Year 1

• Experimental methods

  • Aims -> to investigate…
  • Hypothesis -> directional indicates a certain finding, due to previous research. Non-directional. “There will be difference…”
  • IV = independent variable, manipulated to get findings
  • DV = dependent variable, the variable being measured
  • Levels of the IV = e.g 2 levels if there is a control and an experimental condition. Required for comparison.
  • Operationalise = detail the variables, e.g unit of measurement for DV, unit of change for the IV.
• Extraneous variables

  Any external variables that can potentially interfere with the IV or DV, should be controlled or removed. Should be identified at the start of a study by the researcher. Can minimise by:

  • Standardisation : instructions, same location at the same time of the day, same materials, same debriefs.
• Cofounding variables

  external variables that does affect the IV. E.g individual differences. Can minimise by:

  • Randomisation
• Demand characteristics 


  Relates to participant reactivity. Demand characteristics are any clue from the researcher or the research situation that may be interpreted by the participants as revealing the purpose of the study. This may change participant behaviour. Can minimise by:

  • Deception (can be an ethical issue if p’s aren’t deceived and given a right to withdraw)
  • Double-blind study = getting a third party to conduct the experiment when they don't know the aim either.
• Investigator effects

  any effect of the investigators behaviour on the research outcome (DV). Coolican points out that this can include expectancy effects and unconscious cues. A good example = leading questions. Can minimise by:

  • standardisation
• Randomisation + standardisation

  R = reduced bias from researcher

  S = making sure all p’s know they same thing, are shown the same thing, are instructed the same thing, debriefed the same thing…
• Independent groups

  One group is a control, where the other is the experimental condition. Made up of two different groups of people.

  • Participant variables are a problem, causality is less clear as a result. Minimised by random allocation to each group.
  • Twice as many p’s needed, increases money spent and time on the study, less economical.
  • No order effects e.g no bordem
• Repeated measures

  Each participant would do one condition, then the other.

  • Needs to be counterbalanced, half p’s do condition 1 then 2, others do condition 2 then 1. Reduce order effects.
  • Order effects of bordem or increased ability on the second try f a skills-based task.
  • Demand characteristics are more present as all p’s do all conditions.
  • Participant variables are controlled.
  • Less people needed
• Matched pairs

  participants appeared together on a variable relevant to the experiment. E.g in a study of memory, participants might be matched on their IQ. Pair with highest = a pair, and so on. Each pair would be allocated to a different condition.

  • Order effects and demand characteristics less of a problem
  • Participants can never be matched exactly, even identical twins.
  • Time consuming and expensive, especially when a pilot is required
• Lab experiments

  controlled environment, research are manipulates the IV and records the effect on the DV whilst maintaining strict control of extraneous variables.

  • Strength = high control can establish causality, high internal validity, can be replicated and tested for reliability.
  • Limitation = lack external validity, hard to generalise, environment does not reflect every day situations, they are artificial. Low mundane realism.
• Field experiments

  takes place in a natural setting, the researcher still manipulates the IV and records the effect on the DV.

  • Strength = higher mundane realism than lab, more authentic and can be generalised
  • Limitation = loss of control over extraneous variables, causality is harder to establish. Ethical issues such as consent and invasion of privacy.
• natural experiments

  Researcher has no control over the IV, for example a natural disaster, measures the effect on the DV. participants can still be tested in a lab.

  • Strength = provides research opportunities that may not otherwise be taken for practical or ethical reasons. An example, could be the institutionalised Romanian orphans. High external validity.
  • Limitation = can be rare. Participants cannot be randomly allocated to experimental conditions. Causality how to establish, no control over extraneous variables. Lacks realism, demand characteristics may be an issue.
• Quasi-experiments

  IV is based on an existing difference between people e.g eyecolour, height.

  • Strengths = controlled conditions, share strengths of a lab experiment
  • Limitation = confounding variables as cannot randomly allocate.
• Random sample

  All members of target population has equal chance of being selected. Obtain a complete list of population, assign the names a number, use a lottery method such as picking numbers from a hat.

  • Strength = potentially unbiased
  • Limitation = difficult and time-consuming, may still end up with a sample that is unrepresentative, participants may refuse to take part, this turns it into more of a volunteer sample
• systematic sample

  Every nth member of target population is selected, e.g every third house on the street. Sampling frame is produced which lists the target population organised into alphabetical order. Sampling systems such as every 6th person is used.

  • Strength = objective, unbiased
  • Limitation = Time-consuming, participants may refuse to take part, turning it into a volunteer sample
• Stratified sample

  Composition of the sample reflects the proportions of people in certain subgroups (strata) within the target population. First identifies the different strata that make up the population, Proportions needed to make the sample representative is calculated, each Stratham are selected using random sampling. E.g 20% Asian people in population, should be 20% in sample.

  • Strength - representative, can be generalised.
  • Limitation - the identified strata cannot reflect all the way possible, that people are different, so complete representation is difficult
• opportunity sample

  When researches decide to select anyone who happens to be willing and available. For example, asking anyone on the street.

  • Strength - less expensive, convenient as no need to divide the population
  • Limitation - subject to researcher bias as they have complete control over who they ask. Unrepresentative as participants are asked within a certain area e.g a park
• volunteer sample

  When participants want to be part of the study, from advertising such as an advert in a local newspaper.

  • Strength - requires minimum effort, participants are more engaged as they want to be part of it
  • Limitation - volunteer bias. May attract people pleasers, the generalisability of the study
• informed consent

  Participants are made aware of the aims of the research procedures, their rights and what their data will be used for. This does increase the risk of demand characteristics, however.

  • Participants should be issued a consent letter detailing all relevant information which might affect their decision to participate. If they agree, the document is signed. for participants under the age of 16 parental consent is required
• Different types of consent :

  • Presumptive = not consent from p’s but a small outside group saying if the study is acceptable or not
  • prior general = p‘s given consent to take part in number of studies, not knowing that they’ll be taking part in
  • Retrospective = p’s are asked for consent in the debrief, after the study
• deception

  Intentionally misleading or withholding information from participants at any stage of an investigation.

  • Participants should receive a full debrief after the study.
  • Participants should be given a right to withhold their data
• Protection from harm


  If participants are subject to stress or embarrassment, the researcher should compensate with counselling which they will fund
• privacy

  All participants have the right of privacy, in law under the Data Protection Act.

  • Participant data should be protected at all times.
  • Maintain anonymity e.g referring to a participant as a number or their initials
• pilot studies


  A small scale test run of the actual investigation.

  • Used to check the accuracy of behavioural categories in observational research, can also check camera positioning in a recorded observation.
  • Used to check if questions in questionnaires and interviews are ambiguous.
• Single-blind procedure


  When information is kept from participants, such as what condition they are in, or even knowing if there are any other conditions. Reduces any information that may create subconscious expectations.

  • Controls effects of demand characteristics.
• double-blind procedure

  Neither the participants, nor the researcher who conduct the study is aware of the aims. A third party does the study.

  • Often used for drug studies, so the researcher don’t know who took the real drug, and who took the placebo. Reduces investigator effects
• types of observation

  • Naturalistic = Natural setting
  • Controlled = e.g Ainsworth Strange Situation, put in a room with a two-way mirror, similar control to a lab study.
  • Covert = Observation is done in secret and participants are unaware -> must be done in public to be ethical as observation is happening anyway.
  • Overt = participants consent, they know that they are being watched.
  • Participant = When the observer goes into the situation undercover.
  • Non-participant = observer remains separate to participants
• Evaluation of types of observation

  all observations are open to observer bias, where the interpretation of a situation may be affected by their expectations. This can be reduced by using more than one observer.

  • Naturalistic = external validity
  • Controlled = can be replicated, less extraneous variables
  • Covert = reduces demand characteristics, ethic issue
  • Overt=Demand characteristics, may change behaviour if they know that they are being watched
  • Participant =increased insight, loses objectivity as line between being a researcher and being a participant is blurred
  • Non-participant=objective
• observational design

  Unstructured - writing down everything you see

  • Produces rich data, suitable for small samples, however cannot record all behaviour, greater risk for observer bias

  Structured - using a behavioural checklist with behavioural categories.

  • Produces quantitative, numerical data, which can easily be analysed in chart.
• Observational design (2)

  Behavioural categories - operationalised behaviours which observers look for in an observation. -> behavioural checklist. Need to be measurable, making sure there’s no overlap e.g ‘kissing’ + ‘ snogging’.

  Two sampling methods for structured obervations:

  • Event sampling - when behaviour is established, then the researcher records every time it occurs, useful when behaviour is infrequent and could be missed in time sampling.
  • Time Sampling - researcher records behaviour in a fixed timeframe. Reduces number of observations , but may be unrepresentative.
• Observational design (3)

  sampling method for unstructured observations:

  • Continuous recording - Used for very complex behaviours. However, this method may not be practical or feasible. Basically same thing as event sampling.
• Self report techniques - questionnaires


  Technique which uses close and open questions to ask participants, their thoughts and feelings on a topic or their own behaviour.

  • Open questions - don’t have set options for answers. Produces rich qualitative data, difficult to analyse under sometimes ambiguous to the participant.
  • Closed questions - has a fixed number of responses e.g yes or no

  Types of closed questions

  Likert scales - agree to strongly disagree

  Rating scales - rating of a number

  Fixed choice option - limited answers
• self report techniques - interviews

  Can be online, face-to-face in real time. Three types of interviews:

  • Structured interviews - predetermined sets of questions asked in a fixed order.
  • Unstructured interviews - similar to a conversation. There is no set of questions, but a general aim of what will be discussed. Interviewers can prompt participants to elaborate.
  • Semi-structured interviews - e.g good job interview. A list of questions has been established, but interviewers are free to ask follow-up questions.
• designing an interview

  Most interviews involved and interview schedule, which is the list of questions that the interviewer intends to cover. These questions need to be standardised to reduce the effect of interviewer bias.

  • One-to-one interviews should be carried out in a quiet room away from other people.
  • Group interviews may be appropriate, especially in clinical settings
• Self report techniques - importance of good questions


  Avoid

  • Overuse of jargon
  • Emotive language
  • Leading questions
  • Double-barrelled questions -> two questions in one, but respondents may only agree with one half of the question.
  • Double negatives -> just confusing e.g ‘Are you not unhappy at your job? Agree/Disagree
• correlations

  Illustrates the direction of an association between two or more co-variables. Plotted on a scattergram, with a line of best fit.

  • Positive correlation = as one co-variable increases, so does the other.
  • Negative correlation = as one co-variable increases, the other decreases
  • Zero correlation = no relationship

  Does not establish causality. Useful for preliminary research, if there is a strong positive correlation, it may suggest to do research. Quick and economical carry out. Has a third-variable problem, maybe a third variable involved intervening with the result
• types of data

  Qualitative data - expressed in words, non-numerical.

  • Detailed, doesn’t limit what participants can say, external validity because it’s more meaningful. Hard to analyse.

  Quantitative data - numerical data

  • Opposite of qualitative

  Primary data - collected for the purpose of the investigation by the researcher.

  • Specific, time-relevant, requires effort

  Secondary data - collected by other people previously.

  • Easy accessed, in expensive, doesn’t guarantee quality and accuracy
• measures of central tendency

  Mean - adding up all values and then dividing by the number of values. Easily distorted by extreme values, representative of the data as a whole. -> ideal one we want to use

  Median - arranging the valleys from lowest to highest, identifying the middle number. When there is an even amount, it’s a value in between the two middle ones. Extreme scores do not affect it, less sensitive to the data as a whole.

  Mode - which occurs the most in a data set. Not representative of the data, but very easy to calculate.
• measures of dispersion

  Range - working out the difference between the highest and lowest value.

  • affected by extreme scores, easy to calculate, unrepresentative of the data as a whole

  Standard deviation - that tells us how far the scores deviate from the mean. The larger the value, the more spread out the values are. if the value is small, it shows that participants responded the same way.

  • Precise, but similar to the main, it can be affected by extreme scores.
• presenting quantitative data

  • Using a table
  • Using bar charts - discrete data -> data organised into categories, not continuous data. Conditions of the experiment are on the X axis
  • Using histograms - continuous data, equal intervals of a single category. Y axis = frequency.
  • Using a scattergram - Used for correlations, doesn’t matter which variable is on each axis.