WikiJournal of Science/Design effect/XML

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    <full_title>WikiJournal of Science/Design effect</full_title>
    <abbrev_title>Wiki.J.Sci.</abbrev_title>
    <issn media_type='electronic'>2002-4436 / 2470-6345 / 2639-5347</issn>
    <doi_data>
     <doi>10.15347/WJS</doi>
     <resource>http://www.WikiJSci.org/</resource>
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   <journal_issue>  
    <publication_date media_type='online'>     
     <year>2024</year>  
    </publication_date>   
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     <volume></volume>
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    <issue></issue>
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   <journal_article publication_type='full_text'>   
    <titles>     
     <title>Design effect</title>
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    <contributors>
    <person_name sequence='first' contributor_role='author'>
     <surname></surname>
    </person_name><person_name sequence='additional' contributor_role='contributors'>
     <surname>et al.</surname><affiliation>Wikipedia editors of Design effect</affiliation><link>https://xtools.wmflabs.org/articleinfo/en.wikipedia.org/Design_effect//2024-05-06</link>
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    <publication_date media_type='online'>     
     <year>2024</year>
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     <doi></doi>     
     <resource>https://en.wikiversity.org/wiki/WikiJournal of Science/Design effect</resource>
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This is an open access article distributed under the&nbsp;[http://creativecommons.org/licenses/by-sa/3.0/ Creative Commons Attribution ShareAlike License], which permits unrestricted use, distribution, and reproduction, provided the original author and source are credited.</license-p>
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In survey research, the design effect is a number that shows how well a sample of people may represent a larger group of people for a specific measure of interest (such as the mean). This is important when the sample comes from a sampling method that is different than just picking people using a simple random sample.  The design effect is a positive real number, represented by the symbol   . If   , then the sample was selected in a way that is just as good as if people were picked randomly. When   , then inference from the data collected is not as accurate as it could have been if people were picked randomly.  When researchers use complicated methods to pick their sample, they use the design effect to check and adjust their results. It is also used when planning a study in order to figure out how many people should be in the sample.
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