Research Methods in Education 6th Edition

1. CAUSATION AND
EDUCATIONAL RESEARCH
© LOUIS COHEN, LAWRENCE
MANION & KEITH MORRISON

2. STRUCTURE OF THE CHAPTER
• Causes and conditions
• Causal inference and probabilistic causation
• Causation, explanation, prediction and
correlation
• Causal over-determination
• The timing and scope of the cause and the
effect
• Causal direction, directness and indirectness
• Establishing causation
• The role of action narratives in causation
• Researching causes and effects
• Researching the effects of causes
• Researching the causes of effects

3. DECISIONS IN RESEARCHING
CAUSATION
• Is the research seeking to establish causation, and if
so, why?
• When is causation demonstrated (causation is
never 100% certain and often is unobservable)?
• What constitutes a cause and what constitutes an
effect?
• What constitutes evidence of the cause and
evidence of the effect (as causation is often
unobservable)?
• What kind of research and research methodology is
necessary if causation is to be investigated?
• Is the research investigating the cause of an effect,
the effect of a cause, or both?

4. EXAMINE CAUSAL PROCESSES
RATHER THAN SINGLE EVENTS
BECAUSE . . .
• An effect is often the result of several causes;
• A single cause can have many effects;
• Causes are different from reasons, motives,
determination, entailment, prediction, explanation,
correlation;
• Causes may have a direct and/or indirect effect on
outcomes.

5. A B C
A
?
?
B
C
A C
B
?? and/or A
C
B
A C
?
?
A ?
?
C
?? A ?? ?? C
?? B ?? C
D B C
D C
MODELLING CAUSES
AND EFFECTS
D = Known cause, but not included
?? = UNKNOWN CAUSE
D
??
??
C
??
??

6. DEMONSTRATING CAUSALITY
• Look for counterfactuals: the absence of X
(the supposed cause) would have led to the
absence of Y (the effect).
BUT
• Counterfactuals are difficult to prove.
HENCE
• Researchers use the experimental method (a
control group and an experimental group).

7. CAUSES AND CONDITIONS
• An effect occurs when all the necessary
and/or sufficient conditions are present.
THEREFORE
• Examine the necessary and/or conditions and
circumstances for the cause to bring about an
effect.

8. CAUTIONS IN RESEARCHING
CAUSATION
• A cause may bring about an effect but not vice
versa.
• Causation is different from correlation
• Correlation, even if repeated, does not prove
causation
• Causation is usually probabilistic, conjectural,
inferential rather than definite and absolute
• A small cause may bring about a small/large/no
effect
• A large cause may bring about a small/large/no
effect

9. CORRELATION IS NOT CAUSATION
• A low barometric reading correlates
with a storm but does not cause it;
they are both caused by a third
factor
Atmospheric
conditions
Low barometric
reading
Storm

10. WHEN IS A CAUSE A CAUSE?
• Context, conditions and triggers: the trigger is
the final cause in a chain or web of causes and
conditions that bring about an effect.
• It is sometimes unclear when a cause or an
effect begin and end, e.g. when does smoking
start to cause cancer?
• How far back in time, and how far out (or in) in
terms of context does the researcher have to go
in order to establish causation?

11. HUME’S INDICATORS OF CAUSATION
• Contiguity (of space and time) (the cause is
contiguous with the effect);
• Priority/succession (the cause precedes the
effect);
• Constant conjunction (the coupling of one
event and its successor are found to recur
repeatedly);
• Necessary connection (which is learned from
experience, habit and custom rather than from
deductive, logical, necessary proof).

12. EXOGENOUS AND ENDOGENOUS
VARIABLES
• An exogenous variable: a variable whose values are
determined outside the causal model in which that
variable is being used, or which is considered not to
be caused by another variable in the model, or which
is extraneous to the model.
• An endogenous variable: a variable whose values or
variations are explained by other variables within the
model, or which is caused by one or more variables
within the model.
THEREFORE
• Identify which causes mediate, and are mediated by,
other causes.
• Make sure that all the relevant variables are included
in the causal model.

13. CONTROLLING FOR VARIABLES
• The researcher has to control for the effects of
one or more antecedent variables in establishing
causation.
• Controlling variables means holding them
constant (matched) so that fair attribution of
causality can be assessed.
• If we wish to examine the effect of A on C then
we have to control for the effect of B.
A B C

14. RANDOMIZATION
• Isolation and control of variables may be
addressed through randomization.
• Random allocation of individuals to a control
group or an experimental group can allow for the
many uncontrolled variables that are part of the
make-up of the groups in a study.
• Randomization adopts the ceteris paribus
condition (all other things being equal), and
assumes that the distribution of many other
variables is evenly distributed across the control
and experimental groups, such that there is no
need to control for them.

15. CAUSAL OVERDETERMINATION
• An effect may be the result of any one or more
causes, each of which may be sufficient to bring
about the effect.
• A child behaves well or badly in school as a
result of many causes, each of which on their
own might bring about the behaviour.

16. TIMING OF CAUSES AND EFFECTS
• A cause from a long time prior to an effect may
contribute to an effect much later.
• Effects may be short-term, delayed. immediate,
instantaneous, cumulative and long-term.
• The full effects of a cause may not be revealed in
a single instance.
• Temporality and causation are closely connected
but separate.
• Between a cause and effect may lie many other
factors and time frames.

17. A B C
A
?
?
B
C
A C
B
?? and/or A
C
B
A C
?
?
A ?
?
C
?? A ?? ?? C
?? B ?? C
D B C
D C
MODELLING CAUSES
AND EFFECTS
D = Known cause, but not included
?? = UNKNOWN CAUSE
D
??
??
C
??
??

18. CAUSES AND EFFECTS MAY BE
MASKED
I have a
headache
I take a
paracetamol
The headache
increases
I conclude that
the paracetamol
has not worked
A WRONG CONCLUSION
I have a
headache
I take a
paracetamol
The headache
increases
The paracetamol has
slowed the progress
of the headache (the
headache could have
been even worse)
A SAFER CONCLUSION

19. CAUSES AND EFFECTS MAY BE
MASKED
I feel stressed
I sleep
The still feel
stressed
I conclude that
sleeping does
not reduce stress
A WRONG CONCLUSION
I feel stressed
I sleep
I still feel
stressed
Sleeping reduces
stress, but its
effects may be
masked by
other stressors
A SAFER CONCLUSION
Stressors
Lesson: always consider possible rival explanations of
causes and effects

20. ACTION NARRATIVES
• Qualitative data may provide closer detail on the
processes of cause and effect – the ‘why’ and
‘how’ of cause and effect – than numerical data
(the ‘what’ of cause and effect).
• Qualitative data can include intentionality and
agency in cause and effect.
• Qualitative data can examine the micro-nature of
causation.
• Example: it may be unhelpful to say that
increasing the time spent on reading causes
students’ reading to improve. A researcher may
wish to know how and why the increase in time
spent on reading improves it.

21. MODELS OF CAUSATION
• Behind models of causation lie theories of
causation (theories of what causes what).
• Models of causation (e.g. A causes B) often use
proxy variables (e.g. ‘sex’: but how does ‘sex’
(A) actually cause an effect (B)?).
• Longitudinal studies might be more useful than
‘one-off’ studies in investigating causation, as
‘one-off’ studies do not include temporality, and
temporality (e.g. one event (cause) preceding
another (effect) is an essential element of
causation).
A
B
C
D

22. QUESTIONS TO ASK IN RESEARCHING
CAUSES AND EFFECTS
• What is the causal connection between the cause and
the effect (how does the cause bring about the effect
and how has the effect been brought about by the
cause)?
• What are the causal processes at work in the situation
being investigated?
• What constitutes the evidence of the causal connection?
• On what basis will the inference of causality be made?
• What constitutes the evidence that a cause is a cause
and that an effect is an effect?
• What constitutes the evidence that a cause is the cause
(and that there is not another cause) and that an effect is
the effect (and that there is not another effect)?

23. QUESTIONS TO ASK IN RESEARCHING
CAUSES AND EFFECTS
• Is the research investigating the effects of a cause (an
interventionist strategy) or the cause of an effect (a
post hoc investigation)?
• How will the research separate out a range of possible
causes and effects, and how will decisions be made to
include and/or exclude possible causes and effects?
• What methodology will be chosen to examine the
effects of causes?
• What methodology will be chosen to examine the
causes of effects?
• What kind of data will establish probabilistic
causation?
• When will the data be collected from which causation
will be inferred?

24. RESEARCHING THE EFFECTS OF CAUSES
(Interventionist research)
• Action research;
• Experimental approaches;
• Participant observation.

25. RESEARCHING THE EFFECTS OF CAUSES
(Interventionist research)
Note that:
• The context of the intervention and the power of the
situation can affect the outcomes and behaviours of
participants;
• The same causes do not always produce the same
effects;
• Inappropriate timing of the pre-test and post-test
measurements of effects can undermine the
reliability of statements of the effects of a cause;
• It may not be possible or desirable to conduct a
randomized controlled trial;
• A randomized controlled trial may not indicate how
causes bring about effects (i.e. how causation really
happens);

26. RESEARCHING THE EFFECTS OF CAUSES
(Interventionist research)
Note that:
• There is a problem of accuracy, as groups and
individuals cannot both be in a group that is and is
not receiving an intervention (Holland’s (1986)
‘fundamental problem of causal inference’, which
may not be sufficiently attenuated by
randomization);
• Process variables, not only input variables, feature
in understanding causation;
• The characteristics, personae and specific individual
features of participants and their agency influence
interventions and their effects.

27. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
Determining the causes of effects is more tentative,
provisional and inferential than determining the
effects of causes, because:
•Data are incomplete;
•Backtracking along causal chains and/or
searching within causal nets/webs is difficult (as it
involves a search for clues and testing of rival
hypotheses about causation).
•Researchers can generate a huge number of
potential causes of observed effects, i.e. there is a
problem in deciding which cause(s) is/are correct.

28. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
A seven-stage process of tracing causes from effects:
Stage 1: Establish exactly what has to be explained.
Stage 2: Set out possible theoretical foundations for
the investigation.
Stage 3: Examine, evaluate and eliminate rival
theoretical foundations, selecting the most
fitting.
Stage 4: Hypothesize a causal explanation on the
basis of the best theoretical foundation.
Stage 5: Set out the assumptions underlying the
causal explanation.
Stage 6: Test the causal hypotheses empirically.
Stage 7: Draw conclusions based on the test.

29. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
• Indicate what needs to be done to test the theory
and to falsify it.
• Identify the kinds of data required for the theory to
be tested.
• Identify the actual data required to test the theory.
• Identify the test conditions and criteria.
• Construct the empirical test.
• Consider using primary and secondary data.
• Consider using existing published evidence as
part of the empirical test.
• Ensure that action narratives and intentionality are
included in causal accounts.

30. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
Ways in which causes may be inferred from effects:
• A high level of detail may be required in order to
establish causation;
• Identify several causal chains, mechanisms and
processes in a situation;
• Combine micro- and macro- levels of analysis;
• Address both agency and structure;
• Underpin the data analysis and causal
explanation with theory;
• Use different kinds of ex post facto analysis;

31. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
Ways in which causes may be inferred from effects:
• Use correlational and causal-comparative, criterion
group analysis;
• Ensure matching of groups in samples and that
similar causes apply to both groups;
• Ensure clarity on the direction of causation;
• Use empirical data to test the causal explanation;
• Identify which is cause and which is the effect,
• Identify which effect subsequently, becomes a cause;
• Avoid over-selective data;
• Ensure that the data fairly represent the phenomenon
under investigation;

32. RESEARCHING THE CAUSES OF
EFFECTS (Post hoc research)
Ways in which causes may be inferred from effects:
• Recognize that cause and effect may be blurred;
• Accept that effects may become causes in a
cyclical sequence of causation;
• Seek out and recognize over-determination at
work in causal accounts;
• Keep separate the explanans (the explanation)
from the explanandum (that which is to be
explained);
• Ensure that alternative theories and causal
explanations are explored and tested;
• Draw conclusions based on the evidence, and the
evidence alone.

33. CHALLENGES IN RESEARCHING
CAUSATION
• Focus more on causal processes than
input/output/results models of causation;
• Establish causation other than through reduction and
recombination of atomistic, individual items and
elements;
• Regard causation as the understanding of the
emergent history of a phenomenon or a whole;
• Investigate multiple and simultaneous causes and
their multiple and simultaneous effects in a multiply-
connected and networked world;
• Separate causation from predictability, and recognize
that a causal sequence may not be repeatable;
• Remember that randomness often ‘trumps’ causation;
• Indicate the utility of understanding causation if it has
little subsequent predictive strength;

34. CHALLENGES IN RESEARCHING
CAUSATION
• Understand how to investigate causation in
holistic webs of connections, i.e. how is it
possible to discover or demonstrate causation
when looking at events holistically;
• Investigate causation and causal processes in a
multi causal, multi-effect, non-linear and‑
multiply-connected world;
• Identify the causal processes at work in
determining social and macro structures from‑
the actions and interaction of individuals and,
conversely, in determining actions and
interactions of individuals from the structures of
society and its institutions.