This is important for measures of a construct.
For example, when measuring a patient’s blood pressure, the blood pressure cuff should consistently measure in the same way. So, when doing every 15-minute vital signs after surgery, the blood pressure cuff should measure consistently every 15 minutes.
Validity
Measures the concept it is
supposed
to measure
This is important to be able to measure the intended construct.
For example, a measure of critical thinking is an accurate measure of critical thinking and not expert practice.
Another example: a measure of stress level should measure stress level, not pain level.
Leibold, 2020
In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.
A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population. For example, if we wanted to investigate heart failure, there would be no possible way to measure every single human with heart failure. Therefore, researchers will attempt to select a sample of that large population which would most likely reflect (AKA: be a representative sample) the larger population of those with heart failure. Remember, in quantitative research, the results should be generalizable to the population studied.
A researcher will specify population characteristics through eligibility criteria. This means that they consider which characteristics to include ( inclusion criteria ) and which characteristics to exclude ( exclusion criteria ).
For example, if we were studying chemotherapy in breast cancer subjects, we might specify:
Sampling Designs:
There are two broad classes of sampling in quantitative research: Probability and nonprobability sampling.
Probability sampling : As the name implies, probability sampling means that each eligible individual has a random chance (same probability) of being selected to participate in the study.
There are three types of probability sampling:
Simple random sampling : Every eligible participant is randomly selected (e.g. drawing from a hat).
Stratified random sampling : Eligible population is first divided into two or more strata (categories) from which randomization occurs (e.g. pollution levels selected from restaurants, bars with ordinances of state laws, and bars with no ordinances).
Systematic sampling : Involves the selection of every __ th eligible participant from a list (e.g. every 9 th person).
Nonprobability sampling : In nonprobability sampling, eligible participants are selected using a subjective (non-random) method.
There are four types of nonprobability sampling:
Convenience sampling : Participants are selected for inclusion in the sample because they are the easiest for the researcher to access. This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.
Quota sampling : Participants are from a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, the researcher could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.
Consecutive sampling : A sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Consecutive sampling is defined as a nonprobability technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Here, the researcher selects a sample or group of people, conducts research over a period, collects results, and then moves on to another sample.
Purposive sampling : A group of non-probability sampling techniques in which units are selected because they have characteristics that the researcher needs in their sample. In other words, units are selected “on purpose” in purposive sampling.
There are various methods that researchers use to collect data for their studies. For nurse researchers, existing records are an important data source. Researchers need to decide if they will collect new data or use existing data. There is also a wealth of clinical data that can be used for non-research purposed to help answer clinical questions.
Let’s look at some general data collection methods and data sources in quantitative research.
Existing data could include medical records, school records, corporate diaries, letters, meeting minutes, and photographs. These are easy to obtain do not require participation from those being studied.
Collecting new data:
Let’s go over a few methods in which researcher can collect new data. These usually requires participation from those being studied.
Self-reports can be obtained via interviews or questionnaires . Closed-ended questions can be asked (“Within the past 6 months, were you ever a member of a fitness gym?” Yes/No) or open-ended questions such as “Why did you decide to join a fitness gym?” Important to remember (this sometimes throws students off) is that conducting interviews and questionnaires does not mean it is qualitative in nature! Do not let that throw you off in assessing whether a published article is quantitative or qualitative. The nature of the questions, however, may help to determine the type of research (quantitative or qualitative), as qualitative questions deal with ascertaining a very organic collection of people’s experiences in open-ended questions.
Advantages of questionnaires (compared to interviews):
Advances of interviews (compared to questionnaires):
Psychosocial scales are often utilized within questionnaires or interviews. These can help to obtain attitudes, perceptions, and psychological traits.
Likert Scales :
Visual Analog Scale:
Observational Methods include the observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research .
When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation. When individuals are observed in their natural environment, this is known as naturalistic observation. In participant observation, the researcher immerses himself or herself in the environment and becomes a member of the group being observed.
Biophysiologic Measures are defined as ‘those physiological and physical variables that require specialized technical instruments and equipment for their measurement’. Biophysiological measures are the most common instruments for collecting data in medical science studies. To collect valid and reliable data, it is critical to apply these measures appropriately.
Let’s watch a video about Sampling and Data Collection that I made a couple of years ago.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .
This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .
A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
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Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research question. Characteristics of individuals included in the sample population should be clearly defined to determine eligibility for study participation and improve power. Sample selection methods differ based on study design. The purpose of this short report is to review common sampling considerations and related errors.
Keywords: research design; sample size; sampling.
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Last updated
27 February 2023
Reviewed by
Cathy Heath
When researching perceptions or attributes of a product, service, or people, you have two options:
Survey every person in your chosen group (the target market, or population), collate your responses, and reach your conclusions.
Select a smaller group from within your target market and use their answers to represent everyone. This option is sampling .
Sampling saves you time and money. When you use the sampling method, the whole population being studied is called the sampling frame .
The sample you choose should represent your target market, or the sampling frame, well enough to do one of the following:
Generalize your findings across the sampling frame and use them as though you had surveyed everyone
Use the findings to decide on your next step, which might involve more in-depth sampling
Dovetail streamlines research to help you uncover and share actionable insights
Valery Glivenko and Francesco Cantelli, two mathematicians studying probability theory in the early 1900s, devised the sampling method. Their research showed that a properly chosen sample of people would reflect the larger group’s status, opinions, decisions, and decision-making steps.
They proved you don't need to survey the entire target market, thereby saving the rest of us a lot of time and money.
We’ve already touched on the fact that sampling saves you time and money. When you get reliable results quickly, you can act on them sooner. And the money you save can pay for something else.
It’s often easier to survey a sample than a whole population. Sample inferences can be more reliable than those you get from a very large group because you can choose your samples carefully and scientifically.
Sampling is also useful because it is often impossible to survey the entire population. You probably have no choice but to collect only a sample in the first place.
Because you’re working with fewer people, you can collect richer data, which makes your research more accurate. You can:
Ask more questions
Go into more detail
Seek opinions instead of just collecting facts
Observe user behaviors
Double-check your findings if you need to
In short, sampling works! Let's take a look at the most common sampling methods.
There are two main sampling methods: probability sampling and non-probability sampling. These can be further refined, which we'll cover shortly. You can then decide which approach best suits your research project.
Probability sampling is used in quantitative research , so it provides data on the survey topic in terms of numbers. Probability relates to mathematics, hence the name ‘quantitative research’. Subjects are asked questions like:
How many boxes of candy do you buy at one time?
How often do you shop for candy?
How much would you pay for a box of candy?
This method is also called random sampling because everyone in the target market has an equal chance of being chosen for the survey. It is designed to reduce sampling error for the most important variables. You should, therefore, get results that fairly reflect the larger population.
In this method, not everyone has an equal chance of being part of the sample. It's usually easier (and cheaper) to select people for the sample group. You choose people who are more likely to be involved in or know more about the topic you’re researching.
Non-probability sampling is used for qualitative research. Qualitative data is generated by questions like:
Where do you usually shop for candy (supermarket, gas station, etc.?)
Which candy brand do you usually buy?
Why do you like that brand?
Here are five ways of doing probability sampling:
Simple random sampling (basic probability sampling)
Stratified sampling.
Cluster sampling
Simple random sampling.
There are three basic steps to simple random sampling:
Choose your sampling frame.
Decide on your sample size. Make sure it is large enough to give you reliable data.
Randomly choose your sample participants.
You could put all their names in a hat, shake the hat to mix the names, and pull out however many names you want in your sample (without looking!)
You could be more scientific by giving each participant a number and then using a random number generator program to choose the numbers.
Instead of choosing names or numbers, you decide beforehand on a selection method. For example, collect all the names in your sampling frame and start at, for example, the fifth person on the list, then choose every fourth name or every tenth name. Alternatively, you could choose everyone whose last name begins with randomly-selected initials, such as A, G, or W.
Choose your system of selecting names, and away you go.
This is a more sophisticated way to choose your sample. You break the sampling frame down into important subgroups or strata . Then, decide how many you want in your sample, and choose an equal number (or a proportionate number) from each subgroup.
For example, you want to survey how many people in a geographic area buy candy, so you compile a list of everyone in that area. You then break that list down into, for example, males and females, then into pre-teens, teenagers, young adults, senior citizens, etc. who are male or female.
So, if there are 1,000 young male adults and 2,000 young female adults in the whole sampling frame, you may want to choose 100 males and 200 females to keep the proportions balanced. You then choose the individual survey participants through the systematic sampling method.
This method is used when you want to subdivide a sample into smaller groups or clusters that are geographically or organizationally related.
Let’s say you’re doing quantitative research into candy sales. You could choose your sample participants from urban, suburban, or rural populations. This would give you three geographic clusters from which to select your participants.
This is a more refined way of doing cluster sampling. Let’s say you have your urban cluster, which is your primary sampling unit. You can subdivide this into a secondary sampling unit, say, participants who typically buy their candy in supermarkets. You could then further subdivide this group into your ultimate sampling unit. Finally, you select the actual survey participants from this unit.
Probability sampling has three main advantages:
It helps minimizes the likelihood of sampling bias. How you choose your sample determines the quality of your results. Probability sampling gives you an unbiased, randomly selected sample of your target market.
It allows you to create representative samples and subgroups within a sample out of a large or diverse target market.
It lets you use sophisticated statistical methods to select as close to perfect samples as possible.
To recap, with non-probability sampling, you choose people for your sample in a non-random way, so not everyone in your sampling frame has an equal chance of being chosen. Your research findings, therefore, may not be as representative overall as probability sampling, but you may not want them to be.
Sampling bias is not a concern if all potential survey participants share similar traits. For example, you may want to specifically focus on young male adults who spend more than others on candy. In addition, it is usually a cheaper and quicker method because you don't have to work out a complex selection system that represents the entire population in that community.
Researchers do need to be mindful of carefully considering the strengths and limitations of each method before selecting a sampling technique.
Non-probability sampling is best for exploratory research , such as at the beginning of a research project.
There are five main types of non-probability sampling methods:
Purposive sampling, voluntary response sampling, snowball sampling, quota sampling.
The strategy of convenience sampling is to choose your sample quickly and efficiently, using the least effort, usually to save money.
Let's say you want to survey the opinions of 100 millennials about a particular topic. You could send out a questionnaire over the social media platforms millennials use. Ask respondents to confirm their birth year at the top of their response sheet and, when you have your 100 responses, begin your analysis. Or you could visit restaurants and bars where millennials spend their evenings and sign people up.
A drawback of convenience sampling is that it may not yield results that apply to a broader population.
This method relies on your judgment to choose the most likely sample to deliver the most useful results. You must know enough about the survey goals and the sampling frame to choose the most appropriate sample respondents.
Your knowledge and experience save you time because you know your ideal sample candidates, so you should get high-quality results.
This method is similar to convenience sampling, but it is based on potential sample members volunteering rather than you looking for people.
You make it known you want to do a survey on a particular topic for a particular reason and wait until enough people volunteer. Then you give them the questionnaire or arrange interviews to ask your questions directly.
Snowball sampling involves asking selected participants to refer others who may qualify for the survey. This method is best used when there is no sampling frame available. It is also useful when the researcher doesn’t know much about the target population.
Let's say you want to research a niche topic that involves people who may be difficult to locate. For our candy example, this could be young males who buy a lot of candy, go rock climbing during the day, and watch adventure movies at night. You ask each participant to name others they know who do the same things, so you can contact them. As you make contact with more people, your sample 'snowballs' until you have all the names you need.
This sampling method involves collecting the specific number of units (quotas) from your predetermined subpopulations. Quota sampling is a way of ensuring that your sample accurately represents the sampling frame.
You can use non-probability sampling when you:
Want to do a quick test to see if a more detailed and sophisticated survey may be worthwhile
Want to explore an idea to see if it 'has legs'
Launch a pilot study
Do some initial qualitative research
Have little time or money available (half a loaf is better than no bread at all)
Want to see if the initial results will help you justify a longer, more detailed, and more expensive research project
Sampling bias can fog or limit your research results. This will have an impact when you generalize your results across the whole target market. The two main causes of sampling bias are faulty research design and poor data collection or recording. They can affect probability and non-probability sampling.
If a surveyor chooses participants inappropriately, the results will not reflect the population as a whole.
A famous example is the 1948 presidential race. A telephone survey was conducted to see which candidate had more support. The problem with the research design was that, in 1948, most people with telephones were wealthy, and their opinions were very different from voters as a whole. The research implied Dewey would win, but it was Truman who became president.
This problem speaks for itself. The survey may be well structured, the sample groups appropriate, the questions clear and easy to understand, and the cluster sizes appropriate. But if surveyors check the wrong boxes when they get an answer or if the entire subgroup results are lost, the survey results will be biased.
To get results you can rely on, you must:
Know enough about your target market
Choose one or more sample surveys to cover the whole target market properly
Choose enough people in each sample so your results mirror your target market
Have content validity . This means the content of your questions must be direct and efficiently worded. If it isn’t, the viability of your survey could be questioned. That would also be a waste of time and money, so make the wording of your questions your top focus.
If using probability sampling, make sure your sampling frame includes everyone it should and that your random sampling selection process includes the right proportion of the subgroups
If using non-probability sampling, focus on fairness, equality, and completeness in identifying your samples and subgroups. Then balance those criteria against simple convenience or other relevant factors.
Self-selection bias. If you mass-mail questionnaires to everyone in the sample, you’re more likely to get results from people with extrovert or activist personalities and not from introverts or pragmatists. So if your convenience sampling focuses on getting your quota responses quickly, it may be skewed.
Non-response bias. Unhappy customers, stressed-out employees, or other sub-groups may not want to cooperate or they may pull out early.
Undercoverage bias. If your survey is done, say, via email or social media platforms, it will miss people without internet access, such as those living in rural areas, the elderly, or lower-income groups.
Survivorship bias. Unsuccessful people are less likely to take part. Another example may be a researcher excluding results that don’t support the overall goal. If the CEO wants to tell the shareholders about a successful product or project at the AGM, some less positive survey results may go “missing” (to take an extreme example.) The result is that your data will reflect an overly optimistic representation of the truth.
Pre-screening bias. If the researcher, whose experience and knowledge are being used to pre-select respondents in a judgmental sampling, focuses more on convenience than judgment, the results may be compromised.
Focus on the bullet points in the next section and:
Make survey questionnaires as direct, easy, short, and available as possible, so participants are more likely to complete them accurately and send them back
Follow up with the people who have been selected but have not returned their responses
Ignore any pressure that may produce bias
Use the ideas you've gleaned from this article to give yourself a platform, then choose the best method to meet your goals while staying within your time and cost limits.
If it isn't obvious which method you should choose, use this strategy:
Clarify your research goals
Clarify how accurate your research results must be to reach your goals
Evaluate your goals against time and budget
List the two or three most obvious sampling methods that will work for you
Confirm the availability of your resources (researchers, computer time, etc.)
Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints
Make your decision
Effective market research is the basis of successful marketing, advertising, and future productivity. By selecting the most appropriate sampling methods, you will collect the most useful market data and make the most effective decisions.
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Learning objectives.
Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques. The goals and techniques associated with probability samples differ from those of nonprobability samples. We’ll explore those unique goals and techniques in this section.
Unlike nonprobability sampling, probability sampling Sampling techniques for which a person’s likelihood of being selected for membership in the sample is known. refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is known. You might ask yourself why we should care about a study element’s likelihood of being selected for membership in a researcher’s sample. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample A sample that resembles the population from which it was drawn in all the ways that are important for the research being conducted. from which to collect data. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you better make sure that your sample doesn’t contain only women. That’s a bit of an oversimplification, but the point with representativeness is that if your population varies in some way that is important to your study, your sample should contain the same sorts of variation.
Obtaining a representative sample is important in probability sampling because a key goal of studies that rely on probability samples is generalizability The idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. . In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s target population have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection The principle that all elements in a researcher’s target population have an equal chance of being selected for inclusion in the study. . Random selection is a mathematical process that we won’t go into too much depth about here, but if you have taken or plan to take a statistics course, you’ll learn more about it there. The important thing to remember about random selection here is that, as previously noted, it is a core principal of probability sampling. If a researcher uses random selection techniques to draw a sample, he or she will be able to estimate how closely the sample represents the larger population from which it was drawn by estimating the sampling error. Sampling error The extent to which a sample represents its population on a particular parameter. is a statistical calculation of the difference between results from a sample and the actual parameters The actual characteristics of a population on any given variable; determined by measuring all elements in a population (as opposed to measuring elements from a sample). of a population.
There are a variety of probability samples that researchers may use. These include simple random samples, systematic samples, stratified samples, and cluster samples.
Simple random samples The most basic type of probability sample; a researcher begins with a list of every member of his or her population of interest, numbers each element sequentially, and then randomly selects the elements from which he or she will collect data. are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample. To draw a simple random sample, a researcher starts with a list of every single member, or element, of his or her population of interest. This list is sometimes referred to as a sampling frame A list of all elements in a population. . Once that list has been created, the researcher numbers each element sequentially and then randomly selects the elements from which he or she will collect data. To randomly select elements, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks offer such tables as appendices to the text. Perhaps a more accessible source is one of the many free random number generators available on the Internet. A good online source is the website Stat Trek, which contains a random number generator that you can use to create a random number table of whatever size you might need ( http://stattrek.com/Tables/Random.aspx ). Randomizer.org also offers a useful random number generator ( http://randomizer.org ).
As you might have guessed, drawing a simple random sample can be quite tedious. Systematic sampling A researcher divides a study population into relevant subgroups then draws a sample from each subgroup. techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must be able to produce a list of every one of your population elements. Once you’ve done that, to draw a systematic sample you’d simply select every k th element on your list. But what is k , and where on the list of population elements does one begin the selection process? k is your selection interval The distance between elements selected for inclusion in a study. or the distance between the elements you select for inclusion in your study. To begin the selection process, you’ll need to figure out how many elements you wish to include in your sample. Let’s say you want to interview 25 fraternity members on your campus, and there are 100 men on campus who are members of fraternities. In this case, your selection interval, or k , is 4. To arrive at 4, simply divide the total number of population elements by your desired sample size. This process is represented in Figure 7.5 "Formula for Determining Selection Interval for Systematic Sample" .
Figure 7.5 Formula for Determining Selection Interval for Systematic Sample
To determine where on your list of population elements to begin selecting the names of the 25 men you will interview, select a random number between 1 and k , and begin there. If we randomly select 3 as our starting point, we’d begin by selecting the third fraternity member on the list and then select every fourth member from there. This might be easier to understand if you can see it visually. Table 7.2 "Systematic Sample of 25 Fraternity Members" lists the names of our hypothetical 100 fraternity members on campus. You’ll see that the third name on the list has been selected for inclusion in our hypothetical study, as has every fourth name after that. A total of 25 names have been selected.
Table 7.2 Systematic Sample of 25 Fraternity Members
Number | Name | Include in study? | Number | Name | Include in study? | |
---|---|---|---|---|---|---|
1 | Jacob | 51 | Blake | Yes | ||
2 | Ethan | 52 | Oliver | |||
3 | Michael | Yes | 53 | Cole | ||
4 | Jayden | 54 | Carlos | |||
5 | William | 55 | Jaden | Yes | ||
6 | Alexander | 56 | Jesus | |||
7 | Noah | Yes | 57 | Alex | ||
8 | Daniel | 58 | Aidan | |||
9 | Aiden | 59 | Eric | Yes | ||
10 | Anthony | 60 | Hayden | |||
11 | Joshua | Yes | 61 | Brian | ||
12 | Mason | 62 | Max | |||
13 | Christopher | 63 | Jaxon | Yes | ||
14 | Andrew | 64 | Brian |
Number | Name | Include in study? | Number | Name | Include in study? | |
---|---|---|---|---|---|---|
15 | David | Yes | 65 | Matthew | ||
16 | Logan | 66 | Elijah | |||
17 | James | 67 | Joseph | Yes | ||
18 | Gabriel | 68 | Benjamin | |||
19 | Ryan | Yes | 69 | Samuel | ||
20 | Jackson | 70 | John | |||
21 | Nathan | 71 | Jonathan | Yes | ||
22 | Christian | 72 | Liam | |||
23 | Dylan | Yes | 73 | Landon | ||
24 | Caleb | 74 | Tyler | |||
25 | Lucas | 75 | Evan | Yes | ||
26 | Gavin | 76 | Nicholas | |||
27 | Isaac | Yes | 77 | Braden | ||
28 | Luke | 78 | Angel | |||
29 | Brandon | 79 | Jack | Yes | ||
30 | Isaiah | 80 | Jordan | |||
31 | Owen | Yes | 81 | Carter | ||
32 | Conner | 82 | Justin | |||
33 | Jose | 83 | Jeremiah | Yes | ||
34 | Julian | 84 | Robert | |||
35 | Aaron | Yes | 85 | Adrian | ||
36 | Wyatt | 86 | Kevin | |||
37 | Hunter | 87 | Cameron | Yes | ||
38 | Zachary | 88 | Thomas | |||
39 | Charles | Yes | 89 | Austin | ||
40 | Eli | 90 | Chase | |||
41 | Henry | 91 | Sebastian | Yes | ||
42 | Jason | 92 | Levi | |||
43 | Xavier | Yes | 93 | Ian | ||
44 | Colton | 94 | Dominic | |||
45 | Juan | 95 | Cooper | Yes | ||
46 | Josiah | 96 | Luis | |||
47 | Ayden | Yes | 97 | Carson | ||
48 | Adam | 98 | Nathaniel | |||
49 | Brody | 99 | Tristan | Yes | ||
50 | Diego | 100 | Parker | |||
In case you’re wondering how I came up with 100 unique names for this table, I’ll let you in on a little secret: lists of popular baby names can be great resources for researchers. I used the list of top 100 names for boys based on Social Security Administration statistics for this table. I often use baby name lists to come up with pseudonyms for field research subjects and interview participants. See Family Education. (n.d.). Name lab. Retrieved from . |
There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. This is sometimes referred to as the problem of periodicity The tendency for a pattern to occur at regular intervals. . Periodicity refers to the tendency for a pattern to occur at regular intervals. Let’s say, for example, that you wanted to observe how people use the outdoor public spaces on your campus. Perhaps you need to have your observations completed within 28 days and you wish to conduct four observations on randomly chosen days. Table 7.3 "Systematic Sample of Observation Days" shows a list of the population elements for this example. To determine which days we’ll conduct our observations, we’ll need to determine our selection interval. As you’ll recall from the preceding paragraphs, to do so we must divide our population size, in this case 28 days, by our desired sample size, in this case 4 days. This formula leads us to a selection interval of 7. If we randomly select 2 as our starting point and select every seventh day after that, we’ll wind up with a total of 4 days on which to conduct our observations. You’ll see how that works out in the following table.
Table 7.3 Systematic Sample of Observation Days
Number | Day | Include in study? | Number | Day | Include in study? | |
---|---|---|---|---|---|---|
1 | Monday | 15 | Monday | |||
2 | Tuesday | Yes | 16 | Tuesday | Yes | |
3 | Wednesday | 17 | Wednesday | |||
4 | Thursday | 18 | Thursday | |||
5 | Friday | 19 | Friday | |||
6 | Saturday | 20 | Saturday | |||
7 | Sunday | 21 | Sunday | |||
8 | Monday | 22 | Monday | |||
9 | Tuesday | Yes | 23 | Tuesday | Yes | |
10 | Wednesday | 24 | Wednesday | |||
11 | Thursday | 25 | Thursday | |||
12 | Friday | 26 | Friday | |||
13 | Saturday | 27 | Saturday | |||
14 | Sunday | 28 | Sunday |
Do you notice any problems with our selection of observation days? Apparently we’ll only be observing on Tuesdays. As you have probably figured out, that isn’t such a good plan if we really wish to understand how public spaces on campus are used. My guess is that weekend use probably differs from weekday use, and that use may even vary during the week, just as class schedules do. In cases such as this, where the sampling frame is cyclical, it would be better to use a stratified sampling technique A researcher divides the study population into relevant subgroups then draws a sample from within each subgroup. . In stratified sampling, a researcher will divide the study population into relevant subgroups and then draw a sample from each subgroup. In this example, we might wish to first divide our sampling frame into two lists: weekend days and weekdays. Once we have our two lists, we can then apply either simple random or systematic sampling techniques to each subgroup.
Stratified sampling is a good technique to use when, as in our example, a subgroup of interest makes up a relatively small proportion of the overall sample. In our example of a study of use of public space on campus, we want to be sure to include weekdays and weekends in our sample, but because weekends make up less than a third of an entire week, there’s a chance that a simple random or systematic strategy would not yield sufficient weekend observation days. As you might imagine, stratified sampling is even more useful in cases where a subgroup makes up an even smaller proportion of the study population, say, for example, if we want to be sure to include both men’s and women’s perspectives in a study, but men make up only a small percentage of the population. There’s a chance simple random or systematic sampling strategy might not yield any male participants, but by using stratified sampling, we could ensure that our sample contained the proportion of men that is reflective of the larger population.
Up to this point in our discussion of probability samples, we’ve assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let’s say, for example, that you wish to conduct a study of hairstyle preferences across the United States. Just imagine trying to create a list of every single person with (and without) hair in the country. Basically, we’re talking about a list of every person in the country. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling A researcher begins by sampling groups of population elements and then selects elements from within those groups. occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.
Let’s take a look at a couple more examples. Perhaps you are interested in the workplace experiences of public librarians. Chances are good that obtaining a list of all librarians that work for public libraries would be rather difficult. But I’ll bet you could come up with a list of all public libraries without too much hassle. Thus you could draw a random sample of libraries (your cluster) and then draw another random sample of elements (in this case, librarians) from within the libraries you initially selected. Cluster sampling works in stages. In this example, we sampled in two stages. As you might have guessed, sampling in multiple stages does introduce the possibility of greater error (each stage is subject to its own sampling error), but it is nevertheless a highly efficient method.
Jessica Holt and Wayne Gillespie (2008) Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple pychosocial factors affecting intimate partner violence. American Journal of Criminal Justice, 33 , 252–266. used cluster sampling in their study of students’ experiences with violence in intimate relationships. Specifically, the researchers randomly selected 14 classes on their campus and then drew a random subsample of students from those classes. But you probably know from your experience with college classes that not all classes are the same size. So if Holt and Gillespie had simply randomly selected 14 classes and then selected the same number of students from each class to complete their survey, then students in the smaller of those classes would have had a greater chance of being selected for the study than students in the larger classes. Keep in mind with random sampling the goal is to make sure that each element has the same chance of being selected. When clusters are of different sizes, as in the example of sampling college classes, researchers often use a method called probability proportionate to size A cluster sampling technique in which each cluster is given a chance of selection based on its size. (PPS). This means that they take into account that their clusters are of different sizes. They do this by giving clusters different chances of being selected based on their size so that each element within those clusters winds up having an equal chance of being selected.
Table 7.4 Types of Probability Samples
Sample type | Description |
---|---|
Simple random | Researcher randomly selects elements from sampling frame. |
Systematic | Researcher selects every th element from sampling frame. |
Stratified | Researcher creates subgroups then randomly selects elements from each subgroup. |
Cluster | Researcher randomly selects clusters then randomly selects elements from selected clusters. |
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).
We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”
In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.
Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.
This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.
One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.
Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).
OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?
There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).
Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.
This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).
Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.
During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.
A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.
For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.
We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).
Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .
An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.
Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.
Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.
To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.
If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.
The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.
Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.
Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.
Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.
Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.
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Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.
Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.
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Home » Sampling Methods – Types, Techniques and Examples
Table of Contents
Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.
In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.
Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.
Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.
Sampling can be broadly categorized into two main categories:
This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.
Type of Probability Sampling :
This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.
Types of Non-probability Sampling :
Applications of Sampling Methods from different fields:
Probability Sampling Methods Examples:
Non-probability Sampling Methods Examples:
some general steps to conduct sampling methods:
Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.
Sampling methods are particularly useful when:
The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.
Sampling methods allow researchers to:
Here are some characteristics of sampling methods:
Sampling methods have several advantages, including:
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Jeovany martínez-mesa.
1 Faculdade Meridional (IMED) - Passo Fundo (RS), Brazil.
2 University of Adelaide - Adelaide, Australia.
3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.
João luiz bastos.
4 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (RS), Brazil.
In this paper, the basic elements related to the selection of participants for a health research are discussed. Sample representativeness, sample frame, types of sampling, as well as the impact that non-respondents may have on results of a study are described. The whole discussion is supported by practical examples to facilitate the reader's understanding.
To introduce readers to issues related to sampling.
The essential topics related to the selection of participants for a health research are: 1) whether to work with samples or include the whole reference population in the study (census); 2) the sample basis; 3) the sampling process and 4) the potential effects nonrespondents might have on study results. We will refer to each of these aspects with theoretical and practical examples for better understanding in the sections that follow.
In a previous paper, we discussed the necessary parameters on which to estimate the sample size. 1 We define sample as a finite part or subset of participants drawn from the target population. In turn, the target population corresponds to the entire set of subjects whose characteristics are of interest to the research team. Based on results obtained from a sample, researchers may draw their conclusions about the target population with a certain level of confidence, following a process called statistical inference. When the sample contains fewer individuals than the minimum necessary, but the representativeness is preserved, statistical inference may be compromised in terms of precision (prevalence studies) and/or statistical power to detect the associations of interest. 1 On the other hand, samples without representativeness may not be a reliable source to draw conclusions about the reference population (i.e., statistical inference is not deemed possible), even if the sample size reaches the required number of participants. Lack of representativeness can occur as a result of flawed selection procedures (sampling bias) or when the probability of refusal/non-participation in the study is related to the object of research (nonresponse bias). 1 , 2
Although most studies are performed using samples, whether or not they represent any target population, census-based estimates should be preferred whenever possible. 3 , 4 For instance, if all cases of melanoma are available on a national or regional database, and information on the potential risk factors are also available, it would be preferable to conduct a census instead of investigating a sample.
However, there are several theoretical and practical reasons that prevent us from carrying out census-based surveys, including:
All these reasons explain why samples are more frequently used. However, researchers must be aware that sample results can be affected by the random error (or sampling error). 3 To exemplify this concept, we will consider a research study aiming to estimate the prevalence of premalignant skin lesions (outcome) among individuals >18 years residing in a specific city (target population). The city has a total population of 4,000 adults, but the investigator decided to collect data on a representative sample of 400 participants, detecting an 8% prevalence of premalignant skin lesions. A week later, the researcher selects another sample of 400 participants from the same target population to confirm the results, but this time observes a 12% prevalence of premalignant skin lesions. Based on these findings, is it possible to assume that the prevalence of lesions increased from the first to the second week? The answer is probably not. Each time we select a new sample, it is very likely to obtain a different result. These fluctuations are attributed to the "random error." They occur because individuals composing different samples are not the same, even though they were selected from the same target population. Therefore, the parameters of interest may vary randomly from one sample to another. Despite this fluctuation, if it were possible to obtain 100 different samples of the same population, approximately 95 of them would provide prevalence estimates very close to the real estimate in the target population - the value that we would observe if we investigated all the 4,000 adults residing in the city. Thus, during the sample size estimation the investigator must specify in advance the highest or maximum acceptable random error value in the study. Most population-based studies use a random error ranging from 2 to 5 percentage points. Nevertheless, the researcher should be aware that the smaller the random error considered in the study, the larger the required sample size. 1
The sample frame is the group of individuals that can be selected from the target population given the sampling process used in the study. For example, to identify cases of cutaneous melanoma the researcher may consider to utilize as sample frame the national cancer registry system or the anatomopathological records of skin biopsies. Given that the sample may represent only a portion of the target population, the researcher needs to examine carefully whether the selected sample frame fits the study objectives or hypotheses, and especially if there are strategies to overcome the sample frame limitations (see Chart 1 for examples and possible limitations).
Examples of sample frames and potential limitations as regards representativeness
Sample frames | Limitations |
---|---|
Population census | • If the census was not conducted in recent years, areas with high migration might be outdated |
• Homeless or itinerant people cannot be represented | |
Hospital or Health Services records | • Usually include only data of affected people (this is a limitation, depending on the study objectives) |
• Depending on the service, data may be incomplete and/or outdated | |
• If the lists are from public units, results may differ from those who seek private services | |
School lists | • School lists are currently available only in the public sector |
• Children/ teenagers not attending school will not be represented | |
• Lists are quickly outdated | |
• There will be problems in areas with high percentage of school absenteeism | |
List of phone numbers | • Several population groups are not represented: individuals with no phone line at home (low-income families, young people who use only cell phones), those who spend less time at home, etc. |
Mailing lists | • Individuals with multiple email addresses, which increase the chance of selection compared to individuals with only one address |
• Individuals without an email address may be different from those who have it, according to age, education, etc. |
Sampling can be defined as the process through which individuals or sampling units are selected from the sample frame. The sampling strategy needs to be specified in advance, given that the sampling method may affect the sample size estimation. 1 , 5 Without a rigorous sampling plan the estimates derived from the study may be biased (selection bias). 3
In figure 1 , we depict a summary of the main sampling types. There are two major sampling types: probabilistic and nonprobabilistic.
Sampling types used in scientific studies
In the context of nonprobabilistic sampling, the likelihood of selecting some individuals from the target population is null. This type of sampling does not render a representative sample; therefore, the observed results are usually not generalizable to the target population. Still, unrepresentative samples may be useful for some specific research objectives, and may help answer particular research questions, as well as contribute to the generation of new hypotheses. 4 The different types of nonprobabilistic sampling are detailed below.
Convenience sampling : the participants are consecutively selected in order of apperance according to their convenient accessibility (also known as consecutive sampling). The sampling process comes to an end when the total amount of participants (sample saturation) and/or the time limit (time saturation) are reached. Randomized clinical trials are usually based on convenience sampling. After sampling, participants are usually randomly allocated to the intervention or control group (randomization). 3 Although randomization is a probabilistic process to obtain two comparable groups (treatment and control), the samples used in these studies are generally not representative of the target population.
Purposive sampling: this is used when a diverse sample is necessary or the opinion of experts in a particular field is the topic of interest. This technique was used in the study by Roubille et al, in which recommendations for the treatment of comorbidities in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis were made based on the opinion of a group of experts. 6
Quota sampling: according to this sampling technique, the population is first classified by characteristics such as gender, age, etc. Subsequently, sampling units are selected to complete each quota. For example, in the study by Larkin et al., the combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally-advanced melanoma, stage IIIC or IV, with BRAF mutation. 7 The study recruited 495 patients from 135 health centers located in several countries. In this type of study, each center has a "quota" of patients.
"Snowball" sampling : in this case, the researcher selects an initial group of individuals. Then, these participants indicate other potential members with similar characteristics to take part in the study. This is frequently used in studies investigating special populations, for example, those including illicit drugs users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana. 8
In the context of probabilistic sampling, all units of the target population have a nonzero probability to take part in the study. If all participants are equally likely to be selected in the study, equiprobabilistic sampling is being used, and the odds of being selected by the research team may be expressed by the formula: P=1/N, where P equals the probability of taking part in the study and N corresponds to the size of the target population. The main types of probabilistic sampling are described below.
Simple random sampling: in this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers. An example is the study by Pimenta et al, in which the authors obtained a listing from the Health Department of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants. 9
Systematic random sampling: in this case, participants are selected from fixed intervals previously defined from a ranked list of participants. For example, in the study of Kelbore et al, children who were assisted at the Pediatric Dermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order. 10
Stratified sampling: in this type of sampling, the target population is first divided into separate strata. Then, samples are selected within each stratum, either through simple or systematic sampling. The total number of individuals to be selected in each stratum can be fixed or proportional to the size of each stratum. Each individual may be equally likely to be selected to participate in the study. However, the fixed method usually involves the use of sampling weights in the statistical analysis (inverse of the probability of selection or 1/P). An example is the study conducted in South Australia to investigate factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated. 11
Cluster sampling: in this type of probabilistic sampling, groups such as health facilities, schools, etc., are sampled. In the above-mentioned study, the selection of households is an example of cluster sampling. 11
Complex or multi-stage sampling: This probabilistic sampling method combines different strategies in the selection of the sample units. An example is the study of Duquia et al. to assess the prevalence and factors associated with the use of sunscreen in adults. The sampling process included two stages. 12 Using the 2000 Brazilian demographic census as sampling frame, all 404 census tracts from Pelotas (Southern Brazil) were listed in ascending order of family income. A sample of 120 tracts were systematically selected (first sampling stage units). In the second stage, 12 households in each of these census tract (second sampling stage units) were systematically drawn. All adult residents in these households were included in the study (third sampling stage units). All these stages have to be considered in the statistical analysis to provide correct estimates.
Frequently, sample sizes are increased by 10% to compensate for potential nonresponses (refusals/losses). 1 Let us imagine that in a study to assess the prevalence of premalignant skin lesions there is a higher percentage of nonrespondents among men (10%) than among women (1%). If the highest percentage of nonresponse occurs because these men are not at home during the scheduled visits, and these participants are more likely to be exposed to the sun, the number of skin lesions will be underestimated. For this reason, it is strongly recommended to collect and describe some basic characteristics of nonrespondents (sex, age, etc.) so they can be compared to the respondents to evaluate whether the results may have been affected by this systematic error.
Often, in study protocols, refusal to participate or sign the informed consent is considered an "exclusion criteria". However, this is not correct, as these individuals are eligible for the study and need to be reported as "nonrespondents".
In general, clinical trials aim to obtain a homogeneous sample which is not necessarily representative of any target population. Clinical trials often recruit those participants who are most likely to benefit from the intervention. 3 Thus, the more strict criteria for inclusion and exclusion of subjects in clinical trials often make it difficult to locate participants: after verification of the eligibility criteria, just one out of ten possible candidates will enter the study. Therefore, clinical trials usually show limitations to generalize the results to the entire population of patients with the disease, but only to those with similar characteristics to the sample included in the study. These peculiarities in clinical trials justify the necessity of conducting a multicenter and/or global studiesto accelerate the recruitment rate and to reach, in a shorter time, the number of patients required for the study. 13
In turn, in observational studies to build a solid sampling plan is important because of the great heterogeneity usually observed in the target population. Therefore, this heterogeneity has to be also reflected in the sample. A cross-sectional population-based study aiming to assess disease estimates or identify risk factors often uses complex probabilistic sampling, because the sample representativeness is crucial. However, in a case-control study, we face the challenge of selecting two different samples for the same study. One sample is formed by the cases, which are identified based on the diagnosis of the disease of interest. The other consists of controls, which need to be representative of the population that originated the cases. Improper selection of control individuals may introduce selection bias in the results. Thus, the concern with representativeness in this type of study is established based on the relationship between cases and controls (comparability).
In cohort studies, individuals are recruited based on the exposure (exposed and unexposed subjects), and they are followed over time to evaluate the occurrence of the outcome of interest. At baseline, the sample can be selected from a representative sample (population-based cohort studies) or a non-representative sample. However, in the successive follow-ups of the cohort member, study participants must be a representative sample of those included in the baseline. 14 , 15 In this type of study, losses over time may cause follow-up bias.
Researchers need to decide during the planning stage of the study if they will work with the entire target population or a sample. Working with a sample involves different steps, including sample size estimation, identification of the sample frame, and selection of the sampling method to be adopted.
Financial Support: None.
* Study performed at Faculdade Meridional - Escola de Medicina (IMED) - Passo Fundo (RS), Brazil.
What’s the best way to gather data that doesn’t leave you second-guessing?
If you’re dealing with research, you know how important it is to get solid, reliable data.
That’s where quantitative observation steps in.
In this article, we’ll look into everything you need to know about quantitative observation.
We’ll cover what it is, how it’s different from qualitative observation, and why it’s so widely used across various fields like education, healthcare, and marketing.
By the end, you’ll see why this method is a go-to for researchers who need precise, measurable results:
Quantitative observation is a research method that involves collecting and analyzing numerical data about people, objects, or events. It’s often used to measure specific variables, such as frequency, duration, or intensity. Quantitative observation can be conducted in various settings, including laboratories, classrooms, and public places.
When it comes to research, you’ll often hear about two main types of observations: quantitative and qualitative .
Both have their place, but they’re pretty different in what they focus on and how they’re used.
Let’s break it down.
Quantitative observations are all about numbers. If you can count it, measure it, or express it in figures, it falls into the quantitative camp.
Think of things like:
This type of observation gives you hard data that you can analyze and compare.
On the other hand, qualitative observations focus on descriptions. They’re about the qualities of what you’re observing.
For example, instead of saying, “The car is going 60 mph,” you’d say, “The car is moving quickly.” It’s more about what something is like than how much there is of it.
Quantitative observations are usually more objective. The data you gather isn’t influenced by opinions or feelings – it’s just numbers . This makes it reliable when you’re looking for facts that can be backed up by statistical analysis.
Qualitative observations, however, are more subjective.
They depend on the observer’s perspective and interpretation. Two people might describe the same event differently, which can make this type of observation more varied and rich, but also less consistent.
When you gather quantitative data, you’re looking for specific measurements.
This might include things like:
It’s precise and can be used in graphs, charts, and statistical models.
Qualitative data, though, is more about the details that don’t fit into neat little boxes.
It includes things like colors, textures, feelings, and experiences. This data is harder to measure, but it adds depth and context to your research.
Quantitative observation methods are usually standardized. You use the same tools and processes each time to make sure your data is consistent. This is great for making comparisons across different studies or groups.
Qualitative observation, in contrast, is more flexible. It allows you to explore your subject in a more open-ended way, which can lead to new insights and understanding that you might miss with a more rigid approach.
So, whether you’re counting heads or describing feelings, both quantitative and qualitative observations play important roles in research. Each brings something valuable to the table, helping you see the full picture.
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Quantitative observation has attractive advantages, and the most important ones are:
When you’re collecting quantitative observation data, you’re gathering facts that are clear-cut and free from personal bias.
This makes the data objective and reliable, which is a big deal in scientific research.
With these numbers in hand, you can engage in statistical analysis, where patterns and relationships start to emerge.
The beauty of this approach is that it strips away guesswork, leaving you with solid evidence that can back up your findings.
Unlike qualitative observation, which leans on descriptions, quantitative observations give you something concrete to work with.
When it comes to measuring and comparing variables, quantitative research is the tool of choice.
Quantitative observation methods focus on capturing exact values – whether it’s the height of a plant, the number of customers, or the temperature of a liquid.
This precision is key in the research process because it lets you compare different factors head-to-head.
With standardized observation techniques, the data you gather is consistent and reliable across the board.
It doesn’t matter if you’re working on a big project or just trying to understand a small detail, quantitative observations help you keep everything measured and comparable.
In scientific research, testing hypotheses is a key part of the job.
Quantitative observation research plays a huge role here.
Thanks to gathering quantitative data through systematic observation, you can put your ideas to the test.
The numbers you collect can either support your hypothesis or show you where things aren’t adding up.
Plus, as you gather more data, you start to see patterns and trends that weren’t obvious at first.
This is where quantitative and qualitative observation work hand in hand.
The hard numbers from quantitative research point you in the right direction, while qualitative observations add the context you need to understand the bigger picture.
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Quantitative observation can be used in a variety of fields, including:
Imagine a store tracking how many customers stop to look at a new product display or how long they spend browsing a particular aisle.
These numbers tell a story about what catches people’s attention and what doesn’t.
For instance, a study published in the International Journal of Advertising explored the effectiveness of retail window displays as part of the marketing mix.
The researchers worked with Boots the Chemists and Nottingham Business School to measure how window display design influences consumer-buying behavior.
They found that connecting buying behavior to specific marketing elements, like window displays, made sales forecasting more predictable.
If a lot of people are lingering by a new clothing line but not buying, it might suggest they’re interested but need a nudge, maybe a sale or better positioning.
This kind of data helps businesses tweak their strategies to match customer behavior.
In education, teachers often use quantitative observation to see how students are engaging with their lessons.
For example, a study presented in the Journal of Educational Psychology introduced the Behavioral Engagement Related to Instruction (BERI) protocol.
This protocol was specifically designed for large university classrooms to measure student engagement levels through quantitative observation data.
The BERI protocol involves tracking student behaviors in real-time, offering teachers immediate feedback on how well students are engaging with the material.
For instance, if students are actively participating in discussions or focusing on tasks during lectures, the data collected can show high levels of engagement.
On the other hand, if students appear distracted or disengaged, the data can highlight areas where the teaching method might need adjustment.
These numbers help educators identify which teaching strategies are working and which might need a different approach. If the protocol shows that students are more engaged during interactive lessons compared to traditional lectures, it indicates a need to incorporate more interactive elements into the curriculum.
This kind of targeted feedback helps instructors refine their methods to improve student learning outcomes.
Psychologists use quantitative observation to dig into the details of human behavior.
For example, a well-known study in the field of memory research conducted by Ebbinghaus in the late 19th century focused on how quickly people forget information.
In this study, participants were asked to memorize lists of nonsense syllables, and then their recall was tested at different time intervals.
The researchers measured how many syllables participants could remember after varying lengths of time, such as immediately after learning, after a few hours, and after several days.
The numbers collected from these tests helped to map out the “forgetting curve,” which shows that memory retention decreases sharply soon after learning but then levels off over time.
This type of quantitative data is often used in psychology, as it helps researchers understand how memory works and how factors like stress or fatigue might impact recall.
In sociology, quantitative observation helps researchers understand broader social trends.
A notable study published in the American Political Science Review examined voting behavior across various neighborhoods in a large metropolitan area.
The researchers collected quantitative data on voter turnout by tracking the number of people who participated in elections in different districts over several election cycles.
The study revealed that neighborhoods with lower voter turnout often had higher levels of economic disadvantage, lower educational attainment, and less access to transportation.
These patterns were not immediately obvious without the data. By analyzing the numbers, sociologists were able to identify the social factors that contributed to lower voting rates.
This type of research helps sociologists understand the underlying reasons for such trends and suggests potential interventions.
For instance, the findings might prompt community programs aimed at increasing voter education or improving access to polling stations.
Quantitative observation in sociology is essential for uncovering these hidden patterns and driving efforts to address social inequalities.
In healthcare, quantitative observation is useful for evaluating the effectiveness of medical treatments.
A well-known example is the clinical trial of the drug Streptomycin in the treatment of tuberculosis, conducted in the late 1940s.
This was one of the first randomized controlled trials (RCTs) in medical history, which set the standard for future clinical research.
In this study, researchers quantitatively observed and recorded the number of patients who showed improvement in their tuberculosis symptoms after taking Streptomycin compared to those who received a placebo.
The results showed a statistically significant improvement in the recovery rates among those treated with the drug, confirming its effectiveness.
This study provided clear evidence of the drug’s efficacy, shaping the future of tuberculosis treatment and demonstrating the power of quantitative observation in healthcare.
Thanks to systematically tracking patient outcomes, healthcare professionals were able to make informed decisions about adopting Streptomycin as a standard treatment.
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SurveyLab is a tool that takes quantitative observation to the next level.
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With SurveyLab, you can create online tests that score automatically and make data collection straightforward.
It doesn’t matter if you’re measuring customer satisfaction, employee engagement, or any other metric, the platform’s scoring mechanism helps you keep everything in check.
But SurveyLab isn’t just about gathering data – it’s about making sense of it.
The combination of scoring, metrics, data collection, and data analysis tools means you can conduct quantitative observations that lead to real, actionable insights.
It’s like having a full toolkit at your disposal, ready to help you make informed decisions based on solid data.
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Journal of Health, Population and Nutrition volume 43 , Article number: 135 ( 2024 ) Cite this article
Metrics details
By studying the dietary habits of patients with bacterial vaginosis (BV) and the controls, we aim to find out whether the dietary intakes of phytochemicals could reduce the odds of BV. To the best of our knowledge, no study has ever examined the matter before. Therefore, we decided to conduct this secondary analysis of case-control study to examine the association between dietary phytochemicals and BV.
This case-control study was conducted at the gynecological clinic of Imam Hossein Hospital using a convenience sampling method from November 2020 to June 2021. To diagnose BV, all participants underwent examination by a gynecologist, assessing the presence of 3 or 4 criteria from the Amsel criteria. A validated semi-quantitative food frequency questionnaire was used. The phytochemical index was determined using McCarty’s method. To assess the association between dietary phytochemical intake and the odds of BV, binary logistic regression was utilized.
After adjusting for potential confounders, the association between phytochemical index and BV remained significant (odds ratio (OR) = 0.349, 95% confidence interval (CI): 0.176–0.695, p -value = 0.003). Furthermore, each unit increase in fat intake was associated with higher odds of BV (OR = 1.008, 95% CI: 1.002–1.014, p -value = 0.006), and a positive family history of BV continued to show significantly increased odds of BV (OR = 3.442, 95% CI: 2.068–5.728, p -value < 0.001).
In summary, the findings of this study indicate that increased consumption of dietary phytochemicals is associated with a reduced risk of BV among Iranian women of reproductive age. Additional research, especially longitudinal dietary studies, is required to explore the potential impact of dietary modifications on BV.
The vagina hosts a wide variety of bacteria which are dynamic and ever-changing. However, under normal circumstances, lactobacilli make up the majority of them. This optimum proportion is believed to fight off infections by producing lactic acid and maintaining an acidic environment. Disruption of the said proportion, perhaps through the increase in the amount of various anaerobic bacteria, is called bacterial vaginosis (BV) [ 1 ] and is believed to cause several complications for women of childbearing age [ 2 ]. The dysbiosis of the vaginal microbiota might pave the way for the contraction of sexually transmitted infections, urogenital infections, and pelvic inflammatory disease [ 3 ]. Moreover, up to 40% of preterm births are thought to be caused by some sort of vaginal or intrauterine infection [ 4 ]. Ethnical differences, sexual activities, vaginal douching, and fluctuations in estrogen levels are deemed to be the main risk factors. However, despite affecting 1 in every 3 women, many aspects of the disease, especially the dietary aspects, remain unknown and therefore there is still a call for further research.
Phytochemicals are organic compounds produced by plants, mainly to serve protective purposes and, therefore considered secondary metabolites [ 5 ]. So far, thousands of phytochemicals have been discovered, extracted, and carefully studied and are believed to exert many beneficial effects [ 6 ]. Some of the significant phytochemicals are alkaloids, saponins, tannins, flavonoids, and steroids which have also been shown to stimulate strong antioxidant, antimicrobial, antiallergic, and antiviral activities [ 7 , 8 ]. Their antibacterial properties have especially been of notice. Despite being less potent than antibiotics, research has shown phytochemical supplementation in adjunction to antibiotics could strengthen the efficacy of antibiotic therapy, especially when they are beginning to fall short against bacteria due to their increased development of resistance [ 9 ]. Through a recently devised index, we can now estimate the approximate dietary intake of phytochemicals. Observational studies examining the benefits of dietary phytochemicals have shown their efficacy in reducing the risk of cancer, heart disease, and neurodegenerative diseases [ 10 , 11 , 12 ].
By studying the dietary habits of patients with BV and the controls, we aim to find out whether the dietary intakes of phytochemicals could reduce the odds of BV. To the best of our knowledge, no study has ever examined the matter before. Therefore, we decided to conduct this secondary analysis of case-control study to examine the association between dietary phytochemicals and BV.
Study population.
This secondary analysis of case-control study was conducted at the gynecological clinic of Imam Hossein Hospital using a convenience sampling method from November 2020 to June 2021. The sample size was determined based on the study by Fahim et al. [ 13 ]. We enrolled 151 women with BV in the case group and 143 healthy women in the control group. This study was a secondary analysis and detailed information on the sample size calculation and other inclusion criteria can be found in previous studies [ 14 , 15 , 16 , 17 , 18 ]. To diagnose BV, all participants underwent examination by a gynecologist, assessing the presence of 3 or 4 criteria from the Amsel criteria, which include: a homogeneous and watery vaginal discharge, vaginal pH greater than 4.5, presence of 20% clue cells observed during saline microscopy, and a fishy odor detected after adding 10% potassium hydroxide to the discharge slide [ 19 , 20 ].
Eligible participants met the following inclusion criteria: willingness to participate and signing the consent form, aged between 15 and 45 years, not pregnant, not in menopause, and not using antibiotics, probiotics, hormonal contraceptives, vaginal douches, or immunosuppressive medications. They also did not have systemic illnesses, autoimmune diseases, chronic infections, diet-related chronic diseases like diabetes and cardiovascular disease, or any uterine cavity issues such as fibroids, polyps, or hysterectomy. The only difference in inclusion criteria between the case and control groups was the presence of a BV diagnosis for the case group and the absence of ongoing or previous BV or BV treatment for the control group.
Participants in both groups were excluded if they did not complete 60% or more of the food frequency questionnaire (FFQ), if their reported energy intake deviated beyond ± 3 standard deviations (SD) from the average energy intake, or if they expressed unwillingness to continue participating in the study.
A checklist was employed to gather data on participants’ age, family history of BV, polycystic ovary syndrome, and pregnancy, pregnancy history, menstrual cycle, education level, occupational status, smoking habits, number of sexual partners, and monthly family income. Questions regarding alcohol and opium use were omitted due to specific religious and cultural beliefs among Iranians. Anthropometric assessments included weight measurement using a reliable scale with a precision of 100 g, height measurement in the standing position without shoes with 1 mm accuracy, and waist circumference (WC) measurement to assess central adiposity using a measuring tape accurate to the nearest 1 mm, conducted by a trained examiner. Body Mass Index (BMI) was calculated by dividing weight in kilograms by the square of height in meters. Physical activity levels were assessed using the International Physical Activity Questionnaire (IPAQ), the validity and reliability of which have been previously established in studies conducted in Iran [ 21 ].
A validated semi-quantitative FFQ consisting of 168 food items [ 22 ], each with a standard and commonly used serving size in Iran, was used to estimate the participants’ dietary intake over the year preceding the interview. During the interview, participants were informed about the average size of each food item. They then reported how often they consumed each item, specifying the frequency on a daily, weekly, or monthly basis. The reported values for each food were converted to grams using a household scale guide. The average daily intakes of energy and nutrients were calculated using the Iranian food composition Table [ 23 ] and the USDA food composition Table [ 24 ].
The phytochemical index was determined using McCarty’s method [ 25 ] as follows: [phytochemical index = (daily energy from phytochemicals- rich foods (kcal) / total daily energy intake (kcal)) × 100]. First, the energy intake from each phytochemical-rich food item was calculated based on their total gram intake. Then, the total energy intake from all phytochemical-rich foods was determined. These foods included whole grains, legumes, nuts, olives and olive oil, soy products, seeds, tea, coffee, and spices. Natural vegetable and fruit juices, along with tomato sauces, were included in the vegetable and fruit groups because of their high phytochemical content. However, potatoes and pickled vegetables were excluded from the vegetable groups due to their low phytochemical content [ 26 , 27 ]. The total phytochemical index intake was then classified as either below or above the mean intake.
All statistical analyses were conducted using SPSS (Statistical Package for the Social Sciences, version 23, Chicago, IL, United States). The Chi-square and Kruskal-Wallis tests were used to compare categorical and non-parametric baseline variables between tertiles of the phytochemical index in both the case and control groups. Continuous variables were presented as medians (with 25th-75th confidence intervals), and categorical variables as percentages. To assess the association between dietary phytochemical intake and the odds of bacterial vaginosis (BV), binary logistic regression was utilized in both crude and adjusted models (using the Backward LR method for multivariate analysis), calculating odds ratios (OR) with 95% confidence intervals (CI). The second model adjusted for potential confounders, which were selected based on a p -value < 0.25 in the univariate analysis (adjusted for age (years), BMI (kg/m 2 ), fat intake (g/day), and familial history of BV (no/yes)).
Table 1 shows significant differences in age ( p -value = 0.032), BMI ( p -value = 0.002), pregnancy history ( p -value = 0.002), pregnancy number ( p -value = 0.003), and menstrual cycle ( p -value = 0.010) across phytochemical index tertiles within the case group. Additionally, all nutrient and food group intakes differed significantly across phytochemical index tertiles in both case and control groups ( p -value < 0.001), except for seeds ( p -value = 0.066) and legumes ( p -value = 0.174) in the case group, and seeds ( p -value = 0.178) in the control group. Other sources of phytochemical index did not show significant differences ( p -value = 0.100) in the case group.
Table 2 presents the results of both univariate and multivariate regression models assessing the relationship between phytochemical index and other variables with the risk of BV. In the univariate analysis, compared to the lowest tertile of phytochemical index, the highest tertile showed significantly lower odds of BV (OR = 0.514, 95% CI: 0.290–0.909, p -value = 0.022). Additionally, a significantly higher odds of BV was observed in individuals with a positive family history of BV compared to the reference group (OR = 3.595, 95% CI: 2.190–5.900, p -value < 0.001).
After adjusting for potential confounders (variables with p -value < 0.25 in univariate analysis), the association between phytochemical index and BV remained significant (OR = 0.349, 95% CI: 0.176–0.695, p -value = 0.003). Furthermore, each unit increase in fat intake was associated with higher odds of BV (OR = 1.008, 95% CI: 1.002–1.014, p -value = 0.006), and a positive history of BV continued to show significantly increased odds of BV (OR = 3.442, 95% CI: 2.068–5.728, p -value < 0.001).
In the current study, by investigating the dietary habits of patients with BV and controls, we found that a higher intake of dietary phytochemicals is associated with a lower risk of BV. The association remained significant even after adjustment for potential cofounders.
Research investigating the association between diet and BV is scarce. In a study on 208 Iranian women with BV, it was reported that participants who were supplemented daily with vitamin D responded better to the treatment compared to the placebo group [ 28 ]. A case-control study reported that the serum level of 25-hydroxy vitamin D was significantly lower in participants with BV compared to healthy participants [ 29 ]. It has been suggested that sufficient vitamin D could protect women against BV through the production of some antimicrobial peptides that exist in the lysosomes of macrophages and neutrophils [ 30 ]. Furthermore, It has also been shown that subclinical iron deficiency in early pregnancy might lead to BV [ 31 ]. Iron deficiency may weaken the host response against vaginal bacterial colonization [ 32 ]. Administration of probiotic supplements has also been proven efficient in treating BV patients [ 33 ]. Since probiotics produce beneficial metabolites, their impact goes beyond the well-known benefits to the intestines [ 34 , 35 ]. They could lower cholesterol levels [ 36 ] and improve the absorption of magnesium and calcium [ 37 ], all of which are said to help reduce inflammation [ 38 , 39 ].
Research abounds investigating the antimicrobial effects of phytochemicals and they were shown to be significantly efficient against a broad spectrum of bacteria. Among them, Flavonols, Flavonols, and phenolic acids are of significance. They were shown to be able to overcome the development of resistance in bacterial pathogens and fight off bacterial infections [ 40 ]. For instance, a certain flavonoid was reported to reverse the β-lactam antibiotic resistance of S. aureus [ 41 ]. Moreover, Zhao et al. investigated a specific phytochemical in green tea and found that it may inhibit the enzyme β-lactamase that blocks the effects of antibiotics such as cefotaxime and imipenem [ 42 ]. The proposed mechanisms through which they exert their impacts are as follows; they interact with the cytoplasmic membrane, alterations in the bacterial cell wall and cell membrane, reduce the pH values, suppress biofilm formation, and reduce the extracellular polysaccharide activity [ 43 , 44 ].
However, all the mentioned studies have been conducted on a handful of phytochemicals in an in-vitro setting. Despite their accuracy, the process takes a lot of time and resources. Hence, there was still a need to investigate the matter in a wide population. Then came the phytochemical index, which measures the phytochemical content in food composition databases. Subsequently, more population-based studies have emerged to investigate the effects of these compounds on chronic diseases. Most of which, yielded positive results [ 45 ]. For once, Kim et al. showed that high consumption of phytochemical-rich foods is associated with lower inflammation [ 46 ]. A case-control study indicated that higher consumption of phytochemicals is related to lower risk of pre-diabetes [ 47 ]. Another case-control study revealed a reverse association between the consumption of phytochemicals and the risk of breast cancer [ 48 ]. Finally, a meta-analysis of nine cross-sectional studies revealed that a high consumption of phytochemicals is associated with a reduced risk of overweight and obesity [ 49 ].
To the best of our knowledge, this is the first study to investigate the association between dietary-derived phytochemicals and BV which could provide further understanding of these compounds. The mentioned method for the calculation and evaluation of the dietary phytochemical index has been performed on another Iranian population study, so has been validated [ 50 ]. Though the method by which our results were generated is certainly more time and cost-efficient than in vitro studies, some limitations should be noted. First, since FFQ is a memory-dependent assessment tool, the chances of recall bias in reporting dietary intake are high. Moreover, FFQ lacks detailed information on how the food is prepared and is limited to a fixed list of foods, so it may not properly capture the eating patterns of the studied population. Second, the case-control nature of our study was another limitation, as it prevented us from inferring causality. Third, phytochemicals abound in plant foods, such as vegetables, fruit, whole grains, nuts, and legumes [ 51 ]. So the consumption of phytochemical-rich plant foods provides other beneficial nutrients such as fiber, B vitamins, folate, and Vitamin E [ 52 , 53 , 54 ]. Hence, pinning down the reported results only on phytochemicals may not be completely accurate, although we tried to nullify the effects of these nutrients by controlling for them. Finally, there might be a risk of selection bias as our subjects were enrolled from a hospital, thus, the study result might not be attributable to society as many patients may be undiagnosed or might resort to home remedies and not be hospitalized.
In summary, the findings of this study indicate that increased consumption of dietary phytochemicals is associated with a reduced risk of BV among Iranian women of reproductive age. Hence, regular intake of dietary phytochemicals could be introduced as a potentially effective approach in the prevention and management of BV. Additional research, especially longitudinal dietary studies, is required to explore the potential impact of dietary modifications on BV.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Department of Microbiology, School of Medicine, Babol University of Medical Sciences, Babol, Iran
Aynaz Khademian
Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
Morvarid Noormohammadi
Student Research Committee, Faculty of Public Health Branch, Iran University of Medical Sciences, Tehran, Iran
Department of Midwifery, Faculty of Nursing and Midwifery, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
Mozhgan Hafizi Moori
Students’ Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
Maede Makhtoomi
Department of Community Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
Sedighe Esmaeilzadeh & Mehran Nouri
Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Ghazaleh Eslamian
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A.K., M.N., M.H.M., M.M. and M.N.; Contributed to writing the first draft. M.N. and G.E.; Contributed to all data and statistical analysis and interpretation of data. S.E., M.N. and G.E.; Contributed to the research concept, supervised the work, and revised the manuscript. All authors read and approved the final manuscript.
Correspondence to Mehran Nouri or Ghazaleh Eslamian .
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This study was conducted in accordance with the ethical standards of the declaration of Helsinki and was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.NNFTRI.REC.1399.054). All participants read and signed the informed consent form.
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Khademian, A., Noormohammadi, M., Moori, M.H. et al. The association between dietary phytochemical index and bacterial vaginosis risk: secondary analysis of case-control study. J Health Popul Nutr 43 , 135 (2024). https://doi.org/10.1186/s41043-024-00631-2
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DOI : https://doi.org/10.1186/s41043-024-00631-2
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Sampling techniques . Sampling in quantitative research is a critical component that involves selecting a representative subset of individuals or cases from a larger population and often employs sampling techniques based on probability theory. 41 The goal of sampling is to obtain a sample that is large enough and representative of the target ...
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.
Probability-based sampling methods are most commonly used in quantitative research, especially when it's important to achieve a representative sample that allows the researcher to generalise their findings. Non-probability sampling, on the other hand, refers to sampling methods in which the selection of participants is not statistically random.
Types of Sampling Techniques in Quantitative Research. There are two main types of sampling techniques are observed—probability and non-probability sampling (Malhotra & Das, 2010; Sekaran & Bougie, 2016). If the population is known and each element has an equal chance of being picked, then probability sampling applies.
We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include ...
Understand sampling methods in research, from simple random sampling to stratified, systematic, and cluster sampling. Learn how these sampling techniques boost data accuracy and representation, ensuring robust, reliable results. Check this article to learn about the different sampling method techniques, types and examples.
Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.
The purpose of this section is to discuss various sampling methods used in research. After finalizing the research question and the research design, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the "Sampling Method" [Figure 1].
Obtaining Samples for Population Generalizability. In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.. A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population.
Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling. To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance of being selected.
Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research ...
Sampling types. There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1, 2] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee ...
The research used quantitative methodology and relied on two data acquisition techniques. ... of clinical research findings is the sampling method. In this educational article, we are explaining ...
Non-probability Sampling Methods. Another class of sampling methods is known as non-probability sampling methods because not every member in a population has an equal probability of being selected to be in the sample. This type of sampling method is sometimes used because it's much cheaper and more convenient compared to probability sampling ...
These can be further refined, which we'll cover shortly. You can then decide which approach best suits your research project. Probability sampling method. Probability sampling is used in quantitative research, so it provides data on the survey topic in terms of numbers. Probability relates to mathematics, hence the name 'quantitative research'.
Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques.
Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study. Sampling in Qualitative Research Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size.
Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research. Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of ...
Sampling is used in research studies when it is unlikely that researchers will be able to collect all the necessary ... A descriptive quantitative method using Cropwat 8.0 was employed to ...
The essential topics related to the selection of participants for a health research are: 1) whether to work with samples or include the whole reference population in the study (census); 2) the sample basis; 3) the sampling process and 4) the potential effects nonrespondents might have on study results. We will refer to each of these aspects ...
Quantitative observation is a research method that involves collecting and analyzing numerical data about people, objects, or events. It's often used to measure specific variables, such as frequency, duration, or intensity. Quantitative observation can be conducted in various settings, including laboratories, classrooms, and public places. ...
What are sampling procedures? Sampling in qualitative research has a different meaning than it does in quantitative research. Qualitative sampling, you are looking to find a group of individuals or a culture or a social organization in which you can get rich description of the load experience of either the question under inquiry or the culture or social organization under inquiry.
By studying the dietary habits of patients with bacterial vaginosis (BV) and the controls, we aim to find out whether the dietary intakes of phytochemicals could reduce the odds of BV. To the best of our knowledge, no study has ever examined the matter before. Therefore, we decided to conduct this secondary analysis of case-control study to examine the association between dietary ...