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Introduction to Random Survey Sampling

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Introduction to Random Survey Sampling Powered By Docstoc
					Presented by Daniel Toriola
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Introduction to Random Survey Sampling By Ryan J Bell

It may be impossible to interview everybody within the group you'd like to survey. For example, assume that you're surveying the people who live in your city. Unless the city in which you live is very small, your staff won't be able to reach everyone. Likewise, assume that you have been given the task to survey the employees at your company. If your company is large and employs thousands of people across several locations, it may be unrealistic to collect data on each person. In both cases, random sampling may be required. Below, we'll describe the basics of random survey sampling and how to judge the accuracy of your data. Selecting A Representative Sample The pivotal question regarding random sampling for surveying large groups is "how many randomly chosen people do I need to survey in order to draw reliable conclusions about the group as a whole?" The answer to that question depends upon two factors. First, you'll need to determine how many people comprise the entire group. Second, you'll need to decide how accurate you'd like your data to be. In short, the accuracy of your data will be directly correlated with the relative size of the group that you survey. That is, the greater percentage of the original group that you interview, the more confidence you should have in your results. Deriving Conclusions In order to interpret the data that you collect from your population (that is, your randomly chosen sample), you should have an appreciation for the breadth of error in your results and the confidence level you accord the data. You're likely familiar with the concept of error in surveying. Consider political polls. You may remember hearing that the results of a poll were accurate within a certain percentage of error above and below the result (i.e. "plus or minus 4%"). You may be less familiar with the concept of confidence level in survey results. It reflects the likelihood of similar results occurring for an action that is performed 100 times. In the context of surveys, it reflects your confidence that your survey will yield similar data if it is given 100 times. It works hand in hand with your error percentage. For example, you may have a "95% confidence level, given an error of plus or minus 4%." Both factors are a byproduct of the size of your survey population in comparison to the original group.
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What Is The Best Sample Size? The "best" sample size is a subjective variable that is completely up to you. That is, the size of your population will depend upon how large an error rate you can tolerate and the level of confidence you would like to have in your survey results. For example, you may be able to tolerate a 5% error along with a 90% confidence level for one survey while requiring a 3% error with a 95% confidence level for another survey. Executing Your Survey Ideally, you'll be able to achieve a 100% confidence level with a 0% error rate in your data whenever you interview a random sampling of a larger group. Unfortunately, it's impossible. You'll need to tolerate an element of error in order to execute your survey with a reasonable amount of effort and within a reasonable time frame. The key is to determine in advance how accurate you want your results to be given your limitations. Surveying a randomly chosen population from a larger group is not an exact science. However, as long as you're willing to assume the potential for variance in your results, the data can still be valuable. SurveyGizmo is a leading provider of online survey tools, check them out on the web at http://www.surveygizmo.com

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Basics of Choosing a Survey Population By Ryan J Bell

Most surveys are conducted only after the goal of the data has been determined. Depending upon the objective of the data, a target group is identified. When that target group is large, surveying the entire group is often impossible. A representative sample of that group must be chosen. Typically, a random selection process is used to build a survey population (i.e. the people who will participate in the survey). While most people imagine that choosing a survey population and having confidence in the survey results is straightforward, it is not. Below, we'll explain what is involved when choosing a survey population. You'll also learn why survey results can be misleading. Random Sampling, Subgroup Selection, Or Both? Random sampling is an effective way to measure the perspective of a group of people that is too large to interview. For example, surveying Americans about a political candidate would be impossible due to the number of Americans. In this case, a random sample is chosen as the survey population. Conclusions are drawn about Americans' view of the political candidate based upon the results from interviewing the smaller sample. Often, you'll want to divide your random sample into separate groups of people. This is done when the perspective of the groups are expected to vary widely. For example, assume that you have chosen a sample population to survey about the aforementioned political candidate. Your population contains both men and women. Because these two groups often perceive political candidates differently, subgroup selection and interviewing may be worthwhile. It's important to note that doing this can require a significant amount of additional time. If the effort and time involved is too great, you can perform what it called a stratified random sampling of the disparate groups (a topic that is beyond the scope of this article). How Accurate Are Your Results? The accuracy (and thus, reliability) of survey data is often misunderstood. While most people intuitively understand that the survey results of a random sampling will contain some allowance of error, they may not know how the error rate and confidence level is derived. Exacerbating the issue is the fact that these numbers are often unreported. Let's return to our previous example to illustrate this concept. Assume once again that you're surveying Americans about the political candidate. You obviously cannot survey every American. So, you choose a sample population to interview. The level of accuracy in your results will reflect how large a percentage your sample population is in comparison to your larger group (i.e. Americans). The larger your sample, the more accurate you can expect your results will be. Conversely, the higher error rate you're willing to assume in your data, the smaller your sample population must be. Making Corrections When Needed There will be times when a portion of your random sample population fails to complete your survey. This can present a problem. When you choose the number of people for your population, you do so

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based upon the error rate you're willing to assume and the level of confidence you would like to have in your data. When some members of your population fail to respond to your survey, your original assumptions of error and confidence are no longer reliable. To circumvent this problem, estimate a percentage of non-response and build your population based upon your estimate. Choosing your survey population is a matter of making reasonable assumptions and having a clear objective regarding the purpose of your data. Just keep in mind that assumptions are often wrong. Learn to make corrections along the way. SurveyGizmo is a leading provider of online survey tools, check them out on the web at http://www.surveygizmo.com

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