Year 1

• Aim

  Statement of what the researcher intends to investigate: the purpose of the study
• Hypothesis


  A precise and testable prediction about the variables in the study (IV and DV or co-variables)
• Types of alternative hypothesis

  • Non-directional (two-tailed) - predicts a difference/correlation but not in a specific direction
  • Directional (one tailed) - predicts a difference/correlation in a specific direction e.g. whether the change is greater or lesser, positive or negative
• How researchers know which type of alternative hypothesis to use

  If prior research suggests the findings will go in a particular direction = directional

  If there is no previous research, or research findings are contradictory = non-directional
• Null hypothesis


  Predicts that the expected effects will not occur i.e. it simply states that there will be no difference or correlation
• All variables, IV, DV and co-variables should be operationalised- clearly defining variables in terms of how they can be measured, this also allows for precise replication
• Independent variable


  What the researcher changes (manipulates)
• Levels of the IV

  Experimental condition (containing the IV) and control condition (baseline, where the IV is or absent/does not receive experimental treatment)
• Dependent variable


  What the researcher measures
• Co-variables


  The variables investigated in a correlation e.g. weight and height (they are not manipulated)
• In an experiment the researcher manipulates the IV and measures the effect on the DV
• All other variables should be controlled, so the researcher can be confident that the result was due to the IV alone
• Extraneous variables


  Any variable that may affect the DV if not controlled
• Confounding variables

  Any extraneous variable that has been 'found' to have impacted the IV
• If all other variables are controlled for, we can establish cause and effect
• Participant variables

  • Individual differences between participants that may affect the DV e.g. age, motivation, personality, intelligence, concentration and gender
• Situational variables

  • Features of the experimental situation/environment that may affect the DV e.g. noise, weather, instructions, temperature and times of day
• Demand characteristics

  Type of extraneous variable. Participants become aware of a study's aims due to cues from the researcher or research situation. They may act in the way that they think is expected and over-perform to please the researcher or deliberately or under-perform to sabotage the results of the study
• Either way, the participants are not showing natural behaviour
• Investigator effects

  When the researcher consciously or unconsciously influences the outcome of the study
• Ways investigator effects can occur

  • Unconscious cues (encouragement, nodding, smiling, frowning, attitude)

  Factors relating to the study's design (participants chosen, materials used, procedure designed, biased interpretation)

  Physical characteristics e.g. gender, which may influence the response
• Randomisation

  The use of chance used wherever possible to reduce the researcher's influence on the design of the investigation. Aims to control investigator effects
• Randomisation


  • Randomly generating words in a recall task

  Randomising the order of conditions for each participant
• Standardisation

  Ensuring that all participants are subject to the same formalised procedures, instructions, environment and experience
• Standardisation


  • Using the same room, same time of day and same verbal instructions
• Lab experiments

  • Highly controlled environment, researcher manipulates the IV and records the effects on the DV, controlling for extraneous variables

  High in internal validity, as extraneous variables are controlled. Can establish cause and effect

  Easy to replicate, as often uses standardised procedures
• Lab experiments weaknesses

  • Low in ecological validity, as participants may not behave naturally as they are in an artificial setting. Prone to demand characteristics

  Often uses tasks which lack mundane realism, as they may be unfamiliar/artificial
• Field experiments

  • IV is manipulated in a more natural, everyday setting (outside of a lab). The researcher records the effect of the IV on the DV

  Conducted in a natural environment, so higher in ecological validity and usually mundane realism

  As participants are usually unaware they are being studied, demand characteristics are unlikely, making results more valid (true)
• Field experiments weaknesses

  • Loss of control of variables, so lower internal validity as extraneous variables are harder to eliminate

  Precise replication not always possible

  Ethical issues due to the fact participants are unaware they are taking part in an experiment. Lack of informed consent
• Natural experiments

  • Researcher takes advantage of a pre-existing IV and records the effect on the DV. The IV is a naturally occurring external event and is not manipulated by the researcher
• Natural experiments allow research to be conducted where it would be unethical for a researcher to manipulate the IV themselves
• Natural experiments allow researchers to study real-life issues and problems that occur in the world. Therefore, high external validity
• Natural experiments weaknesses

  • Difficult to establish cause and effect as the IV is not being directly manipulated or controlled by the researcher

  Can only be used where conditions occur naturally. Many natural events are one offs so there are limited opportunities for research

  Participants not randomly allocated to conditions, the researcher has no control over which participants are placed in each condition as the IV is pre-existing and changes naturally. Therefore, participant variables may have caused the change in the DV acting as a CV
• Quasi experiments

  • The IV is based on a pre-existing difference between people, e.g. LOC, gender, age, anxiety levels etc.

  Comparisons between different types of people can be made as the IV is a difference between people
• Quasi experiments will share the same strengths as laboratory experiments (e.g. replicable and highly controlled)
• Quasi experiments weaknesses

  • Difficult to establish cause and effect as the IV is not being directly manipulated or controlled by the researcher

  Participants not randomly allocated to conditions as the IV is pre-existing. Participant variables may have caused the change in the DV acting as a CV
• Independent measures design

  • Different participants in each condition, so that the participants only take part in 1 condition

  Order effects and demand characteristics less of a problem, pps take part in one single condition

  Same task can be used with both conditions, because they will not be familiar with it. Using a different task may be an extraneous variable
• Independent measures design weaknesses

  • More expensive and time consuming, twice as many pps need to be recruited than in a repeated measures

  Differences between groups may be due to participant variables (e.g. them being different people who might differ in age, IQ or mood) not the IV, which is an extraneous variable
• Random allocation


  Each participant has an equal chance of being in each condition. This can be done by pulling participants' names out of a hat/ using a random name generator
• Random allocation removes researcher bias and distributes the participant variables evenly