Interviews",
        "Field notes",
        "Documents"
      ],
      "analysis": [
        "Open coding",
        "Axial coding",
        "Selective coding",
        "Constant comparison"
      ],
      "example_questions": [
        "What theoretical framework explains teacher burnout?",
        "Which factors underlie social media addiction?",
        "What constructs emerge from administrators’ leadership practices?",
        "How do migrants make sense of their transition experiences?"
      ],
      "strengths": [
        "Systematic theory generation",
        "Data‑driven"
      ],
      "limitations": [
        "Resource‑intensive",
        "Requires iterative cycles"
      ],
      "references": [
        "Charmaz, K. (2014). Constructing Grounded Theory. SAGE.",
        "Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory. Aldine."
      ]
    },
    {
      "method": "Case Study",
      "paradigm": "Interpretivist / Pragmatist",
      "use_when": "To conduct an in‑depth analysis of a bounded system in context.",
      "data_collection": [
        "Interviews",
        "Observations",
        "Documents",
        "Artifacts"
      ],
      "analysis": [
        "Within‑case analysis",
        "Cross‑case synthesis",
        "Thematic analysis"
      ],
      "example_questions": [
        "What is one student’s learning trajectory in a tech‑rich classroom?",
        "How was STEM implemented in a rural school?",
        "What are the inclusion experiences of a student with special needs?",
        "How did a policy change affect a particular school?"
      ],
      "strengths": [
        "Holistic insight",
        "Flexible data sources"
      ],
      "limitations": [
        "Limited generalizability",
        "Potential researcher bias"
      ],
      "references": [
        "Yin, R. K. (2018). Case Study Research and Applications (6th ed.). SAGE."
      ]
    },
    {
      "method": "Meta‑Analysis",
      "paradigm": "Post‑positivist",
      "use_when": "To statistically synthesize results from multiple quantitative studies.",
      "data_collection": [
        "Published studies",
        "Databases (ERIC, PsycINFO)"
      ],
      "analysis": [
        "Effect size calculation",
        "Heterogeneity testing",
        "Moderator analysis"
      ],
      "example_questions": [
        "What is the combined effect of STEM programs on student achievement?",
        "How does digital game‑based learning impact academic motivation?",
        "Which factors most influence college retention rates?",
        "What is the overall efficacy of preschool interventions on social skills?"
      ],
      "strengths": [
        "High generalizability",
        "Robust evidence synthesis"
      ],
      "limitations": [
        "Dependent on study quality",
        "Risk of publication bias"
      ],
      "references": [
        "Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta‑Analysis. Wiley."
      ]
    },
    {
      "method": "Systematic Review",
      "paradigm": "Post‑positivist",
      "use_when": "To methodically review and synthesize all evidence on a focused question.",
      "data_collection": [
        "Database searches",
        "Inclusion/exclusion screening",
        "Data extraction"
      ],
      "analysis": [
        "Narrative synthesis",
        "Quantitative aggregation"
      ],
      "example_questions": [
        "What do mobile learning studies reveal about effectiveness and challenges?",
        "What common findings exist on parental involvement and student success?",
        "What are trends in teacher education over the last decade?",
        "What does the literature say about AI tools in K‑12 education?"
      ],
      "strengths": [
        "Comprehensive",
        "Transparent process"
      ],
      "limitations": [
        "Time‑intensive",
        "Dependent on study quality"
      ],
      "references": [
        "Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences. Blackwell."
      ]
    },
    {
      "method": "Design‑Based Research",
      "paradigm": "Pragmatist",
      "use_when": "To iteratively design, implement, and refine educational interventions.",
      "data_collection": [
        "Observations",
        "Interviews",
        "Artifact analysis"
      ],
      "analysis": [
        "Design iteration analysis",
        "Mixed qualitative and quantitative measures"
      ],
      "example_questions": [
        "How can a game‑based math environment be optimized for middle school?",
        "What design cycles improve a preschool literacy app?",
        "How does digital storytelling impact high school creativity?",
        "How can interaction in online courses be enhanced through design iterations?"
      ],
      "strengths": [
        "High ecological validity",
        "Bridges theory and practice"
      ],
      "limitations": [
        "Complex design",
        "Difficult to isolate variables"
      ],
      "references": [
        "Design‑Based Research Collective. (2003). Educational Researcher, 32(1), 5‑8."
      ]
    },
    {
      "method": "Autoethnography",
      "paradigm": "Constructivist",
      "use_when": "To use personal experience as primary data to explore broader contexts.",
      "data_collection": [
        "Personal narratives",
        "Journals",
        "Reflections"
      ],
      "analysis": [
        "Narrative analysis",
        "Reflexive interpretation"
      ],
      "example_questions": [
        "How do a teacher’s own classroom experiences illuminate discipline practices?",
        "What does an expat student’s journal reveal about cross‑cultural adaptation?",
        "How does chronic illness shape daily life narratives?",
        "What do women academics’ experiences reveal about systemic barriers?"
      ],
      "strengths": [
        "Deep personal insight",
        "Emotive resonance"
      ],
      "limitations": [
        "Highly subjective",
        "Limited generalizability"
      ],
      "references": [
        "Ellis, C., Adams, T. E., & Bochner, A. P. (2011). Autoethnography: An Overview. FQS."
      ]
    },
    {
      "method": "Concept Mapping",
      "paradigm": "Constructivist",
      "use_when": "To visually represent relationships among concepts.",
      "data_collection": [
        "Brainstorming sessions",
        "Group workshops"
      ],
      "analysis": [
        "Map scoring",
        "Structural analysis"
      ],
      "example_questions": [
        "How do students map relationships among scientific concepts?",
        "What does a concept map reveal about prior knowledge structures?",
        "How does concept mapping in teacher training affect learning outcomes?",
        "How are key curriculum concepts visually organized by learners?"
      ],
      "strengths": [
        "Visual clarity",
        "Identifies gaps"
      ],
      "limitations": [
        "Subjective scoring",
        "Training required"
      ],
      "references": [
        "Novak, J. D., & Cañas, A. J. (2008). The Theory Underlying Concept Maps. IHMC."
      ]
    },
    {
      "method": "Think‑Aloud Protocol",
      "paradigm": "Cognitive Psychology",
      "use_when": "To uncover participants’ cognitive processes during tasks.",
      "data_collection": [
        "Verbal reports",
        "Screen recordings (optional)"
      ],
      "analysis": [
        "Protocol analysis",
        "Thematic coding"
      ],
      "example_questions": [
        "What thought processes do students use solving complex math problems?",
        "How do users describe decision‑making when using new educational software?",
        "Which cognitive steps do experienced teachers follow when lesson‑planning?",
        "What do readers verbalize when constructing meaning from a text?"
      ],
      "strengths": [
        "Real‑time cognitive insights",
        "Easy to set up"
      ],
      "limitations": [
        "Reactivity (thinking process may change)",
        "Coding complexity"
      ],
      "references": [
        "Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis. MIT Press."
      ]
    },
    {
      "method": "Heuristic Evaluation",
      "paradigm": "Usability Engineering",
      "use_when": "To quickly evaluate interface usability via expert heuristics.",
      "data_collection": [
        "Annotated screenshots",
        "Severity ratings"
      ],
      "analysis": [
        "Issue categorization",
        "Priority scoring"
      ],
      "example_questions": [
        "What usability issues exist in an online learning platform's UI?",
        "How does a mobile educational app fare against Nielsen’s heuristics?",
        "Which heuristic violations hinder a school website’s user experience?",
        "What usability gaps do experts identify in an LMS?"
      ],
      "strengths": [
        "Fast",
        "Cost‑effective"
      ],
      "limitations": [
        "Expert‑dependent",
        "May miss real‑user issues"
      ],
      "references": [
        "Nielsen, J. (1994). Usability Engineering. Morgan Kaufmann."
      ]
    },
    {
      "method": "Experimental Simulation",
      "paradigm": "Positivist / Experimental",
      "use_when": "To simulate real‑world scenarios safely in lab conditions.",
      "data_collection": [
        "Behavioral logs",
        "Physiological measures"
      ],
      "analysis": [
        "Statistical tests",
        "Comparative analysis"
      ],
      "example_questions": [
        "How can classroom management strategies be tested via simulation?",
        "Does VR lab learning reflect real‑world outcomes?",
        "How do disaster‑response simulations affect risk perception?",
        "What transfer occurs from driving simulators to real‑life skills?"
      ],
      "strengths": [
        "Controlled variables",
        "Participant safety"
      ],
      "limitations": [
        "External validity concerns",
        "Artificiality of setting"
      ],
      "references": [
        "Gredler, M. E. (2004). Games and Simulations and Their Relationships to Learning. ET R&D."
      ]
    },
    {
      "method": "Eye‑Tracking in Education",
      "paradigm": "Cognitive Psychology",
      "use_when": "To study visual attention and cognitive load.",
      "data_collection": [
        "Fixations",
        "Saccades",
        "Heatmaps"
      ],
      "analysis": [
        "AOI metrics",
        "Scanpath analysis"
      ],
      "example_questions": [
        "What gaze patterns emerge when reading digital text?",
        "Which visuals draw the most attention in a storybook?",
        "How do slide designs influence students’ visual attention?",
        "What do eye movements reveal about problem‑solving strategies?"
      ],
      "strengths": [
        "Objective metrics",
        "Process data"
      ],
      "limitations": [
        "Equipment cost",
        "Complex interpretation"
      ],
      "references": [
        "Holmqvist, K. et al. (2011). Eye Tracking: A Comprehensive Guide. Oxford UP."
      ]
    },
    {
      "method": "Learning Analytics",
      "paradigm": "Pragmatist / Data‑Driven",
      "use_when": "To analyze educational data for insights and interventions.",
      "data_collection": [
        "LMS logs",
        "Assessment data",
        "Clickstreams"
      ],
      "analysis": [
        "Predictive modeling",
        "Clustering",
        "Dashboards"
      ],
      "example_questions": [
        "What engagement patterns emerge in LMS data?",
        "How can clickstream data predict at‑risk learners?",
        "Can analytics identify students needing early support?",
        "Which variables best predict course completion?"
      ],
      "strengths": [
        "Scalable insights",
        "Data‑informed decisions"
      ],
      "limitations": [
        "Privacy concerns",
        "Requires infrastructure & expertise"
      ],
      "references": [
        "Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning. EDUCAUSE Review."
      ]
    },
    {
      "method": "Experience Sampling Method",
      "paradigm": "Mixed Methods",
      "use_when": "To capture real‑time experiences via random prompts.",
      "data_collection": [
        "Mobile prompts",
        "Brief surveys"
      ],
      "analysis": [
        "Time‑series analysis",
        "Descriptive statistics"
      ],
      "example_questions": [
        "What time of day do students report highest motivation?",
        "When do teachers’ stress levels peak during the day?",
        "How do technology experiences fluctuate throughout a workday?",
        "What moments yield highest job satisfaction?"
      ],
      "strengths": [
        "High ecological validity",
        "Reduces recall bias"
      ],
      "limitations": [
        "Participant burden",
        "Complex data management"
      ],
      "references": [
        "Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience Sampling Method. SAGE."
      ]
    },
    {
      "method": "Cognitive Task Analysis",
      "paradigm": "Cognitive Psychology",
      "use_when": "To elicit expert cognitive processes during tasks.",
      "data_collection": [
        "Structured expert interviews",
        "Concept mapping"
      ],
      "analysis": [
        "Critical Decision Method",
        "Thematic coding"
      ],
      "example_questions": [
        "What decision steps do surgeons use in planning operations?",
        "How do expert programmers break down complex coding tasks?",
        "What mental strategies guide chess grandmasters’ moves?",
        "Which tacit decisions inform teachers’ lesson planning?"
      ],
      "strengths": [
        "Reveals tacit knowledge",
        "Informs design and training"
      ],
      "limitations": [
        "Time‑intensive",
        "Requires skilled facilitation"
      ],
      "references": [
        "Clark, R. E. et al. (2008). Cognitive Task Analysis. In Handbook of Research on Educational Communications and Technology. Erlbaum."
      ]
    },
    {
      "method": "Cross‑Case Analysis",
      "paradigm": "Comparative",
      "use_when": "To compare multiple cases for patterns and differences.",
      "data_collection": [
        "Data sets from multiple cases"
      ],
      "analysis": [
        "Thematic synthesis",
        "Variable‑oriented comparison"
      ],
      "example_questions": [
        "What success factors emerge across mentoring programs in different universities?",
        "How do STEM initiatives compare across three countries?",
        "What patterns arise when comparing climate initiatives in urban vs. rural schools?",
        "What common themes appear in remote learning experiences across similar contexts?"
      ],
      "strengths": [
        "Enhances generalizability",
        "Pattern discovery"
      ],
      "limitations": [
        "May lose within‑case nuances",
        "Complex coding"
      ],
      "references": [
        "Stake, R. E. (2006). Multiple Case Study Analysis. Guilford."
      ]
    },
    {
      "method": "Educational Data Mining",
      "paradigm": "Data‑Driven",
      "use_when": "To apply mining techniques on large educational datasets.",
      "data_collection": [
        "LMS logs",
        "Sensor data"
      ],
      "analysis": [
        "Classification",
        "Clustering",
        "Sequence mining"
      ],
      "example_questions": [
        "Which algorithms best predict student success from LMS data?",
        "What hidden patterns exist in forum discussions?",
        "Can clustering reveal learning pathways?",
        "How do sequence‑mining techniques map students’ study orders?"
      ],
      "strengths": [
        "Scalable insights",
        "Predictive power"
      ],
      "limitations": [
        "Data quality issues",
        "Technical expertise required"
      ],
      "references": [
        "Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review. IEEE."
      ]
    },
    {
      "method": "Discourse & Conversation Analysis",
      "paradigm": "Social Constructivist",
      "use_when": "To study language use and interaction structures.",
      "data_collection": [
        "Transcripts",
        "Recordings",
        "Forum posts"
      ],
      "analysis": [
        "Turn‑taking analysis",
        "Critical discourse coding"
      ],
      "example_questions": [
        "How do power dynamics emerge in classroom talk?",
        "What discourse strategies do students use in online forums?",
        "How do teacher–student dialogues reveal learning strategies?",
        "What rhetoric shapes social media debates on education policy?"
      ],
      "strengths": [
        "Deep social insight",
        "Linguistic detail"
      ],
      "limitations": [
        "Transcription‑intensive",
        "Complex interpretation"
      ],
      "references": [
        "Gee, J. P. (2011). How to Do Discourse Analysis. Routledge.",
        "Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). Turn‑taking in Conversation. Language."
      ]
    },
    {
      "method": "Experimental Research",
      "paradigm": "Positivist",
      "use_when": "To establish causal relationships through controlled manipulation.",
      "data_collection": [
        "Pre‑test/post‑test measures",
        "Random assignment to groups",
        "Lab observations"
      ],
      "analysis": [
        "ANOVA",
        "t‑tests",
        "Regression analyses"
      ],
      "example_questions": [
        "How does program X affect math achievement in controlled trials?",
        "What is the effect of small‑group work on self‑efficacy?",
        "How do feedback types causally influence learning outcomes?",
        "How do face‑to‑face and online groups compare under random assignment?"
      ],
      "strengths": [
        "High internal validity",
        "Controlled variables"
      ],
      "limitations": [
        "Lower external validity",
        "Artificial settings",
        "Ethical/practical constraints"
      ],
      "references": [
        "Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi‑Experimental Designs. Houghton Mifflin."
      ]
    },
    {
      "method": "Quasi‑Experimental Research",
      "paradigm": "Post‑positivist",
      "use_when": "To approximate causal inference when randomization isn't possible.",
      "data_collection": [
        "Pre/post measures without random assignment",
        "Naturally formed groups",
        "Existing data comparisons"
      ],
      "analysis": [
        "ANCOVA",
        "Matched‑pairs t‑tests",
        "Regression discontinuity"
      ],
      "example_questions": [
        "How does curriculum change impact test scores without randomization?",
        "What effects arise when comparing naturally occurring groups?",
        "How do matched schools differ after an intervention?",
        "How to analyze group differences when random assignment isn’t feasible?"
      ],
      "strengths": [
        "Field applicability",
        "Some causal inference"
      ],
      "limitations": [
        "Threats to internal validity",
        "Confounding variables"
      ],
      "references": [
        "Cook, T. D., & Campbell, D. T. (1979). Quasi‑Experimentation. Houghton Mifflin."
      ]
    },
    {
      "method": "Correlational Research",
      "paradigm": "Post‑positivist",
      "use_when": "To discover relationships among variables without inferring causation.",
      "data_collection": [
        "Surveys and scales",
        "Archival data",
        "Numeric observations"
      ],
      "analysis": [
        "Correlation coefficients",
        "Regression analysis",
        "Factor analysis"
      ],
      "example_questions": [
        "What is the relationship between study time and GPA?",
        "How do teacher job satisfaction and motivation correlate?",
        "Is class size correlated with student achievement?",
        "How does social media use relate to academic performance?"
      ],
      "strengths": [
        "Examines multiple variables simultaneously",
        "Large samples yield generalizable findings"
      ],
      "limitations": [
        "Cannot establish causation",
        "Unmeasured variables may confound"
      ],
      "references": [
        "Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). Routledge."
      ]
    },
    {
      "method": "Causal‑Comparative Research",
      "paradigm": "Post‑positivist",
      "use_when": "To examine cause‑effect relationships by comparing existing groups.",
      "data_collection": [
        "Identifying naturally occurring groups",
        "Historical data analysis",
        "Group‑based surveys/tests"
      ],
      "analysis": [
        "t‑tests, ANOVA",
        "Chi‑square tests",
        "ANCOVA"
      ],
      "example_questions": [
        "Do private school students differ in self‑confidence from public school students?",
        "How do bachelor’s vs. associate’s degree holders differ in employment timelines?",
        "What differences emerge in academic outcomes across instructional methods?",
        "Do tablet‑using vs. traditional learners differ in class participation?"
      ],
      "strengths": [
        "Explores causality where experiments can’t be done",
        "Uses existing groups"
      ],
      "limitations": [
        "Lower internal validity",
        "Group differences may stem from other factors"
      ],
      "references": [
        "Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw‑Hill."
      ]
    },
    {
      "method": "Explanatory Sequential Mixed Methods",
      "paradigm": "Pragmatist / Mixed",
      "use_when": "To follow quantitative results with qualitative data for deeper explanation.",
      "data_collection": [
        "Phase 1: Surveys/tests (quantitative)",
        "Phase 2: Interviews/focus groups (qualitative)",
        "Integration of both phases"
      ],
      "analysis": [
        "Quantitative statistics",
        "Qualitative thematic analysis",
        "Integrated interpretation"
      ],
      "example_questions": [
        "What factors predict exam anxiety, and why do high‑anxiety students feel that way?",
        "How do teachers’ attitudes toward technology explain survey findings?",
        "Which variables drive achievement, and how do participants describe them?",
        "What do focus groups reveal about high‑ and low‑satisfaction survey respondents?"
      ],
      "strengths": [
        "Adds depth to numerical findings",
        "Combines generalizability and insight"
      ],
      "limitations": [
        "Time‑consuming",
        "Requires expertise in both methods"
      ],
      "references": [
        "Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE."
      ]
    },
    {
      "method": "Convergent Parallel Mixed Methods",
      "paradigm": "Pragmatist / Mixed",
      "use_when": "To collect quantitative and qualitative data simultaneously and integrate results.",
      "data_collection": [
        "Concurrent surveys and interviews",
        "Parallel observation and testing",
        "Combined data gathering"
      ],
      "analysis": [
        "Separate quantitative and qualitative analysis",
        "Side‑by‑side comparison and integration"
      ],
      "example_questions": [
        "How do LMS engagement logs and focus group insights align on student participation?",
        "What do test scores and classroom observations jointly reveal about program impact?",
        "How do survey ratings and interview themes converge on technology acceptance?",
        "How consistent are quantitative usage patterns with qualitative student feedback?"
      ],
      "strengths": [
        "Triangulates data for credibility",
        "Offers multifaceted perspectives"
      ],
      "limitations": [
        "Difficult to reconcile conflicting results",
        "High analytic workload"
      ],
      "references": [
        "Creswell, J. W. (2014). Research Design (4th ed.). SAGE."
      ]
    },
    {
      "method": "Digital Ethnography",
      "paradigm": "Interpretivist / Constructivist",
      "use_when": "To study online communities, social media, and digital cultural practices.",
      "data_collection": [
        "Social media posts/comments",
        "Online forum observation",
        "Digital diaries/blogs"
      ],
      "analysis": [
        "Content analysis",
        "Thematic coding",
        "Discourse analysis"
      ],
      "example_questions": [
        "How does gamer jargon shape online community culture?",
        "What narratives emerge around an education hashtag on Twitter?",
        "How do remote teams communicate on digital platforms?",
        "What do YouTube comment threads reveal about educational content reception?"
      ],
      "strengths": [
        "Captures natural online behaviors",
        "Access to large, diverse data"
      ],
      "limitations": [
        "Ethical/privacy concerns",
        "Lacks face‑to‑face context",
        "Complex data volume"
      ],
      "references": [
        "Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. SAGE."
      ]
    },
    {
      "method": "Mobile Learning Research",
      "paradigm": "Pragmatist / Technology‑Focused",
      "use_when": "To investigate learning via mobile devices and their educational impact.",
      "data_collection": [
        "App usage logs",
        "In‑app surveys/feedback",
        "Location‑based data"
      ],
      "analysis": [
        "Usage pattern analysis",
        "Pre‑/post‑test comparisons",
        "Interaction content analysis"
      ],
      "example_questions": [
        "How do usage patterns correlate with language learning outcomes?",
        "What is the effect of a mobile math app on student motivation?",
        "Which out‑of‑school mobile learning activities predict academic success?",
        "How do mobile environments foster self‑regulated learning?"
      ],
      "strengths": [
        "Real‑world context data",
        "Captures learning across time and place"
      ],
      "limitations": [
        "Requires continuous updates",
        "Device variability affects compatibility",
        "Privacy/security concerns"
      ],
      "references": [
        "Traxler, J. (2007). Defining mobile learning. The International Review of Research in Open and Distributed Learning."
      ]
    }
  ],
  "concepts": [
    {
      "concept": "Triangulation",
      "definition": "Using multiple methods, data sources, investigators, or theories to increase credibility.",
      "types": [
        "Data",
        "Method",
        "Investigator",
        "Theory"
      ],
      "importance": "Enhances trustworthiness and minimizes bias.",
      "example_usage": "Combining interviews, surveys, and observations.",
      "references": [
        "Denzin, N. K. (1978). The Research Act."
      ]
    },
    {
      "concept": "Reflexivity",
      "definition": "Critical self‑reflection by researchers on their influence on the research.",
      "components": [
        "Positionality",
        "Ethical awareness",
        "Subjectivity"
      ],
      "importance": "Improves transparency and credibility.",
      "example_usage": "Maintaining a reflexive journal during data collection.",
      "references": [
        "Finlay, L. (2002). Outing the Researcher. Qual Health Res."
      ]
    },
    {
      "concept": "Data Saturation",
      "definition": "Point where additional data yields no new themes.",
      "indicators": [
        "Redundant codes",
        "No new insights"
      ],
      "importance": "Signals adequate data collection.",
      "example_usage": "Stopping interviews after redundancy.",
      "references": [
        "Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? Field Methods."
      ]
    },
    {
      "concept": "Theoretical Sampling",
      "definition": "Selecting data sources based on emerging theory needs.",
      "use_when": "Grounded theory studies.",
      "characteristics": [
        "Iterative",
        "Purposeful"
      ],
      "references": [
        "Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory."
      ]
    },
    {
      "concept": "Internal Validity",
      "definition": "Extent to which outcomes can be attributed to the intervention.",
      "improvement": [
        "Randomization",
        "Control groups"
      ],
      "references": [
        "Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi‑Experimental Designs."
      ]
    },
    {
      "concept": "External Validity",
      "definition": "Generalizability of findings to broader populations.",
      "improvement": [
        "Replication",
        "Diverse sampling"
      ],
      "references": [
        "Cook, T. D., & Campbell, D. T. (1979). Quasi‑Experimentation."
      ]
    },
    {
      "concept": "Reliability",
      "definition": "Consistency and stability of a measurement instrument.",
      "types": [
        "Test–retest reliability",
        "Internal consistency (e.g., Cronbach’s alpha)",
        "Inter‑rater reliability"
      ],
      "importance": "Ensures reproducible and consistent results.",
      "references": [
        "Crocker, L., & Algina, J. (1986). Introduction to Classical and Modern Test Theory."
      ]
    },
    {
      "concept": "Construct Validity",
      "definition": "Degree to which a test measures the intended theoretical construct.",
      "established_by": [
        "Convergent validity",
        "Discriminant validity",
        "Theoretical coherence"
      ],
      "importance": "Ensures tests truly measure the target concept.",
      "references": [
        "Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests."
      ]
    },
    {
      "concept": "Criterion Validity",
      "definition": "Extent to which test scores relate to an external criterion.",
      "types": [
        "Predictive validity",
        "Concurrent validity"
      ],
      "importance": "Demonstrates practical usefulness of measurements.",
      "references": [
        "Carmines, E. G., & Zeller, R. A. (1979). Reliability and Validity Assessment."
      ]
    },
    {
      "concept": "Credibility",
      "definition": "Confidence in the truth of qualitative findings.",
      "strategies": [
        "Member checking",
        "Prolonged engagement",
        "Triangulation"
      ],
      "references": [
        "Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry."
      ]
    },
    {
      "concept": "Transferability",
      "definition": "Applicability of findings in other contexts.",
      "strategies": [
        "Thick description"
      ],
      "references": [
        "Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry."
      ]
    },
    {
      "concept": "Audit Trail",
      "definition": "Detailed documentation of research decisions and processes.",
      "components": [
        "Raw data",
        "Memos",
        "Coding logs"
      ],
      "references": [
        "Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry."
      ]
    },
    {
      "concept": "Trustworthiness",
      "definition": "Overall rigor and quality in qualitative research.",
      "criteria": [
        "Credibility",
        "Transferability",
        "Dependability",
        "Confirmability"
      ],
      "references": [
        "Guba, E. G., & Lincoln, Y. S. (1989). Fourth Generation Evaluation."
      ]
    },
    {
      "concept": "Sampling Strategies",
      "definition": "Approaches to selecting participants or cases.",
      "types": [
        "Random",
        "Stratified",
        "Purposive",
        "Snowball"
      ],
      "references": [
        "Teddlie, C., & Yu, F. (2007). Mixed Methods Sampling."
      ]
    },
    {
      "concept": "Bias Types",
      "definition": "Systematic errors affecting research validity.",
      "types": [
        "Sampling bias",
        "Measurement bias",
        "Confirmation bias",
        "Social desirability bias"
      ],
      "prevention": [
        "Blinding",
        "Pilot testing",
        "Reflexivity"
      ],
      "references": [
        "Creswell, J. W. (2014). Research Design."
      ]
    },
    {
      "concept": "Member Checking",
      "definition": "Validating findings with participants for accuracy.",
      "importance": "Enhances credibility.",
      "references": [
        "Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member Checking. Qual Health Res."
      ]
    },
    {
      "concept": "Methodological Coherence",
      "definition": "Alignment among research questions, paradigms, methods, and analysis.",
      "importance": "Ensures internal consistency.",
      "references": [
        "Morse, J. M. (1991). Ensuring Rigor in Qualitative Research."
      ]
    },
    {
      "concept": "Data Visualization Strategies",
      "definition": "Techniques for visually communicating research findings.",
      "tools": [
        "Excel",
        "R (ggplot2)",
        "Python (matplotlib)",
        "Tableau"
      ],
      "references": [
        "Cairo, A. (2016). The Truthful Art."
      ]
    },
    {
      "concept": "Ontological & Epistemological Alignment",
      "definition": "Consistency between assumptions about reality and knowledge generation methods.",
      "importance": "Supports coherent research design.",
      "references": [
        "Crotty, M. (1998). The Foundations of Social Research."
      ]
    },
    {
      "concept": "Poststructuralism",
      "definition": "Approach asserting that meaning and reality are constructed through language, discourse, and social context, rejecting singular objective truths.",
      "principles": [
        "Knowledge is socially constructed through discourse.",
        "Multiple, shifting realities exist rather than one objective truth.",
        "Power relations and norms are critically examined."
      ],
      "importance": "Challenges assumptions and fosters alternative interpretations.",
      "references": [
        "St. Pierre, E. A. (2014). A Brief History of Poststructuralism. Journal of Curriculum Theorizing."
      ]
    },
    {
      "concept": "Critical Realism",
      "definition": "Approach positing that reality exists independently of perception but involves complex layers; seeks underlying mechanisms behind observable phenomena.",
      "principles": [
        "Reality comprises structures beyond direct observation.",
        "Research uncovers underlying causal mechanisms."
      ],
      "importance": "Offers deep explanatory power by integrating objective structures and subjective interpretations.",
      "references": [
        "Maxwell, J. A. (2012). A Realist Approach for Qualitative Research. SAGE."
      ]
    },
    {
      "concept": "Ethical Principles",
      "definition": "Core rules ensuring participant rights and scientific integrity during research.",
      "principles": [
        "Respect for autonomy (informed consent, voluntary participation)",
        "Non‑maleficence and beneficence",
        "Justice in selection and benefits",
        "Privacy and confidentiality"
      ],
      "importance": "Safeguards human rights and research credibility.",
      "references": [
        "National Commission. (1979). The Belmont Report."
      ]
    },
    {
      "concept": "Data Security",
      "definition": "Practices to protect research data from unauthorized access, disclosure, or corruption.",
      "practices": [
        "Anonymization/encryption",
        "Password‑protected storage",
        "Restricted access",
        "Ethical/legal compliance"
      ],
      "importance": "Builds trust and meets legal/ethical obligations.",
      "references": [
        "Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2014). Managing and Sharing Research Data."
      ]
    },
    {
      "concept": "Open Science Practices",
      "definition": "Approaches fostering transparency, accessibility, and reproducibility in research.",
      "practices": [
        "Open access publishing",
        "Sharing data and code",
        "Pre‑registration",
        "Encouraging replications"
      ],
      "importance": "Enhances dissemination, reliability, and accountability of science.",
      "references": [
        "Munafò, M. R., et al. (2017). A Manifesto for Reproducible Science. Nature Human Behaviour."
      ]
    }
  ]
}