1. Lecture 6: Primary Data Collection and Sampling
Research Methods I
Lecture 6: Primary Data Collection and Sampling

2. Primary Data
There are various methods for collecting primary (original) data
For example: 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