Experiments

• Lab experiments

  IV is manipulated by the researcher and is therefore controlled
• Lab experiments strengths

  Control in a laboratory experiment will produce scientific research, which ensures that the variable we are manipulating is really the only thing affecting behaviour
• Lab experiment weaknesses


  Less ecological validity as it is an artificial setting which do not reflect real life, so therefore the behaviour may also be artificial
• Field experiment


  IV is manipulated by the researcher but is not controlled
• Field experiments strengths

  • Can offer a more realistic setting for a study and therefore can have more ecological validity
  • Participants may be unaware they are being studied, and therefore are less likely to be effected by demand characteristics
• Field experiments

  • Control over extraneous variables is more difficult because the situational extraneous variables are difficult to control. This can make them less reliable and less easy to control
  • Lack of control of the environment means researcher cannot be sure it is the IV that effected the DV
  • Participants could be unaware that they are being studied as many field experiments are in natural environments, which could raise ethical issues
• Quasi experiment


  IV is naturally occuring
• Quasi experiment strengths

  • Due to the IV naturally occurring within the individual it may be more reflective of the individual
  • They allow researchers to investigate variables that would be unethical to manipulate
• Quasi experiment weaknesses

  • Control over an extraneous variable is often difficult. As the researcher is not manipulating the IV, they can be less sure that it caused an effect on the DV
  • They are generally hard to replicate and therefore can lack internal and external reliability
• Repeated measures design


  When the same participants participate in each condition
• Repeated measures design strengths

  • Individual differences are unlikely to distort the effect of the IV on the DV, as participants do both levels
  • Uses fewer participants, so may be more time efficient
  • By comparing each person with themselves, the likelihood that the individual differences between the subjects is reduced. This is the best design, therefore for controlling subjects in an experiment
• Repeated measures design weaknesses

  • Order of effects such as practice and fatigue may effect the results, so it requires counterbalancing to control for these.
  • Participants see the experimental task more than once meaning they are more likely to guess the aim, and therefore more likely to suffer from demand characteristics
• Independent measures design 


  When different participants participate in each condition
• Independent measures design strengths

  • Is not affected by the order of effects as each participant has only been tested once
  • Less likely to be effected by demand characteristics as they only have one chance to figure out the IV and act accordingly
• Independent measures design

  • Does not control extraneous variables so individual participants may confound the findings
  • Large samples are often needed in order to be sure that any effect on the IV is caused by the DV and not independent differences, which is difficult to gather if more than one test is being carried out
• Matched pairs design


  This is when different participants participate in each condition, however, each participant in one group is matched on a certain characteristic of a participant in the other group.
• Matched pairs design strengths

  • Different participants are used in each condition so there is no order of effects
  • Participants only see the experimental task taken once, meaning they are less likely to guess the aims of the study and therefore reduces demand characteristics
  • The effects of individual differences are highly controlled, so less chance of participant extraneous variables.
• Matched participant weaknesses

  • Matched participants can be very time-consuming and hard
  • It is almost impossible to match participants on enough variables to be sure there are no possible extraneous variables that might confound the study
• Alternative hypothesis


  Predicts how one variable (the IV) is likely to effect another variable (the DV). An alternative hypothesis predicts the IV will affect the DV
• One-tailed hypothesis


  This is a hypothesis that predicts a certain direction the independent variable will affect the dependent variable. This means you must state the conditions of the IV the measure used for the DV and how it was scored.

  EFFECT not correlation
• Two-tailed hypothesis


  This is a hypothesis that predicts that effect will occur, it just doesn't state the direction of that effect
• Null hypothesis


  This is a hypothesis that predicts no effect on the dependent variable and that any result found is due to error or chance
• Self-selected Sampling


  Participants choose themselves to take part in the study.
• Self-selecting strengths

  • Reduces the amount of time taken to search for necessary participants
  • Participants are more likely to be more willing to take part
  • Can reach a wider variety of participants through emails, posters, advertisements etc. as opposed to opportunity samples, which will only cover a small area.
• Self-selecting weaknesses

  • Sample bias, usually certain individuals volunteer for studies and therefore it may not be representative of the general population
  • Sometimes there will not be enough interest in your studies advert which can lead to a small sample, making it unrepresentative and lack ecological validity
• Opportunity sampling


  Anyone who is available at the time of your research.
• Opportunity sampling strengths

  • It is quick and easy to carry out. This is because it relies on people who are around at the time. This is a strength as it is very easy to replicate and is far more time-efficient than other sampling methods
  • Can help to collect participants with similar characteristics as people who share characteristics tend to segregate in the same area, meaning it will accurately generalise findings to a target population
• Opportunity sampling weaknesses

  • Not representative as the kinds of people available are likely to be limited and therefore similar, this makes the sample difficult to generalise to a wider population
  • Increased chance of researcher bias as they may only approach people who feel they will give them what they want
• Random sampling


  Every member of the population has an equal chance of taking part.
• Random sampling strengths

  • The most representative sampling technique to use as all types of people in the population are equally likely to be chosen
  • Provides unbiased sample as the researcher has no part in deciding who is selected therefore reduces the chance of researcher bias, increasing validity
  • Prevents the researcher from choosing people who may support their hypothesis
• Random weaknesses

  • Time-consuming and hard to ensure that everyone is equally chosen
  • The sample could still be biased for example if it is only girls that have been chosen
  • Some people may refuse to take part.
• Snowball sampling


  People are recruited through friends/colleagues of existing participants
• Snowball strengths

  • Quick and easy to carry out as you only have to find a few participants and they find the rest for you
  • It is a convenient way to find a sample with certain characteristics as friends are likely to be similar and therefore representative of the target population
• Snowball weaknesses

  • Sample bias due to having similar characteristics/culture as they are all friends and therefore less generalisable
  • More chance of social desirability of all participants are friends with each other
  • There is a chance of lack of communication between the previously selected sample and what they tell their acquaintances.
• Qualitative data strengths

  • Richer more detailed data on the person or behaviour
  • Provides insight on the person
• Qualitative data weaknesses

  • Difficult to analyse statistically
  • More time consuming to analyse and compare
• Quantitative data strengths

  • Get larger responses so its easier to generate to a larger population
  • Easy to compare
  • Easy to see trends
• Quantitative data weaknesses

  • People could lie more easily
  • Lacks depth of detail about the person or behaviour
  • Lacks insight for personal experienece
• Ball in the bucket

  • Noisy condition - applause and cheering sound on youtube
  • Silent condition
  • Three attempts in each condition