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Summary Causal analysis techniques 2024
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ANOVA (Analysis of Variance):• Independent Variable(s): Categorical (nominal) variables that has more than 2
categories (e.g., groups or categories).
• Dependent Variable: Continuous (interval/ratio) variable.
Pearson Correlation:
• Independent Variable: Continuous (interval/ratio) variable.
• Dependent Variable: Continuous (interval/ratio) variable.
Partial Correlation:
• Independent Variable: Continuous (interval/ratio) variable.
• Dependent Variable: Continuous (interval/ratio) variable.
• Control Variable: Continuous (interval/ratio) variable (the variable being
controlled for).
Semi-Partial Correlation:
• Independent Variable: Continuous (interval/ratio) variable.
• Dependent Variable: Continuous (interval/ratio) variable.
• Control Variable: Continuous (interval/ratio) variable (the variable being
controlled for, but only in relation to one of the variables).
Bivariate Regression:
• Independent Variable: Continuous (interval/ratio) variable (or categorical if
using dummy coding).
• Dependent Variable: Continuous (interval/ratio) variable.
Multiple Regression:
• Independent Variable(s): a large number of continuous (interval/ratio) variables
(or categorical if using dummy coding).
• Dependent Variable: Continuous (interval/ratio) variable.
Linear regression:
• One continuous dependent variable Y
• And one or more continuous (or Dummy) independent variables X
Logistic regression
• Dichotomous dependent variable (die or not die, pregnant or not)
, • ANOVA: Used to compare means across multiple groups and assess whether
any of those means are statistically significantly diIerent from each other.
• Pearson Correlation: Measures the strength and direction of the linear
relationship between two continuous variables.
• Partial Correlation: Assesses the relationship between two variables while
controlling for the influence of a third variable, helping to clarify the nature of
the association.
• EIect Size: Important for understanding the practical significance of results,
not just statistical significance.
Lecture 1 ANOVA
You will learn …
¨ in which situation ANOVA is applicable
¨ what hypotheses can be tested with ANOVA
¨ the key logic behind ANOVA
¨ how to calculate the deviations from the mean(s)
Y= dependent variable
X= independent variable
Systematic error = taking other variables into account
Random = taking other variables not into account (can’t explain the fluctuation)
, • Substantive hypothesis: A person’s degree of organizational commitment (Y)
depends on the team in which the person works (X)
o Team = categorical level
Team in which someone works (X) (independent) ---à Organizational Commitment (Y)
(dependent)
o Question: if the hypothesis is correct, what would you expect to find with
regard to diIerences in average commitment between the teams?
§ DiIerent means
o Imagine that we have collected data of measurements of organizational
commitment for 3 teams
o 2 scenarios with regard to the data...
Key idea of ANOVA is:
• When there are 2 or more groups, can we make a statement about possible -
significant- diIerences between the mean scores of the groups?
• Intermezzo: What could we do if there were only 2 groups?
o (Answer: t-test)
Fundamental principle of ANOVA:
ANOVA analyses the ratio of the two components of total variance in data:
between-group (group means) variance and within-group variance
information on variance of average scores between groups
information on variance of scores within groups
OR
ANOVA analyses ratio in which:
• between-group variance measures systematic diIerences between groups and
all other variables that influence Y, either systematically or randomly (‘residual
variance’ or ‘error’)
and
age en income
o systematic: de gemeten variabelen en de random: ‘nog onbekende’
variables
, • within-group variance measures influence of all other variables that influence Y
either systematically or randomly (‘residual variance’ or ‘error’)
o andere variabele bijv education erbij doen
à verschillen in fertile soil zijn niet door de olie maar door een andere oorzaak bijv
genen.
Logic of Anova
Important to realize:
1. Any diIerences within a group cannot be due to diIerences between
the groups because everyone in a particular group has the same group
score; so, within-group diIerences must be due to systematic
unmeasured factors (e.g., individual diIerences) or random
measurement error
2. Any observed diIerences between groups are probably not only pure
between-group diIerences, but also diIerences due to systematic
unmeasured factors or random measurement error
systematic = there are no other unmeasured variables
Compare…
between-group variability (= systematic group e9ect + error/random)
to
within-group variability (= error/random)
… to learn about the size of the systematic group e9ect
We can speak of rejecting null hypothesis when there is at least 1 significance
(mean van groep 1= 3 en groep 2= 3 en groep 3 =9, we can reject null)
The more test we do, the alpha level gets larger (bijvoorbeeld 3 test is 0,05 x 100=
5%.x 3 = 15 procent of faults
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