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Sampling methodologies in research mrhod
- 2. 2 Learning objectives At the end of the session the participant/student will be able to: Differentiate source population, study population and sample population Calculate sample size for the proposed study Apply appropriate sampling techniques for the selection of study units
- 3. Population • In research, measurements are taken from few people and estimates are derived from these measurements. • All kinds of errors prior, during and after the study can be responsible for bias in the final results. • This bias can be caused by measurement errors, as well as through poorly chosen source and study populations. 3
- 4. Population cont.. • Bias can also be introduced during the sampling procedure. • The generalizability of the results could be limited by these types of bias. 4
- 5. Target population • Refers to the entire group of individuals or objects to which researchers are interested to generalize the conclusions. • But, because of practicalities, entire target population often cannot be studied. • Also known as the theoretical population. 5
- 6. Study population • This population is a subset of the target/source population and is also known as the accessible population. • It is from this accessible population that researchers draw their samples. • E.g Female patients who are older than 50 years admitted with a diagnosis of diabetes mellitus. 6
- 7. Sample population • Is a population selected and included in the study. • Samples are subsets of study populations used in research because often not every member of study population can be measured. • However, the results drawn from the investigation of the sample are interpreted and applied directly to the study population. 7
- 8. 8 Sampling methods • Sampling is a process of choosing a section of the population for study • The conclusions drawn from the study are often based on generalizing the results observed in the sample to the entire population from which the sample was drawn • The accuracy of the conclusions will depend on how representative the sample is for the target population
- 9. 9 Why sampling? • There are several reasons why samples are chosen for a study, rather than studying the entire population • A researcher wants to minimize the costs of – Data collection – processing and – reporting on the results
- 10. 10 How to do sampling • Sample should be representative of the population • This requires knowledge of the variables and their distribution in the population • A representative sample has all the important characteristics of the population from which it is drawn
- 11. Sampling methods • There are two types of sampling methods: A. Probability Sampling methods B. Non-probability methods 11
- 12. Probability sampling methods • Points to be considered – Heterogeneity of the population – Area coverage – Frame availability – Analysis to be performed 12
- 13. Probability sampling methods • Simple random sampling • Systematic random sampling • Stratified Random sampling • Cluster random sampling • Stratified-cluster sampling • Multistage random sampling 13
- 14. Simple random sampling • Each individual in the population should have an equal chance to be selected • Sampling frame is necessary • Select the required number of study units using lottery method (for small population) or a table of random numbers (for large population) 14
- 16. Stratified random sampling • The total population is divided into smaller groups (strata) to complete the sampling process. • The strata is formed based on some common characteristics in the population. • After dividing the population into strata, randomly select the sample. 16
- 17. Stratified random sampling cont… 17
- 18. Stratified random sampling cont.. Types of allocation in stratified sampling 1) Proportional allocation – if the same sampling fraction is used for each stratum 2) Non-proportional allocation – if a different sampling fraction is used for each stratum or - if the strata are unequal in size and a fixed number of units is selected from each stratum 18
- 19. Stratified Random sampling cont… 19
- 20. Cluster sampling • Cluster (group of population elements) constitutes the sampling unit, instead of a single element of the population. • The main reason for cluster sampling is cost efficiency 20
- 22. Cluster sampling cont.. Simple one-stage cluster sampling • List all the clusters in the population • From the list, select the clusters – usually by simple random sampling • All units (elements) in the sampled clusters are selected. 22
- 23. Cluster sampling cont.. Simple two-stage cluster sample • List all the clusters in the population. • First, select the clusters. • The units (elements) in the selected clusters of the first-stage are then sampled in the second-stage. 23
- 24. Cluster sampling cont.. Multi-stage sampling • When sampling is done in more than one stage. • In practice, clusters are also stratified. 24
- 26. Non-probability sampling methods • Convenience sampling • Purposive sampling • Snowball sampling • Quota sampling • Extreme or deviant case sampling • homogenous sampling • maximum variation sampling (heterogeneous sampling) 26
- 27. Non-probability sampling methods cont.. Convenience sampling For convenience sake the study units that happen to be available at the time of data collection are selected 27
- 28. Non-probability sampling methods cont.. Quota sampling • Ensures that a certain number of sample units from different categories with specific characteristics are represented • The investigator interviews as many people in each category of study unit as he can find until he has filled his quota 28
- 29. Non-probability sampling methods cont.. Purposive sampling • Involves selection of the most productive sample to answer a research question • Ongoing interpretation of data will indicate who should be approached, including identification of “missing” voices. 29
- 30. Non-probability sampling methods cont.. Snowball or chain sampling: • Mainly applied when researcher is not familiar with the research area • Is used when the desired sample characteristic is rare. • It may be extremely difficult or cost prohibitive to locate respondents in these situations. 30
- 31. Non-probability sampling methods cont.. • Snowball sampling relies on referrals from initial subjects to generate additional subjects. • It introduces bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population 31
- 32. Non-probability sampling methods cont.. Extreme or deviant case sampling Useful to test emerging theories by learning from highly unusual manifestations and/or atypical situations 32
- 33. Non-probability sampling methods cont... Homogenous sampling • Useful to identify most common phenomenon • Focus on similar type of respondents 33
- 35. Sample size determination and Types of data Yordanos S.
- 36. Sample size • In general it is much better to increase the accuracy of data collection than to increase sample size after a certain point • Also try to get a representative sample rather than to get a very large sample • Nowadays computers have made the calculation of sample size easier 36
- 37. Rules of thumb 1. For smaller samples (N ‹ 100), there is little point in sampling. Survey the entire population. 2. If the population size is around 500, 50% should be sampled. 3. If the population size is around 1500, 20% should be sampled. 4. At least 20 respondents for each independent variable should be considered 37
- 38. 38 Single population proportion formula Where: n - is the sample size Zα/2 -is the value of Z from standard normal curve at α/2 For α= 0.05 the Z0.025=1:96 For α =0:1 the Z0.05 = 1:65 and so on. p= Best estimate of population proportion (When using the formula, if you let p* = 0.5, this produces the maximum possible value for n for any given E and α) E=Margin of error or maximum acceptable difference 2 * * 2 2 ) 1 ( E p p Z n
- 39. Single population proportion cont.. .. Margin of error (E) • The margin of error (E) measures the precision of the estimate • Small value of E indicates high precision • It lies in the interval (0%, 5%] • For p close to 50%, E is assumed to be close to 5% • For smaller value of p, E is assumed to be lower than 5% 39
- 41. Single population proportion cont.. Example: • We wish to estimate the proportion of males in ‘Country X’ who smoke. • What sample size do we require to achieve a 95% confidence interval of width ± 5% ( that is to be within 5% of the true value) ? In a study some years ago that found approximately 30% were smokers. 41
- 42. Single population proportion p=0.3 E=.05 (1-α)=0.95 Z(1-α)= 1.96 for 95% confidence level Then n = (1.96)2(0.3)(0.7)/(0.05)2 = 322.7 ≈ 323 42
- 43. Single population proportion cont.. Design Effect • It is a correction of bias in the variance introduced in the sampling design, by selecting subjects due to the use of clusters. • The design effect is 1 (i.e., no design effect) when taking a simple random sample. • The design effect varies using cluster sampling • It is usually estimated that the design effect is 2 in multistage sampling having cluster sampling. 43
- 44. Group Assignment with presentation 1.What are data? 2.What are the types of data?