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Data Collection and Sampling

Data Collection and Sampling. Primary Data. There are various methods for collecting primary (original) data Eg questionnaire, survey, interview, observation Control over investigation much greater Can more easily avoid “data-driven” research Cost can be prohibitive

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Data Collection and Sampling

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  1. Data Collection and Sampling

  2. Primary Data • There are various methods for collecting primary (original) data • Eg questionnaire, survey, interview, observation • Control over investigation much greater • Can more easily avoid “data-driven” research • Cost can be prohibitive • Pilot studies can be very helpful

  3. Choice of method • Shipman: choice often between sampling and case study • Intensive versus extensive research design • Qualitative versus quantitative data • Interpretivists favour the former; positivists favour the latter • All primary research involves selection • Most methods require sampling

  4. Sampling: general principles • No a priori superiority of any method • Trade-offs: standardisation versus control, generalisability versus flexibility • Shipman: sampling method used dependent on nature of study undertaken • Basis for sample must be transparent • Cost of surveying entire population is prohibitive (e.g. census) • Constraint of feasibility

  5. Sampling: definitions • Population: must be defined • Finite population: e.g. voters • Sampling unit: single potential member of sample • Sampling frame: list of sampling units (NB 1936 US Presidential election) • Sample: drawn from sampling frame

  6. Probability Sampling • Probability of each sampling unit being chosen is known (often equal probability) • Simple random sampling: classic method, regarded as most reliable, least biased • List numbered sampling frame members and select via random number generator • Other probabilistic methods are available

  7. Systematic sampling • List members of sampling frame • Choose first sample member randomly • Then choose every Kth unit, where K=N/n • More convenient than SRS for large popn • Can be a systematic pattern in sample list, leading to bias; e.g. corner shops

  8. Stratified sampling • Divide population into groups of alike members • Strata sizes usually proportionate to popn • Draw randomly from groups • Cost effective • Ensure representativeness • Can lead to excessive number of sub-groups

  9. Cluster Sampling • Select large groups • Select sampling units from clusters randomly • Example: take a city, divide into areas, number areas, select areas randomly, number units within areas, select units randomly • Very cost-effective • Very good if sampling frame poorly defined

  10. Non-probability Sampling • Convenience sampling: select whoever is available • Quota sampling: collect data according to proportions of the population • Selection of subjects absolutely crucial • Requires great skill of interviewers • Snowball sampling: select next subject from previous subject

  11. Non-Probability Sampling • Theoretical sampling: select those most likely to be affected by an issue • Can ignore things which do not fit • Can interpret observations according to the theory • Non-prob sampling cannot claim representativeness as easily but gives much more discretion and control

  12. Response Rates • Another possible trade-off is on response rates • R = 1 - (n-r)/n • Even if initial sample size is appropriate (n’ = n/(1+(n/N)) where n = s2/SE2: see F-N and N: 194-9) response rates can be low • Postal questionnaires: typically 20-40% • Non-response bias

  13. Response Rates • Non-respondents could affect findings • If reason for non-response is related to issue: e.g. reluctance to interview drunks hampers study on alcoholism • Response rate can be improved by cover letter, callbacks, skill of researcher, length of questionnaire, types of question

  14. Conclusions • All types of primary data require selection • If sampling used: various methods possible • Sampling method relates to research tool • Different data collection techniques: questionnaires, interviews, etc. - all to be studied in Research Methods 2 - all have advantages and disadvantages

  15. Secondary Data

  16. Introduction • Primary quantitative data has several advantages, particularly control; qualitative data too • Do not equate primary and qualitative • Today: advantages of secondary data • Searching on electronic data sources including the Internet

  17. Secondary data • Primary/secondary is not = qualitative/quantitative • Qualitative can include secondary data sources such as personal documents, auto/biographies, etc. • Secondary: collected by someone else, e.g. another academic researcher, business, government agency, etc.

  18. Secondary data • Used extensively in social science • Durkheim: suicide • Marx: wages, incomes, prices • Weber: church records • Economists mainly use secondary data

  19. Advantages of Secondary Data • Might be the only data available • Enables longitudinal /time series work • Cheaper (cost and time) and more convenient than primary data • Aids generalisation • Arises from natural settings (nonreactive/unobtrusive data) • Allows replication and checking - validity

  20. Disadvantages of Secondary Data • May be not exactly the data required • Differences in underlying sampling, design, questions asked, method of ascertaining information, etc. • Differences lead to bias • Method of data generation crucial to econometric studies

  21. Electronic Data Sources • Through the library system • Through the internet • Known versus unknown sources • Known sources via library catalogue • Problem of reliability/credibility is common to all electronic sources (more than non-electronic sources)

  22. Electronic Data - Literature • You can search by author or subject across journals, via several static websites/portals: • www.econlit.org/ • www.sosig.ac.uk • www.mimas.ac.uk • www.economics.ltsn.ac.uk • www.esds.ac.uk

  23. Electronic Data: Databases • There are many databases available online • Most have standardised, national data free to download in various formats • Common file format is .csv; but .html and even .xls files also common

  24. OECD: • ONS: • UN: • Penn World Tables: • BEA (US): • Ameristat: • Eurostat: • World Bank: • CIA: • US Statistical Abstract: • See Dissertation homepage/hb

  25. Conclusions • Secondary data has many advantages and disadvantages relative to primary • There is a wide range of secondary data available • Much data is available on the internet • Internet sources must be scrutinised more closely than other sources

  26. Qualitative Data

  27. Introduction • Principals of research design and sampling basically hold for quantitative and qualitative data • However, they apply most easily to quantitative analysis • Qualitative analysis has different foci • Qualitative analysis relatively (to quant; other soc sci) unused in economics

  28. Qualitative techniques: types • Case study • Fieldwork (ethnography) • Observation • Unstructured interviews • Analytic induction/grounded theory • Discourse analysis • Theoretical sampling

  29. Qualitative techniques: principals • Qual often = not quantitative • Can use quant for pattern detection, qual for causal analysis • Or use qual and quant as equals in inference (triangulation) • Quantification often inappropriate

  30. Qualitative techniques: principals • Interpretivism, verstehen • Used to be associated only with using autobiography, letters, personal documents, diaries • Ethnography fairly recent: • Focus on cases rather than generality

  31. Qualitative techniques: principals • Analysis not really a separate stage of research • Design, data collection and analysis all simultaneous and continuous • Open-ended approach: Theory and conclusions formed iteratively • Imagination is crucial • Recognise importance of exceptions • Context is crucial

  32. Fieldwork • Study of people acting in their daily lives • Access a group but remain somewhat detached • Approach with key questions • Teams get range of perspectives • Danger of self-perception and bias

  33. Participant Observation • Adopt perspectives of subject group in order to understand them • Learning language, customs, behaviours, work, leisure, etc. • Hanging around and learning the ropes • Being an outsider can changes subjects’ behaviour • Complete participation - researcher wholly concealed –  contamination and artificiality

  34. Participant Observation • Researchers can go native (internalise group lifestyle) • Covert researchers can be in danger or create detrimental behaviour • Researchers can be “piggy in the middle” • Covert: recording observations can be difficult (e.g. need hidden cameras) • Serious ethical issues with covert observation

  35. Employ analytic induction • Go in with prejudices and theories • Revise theory in light of evidence • Generate new theories until evidence seems to fit • Flexibility accorded but also required by the researcher • Need to be open to disconfirming cases

  36. Grounded theory • Data collected • Develop categories (with inevitable theoretical priors and language) • Categories checked by data • Once categories seem secure and grounded in the evidence, formulate interconnection between categories

  37. Evaluation • Broad range of qualitative techniques • Control over the investigation; less data driven; flexibility much greater than quantitative studies • Logistically difficult: Huge amounts of data produced and problems with manipulation (although Nvivo will help with this) • Must be careful to collect evidence widely to avoid bias • Can be ethical issues re: data collection and reporting

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