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BMGT 230 Week 2 Notes

by: Drew Herring

BMGT 230 Week 2 Notes BMGT230

Marketplace > University of Maryland > Business > BMGT230 > BMGT 230 Week 2 Notes
Drew Herring

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These notes cover chapter 8 of the textbook, and everything important from week 2 of class.
Business Statistics
Radu Lazar
Class Notes
business, Statistics
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This 4 page Class Notes was uploaded by Drew Herring on Monday September 12, 2016. The Class Notes belongs to BMGT230 at University of Maryland taught by Radu Lazar in Spring 2016. Since its upload, it has received 13 views. For similar materials see Business Statistics in Business at University of Maryland.


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Date Created: 09/12/16
Week 2 Notes  Surveys and Sampling      I. Three Ideas of Sampling  A) Idea 1: Sample ­ Examine a part of the whole  ○ The group of people that you are sampling is called a ​population​. A population  includes the people that you want to survey.  ○ Since it would be nearly impossible to sample an entire population, a smaller  representative group is examined: called a ​sample​. A sample is the people that are  actually surveyed.  i. The concept here is that ­­ A small sample, if selected properly, can  represent the whole population.  ○ One kind of sample is a ​sample survey​ ­­ a poll that is designed to ask questions  to a small group of people in order to gain information about a larger group of  people, i.e., a population.  ○ Some samples are subject to ​bias​ ­ characteristics of a sample that differ  significantly from the majority of the population it is trying to represent. For  example, a sample attempting to poll Americans’ feelings toward desserts and  sweets would be greatly biased if a large amount of dentists were polled. Our  hopes of avoiding bias lead us to Idea 2…  B) Idea 2: Randomize  ○ The prin ​ ciple here i​s to select​ air and​ iverse sample. Interviewing some  dentists might be okay, but don’t go conducting all of your surveys in dentist’s  offices. Try other places too.  C) Idea 3: It’s all about sample size  ○ Contrary to what you may think, the size of the sample determines the accuracy of  the conclusion. The size of th​ e population doesn’t matter at ​all. There is no magic  ratio, fraction, or percentage required to provide an accurate representation. A poll  of 100 random college students is as good as a poll of 1,000 random college  students. Studies have shown that once the threshold of several hundred  respondents has been reached, polls don’t become significantly more accurate.  i. A Sample that includes an entire population is called a ​census​. However,  censuses can be difficult to complete and over the course of a census, the  population will usually change.    II.​ opulations and Parameters  A) Parameters​ are mathematical components (values) that are generalized from a model to  reality (i.e. economic models compared to actual economic events). Parameters are  essentially the unmeasured statistics that the survey is trying to find. Parameters having to  do with generalizations towards a population are called ​population parameters​.  Parameters have to do with populations. Statistics go with samples.  ○ Any summary found from the data used in parameters is called a ​statistic​. When  statistics are matched with the parameters they calculate, they are called ​sample  statistics.​   ○ Any sample statistic that does truly generalize from a sample to a population is  said to be ​ epresentative.​     III.​ ommon Sampling Designs ​ [​Important!​]  A) Simple Random Sample (SRS)​ ­ A sample that gives every combination of individuals  an equal chance of being sampled. The SRS is considered to be the standard among  sampling methods. Always applies to a specific population.  B) Since pollsters don’t often have a list of the entire population that they want to survey,  the list that they actually draw from is called a ​Sampling Frame​. The sampling frame is  the group of people that can be reached.  ○ Respondents will typically give different answers to a survey. The possibility for  many different samples in a given population to give different answers is called  sampling variability​, or ​sampling error​ (even though there really is no error at  all).  ​ C) Stratified Random Sampling ​ ­ The combination of survey answers ​after the population  has been split into smaller, identical groups. These groups are called ​strata​. Examples of  strata can include big differences, such as gender, age range, income, but they can also  include smaller differences, such as political party affiliation, favorite football team,  ​ hometown, birth month, etc. All that matters is that something i of strata. Strata are always based on prior knowledge. While strata are different, the  individuals within each set of strata are similar. ​Stratified Samples​ are really  combinations of ​Simple Random Samples​ from within each strata.  D) Cluster and Multistage Sampling  ○ Cluster Sampling​ is similar to stratified sampling in that populations are split  into groups. However, in cluster sampling, these groups are generally ​clusters​ of  strata, such as people born in certain cities of a county, or fans of any/all sports, or  people born during the winter months. Drug test teams example.  ○ Sampling schemes that combine more than one method of sampling are called  Multistage Samples​.  E) Systematic Samples​ are samples that choose respondents through some function, or  system. Examples include picking every 5th person on a list, reading every 10th sentence  of an essay, or listening to every other song on an album. Use unless there is ​cyclical  data​.    IV. ​The Valid Survey  A) In order to make sure that your survey is actually valid, you’ll need to think about  some key points:  ​ a) Know what you want to know ­­  What are you trying to discover? What’s  the point of this survey? Why do you need a sample’s input on your point  of question?  ​ b) Use the right sampling frame ­­ Is your sample representative of the  population at large? Is it subject to bias? Is it diverse enough to provide  accurate information?  ​ c) Use specific questions over general questions ­­ Seriously, the more  specific and detailed information that you can get from a survey, the more  you’ll be able to learn from it.  d) Watch for biases  i) Nonresponse Bias​ is found in people not likely to give a response  due to {some variable}  ii) Voluntary Response Bias ​is found when people are more likely to  give a response due to {some other variable}  ​ e) Be careful with phrasing ­­ Avoid tricky wording. Be deliberate with how  you construct your questions and/or answer options so as to eliminate  potential for error.  i) Inaccurate OR accidental responses are called ​Measurement  Errors​. The best way to avoid these errors is through the use of a  Pilot Test​. A pilot test is essentially a “rough­draft” form of the  survey/poll being conducted.    V. ​ ow to Sample Badly  A) Voluntary Response Sample ​­ A survey in which a large population is invited to  respond. In this sample type, all answers are both counted and given equal credibility.  ​ Voluntary response samples are almost a to draw accurate conclusions from them. Applies to a broad population.  B) Convenience Sampling​ is exactly what it sounds like. This method includes whatever  respondents are convenient for the administration of the survey. An example would be a  text­response survey geared towards teenagers. The question to ask here is, are teenagers  with cell­phones representative of all teenagers? Likely not.  C) Many survey designs suffer from ​undercoverage​ ­ some portion of a population either  isn’t sampled or is given a disproportionately small sampling size. Undercoverage can  sometimes lead to bias.  


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