Stratified sampling: Difference between revisions

Revision as of 05:30, 16 February 2025

A method of sampling that involves dividing a population into subgroups

Stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratified sampling is a method of probability sampling (e.g., simple random sampling) in which the population is divided into different "strata" and a sample is taken from each stratum.

Overview

Stratified sampling involves dividing the population into homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded.

Process

The process of stratified sampling involves several steps:

Identify the strata: Determine the characteristics that define the strata. These characteristics should be relevant to the research question.
Divide the population: Partition the population into strata based on the identified characteristics.
Sample from each stratum: Use a probability sampling method to select a sample from each stratum. This can be done using simple random sampling or systematic sampling.
Combine the samples: Combine the samples from all strata to form the complete stratified sample.

Advantages

Stratified sampling has several advantages:

Increased precision: By ensuring that each subgroup is represented, stratified sampling can increase the precision of the overall sample.
Reduced variability: Stratified sampling can reduce the variability of the sample estimates.
Ensures representation: It ensures that all subgroups of interest are represented in the sample.

Disadvantages

Despite its advantages, stratified sampling also has some disadvantages:

Complexity: The process of identifying strata and dividing the population can be complex and time-consuming.
Requires detailed information: Detailed information about the population is required to effectively stratify it.

Applications

Stratified sampling is widely used in various fields such as:

Market research: To ensure that different segments of the market are represented.
Public health: To study different demographic groups within a population.
Education: To assess the performance of different student groups.

Related pages

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-'''Stratified sampling''' is a method of [[sampling (statistics)|sampling]] from a [[population (statistics)|population]]. In statistical surveys, when subpopulations within an overall population vary, it is advantageous to sample each subpopulation ([[stratum]]) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded.
+{{Short description|A method of sampling that involves dividing a population into subgroups}}
-== Overview ==
+[[File:Stratified_sampling.PNG|thumb|right|Diagram illustrating stratified sampling]]
-Stratified sampling strategies can be divided into two groups: proportionate and disproportionate. Whether to use proportionate or disproportionate stratification depends on the researcher's knowledge about the population of interest and the goals of the research.
-=== Proportionate stratification ===
+'''Stratified sampling''' is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratified sampling is a method of probability sampling (e.g., simple random sampling) in which the population is divided into different "strata" and a sample is taken from each stratum.
-In proportionate stratification, the size of the sample from each stratum is proportional to the size of the stratum. For example, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x1 = m/X males, x2 = f/X females) should reflect this ratio.
-=== Disproportionate stratification ===
+==Overview==
-In disproportionate stratification, the size of the sample from each stratum is not proportional to the size of the stratum. This type of stratification is used when the researcher wants to highlight a subgroup within the population. This may be the best strategy when a subgroup is small and the research requires a larger sample from that group than would be needed if the sample were drawn proportionately.
+Stratified sampling involves dividing the population into homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded.
-== Advantages and disadvantages ==
+==Process==
-Stratified sampling has several advantages over simple random sampling. For example, using stratified sampling, it may be possible to reduce the sample size required to achieve a given precision. Or it may be possible to increase the precision with the same sample size.
+The process of stratified sampling involves several steps:
-However, stratified sampling also has disadvantages. It can be complex to decide how to stratify a population and what stratification to use. It can also be difficult to manage the process of drawing a stratified sample.
+# '''Identify the strata''': Determine the characteristics that define the strata. These characteristics should be relevant to the research question.
+# '''Divide the population''': Partition the population into strata based on the identified characteristics.
+# '''Sample from each stratum''': Use a probability sampling method to select a sample from each stratum. This can be done using simple random sampling or systematic sampling.
+# '''Combine the samples''': Combine the samples from all strata to form the complete stratified sample.
-== See also ==
+==Advantages==
+Stratified sampling has several advantages:
+* '''Increased precision''': By ensuring that each subgroup is represented, stratified sampling can increase the precision of the overall sample.
+* '''Reduced variability''': Stratified sampling can reduce the variability of the sample estimates.
+* '''Ensures representation''': It ensures that all subgroups of interest are represented in the sample.
+==Disadvantages==
+Despite its advantages, stratified sampling also has some disadvantages:
+* '''Complexity''': The process of identifying strata and dividing the population can be complex and time-consuming.
+* '''Requires detailed information''': Detailed information about the population is required to effectively stratify it.
+==Applications==
+Stratified sampling is widely used in various fields such as:
+* '''Market research''': To ensure that different segments of the market are represented.
+* '''Public health''': To study different demographic groups within a population.
+* '''Education''': To assess the performance of different student groups.
+==Related pages==
+* [[Simple random sampling]]
+* [[Systematic sampling]]
 * [[Cluster sampling]]
-* [[Quota sampling]]
+* [[Sampling (statistics)]]
-* [[Simple random sample]]
-* [[Systematic sampling]]
 [[Category:Sampling techniques]]
-[[Category:Design of experiments]]
-[[Category:Survey methodology]]
-{{stub}}
-{{dictionary-stub1}}