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What are homogeneous and heterogeneous populations?

what is homogeneous population in research

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Homogeneity, Homogeneous Data & Homogeneous Sampling

Statistics Definitions > Homogeneity & Homogeneous Data

What is Homogeneity?

A data set is homogeneous if it is made up of things (i.e. people, cells or traits) that are similar to each other. For example a data set made up of 20-year-old college students enrolled in Physics 101 is a homogeneous sample .

What is Homogeneous Sampling?

In homogeneous sampling, all the items in the sample are chosen because they have similar or identical traits. For example, people in a homogeneous sample might share the same age, location or employment. The selected traits are ones that are useful to a researcher. It is a type of purposive sampling and is the opposite of maximum variation sampling .

Homogeneous samples tend to be:

Made up of similar cases.

The opposite of a homogeneous sample is a heterogeneous sample. For this example, you might have a heterogeneous sample of 18-21 year old students in history 112, chemistry 211 and physics 101. The same is true for a heterogeneous population (all items in the population have different characteristics) and a homogeneous population (all items in the population have the same characteristics).

Homogeneous in More General Terms

In data analysis , a set of data is also considered homogeneous if the variables are one type (i.e. binary or categorical); if the variables are mixed (i.e. binary + categorical), then the data set is heterogeneous.

While it’s common in statistics to use “homogeneous” to mean the general sense of being the same, a data set can be analyzed mathematically to see if the data set is homogeneous. There are several ways to achieve this:

Compare boxplots of the data sets.
Compare descriptive statistics (especially the variance , standard deviation and interquartile range .
Run a statistical test for homogeneity.

Statistical Tests

Running statistical tests for homogeneity becomes important when performing any kind of data analysis, as many hypothesis tests run on the assumption that the data has some type of homogeneity. For example, an ANOVA test assumes that the variances of different populations are equal (i.e. homogeneous).

One example of a test is the Chi-Square Test for Homogeneity . This tests to see if two populations come from the same unknown distribution (if they do, then they are homogeneous). The test is run the same way as the standard chi-square test; the Χ 2 statistic is computed, and the null hypothesis (that the data comes from the same distribution) is either accepted or rejected.

Homogeneity of Variance

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An Introduction to Model-Based Survey Sampling with Applications

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3 Homogeneous Populations

Published: January 2012
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This chapter describes the simplest possible model for a finite population: the homogeneous population model. It is appropriate when there is no auxiliary information that can distinguish between different population units. The homogeneous population model assumes equal expected value and variance for the variable of interest for all population units. Values from different units are assumed to be independent although this is relaxed in the last section of the chapter. The empirical best and best linear unbiased predictor of a population total are derived under the model. Inference, sample design and sample size calculation are also discussed. The most appropriate design for this kind of population is usually simple random sampling without replacement. The urn model (also known as the hypergeometric model), a special case of the homogeneous population model, is also discussed.

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Methodology

Stratified Sampling | Definition, Guide & Examples

Stratified Sampling | Definition, Guide & Examples

Published on September 18, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a stratified sample , researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender identity, location, etc.). Every member of the population studied should be in exactly one stratum.

Each stratum is then sampled using another probability sampling method, such as cluster sampling or simple random sampling , allowing researchers to estimate statistical measures for each sub-population.

Researchers rely on stratified sampling when a population’s characteristics are diverse and they want to ensure that every characteristic is properly represented in the sample. This helps with the generalizability and validity of the study, as well as avoiding research biases like undercoverage bias .

When to use stratified sampling, step 1: define your population and subgroups, step 2: separate the population into strata, step 3: decide on the sample size for each stratum, step 4: randomly sample from each stratum, other interesting articles, frequently asked questions about stratified sampling.

To use stratified sampling, you need to be able to divide your population into mutually exclusive and exhaustive subgroups. That means every member of the population can be clearly classified into exactly one subgroup.

Stratified sampling is the best choice among the probability sampling methods when you believe that subgroups will have different mean values for the variable(s) you’re studying. It has several potential advantages:

Ensuring the diversity of your sample

A stratified sample includes subjects from every subgroup, ensuring that it reflects the diversity of your population. It is theoretically possible (albeit unlikely) that this would not happen when using other sampling methods such as simple random sampling .

Ensuring similar variance

If you want the data collected from each subgroup to have a similar level of variance , you need a similar sample size for each subgroup.

With other methods of sampling, you might end up with a low sample size for certain subgroups because they’re less common in the overall population.

Lowering the overall variance in the population

Although your overall population can be quite heterogeneous, it may be more homogenous within certain subgroups.

For example, if you are studying how a new schooling program affects the test scores of children, both their original scores and any change in scores will most likely be highly correlated with family income. The scores are likely to be grouped by family income category.

In this case, stratified sampling allows for more precise measures of the variables you wish to study, with lower variance within each subgroup and therefore for the population as a whole.

Allowing for a variety of data collection methods

Sometimes you may need to use different methods to collect data from different subgroups.

For example, in order to lower the cost and difficulty of your study, you may want to sample urban subjects by going door-to-door, but rural subjects using mail.

Because only a small proportion of this university’s graduates have obtained a doctoral degree, using a simple random sample would likely give you a sample size too small to properly compare the differences between men, women, and those who do not identify as men or women with a doctoral degree versus those without one.

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Like other methods of probability sampling , you should begin by clearly defining the population from which your sample will be taken.

Choosing characteristics for stratification

You must also choose the characteristic that you will use to divide your groups. This choice is very important: since each member of the population can only be placed in only one subgroup, the classification of each subject to each subgroup should be clear and obvious.

Stratifying by multiple characteristics

You can choose to stratify by multiple different characteristics at once, so long as you can clearly match every subject to exactly one subgroup. In this case, to get the total number of subgroups, you multiply the numbers of strata for each characteristic.

For instance, if you were stratifying by both race and gender identity, using four groups for the former and three for the latter, you would have 4 x 3 = 12 groups in total.

Next, collect a list of every member of the population, and assign each member to a stratum.

You must ensure that each stratum is mutually exclusive (there is no overlap between them), but that together, they contain the entire population.

Combining these characteristics, you have nine groups in total. Each graduate must be assigned to exactly one group.

Characteristic	Strata	Groups

First, you need to decide whether you want your sample to be proportionate or disproportionate.

Proportionate versus disproportionate sampling

In proportionate sampling, the sample size of each stratum is equal to the subgroup’s proportion in the population as a whole.

Subgroups that are less represented in the greater population (for example, rural populations, which make up a lower portion of the population in most countries) will also be less represented in the sample.

In disproportionate sampling, the sample sizes of each strata are disproportionate to their representation in the population as a whole.

You might choose this method if you wish to study a particularly underrepresented subgroup whose sample size would otherwise be too low to allow you to draw any statistical conclusions.

Sample size

Next, you can decide on your total sample size. This should be large enough to ensure you can draw statistical conclusions about each subgroup.

If you know your desired margin of error and confidence level as well as estimated size and standard deviation of the population you are working with, you can use a sample size calculator to estimate the necessary numbers.

Finally, you should use another probability sampling method , such as simple random or systematic sampling , to sample from within each stratum.

If properly done, the randomization inherent in such methods will allow you to obtain a sample that is representative of that particular subgroup.

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.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

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Introduction

Data Homogeneity

It is often important to determine if a set of data is homogeneous before any statistical technique is applied to it. Homogeneous data are drawn from a single population. In other words, all outside processes that could potentially affect the data must remain constant for the complete time period of the sample. Inhomogeneities are caused when artificial changes affect the statistical properties of the observations through time. These changes may be abrupt or gradual, depending on the nature of the disturbance. Realistically, obtaining perfectly homogeneous data is almost impossible, as unavoidable changes in the area surrounding the observing station will often affect the data.

Many observed trends are the result of inhomogeneities and not some other large-scale climatic change (e.g., global warming).
Characterizing a short-term artificially-induced trend as natural variations in the climate system can cause substantial errors in conclusions drawn from the data.
The interpretation of a given process depends on the time scales considered (i.e., analyses using short time scales should not be used as evidence to support phenomena observed over larger time scales).

Analyzing the Homogeneity of a Dataset

Calculate the median.
Subtract the median from each value in the dataset.
Count how many times the data will make a run above or below the median (i.e., persistance of positive or negative values).
Use significance tables to determine thresholds for homogeneity.

Locate Dataset, Variable, and Station	dataset. . You have selected the station identification number for Sherbrooke. To get general information on finding station ID's, click the following link to the tutorial:
Select Temporal Domain	in the Time text box.
Compute Yearly Mean Minimum Temperature	This command computes the mean minimum temperature for each year by taking a 365-day average of the minimum daily temperature. This is not an exact yearly average because every 4 years is a leap year, with one extra day. Every four years, the 365-day range will start one day earlier. This being ignored, we are still left with a good approximation of the mean minimum temperature per year.
View Yearly Mean Minimum Temperature Time Series	A gradual upward trend is noticeable over the selected time range. The increase in temperature may have been caused by urbanization in the region surrounding the observing station. Has the urbanization made a sufficient impact on the data so that it may no longer be considered homogeneous over this time period? To answer this question, it is necessary to analyze the distribution of the data around the median.
Subtract Median From Dataset	The median should be located below the expert mode text box in bold: 0.8683567 degrees Celsius. Take note of this value. The function is further explained in the section. link. This will undo the medianover command. The above command subtracts the median (0.8683567° Celsius) from each value in the dataset.
Analyze Homogeneity of Data	link. A table will appear with Time in one column and (Min Temp - 0.8683567) in the other column. The day in the Time column changes every four years because of the leap year issue mentioned earlier. The table lists the number of runs for a given number above (N ) and below (N ) the median. For a 40 year series, for example, N = N = 20. If the number of runs falls between the .10 and .90 significance limits, there is a high probability that the data is homogeneous. Other significance tables can be obtained for sample sizes not contained in the table. = N
10	8	13
11	9	14
12	9	16
13	10	17
14	11	18
15	12	19
16	13	20
17	14	21
18	15	22
19	16	23
20	16	25
25	22	30
30	26	36
35	31	41
40	35	47
45	40	52
50	45	57

Oliver, John E. Climatology: Selected Applications. p 7.

There are 18 runs in the Sherbrooke data from 1920 to 1970. The total number of elements that make up the sample is 50 (each yearly mean minimum temperature constitutes one element). According to the table, at a .10 significance limit there should be at least 22 runs. We can therefore conclude, with 90% confidence, that this data is not homogeneous. Is this inhomogeneity caused by a large-scale climatic change or by an inconsistancy in the area surrounding the observing station? To answer this question, we analyze the mean minimum temperature at another station only a few miles away.

Locate Dataset and Variable	dataset.
Select Temporal Domain and Station	in the Time text box. in the ISTA Station text box. The station ID 7018000 is for Shawinigan. To get more information on finding station ID's, click the following link to the tutorial:
Compute Yearly Mean Minimum Temperature
View Yearly Mean Minimum Temperature Time Series	Based on visual inspection, these data appear to be more homogeneous than the data that taken at Sherbrooke. There isn't a distinct upward trend in the minimum temperatures, as there was in the Sherbrooke data.
Subtract Median From Dataset	The median should be -0.6845208 degrees Celsius. Take note of this value. link. The above command subtracts the median (-0.6845208° Celsius) from each value in the dataset.
Analyze Homogeneity of Data	link. A table will appear with Time in one column and (Min Temp - -0.6845208) in the other column. Shawinigan is only located a few miles northwest of Sherbrooke across the St. Lawrence River, yet the minimum temperature at Sherbrooke exhibited a noticeable upward trend over the time period while the minimum temperature at Shawinigan did not. Therefore, we can conclude that the inhomogeneity at Sherbrooke is not the result of large-scale climatic change. Instead, from 1920 to 1970, Sherbrooke had been heavily affected by human development. The increased density and height of buildings surrounding the observing station in Sherbrooke caused a small heat island, which in turn created an inhomogeneity in the data. Shawinigan, on the other hand, was not affected by development and in turn, did not experience a gradual warming over the period.

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Using Measures of Variability to Inspect Homogeneity of a Sample: Part 1

Posted on 24th February 2022 by Tarik Suljic

My previous blog explored how measures of central tendency (MCT) are used in the majority of clinical research papers, and that reporting MCTs means very little, if anything, in the absence of other secondary data. In this two-part blog, I will discuss the basics of measures of variability and explain how we can use these to evaluate data homogeneity further.

The first part will cover named variability measures (variability measures bearing units with them) and the second part will cover unnamed variability measures (coefficient of variation and z-score).

In this blog, you will learn about:

What homogeneity of a sample is and how to inspect it;
What internal and external validities are and how to assess them;
What causes variability and how to measure it;
Basic concepts of range, standard deviation, and variance and how they help us inspect sample homogeneity, as well as internal and external validities.

What are measures of variability?

Measures of variability are statistical tools that assess data homogeneity thus providing us with information on the quality of the sample middle.

Generally, causes of data variations can be classified into three groups:

Biological causes are characteristics that are inherent to people and that we cannot change, but may be able to control for in the analysis afterwards. Examples of biological causes are sex, age, and genetics.
Temporal causes . Results may vary based on when they are acquired. For example, blood glucose results will vary across a day, based on a patient’s daily activities and the physiology of blood glucose control.
Errors in measurements occur when measuring the same thing with different equipment. For example, two spectrophotometers – machines that can measure a concentration of a substance – that are not calibrated equally will produce inevitable differing blood glucose readings.

Mistakes that encompass these errors, if not taken into consideration, are likely to result in distorted validity and systematic errors, therefore resulting in bias. The validity can be defined as the degree to which measurement and/or study represent true inferences. For a study sample, internal validity may be true; however, the external validity that applies to a certain population, may not be.

For example, if a study of the effectiveness of drug A vs drug B for chronic myeloid leukaemia (which occurs mostly in older people) recruits a high proportion of younger patients, the results that arise from this study may be true for the study sample (internal validity), but the results may not apply to the greater population (poor external validity).

One tool we can use to assess the homogeneity of a study sample are measures of variability. We can split them into two categories: (1) those that have units– measures of variability in a narrow sense and (2) those that do not – coefficients. The examples below are all from the first category.

The easiest of all variability measures is range. It represents a span between the two extreme numbers of a set.

For instance, if a set has entries of 15, 17, 19, 20 and 21, the range would be from the minimum number, 15, and the maximum number, 21. Hence the range is calculated as 21-15=6. Values in a range include the units; for example, kilograms (kg) if we are describing the weight of young children.

A range of 6kg in a study of young children is quite large. If it was an intervention research study, this may introduce bias as these children are likely to demonstrate different physiology. If, however, the research study was about a diagnostic radiographic test, then it would be encouraged to have diverse baseline differences because we need to know that a certain device can detect a certain disease in individuals of different ages.

The important thing is that the range of any dataset is taken into context of the study which the data comes from.

Standard deviation – the most used measure of variability in research

In the simplest terms, standard deviation tells you how spread out the data is. Standard deviation (SD) is a linear measure of how far each observed value is from the mean , and describe it not only as a raw value but as being ‘n-standard-deviations away from the mean.’

How do we calculate standard deviation?

Let’s first imagine that we have a set A={5, 9, 2, 7, 1}. The formula for SD is:

Where σ is SD, x i is a set constituent, μ is the mean, and N is the number of set constituents.

First, we need to calculate the mean by adding up all constituents of the set and then dividing by the total number of set constituents:

Next, we calculate the square difference of each set constituent and then add them all up before dividing with the total number of set constituents, and finally square-rooting it.

The square difference is calculated by subtracting each set constituent from the mean.

Considering that we obtained (one) SD of approximately ±3 units, we can now draw a numbered line including all the data, and break it down in equal portions, according to the standard deviation.

How do we interpret standard deviation?

We said that SD is a linear measure of spread from the mean. Accordingly, the mean of our dataset is 4.8 units, and the SD is ±3 units. So, anything 3 units above the mean, which is 7.8 units, is 1SD away from the mean. Likewise, anything 3 units below the mean is said to be minus 1SD away from the mean. Similarly, if something is 6 units away from the mean, in either direction, it is said to be ±2-standard-deviations away from the mean.

The greater the standard deviation, the greater the variability of the data.

Why is standard deviation relevant?

In a total population of students who are being graded 1-5 in several subjects, we expect the majority of them to stick around the (hypothetical) mean, which is 3. Students whose grade point average (GPA) turns out to be either 1 or 5 can be considered as exceptional results. This is true for a normal distribution in which ~68% of sample data is found within 1SD from the mean, ~95.5% within 2SD. Anything above or below 2SD may be considered extreme. Therefore, standard deviation, as a measure of variability, helps us understand where each of our constituents falls under the curve of normal distribution, also known as Gaussian or bell curve.

Variance – the most avoided of variability measures in research

Variance, like SD, describes how distant set constituents are from the mean. However, the key difference is that variance is an average of squared differences from the sample mean, thus informing us about the average degree to which each dataset constituent differs from the mean.

The formula for variance is as follows:

In the previous section, the SD was ±2.96 units. Should we want to obtain the variance, we just square it.

The figure above is a graphical representation of how to calculate population variance, which we will talk through.

Each blue marker represents a data point.
Step 1 is to find the mean value, which for this dataset is 4.8, depicted by the red line.
Then, for each data point, subtract the data value from the mean to find its distance from the mean (9 – 4.8 = 4.2).
Then, you have to square this value (4.2×4.2 (units) = 17.64 (units)^2).
For each of the points, the size of the square represents the amount of variance which is the same whether a point is above or below the mean.
Next, we have to sum the areas of the squares and finally divide them by the number of squares to calculate the overall we will get the variance – an average squared distance of each dataset constituent from the average line.

The variance tells us how variable the data is, as a squared value. This is less widely used than standard deviation (which is the linear equivalent of variance) as the standard deviation is in the same units as the mean.

Sample vs population variance and standard deviation

According to the American Lung Association , an estimated prevalence of lung cancer for 2020 in the USA was around 541,000. Should there be research investigating efficacy of a therapy in lung cancer patients in the USA, it would not be possible for all 541,000 patients to be enrolled in the research. Instead, only a representative number of the total number of patients would be enrolled. This representative subset of the lung cancer population is called a ‘sample’.

The formulae above for both standard deviation and variance were written as for a population , and not for a sample , although a substantial amount of research is actually conducted on samples representing population and not on a population itself. For this reason, (bio)statisticians use adjusted formulae to calculate standard deviation and variance of a sample. The key differences between these two formulae are listed in the table below:

Let’s look at this through an example…

If we take two studies which recruit people of different ages (in years):


A	65, 44, 52, 18, 46, 73, 25, 34, 46, 49, 53, 61
B	47, 51, 44, 39, 47, 50, 43, 49, 51, 40, 39, 52

Which group of patients has a narrower spread of ages?
Which group do you think would have greater chances to have internal validity but not external validity?
If this study is of a disease that primarily affects the middle-aged population, which study would have a greater chance of achieving both internal and external validity?

Looking at the table below, Group A has a more diverse, variable dataset than Group B. Therefore, the results that come out of Group A have a greater chance that they will apply to all of the members of the dataset (internal validity).

We are looking for a study of middle-aged participants. The age range in Group B (39-52) is lower than in Group A (18-73). The results of Group B have greater chances of applying to the majority of patients suffering from this disease than the results of Group A (external validity), as well as to the patients from the study sample (internal validity).

Furthermore, from the table, we can see that the measures of central tendency appear to be very similar between Group A and Group B. Yet, the variance and standard deviation are by far and large greater in Group A compared to Group B, indicating that the dataset has quite a large variability in its constituents. Hence, we can say that Group B has a more homogenous dataset than Group A.

Variability is the extent to which data point diverge from the mean. It can be caused by either measurement errors, biological, or temporal causes.
Measures of variability are statistical tools that help us assess data variability by informing us about the quality of a dataset mean.
Range informs us about two end values of dataset; the minimum and the maximum value. On the other hand, standard deviation and variance describe how diverse our dataset is.
Standard deviation is a linear measure of dataset spread around mean and thus it enables us to use and compare it with average, whilst variance is a non-linear measure of dataset.
Regardless of that, as a rule of thumb, it is true for both standard deviation and variance that the greater they are, the greater variability of a dataset is.
In research, these measures help us assess if a dataset meets internal and external validity; that is, it helps us understand if the results truthfully represent sample (internal validity) and population (external validity).

You can continue your learning in my next blog , which discusses the coefficient of variation and z-score.

Tarik Suljic

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Using measures of variability to inspect homogeneity of a sample: part 2.

Measures of variability are statistical tools that help us assess data variability by informing us about the quality of a dataset mean. This second of two blogs on the topic will look at coefficient of variation and the z-score.

Stick person, confused, with 2 equations either side of head

A beginner’s guide to standard deviation and standard error

A beginner’s guide to standard deviation and standard error: what are they, how are they different and how do you calculate them?

Nominal, ordinal, or numerical variables?

How can you tell if a variable is nominal, ordinal, or numerical? Why does it even matter? Determining the appropriate variable type used in a study is essential to determining the correct statistical method to use when obtaining your results. It is important not to take the variables out of context because more often than not, the same variable that can be ordinal can also be numerical, depending on how the data was recorded and analyzed. This post will give you a specific example that may help you better grasp this concept.

11.4 Test for Homogeneity

The goodness-of-fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity , can be used to draw a conclusion about whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence.

The expected value for each cell needs to be at least five for you to use this test.

Hypotheses H 0 : The distributions of the two populations are the same. H a : The distributions of the two populations are not the same.

Test Statistic Use a χ 2 χ 2 test statistic. It is computed in the same way as the test for independence.

Degrees of freedom ( df ) df = number of columns – 1

Requirements All values in the table must be greater than or equal to five.

Common Uses Comparing two populations. For example: men vs. women, before vs. after, east vs. west. The variable is categorical with more than two possible response values.

Example 11.8

Do male and female college students have the same distribution of living arrangements? Use a level of significance of 0.05. Suppose that 250 randomly selected male college students and 300 randomly selected female college students were asked about their living arrangements: dormitory, apartment, with parents, other. The results are shown in Table 11.19 . Do male and female college students have the same distribution of living arrangements?


72	84	49	45
91	86	88	35

H 0 : The distribution of living arrangements for male college students is the same as the distribution of living arrangements for female college students. H a : The distribution of living arrangements for male college students is not the same as the distribution of living arrangements for female college students. Degrees of freedom ( df ): df = number of columns – 1 = 4 – 1 = 3 Distribution for the test: χ 3 2 χ 3 2 Calculate the test statistic: χ 2 = 10.1287 (calculator or computer) Probability statement: p -value = P ( χ 2 >10.1287) = 0.0175

Using the TI-83, 83+, 84, 84+ Calculator

Compare α and the p -value: Since no α is given, assume α = 0.05. p -value = 0.0175. α > p -value. Make a decision: Since α > p -value, reject H 0 . This means that the distributions are not the same. Conclusion: At a 5 percent level of significance, from the data, there is sufficient evidence to conclude that the distributions of living arrangements for male and female college students are not the same. Notice that the conclusion is only that the distributions are not the same. We cannot use the test for homogeneity to draw any conclusions about how they differ.

Try It 11.8

Do families and singles have the same distribution of cars? Suppose that 100 randomly selected families and 200 randomly selected singles were asked what type of car they drove: sport, sedan, hatchback, truck, van/SUV. The results are shown in Table 11.20 . Do families and singles have the same distribution of cars? Test at a level of significance of 0.05.

	Sport	Sedan	Hatchback	Truck	Van/SUV
	5	15	35	17	28
	45	65	37	46	7

Example 11.9

Both before and after a recent earthquake, surveys were conducted asking voters which of the three candidates they planned on voting for in the upcoming city council election. Has there been a change since the earthquake? Use a level of significance of 0.05. Table 11.21 shows the results of the survey. Has there been a change in the distribution of voter preferences since the earthquake?


167	128	135
214	197	225

H 0 : The distribution of voter preferences was the same before and after the earthquake. H a : The distribution of voter preferences was not the same before and after the earthquake. Degrees of freedom ( df ): df = number of columns – 1 = 3 – 1 = 2 Distribution for the test: χ 2 2 χ 2 2 Calculate the test statistic: χ 2 = 3.2603 (calculator or computer) Probability statement: p -value= P ( χ 2 > 3.2603) = 0.1959

Press the MATRX key and arrow over to EDIT . Press 1:[A] . Press 2 ENTER 3 ENTER . Enter the table values by row. Press ENTER after each. Press 2nd QUIT . Press STAT and arrow over to TESTS . Arrow down to C:χ2-TEST . Press ENTER . You should see Observed:[A] and Expected:[B] . Arrow down to Calculate . Press ENTER . The test statistic is 3.2603 and the p -value = 0.1959. Do the procedure a second time but arrow down to Draw instead of Calculate .

Compare α and the p -value: α = 0.05 and the p -value = 0.1959. α < p -value.

Make a decision: Since α < p -value, do not reject H o .

Conclusion: At a 5 percent level of significance, from the data, there is insufficient evidence to conclude that the distribution of voter preferences was not the same before and after the earthquake.

Try It 11.9

Ivy League schools receive many applications, but only some can be accepted. At the schools listed in Table 11.22 , two types of applications are accepted: regular and early decision.

Application Type Accepted	Brown	Columbia	Cornell	Dartmouth	Penn	Yale
	2,115	1,792	5,306	1,734	2,685	1,245
	577	627	1,228	444	1,195	761

We want to know if the number of regular applications accepted follows the same distribution as the number of early applications accepted. State the null and alternative hypotheses, the degrees of freedom and the test statistic, sketch the graph of the p -value, and draw a conclusion about the test of homogeneity.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute Texas Education Agency (TEA). The original material is available at: https://www.texasgateway.org/book/tea-statistics . Changes were made to the original material, including updates to art, structure, and other content updates.

Access for free at https://openstax.org/books/statistics/pages/1-introduction

Authors: Barbara Illowsky, Susan Dean
Publisher/website: OpenStax
Book title: Statistics
Publication date: Mar 27, 2020
Location: Houston, Texas
Book URL: https://openstax.org/books/statistics/pages/1-introduction
Section URL: https://openstax.org/books/statistics/pages/11-4-test-for-homogeneity

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Research Design Review

A discussion of qualitative & quantitative research design, focus groups: heterogeneity vs. homogeneity.

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 107-109).

Fundamental to the design of a focus group study is group composition. Specifically, the researcher must determine the degree of homogeneity or heterogeneity that should be represented by the group participants. As shown below, there are many questions the researcher needs to contemplate, such as the extent of similarity or dissimilarity in participants’ demographic characteristics, as well as in their experiences and involvement with the subject matter.

A few of the questions the focus group researcher might consider when determining the desired heterogeneity or homogeneity among group participants include:

Whether or not—or the degree to which—group participants should be homogeneous in some or all characteristics has been at the center of debate for some years. On the one hand, Grønkjær, Curtis, Crespigny, and Delmar (2011) claim that at least some “homogeneity in focus group construction is considered essential for group interaction and dynamics” (p. 23)—for example, participants belonging to the same age group may have similar frames of reference and feel comfortable sharing their thoughts with people who have lived through the same experience. In the same vein, Sim (1998) states that, “the more homogeneous the membership of the group, in terms of social background, level of education, knowledge, and experience, the more confident individual group members are likely to be in voicing their [own] views” (p. 348). Even among strangers, there is a certain amount of comfort and safety in the group environment when the participants share key demographic characteristics, cultural backgrounds, and/or relevant experience.

A problem arises, however, if this comfortable, safe environment breeds a single-mindedness (or “groupthink”) that, without the tactics of a skillful moderator, can stifle divergent thinking and result in erroneous, one-sided data. Heterogeneity of group participants (e.g., including users and nonusers of a particular child care service within the same focus group) potentially heads off these problems by stimulating different points of view and a depth of understanding that comes from listening to participants “defend” their way of thinking (e.g., product or service preferences). As Grønkjær et al. (2011) state, “a group may be too homogeneous; thus influencing the range and variety of the data that emerges” (p. 26). The tension that heterogeneity may create in a group discussion can serve to uncover deeper insights into what is being studied, providing the moderator is able to channel this tension in constructive directions. In addition to a heightened level of diversity, heterogeneous groups may also be a very pragmatic choice for the researcher who is working with limited time and financial resources, or whose target population for the research is confined to a very narrow group (e.g., nurses working at a community hospital).

Ultimately, the answer to the question of whether group participants should be homogeneous or heterogeneous is “it depends.” As a general rule, group participants should have similar experiences with, or knowledge of, the research topic (e.g., using the Web to diagnose a health problem, weekly consumption of fat-free milk), but the need for “sameness” among participants on other parameters can fluctuate depending on the circumstance. Halcomb, Gholizadeh, DiGiacomo, Phillips, and Davidson (2007), for example, report that homogeneity of age is particularly important in non-Western countries where younger people may believe it is disrespectful to offer comments that differ from those stated by their elders. Homogeneous groups are also important when investigating sensitive topics, such as drug use among teenagers, where a more mixed group of participants with people who are perceived as “different” (in terms of demographics and knowledge/experience with drugs) may choke the discussion and lead to a struggle for control among participants (e.g., one or more participants trying to dominate the discussion).

Homogeneity of gender, on the other hand, may or may not be important to the success (usefulness) of a focus group study. For example, an organization conducting employee focus group research to explore employees’ attitudes toward recent shifts in management would need to conduct separate groups with men and women in order to discover how the underlying emotional response to new management differs between male and female employees. In contrast, a focus group study among city residents concerning public transportation might benefit from including both men and women in the same discussion, among whom the varied use and perceptions of the transportation services would serve to stimulate thinking and enrich the research findings. The heightened level of dynamics in groups that are heterogeneous in gender and other aspects may also provoke conversations on taboo subjects (e.g., racism) that might not be forthcoming in other methods such as in-depth interviews.

Grønkjær, M., Curtis, T., de Crespigny, C., & Delmar, C. (2011). Analysing group interaction in focus group research: Impact on content and the role of the moderator. Qualitative Studies , 2 (1), 16–30.

Halcomb, E. J., Gholizadeh, L., DiGiacomo, M., Phillips, J., & Davidson, P. M. (2007). Literature review: Considerations in undertaking focus group research with culturally and linguistically diverse groups. Journal of Clinical Nursing , 16 (6), 1000–1011. https://doi.org/10.1111/j.1365-2702.2006.01760.x

Sim, J. (1998). Collecting and analysing qualitative data: Issues raised by the focus group. Journal of Advanced Nursing , 28 (2), 345–352. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9725732

Images captured/created from: https://www.thoughtco.com/heterogeneous-definition-and-example-606355

Homogenous sampling

Homogenous sampling involves selecting similar cases to further investigate a particular phenomenon or subgroup of interest.

The logic of homogenous sampling is in contrast to the logic of maximum variation sampling.

In a recent evaluation of village level revitalization in Aceh, post-tsunami, leadership was identified as a contributing factor to village success. Those villages with effective leaders were able to rebuild more productively than those without effective leadership. A homogenous sample of village leaders could be a useful augment to the study, identifying common leadership characteristics and circumstances.

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Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards

Sampling is a key feature of every study in developmental science. Although sampling has far-reaching implications, too little attention is paid to sampling. Here, we describe, discuss, and evaluate four prominent sampling strategies in developmental science: population-based probability sampling, convenience sampling, quota sampling, and homogeneous sampling. We then judge these sampling strategies by five criteria: whether they yield representative and generalizable estimates of a study’s target population, whether they yield representative and generalizable estimates of subsamples within a study’s target population, the recruitment efforts and costs they entail, whether they yield sufficient power to detect subsample differences, and whether they introduce “noise” related to variation in subsamples and whether that “noise” can be accounted for statistically. We use sample composition of gender, ethnicity, and socioeconomic status to illustrate and assess the four sampling strategies. Finally, we tally the use of the four sampling strategies in five prominent developmental science journals and make recommendations about best practices for sample selection and reporting.

When we undertake to study some phenomenon, we wish to know something about that phenomenon in a population, but in practice we study the phenomenon in a group of individuals who purportedly represent the target or reference population to whom we wish our results to generalize. That is, we sample the population. We sample because we normally do not command the resources (time, money, or personnel) to assess the entire population of interest. Sampling is therefore a key feature of every study in developmental science, and sampling has far-reaching implications in all studies. This article is concerned with sampling in developmental science. As we point out, different sampling strategies exist, and each has its implications. Employing sub-optimal sampling strategies is far too common in developmental research, compromises the validity and utility of the research, renders replication and cross-study comparisons difficult, and most generally impedes progress in the field of developmental science.

In this article, we briefly describe and illustrate four prominent strategies that answer the sampling challenge, and we evaluate each in terms of some fundamental, meaningful, and practical criteria. The four strategies include (a) population-based probability sampling as well as nonprobability sampling strategies such as (b) convenience sampling, (c) quota sampling, and (d) homogeneous sampling. The five criteria by which we appraise these sampling strategies include (a) whether they yield representative and generalizable estimates of a study’s target population (e.g., estimates of intelligence among the population when all sociodemographic groups are collapsed), (b) whether they yield representative and generalizable estimates of sociodemographic group differences within a study’s target population (e.g., how estimates of intelligence vary across a population’s ethnic groups), (c) the recruitment efforts and costs they entail, (d) whether they provide sufficient power to detect sociodemographic group differences, and (e) whether they introduce noise related to variation in sociodemographic factors and whether that noise can be accounted for statistically. After overviewing the four sampling strategies, we examine how the sociodemographic composition of a sample in terms of gender, ethnicity, and SES can compromise a study’s findings – regardless of the study goals. We then recount the use of each prominent sampling strategy in five high-profile journals in contemporary developmental science. On these bases, we arrive at conclusions and recommendations about best practices and practical considerations, including ethical issues, and discuss the importance of weighing the research question when considering the merits of various sampling strategies.

This article is not comprehensive, and we have not assumed some related burdens. By now demographers, sociologists, and others in many disciplines have weighed the pros and cons of different sampling strategies ( Davis-Kean & Jager, 2011 ; Henry, 1990 ; Onwuegbuzie & Collins, 2007 ; Sue, 1999 ; Watters & Biernacki, 1989 ). This article does not provide a tutorial on sampling (see http://stattrek.com/statistics/data-collection-methods.aspx?Tutorial=Stat ). We also eschew technical details in favor of highlighting “big picture” issues of design and practicality in an accessible way. Although our examples and arguments are applicable to any single sociodemographic factor or set of sociodemographic factors, here we limit our focus to gender, ethnicity, and SES. Also, although we fully recognize that gender, ethnicity, and SES are non-independent (ethnicity and SES in particular) but interact in myriad complex ways, when discussing the implications of these three sociodemographic factors we typically limit our examples to a single factor for the sake of conceptual clarity. Finally, for the purposes of this exposition about sampling, we combine “race” and “ethnicity” as used by the U.S. Government and its agencies and define six ethnic categories (see Table 1 ). We acknowledge that these ethnic groups are also heterogeneous in that each group contains people who originated from many different countries with different cultural practices.

Ethnicity distribution in the United States in 2010

Ethnicity	Percentage
White	63.75%
Hispanic	16.35%
Black	12.21%
Asian	4.69%
American Indian/Alaskan Native	0.73%
Hawaiian/Other Pacific Islander	0.16%

Note . Adapted from Table 1 in Humes, Jones, and Ramirez (2011) .

Common Sampling Strategies in Developmental Science

Here we describe four of the most used sampling strategies, and we assess their advantages, disadvantages, and limitations. How each of the four sampling strategies fares on the five criteria is summarized in Table 2 .

Evaluation of sampling strategies based on five criteria

	Population-based	Convenience	Quota	Homogeneous
Generalizable estimates of target population	Yes	No	No	No
Generalizable estimates of sociodemographic differences	Yes	No	No	No
Ease of recruitment	Low	High	Intermediate	High
Power to detect sociodemographic group differences	Yes	No	Yes	No
Absence of sociodemographic “noise” in results	No	No	No	Yes

Population-Based Probability Sampling

A principal set of sampling strategies falls under the category of “population-based probability sampling.” These strategies include simple random sampling as well as more complex sampling designs such as stratified sampling and cluster sampling (and its variants such as probability proportional to size sampling). Because a detailed exposition of these strategies is beyond this scope of this paper, here we only provide basic descriptions (for more thorough reviews see Cochran, 1977 , or Levy & Lameshown, 2011 ). In simple random sampling, a random subset ( n ) of the target population ( N ) is selected, with each member of N having an equal probability of selection. In stratified sampling, the population is divided into separate groups called “strata” (such as ethnic groups), and then a probability sample (often a simple random sample) is drawn from each stratum. With cluster sampling, the target population is divided into separate geographic groups called “clusters” (such as schools, neighborhoods, businesses), a simple random sample of clusters is selected from the population, and data collection is limited to those who fall within these randomly selected clusters. Within each selected cluster, data collection can be probability based (i.e., based on a simple random sample or a stratified design) or complete (i.e., every individual within a given cluster is eligible to participate in the study). Although these population-based probability sampling strategies differ from one another in important ways, they all, when carried out properly, yield an unbiased sample that is representative of the target population (i.e., the sociodemographic characteristics of the sample faithfully reflect the sociodemographic characteristics of the target population). For example, the National Longitudinal Study of Adolescent Health (Add Health; Bearman, Jones, & Udry, 1997 ), which used a clustered sampling design that applied stratified sampling within cluster (school), is a population-based probability sample of 7 th -12 th graders in the United States during the 1994–1995 school year. The sample has been re-interviewed three times, the most recent being in 2008, when the sample was ages 24 to 32. Because Add Health is a population-based probability sample with a clear target population, researchers and practitioners can be confident that findings from studies utilizing Add Health data generalize to the U.S. population of adolescents and young adults as a whole.

Based on our five criteria, population-based probability sampling appears to have more advantages than disadvantages. Focusing first on its advantages, in terms of representativeness and generalizability, when carried out properly, these sorts of samples yield generalizable estimates of the target population (i.e., all sociodemographic groups combined) and of sociodemographic group differences (e.g., gender, ethnic, or SES differences within a target population). Additionally, assuming the subsamples for a given sociodemographic factor are sufficiently large (say ≥ 45; based on .80 power to detect a medium effect of f = .25, α = .05, in an ANOVA design with four groups 1 ; Faul, Erdfelder, Lang, & Buchner, 2007 ), this sampling strategy yields sufficient power to detect differences among sociodemographic subgroups within the target population. Finally, for researchers not interested in subgroup differences, probability samples also allow accounting for noise introduced by variation in sociodemographic factors. Taking advantage of the substantial sociodemographic variation in these studies, researchers can take steps to control for sociodemographic group differences, or, better, researchers can simply examine their research question separately for each sociodemographic subgroup (i.e., examine questions separately by gender using multiple-group analyses) and compare the findings.

Regarding its disadvantages, when done properly the recruitment costs and efforts for population-based probability sampling are high. Regardless of the population-based sampling strategy used, researchers need to carefully define the target population and clarify its sociodemographic composition. Depending on the target population, doing so can be straightforward (e.g., the ethnic composition of the U.S. population is tracked by the U.S. Census). However, in many cases the sociodemographic compositions of other, smaller target populations are not fully known and may require great efforts to accurately determine. In addition, when researchers sample from geographic areas smaller than their country, geographical decisions can be arbitrary. Suppose one were to sample from the geographic home of the National Institutes of Health (NIH). What would define that sampling area? The main campus of the NIH? The town of Bethesda? Montgomery Country? The state of Maryland? The Middle Atlantic States? Second, population-based probability-sample sizes need to be quite large, often coming at great costs in terms of money, time, and effort. For example, consider a research group that wishes to collect ethnicity data that is representative of a given target population via simple random sampling. Unless the target population is a single ethnic group (see Homogeneous Sampling below), any target population will consist of a set of ethnic groups that are not distributed equally (i.e., some comprise a far lesser proportion of the target population than others). Therefore, to yield a subsample of each ethnic group that is statistically useful 2 , including those that comprise a small proportion of the target population, requires collecting a substantial amount of data. For example, using the U.S. population as the target population, to yield a Hispanic n of 45 (a rather modest n for making generalizations to the entire U.S. population of Hispanics) calls for a total representative sample N of 275. To yield n s of 45 for non-Hispanic Blacks, Asians, American Indians, and Hawaiian/Other Pacific Islanders would call for total representative sample N s of 369, 959, 6,164, and 28,125, respectively. Of course the required sample size balloons even higher when multiple sociodemographic factors are considered (e.g., to yield an n of 45 for Hawaiian/Other Pacific Islander females would call for a total sample N of 56,250).

One of the benefits of clustered and stratified sampling designs is that, relative to simple random sampling, the recruitment costs and efforts are often lower ( Groves, 1989 ; Heeringa et al., 2004 ), although still considerable. Within stratified designs, strata that are underrepresented within the population (e.g., the American Indian stratum among ethnicity strata or the highly affluent stratum among SES strata) can be oversampled (termed “disproportionate allocation”) reducing the overall N required to yield a statistically useful subsample of each subgroup. Sample weights, which “down weight” the oversampled strata, can then be applied to the data to yield estimates that are generalizable to the total population. Additionally, although cluster designs do not necessarily reduce the number of individuals that need to be recruited, they can reduce recruitment costs. In terms of effort and time, it is far easier to sample, for example, 100 individuals within a single sampling cluster (i.e., neighborhood or school) than it is to sample 100 individuals scattered across a number of different sampling clusters. Stratified and clustered sampling designs also have their disadvantages relative to simple random sampling. Complex sampling strategies are less straightforward to implement and require the use of specialized analytical techniques to obtain accurate variance estimates ( Davis-Kean & Jager, 2011 ).

In summary, population-based probability sampling strategies allow for clear generalizability to both the target population and its sociodemographic subpopulations, and they enable researchers to account for the noise introduced by variation in sociodemographic factors. Despite these important advantages, population-based sampling strategies are often prohibitively costly and labor-intensive.

Due to the substantial costs of population-based sampling strategies, the use of nonprobability samples, which are typically less expensive in all ways, is much more common in developmental research. Next, we review and critique three specific types of nonprobability samples: convenience sampling, quota sampling, and homogenous sampling.

Convenience Sampling

Unlike population-based sampling strategies, convenience sampling is a nonprobability sampling strategy where participants are selected based on their accessibility and/or proximity to the research. One of the most common examples of convenience sampling within developmental research is the use of student volunteers as study participants. This strategy entails recruiting a sample that has some ad hoc sociodemographic composition, which is tailored neither to the United States nor to any other identifiable target population but rather settles for whatever convenience sample the researcher recruits (presumably) on a first come-first recruited basis.

This strategy’s clear advantage is that, of all the sampling strategies, convenience sampling is the easiest, least time-intensive, and least expensive to implement, perhaps accounting for its popularity in developmental research. Regarding its disadvantages, results that derive from convenience sampling have known generalizability only to the sample studied. Thus, any research question addressed by this strategy is limited to the sample itself. The same limitation holds true for estimates of differences between sociodemographic subgroups. As another disadvantage, convenience samples typically include small numbers of underrepresented sociodemographic subgroups (e.g., ethnic minorities) resulting in insufficient power to detect subgroup differences within a sociodemographic factor or factors. Moreover, although small in number, these underrepresented sociodemographic subgroups introduce modest amounts of variation into the sample, enough variation to produce statistical noise in the analyses but not enough variation to harness or control statistically. Indeed, the widespread use of convenience sampling may be partly responsible for the host of small and inconsistent effects that pervade developmental science, why sizes of effects often vary depending on the variables considered, and why research shows links between particular setting conditions and outcomes for some, but not other, groups ( Bornstein, 2013 ).

In summary, convenience sampling is a common strategy, but its scientific disadvantages appear to outweigh its practical advantages. Relative to population-based probability sampling, convenience sampling is far easier and less expensive to implement. However, unlike population-based probability sampling, convenience sampling produces estimates that lack generalizability to any identifiable target population or subpopulations (except for the sample studied), provides insufficient power to detect differences among sociodemographic subgroups, and includes noise due to sociodemographic variation that cannot be controlled or accounted for.

Quota Sampling

Because of the well-intentioned movement to improve the representation of underrepresented groups in developmental research, there has developed a sampling strategy of recruiting fixed numbers of participants from different sociodemographic groups (e.g., sample n s of 45 for each ethnic group within the target population). Like convenience sampling, quota samples are typically nonprobability samples. Although quota sampling (also referred to as “equal sampling”) bears some resemblance to stratified sampling using disproportionate allocation (i.e., oversampling underrepresented groups or strata), it is distinct from stratified sampling in two important ways. First, stratified sampling using disproportionate allocation draws a probability sample from each stratum under investigation, but quota sampling typically draws a nonprobability sample from each group under investigation (i.e., a non-random sample of each ethnic group within the target population). Second, stratified sampling using disproportionate allocation is typically accompanied by the use of sample weights, which render estimates that are generalizable to the target population, but quota sampling rarely involves the calculation or application of sample weights.

Based on our five criteria, quota sampling has more disadvantages than advantages. One advantage of quota sampling is that, because it often entails oversampling of underrepresented groups, quota sampling typically provides sufficient statistical power to detect group differences. Another advantage of quota sampling is that, for researchers not interested in differences across subgroups of a sociodemographic factor, quota sampling permits accounting statistically for noise introduced by sociodemographic variation. However, because quota sampling typically draws nonprobability samples from each group under investigation, it does not yield generalizable estimates of the target population or of subgroup differences within the target population. Indeed, that sample weights are rarely applied renders estimates of the target population all the more biased. A second disadvantage of quota sampling is that, when done properly, the recruitment costs and efforts are intermediate. Although not as large as population-based samples, the size of the overall sample still needs to be fairly large. That is, the sample still needs to include a statistically useful sub-sample of each subgroup of a sociodemographic factor, which at the low end amounts to an n = 45 (for a 4-group ANOVA). For example, for studies focused on ethnicity, based on the six-category definition of ethnicity, a minimum total sample N of 270 would be required (i.e., a subgroup n of 45, by 6 subgroups, equals a total N of 270). Locating high numbers of those in underrepresented groups to participate in a study can be challenging.

In summary, relative to convenience samples, quota samples are equally disadvantaged when it comes to producing estimates of the target population and of subgroup differences that are generalizable and more disadvantaged with respect to recruitment costs. However, unlike convenience samples, quota sampling typically provides the statistical power necessary to detect subgroup differences among sociodemographic factor(s) of interest (i.e., the sociodemographic factor(s) whose subgroups are quota sampled) and permits accounting statistically for noise introduced by variation in the sociodemographic factor(s) of interest. Therefore, relative to convenience samples, quota samples are better suited for examining subgroup differences within a sociodemographic factor or factors.

Homogeneous Sampling

In homogenous sampling, researchers undertake to study a sociodemographically homogeneous population (e.g., the overall sample is comprised of just females). Homogenous samples can vary in their degree of sociodemographic homogeneity. For example, samples that restrict variation on multiple sociodemographic factors (e.g., a sample limited to female, European American adults) are more homogeneous than samples that restrict variation on a single sociodemographic factor (e.g., a sample limited to females that includes all ethnic groups). Like convenience and quota samples, homogenous samples are typically nonprobability samples.

Based on our five criteria, homogeneous sampling has important advantages and disadvantages. Focusing first on its advantages, the recruitment costs and efforts are generally low for homogeneous samples, although relative to homogenous samples of overrepresented sociodemographic groups (e.g. European Americans in the United States), homogeneous samples of underrepresented sociodemographic groups (e.g., Native Americans) can be more costly and time consuming. An additional advantage is, because the homogeneous sampling design eliminates all variation associated with one or more sociodemographic factors, it adds no noise associated with those sociodemographic factors to the overall results. That is, by including only one ethnic group, for example, the research intentionally avoids the noise associated with ethnic confounds that can cloud the findings if different ethnic groups are combined, thereby improving the accuracy and quality of the resultant data. Provided homogeneous samples are nonprobability samples, which in practice is typically the case, a key shortcoming is that they yield estimates that are not generalizable to the target population (e.g., a non-probability, homogenous sample of Native Americans would not yield estimates that are generalizable to the population of Native Americans). However, this limitation does not hold for probability homogeneous samples. Additionally, because the target population is defined as a particular sociodemographic group, homogeneous samples are often ill-suited to examine sociodemographic differences. For example, homogeneous samples limited to females cannot be used to examine gender differences, and homogeneous samples limited to African American females cannot be used to examine ethnic differences, gender differences, or ethnic differences within gender.

In summary, because homogeneous samples are typically nonprobability samples, they are equally disadvantaged relative to convenience samples and quota samples when it comes to producing estimates of the target population that are generalizable. Like convenience samples, recruiting costs for homogeneous samples are typically low, and lower than the costs associated with population-based probability and quota samples. Unlike convenience samples and quota samples, homogeneous samples eliminate noise due to variation in one or more sociodemographic factors. Therefore, homogeneous samples provide key advantages over both convenience samples (i.e., noise due to variation in one or more sociodemographic factors is eliminated in homogenous samples) and quota samples (i.e., recruitment costs are typically lower for homogeneous samples).

Why the Sociodemographic Composition of Samples Matters

Given the advantages and disadvantages of the four sampling strategies, it is important to note how sociodemographic characteristics can affect study outcomes and the interpretation of study results. Gender, ethnicity, and SES variation in many characteristics—physical and mental health ( Adkins, Wang, Dupre, van den Oord, & Elder, 2009 ; American Public Health Association, 2004 ; Breslau et al., 2005 ; Crimmins & Saito, 2001 ; National Center for Health Statistics, 2011 ), beliefs and cognitions ( Burke et al., 1992 ; Courtenay, McCreary, & Merighi, 2002 ; Diala et al., 2000 ), and behaviors and practices ( Blum et al., 2000 ; Burke et al., 1992 ; Courtenay et al., 2002 ; National Center for Health Statistics 2011 ; Snowden & Yamada, 2005 )—is ubiquitous. Beyond mean-level differences in these constructs, cross-sectional associations as well as developmental linkages among them and other salient constructs also vary by gender ( Card, Stucky, Sawalani, & Little, 2008 ; Griffin, Botvin, Scheier, Diaz, & Miller, 2000 ), ethnicity ( Amey, Albrecht, & Miller, 1996 ; Burke et al., 1992 ; Jager, 2011 ), and SES ( Geoffroy et al., 2007 ; Sachs-Ericsson et al., 2007 ). These broad and entrenched sociodemographic differences in both the levels and the correlates of significant developmental characteristics—constructs, structures, functions, or processes—place demands on developmental researchers regarding the sociodemographic composition of their analytic samples ( Bornstein, 2010 ; Davis-Kean & Jager, 2011 ; Henry, 1990 ; Onwuegbuzie & Collins, 2007 ; Sue, 1999 ; Watters & Biernacki, 1989 ). Moreover, the practical implications of a sample’s sociodemographic composition vary depending on whether a study is (a) focused on one or more sociodemographic factors as a source of heterogeneity or (b) focused on broad developmental patterns (not focused on sources of heterogeneity of any kind).

Sampling Implications for Research Focused on Sociodemographic Factors as a Source of Heterogeneity

The roots of sociodemographic differences – including gender, ethnicity, and SES differences - in developmental characteristics are complex and likely the product of layered interactions among biological, behavioral, and sociocultural factors ( Betencourt & Lopez, 1993 ; Courtenay et al., 2002 ; Crimmins & Saito, 2001 ; Phinney, 1996 ). Nonetheless, they are important to unpackage because without a scientific base of knowledge regarding human health and behavior that takes into account the sociodemographic diversity of the population, health care delivery, planning, and policy making would be compromised by inadequate information and potentially misleading generalizations ( Betencourt & Lopez, 1993 ; Hahn & Stroup, 1994 ; Mays, Ponce, Washington, & Cochran, 2003 ). Indeed, a sizable amount of developmental research has been devoted to examining sociodemographic differences in health and development. However, if research does not include a broad, representative sample of the sociodemographic groups under examination, then a distorted view of sociodemographic differences likely results. Additionally, if research does not include adequately large samples of each sociodemographic group under examination, then sociodemographic differences that exist in the target population may go undetected (i.e., Type II errors) due to a lack of power. Thus, a proper examination of socioeconomic differences in a given developmental characteristic demands an analytical sample whose sociodemographic composition is (a) varied enough to represent the diversity of the sociodemographic groups under examination and (b) large enough to yield sufficient power to detect differences among the sociodemographic groups under examination.

Studies whose sociodemographic compositions do not meet these criteria are problematic because (a) when considered individually it is unclear whether a study’s findings generalize to the intended target population and (b) when considered collectively, the findings from these studies are difficult to synthesize. For purposes of illustration, consider the following three hypothetical studies exploring ethnic differences in depression within the United States, all of which poorly sample ethnicity (either in terms of representation, number, or both). Study A utilized a sample of 1,000 middle-aged, married adults from an affluent East Coast suburban community and found that European Americans reported higher rates of major depressive disorders than all other ethnic groups; here, the sample is large but not representative of the ethnic distribution of the United States. Study B utilized a diverse sample of 200 adults and found that all ethnic groups reported equivalent rates of major depressive disorders; here the sample is representative but not large. Study C, whose sample is neither large nor representative, utilized an impoverished, rural sample of 150 adults and found one group difference: African Americans reported higher rates of major depressive disorders than did European Americans. When considering any one of these three studies individually, can one confidently conclude that its findings regarding ethnic differences in depression are a true reflection of the United States population? Although Study A’s sample is adequately large to yield sufficient power to detect group differences, its estimates of group difference may be biased due to the characteristics of the sample (i.e., affluent, married, suburban adults). Although Study B’s sample is more diverse, it is also much smaller and likely insufficiently powered to detect ethnic group differences. As a result, its finding that the ethnic groups did not differ from one another could simply reflect lack of power (i.e., a Type II error). Finally, the one group difference found by Study C could be an artifact of its biased sample, and its failure to detect any other group differences could be ascribable to insufficient power on account of the small sample size. Additionally, when considered collectively, how does one go about integrating the findings from these three studies? Their findings regarding ethnic differences in depression are inconsistent; therefore, they cannot all be correct. But is one more correct (or less incorrect)? Unfortunately, determining the answers to these questions is difficult because each study’s sample is deficient in representation and/or sample size. Although this example involved a set of hypothetical studies, substantial variation in the size and sociodemographic composition of samples is all too common across studies examining sociodemographic differences in a given developmental characteristic in an equivalent target population. These variations make it difficult to determine whether inconsistencies across studies represent true population differences or instead are artifacts of differences in sample composition.

Sampling Implications for Research Not Focused on Sociodemographic Factors as a Source of Heterogeneity

Even for developmental research not focused on sociodemographic factors as a source of heterogeneity, issues pertaining to the sociodemographic composition of the sample still warrant consideration ( LaVeist, 2005 ; Phinney, 1996 ; Williams, 1999 ). For example, much developmental research focuses on developmental patterns in a particular target population (e.g., mental health trajectories among children, romantic relationship formation among young adults, and so forth). Even though this sort of research is concerned with population norms and not sources of heterogeneity (such as gender, ethnicity, and SES), the nature of the sociodemographic composition of the sample still influences the patterns found. In studies where heterogeneity due to sociodemographic factors is not the focus, variation introduced by sociodemographic factors can be thought of as “noise” or nuisance variance that, although tangential to the topic of study, should ideally be accounted for. When left unaccounted for, this noise at the very least introduces additional variance resulting in unnecessarily inflated standard errors and increases the likelihood of Type II errors. In addition to inflating the standard errors of parameter estimates, this noise can also bias or alter parameter estimates themselves. For example, consider two studies designed to examine developmental linkages between alcohol abuse and major depressive disorders in a target population. The two studies’ samples are the same except for their gender composition; Study A’s sample is 60% male, and Study B’s sample is 60% female. Because the association between alcohol abuse and major depressive disorders is markedly higher among females ( Kessler et al., 1997 ; Poulin, Hand, Boudreau, & Santor, 2005 ; Zilberman, Tavares, Blume, & el-Guebaly, 2003 ), the association between alcohol abuse and major depressive disorders will vary across the two studies. Specifically, the association is likely higher for Study B because its sample included a higher proportion of females. Thus, two studies focusing on population norms and examining the same question (in this case the association between alcohol abuse and major depressive disorders) could reach different conclusions due to differences in sample composition (in this case gender).

A popular option for dealing with the noise introduced by sociodemographic factors is to control for it statistically, but in many cases attempts to do so prove unsatisfactory. To be able to adequately control for a sociodemographic factor, there must be sufficient variance in it. Thus, if a study has only a small number of individuals in one or more ethnic groups, for example, then the ability of the study design to effectively control for ethnicity is reduced. Because gender, ethnicity, and (in some instances) SES are categorical variables, a common method for controlling for the noise they introduce is to use a series of dummy variables. However, this method of controlling for sociodemographic “noise” is problematic for two reasons. First, this technique only controls for sociodemographic differences in levels of variables; it does not account for sociodemographic differences in associations among variables (i.e., the technique assumes that the slope of the regression line in each sociodemographic group is the same). Thus, it only partially accounts for the noise attributed to sociodemographic differences. Second, this technique successfully controls for level differences across sociodemographic groups; however, if dummy-coding (as opposed to effects-coding) is used, it yields findings that generalize only to the reference group. To account properly for the noise introduced by sociodemographic variation, we suggest using one of the two following options. One option is to conduct preliminary analyses testing for sociodemographic differences (in both means and covariances) and if differences are found to conduct primary analyses separately for each sociodemographic group (i.e., multiple-group analyses). Option two is to recruit a sample that contains no variation in a sociodemographic factor (i.e., a data set limited to a single SES group; see Homogeneous Sampling above).

How Sampling is Used in Developmental Science

To determine the frequency with which the four different sampling methods are used in contemporary developmental science, we surveyed five years (2007–2011) of five high-profile developmental journals. The five journals included two that generally focus on abnormal development, ( Journal of the American Academy of Child and Adolescent Psychiatry (JAACAP) and Development and Psychopathology ), two that generally focus on normative development ( Developmental Psychology and Child Development ), and one that focuses on experimental studies related to development ( Developmental Science ). As we have done previously, for purposes of illustration and the sake of simplicity we limit our focus to just one of the three sociodemographic factors – ethnicity. For all published articles that included sample descriptions, we recorded the country of data collection, the sample size, and the percentages of participants who were European American or White, Hispanic American or Latino, African American or Black, Asian American or Asian, and another ethnicity. We also noted if the study did not report ethnicity and if the study mentioned that the sample was nationally representative. If ethnicity was reported on multiple groups or in multiple studies within an article, each sample was documented separately. Meta-analyses, reviews, commentaries, and editorials were ignored unless they presented new data. Because we were only interested in documenting the sampling distributions of U.S. samples for this illustration, we excluded any non-U.S. samples from our analysis. Any article that did not report the sample distribution of ethnicity, or that did not provide enough information to evaluate the ethnic distribution (e.g., “most participants were European American”), was coded as “not reported.”

Population-based probability sampling was coded if the authors reported that the study sample was nationally representative or if the distribution of ethnicities in the sample was ordered as it is in the population of the United States (with European Americans represented in higher proportions than Hispanic Americans, Hispanic Americans higher than African Americans, and African Americans higher than Asian Americans; Table 1 ). This decision constitutes a generous way to code population-based probability sampling because some studies that approximate the distribution of ethnicities in the United States were not truly representative. Quota sampling was coded when the authors mentioned that a particular ethnic group was oversampled (but not part of a probability sample), or when the sample included only 2 ethnic groups, each representing 45–55% of the sample, or when the sample included only 3 ethnic groups, each representing 28–38% of the sample. Homogeneous sampling was coded when the entire sample was comprised of a single ethnic group. Finally, convenience sampling was coded for the remainder of the samples.

Table 3 presents the categorization of samples in each of the five journals. The number of samples drawn from the United States ranged from 226 to 647 across the five journals. The range of sample sizes for each journal was wide, and the median sample sizes varied across journals, Kruskal-Wallis χ 2 (4, N =2104) = 293.96, p < .001. Post-hoc tests indicated that the median sample size for Developmental Science was smaller than all other journals, and the median sample size for Development and Psychopathology was larger than all other journals. Notably, depending on the journal, ethnicity was not adequately reported for one-quarter to more than two-thirds of the samples. As expected, the most prevalent type of sample in every journal was the convenience sample (78–88% of codable samples). Homogeneous sampling was the next most prevalent (5–19% of codable samples), followed by population-based probability samples (3–7% of codable samples) and quota samples (0–2% of codable samples). It could be that most samples for which ethnicity was not reported were convenience samples. If we assumed that the “not reported” samples were all convenience samples, then 88–98% (overall 91%) of samples would be convenience.

Characteristics of U.S. samples that fell into each sampling category from 2007 to 2011 in five high-profile developmental journals

Journal	5-year impact factor	samples	Median sample size	Range of sample sizes	Percentage of samples drawn using each sampling strategy
Journal	5-year impact factor	samples	Median sample size	Range of sample sizes	Population-based	Convenience	Quota	Homogeneous	Not reported
JAACAP	5.976	443	126	10–16,128,828	3.8/7.2	45.6/85.6	1.1/2.1	2.7/5.1	46.7
D&P	6.688	226	220	20–390,350	3.1/4.2	65.5/88.1	0.9/1.2	4.9/6.5	25.7
DP	5.123	647	121	8–67,124	3.9/5.5	58.1/82.8	1.4/2.0	6.8/9.7	29.8
CD	5.700	517	101	6–21,255	3.5/5.1	57.8/84.7	1.7/2.5	5.2/7.6	31.1
DS	4.597	289	36	10–10,200	0.3/3.1	8.7/78.1	0.0/0.0	2.1/18.8	88.9

Total	--	2122	105	6–16,128,828	3.2/5.5	49.5/84.5	1.2/2.0	4.7/8.0	41.4

Note . The 5-year Impact Factor was abstracted from the 2011 Thomson Reuters Journal Citation Reports Database. JAACAP = Journal of the American Academy of Child and Adolescent Psychiatry . D&P = Development and Psychopathology . DP = Developmental Psychology . CD = Child Development . DS = Developmental Science .

Overall, the five journals were remarkably consistent in their representation of different types of samples. Developmental Science (the experimental journal) had a much larger percentage of samples where ethnicity was not reported adequately, χ 2 (8, N =2122) = 330.66, p < .001, but otherwise the patterns were very similar across journals. We explored whether the type of journal (abnormal, normative, or experimental) was associated with the prevalence of different sampling strategies. When samples where ethnicity was not reported were excluded, the distribution of sampling strategies was similar across journal types, χ 2 (6, N =1243) = 9.85, p = .13 (compare percentages after the slash in Table 3 ).

The median sample sizes for the four sampling strategies differed, Kruskal-Wallis χ 2 (4, N =2104)=326.45, p < .001. Post-hoc tests indicated that the median sample size for population-based probability sampling (Median = 2,741) was larger than the median of all other sampling strategies except quota sampling (Median = 486). The median sample size for quota sampling was larger than the median sample size of homogeneous (Median = 152), convenience (Median = 153), and “not reported” samples (Median = 52). Finally, the median sample sizes for homogeneous and convenience samples were both larger than the median sample size for “not reported” samples, but the median sample sizes for homogeneous and convenience samples did not significantly differ. These results support the proposition that population-based probability sampling requires a relatively large sample size and that there is little difference in sample size for convenience and homogeneous samples.

Implications and Recommendations for Reporting Sample Characteristics

The analysis of published articles reveals several inconsistencies and inadequacies in basic reporting of demographic characteristics of study samples in developmental science. Overall, 41.4% of articles published in five high-profile developmental science journals failed to report ethnicity at all or only reported that the sample was “predominantly White” or “about half minority”, which is not detailed enough to draw conclusions about the representativeness of the sample to a particular population. Many studies also grouped different prominent ethnicities into an “other” category. We recommend that published studies report the percentages of their sample that fall into (at least) the four major ethnic groups (White/European American, Hispanic/Latino American, Black/African American, and Asian American), but more detailed accounting of groups and subgroups is always preferred. Of course, if the study includes an oversampling of other ethnicities or ethnic subgroups, they should also be reported. Similarly, any other sociodemographic characteristics that would help to evaluate the study sample’s representativeness or generalizability (e.g., proportions of females and males, SES, education levels, and ages of participants) should be reported in detail.

A second related recommendation is that each published study explicitly report the population to which the study generalizes. Not all studies intend to represent the entire population of the United States, but in most cases authors do not explain what population their study sample is intended to represent. For example, knowing whether a study generalizes to the entire population of 8- to 10-year-old children in the United States or only to 8- to 10-year-olds in five public schools in Maine frames the interpretation and application of the study’s findings considerably. Furthermore, a study that was designed to represent public school children in Maine should have very different sociodemographics from a study designed to represent 8- to 10-year-olds in the entire United States.

Summary and Conclusions

Here we have recounted four common sampling strategies (population-based probability sampling, convenience sampling, quota sampling, and homogenous sampling) and evaluated each by five meaningful criteria. We find that by far the most common sampling strategy (convenience sampling) is the least desirable in terms of representativeness, generalizability, and noise. Convenience samples require less practical investment in recruitment costs and efforts, but this advantage does not offset its aforementioned consequential scientific disadvantages. We find population-based probability sampling the most desirable strategy, but potentially cost and resource prohibitive for many researchers. The sample size required to adequately represent all sociodemographic groups would be large and the recruitment costs and efforts for this method are consequently considerable. Furthermore, large samples often involve a trade-off with detailed measurement. For example, a small sample may allow a researcher to investigate a topic in greater depth, such as with open-ended questions or more extensive measurement. Larger samples may only allow for more blunt instruments or require even greater allocation of resources for detailed measurement. Whether homogeneous sampling or quota sampling is a better nonprobability option depends on the research question. For research questions focused on sociodemographic factors as a source of heterogeneity (e.g., the sociodemographic factor is a focal variable of interest in the study), quota sampling is the better choice. In this case, quota sampling allows the researcher to recruit sociodemographic groups (e.g., ethnic or SES groups) that are sizeable enough to address the research questions. For research questions not focused on sociodemographic factors as a source of heterogeneity (e.g., the sociodemographic factor is not a focal variable and would likely be used as a covariate), homogeneous samples are preferable because they eliminate sociodemographic noise in the sample that could cloud the study results. Collecting a homogeneous sample might marginally increase recruitment costs and efforts compared to convenience samples (especially if the targeted group is infrequently occurring in the population), but pays off in terms of reducing noise.

One concern many researchers have about homogeneous samples is the success in obtaining protocol approval by internal review boards or to secure funding. Many funding agencies require (or strongly recommend) inclusion of all major sociodemographic groups. For example, the NIH released guidelines about including women and minorities in clinical research in 1994 (revised in 2001; Hohmann & Parron, 1996 ; NIH Office of Extramural Research, 2001 ) which indicate that all grant applications are evaluated for the inclusion of sociodemographic groups, and if groups are omitted, a strong justification is required. As we have outlined, there are circumstances when a homogenous sample is the best available option (e.g., probability samples are too expensive and sociodemographic differences are of little interest). Including sociodemographic variation that cannot be properly addressed statistically (as in a small convenience sample) should not be considered preferable to a homogeneous sample. Researchers are encouraged to make principled theoretical and statistical arguments to support their choice of a better sampling strategy, even if that strategy excludes one or more sociodemographic groups.

Sampling is a necessary evil of developmental science. It is practically and feasibly impossible to conduct a true population study, and so scientific researchers are forced to resort to sampling. Insofar as scientists must sample, they are confronted with the question of defining the profile of their sample. There are different types of samples, and with respect to different scientific questions different sampling strategies have different advantages and disadvantages. Although they are clearly preferable in terms of their generalizability and power, probability samples are infrequently used because of the cost and effort required to plan and execute them. Instead, nonprobability samples are much more common, with faulty convenience samples making up the largest share. However, with only a small amount of extra effort and planning, researchers could recruit a quota or homogeneous sample, thereby improving the characteristics of their sample and study.

Sampling is a key feature of every study in developmental science.
We evaluate 4 prominent sampling strategies in developmental science.
We judge these sampling strategies by criteria, such as representativeness and generalizability.
We tally the use of the four sampling strategies in five prominent developmental science journals.
Finally, we make recommendations about best practices for sample selection and reporting.

Acknowledgments

We thank A. Bradley, O. M. Haynes, P. Horn, A. Mahler, C. Padilla, and C. Yuen. Supported by the Intramural Research Program of the NIH, NICHD.

1 This is only one example for illustrative purposes. The sample size needed for different analyses should always be estimated using an a priori power analysis prior to selecting a sample.

2 A sample large enough to yield sufficient power and to reasonably account for other confounds via random sampling.

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Home » Purposive Sampling – Methods, Types and Examples

Purposive Sampling – Methods, Types and Examples

Table of Contents

Purposive Sampling

Definition:

Purposive sampling is a non-probability sampling technique used in research to select individuals or groups of individuals that meet specific criteria relevant to the research question or objective.

This sampling technique is also known as judgmental sampling or selective sampling, and it is often used when the population being studied is too small, too difficult to access, or too heterogeneous to use probability sampling methods.

Purposive Sampling Methods

Purposive Sampling Methods are as follows:

Expert sampling: In expert sampling, the researcher selects participants who are experts in a particular field or subject matter. This can be useful when studying a specialized or technical topic, as experts are likely to have a deeper understanding of the subject matter and can provide valuable insights.
Maximum variation sampling: Maximum variation sampling involves selecting participants who represent a wide range of characteristics or perspectives. This can be useful when the researcher wants to capture a diverse range of experiences or viewpoints.
Homogeneous sampling : In homogeneous sampling, the researcher selects participants who have similar characteristics or experiences. This can be useful when studying a specific subpopulation that shares common traits or experiences.
Critical case sampling : Critical case sampling involves selecting participants who are likely to provide important or unique insights into the research question. This can be useful when the researcher wants to focus on cases that are particularly relevant or informative.
Snowball sampling : Snowball sampling involves selecting participants based on referrals from other participants in the study. This can be useful when studying hard-to-reach or hidden populations, as it allows the researcher to gain access to individuals who may not be easily identifiable or accessible.

How to Conduct Purposive Sampling

Here are the general steps involved in conducting purposive sampling:

Identify the research question or objective: The first step in conducting purposive sampling is to clearly define the research question or objective. This will help you determine the criteria for participant selection.
Determine the criteria for participant selection : Based on the research question or objective, determine the specific criteria for selecting participants. These criteria should be relevant to the research question and should help you identify individuals who are most likely to provide valuable insights.
Identify potential participants: Once you have determined the criteria for participant selection, identify potential participants who meet these criteria. Depending on the sampling method you are using, this may involve reaching out to experts in the field, identifying individuals who share certain characteristics or experiences, or asking for referrals from existing participants.
Select participants: Based on the identified potential participants, select the individuals who will participate in the study. Make sure to select a sufficient number of participants to ensure that you have a representative sample.
Collect data: After selecting participants, collect data using the appropriate research methods. Depending on the research question and objectives, this may involve conducting interviews, administering surveys, or collecting observational data.
Analyze data: After collecting data, analyze it to answer the research question or objective. This may involve using statistical analysis, qualitative analysis, or a combination of both.

Examples of Purposive Sampling

Here are some examples of how purposive sampling might be used in research:

Studying the experiences of cancer survivors : A researcher might use maximum variation sampling to select a diverse group of cancer survivors, with the aim of capturing a range of experiences and perspectives on the impact of cancer on their lives.
Exploring the perspectives of teachers on a new curriculum : A researcher might use expert sampling to select teachers who are experts in a particular subject area or who have experience teaching the new curriculum. These teachers can provide valuable insights on the strengths and weaknesses of the new curriculum.
Investigating the impact of a new therapy on a specific population: A researcher might use homogeneous sampling to select participants who share certain characteristics, such as a particular diagnosis or age group. This can help the researcher assess the effectiveness of the new therapy on this specific population.
Examining the experiences of refugees resettling in a new country : A researcher might use critical case sampling to select participants who have experienced particularly challenging resettlement experiences, such as those who have experienced discrimination or faced significant barriers to accessing services.
Understanding the experiences of homeless individuals : A researcher might use snowball sampling to identify and select homeless individuals to participate in the study. This method allows the researcher to gain access to a hard-to-reach population and capture a range of experiences and perspectives on homelessness.

Applications of Purposive Sampling

Purposive sampling has a wide range of applications across different fields of research. Here are some examples of how purposive sampling can be used:

Medical research: Purposive sampling is commonly used in medical research to study the experiences of patients with specific medical conditions. Researchers might use homogeneous sampling to select patients who share specific medical characteristics, such as a particular diagnosis or treatment history.
Market research: In market research, purposive sampling can be used to select participants who represent a particular demographic or consumer group. This might involve using quota sampling to select participants based on age, gender, income, or other relevant criteria.
Education research: Purposive sampling can be used in education research to select participants who have specific educational experiences or backgrounds. For example, researchers might use maximum variation sampling to select a diverse group of students who have experienced different teaching styles or classroom environments.
Social science research : In social science research, purposive sampling can be used to select participants who have specific social or cultural backgrounds. Researchers might use snowball sampling to identify and select participants from hard-to-reach or marginalized populations.
Business research: In business research, purposive sampling can be used to select participants who have specific job titles, work in particular industries, or have experience with specific products or services

Purpose of Purposive Sampling

The purpose of purposive sampling is to select participants based on specific criteria relevant to the research question or objectives. Unlike probability sampling techniques, which rely on random selection to ensure representativeness, purposive sampling allows researchers to select participants who are most relevant to their research question or objectives.

Purposive sampling is often used when the population of interest is rare, hard to reach, or has specific characteristics that are important to the research question. By selecting participants who meet specific criteria, researchers can gather valuable insights that can help inform their research.

The ultimate goal of purposive sampling is to increase the validity and reliability of research findings by selecting participants who are most relevant to the research question or objectives. This can help researchers to make more accurate inferences about the population of interest and to develop more effective interventions or solutions based on their findings.

When to use Purposive Sampling

Purposive sampling is appropriate when researchers need to select participants who meet specific criteria relevant to their research question or objectives. Here are some situations where purposive sampling might be appropriate:

Rare populations: Purposive sampling is often used when the population of interest is rare, such as people with a particular medical condition or individuals who have experienced a particular event or phenomenon.
Hard-to-reach populations: Purposive sampling is also useful when the population of interest is hard to reach, such as homeless individuals or individuals who have experienced trauma or abuse.
Specific characteristics: Purposive sampling is appropriate when researchers need to select participants with specific characteristics that are relevant to the research question, such as age, gender, or ethnicity.
Expertise : Purposive sampling is useful when researchers need to select participants with particular expertise or knowledge, such as teachers or healthcare professionals.
Maximum variation : Purposive sampling can be used to select participants who represent a range of perspectives or experiences, such as individuals from different socio-economic backgrounds or who have different levels of education.

Characteristics of Purposive Sampling

Purposive sampling has several characteristics that distinguish it from other sampling methods:

Non-random selection : Purposive sampling involves the deliberate selection of participants based on specific criteria, rather than random selection. This allows researchers to select participants who are most relevant to their research question or objectives.
Small sample sizes: Purposive sampling typically involves smaller sample sizes than probability sampling methods, as the focus is on selecting participants who meet specific criteria, rather than ensuring representativeness of the larger population.
Heterogeneous or homogeneous samples : Purposive sampling can involve selecting participants who are either similar to each other (homogeneous) or who are diverse and represent a range of perspectives or experiences (heterogeneous).
Multiple sampling strategies: Purposive sampling involves a range of sampling strategies that can be used to select participants, including maximum variation sampling, expert sampling, quota sampling, and snowball sampling.
Flexibility : Purposive sampling is a flexible method that can be adapted to suit different research questions and objectives. It allows researchers to select participants based on specific criteria, making it a useful method for exploring complex phenomena or researching hard-to-reach populations.

Advantages of Purposive Sampling

Purposive sampling has several advantages over other sampling methods:

Relevant participants: Purposive sampling allows researchers to select participants who are most relevant to their research question or objectives, ensuring that the data collected is of high quality and useful for the research.
Efficient : Purposive sampling is an efficient method of sampling, as it allows researchers to select participants based on specific criteria, rather than randomly selecting a large number of participants. This can save time and resources, especially when the population of interest is rare or hard to reach.
Representative : Purposive sampling can produce samples that are representative of the population of interest, as researchers can use a range of sampling strategies to select participants who are diverse and represent a range of perspectives or experiences.
Ethical considerations : Purposive sampling can be used to ensure that vulnerable or marginalized populations are included in research studies, ensuring that their voices and experiences are heard and taken into account.

Disadvantages of Purposive Sampling

Some Disadvantages of Purposive Sampling are as follows:

Sampling bias: Purposive sampling is susceptible to sampling bias, as the participants are not randomly selected from the population. This means that the sample may not be representative of the larger population, and the findings may not be generalizable to other populations.
Limited generalizability: The findings obtained from purposive sampling may be limited in their generalizability due to the small sample size and the specific selection criteria used. Therefore, it may not be possible to make broad generalizations based on the findings of a purposive sample.
Lack of transparency : The selection criteria used in purposive sampling may not be transparent, and this can limit the ability of other researchers to replicate the study.
Reliance on researcher judgment : Purposive sampling relies on the researcher’s judgment to select participants based on specific criteria, which can introduce bias into the selection process.
Potential for researcher subjectivity : The researcher’s subjectivity and bias may influence the selection process and the interpretation of the data collected.

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Purposive sampling

Purposive sampling, also known as judgmental , selective or subjective sampling, is a type of non-probability sampling technique . Non-probability sampling focuses on sampling techniques where the units that are investigated are based on the judgement of the researcher [see our articles: Non-probability sampling to learn more about non-probability sampling, and Sampling: The basics , for an introduction to terms such as units , cases and sampling ]. There are a number of different types of purposive sampling, each with different goals. This article explains (a) what purposive sampling is, (b) the eight of the different types of purposive sampling, (c) how to create a purposive sample, and (d) the broad advantages and disadvantages of purposive sampling.

Purposive sampling explained

Types of purposive sampling, advantages and disadvantages of purposive sampling.

Purposive sampling represents a group of different non-probability sampling techniques . Also known as judgmental , selective or subjective sampling, purposive sampling relies on the judgement of the researcher when it comes to selecting the units (e.g., people, cases/organisations, events, pieces of data) that are to be studied. Usually, the sample being investigated is quite small, especially when compared with probability sampling techniques .

Unlike the various sampling techniques that can be used under probability sampling (e.g., simple random sampling, stratified random sampling, etc.), the goal of purposive sampling is not to randomly select units from a population to create a sample with the intention of making generalisations (i.e., statistical inferences ) from that sample to the population of interest [see the article: Probability sampling ]. This is the general intent of research that is guided by a quantitative research design .

The main goal of purposive sampling is to focus on particular characteristics of a population that are of interest, which will best enable you to answer your research questions. The sample being studied is not representative of the population, but for researchers pursuing qualitative or mixed methods research designs , this is not considered to be a weakness. Rather, it is a choice, the purpose of which varies depending on the type of purposing sampling technique that is used. For example, in homogeneous sampling , units are selected based on their having similar characteristics because such characteristics are of particular interested to the researcher. By contrast, critical case sampling is frequently used in exploratory , qualitative research in order to assess whether the phenomenon of interest even exists (amongst other reasons).

During the course of a qualitative or mixed methods research design , more than one type of purposive sampling technique may be used. For example, critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting a maximum variation sampling technique is used to develop a wider picture of the phenomenon. We explain the different goals of these types of purposive sampling technique in the next section.

There are a wide range of purposive sampling techniques that you can use (see Patton, 1990, 2002; Kuzel, 1999, for a complete list). Each of these types of purposive sampling technique is discussed in turn:

Maximum variation sampling

Homogeneous sampling, typical case sampling, extreme (or deviant) case sampling, critical case sampling, total population sampling, expert sampling.

Maximum variation sampling, also known as heterogeneous sampling , is a purposive sampling technique used to capture a wide range of perspectives relating to the thing that you are interested in studying; that is, maximum variation sampling is a search for variation in perspectives, ranging from those conditions that are view to be typical through to those that are more extreme in nature. By conditions , we mean the units (i.e., people, cases/organisations, events, pieces of data) that are of interest to the researcher. These units may exhibit a wide range of attributes, behaviours, experiences, incidents, qualities, situations, and so forth. The basic principle behind maximum variation sampling is to gain greater insights into a phenomenon by looking at it from all angles. This can often help the researcher to identify common themes that are evident across the sample.

Homogeneous sampling is a purposive sampling technique that aims to achieve a homogeneous sample; that is, a sample whose units (e.g., people, cases, etc.) share the same (or very similar) characteristics or traits (e.g., a group of people that are similar in terms of age, gender, background, occupation, etc.). In this respect, homogeneous sampling is the opposite of maximum variation sampling . A homogeneous sample is often chosen when the research question that is being address is specific to the characteristics of the particular group of interest, which is subsequently examined in detail.

Typical case sampling is a purposive sampling technique used when you are interested in the normality/typicality of the units (e.g., people, cases, events, settings/contexts, places/sites) you are interested, because they are normal/typical . The word typical does not mean that the sample is representative in the sense of probability sampling (i.e., that the sample shares the same/similar characteristics of the population being studied). Rather, the word typical means that the researcher has the ability to compare the findings from a study using typical case sampling with other similar samples (i.e., comparing samples, not generalising a sample to a population). Therefore, with typical case sampling, you cannot use the sample to make generalisations to a population, but the sample could be illustrative of other similar samples. Whilst typical case sampling can be used exclusively, it may also follow another type of purposive sampling technique, such as maximum variation sampling, which can help to act as an exploratory sampling strategy to identify the typical cases that are subsequently selected.

Extreme (or deviant) case sampling is a type of purposive sampling that is used to focus on cases that are special or unusual , typically in the sense that the cases highlight notable outcomes , failures or successes . These extreme (or deviant) cases are useful because they often provide significant insight into a particular phenomenon, which can act as lessons (or cases of best practice) that guide future research and practice. In some cases, extreme (or deviant) case sampling is thought to reflect the purest form of insight into the phenomenon being studied.

Critical case sampling is a type of purposive sampling technique that is particularly useful in exploratory qualitative research, research with limited resources , as well as research where a single case (or small number of cases) can be decisive in explaining the phenomenon of interest. It is this decisive aspect of critical case sampling that is arguably the most important. To know if a case is decisive, think about the following statements: ?If it happens there, it will happen anywhere?; or ?if it doesn?t happen there, it won?t happen anywhere?; and ?If that group is having problems, then we can be sure all the groups are having problems? (Patton, 202, p.237). Whilst such critical cases should not be used to make statistical generalisations , it can be argued that they can help in making logical generalisations . However, such logical generalisations should be made carefully.

Total population sampling is a type of purposive sampling technique where you choose to examine the entire population (i.e., the total population ) that have a particular set of characteristics (e.g., specific experience, knowledge, skills, exposure to an event, etc.). In such cases, the entire population is often chosen because the size of the population that has the particular set of characteristics that you are interest in is very small. Therefore, if a small number of units (i.e., people, cases/organisations, etc.) were not included in the sample that is investigated, it may be felt that a significant piece of the puzzle was missing [see the article, Total population sampling , to learn more].

Expert sampling is a type of purposive sampling technique that is used when your research needs to glean knowledge from individuals that have particular expertise . This expertise may be required during the exploratory phase of qualitative research, highlighting potential new areas of interest or opening doors to other participants. Alternately, the particular expertise that is being investigated may form the basis of your research, requiring a focus only on individuals with such specific expertise. Expert sampling is particularly useful where there is a lack of empirical evidence in an area and high levels of uncertainty, as well as situations where it may take a long period of time before the findings from research can be uncovered. Therefore, expert sampling is a cornerstone of a research design known as expert elicitation .

Whilst each of the different types of purposive sampling has its own advantages and disadvantages, there are some broad advantages and disadvantages to using purposive sampling, which are discussed below.

Advantages of purposive sampling

There are a wide range of qualitative research designs that researchers can draw on. Achieving the goals of such qualitative research designs requires different types of sampling strategy and sampling technique . One of the major benefits of purposive sampling is the wide range of sampling techniques that can be used across such qualitative research designs; purposive sampling techniques that range from homogeneous sampling through to critical case sampling , expert sampling , and more.

Whilst the various purposive sampling techniques each have different goals, they can provide researchers with the justification to make generalisations from the sample that is being studied, whether such generalisations are theoretical , analytic and/or logical in nature. However, since each of these types of purposive sampling differs in terms of the nature and ability to make generalisations, you should read the articles on each of these purposive sampling techniques to understand their relative advantages.

Qualitative research designs can involve multiple phases, with each phase building on the previous one. In such instances, different types of sampling technique may be required at each phase. Purposive sampling is useful in these instances because it provides a wide range of non-probability sampling techniques for the researcher to draw on. For example, critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting an expert sampling approach to examine specific issues further.

Disadvantages of purposive sampling

Purposive samples, irrespective of the type of purposive sampling used, can be highly prone to researcher bias . The idea that a purposive sample has been created based on the judgement of the researcher is not a good defence when it comes to alleviating possible researcher biases, especially when compared with probability sampling techniques that are designed to reduce such biases. However, this judgemental, subjective component of purpose sampling is only a major disadvantage when such judgements are ill-conceived or poorly considered ; that is, where judgements have not been based on clear criteria, whether a theoretical framework, expert elicitation, or some other accepted criteria.

The subjectivity and non-probability based nature of unit selection (i.e., selecting people, cases/organisations, etc.) in purposive sampling means that it can be difficult to defend the representativeness of the sample. In other words, it can be difficult to convince the reader that the judgement you used to select units to study was appropriate. For this reason, it can also be difficult to convince the reader that research using purposive sampling achieved theoretical/analytic/logical generalisation . After all, if different units had been selected, would the results and any generalisations have been the same?

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Published: 23 August 2024

Role of anticoagulation therapy in modifying stroke risk associated with new-onset atrial fibrillation after non-cardiac surgery

Omid Azimaraghi 1 na2 ,
Maíra I. Rudolph ORCID: orcid.org/0000-0003-2307-0864 1 , 2 na2 ,
Karuna Wongtangman 1 , 3 ,
Felix Borngaesser ORCID: orcid.org/0009-0009-6431-2525 1 , 4 ,
Maya Doehne 1 ,
Pauline Y. Ng 5 , 6 ,
Dario von Wedel 7 , 8 , 9 ,
Annika Eyth 1 ,
Fengwei Zou 10 ,
Christopher Tam 1 ,
William J. Sauer 1 ,
Michael E. Kiyatkin 1 ,
Timothy T. Houle 11 ,
Ibraheem M. Karaye 1 , 12 ,
Ling Zhang 1 ,
Maximilian S. Schaefer 7 , 8 , 13 ,
Simon T. Schaefer 4 ,
Carina P. Himes 1 ,
Aline M. Grimm ORCID: orcid.org/0009-0009-1610-0907 1 ,
Olubukola O. Nafiu 14 ,
Christian Mpody 1 ,
Aiman Suleiman 1 ,
Brendon M. Stiles 15 ,
Luigi Di Biase 10 ,
Mario J. Garcia 10 ,
The Boston-NYC Afib after non-cardiac surgery collaborators Consortium ,
Deepak L. Bhatt ORCID: orcid.org/0000-0002-1278-6245 16 &
Matthias Eikermann ORCID: orcid.org/0000-0002-7893-0596 1 , 17

Nature Medicine ( 2024 ) Cite this article

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The role of antithrombotic therapy in the prevention of ischemic stroke after non-cardiac surgery is unclear. In this study, we tested the hypothesis that the association of new-onset postoperative atrial fibrillation (POAF) on ischemic stroke can be mitigated by postoperative oral anticoagulation therapy. Of 251,837 adult patients (155,111 female (61.6%) and 96,726 male (38.4%)) who underwent non-cardiac surgical procedures at two sites, POAF was detected in 4,538 (1.8%) patients. The occurrence of POAF was associated with increased 1-year ischemic stroke risk (3.6% versus 2.3%; adjusted risk ratio (RRadj) = 1.60 (95% confidence interval (CI): 1.37–1.87), P < 0.001). In patients with POAF, the risk of developing stroke attributable to POAF was 1.81 (95% CI: 1.44–2.28; P < 0.001) without oral anticoagulation, whereas, in patients treated with anticoagulation, no significant association was observed between POAF and stroke (RRadj = 1.04 (95% CI: 0.71–1.51), P = 0.847, P for interaction = 0.013). Furthermore, we derived and validated a computational model for the prediction of POAF after non-cardiac surgery based on demographics, comorbidities and procedural risk. These findings suggest that POAF is predictable and associated with an increased risk of postoperative ischemic stroke in patients who do not receive postoperative anticoagulation.

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Epidemiology, risk profile, management, and outcome in geriatric patients with atrial fibrillation in two long-term care hospitals

Long-term risk of adverse outcomes according to atrial fibrillation type

Association between atrial cardiopathy and stroke severity in acute ischemic stroke

Data availability.

Access to anonymized patient data is subject to a data-sharing agreement and protocol approval from the institutional review board committee. The study-specific analyzable dataset is, therefore, not publicly available. Due to the sensitive nature of the patient data collected for this study, requests to access the dataset may be directed to the corresponding author, M.E., at [email protected] (14-d response time).

Code availability

Codes for inclusion criteria, baseline characteristics and outcome events were developed based on the International Classification of Diseases code classification, Current Procedural Terminology or data from electronic health records, unless otherwise specified. These are described in the supplementary materials of this manuscript. Statistical analyses were conducted using Stata (version 17), GraphPad Prism (version 9) and G*Power (version 3.1.9.4). Detailed information about the codes and packages used in the analyses can be found in the Supplementary Information . For additional clarification of these codes, contact the corresponding author at [email protected] (14-d response time).

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Acknowledgements

The authors declare no specific grants for this research from any funding agency in the public, commercial or not-for-profit sector.

Author information

These authors contributed equally: Omid Azimaraghi, Maíra I. Rudolph.

Authors and Affiliations

Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

Omid Azimaraghi, Maíra I. Rudolph, Karuna Wongtangman, Felix Borngaesser, Maya Doehne, Annika Eyth, Christopher Tam, William J. Sauer, Michael E. Kiyatkin, Ibraheem M. Karaye, Ling Zhang, Carina P. Himes, Aline M. Grimm, Christian Mpody, Aiman Suleiman, Annika Bald, Jay J. Im, Jonathan Leff, Can M. Luedeke, Timothy Pulverenti, Tina Ramishvili, Flora T. Scheffenbichler, Sujatha Ramachandran & Matthias Eikermann

Department of Anesthesiology and Intensive Care Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

Maíra I. Rudolph

Department of Anesthesiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand

Karuna Wongtangman

Carl von Ossietzky Universität Oldenburg, University Clinic for Anesthesiology, Intensive Care, Emergency Medicine, and Pain Therapy, Klinikum Oldenburg AöR, Oldenburg, Germany

Felix Borngaesser & Simon T. Schaefer

Critical Care Medicine Unit, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

Pauline Y. Ng

Department of Adult Intensive Care, Queen Mary Hospital, Hong Kong SAR, China

Center for Anesthesia Research Excellence, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

Dario von Wedel, Maximilian S. Schaefer & Dario von Wedel

Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

Institute of Medical Informatics, Charité University Medicine Berlin, Berlin, Germany

Dario von Wedel & Dario von Wedel

Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

Fengwei Zou, Luigi Di Biase, Mario J. Garcia, Luigi Di Biase & M. Azeem Latib

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Timothy T. Houle

Department of Population Health, Hofstra University, Hempstead, NY, USA

Ibraheem M. Karaye

Department of Anesthesiology, Duesseldorf University Hospital, Duesseldorf, Germany

Maximilian S. Schaefer

Department of Anesthesiology & Pain Medicine, Nationwide Children’s Hospital and The Ohio State University, Columbus, OH, USA

Olubukola O. Nafiu

Department of Cardiovascular and Thoracic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY, USA

Brendon M. Stiles, Joseph J. DeRose & Stephen J. Forest

Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Deepak L. Bhatt

Klinik für Anästhesiologie und Intensivmedizin, Universität Duisburg-Essen, Essen, Germany

Matthias Eikermann

Department of Medicine, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA

Fran Ganz-Lord

Department of Anesthesiology and Intensive Care Medicine, Ulm University, Ulm, Germany

Flora T. Scheffenbichler

You can also search for this author in PubMed Google Scholar

The Boston-NYC Afib after non-cardiac surgery collaborators Consortium

Omid Azimaraghi
, Annika Bald
, Luigi Di Biase
, Deepak L. Bhatt
, Felix Borngaesser
, Joseph J. DeRose
, Maya Doehne
, Matthias Eikermann
, Annika Eyth
, Stephen J. Forest
, Fran Ganz-Lord
, Mario J. Garcia
, Aline M. Grimm
, Carina P. Himes
, Timothy T. Houle
, Jay J. Im
, Ibraheem M. Karaye
, Michael E. Kiyatkin
, M. Azeem Latib
, Jonathan Leff
, Can M. Luedeke
, Christian Mpody
, Olubukola O. Nafiu
, Pauline Y. Ng
, Timothy Pulverenti
, Tina Ramishvili
, Maíra I. Rudolph
, Maximilian S. Schaefer
, Simon T. Schaefer
, William J. Sauer
, Flora T. Scheffenbichler
, Brendon M. Stiles
, Aiman Suleiman
, Christopher Tam
, Dario von Wedel
, Sujatha Ramachandran
, Karuna Wongtangman
, Ling Zhang
& Fengwei Zou

Contributions

O.A., L.Z., M.S.S. and M.E. had access to the data. M.E. is the guarantor of the manuscript and takes full responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: O.A., M.K., W.J.S., M.S.S., M.G., D.L.B., L.D. and M.E. Acquisition, analysis or interpretation of the data: O.A., T.T.H., L.Z., M.D., M.I.R., F.B. and D.W. Drafting of the manuscript: O.A., M.I.R., F.Z., A.E., D.L.B. and M.E. Critical revision of the manuscript: all authors. Statistical analysis: O.A., I.M.K., M.D., M.I.R., F.B. and K.W. D.L.B. and M.E. supervised critical revisions.

Corresponding author

Correspondence to Matthias Eikermann .

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Competing interests.

M.E. receives funding from the National Institutes of Health (NIH) (R01AG065554 and R01HL132887) that does not pertain to this manuscript. He holds two patents for acyclic curcubiturils for reversal of drugs of abuse and neuromuscular blocking agents (patent numbers 9956229 and 9469648). He is a member of the associated editorial board for the British Journal of Anaesthesia . D.L.B. discloses the following relationships. Advisory Board: Angiowave, Bayer, Boehringer Ingelheim, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma and Stasys; Board of Directors: American Heart Association New York City, Angiowave (stock options), Bristol Myers Squibb (stock), DRS.LINQ (stock options) and High Enroll (stock); Consultant: Broadview Ventures, Hims, SFJ and Youngene; Data Monitoring Committee: Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Boston Scientific (chair, PEITHO trial), Cleveland Clinic, Contego Medical (chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo; for the ABILITY-DM trial, funded by Concept Medical; for ALLAY-HF, funded by Alleviant Medical), Novartis, Population Health Research Institute and Rutgers University (for the NIH-funded MINT Trial); Honoraria: American College of Cardiology (ACC) (senior associate editor, Clinical Trials and News ; chair, ACC Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee, funded by Boehringer Ingelheim; AEGIS-II executive committee, funded by CSL Behring), Belvoir Publications (editor in chief, Harvard Heart Letter ), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), CSL Behring (American Heart Association lecture), Cowen and Company, Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (editor in chief, Journal of Invasive Cardiology ), Journal of the American College of Cardiology (guest editor and associate editor), K2P (co-chair, interdisciplinary curriculum), Level Ex, Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Oakstone CME (course director, Comprehensive Review of Interventional Cardiology), Piper Sandler, Population Health Research Institute (for the COMPASS operations committee, publications committee and steering committee and USA national co-leader, funded by Bayer), WebMD (CME steering committees) and Wiley (steering committee); Other: Clinical Cardiology (deputy editor); Patent: sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital, assigned to Lexicon; neither D.L.B. nor Brigham and Women's Hospital receive any income from this patent); Research Funding: Abbott, Acesion Pharma, Afimmune, Aker Biomarine, Alnylam, Amarin, Amgen, AstraZeneca, Bayer, Beren, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor, Cleerly, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Garmin, HLS Therapeutics, Idorsia, Ironwood, Ischemix, Janssen, Javelin, Lexicon, Eli Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Otsuka, Owkin, Pfizer, PhaseBio, PLx Pharma, Recardio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, Synaptic, The Medicines Company, Youngene and 89Bio; Royalties: Elsevier (editor, Braunwald’s Heart Disease ); Site Co-Investigator: Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St. Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte and Vascular Solutions; Trustee: ACC; Unfunded Research: FlowCo. M.S.S. received funding for investigator-initiated studies from Merck & Co., which does not pertain to this manuscript. He is an associate editor for BMC Anesthesiology . He received honoraria for lectures from Fisher & Paykel Healthcare and Mindray Medical International Limited. He received an unrestricted philanthropic grant from Jeff and Judy Buzen. All other authors have no support from any organization for the submitted work, no financial relationships with any organizations that might have an interest in the submitted work and no other relationships or activities that could appear to have influenced the submitted work.

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Extended data

Extended data fig. 1 kaplan-meier survival estimates..

The figure illustrates the adjusted Kaplan-Meier survival estimates analysis conducted for the occurrence of stroke in patients experiencing new onset postoperative atrial fibrillation(red line) and patients who did not(blue line). The x-axis represents time after surgery in days, while the y-axis indicates the probability of being stroke-free (Hazard Ratio: 2.07 (95% CI 1.92-2.23), p < 0.001).

Extended Data Fig. 2 Risk of new-onset postoperative atrial fibrillation and stroke.

( A ) New-onset atrial fibrillation (POAF) after non-cardiac surgery is associated with postoperative complications, stroke, heart failure, and myocardial infarction. ( B ) For patients who were prescribed oral anticoagulants, the absolute risk difference was 0.34% in all patients and 0.68% in high-risk patients (upper quintile in computational prediction model [>11 points]). There was no significant absolute risk difference in patients who were prescribed oral anticoagulants. The images used were created by the authors using Procreate for iOS17 (Savage Interactive Pty Ltd., Hobart, Australia).

Extended Data Fig. 3 Graphical description of the study design.

Graphical description of the study design, visualizing temporal anchors of exposure (new-onset postoperative atrial fibrillation), outcome/follow-up (ischemic stroke/transient ischemic attack), covariates (baseline demographics, procedure related factors, comorbidities and preexisting medication) and primary effect modifier (oral anticoagulation). The cohort entry date (CED), serving as the primary anchor date for patients entering the study analysis, was the date of the patients' index surgery (Day 0). We excluded patients with preexisting atrial fibrillation, age below 18 years, ASA physical status greater than 4 or missing data for exposure, outcome or covariates. Covariates were assessed during a window extending one year prior to the CED if not otherwise specified [-365,-1]. The time window for the primary exposure - postoperative atrial fibrillation (POAF) - was between day 0 and the end of day post-CED. Oral anticoagulation prescriptions were included between postoperative day 1 and the end of day 365. The outcome ischemic stroke was assessed between day 31 and 365 after surgery. Abbreviations: y, years; ASA, American Society of Anesthesiologists; POAF, new-onset postoperative atrial fibrillation.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Supplementary Tables 1–7 and Supplementary Notes 1 (STROBE checklist) and 2 (Statistical Analysis Plan).

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Azimaraghi, O., Rudolph, M.I., Wongtangman, K. et al. Role of anticoagulation therapy in modifying stroke risk associated with new-onset atrial fibrillation after non-cardiac surgery. Nat Med (2024). https://doi.org/10.1038/s41591-024-03206-0

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Accepted : 19 July 2024

Published : 23 August 2024

DOI : https://doi.org/10.1038/s41591-024-03206-0

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	© RWJF 2008 P.O. Box 2316 College Road East and Route 1 Princeton, NJ 08543	-->Citation: Cohen D, Crabtree B. "Qualitative Research Guidelines Project." July 2006.