Collection of Data — Economics STD 11 Commerce — Question

There are two methods of random sampling.

1. Unrestricted Random Sampling.
2. Restricted random sampling.

Unrestricted Random Sampling: It is also called simple random sample. A simple random sample is one in which each and every item of the universe has V an equal chance of selection. Selection of all items is purely on chance. So each and every item of the universe has an equal chance of being selected or rejected.
There are two most popular methods of taking sample from this method
Lottery Method: It is the simplest method of selecting a random sample. Under this method, all items of the population are numbered or nailed and these numbers or names are written on small slips. These slips are put in a bowl and requisite numbers of slips are withdrawn at random. It is to be noted that all slips must be of same size and colour. It is also recommended that a non party should be asked to pick the slips. This method is widely used in quiz contests for selecting a question for the team, government departments make use of this method for allotment of benefits under schemes etc.
Use of Random Number Tables: When the population size is very large, it becomes a cumbersome task to make so many slips and using lottery method. To save time and energy, random number tables are used. A random number table is a simply a table which is created by scrambling the digits 0-9. The most popular random table is Tippet's Random Number Table which consists of 10,400 four digited random numbers, giving in all 41,600 digits (10,400 × 4). For example, if we have to select 100 persons from a group of 800 then we can select first 100 from Triplett's table which are below 800. There are some other random number tables also. These are:

1. Fisher and Yates Table: It is a table of 15,000 random digits written in the form of 1, 500 groups.
2. Rand Corporation: It consists of one million random digits consisting of 2, 00,000 random numbers of 5 digits each.
3. M.G. Kendall and B.B. Smith Table: this table consists of 1,00,000 digits grouped into 25,000 set of 4-digit random number. Stratified Sampling/Mixed Sampling: In this form of sampling, the population is first divided into two or more mutually exclusive segments based on some categories of variables of interest in the research. It is designed to organize the population into homogenous subsets before sampling, then drawing a random sample within each subset. With stratified random sampling the population of N units is divided into subpopulations of units respectively. These subpopulations, called strata, are non-overlapping and together they comprise the whole of the population. When these have been determined, a sample is drawn from each, with a separate draw for each of the different strata. The sample sizes within the strata are denoted by respectively. If a SRS is taken within each stratum, then the whole sampling procedure is described as stratified random sampling. The primary benefit of this method is to ensure that cases from smaller strata of the population are included in sufficient numbers to allow comparison.

Cluster Sampling: In some instances the sampling unit consists of a group or cluster of smaller units that we call elements or subunits. There are two main reasons for the widespread application of cluster sampling. Although the first intention may be to use the elements as sampling units, it is found in many surveys that no reliable list of elements in the population is available and that it would be prohibitively expensive to construct such a list. In many countries there are no complete and updated lists of the people, the houses or the farms in any large geographical region.
Systematic Sampling: This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements - every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000.
Creating such a sample includes three steps:

• Divide number of cases in the population by the desired sample size. In this example, dividing 10,000 by 1,000 gives a value of 10.
• Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10 - say we pick 7.
• Starting with case number chosen in Step 2, take every tenth record (7, 17, 27, etc.).