Probability Sampling Techniques

Probability Sampling

• Probability sampling is a sampling technique that uses a random method to select individuals, ensuring each member of the population has a known chance of being selected.
• It is referred to as random sampling and allows for generalizations about the population based on the collected data.
• Common Probability Sampling techniques include:
  • Simple Random Sampling
  • Systematic Random Sampling
  • Stratified Sampling
  • Cluster Sampling
  • Multi-stage Sampling

Simple Random Sampling

• This technique assigns equal probabilities of selection to each member of the population.
• It is easy to use but might be less effective if the population is geographically spread out or heterogeneous.
• It is ideal for populations that are homogeneous and don't have a wide geographical distribution.

Systematic Random Sampling

• This technique selects every kth individual from a population, where k is calculated by dividing the population size by the desired sample size.
• It is simple and easy to administer, offering advantages in terms of efficiency.
• It can be less precise if the population has a hidden periodicity, which could lead to biased selection.
• It is suitable if the ordering of the population is considered essentially random.

Stratified Sampling

• This technique divides the population into subgroups called strata, where individuals within each stratum share similar characteristics.
• A simple random sample is then drawn from each stratum.
• This technique is beneficial when information about the population is available and can be used to form strata.
• It is helpful when the population is diverse, ensuring that all subgroups are adequately represented in the sample.

Cluster Sampling

• This technique involves dividing the population into clusters and randomly selecting clusters for sampling.
• It is convenient when dealing with large populations that are geographically spread out.
• It is especially suitable when it is impractical to obtain a complete list of all individuals.

Multi-stage Sampling

• This technique combines different sampling methods in multiple stages.
• For example, it might start with cluster sampling and then use simple random sampling within the selected clusters.
• This approach is useful for handling complex population structures and can increase the efficiency of sampling.

Non-Probability Sampling

• This technique uses non-random selection methods, making it difficult to generalize population findings from the sample.
• It should not be used for statistical inference.
• It is often used in situations where probability sampling is not feasible or cost-effective.
• Common Non-probability Sampling techniques include:
  • Accidental Sampling
  • Quota Sampling
  • Convenience Sampling
  • Purposive Sampling
  • Judgement Sampling

Accidental Sampling

• This technique involves selecting individuals who are readily available or encountered by chance.
• It lacks control and can lead to biased results.

Quota Sampling

• This technique involves selecting individuals based on pre-determined quotas based on specific characteristics like gender, age, or socioeconomic status.
• It seeks to ensure representation of various groups, but might result in selective bias.

Convenience Sampling

• This technique selects individuals who are easily accessible to the researcher, often based on convenience or availability.
• It faces the risk of being highly biased and not representative of the population.

Purposive Sampling

• This technique selects individuals based on specific criteria determined by the researcher.
• It is useful for selecting individuals with specific expertise or experiences, but it can be subjective.

Judgement Sampling

• This technique selects individuals based on the researcher's judgment, often relying on experts or knowledgeable individuals within the field.
• It is prone to bias, as the selection reflects the researcher's personal opinions.

Advantages of Sampling over Complete Enumeration (Census)

• Reduced Labor: Sampling requires less effort than collecting data from the entire population.
• Reduced Cost: Sampling costs are generally lower than conducting a complete census.
• Greater Speed: Sampling allows for faster data collection and analysis.
• Greater Scope: Sampling can study larger and more diverse populations that might be impossible to cover in a census.
• Greater Efficiency: Sampling can provide more focused and accurate data for a specific research question.
• Convenience: Sampling is often more practical and convenient for data collection.
• Ethical Considerations: Sampling might be ethically necessary in situations where collecting complete data from the population would be harmful.

Bias Selection

• It refers to systematic errors in the sampling process that lead to non-representative samples.
• Types of bias include:
  • Deliberately selecting a "representative" sample based on subjective judgments.
  • Failing to define the target population clearly.
  • Undercoverage: Failing to include all members of the target population in the sampling frame.
  • Overcoverage: Including individuals in the sampling frame who are not part of the target population.
  • Multiplicity of Listings: Having multiple entries of the same individual in the sampling frame.
  • Substitution: Replacing a designated individual with a convenient alternative.
  • Nonresponse: Failing to obtain responses from all individuals in the selected sample.
  • Volunteer Sampling: Allowing the sample to consist entirely of volunteers.

Sources of Errors in Sampling

• Non-sampling Error: These are errors that occur during the survey process, unrelated to the selection of the sample. These errors can be attributed to various factors such as:
  • Non-response
  • Interviewer error
  • Misrepresented answers
  • Data Entry Errors
• Sampling Error: These are errors that arise from the fact that a sample is used to represent the population. Statistical techniques are used to estimate the magnitude of the error.