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In case of use of non probability sampling how to determine sample size?
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True or False As the amount of confidence increases the required sample size should decrease Explain your answer?
I can examine this as a question of theory or real life: As a matter of theory, I will rephrase your question as follows: Does theoretical confidence interval of the mean (CI) of a sample, size n become larger as n is reduced? The answer is true. This is established from the sampling distribution of the mean. The sampling distribution is the probability distribution of the mean of a sample, size n. I will also consider the question as a matter of real life: If I take a sample from a population, size 50 and calculate the CI and take a smaller sample, say size 10, will I calculate a larger CI? If I use the standard deviation calculated from the sample, this is not necessarily true. The CI should be larger but I can't say in every case it will belarger. The standard deviation of the sample will vary from sample to sample. I hope this answers your question. You can find more information on confidence intervals at: http://onlinestatbook.com/chapter8/mean.html
What is the probability that exactly 2 have some kind of defect?
The probability is determined by the binomial distribution. We consider p = probability of defect, q = probability of not defect, n = sample size, and x= number of defects in sample, in this case x=2. We calculate the probability as P(X = x) = n!/[(n-x)! x!] pxqn-x If sample size = 10 and p = 0.1 then: P(x= 2) = 10!/(8!x2!)(0.1)2(0.9)8 = 0.1937 You can find more about the binomial distribution under Wikipedia. It is important also to note the assumptions when using this distribution. It must be a random sample and the probability of defects is known.
What kind of validity does the strategy of random sampling increase?
Random Sampling increases the reliability and validity of your research findings. To begin with, Reliability: By randomly picking research participants, the likelihood that they are from different backgrounds/ have different experiences etc. is higher and hence, they are said to be more representative of the population of interest. EG: RQ: Do females have higher IQ? A case of random sampling will pick females who are housewives/ CEOs/ Indian/ 18yrs old/ Divorced etc. the list goes on. While a case of non-random sampling (such as picking participants at a bus stop) may only result in a sample of females who are 20 - 35 years old, working professionals. Validity: As reliability and validity are related, for the research findings to be reliable and generalizable to the population of interest, it first has to be a valid sample. Hence, from the above example, EG1 provides a valid sample, while EG2 is invalid.
On an eight question true false quiz a student guesses each answer What is the probability that he gets at least One of the answers correct?
The probability of getting at least 1 answer correct = 1 - Probability of getting all answers correct.So in your case it for be P(at least 1 answer correct) = 1 - 1/256where 256 is your sample space, |S| = 2^8.
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.
You are in charge of promoting a new flavor of toothpaste yet to be produced in a toothpaste manufacturing unit which sampling techniques will you use to get the required data from a population?
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business, sampling is widely used for gathering information about a populationIt is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. 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. The sample reflects the characteristics of the population from which it is drawn.Sampling methods are classified as either probability or non-probability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In non-probability sampling, members are selected from the population in some non-random manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non probability sampling, the degree to which the sample differs from the population remains unknown.· Random samplingis the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.· Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.· Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.· Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.· Judgment sampling is a common non-probability method. The researcher selects the sample based on judgment. This is usually an extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.· Quota samplingis the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.· Snowball sampling is a special non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.If I were an officer to promote a new flavour of toothpaste yet to be produced, I would use RANDOM SAMPLING METHODØ Random sampling:Random sampling- all members of the population have an equal chance of being selected as part of the sample. You might think this means just standing in the street and asking passers-by to answer your questions. However, there would be many members of the population who would not be in the street at the time you are there; therefore, they do not stand any chance of being part of your sample. To pick a random sample, it is necessary to take all the names on the electoral register (A list of all the people who live in a particular area) and pick out, for example, every fiftieth name. This particular person needs to be interviewed to make the sample truly random. Random sampling is very expensive and time consuming, but gives a true sample of the population.Types of Random sample:A simple random sample is selected so that all samples of the same size have an equal chance of being selected from the population.A self-weighting sample, also known as an EPSEM (Equal Probability of Selection Method) sample, is one in which every individual, or object, in the population of interest has an equal opportunity of being selected for the sample. Simple random samples are self-weighting.Stratified sampling involves selecting independent samples from a number of subpopulations, group or strata within the population. Great gains in efficiency are sometimes possible from judicious stratification.Cluster sampling involves selecting the sample units in groups. For example, a sample of telephone calls may be collected by first taking a collection of telephone lines and collecting all the calls on the sampled lines. The analysis of cluster samples must take into account the intra-cluster correlation which reflects the fact that units in the same cluster are likely to be more similar than two units picked at randomØ Pros and Cons:1. There are lot of bias in Random sampling2. It is feasible and simple as the sampling is done on a random basis.3. Can make sample units in groups.4. Very expensive and time consuming, but gives a true result of the population5. While in toothpaste case, the users can given a sample piece of toothpaste randomly to get the feedback or their opinion from the chosen populationConclusion: Though the Random sampling has couple of de-merits it will help to figure out the result from the chosen population. While all other also may provide the result which may not be best comparing to the sampling method which I have chosen (Random Sampling)
True or False As the amount of confidence increases the required sample size should decrease Explain your answer?
I can examine this as a question of theory or real life: As a matter of theory, I will rephrase your question as follows: Does theoretical confidence interval of the mean (CI) of a sample, size n become larger as n is reduced? The answer is true. This is established from the sampling distribution of the mean. The sampling distribution is the probability distribution of the mean of a sample, size n. I will also consider the question as a matter of real life: If I take a sample from a population, size 50 and calculate the CI and take a smaller sample, say size 10, will I calculate a larger CI? If I use the standard deviation calculated from the sample, this is not necessarily true. The CI should be larger but I can't say in every case it will belarger. The standard deviation of the sample will vary from sample to sample. I hope this answers your question. You can find more information on confidence intervals at: http://onlinestatbook.com/chapter8/mean.html
What is the probability that exactly 2 have some kind of defect?
The probability is determined by the binomial distribution. We consider p = probability of defect, q = probability of not defect, n = sample size, and x= number of defects in sample, in this case x=2. We calculate the probability as P(X = x) = n!/[(n-x)! x!] pxqn-x If sample size = 10 and p = 0.1 then: P(x= 2) = 10!/(8!x2!)(0.1)2(0.9)8 = 0.1937 You can find more about the binomial distribution under Wikipedia. It is important also to note the assumptions when using this distribution. It must be a random sample and the probability of defects is known.
Under what cicumstances should these sampling methods recommended cluster sampling systematic sampling multistage sampling non-probability sampling give examples in each case?
For decades the go to place to find the best quality reviews on pretty much any product in the US has been consumerreports.org. Consumer reports has been around for forever, and they review products that come onto the market and give pretty solid objective reviews. They can help make your decision easier.
When population distribution is right skewed is the sampling also with right skewed distribution?
If the population distribution is roughly normal, the sampling distribution should also show a roughly normal distribution regardless of whether it is a large or small sample size. If a population distribution shows skew (in this case skewed right), the Central Limit Theorem states that if the sample size is large enough, the sampling distribution should show little skew and should be roughly normal. However, if the sampling distribution is too small, the sampling distribution will likely also show skew and will not be normal. Although it is difficult to say for sure "how big must a sample size be to eliminate any population skew", the 15/40 rule gives a good idea of whether a sample size is big enough. If the population is skewed and you have fewer that 15 samples, you will likely also have a skewed sampling distribution. If the population is skewed and you have more that 40 samples, your sampling distribution will likely be roughly normal.
What is the probability of singing a number?
The probability, in my case, is zero.
Does the length of a copyright for a song hinder others who want to sample it?
Duration of protection is one of the frustrations with sampling, but perhaps the bigger issue is that there is no de minimiscopying in digital sampling of sound recordings: that means there's no snippet too small to sue over.The appeals case that lead to this decision is linked below.
What kind of validity does the strategy of random sampling increase?
Random Sampling increases the reliability and validity of your research findings. To begin with, Reliability: By randomly picking research participants, the likelihood that they are from different backgrounds/ have different experiences etc. is higher and hence, they are said to be more representative of the population of interest. EG: RQ: Do females have higher IQ? A case of random sampling will pick females who are housewives/ CEOs/ Indian/ 18yrs old/ Divorced etc. the list goes on. While a case of non-random sampling (such as picking participants at a bus stop) may only result in a sample of females who are 20 - 35 years old, working professionals. Validity: As reliability and validity are related, for the research findings to be reliable and generalizable to the population of interest, it first has to be a valid sample. Hence, from the above example, EG1 provides a valid sample, while EG2 is invalid.
Sample of synonyms?
Synonyms of the word "sample"as a noun:bit, bite, case, case history, constituent, cross section, element, exemplification, fragment, illustration, indication, individual, instance, morsel, part, pattern, piece, portion, representative, sampling, segment, sign, specimen, typification, unitas a verb:examine, experience, experiment, inspect, partake, savor, sip, test
On an eight question true false quiz a student guesses each answer What is the probability that he gets at least One of the answers correct?
The probability of getting at least 1 answer correct = 1 - Probability of getting all answers correct.So in your case it for be P(at least 1 answer correct) = 1 - 1/256where 256 is your sample space, |S| = 2^8.
What is sample unit?
A sampling unit is an object for which data are collected. For example, if you were studying the health of trees in a park, your sampling units would be individual trees; if you wanted to find out how many cars each household had then the unit would be a household. In the latter case you would probably contact only one member of the household but the data collected are relevant to the whole household.
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.
