Master Summary QUANT
Week Theme Bottom line
1 Research design & ● Clear, theory‑driven RQ is the compass.
questions ● Quant methods = toolbox for causal claims & bias‑proofing.
● Workflow: RQ → theory → hypotheses → data → test →
conclusion.
2 Variables, validity ● Identify variable type → plot & summarise.
& reliability ● Validity (internal, external, construct) = accuracy; reliability
= precision (Cronbach’s α).
● SD & 68‑95‑99 rule quantify spread; watch out for skew.
3 Describing ● Correlation shows pattern (−1 ↔ +1) but never causation.
relations ● OLS line summarises conditional means & uses all data.
● Add controls in regression to isolate the pure X‑Y link.
4 Hypothesis testing ● Sampling variation ⇒ SE; t = β̂ / SE, p = tail area.
& simple ● Exogeneity needed for unbiased β̂; residual ≠ error.
regression ● Model fit: F‑test gate, R²/Adj R² effect size, assumption
checks.
5 Multiple ● Extra predictors remove confounding; β now means “holding
regression, others constant.”
moderation & ● Moderation via interactions (when/for whom); mediation
mediation shows why.
● Binary DV → linear probability model or logit; χ² for two
categorical variables.
Quick notation on symbols:
Meaning Type of symbol Example
Data English/Latin letters 𝑥
Calculations Modifications of English/Latin letters 𝑥
The truth Greek letters σ, β, ε, µ
Estimate Modifications of Greek letters β̂
, Week 1: Research design & questions
Why do we need a quantitative research method?
● A toolbox to study the (social) world around us by using the scientific method.
● It helps minimize cognitive assumptions that may distort our interpretation.
● Depending on the state of prior theory and research on the topic, you have to use quantitative
methods to make a useful contribution to our understanding of the world
● The only way to establish causal relationships
There are 2 types of quantitative research: descriptive (what?) & inferential (why?)
A good research question:
● Can be answered and needs answering (the “So what”)
● Improves our understanding of how the world works
● Informs theory
A theory: explains relationships among concepts or events within a set of boundary conditions.
“Science is facts; just as houses are made of stones, so is science made of facts; but a pile of stones is
not a house and a collection of facts is not necessarily science” – H. Poincaré
TL;DR:
● Good theory simplifies and explains complex real-world phenomena.
● Good research questions can and need to be answered by means of statistics.
● RQ → theory → hypotheses → data → test → conclusion
Week Theme Bottom line
1 Research design & ● Clear, theory‑driven RQ is the compass.
questions ● Quant methods = toolbox for causal claims & bias‑proofing.
● Workflow: RQ → theory → hypotheses → data → test →
conclusion.
2 Variables, validity ● Identify variable type → plot & summarise.
& reliability ● Validity (internal, external, construct) = accuracy; reliability
= precision (Cronbach’s α).
● SD & 68‑95‑99 rule quantify spread; watch out for skew.
3 Describing ● Correlation shows pattern (−1 ↔ +1) but never causation.
relations ● OLS line summarises conditional means & uses all data.
● Add controls in regression to isolate the pure X‑Y link.
4 Hypothesis testing ● Sampling variation ⇒ SE; t = β̂ / SE, p = tail area.
& simple ● Exogeneity needed for unbiased β̂; residual ≠ error.
regression ● Model fit: F‑test gate, R²/Adj R² effect size, assumption
checks.
5 Multiple ● Extra predictors remove confounding; β now means “holding
regression, others constant.”
moderation & ● Moderation via interactions (when/for whom); mediation
mediation shows why.
● Binary DV → linear probability model or logit; χ² for two
categorical variables.
Quick notation on symbols:
Meaning Type of symbol Example
Data English/Latin letters 𝑥
Calculations Modifications of English/Latin letters 𝑥
The truth Greek letters σ, β, ε, µ
Estimate Modifications of Greek letters β̂
, Week 1: Research design & questions
Why do we need a quantitative research method?
● A toolbox to study the (social) world around us by using the scientific method.
● It helps minimize cognitive assumptions that may distort our interpretation.
● Depending on the state of prior theory and research on the topic, you have to use quantitative
methods to make a useful contribution to our understanding of the world
● The only way to establish causal relationships
There are 2 types of quantitative research: descriptive (what?) & inferential (why?)
A good research question:
● Can be answered and needs answering (the “So what”)
● Improves our understanding of how the world works
● Informs theory
A theory: explains relationships among concepts or events within a set of boundary conditions.
“Science is facts; just as houses are made of stones, so is science made of facts; but a pile of stones is
not a house and a collection of facts is not necessarily science” – H. Poincaré
TL;DR:
● Good theory simplifies and explains complex real-world phenomena.
● Good research questions can and need to be answered by means of statistics.
● RQ → theory → hypotheses → data → test → conclusion
