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Q: Distinguish between each in section one with sample if possible. ( Answered,   0 Comments )
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 Subject: Distinguish between each in section one with sample if possible. Category: Reference, Education and News > General Reference Asked by: bren-ga List Price: \$30.00 Posted: 15 Oct 2002 20:32 PDT Expires: 14 Nov 2002 19:32 PST Question ID: 77093
 ```Please distinguish between each and if possible give example. 1. Statistic and parameter 2. Sample frame and population 3. Restricted and unrestricted sampling. 4. Standard deviation and standard error 5. Simple random and complex random sampling 6. Convenience and purposive sampling. 7. Sample and precision and sample accuracy 8. Systematic and error variance 9. Variable and attrbute parameters. 10 Point estimate and interval estimate 11. Proportionate and disproportionate sample. Under what kind of conditions would you recommend each of the following? break out your answers. a. A probability sample? A nonprobability sample? b. A simple random sample? A cluster sample? A stratified sample? c Using the finite population adjustment factor? d. A disproportionate stratified probability sample?```
 ```Hello bran!! Here we Go: 1- Statistic and parameter A statistic is a quantity that is computed from the sample. That means it is determinable. A parameter is a quantity that reflects the entire population. We generally do not know its value, but the desire to know it prompts us to select a sample and compute an approximate value (statistic) for it. The reason we sample is so that we might get an estimate for the population we sampled from. If you measure the entire population and calculate a value like a mean or average, we don't refer to this as a statistic, we call it a parameter of the population. ------------------------------------------------------------------ 2- Sample frame and population Population is the entire group under study as specified by the research objectives, e.g., Supermarkets that are part of chains (Safeway, Publix, Woodman’s) located in Wisconsin. It should be defined in specific terms, I.e., instead of saying “everybody who might buy this product,” say “young adults of age 18-24 who live with their parents.” Sample and Sample Unit: • A sample is a subset of the population that should represent the entire group. A sample is said to be “representative” if, in fact, it represents the entire population. • A sample unit is the basic level of investigation, e.g., a college student, a housewife, a purchasing agent, a supermarket, a sea-food restaurant, a bank, etc. The sample frame is a master list of all the sample units in the population. If the population were defined as all chain supermarkets in Wisconsin, then a list containing all such stores would be the sample frame. The sample frame for a mall intercept survey would be all shoppers who were walking through the mall on the days data were collected. Sometimes a physical list is not possible. To sum up: Population: The set of all the relevant units of analysis defined by the researcher. Element or Sample unit: One unit from a population. Sample Frame: A list of all the elements in the population from which a sample may be drawn. Sample: The set of elements selected or drawn from a sample frame to represent the population. PROPERTIES OF A GOOD SAMPLE FRAME a. It contains a list of all of the elements in the population and does not omit any (coverage is complete). b. It does not contain any extraneous elements (only contains elements relevant to the defined population). c. It does not contain duplicates. d. It is current and up-to-date I found a little "practical" variation of this definition: "Target Population --> Sample Frame: The sample frame is the portion of the target population that is accessible to researchers (e.g., persons who read newspapers, persons with phones). Often, the sample frame is some sort of list (e.g., a membership list). But individuals who are accessible may differ from those who are not. For example, persons with phones are different from persons without phones, and physicians who are members of professional organizations are different from those who are not. Readers should carefully judge how the sample frame might systematically differ from the target population." From "Primer on Interpreting Surveys" ACP-ASIM Journal Ene-Feb 2002 (take a look to this article, it is very interesting): http://www.acponline.org/journals/ecp/janfeb02/primer_interpret_surveys.pdf ------------------------------------------------------------------------- 3- Restricted and unrestricted sampling "The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population... Element Selection: Whether the elements are selected individually and directly from the population - viewed as a single pool - or whether additional controls are placed on element, selection may also classify samples. When each sample element is drawn individually from the population at large, it is an unrestricted sample. Restricted sampling covers all other forms of sampling." From: "Sampling Issues- Part I" - Palm Beach Atlantic University - by David M. Compton, Ph.D http://faculty.pba.edu/comptond/callisto/courses/Quantitative_Methods/Sampling-1.html ------------------------------------------------------------------------ 4 - Standard deviation and standard error The standard deviation is one of several indices of variability that statisticians use to characterize the dispersion among the measures in a given population. To calculate the standard deviation of a population it is first necessary to calculate that population's variance (V). Numerically, the standard deviation is the square root of the variance. Unlike the variance, which is a somewhat abstract measure of variability, the standard deviation can be readily conceptualized as a distance along the scale of measurement. SD = V^(1/2) = ((SUM(X-Xi)^2)/n)^(1/2) The Standard Error, or Standard Error of the Mean, is an estimate of the standard deviation of the sampling distribution of means, based on the data from one or more random samples. Numerically, it is equal to the square root of the quantity obtained when s squared is divided by the size of the sample. SE = (SD^2 / n)^(1/2) = SD/(n^1/2) You will find interesting the following document: "Maintaining Standards: Differences between the Standard Deviation and Standard Error, and When to Use Each" by David L Streiner, PhD. http://www.cpa-apc.org/Publications/Archives/PDF/1996/Oct/strein2.pdf -------------------------------------------------------------- 5- Simple random and complex random sampling Simple Random Sampling: Based on a numbered list of the population. Each person or document has an equal chance of being selected. The unrestricted, simple random sample is the simplest form of probability sampling. Since all probability samples must provide a known nonzero chance of selection for each population element, the simple random sample is considered a special case in which each population element has a known and equal chance of selection. Simple random sampling is often impractical. Reasons include (1) it requires a population list (sampling frame) that is often not available; (2) it fails to use all the information about a population, thus resulting in a design that may be wasteful; and (3) it may be expensive to implement in both time and money. These problems have led to the development of alternative designs that are superior to the simple random design in statistical and/or economic efficiency. They called: Complex Random Sampling: Has three forms: -Systematic sampling In this approach, every kth element in the population is sampled, beginning with a random start of an element in the range of 1 to k. The kth element is determined by dividing the sample size into the population size to obtain the skip pattern applied to the sampling frame: .Identify the total number of elements in population. .Identify the sampling ratio (k = total population size divided by size of the desired sample). .Identify the random start. .Draw a sample by choosing every kth entry The major advantage of systematic sampling is its simplicity and flexibility. It is easier to instruct field workers to choose the dwelling unit listed on every kth line of a listing sheet than it is to use a random numbers table. With systematic sampling, there is no need to number the entries in a large personnel file before drawing a sample. -Stratified sampling Most populations can be segregated into several mutually exclusive subpopulations, or strata. The process by which the sample is constrained to include elements from each of the segments is called stratified random sampling. University students can be divided by their class level, school or major, gender, and so forth. After a population is divided into the appropriate strata, a simple random sample can be taken within each stratum. The sampling results can then be weighted and combined into appropriate population estimates. There are three reasons why a researcher chooses a stratified random sample: (1) to increase a sample's statistical efficiency, (2) to provide adequate data for analyzing the various subpopulations, and (3) to enable different research methods and procedures to be used in different strata. Stratification is usually more efficient statistically than simple random sampling and at worst it is equal to it. With the ideal stratification, each stratum is homogeneous internally and heterogeneous with other strata. This might occur in a sample that includes members of several distinct ethnic groups. In this instance, stratification makes a pronounced improvement in statistical efficiency. -Cluster Sampling In a simple random sample, each population element is selected individually. The population can also be divided into groups of elements with some groups randomly selected for study. This is cluster sampling. Cluster sampling differs from stratified sampling in several ways. The idea is to select typical groups that represent the remaining ones. When done properly, cluster sampling also provides an unbiased estimate of population parameters. Two conditions foster the use of cluster sampling: (1) the need for more economic efficiency than can be provided by simple random sampling and (2) the frequent unavailability of a practical sampling frame for individual elements. Statistical efficiency for cluster samples is usually lower than for simple random samples chiefly because clusters are usually homogeneous. Families in the same block (a typical cluster) are often similar in social class, income level, ethnic origin, and so forth. While statistical efficiency in most cluster sampling may be low, economic efficiency is often great enough to overcome this weakness. The criterion, then, is the net relative efficiency resulting from the trade-off between economic and statistical factors. It may take 690 interviews with a cluster design to give the same precision as 424 simple random interviews. But if it costs only \$5 per interview in the cluster situation and \$10 in the simple random case, the cluster sample is more attractive (\$3,450 versus \$4,240). Summed up from "Sampling Issues - Part II and III" by David M. Compton, Ph.D. ------------------------------------------------------------------ 6- Convenience and purposive sampling. Nonprobability sampling does not involve random selection. it does mean that nonprobability samples cannot depend upon the rationale of probability theory. With a probabilistic sample, we know the odds or probability that we have represented the population well; we are able to estimate confidence intervals for the statistic. With nonprobability samples, we may or may not represent the population well, and it will often be hard for us to know how well we've done so. Probability sampling may be superior in theory, but there are breakdowns in its application; for example: the ideal probability sampling may be only partially achieved because of the human element. -Convenience Sampling: Nonprobability samples that are unrestricted are called convenience samples. They are the least reliable design but normally the cheapest and easiest to conduct. Researchers or field workers have the freedom to choose whomever they find, thus the name convenience. Examples include informal pools of friends and neighbors, people responding to a newspaper's invitation for readers to state their positions on some public issue or a TV reporter's "man-on-the-street" intercept interviews, or using employees to evaluate the taste of a new snack food. -Purposive Sampling: In purposive sampling, we sample with a purpose in mind. So a nonprobability sample that conforms to certain criteria is called purposive sampling. There are two major types - judgment sampling and quota sampling. Judgment sampling occurs when a researcher selects sample members to conform to some criterion. In a study of labor problems, you may want to talk only with those who have experienced on-the-job discrimination. Quota sampling is used to improve representativeness. The logic behind quota sampling is that certain relevant characteristics describe the dimensions of the population. For example if in your job are 20% african-americans, 15% hispanics, 10% asiatics and 60% caucasians the quota sampling would call for 20% african-americans, 15% hispanics, 10% asiatics and 60% caucasians in order to prevent race distorsion in the results. Summed up from "Sampling Issues - Part IV" by David M. Compton, Ph.D. In this point I recommend you to read the following text: "Nonprobability Sampling" by William M.K. Trochim http://trochim.human.cornell.edu/kb/sampnon.htm ------------------------------------------------------------------- 7. Sample precision and sample accuracy Accuracy is the degree to which bias is absent from the sample. An accurate (unbiased) sample is one in which the underestimators and the overestimators are balanced among the members of the sample. There is no systematic variance with i.e. there is no variation in measures due to some known or unknown influences that cause the scores to lean in one direction more than another. A second criterion of a good sample design is precision of estimate. No sample will fully represent its population in all respects. The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process. This is called sampling error and reflects the influences of chance in drawing the sample members. Precision is measured by the standard error of estimate, a type of standard deviation measurement; the smaller the standard error of estimate, the higher is the precision of the sample. Summed up from "Sampling Issues- Part I" by David M. Compton, Ph.D. ----------------------------------------------------------------- 8. Systematic and error variance "In nearly every collection of data, there is variability. We are interested in identifying the sources of that variability. When we are focusing our attention on a single source, that source is called systematic variance -- the source of variability that is under investigation. All other sources of variability are lumped into one indefinite mass called error variance. Error variance has little to do with "error" although variability due to errors can be part of the error variance; it really refers to whatever sources of variability on which we are not focusing our attention. Because systematic variance is due to those variables under investigation and error variance encompasses every other source of variability, the two together equal the total observed variance: total variance = systematic variance + error variance" Taken from the following text: "Systematic and error variance" from Southern Illinois University website: http://www.siu.edu/departments/cola/psycho/faculty/young/ResMethodsStuff/varianceStuff.html From this text you can take a numeric example of calculation of systematic and error variance. The following document from the Department of Psychology of the Brock University will bring to you more clarification about this subject (look for the highlighted text): "3P30 Critical thinking - Chapter 8 - Basic Issues in Experimental Research" http://216.239.39.100/search?q=cache:VqoFdFU1SHcC:www.psyc.brocku.ca/course/outlines2001/ch%25208%25203P30%2520web.doc+systematic+error+variance&hl=en&ie=UTF-8=UTF-8 ------------------------------------------------------------------ 9. Variable and attribute parameters. Variable: A characteristic of interest about each individual element of a population or sample. Examples: Age, height, eye color, etc. A variable is any measured characteristic or attribute that can differ for different subjects. For example, if the weight of 30 boxes were measured, then weight would be a variable. Variables can be quantitative or qualitative. (Qualitative variables are sometimes called "categorical variables.") Quantitative variables are measured on an ordinal, interval, or ratio scale; qualitative variables are measured on a nominal scale. If five-year old subjects were asked to name their favorite color, then the variable would be qualitative. If the time it took them to respond were measured, then the variable would be quantitative. An attribute parameter is a parameter value for the associated variable. Note the distinction between a variable and an attribute parameter. An attribute parameter's value is the same for all units of the population, whereas the variable can has different values for each unit. --------------------------------------------------------------------- 10. Point estimate and interval estimate "When a parameter is being estimated, the estimate can be either a single number or it can be a range of scores. When the estimate is a single number, the estimate is called a "point estimate"; when the estimate is a range of scores, the estimate is called an interval estimate. Confidence intervals are used for interval estimates. As an example of a point estimate, assume you wanted to estimate the mean time it takes 12- year-olds to run 100 yards. The mean running time of a random sample of 12-year-olds would be an estimate of the mean running time for all 12-year-olds. Thus, the sample mean, M, would be a point estimate of the population mean, m." From "HyperstatOnline" : http://davidmlane.com/hyperstat/point_estimation.html "Interval estimates: Confidence Intervals The sample mean and sample variance are examples of “point estimates”: they give a single number which is an estimate of a population parameter. It is generally more useful to have an interval estimate, which gives a range of values which we have some confidence contains the true (population) value. This “confidence” is usually expressed as a percentage probability." From "Interval estimates: Confidence Intervals", Department of Statistics and Modelling Science - University of Strathclyde http://www.stams.strath.ac.uk/~steve/53202/confint1.pdf "Interval Estimates - Confidence Intervals Here the objective is to take samples from a population and construct an interval [l,u] for the unknown value of the parameter being estimated. Typically, there is a confidence coefficient associated with the interval, e.g. one may construct a 99% confidence interval or a 96% confidence interval, or in general, a 100*(1-a)% interval where a is a small fraction (typically less than or equal to 0.1) One should be very clear on how to interpret such an interval. Given a 100*(1-a)% confidence interval [l,u] for the value of some parameter q, it is NOT true that "there is a probability of (1-a) that the value of q lies within this interval." In fact, there IS no such probability since the value either lies within the interval (in which case the probability is equal to 1) or it does not lie within the interval (in which case the probability is equal to 0)! A better way to interpret the interval is that there is a probability of (1-a) that the interval covers the (unknown) value of q. More precisely, suppose we were to follow the correct sampling procedure used to construct the interval and we were to repeat this procedure a very large number of times, constructing a confidence interval each time. Note that these intervals will in general be different each time since they are based upon different random samples that will in general be different from each other. If we were to follow this procedure, then we would expect about 100*(1-a)% of the intervals to contain the true value of q." From "Interval estimates: Confidence Intervals" Industrial Engineering University of Pittsburgh. http://www.pitt.edu/~jrclass/stat/notes/OH33.html "Example of Point Estimate" http://www.amstat.org/publications/jse/v5n1/schwarz.supp/pointest.html --------------------------------------------------------- 11. Proportionate and disproportionate sample. "The size of the strata samples is calculated with two pieces of information: (1) how large the total sample should be and (2) how the total sample should be allocated among strata. In deciding how to allocate a total sample among various strata, there are proportionate and disproportionate options. -Proportionate versus Disproportionate Sampling: In proportionate stratified sampling, each stratum is properly represented so the sample drawn from it is proportionate to the stratum's share of the total population. This approach is more popular than any other stratified sampling procedures. Some reasons for this include: .It has higher statistical efficiency than will a simple random sample. .It is much easier to carry out than other stratifying methods. .It provides a self-weighting sample; the population mean or proportion can be estimated simply by calculating the mean or proportion of all sample cases, eliminating the weighting of responses. On the other hand, proportionate stratified samples often gain little in statistical efficiency if the strata measures and their variances are similar for the major variables under study. Any stratification that departs from the proportionate relationship is disproportionate. There are several disproportionate allocation schemes. One type is a judgmentally determined disproportion based on the idea that each stratum is large enough to secure adequate confidence levels and interval range estimates for individual strata. A researcher makes decisions regarding disproportionate sampling, however, by considering how a sample will be allocated among strata. One author states, ´In a given stratum, take a larger sample if the stratum is larger than other strata; the stratum is more variable internally; and sampling is cheaper in the stratum.´ If one uses these suggestions as a guide, it is possible to develop an optimal stratification scheme. When there is no difference in intra-stratum variances and when the costs of sampling among strata are equal, the optimal design is a proportionate sample. While disproportionate sampling is theoretically superior, there is some question as to whether it has wide applicability in a practical sense. If the differences in sampling costs or variances among strata are large, then disproportionate sampling is desirable. It has been suggested that differences of several-fold are required to make disproportionate sampling worthwhile." From "Sampling Issues- Part II" by David M. Compton, Ph.D. ------------------------------------------------------------- Under what kind of conditions would you recommend each of the following?: a. A probability sample? A nonprobability sample? A probability sampling method is any method of sampling that utilizes some random selection. In order to have a random selection method, you must set up some process that assures that the different units in your population have equal probabilities of being chosen. While probability sampling may be superior in theory than nonprobability sampling, there are breakdowns in its application. Even carefully stated random sampling procedures may be subject to careless application by the people involved. The total population may not be available for study in certain cases. At the scene of a major event, it may be infeasible to even attempt to construct a probability sample. A study of past correspondence between two companies must use an arbitrary sample because the full correspondence is normally not available. In another sense, those who are included in a sample may select themselves. In mail surveys, those who respond may not represent a true cross section of those who receive the questionnaire. The receivers of the questionnaire decide for themselves whether they will participate. There is some of this self-selection in almost all surveys because every respondent chooses whether or not to be interviewed. So, the probability sampling is prefered to be used under circunstances where "externals factors" have a very little interference, like scientific research: laboratories´ experiments, medical research, etc. We may use nonprobability sampling procedures because they satisfactorily meet the sampling objectives. While a random sample will give us a true cross section of the population, this may not be the objective of the research. If there is no desire or need to generalize to a population parameter, then there is much less concern about whether the sample fully reflects the population. Often researchers have more limited objectives. They may be looking only for the range of conditions or for examples of dramatic variations. This is especially true in exploratory research where one may wish to contact only certain persons or cases that are clearly atypical. Additional reasons for choosing nonprobability over probability sampling are cost and time. Probability sampling clearly calls for more planning and repeated callbacks to ensure that each selected sample member is contacted. These activities are expensive. Carefully controlled nonprobability sampling often seems to give acceptable results, so the investigator may not even consider probability sampling. It is also possible that nonprobability sampling may be the only feasible alternative. As we said before the total population may not be available for study in certain cases. Ideas taken from: "Sampling Issues- Part III & IV" by David M. Compton, Ph.D. Trochim, William M. "The Research Methods Knowledge Base, 2nd Edition." Internet WWW page, at URL: http://trochim.human.cornell.edu/kb/index.htm ------------------------------------------------------------------- b. A simple random sample? A cluster sample? A stratified sample? Simple random sampling is often impractical. Reasons include (1) it requires a population list (sampling frame) that is often not available; (2) it fails to use all the information about a population, thus resulting in a design that may be wasteful; and (3) it may be expensive to implement in both time and money. So to use simple random sampling we need a small and uniform population. This is what we do when we separate the population in stratas. The stratified sampling is also useful when the researcher wants to study the characteristics of certain population subgroups. Stratified sampling techniques are generally used when the population is heterogeneous, or dissimilar, where certain homogeneous, or similar, sub-populations can be isolated (strata). Simple random sampling is most appropriate when the entire population from which the sample is taken is homogeneous. Some reasons for using stratified sampling over simple random sampling are : a) the cost per observation in the survey may be reduced; b) estimates of the population parameters may be wanted for each sub-population; c) increased accuracy at given cost. Example Suppose a farmer wishes to work out the average milk yield of each cow type in his herd which consists of Ayrshire, Friesian, Galloway and Jersey cows. He could divide up his herd into the four sub-groups and take samples from these. Cluster sampling is typically used when the researcher cannot get a complete list of the members of a population they wish to study but can get a complete list of groups or 'clusters' of the population. It is also used when a random sample would produce a list of subjects so widely scattered that surveying them would prove to be far too expensive, for example, people who live in different postal districts in the U.K. This sampling technique may well be more practical and/or economical than simple random sampling or stratified sampling. Example: Suppose that the Department of Agriculture wishes to investigate the use of pesticides by farmers in England. A cluster sample could be taken by identifying the different counties in England as clusters. A sample of these counties (clusters) would then be chosen at random, so all farmers in those counties selected would be included in the sample. It can be seen here then that it is easier to visit several farmers in the same county than it is to travel to each farm in a random sample to observe the use of pesticides. Source: "Statistics Glossary - Sampling". http://www.cas.lancs.ac.uk/glossary_v1.1/samp.html#randsamp See also "Probability Sampling" by William M.K. Trochim for more examples: http://trochim.human.cornell.edu/kb/sampprob.htm ------------------------------------------------------------------- c. Using the finite population adjustment factor? The formula for the standard error of the mean is accurate only when samples are drawn from a very large or infinite population, or when they’re drawn with replacement to the population. Since this seldom happens in real life we have to apply a correction factor. This allows us to get a more accurate standard error of the mean. If you are sampling from a relative small finite population, the usual variance estimate can be too large. If the sample size n is over 5% of the population size N, you will benefit by using the finite population correction factor. You can see the formula here: http://www.lakeland.cc.oh.us/academic/sh/math/ddavis/telecourse/ppfiles/BMAN07ef/sld066.htm Also read this (it has a nice example: "Sampling Variability and Sampling Distributions" http://home.xnet.com/~fidler/triton/math/review/mat170/sdist/sdist1.htm -------------------------------------------------------------------- d. A disproportionate stratified probability sample? "Disproportionate stratified sample: Appropriate method when certain segments of a population are seen as more important than others, as varying more, or as more expensive to sample. For example, a health insurance company surveys its corporate customers, oversampling firms with larger memberships to reflect their true influence." From: "Griggs-Anderson Research: Glossary - S": http://www.gar.com/primer/glosss.htm "disproportionate stratified sampling: A probability sampling method in which the sample size of a particular stratum is not proportional to the population strata. For example, a stratum could be large supermarkets, which may only account for 20% of all grocery stores - although they account for 80% of grocery sales. In this case, a disproportionate sample would be used to represent the large supermarkets to reflect their sales (i.e. 80%) rather than the number of stores." From "xRefer - Dictionary of Business: Oxford University Press": http://www.xrefer.com/entry/163088 -------------------------------------------------------------------------- wow!! what a question!! I hope this helps you. The search strategy was to look for each subject in Google to complete some concepts. The greater part of the answer can be found in the folowing pages that I used. I have they in my favorites folder(for help in my University studies): - "Sampling Issues - Part I,II,III,IV" by David M. Compton, Ph.D. (Note: At the bottom of this page -Part IV- there are links to part I,II & III) http://faculty.pba.edu/comptond/callisto/courses/Quantitative_Methods/Sampling-4.html - "Research Methods Knowledge Base" by William M. Trochim, Cornell University http://trochim.human.cornell.edu/kb/ Note: in this case follow the Sampling link, and then navigate the page following the next links: http://trochim.human.cornell.edu/kb/sampling.htm If you need clarifications please post a request for it. Best Regards livioflores-ga```