Error Sampling Survey
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Measurement, Coverage and Non-Response November 24, 2011 by Dana Stanley 5 Comments There are 4 generally-accepted types of survey error. By survey error, I mean factors which reduce the survey sampling international accuracy of a survey estimate. It's important to keep each type of survey sampling error calculator survey error in mind when designing, executing and interpreting surveys. However, I suspect some of them are more ingrained random sampling survey in our thinking about research, while others are more often neglected. Imagine if we interviewed 100 researchers and asked each of them ("Family Feud"-style) to name a type of survey
Survey Sampling Frame
error. Which type of survey error do you think would be mentioned most frequently? Which type would be most overlooked? Here is my predicted order of finish in our hypothetical example. Note for the "Feud"-challenged: Number 1 represents the most commonly named type of error in our hypothetical survey of researchers, while number 4 represents the least commonly named. 1. Sampling Error. sampling for survey research My guess is that sampling error would be the most commonly named type of survey error. In a recent Research Access post, "How to Plus or Minus: Understand and Calculate the Margin of Error," I explained the concept of sampling error and gave 3 ways of calculating it. Sampling error is essentially the degree to which a survey statistic differs from its "true" value due to the fact that the survey was conducted among only one of many possible survey samples. It is a degree of uncertainty that we are willing to live with. Even most non-researchers have a basic understanding, or at least awareness, of sampling error due to the media's reference to the "margin of error" when reporting public survey results. 2. Measurement Error. I believe measurement error would be the second most frequently named type of error. Measurement error is the degree to which a survey statistic differs from its "true" value due to imperfections in the way the statistic is collected. The most common type of measurement error is one researchers deal with on a daily basis: poor quest
Consulting Quick Question Consultations Hourly Statistical Consulting Results Section Review Statistical Project Services Free Webinars Webinar Recordings Contact Customer Login Statistically Speaking Login Workshop Center Login All Logins Sampling Error in Surveys by guest Author: Trent Buskirk, PhD. What do you do when you hear the word sampling in survey research methodology error? Do you think you made a mistake? Well in survey statistics, error could imply that
Sampling Survey Methods
things are as they should be. That might be the best news yet-error could mean that things are as they should be. Let's
Poll Sampling Error
break this down a bit more before you think this might be a typo or even worse, an error. Why Sampling Always Creates Error
In sampling theory there are two basic ways to get information about a target http://researchaccess.com/2011/11/4-kinds-of-survey-error-sampling-measurement-coverage-nonresponse/ population. You measure everyone (you take a census) or you measure a subset of the population (you take a sample). If you choose the sample wisely using some sort of random sample design, you should get a reasonable estimate of the population based on the sample. Let’s say you want to know how much television people in your hometown (the target population) watch on a typical day. You would love to measure this on all 40,000 residents in http://www.theanalysisfactor.com/sampling-error-in-surveys/ your town, but practically that's too many people to ask all at once. So instead of polling everyone you decide to take a random sample of 40 residents. Now in this sample there may be folks who don't watch any television and many who do. If you had taken a different random sample of 40 residents, it is possible that every one of them watched some television. So your estimate for the average television watching will differ slightly across the samples. Naturally, not every sample of 40 residents will produce the same estimate. The only way it could is if everyone in the entire population watches exactly the same amount of television on a typical day. The fact that no two samples are likely to be exactly the same means two things. First, estimates derived from them are also likely be slightly different. Second, both estimates can provide information about the entire population. Sampling error is this variation in estimates that results simply because samples differ. In survey statistics where samples are far more common than censuses, having error in estimates is in fact as it should be. The size of the error depends in part on three things: the size of the sample, how variable the thing you are measuring in the population is, and on the sampling design. Margin of Error In Polling, we see a related caccurate, assuming you counted the votes correctly. (By the way, there's a whole other topic in math that describes the errors people can make when they try to measure things like that. But, for now, let's assume you can count with http://www.robertniles.com/stats/margin.shtml 100% accuracy.) Here's the problem: Running elections costs a lot of money. It's simply not practical to conduct a public election every time you want to test a new product or ad campaign. So companies, campaigns and news organizations ask a randomly https://learnandteachstatistics.wordpress.com/2014/09/04/sampling-and-non-sampling-error/ selected small number of people instead. The idea is that you're surveying a sample of people who will accurately represent the beliefs or opinions of the entire population. But how many people do you need to ask to get a representative sample? sampling survey The best way to figure this one is to think about it backwards. Let's say you picked a specific number of people in the United States at random. What then is the chance that the people you picked do not accurately represent the U.S. population as a whole? For example, what is the chance that the percentage of those people you picked who said their favorite color was blue does not match the percentage of people in the entire U.S. who like blue best? error sampling survey Of course, our little mental exercise here assumes you didn't do anything sneaky like phrase your question in a way to make people more or less likely to pick blue as their favorite color. Like, say, telling people "You know, the color blue has been linked to cancer. Now that I've told you that, what is your favorite color?" That's called a leading question, and it's a big no-no in surveying. Common sense will tell you (if you listen...) that the chance that your sample is off the mark will decrease as you add more people to your sample. In other words, the more people you ask, the more likely you are to get a representative sample. This is easy so far, right? Okay, enough with the common sense. It's time for some math. (insert smirk here) The formula that describes the relationship I just mentioned is basically this: The margin of error in a sample = 1 divided by the square root of the number of people in the sample How did someone come up with that formula, you ask? Like most formulas in statistics, this one can trace its roots back to pathetic gamblers who were so desperate to hit the jackpot that they'd even stoop to mathematics for an "edge." If you really want to know the gory details, the formula is derived from the standard deviation of the proportion of times that a researcher gets a sample "right," given a whole bunch of samples. Which is mathemati
Next → Sampling error and non-samplingerror Posted on 4 September, 2014 by Dr Nic The subject of statistics is rife with misleading terms. I have written about this before in such posts as Teaching Statistical Language and It is so random. But the terms sampling error and non-sampling error win the Dr Nic prize for counter-intuitivity and confusion generation. Confusion abounds To start with, the word error implies that a mistake has been made, so the term sampling error makes it sound as if we made a mistake while sampling. Well this is wrong. And the term non-sampling error (why is this even a term?) sounds as if it is the error we make from not sampling. And that is wrong too. However these terms are used extensively in the NZ statistics curriculum, so it is important that we clarify what they are about. Fortunately the Glossary has some excellent explanations: Sampling Error “Sampling error is the error that arises in a data collection process as a result of taking a sample from a population rather than using the whole population. Sampling error is one of two reasons for the difference between an estimate of a population parameter and the true, but unknown, value of the population parameter. The other reason is non-sampling error. Even if a sampling process has no non-sampling errors then estimates from different random samples (of the same size) will vary from sample to sample, and each estimate is likely to be different from the true value of the population parameter. The sampling error for a given sample is unknown but when the sampling is random, for some estimates (for example, sample mean, sample proportion) theoretical methods may be used to measure the extent of the variation caused by sampling error.” Non-sampling error: “Non-sampling error is the error that arises in a data collection process as a result of factors oth