Sampling Population: total category of subjects. Sampling: (quantitative) is obtaining a sample and arguing the characteristics of the sample are the same as or at least close to those of the population as a whole Sampling frame: complete list of cases in population from which your sample will be drawn. E Sampling: (qualitative) means to select information-rich cases that will provide insight into research problem Sampling is Different in Quantitative and Qualitative Research Quantitative · Seek to be as representative of population as possible · Samples are normally big Qualitative · Purpose is not to establish a representative sample but to identify those people who provide understanding/knowledge · Samples are normally smaller Quantitative Sampling Obtaining a representative quantitative sample ... · Probability sampling-> truly random and statistically representative, confident you can generalise o Key concept is random selection o Everyone in target population should have equal chance of selection o Needs sampling frame · Non-probability sampling-> if used with care can be representative o Doesn't need sampling frame o Problems of bias · Probability sampling examples: 1. Random 2. Systematic (Quasi-random) 3. Stratified 4. Clustered 5. Multi stage 2. Non-probability sampling examples: 1. Quota (cheaper and don't need sampling frame but you do need to know the characteristics of the population) 2. Convenience (problems of bias, not representative, useful for research contexts 3. How Big Should a Quantitative Sample Be?
4. Larger samples are more accurate at measuring population but depends on how robust sampling strategy is and variability of population. 5. Various ways of calculating desired sample size on basis of various statistical parameters. 6. Analysis considerations can also dictate sample size Qualitative Sampling Qualitative sampling focuses on in depth on relatively small samples- even single cases (n=1)- selected carefully. o Each project sampled differently o Key part is purposefully selecting information rich participants who meet criteria for study o Must describe and justify sampling procedures and decisions fully, outline strengths and weaknesses of the approach o Exercises care not to over generalise from qualitative sampling · Theoretical Sampling · Select participants as study needs, evolve with it. · Collect data -> reflect on data -> decide who else to involve in study · Associated with Glaser and Strauss (1967) grounded theory · Maximum Variation Sampling · Deliberately select heterogeneous sample and observing commonalities in their experiences. · Useful when exploring abstract concepts · Homogenous Sampling · Selecting sample with similar characteristics. · Purpose is to describe some particular sub-group in depth · Snowball or Chain Sampling · An approach for locating information-rich informants, such as 'who knows a lot about' or 'who should I talk to'. · The chain of recommended informants will typically diverge as many possible sources are mentioned and then converge as same names are mentioned again. · Opportunistic Sampling · Fieldwork often involves on the spot decisions regarding sampling to take advantage of new opportunities. · Emergent. · Qualitative Sampling Size · No rules, sample size depends on: 1. What you want to know