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Sampling

Sampling. We select a sample from the population; change x, measure y. We want to know something about a population. Sampling terms. Population – the universe of units from which the sample is to be selected. For some populations, not all units will be accessible

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Sampling

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  1. Sampling

  2. We select a sample from the population; change x, measure y We want to know something about a population

  3. Sampling terms • Population – the universe of units from which the sample is to be selected. For some populations, not all units will be accessible • Sampling frame – all the units in the population from which the sample will be selected • Sample – the segment of the population that is selected for investigation • Representative sample – a sample that reflects the population accurately so it is a microcosm of the population

  4. Probability sampling • A sample that has been selected using random selection so that each unit in the population has a known chance of being selected • Is assumed to be a representative sample • You can generalise: make valid inferences from the sample to the population

  5. Non-probability sampling • A sample that has not been selected using a random selection method. This implies that some units in the population are more likely to be selected than others. • Inferences from the sample to the population [=generalisations] are less valid

  6. Non-response • A source of non-sampling error that is particularly likely to happen when individuals are being sampled. It occurs whenever some members of the sample refuse to cooperate, cannot be contacted or for some reason do not supply the required data

  7. Types of probability sample • Random – each member of the population has an equal probability of inclusion - the best form of sampling • Stratified random – may want a sample to exhibit proportional representation from certain categories (eg gender, age) – so randomly sample from those categories egveg • Multi-stage cluster – good for ‘national’ research. Rather than sample students from any uni in UK, you group unis by standard region and randomly sample say 2 regions. Then say take 5 unis from each region and 500 students from each uni, so ask 500 students from each of 5 unis from each of 2 regions. egv

  8. Qualities of probability samples • Possible to generalise findings - sample data represents population data. If you want to know about children, then sample from all World’s children • Absolute size of sample is more important than relative size • Kind of analysis – will affect best sample size • Non-response will affect results • Heterogeneity of population will affect results • Time-consuming and potentially expensive

  9. Types of non-probability samples • Convenience sampling – A sample that is available to the researcher by virtue of its accessibility, time, cost • Snowball sampling – Type of convenience sample – make initial contact with small group of relevant people, use them to establish contact with others • Quota sampling – stratify by categories of interest – eg gender, ethnicity, age. Then select people who fit those categories, usually by convenience

  10. Qualities of non-prob samples • Limits to generalisability - so samples may not be representative of population • Sometimes may be the only way to get participants (may not know full extent of population eg studies on hackers) • Legitimate way of carrying out preliminary analysis before doing larger [probabilistic] study • More often used in qualitative research than quantitative research

  11. Sample size depends on • Sampling and non-sampling error • How precise we want final estimates to be • Precision can be specified by confidence level or significance level, eg • 95% confidence is 5% significance • 99% confidence is 1% significance • We later calculate the achieved significance probability = p-value

  12. Sampling error • Is the difference between • observing a sample and • observing the whole population from which the sample is selected • Measured as margin of error • expressed as +/- x% • Reducing sampling error: • Increase sample size n towards population size N • Improve sampling method; random sampling best

  13. Non-sampling error • Is due to non-response or poor design, eg choice of sampling frame, data collection [question wording, interviewing style] • We aim for probabilistic sampling, but obtaining access to the entire population can be impossible, eg all criminals, everyone who eats X • Busy people are likely to be non-responders, but these might provide the best information

  14. Overview • Probability sampling • Random • Stratified random • Multi-stage cluster • Non-probability sampling • Convenience • Snowball • Quota • Sample size

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