Scientific and Statistical Reasoning Summary Block 3
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Scientific and Statistical Reasoning
Hochschule
Universiteit Van Amsterdam (UvA)
Book
Discovering Statistics Using IBM SPSS
This document provides the necessary content to properly understand the content for the third block of SSR. The lectures are summarized perfectly: the necessary information is included and illustrations to better understand the concepts are provided.
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Universiteit van Amsterdam (UvA)
BSc Psychology
Scientific and Statistical Reasoning
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Inhaltsvorschau
Critical thinking about causality
Causal relationship- one thing happening makes the other thing MORE PROBABLE to
happen (statistical relationship)
Correlation does not imply causation
We don’t see causal relationship→ we infer since A happens after B
Causality (John Stuart Mill): X causes Y only if
- Priority: change in X precedes change Y
- Longitudinal study needed
- Consistency: change X varies systematically with change Y
- Covariance is needed
- Exclusivity: there is no alternative explanation for the
relationship
- Manipulation (groups) is needed
*Conclusion is not possible since exclusivity cannot be met (a
third variable can explain the relationship between the variables)
*Priority principle is also not met: self-esteem is considered an
effect not a cause
Reasoning errors
1. Post hoc ergo propter hoc (Y happens after X… then X is the cause)
a. X precedes Y (priority)--> focus on one aspect of Mill’s
criteria and ignore the other two (check for consistency and
exclusivity)
b. X covaries with Y (consistency/correlation)--> ignore priority
and exclusivity
c. X is the only possible cause of Y (exclusivity)--> ignore
priority and consistency
*Insufficient: needs other elements
*Non-redundant: crucial, presence
makes difference
*Unnecessary: there are other ways to
start fire (replaceable)
*Sufficient: factors (set of things)
together are sufficient
,How to check for non-redundancy: have two versions of the world (identical) only
difference is one factor → now you have an ideal counterfactual (perfect counterfactual
does not exist 🙂)
- Create experimental and control group (people are identical except for
randomness/random assignment) ⇒ that is experimental design:
- Useful because of manipulation of variables, random assignment,
counterfactual, control group
Threats to causality:
1. History: influences outside of intervention which influence outcome
2. Maturation: natural changes that may be confused with effect treatment
3. Selection: selection criteria for treatment related to outcomes of treatment/
systematic differences over conditions that could also cause observed effect
4. Attrition: participant's failure, systematically correlated with conditions (dropping
out of participants… condition gets affected)
5. Instrumentation: change in measuring instrument resulting in a difference between
pre-and post-measurement
6. Testing: effect of measurement on measurement (fatigue, habituation, etc.) exposure
to a test can affect scores on subsequent exposures
7. Regression to the mean: extreme scores will be followed by less extreme scores
DAG⇒ makes it easier to: be more specific about what we are
assuming about the causal relationships, identify potential
confounds when estimating the true causal effect of one variable
on another, understand some applied issues ⇒ justified to
conclude that a correlation is causal
Mediation: effect of X and Y is indirect, mediated by Z
Coufounder: common cause→ X and Y correlate because they
share a common cause… distorted association when no control Z
Collider: common effect… distorted association when control Z
*Whether you should adjust for third variable (Z) depends on the situation you are in→
make assumptions explicit→ use causal graphs to help you and the reader out
- Don’t control for collider or mediator but control for confounder (controlling: going
into detail and separating the variable)
Foster (2010)- swamp of ambiguity has arisen around statements about causality
1. Ignoring causality- some authors write down only correlations, without making any
statements about causality.
, 2. Statements of causality are recognized, but unclear assumptions- statements
about causal relationships based on correlational data, but often without specifying
assumptions.
3. Pseudo-correlational statements- no direct statements about causality, but clearly
implied in the conclusion.
● If all confounders are controlled for, a correlation between treatment and outcome
can be seen as causal
○ Does not mean that the more variables are controlled for, the more accurate
the estimation of the causal effect ⇒ purification principle
■ Problem of overcorrection: controlling for mediators on the causal
path could lead to an over\underestimation of the causal effect
■ Collider bias: controlling for common effects will bias the estimation
of a causal relationship between two variables
❖ Indirect effect→ X cannot directly cause Y
❖ For total effect of X Y, don’t control for mediator
❖ For direct effect of X Y, control for mediator
❖ Check effect of X to Z to then check for Z to Y
Mediator is caused by the treatment variable X and is a cause of
the outcome variables
Collider (common effect):
❖ X and Y cause Z⇒ common effect
❖ Do not control for third variable
➢ Otherwise collider bias
■ Correlation (negative) that does not exist
■ X No sprinkler and no rain = wet lawn X
Tinder example: thinking a beautiful personality and a beautiful face are mutually exclusive
➔ Negative correlation between beauty and personality ⇒ because conditioning on
collider ⇒ COLLIDER BIAS
➔ Attractiveness/personality are selected to go out with them on Tinder date
◆ To the degree to one is absent, the other is likely to be more present
Correlation & Simple regression
Simple regression only has one predictor
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