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Research Methods in Education 6th Edition

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  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 24. RESEARCHING THE EFFECTS OF CAUSES (Interventionist research) • Action research; • Experimental approaches; • Participant observation.
  25. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.