EVERYTHING EXPERIMENTS

• LABORATORY EXPERIMENTS

  • Conducted in a laboratory

  (controlled environment)

  • The IV is manipulated, the DV is measured

  • Participants know they are taking part in an experiment even if they do not know the true aim of the research
• LABORATORY- STRENGTHS

  Most scientific of all experiments, high control over extraneous variables.

  1. Direct control of the IV. The IV can be manipulated & the DV measured.
  2. Both the IV & DV can be operationalised (optimises internal validity).
  3. High internal reliability: Consistency within the procedures. Standardised procedures allow for replication.
• LABORATORY- STRENGTHS

  1. Objective: Empirical research findings - based on fact not interpretation, no room for bias when analysing results.
  2. A cause & effect relationship can be established—the IV had a direct effect on the DV (if extraneous variables controlled).
• FEILD EXPERIMENTS

  • An experiment is conducted in a more natural environment (schools, hospitals,

  etc. - environments relevant to the

  research.

  • The IV is manipulated.

  • Participants do not always know they are taking part in an experiment.
• FEILD- STRENGTHS

  1. Good internal reliability: Consistency within the procedures. Standardised procedures allow for replication, however there is less control over the environment.
  2. Insight into everyday behaviour in real life settings. Environment tends to be more realistic (higher mundane realism), and so have good ecological validity.
• FEILD- STRENGTHS

  1. Less likely to be affected by demand characteristics as participants do not know that they are taking part in research and so are less likely to adapt behaviour accordingly.
  2. A cause & effect relationship can be established- the IV has a direct effect on the DV, - HOWEVER, this can be affected by the lack of control of situational variables, which can become confounding.
• FEILD- WEAKNESSES

  1. Cause & effect relationships are more difficult to established compared to laboratory experiments as there is less control over situational variables.
  2. The research will be more difficult to replicate so the research will have lower external replicate unlike laboratory experiments where exact conditions can be replicated
• QUASI EXPERIMENTS

  • Can be conducted in the lab or in the field.
  • The IV is not manipulated, it is something that occurs naturally
• QUASI- NATURAL

  Those with a naturally occurring IV that has come about with some input from the person (e.g., weight, divorce).
• QUASI- TEST OF DIFFERENCE

  IV is the difference between participants that they are born with (e.g., gender, visual ability).
• QUASI- STRENGTHS

  1. Conducted when ethically or practically an IV cannot be manipulated - the DV can still be measured, and cause & effect can be inferred.
  2. Internal Reliability: Consistency within the procedures. Standardised procedures allow for replication, however there is less control of the environment and participant variables (IV).
• QUASI- EXPERIMENTS

  Insight into naturally occurring phenomenon that would be unethical to manipulate and therefore high in mundane realism and ecological validity.
• QUASI- WEAKNESSES

  1. Although there is a cause & effect relationship established, the results may be low in internal validity as there is limited control of confounding variables such as the environmental & participant variables.
  2. Participants are not randomly allocated into groups and therefore a quasi experiment is not a true experiment.
• REPEATED MEASURES

  • All participants receive all levels of the IV. We then compare the DV of the participants in eachcondition.
  • For example: Each participant does a memory task after drinking caffeine, and one week later a similar memory task without caffeine.
• REPEATED MEASURES- ADVANTAGES

  • Controls for participant variables: participant variables are kept constant between conditions as the same participants take part in all conditions.
  • Fewer participants are needed and is therefore more economical.
• REPEATED MEASURES- DISADVANTAGES

  • Order effects: A participant given the same test on a different occasion may do better due to practice (practice effects).

  • Demand characteristics: As participants are taking part in all conditions, they may guess the aims of the study and change their behaviour.
• REPEATED MEASURES- WAYS TO OVERCOME DISADVANTAGES

  1. Practice effects: use two different equivalent tests.
  2. Order Effects: use counterbalancing A then B and B then A.
  3. Demand characteristics: Deception (participants are not aware of the research aims) or a single blind design (they do not know which condition of the experiment they are receiving).
• COUNTERBALANCING

  1. AB or BA

  • Divide participants into 2 groups.
  • Group 1: each participant does A then B Group 2: each participant does B then A

  2. АВВА

  • All participants take part in each condition twice
  • Trial 1: Condition A
  • Trial 2: Condition B
  • Trial 3: Condition B
  • Trial 4: Condition A
• INDEPENDENT MEASURES

  • Participants are placed in separate (independent groups). Each group only does one level of the IV.
  • Example: To test the effect of caffeine on memory, one group of participants are given coffee, another group are not (IV) both groups are given the same list of words to recall
• INDEPENDENT MEASURES-

  ADVANTAGES

  • No Order Effects: Participant only does one condition, performance less likely to improve due to practise OR get worse due to boredom (boredom effect).

  • Demand Characteristics: Less likely to occur, participants are less likely to guess the aim of the study as they're only taking part in one condition
• INDEPENDENT MEASURES- DISADVANTAGES

  • Participant variables differ between groups which could be a confounding variable unless controlled

  • More participants are needed than with repeated measures design and therefore, is less economical
• INDEPENDENT MEASURES- WAYS OF DEALING WITH DISADVANTAGES

  1. Participant variables: Participants should be randomly allocated to conditions which should help evenly distribute participant variables.
  2. This can be done by putting participants name in a hat and drawing out names so that every other person goes to condition 1 / 2
• MATCHED PAIRS

  • Matched pairs is similar to independent measures, each group is randomly assigned to one level of the IV but the participants are matched on key characteristics (age, gender, intelligence) that may affect the outcomes of the results (DV).
  • Each member of the pair is then randomly allocated into group A or B
  • One group of participants are given coffee, another group are not (IV)
• MATCHED PAIRS- ADVANTAGES

  • Participant Variables: kept more constant between conditions
  • No order effects: participant only participates in one condition
  • Demand characteristics: less of a problem, the participant only participates in one condition
• MATCHED PAIRS- DISADVANTAGES

  • Participant variables can never be perfectly matched in every respect.
  • Matching participants is very time consuming and difficult.
  • More participants are required (because they need to be matched and take part in only one level of the IV) therefore, is less economical than independent and repeated measures design.
• MATCHED PAIRS- WAYS OF DEALING WITH DISADVANTAGES

  1. Restrict the number of variables to match on to make it easier.
  2. Conduct a pilot study to consider key variables that might be important when matching.