Spread the love

Stratified sampling is a sampling plan in which we divide the population into several non overlapping strata and select a random sample from each stratum in such a way that units within the strata are homogeneous but between strata they are heterogeneous.

Stratum is a group of elements where all the units of elements “within the strata are homogeneous but between strata they are heterogeneous”. Homogeneous means alike or contains same characteristics and heterogeneous means different from each other or contains different characteristics. [Note: ‘Stratum’ is singular form and ‘strata’ is plural form].

Stratified sampling is a probability sampling.

Stratum is a group of elements where all the units of elements “within the strata are homogeneous but between strata they are heterogeneous”. Homogeneous means alike or contains same characteristics and heterogeneous means different from each other or contains different characteristics. [Note: ‘Stratum’ is singular form and ‘strata’ is plural form].

Stratified sampling is a probability sampling.

Stratified sampling |

**Allocation rules of stratified sampling**

- Equal allocation
- Proportional allocation
- Neymanallocation
- Optimum allocation

**Equal Allocation**

In equal allocation we have to divide the sample size(n) by the number of strata.

**Proportional allocation**

In proportional allocation we have divide the sample size(n) by the total smple size(N) and multiply with stratum size(Ni).

**Neyman or optimal allocation**

Neyman allocation is a special case of optimal allocation.

**Advantages of stratified sampling**

- Stratification tends to decrease the variances of the sample estimates. This results is smaller bound on the error of estimation. This is particularly true if measurement within strata are homogenous.
- By stratification, the cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.
- When separate estimates for population parameters for each sub population within an overall population is required , stratification is rewarding.
- Stratification makes it possible to use different sampling designs in different strata.
- Stratification is particularly more effective when there are extremely values in the population, which can be segregated into separate strata. Thereby reducing the variability within strata.
- It is most effective in handling heterogeneous population.
- In stratified sampling, confidence intervals may be constructed individually for the parameter of interest in each stratum.

### **Disadvantages of stratified sampling**

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

- It requires more administrative works as compared with Simple Random Sampling.
- It is sometimes hard to classify each kind of population into clearly distinguished classes.
- It can be tedious and time consuming job to those who are not keen towards handling such data.

Right here is the perfect website for anyone who

really wants to find out about this topic. You know so much its

almost tough to argue with you (not that I personally would

want to…HaHa). You certainly put a brand new spin on a topic which

has been written about for years. Great stuff, just great!