Sampling methods Survey sampling can be grouped into two broad categories: proba

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Sampling methods

Survey sampling can be grouped into two broad categories: probability-based sampling (also loosely called ‘random sampling’) and non-probability sampling. A probability- based sample is one in which the respondents are selected using some sort of probabilistic mechanism, and where the probability with which every member of the frame population could have been selected into the sample is known. The sampling probabilities do not necessarily have to be equal for each member of the sampling frame.

Types of probability sample include:

• Simple random sampling (SRS) is a method in which any two groups of equal size in the population are equally likely to be selected. Mathematically, simple random sampling selects n units out of a population of size N such that every sample of size n has an equal chance of being drawn.

• Stratified random sampling is useful when the population is comprised of a number of homogeneous groups. In these cases, it can be either practically or statistically advantageous (or both) to first stratify the population into the homogeneous groups and then use SRS to draw samples from each group.

Cluster sampling is applicable when the natural sampling unit is a group or cluster of individual units. For example, in surveys of Internet users it is sometimes useful or convenient to first sample by discussion groups or Internet domains, and then to sample individual users within the groups or domains.

Systematic sampling is the selection of every kth element from a sampling frame or from a sequential stream of potential respondents. Systematic sampling has the advantage that a sampling frame does not need to be assembled beforehand. In terms of Internet surveying, for example, systematic sampling can be used to sample sequential visitors to a website. The resulting sample is considered to be a probability sample as long as the sampling interval does not coincide with a pattern in the sequence being sampled and a random starting point is chosen.

There are important analytical and practical considerations associated with how one draws and subsequently analyzes the results from each of these types of probability-based sam- pling scheme, but space limitations preclude covering then here. Readers interested in such details should consult texts such as Kish (1965), Cochran (1977), Fink (2003), or Fowler (2002).

Non-probability samples, sometimes called convenience samples, occur when either the probability that every unit or respondent included in the sample cannot be determined, or it is left up to each individual to choose to participate in the survey. For probability samples, the surveyor selects the sample using some probabilistic mechanism and the individuals in the population have no control over this process. In contrast, for example, a web survey may simply be posted on a website where it is left up to those browsing through the site to decide to participate in the survey (‘opt in’) or not. As the name implies, such non-probability samples are often used because it is somehow convenient to do so.

While in a probability-based survey par- ticipants can choose not to participate in the survey (‘opt out’), rigorous surveys seek to minimize the number who decide not to participate (i.e., nonresponse). In both cases it is possible to have bias, but in non-probability surveys the bias has the potential to be much greater, since it is likely that those who opt in are not representative of the general population. Furthermore, in non-probability surveys there is often no way to assess the potential magnitude of the bias, since there is generally no information on those who chose not to opt in.

Non-probability-based samples often require much less time and effort, and thus usually are less costly to generate, but generally they do not support statistical inference. However, non-probability-based samples can be useful for research in other ways. For example, early in the course of research, responses from a convenience sample might be useful in developing research hypotheses. Responses from convenience samples might also be useful for identifying

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issues, defining ranges of alternatives, or collecting other sorts of non-inferential data. For a detailed discussion on the application of various types of non-probability-based sampling method to qualitative research, see Patton (2002).

Specific types of non-probability samples include the following.

• Quota sampling requires the survey researcher only to specify quotas for the desired number of respondents with certain characteristics. The actual selection of respondents is then left up to the survey interviewers who must match the quotas. Because the choice of respondents is left up to the survey interviewers, subtle biases may creep into the selection of the sample (see, for example, the Historical Survey Gaffes section).

• Snowball sampling is often used when the desired sample characteristic is so rare that it is extremely difficult or prohibitively expensive to locate a sufficiently large number of respondents by other means (such as simple random sampling). Snowball sampling relies on referrals from initial respondents to generate additional respondents. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself substantially increases the likelihood that the sample will not be representative of the population.

• Judgement sampling is a type of convenience sam- pling in which the researcher selects the sample based on his or her judgement. For example, a researcher may decide to draw the entire random sample from one ‘representative’ Internet-user community, even though the population of interest includes all Internet users. Judgment sampling can also be applied in even less structured ways without the application of any random sampling.

Bias versus variance

If a sample is systematically not representative of the population of inference in some way, then the resulting analysis is biased. For exam- ple, results from a survey of Internet users about personal computer usage is unlikely to accurately quantify computer usage in the general population, simply because the sample is comprised only of those who use computers.

Taking larger samples will not correct for bias, nor is a large sample evidence of a lack of bias. For example, an estimate of average computer usage based on a sample of Internet users will likely overestimate the average usage in the general population regardless of how many Internet users are surveyed. Randomization is used to minimize the chance of bias. The idea is that by randomly choosing potential survey respondents the sample is likely to ‘look like’ the population, even in terms of those characteristics that cannot be observed or known.

Variance, on the other hand, is simply a measure of variation in the observed data. It is used to calculate the standard error of a statistic, which is a measure of the variability of the statistic. The precision of statistical estimates drawn via probabilistic sampling mechanisms is improved by larger sample sizes.

Some important sources of bias

Bias can creep into survey results in many different ways. In the absence of significant nonresponse, probability-based sampling is assumed to minimize the possibility of bias. Convenience sampling, on the other hand, is generally assumed to have a higher likelihood of generating a biased sample. However, even with randomization, surveys of and about people may be subject to other kinds of bias. For example, respondents may be inclined to over-or understate certain things (‘sensitivity bias’), particularly with socially delicate questions (such as questions about income or sexual orientation, for example). Here we just focus on some of the more common sources of bias related to sampling.

Frame coverage bias occurs when the sampling frame misses some important part of the population. For example, an e-mail survey using a list of e-mail addresses will miss those without an e-mail address.

Selection bias is an error in how the individual or units are chosen to participate in the survey. It can occur, for example, if survey participation depends on the respondents having access to particular

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SAMPLING METHODS FOR WEB AND E-MAIL SURVEYS 201

equipment, such as Internet-based surveys that

miss those without Internet access.

Size bias occurs when some units have a greater

chance of being selected than others. For example, in a systematic sample of website visitors, frequent site visitors are more likely to get selected into the sample than those that do not. In a similar vein, when selecting from a frame consisting of e-mail addresses, individuals with multiple e-mail addresses would have a higher chance of being selected into a sample.

Nonresponse bias occurs if those who refuse to answer the survey are somehow systematically different from those who do answer it. Read this article and answer the following questions please

Explanation / Answer

Survey are of two types

1) Probability sampling (sampling is done with probability)

2) Non probability sampling (sampling is done with out probability)

Probabity sampling classifed as

1) Simple sampling (selects n units out of a population of size N such that every sample of size n has an equal chance of being drawn)

2) Statrified sampling (when the population is comprised of a number of homogeneous groups)

3) Cluster sampling (when the natural sampling unit is a group)

4) Systematic sampling (the selection of every kth element from a sampling frame)

Non probability sampling is classifed into

1) Quota sampling ( the survey researcher only to specify quotas for the desired number of respondents with certain characteristics)

2) Snowball sampling (relies on referrals from initial respondents to generate additional respondents)

3) Judgement sampling (convenience sam- pling in which the researcher selects the sample based on his or her judgement)

Bias can creep into sampling. Type of bias are discussed

1) Sensitivity bias (The respondents may be inclined to over-or understate certain things like biological survey)

2) Coverage bias (Occurs when the sampling frame misses some important part of the population)

3) Section bias  (An error in how the individual are chosen to participate in the survey)

4) Size bias (This occurs when some units have a greater chance of being selected than others)

5) nonresponse bias (When some one chooses not to reply or participate in survey)

THE ABOVE SENTENCES ARE COPY WRITED FROM THE SAME ESSAY THAT IS PROVIDED IN THE QUESTION. SO PLEASE DONT TAG THIS ANSWER FOR PLAGARISM.

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