Understanding Sampling Techniques for Research Studies

SAMPLING TECHNIQUES

SAMPLING TECHNIQUE Techniques for selecting items or elements for the samples from the population. Sampling technique is determined before data is collected. Researcher must select\prepare a sample design which should be reliable and appropriate for his research study.

DEFINITION Population Group of things or persons having one or more common characteristics. Ex. students Sample Representative subgroup of the larger population. Ex. P.G. students Used to estimate something about a population Must be similar to population on characteristics being investigated

STEPS IN A SAMPLING TECHNIQUE 1. TYPE OF UNIVERSE- First step in a sample design to clearly define set of objects, also called as Universe\Population There is two type of universe: A. Finite universe: We can count the total no. of items. Ex. Workers in a factory. B. Infinite universe: We have no idea about the total no. of items. Ex. Listeners of a specific radio programme.

2. SAMPLING UNIT- A sampling unit is a geographical area from where we select our sample, it may be a state, a district, a village, a home and it may be an individual. 3.SIZE OF THE SAMPLE- It refers to the no. of items to be selected from the universe. It should be optimum.

4. PARAMETERS OF INTEREST- Researcher must consider the question of the specific population parameters which are of interested. For ex. Samples having some characteristic in the population. 5. BUDGETARY CONSTRAINT- From a practical point of view, cost consideration have a major impact upon decision relating to not only sample but also the type of the sample.

6. SAMPLING PROCEDURES- Researcher must decide the type of sample he will use and the techniques to be used in selecting the items for the sample.

CHARACTERISTICS OF A GOOD SAMPLE DESIGN It should give a truly representative sample. It should avoid sampling errors. It must be viable in the context of funds available for the research study. It should be in such so that systematic bias can be controlled in a better way.

TYPES OF THE SAMPLING TECHNIQUES A. PROBABILITY SAMPLING B. NON-PROBABILITY SAMPLING

A. PROBABILITY SAMPLING- Also known as random sampling or chance sampling. In this every item of the universe has an equal chance of inclusion in the sample. Random sampling ensures the law of statistical in terms of probability. It gives each element in the population an equal probability of getting into the sample. Probability= 1 possible sample

1. SIMPLE RANDOM SAMPLING TECHNIQUE Simple random sampling- It is a method of probability sampling in which every unit has an equal nonzero chance of being selected. ex. Lottery method Simplest method of probability sampling. It gives each element in the population an equal probability of getting into the sample and all choices are independent of one another.

In this method we can use a random number tables For ex. Tippett, Yates and Fisher tables of random numbers.

2952 6641 3992 9792 7979 5911 3170 5624 4167 9525 1545 1396 7203 5356 1300 2693 2370 7483 3408 2769 3563 6107 6913 7691 0560 5246 1112 9025 6008 8126 If we are interested in taking a sample of 10 units from a population of 5000 units, bearing no. from 3001 to 8000. Than we obtain the following no. 6641, 3992, 7979, 5911, 3170, 5624, 4167, 7203, 5356, 7483.

Advantages Disadvantages Simplest technique for sample collection Low cost It gives each possible sample combination an equal probability Not applicable in some situation. For ex. In estimation of mean height of trees.

2. Complex random sampling techniques Such techniques represent a combination of probability and non- probability sampling procedures in selecting a sample. Also known as Mixed sampling designs.

TYPES OF COMLEX RANDOM SAMPLING 1. SYSTEMATIC SAMPLING 2. STRATIFIED SAMPLING 3. CLUSTER SAMPLING 4. AREA SAMPLING 5. MULTI-STAGE SAMPLING

1. SYSTEMATIC SAMPLING The most practical way of sampling is to select every nth item on a list. For ex. If we need 4% sample, the 1stitem would be selected randomly from the 1sttwenty five and there after every 25thitem would automatically be included in the sample.

Advantages Disadvantages Easier Less costlier Most convenient in case of large population. Inefficient in case of a hidden periodicity in the population. For ex. nth item may be defective

STRATIFIED SAMPLING In this sampling the population is divided into several sub-populations that are individually more homogenous than the total population. The different sub-population are called strata. We are able to get more precise estimates for each stratum .

Keep in mind before this technique 1. How to form strata? 2. How should items be selected from each stratum? 3. How many items be selected from each stratum?

HOW TO FORM STRATA? The strata be formed on the basis of common characteristic(s) of the items to be put in each stratum. One should always careful for characteristics to be estimated are normally used to define the strata.

HOW SHOULD ITEMS BE SELECTED FROM EACH STRATUM? After formation of stratum, a researcher selects the items from each stratum through simple random technique. Systematic sample technique can be used if it is considered more appropriate in certain situation.

HOW MANY ITEMS BE SELECTED FROM EACH STRATUM? Regarding the third question, we should follow the proportional allocation under which the size of the samples from different strata are kept proportional to the size of the strata.

For ex. Population size= 8000 Size of the sample= 30 Size of Strata1(N1)= 4000 Size of strata2(N2)=2400 Size of srata3(N3)= 1600 According proportional allocation selected elements from each stratum= P1(elements from N1)=30 4000\8000= 15 P2(elements from N2)=30 2400\8000= 9 P3(elements from N3)=30 1600\8000=6

3. CLUSTER SAMPLING Used if total area of interest happens to be a big one. Total population is divided into a no. of relatively small sub-populations which are themselves cluster of still smaller units. Then some clusters are randomly selected for inclusion in the overall sample. For ex. Estimation of the proportion of defective parts of machine in an inventory(written list of all objects)

Advantages Dis-advantages Low cost Applicable in case of large population Less precise than random sampling

4. AREA SAMPLING If clusters happen to be some geographical subdivisions. primary sampling unit represents a cluster of units based on geographical area.

5. MULTI-STAGE SAMPLING It is a further development of the cluster sampling. For ex. We want to investigate the working efficiency of nationalized banks in India and we want to take a sample of few banks for this purpose. 1ststage is to select large primary sampling unit, that is state. Then we may select certain districts and interview all banks in the chosen districts. We can also select certain towns of the selected districts

NON-PROBABILITY SAMPLING In this sampling, selection of elements is depend on the researcher choice. Chosen by a researcher according his purpose. Only few particular units of the universe are used for constituting a sample. For ex. Pilot study

WHEN TO USE NON-PROBABILITY SAMPLING It can be used when the researcher aims to do a qualitative, pilot or exploratory study. It can be used when randomization is impossible like when the population is almost limitless When a particular trait exists in the population. It is also useful when the researcher has limited budget, time and workforce.

TYPES OF NON-PROBABILITY SAMPLING 1. Convenience Sampling 2. Judgmental Sampling 3. Quota Sampling 4. Snowball Sampling

CONVENIENCE SAMPLING Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time. Use of students in research Mall intercept interviews people on the street interviews

JUDGEMENT SAMPLING This technique is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. In this sampling investigators are impartial, work without bias and have to some experience, so as to take sound judgement. Ex. Selection of engineers in industrial marketing research Expert witnesses used in court

QUOTA SAMPLING This sampling is viewed as two-stage restricted judgmental sampling. 1. The first stage consists of developing control categories, or quotas, of population elements. 2. In the second stage, sample elements are selected based on convenience or judgment. For ex. To calculate the male and female candidates in a given population.

SNOWBALL SAMPLING In snowball sampling, an initial group of respondents is selected, usually at random. After being interviewed, these respondents are asked to identify others who belong to the target population of interest. Subsequent respondents are selected based on the referrals.

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