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

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