सSaral Shiksha Yojna
Courses/Behavioral Research: Statistical Methods

Behavioral Research: Statistical Methods

CG3.402
Vinoo Alluri•Monsoon 2025-26•4 credits

Last-Week Revision Pack

Every item below is something you should be able to recall cold by exam morning. It is not a study list — it is a triage list for the final 5–7 days.

How to use this page (last 5–7 days)

  1. Day −7 to −3:Read every item top-to-bottom. For any item you can't expand into a 2-minute explanation, open the linked unit/chapterand re-learn it. Don't move on until you can recall the item without looking.
  2. Day −2 to −1: Re-read only — speak each item aloud. If you stumble, mark it mentally and drill it twice more.
  3. Exam morning:Skim once, fast. Don't deep-dive anything. The goal is retrieval priming, not learning.

What each tag means and what to do with it

  • CheatsheetA pointer to a fast-skim page in this course. Open it and re-read in the order suggested. 2–5 minutes per item.
  • High YieldA topic almost certain to appear on the exam. Allocate revision time proportional to its expected mark weight — not equal time per item. Drill until you can answer without notes.
  • Weak AreaA topic the cohort historically struggles with. Treat as high-priority and verify your understanding by explaining it aloud or writing a one-paragraph answer.
  • FormulaAn equation you must reproduce verbatim. Write it out from memory once per day until exam day. If you can't derive it, also re-read the relevant chapter.
  • Memory TriggerA "if you see X → reach for Y" cue for the exam room. Memorise the mapping; you'll only have seconds to recall it under pressure. Pair with the linked framework.
  • DerivationA multi-step proof or derivation. Write it from blank paper — not just read. Re-do until you can produce it in under 5 minutes.
  • Common MistakeA specific error the cohort routinely makes. Memorise the correction and the right phrasing — this is the cheapest mark you can save.
Track your progress:mark the page "Finished" (top-right) once you can recall every item below without looking.

The pack (25 items)

1 Cheatsheet · 6 Formula · 3 Memory Trigger · 4 High Yield · 5 Common Mistake · 4 Weak Area · 2 Derivation
CheatsheetWhich test do I use? Categorical DV → χ². 2 indep continuous → independent t (Welch if unequal var) / Mann-Whitney. 2 paired → paired t / Wilcoxon. 3+ indep → one-way ANOVA / Kruskal-Wallis. 3+ paired → RM-ANOVA / Friedman. Continuous × continuous → Pearson r / Spearman / regression.FormulaBayes: . Mammogram → P(cancer | +) ≈ 9%.FormulaCohen's d = (M₁ − M₂) / SD_pooled. 0.2 / 0.5 / 0.8.FormulaANOVA: F = MS_between / MS_within. η² = SS_between / SS_total.Formulaχ² = Σ (O − E)² / E. df = (r−1)(c−1) for independence.FormulaVIF_j = 1 / (1 − R²_j). > 5–10 is severe multicollinearity.FormulaLogit: log(p/(1−p)) = β₀ + β·X. OR = exp(β).Memory TriggerBonferroni: per-test α = α_FW / m. Holm: stepwise FWER. BH: stepwise FDR.Memory TriggerReliability = consistency; Validity = accuracy. Cannot be valid without being reliable.Memory TriggerFrequentist non-significance is NOT evidence for null. Use Bayes Factor.High YieldPower = 1 − β. Factors: n, effect size, α, variance, design (within > between).High YieldSphericity violated in RM-ANOVA → Greenhouse-Geisser (or Huynh-Feldt) corrects df.High YieldPCA = variance reduction (no latent model). FA = latent-variable model with unique error.High Yieldp-value is P(data | H₀). NOT P(H₀ | data).Common MistakeDon't 'accept' H₀ — only 'fail to reject'.Common MistakeDon't switch one-tailed post-hoc — p-hacking.Common MistakeDon't compare raw R² across models with different k — use adjusted R².Common MistakeDon't run pairwise t-tests across 3+ groups — use ANOVA + post-hoc.Common MistakeDon't apply OLS assumptions (normality, homoscedasticity) to logistic regression.Weak AreaBayes update — practice the 3-step pattern (prior, likelihood, evidence).Weak AreaANOVA SS partition by hand on a 3-group, 3-numbers dataset.Weak Areaχ² 2×3 expected counts: E = row_total × col_total / grand_total.Weak AreaLogistic regression OR computation: β = 0.7 → OR = e^0.7 ≈ 2.01.DerivationBessel's correction: sample variance ÷ (n − 1) is unbiased; one DoF spent on x̄.DerivationWhy F = MS_between/MS_within tests means: under H₀ both estimate σ²; under H₁, MS_between estimates σ² + n·Var(μ).