Advantage of Stratified Sampling & Disadvantage of Stratified Sampling

       Advantage of Stratified Sampling:

1. Stratification tends to decrease the variances of the sample estimates. This results in smaller bound on the error of estimation. This is particularly true if measurements within strata are homogeneous.
2. By stratification, the cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.
3. When separate estimates for population parameters for each sub-population within an overall population are required, stratification is rewarding.
4. Stratification makes it possible to use different sampling designs in different strata.
5. Stratification is particularly more effective when there are extreme values in the population, which can be segregated into separate strata, thereby reducing the variability within strata.
6. Stratified sampling is most effective in handling heterogeneous population such as data on wages of industrial workers, amount of rain fall and the like.
7. Stratification provides a chance to improve sampling design considerably if the strata could be formed on the basis of natural characteristics.
8. In stratified sampling, confidence intervals may be constructed individually for the parameter of interest in each stratum. This is an added advantage over other methods of sampling.
9. The estimates in various strata may be made with whatever precision is desired simply by adjusting the sample size selected from each stratum.

        Disadvantage of Stratified Sampling:

The major disadvantage of stratified sampling is that it may take more time to select the sampling than would be the case for simple random sampling. More time sis involved because complete frames are necessary within each of the strata and each stratum must sampled.

Read More

What is Sampling and Non-sampling Error. or Write a Short Note on Sampling and Non-sampling Error

       Write a Short Note on Sampling and Non-sampling Error.

Random sampling error occurs because the particular sample selected/accepted is an imperfect representation of the population of interest. The errors involved in the collection, processing and analysis of a data may be broadly classified under the following two heads:

i)                    Sampling Error and

ii)                  Non-sampling Errors.

         i)        Sampling Errors:

Sampling error is the variation between true mean value for the population and the true mean value of the sample. The error, which arises entirely due to sampling and no other reasons can be attributed to cause such error, is called sampling error.

In other words, sampling errors arise due to the fact that only a part of the population (i.e., sample) has been used to estimate population parameters and draw inferences about the population. As such the sampling errors are absent in a complete enumeration survey.

Sampling errors are due to the following reasons:

@     Faulty selection of the sample

@     Substitution

@     Faulty demarcation of sampling units

@     Constant error due to improper choice of the statistics for estimating the population parameters.

       ii)      Non-Sampling Errors:

Non-sampling error can be attributed to sources other than sampling and they may be random or non-random. Non-sampling errors can occur at every stage of the planning or execution of census or sample survey. Non-sampling errors arise from the following factors.

@     Faulty Planning or Definitions

@     Response Errors

o        Response errors may be accidental

o        Prestige bias

o        Self-interest

o        Error due to interviewer

o        Failure of respondent’s memory

@     Non-response Errors

@     Errors in coverage

@     Compiling errors

@     Publication errors.

Read More

What is Sampling Error? Non-sampling Error? Non-response Error? Response Error?

        What is Sampling Error?

Sampling error is the variation between true mean value for the population and the true mean value of the sample.

          Non-sampling Error:

Non-sampling error can be attributed to sources other than sampling and they may be random or non-random.

        Non-response Error:

Non-response error arises when some of the respondent included in the sample does not respond.

        Response Error

Response error arises when respondent give inaccurate answer or their answers are misreported

Read More

What is Simple Random Sampling?

        What is Simple Random Sampling?

If a sample size n is drawn from a population of size N such a way that every possible sample of size n has the equal chance or probability of being selected in the sample called simple random sampling.

Read More

What is Sampling Frame?

       What is Sampling Frame?

A sampling frame is a representation of the elements of the target population. It consist of a least or a set of narrations for identify the target population.

Example:

@     Name, age, sex of under five children with their father/mother.

@     Name, age of Rickshaw pullers.

Read More

Some Examples of Target Population: Examples of Element: Examples of Sampling Unit:

        Some Examples of Target Population:

@     Under five children who suffer from nutritional status.

@     All inject Able Drug Users (IDU)

@     Sex workers of a brothel

@     Fish processing workers of fish hatchery

@     Garments workers of Dhaka city

@     Bus/Truck drivers

@     Rickshaw puller of J.U

        Examples of Element:

@     Each Rickshaw puller of J.U

@     Each under five children of J.U campus.

@      Each sex worker of a brothel

       Examples of Sampling Unit:

@     All rickshaw puller of J.U campus

@      All under five children  of J.U

@     All sex worker of a brothel

Read More

What at is Target Population? Characteristic of Target Population:

W    

          What at is Target Population?    

The collection of elements of objects that posses the information sought by the researcher and about which inference are to be drawn.

                                                                                                     

C       

        Characteristic of Target Population:                         

@     The target population must be defined precisely.

@     Imprecise definition of the target population will result in research that will be misleading.

@     The target population should be defined in terms of element sampling units.

Read More