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*In non-probability sampling also known as non-random sampling not all members of the population has a chance of participating in the study. This is contrary to probability sampling , where each member of the population has a known, non-zero chance of being selected to participate in the study.*

- More than Just Convenient: The Scientific Merits of Homogeneous Convenience Samples
- Pros and Cons of Different Sampling Methods
- Type of Sampling

*Sampling is the use of a subset of the population to represent the whole population or to inform about social processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling , is a sampling technique in which the probability of getting any particular sample may be calculated.*

## More than Just Convenient: The Scientific Merits of Homogeneous Convenience Samples

Despite their disadvantaged generalizability relative to probability samples, non-probability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and most available probability samples are ill-suited to examine developmental questions. In lieu of focusing on how to eliminate or sharply reduce reliance on convenience samples within developmental science, here we propose how to augment their advantages when it comes to understanding population effects as well as subpopulation differences.

Although all convenience samples have less clear generalizability than probability samples, we argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples. Therefore, when researchers are limited to convenience samples, they should consider homogeneous convenience samples as a positive alternative to conventional or heterogeneous convenience samples. We discuss future directions as well as potential obstacles to expanding the use of homogeneous convenience samples in developmental science.

For this reason, a sizable amount of developmental science research is devoted to understanding developmental processes and trends in specific sociodemographic groups as well as differences across two or more sociodemographic groups.

Developmental scientists should rely more on probability samples, for reasons we describe below. Nonetheless, because convenience samples are commonly used, we focus here on how developmental scientists can limit the disadvantages of convenience samples when it comes to understanding population effects as well as subpopulation differences. As we outline below, relative to conventional or heterogeneous convenience samples i. On this basis, we argue that when researchers are limited to convenience samples, they should adopt homogeneous convenience samples as a positive alternative to conventional convenience samples.

Before distinguishing between conventional and homogeneous convenience samples, we compare and contrast convenience sampling in general with probability sampling and then, using an illustration, discuss in more depth the key disadvantage of all convenience samples: due to poor generalizability they often yield biased estimates of the target population and its sociodemographic subpopulations.

Next, we describe conventional and homogeneous convenience sampling, and explain why, of the two, homogeneous convenience sampling provides clearer generalizability and, therefore, a more accurate account of its target population effects and subpopulation differences.

We conclude by discussing future directions as well as potential obstacles to expanding the use of homogeneous convenience samples within developmental science. Within developmental science, sampling strategies generally fall into two broad categories: non-probability sampling and probability sampling Bornstein et al. Probability sampling strategies are any methods of sampling that utilize some form of random selection, which entails setting up a process or procedure that assures that different members of the target population have equal probabilities of being chosen.

Probability sampling 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; see Bornstein et al. The key advantage of probability sampling strategies is that they all, when carried out properly, should yield an unbiased sample that is representative of the target population.

As a result, researchers can safely assume that estimates obtained from probability samples are both unbiased and generalizable. The key disadvantage of probability sampling strategies is that they present a significant challenge to execute. That is, the sizes of probability samples need to be quite large, often coming at great costs in terms of money, time, and effort.

Moreover, designing probability samples requires substantial expertise. Furthermore, as Davis-Kean and Jager chapter in this SRCD Monograph discuss in more detail, most existing probability samples are ill-suited to examine developmental questions. Non-probability sampling strategies are any methods of sampling that do not utilize some form of random selection.

By far the most common non-probability sampling strategy used within developmental science is convenience sampling for review see Bornstein et al. One of the most common examples of convenience sampling within developmental science is the use of student volunteers as study participants.

The key advantages of convenience sampling are that it is cheap, efficient, and simple to implement. The key disadvantage of convenience sampling is that the sample lacks clear generalizability. Moreover, these advantages and disadvantages apply, albeit in varying degrees, to all types of convenience samples.

Therefore, the advantages and disadvantages of convenience sampling are the reverse of probability sampling. Whereas probability samples yield results with clearer generalizability, convenience samples are far less expensive, more efficient, and simpler to execute.

Even though probability sampling is more advantaged in terms of scientific merit i. Bornstein et al. Among the studies for which the type of sampling strategy could be conclusively determined, Probability sampling accounted for only 5.

Thus, from a tally of recent publications in prestigious journals in developmental science, convenience samples were the norm and were over 16 times more likely to be used than probability samples. Because the generalizability of convenience samples is unclear, the estimates derived from convenience samples are often biased i.

This bias extends to estimates of population effects as well as estimates of subpopulation differences. We illustrate these effects by outlining the known population parameters for the association between harsh parenting and externalizing as well as ethnic differences in that association, and then compare known population parameters to estimates obtained from three hypothetical convenience samples.

For the purposes of this illustration, we use the following target population: White and Black youth between the ages of 10 and 19 in the United States. Based on data from the United States Census and research on the association between harsh parenting and externalizing in this target population, we know the population parameters of the association between harsh parenting and externalizing with some confidence; they are listed in the first row of Table 1. Based on extant research, among the total population i.

Finally, although some research suggests no ethnic difference in association between harsh parenting and externalizing Berlin et al. Correlation between harsh parenting and externalizing, by ethnicity and SES. Population parameters and sample estimates for the association between harsh parenting and externalizing among White and Black adolescents.

To devise hypothetical convenience samples, we use school-based samples as a heuristic. We do so because in developmental science a common sampling frame e. The demographic characteristics and sample estimates for each of three hypothetical convenience samples are listed in Table 1. As a result, in Sample C the ethnic difference in SES is three times larger than it is in the target population. When considered individually, all three convenience samples yield misleading or biased estimates of the overall population effect i.

The estimates for Sample B are also biased; whereas the estimates for Sample A are too high, the estimates for Sample B are too low. When considered collectively, how would researchers integrate the findings from these three convenience samples? Their estimates of population effects and subpopulation differences are inconsistent; therefore, they cannot all be correct estimates of the same target population.

But for most scientific investigations using convenience samples to study a given developmental process, the true population parameters of the target population are not known. If the true population parameters were known, then there would be no reason to undertake the study in the first place.

Therefore, when attempting to integrate inconsistent findings across a set of studies using convenience samples, investigations typically do not have the known population parameters to use as a benchmark. Although this example involved a set of hypothetical studies, substantial variation in the sociodemographic composition of convenience samples is all too common across studies examining a given developmental characteristic in an equivalent target population.

Importantly, these variations make it difficult to determine whether inconsistencies across studies represent true population differences or instead are artifacts of differences in sample composition. Put succinctly, science is supposed to be cumulative; however, the use of convenience samples can translate into across-study inconsistencies that are difficult to integrate and, therefore, build upon.

Despite their disadvantaged generalizability, convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and most probability samples are ill-suited to examine developmental questions. Instead of focusing on how to reduce the use of convenience samples within developmental science, we focus here on how to limit their disadvantages when it comes to understanding population effects as well as subpopulation differences.

Although all convenience samples have less clear generalizability than probability samples, not all convenience samples are the same, and some convenience samples have clearer generalizability than others. We argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples.

In developmental science, homogeneous convenience samples are far less common than conventional convenience samples. Therefore, we believe that one way to minimize the disadvantages of convenience samples is through the strategic use of homogenous convenience samples in place of conventional convenience samples. Below we describe in more detail what we mean by conventional and homogeneous convenience samples, and then we describe why, of the two, homogeneous convenience sampling has clearer generalizability.

Next, we describe their advantages and disadvantages when it comes to estimating population effects as well as subpopulation differences. The sampling frame for conventional convenience samples is not intentionally constrained based on sociodemographic background i. For example, aside from the fact that they were limited to two ethnic groups for the sake of simplicity, the three hypothetical convenience samples listed in Table 1 are conventional convenience samples.

For these samples, the sampling frame was truly ad hoc regardless of sociodemographics, all were welcome to participate provided they volunteered.

In contrast to conventional convenience sampling, the sampling frame for homogeneous convenience sampling is intentionally constrained with respect to sociodemographic background.

In homogeneous convenience sampling researchers undertake to study and therefore sample a population that is homogeneous with respect to one or more sociodemographic factors e. Thus, the target population not just the sample studied is a specific sociodemographic subgroup.

For example, for a sample that is homogeneous with respect to ethnic group, the sampling frame is limited to, say, just Black Americans, and only Black Americans are sampled. Homogeneous samples can differ in their degree of sociodemographic homogeneity. For example, the target population and its matching sample could be limited to one sociodemographic factor such as ethnicity e.

The greater the number of homogeneous sociodemographic factors, the more homogeneous the sample and the narrower the sampling frame. Although relatively rare, homogeneous samples are used in developmental science, often to examine underrepresented sociodemographic groups e. As part of their tally of the types of sampling strategies in developmental science, Bornstein et al. The key advantage of homogeneous convenience samples, relative to conventional convenience samples, is their clearer generalizability.

Because the sampling frame of homogeneous convenience samples is more homogeneous than the sampling frame for conventional convenience samples, researchers can be more confident with respect to generalizability.

Why does a more homogeneous sampling frame translate into clearer generalizability? Logic dictates that the more homogeneous a population, the easier more probable it is to generate a representative sample, even when using convenience sampling.

Therefore, by intentionally constraining the sampling frame to reduce the amount of sociodemographic heterogeneity, the chance of bias in sampling, as it relates to sociodemographic characteristics of the target population, is reduced although not all together eliminated.

Imagine two different convenience samples that seek to examine the same developmental process. Each convenience sample consists of families, and both samples are taken from the same large Midwestern city. The first is a conventional convenience sample and, because it does not limit its sampling frame with respect to any sociodemographic factors, contains at least some amount of heterogeneity on many sociodemographic factors.

The second is a homogeneous convenience sample and, because it limits its sampling frame with respect to ethnicity only samples Black families , SES only samples middle-class families , and national origin only samples families within which both birth parents were born in the United States , it contains no heterogeneity on these sociodemographic factors.

Now imagine that the findings differed between the two samples, which would not be surprising given the stark sociodemographic differences between the two samples. In our view, the findings from the homogeneous convenience sample would have the clearer generalizability. That is, we could be more confident that the findings from the homogeneous convenience sample generalize to middle-class, native-born, Black families than we could be that the findings from the conventional convenience sample generalize to all families regardless of ethnicity, class, or national origin.

This is because, in comparison to the conventional convenience sample, the homogeneous convenience sample should, on average, have a sociodemographic distribution that more closely reflects the sociodemographic distribution of its target population, and therefore, its estimates of its target population should, on average, be more accurate, precise, and valid. The key disadvantage of homogeneous convenience samples, relative to conventional convenience samples, is their narrower generalizability.

Although homogeneous convenience samples have clearer generalizability, their findings also generalize to a more circumscribed population. Returning to the example above, although the findings from the homogeneous convenience sample of middle-class, native-born, Black families have clearer generalizability than do the findings from the conventional convenience sample of all families, the findings from the homogeneous convenience sample, at best, only generalize to middle-class, native-born, Black families.

Therefore, the findings from the homogenous convenience sample reveal very little if anything about families that are not middle-class, native-born, and Black. Another disadvantage of homogeneous convenience samples is that, if they are samples of underrepresented sociodemographic groups, they can be more costly and time consuming relative to conventional convenience samples. After all, to maximize the alignment and therefore generalizability between a sample and its target population, researchers must have a firm and detailed understanding of their target population.

Although the generalizability of homogeneous convenience samples is clearer, if narrower, relative to conventional convenience samples, we emphasize that both homogeneous convenience samples and conventional convenience samples have poor generalizability relative to probability samples.

On a hypothetical continuum of generalizability, probably samples are at one end and conventional convenience samples are at the other end.

## Pros and Cons of Different Sampling Methods

Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. Collectively, these units form the sample that the researcher studies [see our article, Sampling: The basics , to learn more about terms such as unit , sample and population ]. A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection i. Whilst some researchers may view non-probabilit y sampling techniques as inferior to probability sampling techniques, there are strong theoretical and practical reasons for their use. This article discusses the principles of non-probability sampling and briefly sets out the types of non-probability sampling technique discussed in detail in other articles within this site. The article is divided into two sections: principles of non-probability sampling and types of non-probability sampling :. There are theoretical and practical reasons for using non-probability sampling.

Nonprobability sampling is a method of selecting cases from a population without the use of random selection. Random selection requires each case in a population to have an equal chance of being selected. Nonprobability sampling, in contrast, describes any method in which some cases have no chance for selection in the study. Nonprobability sampling is likely to occur when researchers do not know or do not have access to all cases in a target population, which frequently occurs in communication research. For example, it would be extremely difficult for each adult in a city to have the same chance of being selected for an online survey because it would require not only contact information for each person, but each adult to have Internet

When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study. Ensures a high degree of representativeness of all the strata or layers in the population. Possibly, members of units are different from one another, decreasing the techniques effectiveness.

Non-Probability Sampling: Definition, types, Examples, and advantages. non-probability sampling. What is non-probability sampling? Definition: Non.

## Type of Sampling

Conversations about sampling methods and sampling bias often take place at 60, feet. Although these conversations are important, it is good to occasionally talk about what sampling looks like on the ground. At a practical level, what methods do researchers use to sample people and what are the pros and cons of each?

Despite their disadvantaged generalizability relative to probability samples, non-probability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and most available probability samples are ill-suited to examine developmental questions. In lieu of focusing on how to eliminate or sharply reduce reliance on convenience samples within developmental science, here we propose how to augment their advantages when it comes to understanding population effects as well as subpopulation differences. Although all convenience samples have less clear generalizability than probability samples, we argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples. Therefore, when researchers are limited to convenience samples, they should consider homogeneous convenience samples as a positive alternative to conventional or heterogeneous convenience samples. We discuss future directions as well as potential obstacles to expanding the use of homogeneous convenience samples in developmental science.

Knowing some basic information about survey sampling designs and how they differ can help you understand the advantages and disadvantages of various approaches. Probability gives all people a chance of being selected and makes results more likely to accurately reflect the entire population. That is not the case for non-probability.

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Advantages and disadvantages. A major advantage with non-probability sampling is that—compared to probability sampling—it's very cost- and time-effective. It's.

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