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

Published byStephen McLaughlin Modified over 9 years ago

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Presentation on theme: "Qualitative and quantitative sampling. Who are they Black/Blue/Green/Red Thin/Bold Smiling/Normal/Sad                        "— Presentation transcript:

1 Qualitative and quantitative sampling

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

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

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

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

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

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

8 Deviant case sampling

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

10 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

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

12 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

13 Theoretical sampling distribution Population Sample CASEABCDEFGHIJMean SCORE01234567894.5

14 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

15 Simple random sample 12 3 45678910 11 1213141516 17 181920 212223 24 252627282930 3132 33 343536373839 40 41424344 4546 47484950 51525354555657585960 61626364656667686970 71 72737475 76 77787980 8182838485868788 89 90 919293949596979899100 101102103104105106107108109110 111112113114115116117118119120 121122123 124 125126127 128 129130 131132133134135136137138139140 141142143144145146147148149 150 151152153154155156157158159160 161 162 163164165166 167 168169170 171172173174175176177178179180 181182183184 185 186187188189190 191192 193 194195196197 198199 200

16 Systematic sample 123456789 10 111213141516171819 20 212223242526272829 30 313233343536373839 40 414243444546474849 50 515253545556575859 60 616263646566676869 70 717273747576777879 80 818283848586878889 90 919293949596979899 100 101102103104105106107108109 110 111112113114115116117118119 120 121122123124125126127128129 130 131132133134135136137138139 140 141142143144145146147148149 150 151152153154155156157158159 160 161162163164165166167168169 170 171172173174175176177178179 180 181182183184185186187188189 190 191192193194195196197198199 200

17 Stratified sampling

18 Cluster sampling

19 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 20017185.5% 50035270.4% 100054354.3% 200074537.2% 500096019.2% 10000106110.6% 2000011215.6% 5000011602.3% 10000011731.2%

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

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