Random and non random sampling pdf file
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- Random Sampling
- Types of Sampling: Sampling Methods with Examples
- An introduction to simple random sampling
A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. Before I can explain the various probability methods we have to define some basic terms.
Conclusions and Recommendations The final section presents the conclusions of the Task Force. Those conclusions are summarized below. Great advances of the most successful sciences - astronomy, physics, chemistry - were and are, achieved without probability sampling. Statistical inference in these researches is based on subjective judgment about the presence of adequate, automatic, and natural randomization in the population.
No clear rule exists for deciding exactly when probability sampling is necessary, and what price should be paid for it. Probability sampling for randomization is not a dogma, but a strategy, especially for large numbers.
For example, although psychologists sometimes use data from nationally representative probability samples, it is far more common for their studies to be based on convenience samples of college students. In the mids, David Sears raised concerns about this. The picture had not changed when he examined papers in the same journals published five years later.
Although this reliance on unrepresentative samples may have its weaknesses, as Sears argued, psychologists have continued to use samples of convenience for most of their research. Sears was concerned more about the population from which the subjects in psychology experiments were drawn than about the method for selecting them, but it is clear that even the population of undergraduates is likely not represented well in psychology experiments.
The participants in psychology experiments are self-selected in various ways, ranging from their decision to go to particular colleges, to enroll in specific classes, and to volunteer and show up for a given experiment. For example, the vast majority of psychological experiments are not used to estimate a mean or proportion for some particular finite population — which is the usual situation with surveys based on probability samples — but instead are used to determine whether the differences across two or more experimental groups are reliably different from zero.
In still other cases, no quantitative conclusions are drawn e. In general, they differ from the other methods described in this report in two important ways. Second, they tend to avoid surveys or direct questioning of respondents about their attitudes and behaviors to avoid the data collection costs, instead trying to infer these attributes in other ways.
The typical goal of evaluation research is to establish a causal relationship between a treatment and an outcome e. The gold standard of evaluation research is randomized controlled trials, which are characterized by random assignment of subjects to a treatment group or to one of multiple treatment groups or to a control group Shadish et al. The subjects in the treatment group receive the intervention while the control group subjects do not. Where these concerns exist quasi-experimental designs are often used.
These designs do not randomize eligible sample units e. Many of these studies rely on volunteers. They assume that the effect of a new medical procedure observed among the volunteers is likely to hold in the general population. This is a very strong assumption that may not be valid. In other words, they try to address questions about whether the treatment caused the outcome to change and, if so, by how much and in what direction.
More recently, the ideas proposed in this literature have been used to draw samples for surveys that are more descriptive and not aimed at understanding a specific causal relationship. Thus, the application of sample matching is somewhat different in the survey context.
They are often used for cost-savings in cases where traditional methods are available but costly. Hence, it is not always clear when the cost-per-information of such a strategy is actually lower than that of more traditional approaches.
In some populations, standard methods may not even be feasible. For example, sexual minorities may be stigmatized in the larger population and so the identification of target population members in available sampling frames can be difficult e. Zea Other populations, such as small ethnic minorities, are rare enough that samples from available frames would capture them only at a very slow and inefficient rate e.
Kogan et al. In this work, Goodman introduced a variant of link-tracing network sampling which he refers to as s stage k name snowball sampling. This original probabilistic formulation assumes a complete sampling frame is available, and the initial probability sample, or seeds , is drawn from this frame.
Goodman advocates this sampling strategy for inference concerning the number of network structures of various types in the full network. In contrast to the shortcomings of link-tracing samples as currently practiced, he advocates snowball sampling as a method of increasing the efficiency of the sample. In particular, he argues that by using a s-stage snowball to estimate the number of s-polygons for example, 3-waves for triangles, 4-waves for squares , far fewer nodes need be observed for the desired precision in the estimated number of s-polygons than would be required based on a simple random sample of nodes.
It sometimes means a convenience sample see Section 3 acquired by starting with a non-probability sample, then expanding the sample by enrolling typically all of the contacts of each previous participant.
Such samples are clearly not probability samples, as the probability of being sampled is determined first by the initial sample of convenience, and subsequently by having network connections to the earlier convenience sample see, e. Biernacki and Waldorf ; Handcock and Gile Indeed, these and other online forums for social connection provide opportunities for sampling.
The crawling algorithms used are varied and the subject of a great deal of research. Gjoka et al. Other specialized algorithms aim to address other challenging network features. Frontier Sampling , for example, aims to address the possibility that some parts of the network may not be connected through links to other parts of the network Ribeiro and Towsley, Approaches in this vein, however, currently do not ask users to complete new survey questions, but instead collect information available in online content.
This seeming miracle of mediation is attempted with two features: clever sampling design and a healthy dose of assumptions, although some are virtually untestable. On the one hand, a large number of often un-testable assumptions may be required for valid inference.
Because of the dependence among sampled units, the variance of resulting estimators can be quite high. For these reasons, network sampling may not be the first choice for a sample design. These imperfections are rooted in sampling errors due to not observing the full population and nonsampling errors due to inadequacies in measuring the units.
Even though these errors are of concern in both probability and non-probability samples, non-probability samples often receive closer scrutiny because of general unfamiliarity with non-probability methods. See Section 7 for more discussion of the quality of estimates for probability and non-probability samples. Both categories are discussed below. Weights for probability samples begin with the base weights sometimes called the design weight or inverse probability of selection weight.
Weight adjustments are then applied to improve efficiency or to address potential biases, where the biases may be due to nonresponse and coverage errors. Kalton and Flores-Cervantes described adjustments intended to reduce nonsampling errors in probability samples.
With non-probability surveys, less extensive research on variance estimation has been published, as most of the interest has been directed at bias. However, more research in this area is essential as variance estimates are needed for inferences and for evaluation of the estimates.
In the absence of such research, statements such as those of the National Agricultural Statistical Service USDA that discuss the inability to make valid, design-based estimates of variability from non-probability samples are sometimes interpreted to mean that non-probability samples cannot support any variance estimation approach.
They generally are grouped into three categories: coverage error, sampling error, and nonresponse error. These errors are generally seen to arise from four sources: the questionnaire, the interviewer if there is one , the respondent, and the mode of data collection e. Here we might argue that data coming from non-probability samples likely have the same error properties as those coming from probability samples. Both are prone to observational gaps: between the constructs intended to be measured and the measurements devised to quantify them; between the application of the measurements and the responses provided; and between the response provided and the data ultimately recorded and edited.
Whether the survey data come from a probability or non-probability sample should not matter --we should be able to use the same type of measurement error quality indicators.
A literature has developed over the last decade describing attempts to assess the validity of samples from these sources as compared to probability samples of varying quality mostly on the telephone , benchmark data such as censuses, electoral outcomes and other data collected by non-survey methods such as administrative or sales data.
Arguably the most frequently cited work is the Yeager et al. Some of these methods have been described in previous sections and results from studies using opt-in panels have frequently been challenged on grounds of both bias and variance Yeager et al. However, these and other studies like them generally have not looked at the specific sampling methods used. Their focus has tended to be on the panels themselves rather than the techniques used to draw samples from them. From that perspective it seems entirely legitimate to focus on coverage and in turn point out the variety of ways in which individual panels are recruited and maintained.
Likewise, calculating estimates of coverage error and examining the techniques that may be used to adjust for it are essential parts of evaluating the quality of samples from opt-in panels. Researchers who routinely work with probability samples have been investigating alternative approaches to deal with this shortcoming, especially with respect to response rates. For example, R-indicators are one approach to replace or supplement response rates Schouten, Cobben, and Bethlehem If the response rates from all the subgroups are relatively consistent, then there is little evidence of nonresponse bias nonresponse bias occurs when response rates co-vary with the characteristics being estimated.
The indicators quantify this variation in response rates across the subgroups. Survey researchers, on the other hand, are accustomed to using widely accepted quality measures that they believe substantiate and qualify the inferences they make from probability samples. It seems clear that if non-probability methods are to be embraced as valid for surveys then similar measures or methods are needed.
Within the survey profession he is more likely thought of first and foremost as an eminent statistician who in the early s was instrumental in the development of sampling procedures at the Bureau of the Census Aguayo It was during his time at Census that Deming argued that accuracy should not be the sole criteria for evaluating survey data.
Yet increasingly, a considerable body of statistical agency requirements includes additional quality dimensions and criteria by which fit for purpose design decisions might be made. Tracking studies that continually measure phenomena such as product satisfaction or use over time are in some ways similar to data collections by government statistical agencies.
Media measurement services that rack viewership and readership are another example of where precise estimates are desired. In these instances the fitness for use criteria applied are not unlike those described above for government agencies although an emphasis on cost, timeliness and accessibility sometimes predominate. At other times market researchers are more like modelers whose main focus is on data collections that can support development and testing of statistical models that describe, for instance, how personal characteristics and product features interact to make some products successful while others fail.
Advocates of probability samples have come to accept what once they may have thought of as unacceptably high levels of nonresponse. The dramatic rise in the use of opt-in panels has been premised on a willingness to accept overwhelming coverage and selection error.
Those compromises are mostly practical and increasingly accepted, but seldom explicitly set in a fitness for use framework. Yet even in idealized circumstances where the classic constraints of budget, time, and feasibility may not require compromise in design the degree to which survey results are usable by decision makers is now widely recognized as a key driver of design. Search for:. They generally measure lifestyles issues such as the types of activities people engage and their frequency, media use, attitudes toward privacy, and openness to innovation.
For decades survey researchers have relied on measures for assessing data quality based in the probability sampling paradigm. Because they are grounded in probability theory and have been used for decades, they are widely accepted as useful measures of quality. Unfortunately, non-probability samples violate three key assumptions on which many of these measures are based. Those assumptions are: 1 a frame exists for all units of the population; 2 every unit has a positive probability of selection; and 3 the probability of selection can be computed for each unit.
The standard quality metrics are designed to measure the degree to which a specific sample violates these assumptions due to such real-life constraints as incomplete coverage and unit nonresponse. The quality standard guidelines issued by Statistics Canada, the U.
Types of Sampling: Sampling Methods with Examples
Published on August 28, by Lauren Thomas. Revised on October 2, A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected. This method is the most straightforward of all the probability sampling methods , since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomization, any research performed on this sample should have high internal and external validity. Table of contents When to use simple random sampling How to perform simple random sampling Frequently asked questions about simple random 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. Nonprobability sampling does not meet this criterion. Nonprobability sampling techniques are not intended to be used to infer from the sample to the general population in statistical terms. Instead, for example, grounded theory can be produced through iterative nonprobability sampling until theoretical saturation is reached Strauss and Corbin, Thus, one cannot say the same on the basis of a nonprobability sample than on the basis of a probability sample.
An introduction to simple random sampling
Home QuestionPro Products Audience. Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers.
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.
Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. There are essentially two types of sampling methods: 1 probability sampling — based on chance events such as random numbers, flipping a coin etc. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling.
Conclusions and Recommendations The final section presents the conclusions of the Task Force. Those conclusions are summarized below. Great advances of the most successful sciences - astronomy, physics, chemistry - were and are, achieved without probability sampling.
Simple Random Sampling
In statistics , quality assurance , and survey methodology , sampling is the selection of a subset a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question. Two advantages of sampling are lower cost and faster data collection than measuring the entire population. Each observation measures one or more properties such as weight, location, colour of observable bodies distinguished as independent objects or individuals. In survey sampling , weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. In business and medical research, sampling is widely used for gathering information about a population.