# Start Searching the Answers

The Internet has many places to ask questions about anything imaginable and find past answers on almost everything.

The Question & Answer (Q&A) Knowledge Managenet

The Internet has many places to ask questions about anything imaginable and find past answers on almost everything.

Table of Contents

- What is the formula for stratified sampling?
- How do you determine sample size in stratified sampling?
- How do you calculate stratified mean?
- What is the major difference between stratified sampling and quota sampling?
- Why is stratified sampling better than cluster?
- What is the presence of bias of probability sampling?
- What is the difference between stratified and systematic sampling?

For example, if the researcher wanted a sample of 50,000 graduates using age range, the proportionate stratified random sample will be obtained using this formula: (sample size/population size) x stratum size.

The sample size for each strata (layer) is proportional to the size of the layer: Sample size of the strata = size of entire sample / population size * layer size.

The stratified sample mean, st, is given by the weighted average of the sample means of the characteristic of interest from each stratum, and the stratified sample variance is simply the sum of the variances within each stratum.

The main difference between stratified sampling and quota sampling is that stratified sampling would select the students using a probability sampling method such as simple random sampling or systematic sampling. In quota sampling, no such technique is used.

The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called “strata”).

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields. Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity.

In systematic sampling, the list of elements is “counted off”. That is, every kth element is taken. Stratified sampling also divides the population into groups called strata. However, this time it is by some characteristic, not geographically.