Probability Sampling Techniques
Introduction on probability sampling techniques:
Statistical data may be collected by complete enumeration called census inquiry or by partial enumeration called sample inquiry. In the census inquiry, information is collected about each and every item comprising the whole or population in statistical language. In the sample inquiry, information is collected about a small number of items which are representative of the whole so as to form an estimate of the characteristics of the whole. An example of a probability sampling from daily life is, when a house wife examines a few particles of rice under cooking from a big utensil to form an idea about the stage of cooking of the whole lot.
Having problem with slope formula keep reading my articles,
click here to see
Objective of Probability sampling techniques:
The primary objective of probability random sampling techniques is to obtain maximum information about population with minimum effort, and also to set out the limits of accuracy of estimates based on sampling.
Types of Probability Sampling Techniques:
The probability random sampling techniques are as follows:
Simple random sampling
Stratified random sampling
Systematic random sampling
Cluster sampling
Multi stage random sampling
Merits and Demerits of Probability Sampling Techniques:
A probability sampling technique in which each item in the population has a known and equal probability of selection. Every element is selected independently and the sample is drawn by random procedures from a sampling frame.
Merits of simple random sampling:
1. Simple random sampling:
The technique is easily understood and its simplicity for adoption is always recommended.
In this technique each item has got equal representation in the population.
Demerits of simple random sampling:
This technique cannot be used when the items are homogeneous.
This technique lacks representatives when the number of items is very small or extremely high.
2. Stratified random sampling:
A probability technique that uses a two step process to partition the population into sub-populations or strata. Samples are selected from each stratum by a ramdom procedure. The strata should be mutually exclusive and collectively exhaustive.Then the samples are selected from each stratum by simple random sampling method.
Merits of simple random sampling:
This sampling technique is particularly more effective when there are extreme values in the population which can be segregated into seperate data, there by reduce the variability with in strata.
This technique ensures adequate representation to various groups of the population which may be of some interest or importance.
Demerits of simple random sampling:
To divide the population into homogeneous strata, it requires more money, time and expertise.
If proper stratification is not done, the sample will have an effect of bias. If different strata of the population overlap, such a sample will not be a representative one.
3. Systematic sampling technique:
A sampling technique in which only the first unit is selected with the help of random numbers and the rest get selected automatically according to some pre-designed pattern is known as systematic sampling.
Merits of simple random sampling:
The main advantage of systematic sampling is its simplicity of selection, operational convenience and even spread of the sample over population.
Except for population with periodicities, systematice sampling provides an efficient estimate as compared to alternative designs.
Demerits of simple random sampling:
A serious dis advantage of systematic sampling lies its use with populations having unforeseen periodicity which may substantially contribute bias to the estimate of the mean value.
Another disadvantage concerns the draw back of estimating sampling variance of estimators with single sample.
4. Cluster sampling technique:
In random sampling it is presumed that the population has been divided into a finite number of distinct and identifiable units defined as sampling units. The sampling unit into which the population can be divided is called and element of the population. A group of such elements is know as a cluster. When sampling unit is a cluster, the procedure is called Cluster sampling.
Merits of simple random sampling:
The cost of data collection will be much less in cluster sampling as the elements are physically closer than the elements selected by simple random sampling technique.
The sampling efficiency of cluster sampling is likely to decrease with increase in sample size.
Demerits of simple random sampling:
If the elements of a cluster are similar, cluster sampling may be less efficient than simple random sampling.
The costs and problems in statistical analysis are greater with cluster sampling than with simple random sampling.
5. Multistage random sampling:
The combination of two or more probability random techniques is called the multistage random sampling.
by: Omkar Nayak
Two Resorts In One Custom Jewellery Making Carpet Colours Find The Right One Venez Dcouvrir Le Roller En Ligne Importance Of Brochure Stands Smart Itemization Gives Clients The Details They Need What Is A Sms Gateway? Why Custom T-shirts Are So Important? If You Are Thinking Of Moving Overseas You Might Want To Look At Getting Income Insururance Choose The Best Decorators For Beautiful Interiors How To Validate Event Intelligence With Custom Reporting Want To Record Sport Footage? Botox Leeds