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FINAL CONCEPT MAP - Coggle Diagram
FINAL CONCEPT MAP
Week 4: Qualitative Data Collection [Purple]
Methods
Interviews: structured, semi‑structured, unstructured
Focus Groups: group discussion to explore shared meaning
Observations: participant vs non‑participant
Key Concepts
Reflexivity: researcher awareness of bias
Saturation: no new themes emerging
Coding: labeling segments of text
Themes: patterns across codes
Relationships
Researcher ↔ Participant dynamic influences data
Strengths
Rich detail, context, meaning
Limitations
Harder to generalize, time‑intensive
Connections [Gray]
Helps explain “why” behind quantitative patterns
Supports mixed‑methods designs
Week 2: Research Design [Green]
Descriptive Methods
Observations: naturalistic or structured
Surveys: self‑report, cross‑sectional or longitudinal
Case Studies: in‑depth single case
Design Types
Experimental: manipulation + control + random assignment
Quasi‑experimental: manipulation without full control
Non‑experimental: no manipulation (surveys, correlational)
Time Designs
Cross-sectional: one time point
Longitudinal: repeated measures over time
Key Terms
Correlation ≠ causation
Independent vs dependent variables
Connections to Week 1 [Gray]
Research questions determine design
Data type determines method (qual vs quant)
Week 3: Sampling & Recruitment [Orange]
Sampling Types
Probability: random, stratified, systematic
Allows generalization
Non-probability: convenience, purposive, snowball
Useful when populations are hard to reach
Recruitment Strategies
Flyers, email lists, social media, snowball referrals
Comparing Groups
Group differences: mean comparisons (t‑tests, ANOVA)
Control vs treatment groups
Sampling Logic
Larger samples = more stable estimates
Sampling bias reduces external validity
Connections [Gray]
Sampling affects validity (Week 6)
Recruitment influences data quality (Week 4 & 5)
Week 6: Experiments & Validation [Red]
Experiments
Manipulation: changing IV
Control: holding conditions constant
Randomization: equal chance of assignment
Types of Experiments
True experiment: full control + random assignment
Field experiment: real-world setting
Lab experiment: controlled environment
Validity
Internal validity: confidence IV caused DV
Threats: history, maturation, testing, instrumentation
External validity: generalizability
Threats: sampling bias, artificial settings
Measurement validity: accuracy of instruments
Complex Modeling
Multivariate analysis: multiple predictors
Regression: predicting DV from IVs
Mediation: explaining
how
effects occur
Moderation: explaining
when
effects occur
Connections [Gray]
Requires strong sampling (Week 3)
Builds on quantitative data (Week 5)
Supports final knowledge testing (Week 7)
Week 1: What is Research [Blue]
Purpose of Research
Systematic investigation to answer questions
Builds theory, tests predictions, explains phenomena
Types of Data
Quantitative: numbers, measurements, statistical tests
Qualitative: words, meanings, experiences, patterns
Mixed Methods: combines both for fuller understanding
Key Concepts
Variables: measurable characteristics (IV, DV)
Constructs: abstract ideas (e.g., satisfaction, trust)
Operationalization: turning constructs into measurable items
Examples
Construct: “Communication Satisfaction”
Operational definition: 10‑item Likert scale survey
Week 5: Quantitative Data Collection [Teal]
Methods
Surveys: Likert scales, semantic differentials
Instruments: validated scales (reliability + validity)
Behavioral measures: reaction time, frequency counts
Predictions
Hypotheses: directional or non‑directional
Statistical relationships: correlation, regression
Measurement Concepts
Reliability: consistency (Cronbach’s alpha)
Validity: accuracy (construct, criterion, content)
Connections [Gray]
Sets up experiments (Week 6)
Links to comparing groups (Week 3)
Week 7: Testing Your Knowledge [Gold]
Integration of Concepts
How methods connect (qual ↔ quant ↔ mixed)
How designs influence conclusions (experimental vs descriptive)
How validity strengthens claims
Key Study Logic
Good research = clear question + appropriate design + valid measures
Sampling → data quality → analysis → conclusions
Self-Assessment
Can I identify IV/DV?
Can I choose the right design for a question?
Can I explain threats to validity?
Can I interpret basic statistics?
Final Map Goals
Show relationships between all weeks
Demonstrate cumulative understanding
Serve as exam notes