Operationalisation of Variables.
- This means removing ambiguity from variables by defining them in ways that are meaningful and measurable.
- Variables can be operationalised in different ways.
- The different approaches may have different implications within the research but they can still be valid representations of what needs to be investigated.
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Independant and Dependant Variables.
- This means a persons mood.
- You can induce peoples moods in many ways such as the weather, false feedback of a personality questionnaire, music, drugs etc.
- e.g. Weather - test half of the participants on a rainy day and the other half on a sunny day.
- This means recall.
- To measure recall you would need to decide what information you want you participants to recall such as reading comprehension tasks, work lists, a series of pictures etc.
- e.g Reading Comprehension - ask all participants to read a 400 word extract and then answer 10 questions worth one mark rach that are related to the extract. The Recall would then be measure by their score out of 10.
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Co-variables & Confounding/Extraneous Variables
- E.g. If a researcher want to invesigate the the relationship between stress and illness, stress would be one co-variable (CoV1) and illness would be the second co-variable (CoV2).
- Theses are often classified as participant and situation variables (something about the participants could confound the results or the results could be confounded by situational or enviromental factors).
How to overcome these problems
- Age - Mixed groups, random allocation, categories and age group.
- Investigation Bias - Use people who are not influencial, keep emotionless.
- Weather conditions - forecast or take place in a Lab.
- Demand characteristics - Single Blind technique.
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