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STATISTICS
Chapter summaries By Seldzhan Raimova
Based on “Learn Statistics with Jamovi” by Danielle Navaro & David Foxcroft
CHAPTER 2: A brief introduction to research design2
CHAPTER 3: Getting started with jamovi6
CHAPTER 4: Descriptive statistics9
CHAPTER 5: Drawing graphs14
CHAPTER 6: Pragmatic matters19
CHAPTER 7: Probability24
CHAPTER 8: Estimating unknown quantities from a sample28
CHAPTER 9: Hypothesis testing32
CHAPTER 10: Categorical data analysis35
CHAPTER 11: Comparing two means (T-test)40
CHAPTER 12: Correlation and linear regression44
CHAPTER 13: Comparing several means (one-way ANOVA)49
CHAPTER 14: Factorial ANOVA (14.5–14.6 optional per syllabus)54
CHAPTER 15: Factor Analysis (only §15.5 Internal consistency reliability analysis59
APPENDIX: Summary, meaning, symbols63
Good overview63 Symbols & Notation — Plain‑language Glossary64 1 / 4
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CHAPTER 2: A brief introduction to research design
Construct → Measure → Operationalisation → Variable → Design → Reliability → Validity → (then) Statistics.
Core concepts
●Theoretical construct: thing you want to know (e.g., “anxiety”). Not directly observable.●Measure: the tool/procedure producing observation23s (question item, behaviour code, RT).●Operationalisation: the logic that connects construct → measure (how your item/RT “stands for” anxiety). Case-by-case; no single best way.●Variable: the data after you apply the measure (what’s in your dataset).●Predictor / Outcome (IV/DV): variables by role in explanation: � explains, � is explained (notation below). Authors prefer “predictor/outcome” over IV/DV.
Scales of measurement (what maths “makes sense”) ●Nominal (categorical): labels only; no ordering; don’t average (“average eye color” is nonsense).e.g., transport type ●Ordinal: ordered ranks; gaps not equal. 2 / 4
3 e.g., “agree…disagree”
●Interval: equal steps; no true zero.
e.g., °C. Add/subtract okay; ratios not meaningful.
●Ratio: equal steps + true zero.
e.g., response time (RT). All arithmetic valid, including ratios.Continuous vs discrete: nominal/ordinal are always discrete; interval/ratio can be either (e.g., °C continuous; year = discrete).Likert twist (the messy real world): strictly ordinal, but many treat 5- or 7-point Likert as quasi-interval in practice (participants act as if steps are roughly equal). Know the nuance and justify your choice.
Reliability (precision/consistency of a measure)
Reliable = repeatable; valid = correct. You can be reliable but wrong (bathroom scale with potatoes). Reliability is necessary but not sufficient for validity.
Types you should name and recognise:
●Test–retest (over time).
●Inter-rater (between coders).
●Parallel forms (equivalent versions).
●Internal consistency (items agree—covered in Ch 15.5; think α/ω).
When is low internal consistency okay? When a composite intentionally mixes distinct skills/components (the whole isn’t one 3 / 4
4 narrow thing).
Validity (can we trust the conclusion?) ●Internal validity: correct causal story inside the study? (Confounds break this.) ●External validity: will it generalise to other people/tasks/settings/measures? (Not just “students ≠ world”; relevance matters.) ●Construct validity: does your measure really tap the construct? (e.g., “stand up if you’ve cheated” measures something else.) ●Face validity: looks right on the surface (scientifically weak, politically useful).●Ecological validity: lab set-up resembles the real situation (often used as a proxy for external validity, but no guarantee).
Classic threats (know & spot them):
●Selection bias (groups differ at baseline).
●Attrition:
○Homogeneous → harms external validity.○Differential (heterogeneous) → creates confounds; kills internal validity.
●Non-response bias (survey responders differ from non-responders; also item non-response).●Regression to the mean (extremes drift toward average on re-test; fakes “feedback effects”).●Experimenter bias (unknowingly cuing participants; “Clever Hans”).●Demand characteristics (good/negative/faithful/apprehensive participant roles).
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