sampling

• -          a target population is a group of people who share a given set if characteristics about which a researcher wishes to draw a conclusion from – for example a sample group could be all a level psychology student.
• -          but a target population is too large to study a subset of the population is investigated and they are referred to as a sample.
• -          the aim of sampling is to select a sample that is representative of the target population as this allows the findings to be generalised to the target population.
• -          if a sample is not representative then we have a bias sample and can only apply the findings to the participants used in the research
• -          a unrepresentative sample could cause a bias if the sample selected way more men meaning that women were underrepresented or that the sample was all from the same sixth form college meaning that other locations / areas of the UK were not represented.
• -          a general principle is that larger samples are more likely to provide more accurate estimates about the nature of the population from which it has been draw and is less likely to be bias.

  -          however, even if you have a large sample, it is important they are representative of all – not just men
• -          its better to have a representative sample over a large sample which is unrepresentative.
• -          when sampling and design a sample size it’s important to find a balance between the needs to represent accurately the target population on the one hand and the practical considerations on another like how can time and money be saved while selecting the sample
• -          in sampling errors are likely to result, and the researcher’s task is to minimise this error
• sampling techniques

  -          opportunity sampling – participants happen to be available at the time of the study is being carried out so they are recruited conveniently my strength is that this is an easy method of recruitment which is time saving and less costly however they are not representative of the whole population hence they lack generalizability and researcher bias is present as they control who they want to select
• sampling techniques

  -          random sampling – when all members of the population have the same equal chances of being selectee d- each member of the population is assigned a number from either a random number table or a random number generator or using the lottery method to randomly choose a partner
• sampling techniques

  -          random sampling –  a strength is there is no researcher bias as the research has no influence on who is picked however this method can be time consuming as you need to have a list of all members of the population and then contacting them takes time there is also the problem of volunteer bias as participants can refuse to take part if picked so end up with an unrepresentative sample
• sampling techniques

  -          systematic sampling -it's a predetermined system used where every nth  member is selected from the sample frame on this numerical selection is applied consistently a strength of this method is that it avoids research bias and is fairly representative of the population a weakness is that it cannot truly be unbiased unless a researcher uses a random number generator and then uses the systematic sample
• sampling techniques

  -          volunteer sampling- involves self-selection whereby the participants offered to take part either in response to an advert or when asked
• sampling techniques

  -          volunteer sampling-  a positive of this method is that it is quick access to willing participants which makes it easy and not time consuming and participants are willing to take part so they're more likely to cooperate in the study however this method is susceptible to volunteer bias as the study may attract a particular profile of person this means that generalizability is then affected. Motivations like money could be driving participation so participants may not take the study seriously which influences the results.
• sampling techniques

  -          stratified sampling – a researcher will identify strata / different subgroups and there proportion in the larger population and then the sample is made by selecting random participants within each strata so they are represented in proportion in the random sample – if 10% of you population were university graduates 10% of your sample would be too
• sampling techniques

  -          stratified sampling – a strength is that it creates a representative sample which we can generalise to the wider population and avoids researcher bias and it randomly selects participants from each strata however the researcher does decide what strata are important to consider meaning there may be some bias in the selection of strata and tis method is time consuming and difficult