Some of the types of sampling are 1 simple random sampling. Mostly used for the type of population which is homogeneous. The purpose of all the sampling techniques is to give the equal chance of any item to be selected without bias. Sampling refers to the statistical process of selecting and studying the characteristics of a relatively small number of items from a relatively large population of such items,, to draw statistically valid inferences about the characteristics about the entire population.
There are two broad methods of sampling used by researchers, nonrandom or judgment sampling and random or probability sampling. In judgement sampling the researcher selects items to be drawn from the population based on his or her judgement about how well these items represent the whole population. The sample is thus based on someones knowledge about the population and the characteristics of individual items within it. The chance of an item being included in the sample are influenced by the characteristic of the item as judged by an expert selecting the item.
A judgement sampling system is simple and less expensive to use. Also when there is very little known about the population under study a pilot study based on judgement sample is carried out to permit design of a more rigorous sampling system for a detailed study.
In random sampling, individual judgement plays no part in selection of sample. Each item in the sample stands equal chance of being included in the sample. In case of random sampling, the researcher is required to use specific statistical processes to ensure this equal probability of every item in the population. Researchers will also need to consider the margin of error , the reliability that the data collected is generally accurate; and the confidence level , the probability that your margin of error is accurate.
Finally, researchers must take into account the standard deviation they expect to see in the data. Standard deviation measures how much individual pieces of data vary from the average data measured. For instance, soil samples from one park will likely have a much smaller standard deviation in their nitrogen content than soils collected from across a whole county. Large sample sizes are needed for a statistic to be accurate and reliable, especially if its findings are to be extrapolated to a larger population or group of data.
Say you were conducting a survey about exercise and interviewed five people, two of whom said they run a marathon annually. If you take this survey to represent the population of the country as a whole, then according to your research, 40 percent of people run at least one marathon annually -- an unexpectedly high percentage.
The smaller your sample size, the more likely outliers -- unusual pieces of data -- are to skew your findings. The sample size of a statistical survey is also directly related to the survey's margin of error.
Margin of error is a percentage that expresses the probability that the data received is accurate. Cluster sampling is commonly implemented as multistage sampling. This is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster rather than using all units contained in all selected clusters.
In following stages, in each of those selected clusters, additional samples of units are selected, and so on. All ultimate units individuals, for instance selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random subsamples of preceding random samples. Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed before other sampling methods could be applied.
By eliminating the work involved in describing clusters that are not selected, multistage sampling can reduce the large costs associated with traditional cluster sampling. In quota sampling , the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is used to select the subjects or units from each segment based on a specified proportion.
For example, an interviewer may be told to sample females and males between the age of 45 and It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non- random. For example, interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for several years.
In imbalanced datasets, where the sampling ratio does not follow the population statistics, one can resample the dataset in a conservative manner called minimax sampling. The minimax sampling has its origin in Anderson minimax ratio whose value is proved to be 0.
This ratio can be proved to be minimax ratio only under the assumption of LDA classifier with Gaussian distributions. The notion of minimax sampling is recently developed for a general class of classification rules, called class-wise smart classifiers.
In this case, the sampling ratio of classes is selected so that the worst case classifier error over all the possible population statistics for class prior probabilities, would be the. Accidental sampling sometimes known as grab , convenience or opportunity sampling is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient.
It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone.
The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include:.
In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions. The voluntary sampling method is a type of non-probability sampling.
A voluntary sample is made up of people who self-select into the survey. Often, these subjects have a strong interest in the main topic of the survey. Volunteers may be invited through advertisements on Social Media Sites . This method is suitable for a research which can be done through filling a questionnaire. The target population for advertisements can be selected by characteristics like demography, age, gender, income, occupation, education level or interests using advertising tools provided by the social media sites.
The advertisement may include a message about the research and will link to a web survey. 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:.
Video: What is Sampling in Research? - Definition, Methods & Importance - Definition, Methods & Importance The sample of a study can have a profound impact on the outcome of a study.
Sampling is a process used in statistical analysis in which a group of observations are extracted from a larger set.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to . In research, a sample is a subset of a population that is used to represent the entire group. Learn more about why sampling is used.
It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection. RESEARCH METHOD - SAMPLING 1. Sample size is a count the of individual samples or observations in any statistical setting, such as a scientific experiment or a public opinion survey. Too small a sample yields unreliable results, while an overly large sample demands a good deal of time and resources.