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How many full-length hairs from all area of the scalp are needed to ensure a representative sampling of head hair?
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What are the advantages of sampling plans based on statistical laws and guidelines?
A) more economical B)fewer personnel needed C) Less handling
What are the major types of nonprobability sampling designs?
Non-probability SamplingSocial research is often conducted in situations where a researcher cannot select the kinds of probability samples used in large-scale social surveys. For example, say you wanted to study homelessness - there is no list of homeless individuals nor are you likely to create such a list. However, you need to get some kind of a sample of respondents in order to conduct your research. To gather such a sample, you would likely use some form of non-probability sampling.To reiterate, the primary difference between probability methods of sampling and non-probability methods is that in the latter you do not know the likelihood that any element of a population will be selected for study.There are four primary types of non-probability sampling methods:Availability SamplingAvailability sampling is a method of choosing subjects who are available or easy to find. This method is also sometimes referred to as haphazard, accidental, or convenience sampling. The primary advantage of the method is that it is very easy to carry out, relative to other methods. A researcher can merely stand out on his/her favorite street corner or in his/her favorite tavern and hand out surveys. One place this used to show up often is in university courses. Years ago, researchers often would conduct surveys of students in their large lecture courses. For example, all students taking introductory sociology courses would have been given a survey and compelled to fill it out. There are some advantages to this design - it is easy to do, particularly with a captive audience, and in some schools you can attain a large number of interviews through this method.The primary problem with availability sampling is that you can never be certain what population the participants in the study represent. The population is unknown, the method for selecting cases is haphazard, and the cases studied probably don't represent any population you could come up with.However, there are some situations in which this kind of design has advantages - for example, survey designers often want to have some people respond to their survey before it is given out in the "real" research setting as a way of making certain the questions make sense to respondents. For this purpose, availability sampling is not a bad way to get a group to take a survey, though in this case researchers care less about the specific responses given than whether the instrument is confusing or makes people feel bad.Despite the known flaws with this design, it's remarkably common. Ask a provocative question, give telephone number and web site address ("Vote now at CNN.com), announce results of poll. This method provides some form of statistical data on a current issue, but it is entirely unknown what population the results of such polls represents. At best, a researcher could make some conditional statement about people who are watching CNN at a particular point in time who cared enough about the issue in question to log on or call in.Quota SamplingQuota sampling is designed to overcome the most obvious flaw of availability sampling. Rather than taking just anyone, you set quotas to ensure that the sample you get represents certain characteristics in proportion to their prevalence in the population. Note that for this method, you have to know something about the characteristics of the population ahead of time. Say you want to make sure you have a sample proportional to the population in terms of gender - you have to know what percentage of the population is male and female, then collect sample until yours matches. Marketing studies are particularly fond of this form of research design.The primary problem with this form of sampling is that even when we know that a quota sample is representative of the particular characteristics for which quotas have been set, we have no way of knowing if sample is representative in terms of any other characteristics. If we set quotas for gender and age, we are likely to attain a sample with good representativeness on age and gender, but one that may not be very representative in terms of income and education or other factors.Moreover, because researchers can set quotas for only a small fraction of the characteristics relevant to a study quota sampling is really not much better than availability sampling. To reiterate, you must know the characteristics of the entire population to set quotas; otherwise there's not much point to setting up quotas. Finally, interviewers often introduce bias when allowed to self select respondents, which is usually the case in this form of research. In choosing males 18-25, interviewers are more likely to choose those that are better-dressed, seem more approachable or less threatening. That may be understandable from a practical point of view, but it introduces bias into research findings.Purposive SamplingPurposive sampling is a sampling method in which elements are chosen based on purpose of the study. Purposive sampling may involve studying the entire population of some limited group (sociology faculty at Columbia) or a subset of a population (Columbia faculty who have won Nobel Prizes). As with other non-probability sampling methods, purposive sampling does not produce a sample that is representative of a larger population, but it can be exactly what is needed in some cases - study of organization, community, or some other clearly defined and relatively limited group.Snowball SamplingSnowball sampling is a method in which a researcher identifies one member of some population of interest, speaks to him/her, then asks that person to identify others in the population that the researcher might speak to. This person is then asked to refer the researcher to yet another person, and so on.Snowball sampling is very good for cases where members of a special population are difficult to locate. For example, several studies of Mexican migrants in Los Angeles have used snowball sampling to get respondents.The method also has an interesting application to group membership - if you want to look at pattern of recruitment to a community organization over time, you might begin by interviewing fairly recent recruits, asking them who introduced them to the group. Then interview the people named, asking them who recruited them to the group.The method creates a sample with questionable representativeness. A researcher is not sure who is in the sample. In effect snowball sampling often leads the researcher into a realm he/she knows little about. It can be difficult to determine how a sample compares to a larger population. Also, there's an issue of who respondents refer you to - friends refer to friends, less likely to refer to ones they don't like, fear, etc.
What are Advantages and disadvantages in multistage sampling?
Advantages are: 1. Fewer investigators are needed 2. It is not so costly to obtain a sample Disadvantages are: 1. There is the possibility of bias if, for example, only if a small number of regions are selected 2.The method is not truly random as once the final sampling areas have been selected the rest of the population cannot be in the sample. If the population is heterogeneous, the areas chosen should reflect the full range of the diversity. Otherwise, choosing some areas and excluding others (even if it is done randomly) will result in a biased sample.
Why use standard deviation In what situations is it special?
I will restate your question as "Why are the mean and standard deviation of a sample so frequently calculated?". The standard deviation is a measure of the dispersion of the data. It certainly is not the only measure, as the range of a dataset is also a measure of dispersion and is more easily calculated. Similarly, some prefer a plot of the quartiles of the data, again to show data dispersal.t Standard deviation and the mean are needed when we want to infer certain information about the population such as confidence limits from a sample. These statistics are also used in establishing the size of the sample we need to take to improve our estimates of the population. Finally, these statistics enable us to test hypothesis with a certain degree of certainty based on our data. All this stems from the concept that there is a theoretical sampling distribution for the statistics we calculate, such as a proportion, mean or standard deviation. In general, the mean or proportion has either a normal or t distribution. Finally, the measures of dispersion will only be valid, be it range, quantiles or standard deviation, require observations which are independent of each other. This is the basis of random sampling.
Is a data file needed to make a bar graph?
Yes.
Why sampling needed?
Sampling is needed in order to determine the properties of a distribution or a population. Sampling allows the scientist to determine the variance in an estimate.
What is a sentence with the word representative?
I needed a representative to do my presentaion because I was sick.
Why is core sampling needed?
Core sampling is used by geologists and other scientists for scientific knowlege, geologic mapping, and mineral and resource discovery and management.
Is a normally distributed variable needed to have a normally distributed sampling distribution.?
Yes, it is.
In the internal control evaluation plan it is helpful to include the sampling relating to each evaluation area.?
Yes, including sampling in the internal control evaluation plan helps determine the extent of testing needed to assess the effectiveness of controls in each area. It allows for a systematic and structured approach to evaluating controls, ensuring a representative sample is tested to draw conclusions about the overall control environment. Sampling helps manage resources efficiently by focusing on key areas without testing every single transaction or process.
What environment is needed to ensure that a supercomputer can work properly?
A cool temperature is needed.
What type of education needed to be in the us house of representative?
you need to study law.
What equipment is needed for Dosimetry?
You'll need the following for dosimetry: Sampling Media (Dosimeter or Cassette) Sampling Pump (If applicable) Calibration Standard for Pump (If applicable) Appropriate PPE for you Pen Field Notebook Camera Chain of Custody form
Why is pap smear needed?
to ensure that you do not have a malignancy of your reproductive system
What are the advantages of sampling plans based on statistical laws and guidelines?
A) more economical B)fewer personnel needed C) Less handling
How much data is needed to have a representative sample of the population?
The answer depends on the variability of the characteristic that is being measured.
What is a cluster sampling?
Cluster sampling is sampling technique that is used when natural groups are evident. In one-stage cluster sampling (Elementary Statistics, p. 24, by Triola), all members of these selected clusters are surveyed. Rigorous rules to define members of the cluster are needed. A member can not belong to two clusters. Surveys of people conducted in geographical different areas of the country would be an example of cluster sampling. Because different definitions/ explanations exist, I've included three links for further reading. See the following links: http://stattrek.com/Lesson6/CLS.aspx?Tutorial=Stat http://en.wikipedia.org/wiki/Cluster_sampling http://www.answers.com/topic/cluster-sampling
