Summary of unit 14, 16, 17 (and 18) from RDMS, all you need to know!
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Course
Research Methodology and Descriptive Statistics
Institution
Universiteit Twente (UT)
Comprehensive summary for research descriptive methodology statistics of unit 14, 16, 17 (and 18). The most important keywords have been added with explanations.
Unit 14 – Causality and the effect of third variables
14.1. The effect of third variables - thinking about trivariate hypotheses
Tes$ng bivariate hypothesis is checking,
® Time order of cause and effect
® Correla4on/associa4on
® Effect of third variables, when you introduce the third variable, you check,
o Theorizing about the effect of third variables
o Formula0ng a trivariate hypothesis
o Tes0ng the trivariate hypothesis
Poten&al effects of ‘third variables’
Addi&on: another independent variable
Example > rela0onship between social class and obesity
Confounding: a confounding variable
1
,Interpreta&on: an intervening variable
Confounding or interpreta$on?
It is important to look at the 4me order,
® If the (original) independent variable precedes the third variable in 4me >
interpreta(on.
® If the (original) independent variable is explained by the third variable > confounding.
Trivariate hypothesis
® AHer the introduc4on of the third (test) variable (men(on the third variable). The
bivariate rela4onship (men(on the original bivariate hypothesis > disappears,
changes, remains the same). And the test variable is related to the other variables
(outline the model).
14.2. The effect of third variables – confounding
Example > rela0onship between # of storks and # of babies (per capita) in municipali0es
The bivariate expecta$on in a model
Supposed there is a rela4onship between the numbers of babies and the numbers of storks >
you may assume that there is a causal rela4onship, but then you nothing about biology.
® Why is there no rela4onship?
2
,The expecta&on in a graph
Tes&ng the causal rela&onship
® Correct 4me order is assumed
® We found an associa4on
What would be the ‘theore4cal argumenta4on’ about why this is s4ll NOT a causal
rela4onship? > it has to do with the ‘third variable’…
Confounding in a model
Theorizing why there is no causal rela4onship between the number of babies and the number
of storks.
3
, ® The rela4onship between the independent and the dependent variable is produced by
a confounder variable > effects both the independent variable and dependent
variable. But there is no rela4onship between the independent and dependent
variable.
14.3. The effect of third variables – interpreta?on
Example > rela0onship between study 0me and grades of students
A bivariate expecta$on can be shown in a model like this
Grades > dependent variable (y-as)
Study 4me > independent variable (x-as)
® The rela4onship is posi4ve
The expecta$ons can also be displayed in a graph
Tes$ng the causal rela$onship
• (correct 4me order is assumed)
• (we found an associa4on)
4
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