Sampling and its types

Sampling and its types

1. Sampling and its Types By: Prabhleen Arora

2. Population – the population is a group that is studied by researcher. Sampling – the process of selecting a part of population. Sample – the selected part of the population. Sample size – the number of people in the selected sample. Sampling frame- the list of individual or people included in the sample. Sampling technique – it refers to the technique or procedure used to select members of the sample. Introduction

3. S ample

4. Stages in the selection of a sample

5. Need of Sampling Large population is conveniently covered. Used when the data is unlimited. Lower cost Less time consuming. Accuracy of result.

6. Advantages of Sampling Time Saving Economical Accuracy Covers size of population

7. Disadvantages of Sampling Biasedness. Need for specialized knowledge. Selection of true representative sample. Ex- difficult to select the right representative sample. Difficulty in finding sample. Ex- Sometimes population is too small or too heterogeneous .

8. Sampling Types There are mainly two types of Sampling: Probability sample – a method of sampling that uses of random selection so that all units/ cases in the population have an equal probability of being chosen. Non-probability sample – does not involve random selection and methods are not based on the rationale of probability theory .

9. Probability Sampling A sample that selects subjects with a known probability. Every unit in the population has equal chances of being selected as a sample unit. Probability samples are important when one wishes to generalize to the larger population because one knows the responses will fit the characteristics of the population.

10. Simple Random Sampling Assures that each element in the population has an equal chance of being selected. Selection is free from bias. Suppose your college has 500 students (population)and you need to conduct a short survey on the quality of the food served in the cafeteria. You decide that a sample of 70 students (sample) should be sufficient for your purposes. In order to get your sample, you; a. Assign a number from 001 to 500 to each students, b. Use a table of randomly generated numbers (Random Number Tables)

11. Systematic Random Sampling There is a gap, or interval, between each selected unit in the sample. Selection of units is based on sample interval “k” starting from a determined point, where k = N/n Steps: i. Number the units on your frame from 1 to N. ii. determine point/ the random start. iii. Afterwards, every k- th must be drawn until the total sample has been drawn. E.g., If population is 500 and we want a sample of 50, 500/50=10. We will choose every 10 th subject or object from the population.

12. Stratified Random Sampling A population is divided into homogenous, mutually exclusive subgroups, called strata and a sample is selected from each stratum. Goal: To guarantee that all groups in the population are adequately represented. Within stratum - uniformity (homogenous), Between strata – differences (heterogeneous). Can be stratified by any variable that is available e.g. Gender (Male & Female), Education Level (Undergraduate, Graduate & Postgraduate) etc.

13. Cluster Sampling To reduce the cost of sampling a population scattered over a large geographic area. To gather data quickly and cheaply. It helps in winding up with respondents who come from all over the area. Divides the population into groups or clusters. Within cluster- differences (heterogeneous) Between cluster– uniformity (homogenous). Select clusters at random - all units within selected clusters are included in the sample.

14. Non-Probability Sampling Non - probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

15. Types of Non-Probability Sampling Quota Sampling : Quota sampling is a method for selecting survey participants that is a non-probabilistic version of stratified sampling.

16. 2) Covenience Sampling: A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access .

17. 3 ) Purposive sampling : It is also known as judgmental sampling, reflects a group of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units that are to be studied.

18. 4) Snowball Sampling: Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach. These include populations such as drug addicts, homeless people, individuals with AIDS/HIV, prostitutes, and so forth.

19. 5) Self-Selection Sampling: Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organizations, to choose to take part in research on their own accord.