1.3 Types of sampling designs
4 min read•august 9, 2024
Sampling designs are the backbone of survey research, shaping how we gather data from populations. From to complex multistage methods, each approach has its strengths and weaknesses. Understanding these designs is crucial for collecting representative data and making valid inferences.
ensures every unit has a known chance of selection, while non-probability methods rely on researcher judgment or convenience. The choice between these approaches depends on research goals, resources, and population characteristics. Mastering sampling designs empowers researchers to make informed decisions and conduct robust studies.
Probability Sampling
Simple and Stratified Random Sampling
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- Simple random sampling selects units from the population with equal probability
- Involves assigning numbers to each unit and using a random number generator
- Ensures every member of the population has an equal chance of selection
- Requires a complete of the entire population
- Works well for homogeneous populations (student body of a small college)
- divides the population into subgroups before sampling
- Subgroups (strata) are mutually exclusive and collectively exhaustive
- Samples are drawn independently from each stratum
- Improves precision for heterogeneous populations (employees in a large corporation)
- Allows for different sampling methods or sample sizes within each stratum
- Formula for sample size allocation:
- Where is the sample size for stratum h, is the total sample size, is the population size for stratum h, and is the total population size
Cluster and Systematic Sampling
- selects groups of units rather than individual units
- Divides the population into clusters, typically based on geographic areas
- Randomly selects a subset of clusters
- All units within selected clusters are included in the sample
- Reduces travel costs for in-person surveys (neighborhoods in a city)
- Can lead to higher if clusters are not representative
- selects units at fixed intervals after a random start
- Calculates sampling interval k = N/n, where N is population size and n is sample size
- Randomly selects a starting point between 1 and k
- Selects every kth unit thereafter
- Easy to implement and can provide good coverage of the population
- Works well for ordered lists (selecting every 10th customer from a database)
Multistage Sampling
- combines multiple sampling methods in successive stages
- Often used for large-scale surveys covering wide geographic areas
- First stage typically involves cluster sampling (selecting counties)
- Subsequent stages may use other methods (simple random sampling within selected counties)
- Allows for efficient sampling of geographically dispersed populations
- Can be adjusted at each stage to balance cost and precision
- Commonly used in national surveys (U.S. Census Bureau's American Community Survey)
- Provides flexibility in sample design and allocation of resources
- Can concentrate resources on a subset of primary sampling units
- Allows for different sampling fractions at different stages
- Requires careful planning and weighting to ensure representativeness
Non-Probability Sampling
Convenience and Purposive Sampling
- selects readily available units
- Based on ease of access rather than random selection
- Quick and inexpensive method for gathering data
- Often used in pilot studies or exploratory research
- Highly susceptible to selection and limits
- Commonly employed in street interviews or online surveys
- selects units based on the researcher's judgment
- Also known as judgmental sampling
- Aims to include specific characteristics or expertise in the sample
- Used when studying rare populations or specific phenomena
- Allows for in-depth exploration of particular cases or perspectives
- Frequently utilized in qualitative research (interviewing industry experts)
Snowball and Quota Sampling
- uses initial participants to recruit additional subjects
- Particularly useful for hard-to-reach populations
- Starts with a small group of known participants
- Each participant refers other potential subjects from their network
- Sample size grows like a rolling snowball, hence the name
- Effective for studying hidden populations (undocumented immigrants)
- Can introduce bias as participants tend to know similar others
- sets quotas for specific subgroups within the sample
- Aims to create a sample that reflects known population characteristics
- Researcher determines the proportion of each subgroup to include
- Combines elements of stratified and purposive sampling
- Often used in market research to ensure representation of key demographics
- Does not involve random selection within quotas
- Can lead to biased results if quotas are not properly set or filled
Key Terms to Review (19)
Bias: Bias refers to a systematic error that leads to an inaccurate representation of a population in sampling or survey results. It can occur in various forms, affecting the validity and reliability of research findings. Understanding bias is crucial as it influences sampling designs, estimation processes, and ultimately the interpretation of data.
Cluster Sampling: Cluster sampling is a statistical technique used to select a sample from a population by dividing it into clusters or groups and then randomly selecting entire clusters for study. This method connects closely to concepts of probability and non-probability sampling, as well as different sampling designs, by providing a structured approach to reduce costs and logistical challenges in data collection.
Convenience Sampling: Convenience sampling is a non-probability sampling technique where subjects are selected based on their easy availability and proximity to the researcher. This method often leads to biases, as the sample may not accurately represent the larger population, but it is commonly used for its simplicity and low cost in various research contexts.
Generalizability: Generalizability refers to the extent to which findings from a sample can be applied to a larger population. It is crucial because it helps researchers understand how well the results of a study represent broader trends and behaviors. High generalizability indicates that the results can confidently inform practices or policies across different settings or groups, while low generalizability suggests limitations in applicability, often due to sampling methods or biases.
Law of Large Numbers: The law of large numbers states that as the size of a sample increases, the sample mean will get closer to the population mean. This principle is fundamental in statistics, as it reassures researchers that larger samples will yield results that are more representative of the entire population, enhancing the reliability of conclusions drawn from data. Understanding this law helps in evaluating various sampling designs and also clarifies the benefits and potential drawbacks of simple random sampling.
Multistage sampling: Multistage sampling is a complex form of sampling that involves selecting samples in multiple stages, typically using a combination of random sampling techniques. This method allows researchers to create a more manageable sample size while still obtaining a representative sample from a larger population. By breaking down the sampling process into stages, multistage sampling can be particularly useful when dealing with large and geographically dispersed populations.
Non-probability sampling: Non-probability sampling is a technique where the samples are selected based on subjective judgment rather than random selection. This means that not all individuals in the population have a chance of being included, which can lead to biases in the results. Understanding this method is crucial for evaluating the reliability and validity of research findings, especially when contrasting it with probability sampling techniques.
Probability Sampling: Probability sampling is a sampling technique where each member of a population has a known and non-zero chance of being selected in the sample. This method ensures that the sample accurately represents the population, allowing for generalizations about the whole group based on the results. By utilizing this technique, researchers can minimize bias and enhance the reliability of their findings, which is essential for effective data analysis and interpretation.
Purposive Sampling: Purposive sampling is a non-probability sampling technique where researchers select participants based on specific characteristics or qualities that are relevant to the research objectives. This method allows researchers to target specific groups or individuals who can provide the most insightful information, making it particularly useful when a researcher is looking for in-depth understanding rather than generalizability. By focusing on particular traits, purposive sampling stands out in both qualitative and exploratory research designs.
Quota Sampling: Quota sampling is a non-probability sampling technique where the researcher ensures equal representation of specific subgroups within a population by setting quotas for each subgroup. This method connects with various concepts, including its classification as non-probability sampling, the historical evolution of sampling techniques that highlight its emergence, and its role in different sampling designs. Furthermore, it allows for a practical approach when conducting surveys, especially in contexts such as telephone and online surveys, where demographic diversity is essential.
Representative Sample: A representative sample is a subset of a population that accurately reflects the characteristics and diversity of the whole group. This concept is crucial for ensuring that the results of a survey or study can be generalized to the larger population, making it essential in various sampling designs and strategies for allocating sample sizes effectively across different segments or strata.
Sample Population: A sample population refers to the specific group of individuals from a larger population that is selected for a study or survey. This subset is intended to represent the characteristics of the overall population, allowing researchers to make inferences about the whole group based on the analysis of this smaller segment. The accuracy and reliability of the results heavily depend on how well this sample reflects the larger population.
Sampling error: Sampling error is the difference between the results obtained from a sample and the actual values in the entire population. This error arises because the sample may not perfectly represent the population, leading to inaccuracies in estimates such as means, proportions, or totals.
Sampling frame: A sampling frame is a list or database from which a sample is drawn for a study, serving as the foundation for selecting participants. It connects to the overall effectiveness of different sampling methods and is crucial for ensuring that every individual in the population has a known chance of being selected, thus minimizing bias and increasing representativeness.
Simple random sampling: Simple random sampling is a basic sampling technique where every individual in a population has an equal chance of being selected. This method is vital for ensuring that samples are representative of the whole population, which helps to avoid bias and enhances the validity of statistical results.
Snowball Sampling: Snowball sampling is a non-probability sampling technique where existing study subjects recruit future subjects from their acquaintances. This method is especially useful in populations that are hard to access or identify, as it allows researchers to leverage social networks to find participants. It connects to broader concepts of probability and non-probability sampling, various types of sampling designs, and comparisons between different sampling techniques.
Stratified Random Sampling: Stratified random sampling is a sampling technique that involves dividing a population into distinct subgroups, or strata, and then taking a random sample from each stratum. This method ensures that each subgroup is adequately represented in the overall sample, which can lead to more precise estimates of the population parameters. By using this approach, researchers can analyze differences between strata and improve the accuracy of their findings.
Systematic sampling: Systematic sampling is a probability sampling method where researchers select participants based on a fixed interval from a randomly chosen starting point in a population list. This method offers a structured approach to sampling, making it easier to implement compared to other methods, and is often used in various research designs due to its efficiency and simplicity.
Target Population: The target population refers to the entire group of individuals or items that a researcher aims to study and draw conclusions about. This population is critical because it defines the scope of the research and determines which individuals will be included in the sample, influencing the reliability and validity of the findings. Accurately identifying the target population ensures that the sampling method aligns with the research goals, ultimately impacting data analysis and interpretations.