Observational Design (AQA A Level Psychology)

Structured & unstructured observation

Structured observation

• A structured observation may be chosen by a researcher when observing large samples in busy environments where many different behaviours are likely to occur

• Selecting a structured observation allows the researcher to observe a few specific, clearly defined behaviours rather than trying to make sense of too much information e.g.

  • ignoring instructions/obeying instructions

  • dropping litter/putting litter in a bin

  • helping/not helping

• The emphasis in structured observation is on gathering quantitative data e.g.

  • the number of times shoppers go over to look inside a box when the instruction 'Look inside this box' is placed next to it

  • the frequency with which litter is dropped in a school playground at lunchtime

  • the number of times pedestrians help a confederate, who is pretending to be blind, cross the road

• Researchers conducting structured observations are interested in a limited set of behaviours

  • This allows them to quantify the observed behaviours

  • Rather than recording every single behaviour available, they focus only on the predetermined areas of interest

Evaluation of structured observation

Strengths

• Using quantitative data is a quick and easy method which can be presented visually in graphical form or converted to percentages and statistics

  • This is a strength as the data can show trends and frequencies of behaviour across a large sample

  • Large samples and quantitative data lead to reliable results

• Using predetermined categories means that the researcher is not likely to become distracted or unfocused

  • They can ignore any behaviours which do not align with the behavioural categories they have decided upon

  • This ensures that what is being observed is relevant to the research aim

Limitations

• Quantitative data can shed light on what was observed but not on why that behaviour occurred

  • This means that structured observations lack explanatory power

• Using pre-determined categories means that the researcher is unable to include any behaviours which may be interesting and highly relevant to the study if they have not been included in the categories beforehand

  • This limits the usefulness of structured observations

Unstructured observation

• An unstructured observation may be chosen by a researcher when observing small samples in more intimate environments where interpersonal interaction is the focus of the observation

  • Selecting an unstructured observation allows the researcher to observe everything that is happening throughout the session e.g.

    • a married couple discussing their feelings about how equitable their relationship is

    • a group of five-year-old children playing with gendered toys

    • identical twins talking about the costs and benefits of being identical

• Unstructured observations are more flexible and open-ended than structured observations because:

  • they are likely to be conducted in a fairly relaxed manner

  • they will generally not use predetermined behavioural categories but instead they simply 'go with the flow' of the observation session

• The emphasis in unstructured observation is on gathering qualitative data e.g.

  • verbal and non-verbal communication

  • the use of the environment e.g. where people choose to sit/stand/place themselves

  • the quality of conversation and spoken utterances e.g. light-hearted/serious/aggressive/bored/confused/sarcastic etc.

• Researchers conducting unstructured observations are interested in the full range of behaviours on display

• Observing all behaviours allows them to see 'inside' the behaviour by not restricting it to a few, limited categories

Evaluation of unstructured observation

Strengths

• This type of observation allows a researcher to gain rich, insightful data full of depth and detail

  • The recorded data is likely to be highly subjective and personal to those being observed

  • Thus it is high in ecological validity

• By focusing on the unique experiences, opinions and personal 'journey' of a participant an unstructured observation is a good method to use when conducting a case study

  • The data from the observation session(s) can then be used in conjunction with other methods

  • This would result in the triangulation of data and method which increases the validity of a study

Limitations

• Due to the highly personal and subjective nature of unstructured observations, the researcher may lose their sense of objectivity

  • They may become too close to the participants

  • They may use confirmation bias when analysing their record of the sessions

  • They may (consciously or unconsciously) overlook or miss some important details from the observation sessions

  • A lack of objectivity means that the findings would be unreliable

• Analysing the data from unstructured observations is time-consuming and depends largely on the researcher's interpretation of the data

  • This means that the published findings may not be a valid account of the observation process

Behavioural categories

• Behavioural categories are used to record specific behaviours during one observation session

• The categories must be designed to only record observable behaviours; there can be no ambiguity about what is being observed e.g.

  • punching/kicking/shoving rather than just 'aggressive behaviour'

  • crossing arms/frowning/pursing lips rather than just 'defensive body language'

  • pressing a button/looking at a notice/stopping at a sign rather than just 'obedient behaviour'

• The researchers will have previously agreed on which specific behaviours should be recorded so that all observers agree before the observation begins

• The behaviour categories should be unambiguous e.g.

  • in an observation of children’s aggression the categories could be:

    • ABL = Aggressive body language

    • AGB = Aggressive behaviour

    • NABG = Non-aggressive behaviour

• These behavioural categories would then have to be operationalised to make sure that they are specific and cannot be confused with anything else e.g.

  • ABL could be subdivided into ‘pointing’, ‘shaking fist’, ‘baring teeth’

  • AGB could be subdivided into ‘punching’, ‘kicking’, ‘shoving’

  • NABG could be subdivided into ‘smiling’, ‘arm-linking’, ‘hugging’

• These categories could then be arranged into whether it is boys or girls who are being aggressive/non-aggressive and to whom the aggression/non-aggression is directed e.g.

  • aggression towards another boy(s) or another girl(s) for even more detail and insight into the behaviour

• Even when categories of behaviour are firmly established an observation can still be affected by researcher bias

• Researchers can test the reliability of their observations by comparing them with another researcher's recording of their behaviours

  • The level of consistency between the two records is then compared

• Inter-observer reliability is the level of consistency between two or more trained observers when they conduct the same observation

  • All observers must agree on the behaviour categories and how they are going to record them before the observation begins

  • The observation is conducted separately/individually by each observer to avoid conformity (i.e. one observer may be influenced by one or more other observers)

  • After the observational period

    • the observers compare the two independent data sets (often designed as a tally chart)

    • they then test the correlation between the two sets

    • if there is a strong positive correlation between the sets then this shows that there is good inter-observer reliability and that the behaviour categories are reliable

• Establishing good inter-observer reliability means that there is less chance that researcher bias has interfered with the observation

Evaluation of behavioural categories

Strengths

• The use of clearly defined, unambiguous categories of behaviour enables the researcher to achieve an objective view of what is being observed

  • This means that subjectivity and the need to interpret the behaviour is eliminated

  • Eliminating subjectivity moves the process closer to the scientific method

• The use of more than one observer should ensure inter-observer reliability

  • Being able to claim reliability means that the research is less likely to be criticised during the peer review process

Limitations

• The predetermined behavioural categories may be limiting in terms of the types of behaviours enacted during an observation session

  • If one or more behaviours recur without there being categories for them then this means that the research does not accurately represent what occurred during the session

  • This in turn would lower the validity of the findings

• One issue with inter-observer reliability is that it does not allow for the possibility that raters simply guessed rather than scoring the categories according to strict criteria

  • Thus it may overestimate the true agreement between observers

Sampling methods: event sampling & time sampling

• It can be difficult to observe all behaviours at all times during an observation, especially if it is a continuous observation

• Researchers therefore can choose a sampling procedure to help structure and organise the observation session using either (and sometimes both)

  • event sampling

  • time sampling

• With event sampling the researcher records/tallies every time a behaviour from a specific behavioural category occurs e.g.

  • every time litter is dropped rather than put into a bin

  • the frequency with which children in a classroom raise their hands to ask a question

• With time sampling the researcher records all behaviours during a set time frame, at a set point e.g.

  • they record their observations for 20 seconds at a time every 15 minutes over a 2-hour observation schedule

  • they record their observations for 15 minutes every 3 hours of a two-day observation schedule

• The researcher decides which time sample is most appropriate for that specific piece of research 

Evaluation of event sampling & time sampling

Strengths

• Event sampling ensures that specific behaviours will not be missed or overlooked as they are set out in the behavioural categories

• Time sampling allows the researcher flexibility to record any behaviours which may be relevant to the research

• Time sampling also offers researchers the opportunity to record unexpected behaviours which may trigger new research projects for future reference

Limitations

• If too many of the specific behaviours occur at once and are overly complex it is difficult for event sampling to capture them all

  • This limits the validity of the method as it would not provide a true reflection of what occurred during the observation session

• Time sampling can miss any behaviours that occur outside of the set time frame

  • This limits the validity of the method as some behaviours will be over-represented in the findings