Research Methods

• Independent variable 


  the variable that manipulates a variable to see its cause and effect of the DV
• Dependent variable


  The variable the researcher measures
• Operationalisation 


  When variables must be clearly specified usually the IV and DV.
• Why is operationalisation important?

  It is important to ensure the research is objective. This would mean researchers do not need to subjectively interpret how they will manipulate the IV or how they will measure the DV.
• The experiment method 


  In an experiment, a researcher will manipulate a variable such as the amount of caffeine participants consume. They do this to measure what effect this has on another variable such as reaction time. The variable the researcher manipulates is called the independent variable and the variable the research measures is called the dependent variable.
• Extraneous variables


  refer to variables other than the IV that may affect the dependent variable.
• Order effects

  refers to differences obtained in the DV that result from the order of the experimental conditions. For example, if participants are repeating the same task in different conditions, they may be better in the second condition due to practise. Also, they may be fatigued or bored resulting in worse performance.
• Participant variables


  participant variables refer to differences obtained in the DV that result from differences in the characteristics of participants between conditions. This could include age, IQ, gender..
• investigator effects 


  Refers to differences obtained in the DV that result from differences in the characteristics or behaviours of the researchers e.g. their age, gender, instructions provided
• situational variables


  refer to differences obtained in the DV that result from differences in the environment between conditions e.g. noise, light, weather..
• Task variables 


  refer to differences obtained in the DV that result from the differences in the nature or difficulty of the tasks between conditions
• Confounding variables


  refer to when extraneous variables are not controlled and are left to affect the DV. decreasing the validity of the findings.
• Counterbalancing - why


  counterbalancing controls the impact of order effects such as practise, fatigue or boredom when participants complete each condition of the IV.
• Counterbalancing - how


  Counterbalancing is achieved by splitting participants into two groups. One half complete condition A first and then condition B. The other half complete condition B first then condition A.
• counterbalancing - limitation


  does not eliminate order effects because participants still take part in each condition and therefore order effects will exist in each group. in other words counterbalancing allows order effects to be distributed evenly across conditions.
• Random allocation - why

  involves randomly assigning participants into conditions to remove researcher bias in determining which participants are allocated to which condition. This means that each participant has an equal chance of being placed into each condition. This helps reduce participant variables from impacting DV.
• random allocation - how (20 participants)

  assign participants from a number of 1 - 20. separate these pieces of paper and place them into a hat. shuffle, without looking pull one out and assign them to condition A. The next number pulled will be assigned to condition B, and so one till theres no more numbers left.
• standardisation - why


  involves having standardised procedures so that everything is kept the same for participants across conditions. Ensures that all participants have the same experience and in doing so helps to prevent situational, investigator and task variables from impacting the DV.
• standardisation - how


  providing the participants with the same instructions

  asking the participants the same questions in the same order.

  keeping the environment the same e.g. the noise

  keeping the task the same or equal difficulty e.g. same number of words to remember
• Randomisation - why

  used in the presentation of trials in an experiment to avoid any predictable order. It therefore helps to reduces task variables from impacting the DV.
• Randomisation - How


  for example, all on the shapes to be presented could be printed on separate pieces of paper and placed into a hat. Without looking papers will be pulled out of the hat one at a time and the order in which they were pulled out would be written down. this order is then used to determine the order in which participants will be exposed to the shapes in the experiment.
• IGD - independent groups design

  Different participants take part in each condition of the experiment. Independent groups designs are therefore used to compare one group against another e.g. to see whether drug therapy or talking therapy is more effective for treating depression.
• repeated measures design


  Each participant takes part in all conditions of the experiment. Repeated measures designs are therefore used when comparing participants’ before and after performance e.g. to see whether students’ test scores differ before and after a revision session
• matched pairs design


  Different but similar participants take part in each condition of the experiment. 1. Participants are matched on variables that could impact the DV e.g. age. 2. One matched pair would have their names written on individual slips of paper and placed into a hat. 3. The first name pulled out will be assigned to condition A while the remaining name of the pair will be allocated to condition B. 4. This random allocation is repeated for all matched pairs.
• What is a strength of independent groups designs and matched pairs designs?

  No risk of order effects
• Why is the absence of order effects a strength?

  It prevents impact on the dependent variable
• What do practice effects and boredom/fatigue affect?

  The dependent variable's validity
• What is a limitation of repeated measures designs?

  Risk of order effects
• Why do repeated measures designs have a risk of order effects?

  Participants take part in all conditions
• How do order effects impact the dependent variable?

  They can cause practice effects or fatigue
• What is a strength of independent groups designs regarding task variables?

  Higher control over task variables
• Why is there higher control over task variables in independent groups designs?

  Participants only take part in one condition
• How does using the same tasks/materials affect the dependent variable?

  It prevents impact from task difficulty
• What is a limitation of repeated measures designs regarding task variables?

  Less control over task variables
• Why do researchers need to use different tasks/materials in repeated measures designs?

  To reduce practice effects
• How can differences in tasks impact the dependent variable?

  They can affect the nature or difficulty
• What is a strength of independent groups designs regarding demand characteristics?

  Reduced risk of demand characteristics
• Why is there a reduced risk of demand characteristics in independent groups designs?

  Participants only know their condition
• How does reduced demand characteristics affect participant behavior?

  Less likely to guess research aims
• What is a limitation of repeated measures designs regarding demand characteristics?

  Increased risk of demand characteristics