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Summary Lectures Applied Methods and Statistics
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Summary: Applied Methods and StatisticsLecture 1: Basic Elements of Path Analysis
Path Analysis
Extended version of regression -> combines regressions in larger model
There are many different hypotheses in one model
-> mediation model
Can the correlations between a group of variables be explained by a causal model?
It is more a causality than a regression
Factor Analysis
Can the correlations between a group of variables be explained by one underlying
construct (or multiple)?
Structural equation modelling
Factor analysis and path analysis come together
, Can the correlations between a group of variables be explained by underlying
constructs and causal effects between those?
Goal of path model
The goal of a path model is to capture theories such as Hank Green’s in a formal form, the
“path diagram”, to examine whether the assumed theory corresponds to observed
correlations in reality.
The Basic Elements of a Path Model
Variables (=squares in the model)
Relations between variables
Covariation (correlation)
Causal effects
Types of relations/effects
Direct
Indirect
Unknown
Spurious
Reciprocal
Conditional effects
Variables
Are characteristics of research units (such as people, couples, schools…) that you are
interested in
Research unit = entity you are studying (also time points)
There must be variation in the characteristics across research units
If there is no variation in the variables it is not a variable but a constant
E.g. for this course’s students is “student/no student” a constant
Common mistakes:
Confusion of the values of the variable with the variable itself
“rich” and “poor” are two values of the same variable “income” -> do not include
them in path model separately
Confusion of a process or theory with a variable
E.g. attribution theory, math-related career choice process
,Variables:
Neglecting your care/not taking medication -> self-care
Stress
Emotional states
Lifestyle/behavioral factors
Biological factors
Isolation -> social interactions
Relations
A statement in which:
Two variables occur
Higher/lower values on one variable are associated with higher/lower values on the
other variable
Two types of statements
1. Covariation statement
2. Causal statement
Causal relation:
A causal relation means that change in one variable leads to change in the other
If you change the independent, then also the dependent will change
“Paying attention leads to knowledge” -> if you switch these variables meaning
would change: “Knowledge leads to paying attention”
Signal words: produce, cause, influence, result in, is more likely to
Spurious relations
(Causal statement 1) “Chocolate causes happiness”
(Causal statement 2) “Chocolate causes longevity”
Therefore also:
(Covariation statement) “Happy people live longer”
General:
(Statement 1) “Variable x causes 𝑦 ”
(Statement 2) “Variable x causes 𝑦 ”
(Covariation statement) “𝑦 is related to 𝑦 ”
, More ice cream sold more people were attacked by sharks
Cannot assume that, it is merely related but not a cause -> it is a covariation
Spurious relation -> actually caused by hot weather
Correlation and causation are NOT the same
Direct and indirect effects
“People characterized by their optimism, happiness, love and positive feelings often live
significantly longer”
(C1) “Feeling blue leads to neglecting self-care”
(C2) “Neglecting self-care leads to bad health”
Valence of emotion:
Direct effect on self-care
Indirect effect on health (via mediator self-care)
“Negative emotions are related to health because of the behavioural factor ‘self-care’”
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