Convenience sampling is easy to carry out, but one large disadvantage is that the sample is likely to be biased. Finally, quota sampling is another method of nonprobability sampling.
This is when different subgroups are identified and participants are selected through convenience from each different subgroup. For example, say a researcher wanted to select a sample of students to participate in a study using a convenience sample but wanted to ensure that an equal number of boys and girls were selected — quota sampling would be the best method for them to use. This type of sampling can help to control a convenience sample but may results in a biased sample, which would not be a good representative of the wider population.
As I mentioned earlier, the goal of research is to study a sample of participants and then generalise the results to the larger population. How far we can extend such results to generalise to a population is dependant on how closely the sample resembles the population — the representativeness.
The main threat to representativeness is bias. A biased sample is one which contains characteristics that are different from those of the population. This bias may happen by chance, but usually is down to selection bias. Selection bias is when participants are selected in a way that increases the probability of acquiring a biased sample.
For example, if a researcher recruits participants from a gym, they are more likely to be healthier and fitter than the rest of the general public. I can definitely say that the selection of participants is a very vital part of planning research.
Without carefully planning and choosing an appropriate method for sampling it is very easy to obtain a biased sample that does not represent the population.
When this happens, it is difficult to extend findings to a wider population and the validity of the experiment decreases. In order to produce influential and meaningful results, researchers must ensure that they have chosen an appropriate sampling method to select a representative sample of participants. Behavioral study of obedience. Journal of Abnormal and Social Psychology, 72, This is a very interesting blog. I have written in one of my blogs about generalisation, and have never mentioned or considered sampling methods.
Like you have mentioned convenience sampling is a cheap and easy way to recruit participants, and can lead to biases, which is something that I get really angry about, I know that it is impossible to be perfect on representing the entire population, but I just feel like some methods that are used shut off a large amount of people.
It is unlikely that research will ever represent the whole population, but it is good that there are a number of methods so that representation can be increased, and at the same time reduce biases. Homework for my TA. I have a question related to the sampling techniques described here. I will be grateful if you respond my queries please. Hi, suraiya khatoon, this was every intresting type of convenience sampling. The turn up was good hence less bias. I got what you intend, thankyou for putting up.
Woh I am lucky to find this website through google. Stratified sampling random within target groups There are specific sub-groups to investigate eg. Systematic sampling every nth person When a stream of representative people are available eg. Cluster sampling all in limited groups When population groups are separated and access to all is difficult, eg. Method Best when Quota sampling get only as many as you need You have access to a wide population, including sub-groups Proportionate quota sampling in proportion to population sub-groups You know the population distribution across groups, and when normal sampling may not give enough in minority groups Non-proportionate quota sampling minimum number from each sub-group There is likely to a wide variation in the studied characteristic within minority groups.
Method Best when Purposive sampling based on intent You are studying particular groups Expert sampling seeking 'experts' You want expert opinion Snowball sampling ask for recommendations You seek similar subjects eg. Method Best when Snowball sampling ask for recommendations You are ethically and socially able to ask and seek similar subjects.
Convenience sampling use who's available You cannot proactively seek out subjects. Judgment sampling guess a good-enough sample You are expert and there is no other choice. Method Best when Selective sampling gut feel Focus is needed in particular group, location, subject, etc. Theoretical sampling testing a theory Theories are emerging and focused sampling may help clarify these.
Home Top Menu Quick Links. Probability methods This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results. After voluntary following the link and submitting the web based questionnaire, the respondent will be included in the sample population. This method can reach a global population and limited by the advertisement budget.
This method may permit volunteers outside the reference population to volunteer and get included in the sample. It is difficult to make generalizations about the total population from this sample because it would not be representative enough. Line-intercept sampling is a method of sampling elements in a region whereby an element is sampled if a chosen line segment, called a "transect", intersects the element.
Panel sampling is the method of first selecting a group of participants through a random sampling method and then asking that group for potentially the same information several times over a period of time. Therefore, each participant is interviewed at two or more time points; each period of data collection is called a "wave".
The method was developed by sociologist Paul Lazarsfeld in as a means of studying political campaigns. Panel sampling can also be used to inform researchers about within-person health changes due to age or to help explain changes in continuous dependent variables such as spousal interaction. Snowball sampling involves finding a small group of initial respondents and using them to recruit more respondents. It is particularly useful in cases where the population is hidden or difficult to enumerate.
Theoretical sampling  occurs when samples are selected on the basis of the results of the data collected so far with a goal of developing a deeper understanding of the area or develop theories. Sampling schemes may be without replacement 'WOR'—no element can be selected more than once in the same sample or with replacement 'WR'—an element may appear multiple times in the one sample. For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once.
However, if we do not return the fish to the water, this becomes a WOR design. If we tag and release the fish we caught, we can see whether we have caught a particular fish before. Sampling enables the selection of right data points from within the larger data set to estimate the characteristics of the whole population.
For example, there are about million tweets produced every day. It is not necessary to look at all of them to determine the topics that are discussed during the day, nor is it necessary to look at all the tweets to determine the sentiment on each of the topics. A theoretical formulation for sampling Twitter data has been developed. In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage and controller data are available at short time intervals.
To predict down-time it may not be necessary to look at all the data but a sample may be sufficient. Survey results are typically subject to some error. Total errors can be classified into sampling errors and non-sampling errors.
The term "error" here includes systematic biases as well as random errors. Non-sampling errors are other errors which can impact the final survey estimates, caused by problems in data collection, processing, or sample design. After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis.
A particular problem is that of non-response. Two major types of non-response exist: In this case, there is a risk of differences, between respondents and nonrespondents, leading to biased estimates of population parameters. This is often addressed by improving survey design, offering incentives, and conducting follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame.
Nonresponse is particularly a problem in internet sampling. Reasons for this problem include improperly designed surveys,  over-surveying or survey fatigue ,   and the fact that potential participants hold multiple e-mail addresses, which they don't use anymore or don't check regularly. In many situations the sample fraction may be varied by stratum and data will have to be weighted to correctly represent the population.
Thus for example, a simple random sample of individuals in the United Kingdom might include some in remote Scottish islands who would be inordinately expensive to sample. A cheaper method would be to use a stratified sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate. More generally, data should usually be weighted if the sample design does not give each individual an equal chance of being selected.
For instance, when households have equal selection probabilities but one person is interviewed from within each household, this gives people from large households a smaller chance of being interviewed. This can be accounted for using survey weights.
Similarly, households with more than one telephone line have a greater chance of being selected in a random digit dialing sample, and weights can adjust for this. Random sampling by using lots is an old idea, mentioned several times in the Bible. In Pierre Simon Laplace estimated the population of France by using a sample, along with ratio estimator. He also computed probabilistic estimates of the error. His estimates used Bayes' theorem with a uniform prior probability and assumed that his sample was random.
Alexander Ivanovich Chuprov introduced sample surveys to Imperial Russia in the s. In the USA the Literary Digest prediction of a Republican win in the presidential election went badly awry, due to severe bias .
More than two million people responded to the study with their names obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large, was deeply flawed. The textbook by Groves et alia provides an overview of survey methodology, including recent literature on questionnaire development informed by cognitive psychology:.
The other books focus on the statistical theory of survey sampling and require some knowledge of basic statistics, as discussed in the following textbooks:. The historically important books by Deming and Kish remain valuable for insights for social scientists particularly about the U. From Wikipedia, the free encyclopedia. For computer simulation, see pseudo-random number sampling.
This section needs expansion. You can help by adding to it. How to conduct your own survey. Model Assisted Survey Sampling. The" panel" as a new tool for measuring opinion. The Public Opinion Quarterly, 2 4 , — Analysis of Sampling Algorithms for Twitter. International Joint Conference on Artificial Intelligence. Survey nonresponse in design, data collection, and analysis. Internet, mail, and mixed-mode surveys: The tailored design method.
Nonresponse in web surveys. New directions for institutional research pp. Moore and George P. Mean arithmetic geometric harmonic Median Mode. Central limit theorem Moments Skewness Kurtosis L-moments. Grouped data Frequency distribution Contingency table.
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Techniques > Research > Sampling > Choosing a sampling method Probability | Quota | Selective | Convenience | Ethnographic | See also There are many methods of sampling when doing research.
Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each.
Feb 19, · So, in this weeks blog I am going to be discussing the different sampling techniques and methods, and considering the issue of sampling bias and the problems associated in research. There are a variety of different sampling methods available to researchers to select individuals for a . Simple Random Sampling (SRS) Stratified Sampling; Cluster Sampling; Systematic Sampling; Multistage Sampling (in which some of the methods above are combined in stages) Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling.
RESEARCH METHOD - SAMPLING 1. Sampling Techniques & Samples Types 2. Outlines Sample definition Purpose of sampling Stages in the selection of a sample Types of sampling in quantitative researches Types of sampling in qualitative researches Ethical Considerations in Data Collection. This type of research is called a census study because data is gathered on every member of the population. Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. Sampling methods are classified as either probability or.