SOC2002: Sociological Analysis and Research Methods LECTURE 7: Data Collection (3) Sampling (statistical and purposive) Le...
The research process: today… Reporting Topic/Object 1 2 3 4 5 6 LECTURES 5, 6, 7, 9 & 10 Research question Research design...
Data Collection (3):  Overview <ul><li>What is sampling and why do we do it? </li></ul><ul><li>Two types: </li></ul><ul><u...
What is ‘sampling’? <ul><li>A ‘sample’ is </li></ul><ul><ul><li>“ a portion or subset  of a larger group called a populati...
Why sample? <ul><li>Where the population is large it may be impractical/impossible to study all units </li></ul><ul><li>Th...
Sampling rationales <ul><li>May be different for quantitative and qualitative research: </li></ul><ul><ul><li>Different ai...
Statistical sampling <ul><li>Key concepts: </li></ul><ul><ul><li>Representativeness </li></ul></ul><ul><ul><ul><li>A repre...
Steps in statistical sampling (1) <ul><li>Define your population and units of analysis </li></ul><ul><ul><li>What is your ...
Steps in statistical sampling (2) <ul><li>If you have a complete sampling frame,  probabilistic (random) sampling  is best...
Steps in statistical sampling (3) <ul><li>If you do not have a complete sampling frame,  non-probability (non-random) samp...
Steps in statistical sampling (4) <ul><li>Sample size </li></ul><ul><ul><li>How big your sample needs to be depends on you...
Bias in statistical sampling <ul><li>“ A biased sample is one that does not represent the population from which the sample...
Purposive sampling <ul><li>Corpus construction </li></ul><ul><li>Sampling where the population is unknown or unknowable </...
Purposive sampling <ul><li>Key Concepts </li></ul><ul><ul><li>Systematicity </li></ul></ul><ul><ul><ul><li>A systematic se...
Steps in corpus construction (1) <ul><li>Proceed stepwise </li></ul><ul><ul><li>Select, analyse and select again </li></ul...
Steps in corpus construction (2) <ul><li>Overcoming the paradox: </li></ul><ul><ul><li>“ Strata and function variety prece...
Data Collection (3):  Summary <ul><li>What is sampling and why do we do it? </li></ul><ul><li>Two types: </li></ul><ul><ul...
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SOC2002 Lecture 7

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  • HEY MISS BONNIE GREEN I LIKE YOUR LECTURE THANK YOU SO MUCH. PLEASE E-MALED YOUR DETAIL INFORMATION FOR ME BY belayesubalew9@gmail.com SINCE I WANT TO MEET WITH U AND EXCHANGE SOME IDEAS
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  • Transcript of "SOC2002 Lecture 7"

    1. 1. SOC2002: Sociological Analysis and Research Methods LECTURE 7: Data Collection (3) Sampling (statistical and purposive) Lecturer: Bonnie Green [email_address]
    2. 2. The research process: today… Reporting Topic/Object 1 2 3 4 5 6 LECTURES 5, 6, 7, 9 & 10 Research question Research design Data collection Data analysis Interpretation Literature review, and/or field reconnaissance Choosing indicators & Project Planning Ethics Quality
    3. 3. Data Collection (3): Overview <ul><li>What is sampling and why do we do it? </li></ul><ul><li>Two types: </li></ul><ul><ul><li>Statistical </li></ul></ul><ul><ul><li>Purposive </li></ul></ul>
    4. 4. What is ‘sampling’? <ul><li>A ‘sample’ is </li></ul><ul><ul><li>“ a portion or subset of a larger group called a population” (Fink, 1995:1 In May, 2001:93) </li></ul></ul><ul><li>A ‘population’ is </li></ul><ul><ul><li>“ the universe of units from which the sample is to be selected” (Bryman, 2001:85) </li></ul></ul><ul><ul><li>eg. “ all Americans, residents of California during the 1994 earthquake, and all people over 85 years of age” (Fink, 1995:1 In May, 2001:93) </li></ul></ul><ul><li>“ A good sample is a miniature version of the population – just like it, only smaller” (ibid.) </li></ul>
    5. 5. Why sample? <ul><li>Where the population is large it may be impractical/impossible to study all units </li></ul><ul><li>Therefore, “we steer the middle way between enumeration of a population and convenient selection” (Bauer and Aarts, 2000: 20) </li></ul><ul><li>But, we must be able to justify the selection upon which we base our knowledge claims </li></ul><ul><ul><li>i.e. we must have an appropriate sampling rationale </li></ul></ul>
    6. 6. Sampling rationales <ul><li>May be different for quantitative and qualitative research: </li></ul><ul><ul><li>Different aims and goals </li></ul></ul><ul><ul><ul><li>Enumerating v. mapping </li></ul></ul></ul><ul><ul><li>Populations are qualitatively different </li></ul></ul><ul><ul><ul><li>Known v. unknown </li></ul></ul></ul><ul><li>Different procedures </li></ul><ul><ul><li>Statistical (representative) sample </li></ul></ul><ul><ul><li>Purposive sampling (corpus construction) </li></ul></ul><ul><li>Functionally equivalent </li></ul><ul><ul><li>Both are systematic methods for selecting a sample of material “to characterise a whole” (Bauer and Aarts, 2000: 20) </li></ul></ul><ul><li>Two instances in which sampling is not required: </li></ul><ul><ul><li>The case study (n=1) </li></ul></ul><ul><ul><li>The census (n=N) </li></ul></ul>
    7. 7. Statistical sampling <ul><li>Key concepts: </li></ul><ul><ul><li>Representativeness </li></ul></ul><ul><ul><ul><li>A representative sample is one which can be shown to represent the population according to some key characteristics </li></ul></ul></ul><ul><ul><li>Generalisability (aka external validity ) </li></ul></ul><ul><ul><ul><li>Where the research aim is to draw reliable conclusions about a population, your ability to do this depends upon the representativeness of the sample </li></ul></ul></ul><ul><li>NB “Strictly speaking only probability … samples allow a statistical generalization from sample to population” (May, 2001: 93) </li></ul>
    8. 8. Steps in statistical sampling (1) <ul><li>Define your population and units of analysis </li></ul><ul><ul><li>What is your ‘universe’? </li></ul></ul><ul><ul><li>What/who are the individuals you wish to study? (e.g. persons, households, towns, instances, texts) </li></ul></ul><ul><li>Is there a complete ‘list’ – or sampling frame – that covers the entire population? </li></ul><ul><li>Probability v. non-probability sampling </li></ul><ul><li>Sample size </li></ul>
    9. 9. Steps in statistical sampling (2) <ul><li>If you have a complete sampling frame, probabilistic (random) sampling is best indicated </li></ul><ul><ul><li>Probablistic: each unit within the sampling frame has an equal chance of being selected </li></ul></ul><ul><li>Types of probability samples: </li></ul><ul><ul><li>Simple, random sampling </li></ul></ul><ul><ul><li>Systematic sampling </li></ul></ul><ul><ul><li>Stratified random sampling </li></ul></ul><ul><ul><li>Cluster sampling </li></ul></ul>
    10. 10. Steps in statistical sampling (3) <ul><li>If you do not have a complete sampling frame, non-probability (non-random) sampling may be the best you can do </li></ul><ul><ul><li>N.B. “non-random sampling procedures provide only a weak basis for generalisation” (Bouma and Ling, 2004: 115) </li></ul></ul><ul><li>Types of non-probability samples: </li></ul><ul><ul><li>Accidental sampling ( do not confuse this with random sampling! ) </li></ul></ul><ul><ul><li>Accidental quota sampling </li></ul></ul><ul><ul><li>Snowballing </li></ul></ul><ul><ul><li>Systematic matching </li></ul></ul>
    11. 11. Steps in statistical sampling (4) <ul><li>Sample size </li></ul><ul><ul><li>How big your sample needs to be depends on your project and its aims </li></ul></ul><ul><li>Consider </li></ul><ul><ul><li>If you are doing statistical analysis, do the techniques require a certain number of units to achieve significance </li></ul></ul><ul><ul><li>Greater detail and more questions may require more respondents </li></ul></ul><ul><ul><li>A large sample is not intrinsically better than a small one – representativeness is the key </li></ul></ul><ul><li>Rules of thumb for SOC2002 </li></ul><ul><ul><li>See Bouma and Ling (2004: 125-131) </li></ul></ul>
    12. 12. Bias in statistical sampling <ul><li>“ A biased sample is one that does not represent the population from which the sample was drawn” (Bryman, 2001: 86) </li></ul><ul><li>A serious problem for reliability and validity </li></ul><ul><li>Types of bias: </li></ul><ul><ul><li>Sampling error </li></ul></ul><ul><ul><li>Non-coverage (non-sampling) error </li></ul></ul><ul><ul><li>Non-response </li></ul></ul>
    13. 13. Purposive sampling <ul><li>Corpus construction </li></ul><ul><li>Sampling where the population is unknown or unknowable </li></ul><ul><li>Structurally different from statistical sampling though functionally equivalent </li></ul><ul><li>“ corpus construction helps typifying unknown representations, while…representative sampling describes the distribution of already known representations in society” (Bauer and Aarts, 2000: 32) </li></ul>
    14. 14. Purposive sampling <ul><li>Key Concepts </li></ul><ul><ul><li>Systematicity </li></ul></ul><ul><ul><ul><li>A systematic selection – not just what you think is important/relevant </li></ul></ul></ul><ul><ul><li>Comprehensiveness </li></ul></ul><ul><ul><ul><li>As complete a collection as possible </li></ul></ul></ul><ul><ul><li>Internal variability </li></ul></ul><ul><ul><ul><li>Maximise internal variability. A good corpus contains the widest possible range of representations – not a representative number of a few known types </li></ul></ul></ul><ul><li>Paradox </li></ul><ul><ul><li>If the population is unknown/unknowable how do you know what a functionally representative corpus should contain? </li></ul></ul>
    15. 15. Steps in corpus construction (1) <ul><li>Proceed stepwise </li></ul><ul><ul><li>Select, analyse and select again </li></ul></ul><ul><ul><li>Criteria: </li></ul></ul><ul><ul><ul><li>Relevance </li></ul></ul></ul><ul><ul><ul><li>Homogeneity </li></ul></ul></ul><ul><ul><ul><li>Synchronicity </li></ul></ul></ul><ul><li>Aim is to achieve saturation </li></ul>
    16. 16. Steps in corpus construction (2) <ul><li>Overcoming the paradox: </li></ul><ul><ul><li>“ Strata and function variety precedes variety of representations” (Bauer and Aarts, 2000: 33) </li></ul></ul><ul><ul><li>“ Characterising variety of representations has priority over anchoring them in existing categories of people” (ibid.) </li></ul></ul><ul><ul><li>Maximise the variety of representations by extending the range of strata/functions considered” (ibid.) </li></ul></ul><ul><li>In practice: </li></ul><ul><ul><li>Consider conventional categories for stratifying social space </li></ul></ul><ul><ul><li>Investigate the variety of representation in these </li></ul></ul><ul><ul><li>Extend/deepen the range of categories considered, and reinvestigate </li></ul></ul><ul><ul><li>Repeat until saturation is achieved </li></ul></ul>
    17. 17. Data Collection (3): Summary <ul><li>What is sampling and why do we do it? </li></ul><ul><li>Two types: </li></ul><ul><ul><li>Statistical </li></ul></ul><ul><ul><li>Purposive </li></ul></ul>

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