1. Concepts to be included
Causality
Time order (see: reverse causation)
Association (between variables)
Spurious relationship
Cross-sectional research

2. Cross-sectional research
Henk van der Kolk

3. Aim
Introducing cross-sectional research Some threats to causal inference in cross-sectional research

4. Cross-sectional research (def.)
A research design, in which... all variables of a set of units are measured at the same time and none of the variables is manipulated differently for a sub-set of units.

5. Example Does the amount of e-marketing increase sales? + Amount of
Companies (?)
Months in one company (?)

6. Example
Collecting data about a set of online shops … Asking both e-marketing budgets and sales.

7. Three aspects of causality
Association; X and Y are correlated
Amount of e-marketing spending
Sales

8. Three aspects of causality
Association; X and Y are correlated Time order; X precedes Y in time Non spuriousness; no other variable (Z) produces the correlation.

9. Assessing causal research
Measurement validity (and reliability)
External validity
Internal validity
Statistical conclusion validity
Time order
Non-spuriousness
Correlation

10. Main Threats to internal validity in cross-sectional research
Reverse causation cannot be ruled out
Third variables may affect the relationship (relationship may be spurious)

11. Reverse causation
Since data are collected at one moment in time, reverse causation cannot be ruled out Example: Sales increases the budget for e-marketing
Amount of e-marketing spending
Sales

12. Measurement bias may produce reverse causation
Sometimes, the problem of reverse causation can be reduced in cross-sectional research. Example: ask for the budget of last year, and this years sales.

13. Measurement bias produces reverse causation
Measuring both variables at the same time sometimes produces reverse causation. Example: Does a ‘happy childhood’ make you a more ‘happy adult’?

14. Third variables (spuriousness)
Third variables may affect the relationship (relationship may be spurious) Example: Maybe the presence of ‘young and dynamic managers’ (Z) affect both sales (Y) and the e-marketing budget (X)?

15. Number of young and dynamic managers
Confounding
Number of young and dynamic managers
E-marketing budget
Sales

16. Third variables (spuriousness)
‘Confounding’ is a poblem in cross-sectional research. Taking into account the possible effect of third variables, may reduce the problems of spuriousness. ‘Controlling’ for third variables.

17. Evaluating cross-sectional research
Weak in internal validity (reverse causation / third variables) Potentially strong in external validity (sampling) The effect of many independent variables cannot be studied in other types of research designs

18. Aim
Introducing cross-sectional research Some threats to causal inference in cross-sectional research

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