Stratified sampling

Stratified sampling
Since the word stratify means arranging in a sequence of grades or ranks. So we think that the stratified sampling can be the selection of samples on the basis of certain criteria when the population is randomly selected. Criteria for selection can be anything like their characteristics, physical location, socio economic status, etc.

Meaning:-
The population is divided into strata and a random sample is taken from each stratum.
Stratification means division of the universe into groups according to geographical, sociological, or economic characteristics. When the population is heterogeneous with respect to the variable or characteristics under study then the technique of stratified sampling is use to obtain more efficient and accurate results.

Stratified sampling is where we begin by grouping elements that share certain characteristics, or dividing the population into several large groups, or clusters. Its purpose is to classify populations into subpopulations or strata based on some supplementary information and then a selection of separate samples from each of the strata.

The two types of stratified sampling are
1. Proportionate stratified
2. Disproportionate stratified

1. Proportional Stratified:
In this type, the strata sample size is made proportional to the strata population size. It is used to get a more representative sample than might be expected under Simple Random Sampling. Reduces sampling errors between strata with respect to the relative numbers selected. This is true when there are homogeneous groups. Population strata must be known in order to draw a proportionate stratified sample.

2. Disproportionate stratified:
It is used to manipulate the number of cases selected in order to improve efficiency of the design. The main interest is to study separate sub-populations represented by the strata rather than on the entire population. Here a varying sampling is used. In disproportional stratified sampling, the sub samples are not proportional to their sizes in the population.
Here is an example showing the difference between proportional and disproportional stratified sampling:
If the population is 75% female and 25% male. And if a researcher wants a sample of size 100 and wants to stratify on the variable called gender.
For proportional stratified sampling, researcher would randomly select 75 females and 25 males from the population.
For disproportional stratified sampling, researcher might randomly select 50 females and 50 males from the population.

Advantages:
Stratification will always achieve greater precision provided that the strata have been chosen so that members of the same stratum are as similar as possible in respect of the characteristic of interest. The bigger the differences between the strata, the greater the gain in precision. For example, if you were interested in Internet usage you might stratify by age, whereas if you were interested in smoking you might stratify by gender or social class.
It is often administratively convenient to stratify a sample. Interviewers can be specifically trained to deal with a particular age-group or ethnic group, or employees in a particular industry. The results from each stratum may be of intrinsic interest and can be analyzed separately.
It ensures better coverage of the population than simple random sampling.

Disadvantages:
Difficulty in identifying appropriate strata.
More complex to organize and analyze results.

References:
http://www.southalabama.edu/coe/bset/johnson/lectures/lec7.htm

http://www.mis.coventry.ac.uk/~nhunt/meths/strati.html

From,
Vipula Khanolkar 07
Aditee Moghe 11