Population definition[ edit ] Successful statistical practice is based on focused problem definition.
A Brief Introduction to Sampling Researchers usually cannot make direct observations of every individual in the population they are studying.
Ideally, the sample corresponds to the larger population on the characteristic s of interest. In that case, the researcher's conclusions from the sample are probably applicable to the entire population. Public opinion polls that try to describe the percentage of the population that plans to vote for a particular candidate, for example, require a sample that is highly representative of the population.
Probability samples and convenience samples Two general approaches to sampling are used in social science research. With probability sampling, all elements e. Recruiting a probability sample is not always a priority for researchers. A scientist can demonstrate that a particular trait occurs in a population by documenting a single instance.
For example, the assertion that all lesbians are mentally ill can be refuted by documenting the existence of even one lesbian who is free from psychopathology. Another situation in which a probability sample is not necessary is when a researcher wishes to describe a particular group in an exploratory way.
For example, interviewing 25 people with AIDS PWAs about their experiences with HIV could provide valuable insights about stress and coping, even though it would not yield data about the proportion of PWAs in the general population who share those experiences.
Types of probability samples Many strategies can be used to create a probability sample. Each starts with a sampling frame, which can be thought of as a list of all elements in the population of interest e.
The sampling frame operationally defines the target population from which the sample is drawn and to which the sample data will be generalized. Probably the most familiar type of probability sample is the simple random sample, for which all elements in the sampling frame have an equal chance of selection, and sampling is done in a single stage with each element selected independently rather than, for example, in clusters.
Somewhat more common than simple random samples are systematic samples, which are drawn by starting at a randomly selected element in the sampling frame and then taking every nth element e.
In yet another approach, cluster sampling, a researcher selects the sample in stages, first selecting groups of elements, or clusters e. An example Suppose some researchers want to find out which of two mayoral candidates is favored by voters.
Obtaining a probability sample would involve defining the target population in this case, all eligible voters in the city and using one of many available procedures for selecting a relatively small number probably fewer than 1, of those people for interviewing. For example, the researchers might create a systematic sample by obtaining the voter registration roster, starting at a randomly selected name, and contacting every th person thereafter.
Or, in a more sophisticated procedure, the researchers might use a computer to randomly select telephone numbers from all of those in use in the city, and then interview a registered voter at each telephone number.
This procedure would yield a sample that represents only those people who have a telephone. Several procedures would also be available for recruiting a convenience sample, but none of them would include the entire population as potential respondents.
For example, the researchers might ascertain the voting preferences of their own friends and acquaintances. Or they might interview shoppers at a local mall. Or they might publish two telephone numbers in the local newspaper and ask readers to call either number in order to "vote" for one of the candidates.
The important feature of these methods is that they would systematically exclude some members of the population respectively, eligible voters who do not know the researchers, do not go to the shopping mall, and do not read the newspaper.
Consequently, their findings could not be generalized to the population of city voters. Evaluating samples Samples are evaluated primarily according to the procedures by which they were selected rather than by their final composition or size. In the example above, it would be impossible to know if the convenience sample consisting of the researchers' friends or mall shoppers is representative, even if its demographic characteristics closely resembled those of the city electorate e.
And even if several thousand people called the published telephone numbers, the sample would be seriously biased. Of course, results from a probability sample might not be accurate for many reasons.
Using probability sampling procedures is necessary but not sufficient for obtaining results that can be generalized with confidence to the entire population.How big should a sample be?
Sample size is an important consideration in qualitative research. Typically, researchers want to continue sampling until having achieved informational redundancy or saturation -- the point at which no new information or themes are emerging from the data. SAMPLING IN RESEARCH Sampling In Research Mugo Fridah W.
INTRODUCTION This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with. 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.
Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. Each observation measures one or more properties.
A probability sampling method is any method of sampling that utilizes some form of random vetconnexx.com 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.
The following Slideshare presentation, Sampling in Quantitative and Qualitative Research – A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding. Sampling Bias. Author(s) David M. Lane. Prerequisites.
Inferential Statistics (including sampling) Learning Objectives. Recognize sampling bias; Distinguish among self-selection bias, undercoverage bias, and survivorship bias.