Sampling Methods in Research: A Comprehensive Guide
UNIT 5: SAMPLING – CONCEPT AND CLASSIFICATION
Population and Sample
Population refers to the entire group you want to gather information about (e.g., individuals, families, households). A sample is a subset of the population selected to represent the whole. Information gathered from the sample is used to make inferences about the entire population. The process of selecting a sample is called sampling and depends on the chosen research technique.
A sample must be representative, meaning the attributes of the population are proportionally reflected in the sample. This representation allows for generalizability, where conclusions drawn from the sample can be applied to the population. The sampling procedure should ensure that the sample accurately reflects the population’s characteristics.
Sample Size
Determining the appropriate sample size depends on several factors:
- Time and Resources: Available time and resources significantly influence the final sample size.
- Sampling Mode:
- Probability Sampling: Requires a larger sample size.
- Non-Probability Sampling: Typically uses a smaller sample size.
- Data Diversity: When applying multivariate statistical techniques, a larger sample size helps reduce sampling errors and ensures sufficient data for analysis.
- Population Variance: A more heterogeneous population with greater variance requires a larger sample size to capture its diversity.
- Margin of Error: In probability sampling, a larger sample size generally leads to a smaller margin of error, increasing the accuracy of estimations.
- Confidence Level: This refers to the certainty that the sample accurately reflects the population. Common confidence levels are 68%, 95.5%, and 99.7%. The most frequently used level is 95.5%.
Formula for Sample Size Calculation:
When the population size is greater than 100,000: [Formula not provided in the original text]
When the population size is 100,000 or less: [Formula not provided in the original text]
TYPES OF SAMPLING
Sampling methods fall into two broad categories: probability sampling and non-probability sampling.
Probability Sampling
In probability sampling, each element in the population has a known probability of being selected for the sample. This random selection process is a key characteristic.
Features of Probability Sampling:
- Random Selection: Every element has an equal chance of being included in the sample.
- Equal Opportunity: All population elements have the same probability of selection.
- Measurable Error and Confidence: The sampling error and confidence level can be calculated.
- Generalizability: Results obtained from the sample can be generalized to the larger population.
- Representativeness Assessment: Probability sampling provides a method for evaluating the sample’s representativeness.
- Higher Cost: Probability sampling methods tend to be more expensive and time-consuming.
- Complexity: These methods can be more complex to implement than non-probability methods.
Non-Probability Sampling
Non-probability sampling does not rely on random selection, and the probability of each element being included in the sample is unknown. This approach introduces subjectivity into the selection process.
Characteristics of Non-Probability Sampling:
- Unequal Selection Probability: Elements in the population do not have an equal chance of being selected.
- Difficulty in Estimating Error: Calculating the sampling error and confidence level is challenging and not solely dependent on sample size.
- Researcher Bias: The researcher’s subjective judgment in selecting the sample can introduce bias.
- Lower Cost: Non-probability sampling methods are generally less expensive and quicker to implement.
While non-probability sampling offers practicality, probability sampling is generally preferred for its ability to minimize bias and provide a more accurate representation of the population.
Types of Probability Sampling
1. Simple Random Sampling
In simple random sampling, every element in the population has an equal chance of being selected. This method ensures fairness in the selection process. However, it is not commonly used in social research unless the population is small and homogeneous.
Features:
- Equal Probability: All elements have the same chance of selection.
- Random Selection: The sample is formed through a completely random process.
2. Systematic Random Sampling
Systematic random sampling involves selecting a random starting point and then choosing every ‘kth’ element from the population list. The sampling interval ‘k’ is determined by dividing the population size by the desired sample size (N/n).
Example:
Population size (N) = 8000
Desired sample size (n) = 500
Sampling interval (k) = N/n = 16
A random number between 1 and 16 is selected as the starting point. For instance, if the random number is 12, the sample would include elements 12, 28 (12+16), 44 (28+16), and so on.
3. Stratified Random Sampling
Stratified random sampling is used when the population can be divided into distinct subgroups or strata based on specific characteristics relevant to the study. This method ensures representation from each stratum in the sample.
Key Aspects:
- Stratification: The process of forming strata, ensuring homogeneity within strata and heterogeneity between them.
- Allocation: Determining the sample size for each stratum. Common allocation methods include:
- Simple: All strata have the same sample size.
- Proportional: Sample size for each stratum is proportional to its size in the population (most commonly used).
- Optimum: Considers both the stratum’s proportion and variability regarding the study variable.
Stratified random sampling is widely used in social research due to its ability to provide a representative sample that reflects the population’s diversity.
4. Cluster Random Sampling
Cluster sampling is employed when dealing with large, geographically dispersed populations. Instead of selecting individual elements, clusters or groups of elements are randomly selected. This method is efficient for large-scale surveys.
Multistage or Multi-Phase Sampling:
If a new sample is drawn from the selected clusters, it is referred to as multistage or multi-phase sampling. This approach allows for greater coverage and diversity in the sample.
Example:
N = 8000. n = 500 N / n = 16 and will be chosen at random from the first 16 elements. Eg 12. The first number would be 12 +18 = 28, the second 28 +16 = 44, and so on up to sample number.Stratified random sampling: Use when there are people in different classes or strata defined by some attribute or characteristic associated with the variable being studied. Involves dividing the population into strata, is the most used technique in social research. Two basic criteria: * Stratification: training approach of the strata, they must be homogeneous in composition and heterogeneous between them. * Allocation: criterion for determining the sample size of the different strata, there are three types of affixation: 1 Simple (all strata have the same number of elements in the sample). 2 Proportional (strata have a number of elements in the sample in terms of size, within the population is the most used). 3 Optimum (also taking into account their proportion of the population, they value their variability with respect to the variable to study.) Random cluster sampling: Used in large geographically dispersed populations. The difference with the simple random sampling, is that instead of selecting population elements are selected clusters, groups with heterogeneous elements but homogeneous clusters. While in the stratified random sampling randomly selected individuals (the unit is the individual) in the random cluster sampling randomly selected clusters (the unit is the cluster). If from a cluster sample a new sample was removed references to which were the previous cluster, this would be a multistage or multi-phase sampling. This type of sampling allows access to larger and more diverse populations, what is done is to select a cluster within another and final sampling unit is not the initial cluster but subdivisions of these. Two phases in the samples simple: a loas groups are selected members of the study population (PSUs). Two randomly chosen members of the population study of primary sampling units. It is therefore important to seek higher-order units: 1 were counted different geographical areas. A randomly chosen geographical area. 2. populations were counted chosen geographical area. 3. population is randomly chosen. 4. tired the different streets and families are interviewed. 5. is repeated many times until reaching the sample. Typically multi-stage or multi-phase sampling is carried out in three or four stages. In national sample Multi-stage sampling is used. Sampling of random routes: is so named because the interviewer, using a randomly selected route on a map in the selection of sampling units in the map are marked the starting points (streets, buildings, etc), the interviewer selected route must meet the following standards: one set of rotations (right or left). 2 enter certain buildings. 3 to interview the persons specified. The main drawback is the over-representation, ie that housing can make the same kind of people (pensioners, unemployed, housewives, etc) so it is advisable to interview specific individuals and three times less stress. types of tests were not probalísticos: quota sampling: used especially in market studies and opinion polls.Quota sampling has shared stages with stratified sampling: 1 identifies homogeneous groups (age, sex, education, etc). 2 determines the sample size in each group. It is in the next phase where the difference lies because the elements of the sample was not randomly chosen, but through contributions, the interviewer follows an approach based on certain characteristics or contributions. Total praise categories on which information is needed: 2x3x4 = 24 groups. It is difficult to identify a sample size for each group. Or convenience sampling strategy: often used in exploratory research and reduced cost. The criterion used is subjective, that is, depends on the interests of the researcher. Sampling “snowball”: also called chain, is used in small groups, difficult to locate as illegal immigrants, cults, gangs, etc.. The approach is to identify one component of the sample, the following are selected on the basis of the references wagered by individuals and interviewed, the sample size is determined by the investigator. The main disadvantage is that they usually are interviewed the most visible.