Research Methods

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  • Created by: Em
  • Created on: 25-05-16 11:53



  • The process of selecting participants from a population of interest
  • Sampling is essential to make sure researchers can minimise extraneous and confounding variables

Random Sampling

  • Every member of the population of research interest has an equal chance of being selected to be part of a sample
  • This method of sampling helps ensure the sample is highly representative, the larger the sample the more likely this is going to occur
  • However this method of sampling can only happen if there is a full list of the target population is available, and this can be hard to obtain
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Stratified Sampling

  • Dividing the population into different sub-groups (strata) 
  • Then selecting a separate sample from each sub-group (stratum) in the same proportions as they occur in the population of interest
  • Examples of stratum/subgroups are socio-cultural factors like
    • Residential area
    • Type of accomodation
    • Income level
    • Age
    • Sex etc
  • This method of sampling gives the researcher the ability to sample from specific groups within populations for comparison purposes, giving the study greater precision
  • However this method of sampling can be time consuming and expensive since it takes up people's time
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Convenience (Opportunity) Sampling

  • The researcher may go to locations known to be frequented by the required participants 
  • The researcher will then select the first individuals they meet who are in the target population and willing to participate 
  • The researcher may do this because the target population is not readily available
  • Convenience/opportunity sampling is quick, easy and inexpensive
  • However this method of sampling often produces a biased sample as researchers may purposely choose participants willing to cooperate and may leave other people and their opinions out of the study
  • Results from these samples can't be generalised to the entire population
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Experimental Design

Independent Groups Design

  • Participants only take part in one condition of the experiment (i.e. two different groups exposed to 2 different variables)
  • Can all be done at once an drop-outs are unlikely 
  • But a large number of participants are needed so the participant variables in the sample can match the population 

Repeated Measures Design 

  • Participants take part in both of the conditions of the experiment (i.e. everyone in one group is exposed to two variables) 
  • The participants are the experimental and the control group so the researcher must consider any order or practice effects
  • Individual participant variables are controlled and fewer participants are required 
  • But order and practice effects may do better (they know what to expect during the experiment) or worse (they know it will be boring or they are tired)
  • Participants may drop out because this experimental design takes a long time
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Experimental Design

Matched Particpants Design

  • Participants are matched in each condition for characteristics that may affect their performance 
  • For example, participants who scored a 3 on a memory test are paired with someone who also got a 3
  • The variable which participants have been matched won't affect the results because the researcher has matched people with similar abilities 
  • However this is time consuming and expensive as lots of testing is done to find an appropriate pair and if one person from a pair drops out, the remaining person can't continue 
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  • The way that participants are placed into the groups for the study (these groups are often experimental and control groups)

Random Allocation

  • A procedure used to place participants in a group so they are as likely to be in one group as the other
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Independent Variable

  • The variable that is changed in order to measure its effects on the dependent variable 

Dependent Variable

  • Used to measure the effects of the independent variable
  • Called the dependent variable because whether it changes or not depends on the effects of the independent variable

Extraneous Variable

  • Any variable (other than the independent variable) which may influence the dependent variable 

Confounding Variable 

  • Any variable (other than the independent variable) that has influenced tha dependent variable
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