When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method. There are two primary types of sampling methods that you can use in your research:
- Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
- Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
Population vs. sample
First, you need to understand the difference between a population and a sample, and identify the target population of your research.
- The population is the entire group that you want to draw conclusions about.
- The sample is the specific group of individuals that you will collect data from.
Sampling frame
The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
Sample size
The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design.
Probability sampling methods
Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.
There are four main types of probability sample.
1. Simple random sampling
In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.
A researcher selects 100 students from a school's enrollment list using a random number generator to participate in a survey about study habits.
2. Systematic sampling
Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
3. Stratified sampling
Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.
To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).
To study income distribution, a researcher divides a city's population into income brackets (low, medium, high) and then selects a random sample of 50 individuals from each bracket.
4. Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.This is called multistage sampling.
The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.
Non-probability sampling methods
In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.
1. Convenience sampling
A convenience sample simply includes the individuals who happen to be most accessible to the researcher.
You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.
2. Voluntary response sampling
Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).
You send out the survey to all students at your university and a lot of students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.
3. Purposive sampling
This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.
You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.
4. Quota sampling
Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.
You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota.
A political pollster aims to interview 200 men and 200 women of various age groups to ensure a representative sample for a survey on voting intentions.
What is Quantitative Research?
- Quantitative research is a way to learn about a sample population (or a group of people chosen from the whole). Using scientific inquiry, quantitative research relies on observed or measured data to examine questions about this sample population.
- This type of research focuses on numbers, and is thus precise.
- Uses statistics
Purpose?
- Typically it is to determine the relationship between two variables.
Sample Size
- Usually large samples
- Often simple random probability sampling
Types of Data
- Tests, surveys, frequently multiple-choice questionnaires
Sampling In Quantitative Research:
- Sampling Techniques: Quantitative research often uses probability sampling techniques, such as simple random sampling, stratified sampling, and cluster sampling, to ensure that the sample is representative of the population. These techniques help minimize bias and allow for generalization of the findings.
- Sample Size: Determining the sample size in quantitative research involves considerations such as the desired level of confidence, the expected variability in the population, and the margin of error. Larger samples generally provide more reliable results, but they can also be more costly and time-consuming to collect and analyze.
- Sampling Bias: Despite efforts to create representative samples, sampling bias can still occur in quantitative research. This bias can result from factors such as non-response bias, where certain individuals are more likely to respond to surveys, or selection bias, where certain groups are underrepresented in the sample.
- Statistical Analysis: Quantitative research relies on statistical analysis to draw conclusions about the population based on the sample. Techniques such as hypothesis testing, regression analysis, and analysis of variance (ANOVA) are used to analyze quantitative data and test research hypotheses.
What is Qualitative Research?
- Uses interpretive techniques to describe, decode, and translate the meaning -- not the frequency -- of certain naturally occurring phenomena.
- Uses non-statistical techniques
- interested in how people interpret their experiences, how they construct their worlds, and what meaning they attribute to their experiences
- Case studies tend to be qualitative
Purpose
- To understand a phenomenon
Sample Size
- small
- purposeful sampling -- the investigator wants to discover, understand, and gain insight and therefore must select a sample from which the most can be learned. There is an emphasis on in-depth understanding of specific cases.
Types of Data
- Focus groups, interviews, field observation, historical comparison
- mostly open-ended questions
Sampling in Qualitative Research:
- Purposeful Sampling: Qualitative research often uses purposeful sampling techniques, where participants are selected based on their ability to provide rich and meaningful data related to the research questions. Common types of purposeful sampling include maximum variation sampling, homogenous sampling, and critical case sampling.
- Data Saturation: In qualitative research, the concept of data saturation is important. Data saturation occurs when no new information or themes are emerging from the data, indicating that the sample size is adequate to address the research questions. Researchers often continue sampling until data saturation is reached.
- Contextual Understanding: Sampling in qualitative research aims to provide a deep, contextual understanding of the research topic. This often involves selecting participants who can offer diverse perspectives and experiences, allowing for a comprehensive exploration of the phenomenon under study.
- Interpretive Analysis: Qualitative research involves interpretive analysis, where researchers analyze the data to uncover patterns, themes, and meanings. Techniques such as thematic analysis, content analysis, and narrative analysis are used to analyze qualitative data and generate findings.
Comments
Post a Comment