Observational design

• unstructured observation


  Observation in which all behaviour and activities are recorded in rich detail and usually involves small scale experiments with few ppts
• Structured observations

  R quantifies their observation using pre-determined list of behaviours and sampling methods. Often used when there is too much observation
• Strengths to structured observations

  Makes recording data easier and more systematic. Data produced more likely to be numerical: quantitative data meaning analysing and comparing data of behaviour observed between ppts is straightforward.
• Strength to unstructured observations


  Benefits from more richness and depth of detail in data collected
• Limitations to unstructured observations

  Greater risk of observer bias with With UOs as objective behavioural categories that are a feature of SOs are not present here
• Qualitative data with UOs

  Also, use of qualitative data may be more difficult to record and analyse
• Behavioural Categories

  To produce a structured method of what R sees (hears) , its necessary to break the target behaviour up into a set of behavioural categories
• Behavioural checklist


  These behavioural categories can be called a behaviour checklist. Before O begins, R should make sure they have as much as possible included all the ways in which target behaviour may occur in their behavioural checklist
• Target behaviours

  Should be precisely defined and made observable and measurable
• Strengths to behavioural categories

  Behavioural categories can make data collection more structured and objective
• Limitations to behavioural categories

  Rs must ensure that these categories are as clear and unambiguous as possible and they must be observable, measurable and self-evident.
• Target behaviours in checklist

  Rs should ensure all possible forms of target behaviour are included in the checklist and that they don't over lap and be exclusive. E.g.: grinning and smiling
• Sampling methods- Continuous sampling

  Continuous recording of behaviour is a key feature of UOs in which all instances of a target behaviour are recorded
• Systematic way of sampling

  For complex behaviour, this may not be practical but in SOs R may use a systematic way of sampling their observations
• Event sampling


  Counting number of times a particular behaviour occurs in a target individual/group
• Time sampling

  Recording behaviour within a pre-established time frame
• Strengths to Event sampling

  Useful when target behaviour or event happens quite infrequently and could be missed if time sampling used
• Limitation to Event sampling

  If specified event is too complex, observer may overlook important details if using event sampling
• Strengths to Time sampling

  Effective in reducing number of observations that have to be made
• Limitations to Time sampling

  But, those instances when behaviour is sampled might be unrepresentative, of the observation as a whole