Qualitative and quantitative sampling. Who are they Black/Blue/Green/Red Thin/Bold Smiling/Normal/Sad                        

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Presentation transcript:

Qualitative and quantitative sampling

Who are they Black/Blue/Green/Red Thin/Bold Smiling/Normal/Sad                                                                                                                                                                                                                                                                                                                                  

Non-Probability Sampling Haphazard sampling Quota sampling Purposive sampling Snowball sampling Deviant case sampling Sequential sampling Theoretical sampling

  Haphazard / Accidental / Convenience sample                                                                                                                                                                                                                                                                                                                                                    

   Quota sample                                                                                                                                                                                                                                                                                                                                                               

   Purposive sample                                                                                                                                                                                                                                                                                                                               

Snowball sample                                                                                                                                                                                                                                                                                                                                  

Deviant case sampling

Sequential sampling (= purposive + collect cases until marginal utility drops significantly)                                                                                                                                                                                                                                                                                                                                  

Theoretical sampling Based on (grounded) theory –Theory develops from initial research –Cases are selected that are expected to further deepen the theory Eg. Theory developed from data collected during day time  next collect data at night

Probability Sampling Population (delinquents) N Target population (thieves) Sampling frame (known as “thieves” by police) Sample n                                                                                                                                                                                                                                                                                        Sampling ratio

Probability Sampling Population is often (usually) unknown, therefor a population is described with theoretical values (parameters) eg: N( ,  ) Random sample –Equal chance for all elements to figure in the sample Sampling error (deviation from representativeness) Sampling distribution Central limit theorem Other demos : 1, 212

Theoretical sampling distribution Population Sample CASEABCDEFGHIJMean SCORE

Probability sampling techniques Simple Random Sample (SRS) –Random numbers Systematic sampling (danger: cycles or patterns in sampling frame) –Random start –Sampling interval Stratified sampling –Subpopulating (stratification) –SRS from all strata Cluster sampling –Identify clusters and draw SRS from those –Then draw SRS of elements from the selected clusters –Within-Household sample: whom should researcher ask? (always first to pick up the phone or open the door...?) –Probability Proportionate to Size (equal probabilities) Random Digit Dialling A link

Simple random sample

Systematic sample

Stratified sampling

Cluster sampling

How large should a sample be?... It depends on... –population size, characteristics (homogeneous  less / heterogeneous  more) –spread of the data (sd) –Purposes (descriptive? testing? (power)) PopulationSample Sampling ratio % % % % % % % % %

Haphazard sampling =Accidental-, Convenience-, Availability - Cluster sampling Multi stage sampling Quota sampling Random sampling Snowball sampling Stratified sampling Systematic sampling