sampling techniques

• opportunity sampling


  selecting anyone who is willing and available to take part at the time - e.g. approaching people in the street - most common sampling technique and often results in students being used since most research takes place within universities
• opportunity sampling advantage


  quick, convenient and economical - doesn't require level of planning and preparation many other methods require like stratified - e.g. study into stress levels during shopping can simply involve researcher approaching shoppers rather than pre-identifying participants - leads to less delay in research process and less money spent
• opportunity sampling disadvantage


  can be biased and unrepresentative - participants that happen to be available at the time of the study may not represent everyone in the target population - e.g. study conducted in middle of working day, sample may only include people who work reduced hours or are unemployed - researchers may avoid people they don't like the look of (researcher bias) - sample may be biased ad can't be generalised to everyone lowering population validity
• volunteer sampling


  selecting individuals who have put themselves forward to take part in research - sometimes referred to as a self selected sample - researchers may place adverts in newspapers or posters on a university notice board
• volunteer sampling advantages


  Quick, convenient and economical - doesn't require level of planning and preparation that other methods require - e.g. researcher studying memory can advertise for participants and participants should present themselves - less delays in research process and less money spent
• volunteer sampling disadvantages


  can be biased and unrepresentative - volunteers tend to be a certain type of person - e.g. tend to be more confident and motivated than most - sample may be biased (volunteer bias) and findings can't be generalised to everyone lowering population validity
• systematic sampling 


  selecting every nth number of the target population - involves obtaining a list of names of everyone in target population (e.g. school register) organised in some way (alphabetical order) and choosing, for example, every 5th name
• systematic sampling advantage


  avoids researcher bias - researcher has no influence over who is chosen as it simply happens to be in certain positions in a list that are selected - e.g. picking whoever happens to be in every 5th position on an alphabetical list prevents them from only choosing people they think will help support their hypothesis - research is less biased, more objective and less open to abuse or researcher influence
• systematic sampling disadvantages


  not guaranteed to be representative - every nth name on the list could lead to only a certain type of person being selected - e.g. every nth name could be male even though there are just as many females on the list - still element of bias involved as not everyone has an equal chance of being selected - findings can't be generalised to everyone lowering population validity
• random sampling


  everyone in target population has an equal chance of being selected - obtain a list of everyone in target population (school register) - all names assigned number - sample generated through lottery method (paper in a hat)
• random sampling advantage


  avoids researcher bias - researcher has no influence over who is being selected - e.g. picking names out of a hat prevents them from only choosing people they think will support their hypothesis - less biased and more objective and less open to abuse or researcher influence
• random sampling disadvantage


  not guaranteed to be representative - drawing names randomly from a hat could still lead to only a certain type of person being selected - e.g. every name drawn could be male even though there are just as many females in the hat - findings can't be generalised to everyone lowering population validity
• stratified sampling


  selecting a sample that reflects proportions of people in different subgroups according to their frequency within the population - e.g. if 15% of target population are from particular age group then 15% of sample should be from that age group - researcher needs to identify the different subgroups that make up the population - researcher works out proportions needed to make the sample representative - participants from each subgroup are chosen randomly e.g. putting names of all males in one hat and all females in another and selecting right proportion of names from each hat
• stratified sampling advantages


  highly representative - avoids researcher bias and ensures all subgroups are proportionally represented in sample - e.g. picking names from hats prevents researcher from only choosing people they think will help support hypothesis but also focus on subgroups means the process isn't left entirely to chance to represent all types of people - likely to become representative than other techniques
• stratified sampling disadvantage


  time consuming and inconvenient - takes a great deal of planning to identify relevant subgroups and count frequencies within each subgroup prior to starting the random selection process - e.g. none of this level of planning is necessary with opportunity or volunteer sampling - more delays in research process and more money spent