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by: Christina Turcotte

17

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3

Christina Turcotte
WVU
GPA 3.64

Gerald Blakely

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COURSE
PROF.
Gerald Blakely
TYPE
Class Notes
PAGES
3
WORDS
KARMA
25 ?

## Popular in Course

This 3 page Class Notes was uploaded by Christina Turcotte on Saturday September 12, 2015. The Class Notes belongs to BADM 621 at West Virginia University taught by Gerald Blakely in Fall. Since its upload, it has received 17 views. For similar materials see /class/202744/badm-621-west-virginia-university in Business Administration at West Virginia University.

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Date Created: 09/12/15
Chi Square sometimes called contingency table analysis or categorical data analysis Chi Square is a statistic that in this class we use to examine whether two categorical variables eg gender state of residence race are related or are independent from one another A typical table might look like this Gender Males Females Sport Basketball 117 56 Football 121 88 We would examine this table to determine is there is any relationship between preferred sport and gender The way this statistic is calculated is Expected cell value if independent row total X column totaltotal count in table The formula for Chisquare H2EEfojfeij2 feu where o is the observed count and e is the expected count for each cell Sampling A a procedure by which you can infer characteristics about a large body of people a population by getting data from only a few the sample A good sample is one in which every member ofthe population has an equal probability of being selected for the sample 1 2 Sampling error the difference in the sample estimate and what we would have found by measuring the entire population variance Sampling biases nonrandom errors due to inadequate data or clerical mistakes for example an interviewer always interviews the person who answers the doortypically the youngest family member Nonsampling biases clerk writes 4s that look like 9s In other words the kinds of errors that might occur even if we sampled the entire population Sample Design Probability samples 1 Simple random sample each unit in the population has a known and equal chance of being selected like drawing names out ofa hat without replacement Systematic random sample Find the sampling interval then pick a random starting number then every nth person on our list Must have the list in some order and must assume theres nothing of importance in that ordering Suppose we want a sample of 150 out of 6000 then 6000150 40 thus 40 is the sampling interval May save time and costs with large populations Strati ed samples if we know that certain population characteristics are important and we want to make sure theyre adequately included in the sample Divide into subgroups strata or layers and draw sample from each strata Cluster samples divides population geographically census tracts voting precincts counties etc Then the clusters are selected randomly usually multistage on down to individual or household level Many national studies are done this way For the same level of precision strati ed samples require the fewest respondents and cluster samples require the most C Sampling design nonprobability samples 1 2 Purposive respondents are deliberately sampled for example individual knowledge about a certain topic but we cant generalize Quota samples for example 5 men and 5 women underthe age of 40 and 5 men and 5 women 40 and older 3 Chunk samples interviewing 5 people on the street about some topic doesn t represent the whole population and probably not even the people on the street 4 Volunteer samples 5 Convenience samples Sample size depends on homogeneity variance of sample kind of sampling procedure used the number of categories you want to break the responses into how much error were willing to tolerate con dence level

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