The document discusses different types of sampling techniques used in research. It explains that sampling involves selecting a subset of a larger population to make estimates about the population's characteristics. There are two main types of sampling: probability sampling, which uses random selection, and non-probability sampling, which does not. Some examples of probability and non-probability sampling techniques are provided.

2. A process of selecting units from a population A process of selecting a sample to determine certain characteristics of a population A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth. Concept of sampling

3. Sampling enables researchers to make estimates of some unknown characteristics of the population in question A finite group is called population whereas a non-finite (infinite) group is called universe A census is a investigation of all the individual elements of a population

4. 29 Population Sample A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth. Sampling enables researchers to make estimates of some unknown characteristics of the population in question A finite group is called population whereas a non-finite (infinite) group is called universe A census is a investigation of all the individual elements of a population

5. Get information about large populations Less costs Less field time More accuracy i.e. Can Do A Better Job of Data Collection When it’s impossible to study the whole population Why sampling

6. Classification of Sampling Techniques Probability Sampling Techniques Stratified Sampling Cluster Sampling Simple random Sampling Sampling Techniques Non-probability Sampling Techniques Convenience Sampling Judgment Samples Quota Sampling Snowball Sampling Systematic Sampling

7. Probability Sampling: utilizes some form of random selection. A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample. Non-probability sampling : does not involve random selection

8. Simple random Stratified random Systematic random Cluster/area random Multi-stage random

9. Non-probability Sampling are of following types Convenience Sampling Judgment Sampling Quota Sampling Snow ball Sampling

10. Probability selected = n i /N When population is rather uniform (e.g. school/college students, low-cost houses) Simplest, fastest, cheapest Could be unreliable, why? A T Y W B P G E S C K L G N Q B T G K Population Sample Population not uniform Wrong procedure ?

11. Pick any “element” Use random table

12. Break population into “meaningful” strata and take random sample from each stratum Can be proportionate or disproportionate within strata When: * population is not very uniform (e.g. shoppers, houses) * key sub-groups need to be represented -> more precision * variability within group affects research results 1 4 8 12 3 6 13 2 10 20 15 7 14 11 16 3 7 10 16 Population Sample Stratum 2 = even no. Stratum 1 = odd no.

13. Simple or stratified in nature Systematic in the “picking-up” of element. E.g. every 5 th . visitor, every 10 th . House, every 15 th . minute Steps: * Number the population (1,…,N) * Decide on the sample size, n * Decide on the interval size, k = N/n * Select an integer between 1 and k * Take case for every k th . unit

14. Research involves spatial issues ( e.g. do prices vary according to neighbourhood’s level of crime? ) Sampling involves analysis of geographic units Sampling involves extensive travelling -> try to minimise logistic and resources Steps: * Divide population into “clusters” (localities) * Choose clusters randomly (simple random, stratified, etc.) * Take all cases from each cluster Efficient from administrative perspective

15. Section 5 Section 2 Section 1

16. Convenience Samples Non-probability samples used primarily because they are easy to collect. Judgment Samples Non-probability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study.

17. Quota Samples Non-probability samples in which population subgroups are classified on the basis of researcher judgment. Snowball Samples Non-probability samples in which selection of additional respondents is based on referrals from the initial respondents.

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