21 Apr In the realm of research methodology, the choice of a sampling technique is a crucial decision that impacts the validity and generalizability of stu
Chapter 6
In the realm of research methodology, the choice of a sampling technique is a crucial decision that impacts the validity and generalizability of study findings. Drawing from the chapter outline on sampling techniques, let's engage in a discussion about the fundamental aspects of sampling in research.
TEXTBOOK(S) AND REQUIRED MATERIALS:
Title: Introduction to Research Methods: A Hands-On Approach Author: Pajo, Bora Publisher: SAGE Vantage Year Published: 2023 Edition: 2nd ISBN: 19781544391724 (electronic) 9781544391700 (paperback)
Introduction to Research Methods: A Hands-on Approach, 2nd Edition Chapter 6: Sampling
Sampling (1 of 3)
Sampling.
Sample.
Increase likelihood of representative population subset.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.1: Explain the purposes of sampling.
Sampling: the procedure used by researchers to select a subset of the population that can be used to conduct scientific study.
Sample: the subset of the population used in sampling.
By following sampling procedures, researchers increase the likelihood of getting a representative subset of the population as their sample.
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Sampling (2 of 3)
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.1: Explain the purposes of sampling.
Figure 6.1
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Sampling (3 of 3)
Population.
Representative sample.
Population of interest and sampling frame.
Sampling frame.
Alternative population sampling methods.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.1: Explain the purposes of sampling.
Population: the entire group of people that are the focus of the study.
Representative sample: one that reflects the same features of interest as the entire population.
The first step toward finding the best sampling technique is to determine the population of interest and develop a sampling frame.
Sampling frame: a list of the entire population of the study.
Researchers have developed different ways to sample from a population even if a sampling frame is not possible.
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Probability and Non-Probability Sampling (1 of 3)
Probability.
Non-probability.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.2: Compare and contrast probability and non-probability sampling.
Probability: participants are randomly selected and a sampling frame is often used.
Non-probability: participants are often not randomly selected and a sampling frame may not be available.
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Probability and Non-Probability Sampling (2 of 3)
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.2: Compare and contrast probability and non-probability sampling.
Figure 6.2
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Probability and Non-Probability Sampling (3 of 3)
Randomization for sampling.
Qualitative versus quantitative study sampling types.
Sample sizes for each probability sampling type.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.2: Compare and contrast probability and non-probability sampling.
Randomization for sampling means everyone in the population has an equal chance of participating in the study.
It is common for qualitative studies to use non-probability sampling types and for quantitative studies to use probability sampling types.
Probability sampling often results in a larger sample size while non-probability sampling results in a smaller sample size.
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Types of Non-Probability Sampling (1 of 10)
No equal change of participation.
Difficult populations or no sampling frame.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Non-probability sampling includes various techniques that do not include an equal change of participation.
They often involve populations that are difficult to reach or where a sampling frame does not exist.
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Types of Non-Probability Sampling (2 of 10)
Convenience Sampling
Convenience sampling.
Accidental sampling.
Pilot studies.
Help ensure well-conducted studies.
Cost-effective study testing method.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Convenience sampling: technique that allows the researcher to select any participants who are available to participate in a study, even if they are not representative of a population.
Their participation happens by availability and accident, so this is often also called accidental sampling.
Pilot studies: initial inquiries that pave the way for larger studies.
Helps avoid badly conducted larger studies.
Cost-effective way to test aspects of a study.
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Types of Non-Probability Sampling (3 of 10)
Convenience Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Figure 6.3: Convenience Sampling
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Types of Non-Probability Sampling (4 of 10)
Snowball Sampling
Snowball sampling.
Some participants help find others.
May not represent whole population.
May begin with convenience, then snowball.
Encourage participant variety.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Snowball sampling: technique in which participants are selected by word of mouth.
One or a few participants are willing to help find more participants.
May not be representative of the entire population because the researcher selects only participants who know each other.
A researcher can start with convenience sampling and continue with snowball.
A researcher should try to introduce as much participant variety as possible by encouraging participants to recommend people with specific characteristics to participate.
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Types of Non-Probability Sampling (5 of 10)
Snowball Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Figure 6.4: Snowball Sampling
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Types of Non-Probability Sampling (6 of 10)
Purposive Sampling
Purposive sampling.
Homogenous sampling.
Each participant must have specific characteristic.
Deviant case sampling.
Low external validity.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Purposive sampling: technique that allows the researcher to select participants of interest for the study; also called judgmental sampling.
Homogenous sampling: type of purposive sampling technique in which participants are chosen based on a trait of characteristic of interest.
Each participant must have the specific characteristic the researcher is looking for.
Deviant case sampling: focuses on unusual or very specific cases.
Purposive sampling allows a researcher to pick participants according to the characteristic under the study, but that means the results of the study cannot be generalized to the greater population and offer low external validity.
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Types of Non-Probability Sampling (7 of 10)
Purposive Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Figure 6.5: Purposive Sampling
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Types of Non-Probability Sampling (8 of 10)
Quota Sampling
Quota sampling.
Sample of quota or proportions needed.
Proportional quota sampling.
Non-proportional quota sampling.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Quota sampling: technique that compares different groups within the population of interest; found in both qualitative and quantitative designs.
The researcher needs a sample of predetermined quota or proportions.
Proportional quota sampling: the sample’s representation of the same proportion as it exists in the entire population of interest.
Non-proportional quota sampling: uses a different quota from the one found in the population of interest because the study’s aim is to compare two or more different groups of interest.
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Types of Non-Probability Sampling (9 of 10)
Quota Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Figure 6.6: Proportional Quota Sampling
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Types of Non-Probability Sampling (10 of 10)
Quota Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.3: Describe the types of non-probability sampling.
Figure 6.7: Non-Proportional Quota Sampling
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Types of Probability Sampling (1 of 8)
All types use randomization.
Method of choosing participants varies.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
All types of probability sampling use randomization, but they vary in the methodology used to choose participants, which depends on the aim of the study.
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Types of Probability Sampling (2 of 8)
Simple Random Sampling
Simple random sampling.
Conducted in one step.
Ensures population representation.
Requires list of complete population.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Simple random sampling: sampling procedure that relies on complete randomization without any specific boundaries.
Conducted in one step using techniques such as random number generation.
Every member has an equal chance of participating and it ensures representation of the population.
It requires a complete list of the population from which the sample is drawn.
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Types of Probability Sampling (3 of 8)
Simple Random Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Figure 6.8: Simple Random Sampling
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Types of Probability Sampling (4 of 8)
Systematic Random Sampling
Systematic random sampling.
Random selection from sampling frame.
Assumes random order in sampling frame.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Systematic random sampling: same technique as simple random sampling but has a level of organization embedded in it.
A systematic way of randomly selecting participants from a sampling frame.
Operates under the assumption that participants are randomly order in a sampling frame so everyone in the list has the same chance of participating in the study.
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Types of Probability Sampling (5 of 8)
Stratified Random Sampling
Stratified random sampling.
Requires equal participants from each group.
Strata.
Proportionate stratified sampling.
Disproportionate stratified sampling.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Stratified random sampling: a technique that become valuable when the study is focused on understanding, comparing, or analyzing different groups of a population.
Requires equal numbers of participants from each group.
Strata: lists of different groups to choose participants from.
Proportionate stratified sampling: type of sampling that follows the proportions of the population but also creates specific strata that are of interest to the study.
Disproportionate stratified sampling: a sample in which the proportions are not equivalent to the proportions in the entire population.
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Types of Probability Sampling (6 of 8)
Stratified Random Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Figure 6.9: Proportionate Stratified Sampling
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Types of Probability Sampling (7 of 8)
Stratified Random Sampling
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Figure 6.10: Disproportionate Stratified Sampling
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Types of Probability Sampling (8 of 8)
Cluster Random Sampling
Cluster random sampling.
Researcher steps:
Clusters are randomly defined.
Cluster.
Participants randomly selected from each cluster.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.4: Summarize the types of probability sampling.
Cluster random sampling: useful technique for when the population of interest is widely spread out.
Researcher follows two steps:
Clusters are randomly defined.
A cluster is a group of population in a specific geographical area.
Participants are randomly selected from each participating cluster.
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Sampling Assessment (1 of 3)
Sampling Error
Sampling error.
Tool for quantitative studies.
Determines sample representativeness.
Small sample size is good match.
Measure for focused population parameters.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.5: Understand sampling error, confidence interval, and saturation.
Sampling error is a statistical measurement that broadly represents how different the sample size is from the population of interest.
Used in quantitative studies.
Helps to determine whether the sample is sufficiently representative to the entire population.
A small sampling size indicates a good match between the sample and entire population, which allows researchers to generalize their findings beyond the sample.
Sampling error is measured for any different parameters of the population that are the focus of the study.
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Sampling Assessment (2 of 3)
Confidence Interval
Confidence interval.
Key in quantitative studies.
Higher confidence with higher sample sizes.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.5: Understand sampling error, confidence interval, and saturation.
Confidence interval (CI) measures the confidence of lack of confidence in the sample size and sampling method.
One of the first indexes looked at in a quantitative study.
Higher sample sizes lead to higher confidence.
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Sampling Assessment (3 of 3)
Saturation
Saturation.
No new information in qualitative data collection.
Sample size sufficient.
Pajo, Introduction to Research Methods: A Hands-on Approach, 2nd Edition. © 2023 SAGE Publishing.
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LO 6.5: Understand sampling error, confidence interval, and saturation.
Saturation is the measure used to estimate the right sample size in qualitative work.
Saturation refers to the moment in qualitative data collection where no new information is coming in.
Reaching saturation means the sample size is sufficient and data collection can stop.
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