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160309t20162016caua ob 000 0 eng d |
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|a AWC
|b eng
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|d JSTOR
|d COO
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|a 945391446
|a 1272921605
|a 1374863660
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|a 9780833094445
|q (electronic bk.)
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|a 0833094440
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|z 9780833094148
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|z (OCoLC)1374863660
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|a 22573/ctt1b6gkg8
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|b .J34 2016eb
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|a RR-1197-A
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|a UAMI
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|a Jahedi, Salar,
|e author.
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|a Searching for information online :
|b using big data to identify the concerns of potential Army recruits /
|c Salar Jahedi, Jennie W. Wenger, Douglas Yeung.
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|a Santa Monica, Calif. :
|b RAND Corporation,
|c [2016]
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|c ©2016
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|a 1 online resource (22 pages) :
|b color illustrations
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
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|b PDF
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|a Rand Corporation research report series ;
|v RR-1197-A
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|a Series from web site.
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|a "Arroyo Center."
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|a Includes bibliographical references (pages 21-22).
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|a Introduction -- What is big data? -- What can internet search data tell us about attitudes and trends? -- Internet search data : three publicly available analytic tools and Army-relevant examples -- Implications and future work -- Tips, tricks, and other considerations.
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|a "This report assesses empirical applications of web search data and discusses the prospective value such data can offer Army recruiting efforts. The authors examine three different tools -- Google Trends, Google AdWords, and Google Correlate -- that can be used to access and analyze readily available, anonymous data from Internet searches related to the Army and to Army service. They found that Google search queries can inform how interest in military careers has evolved over time and by geographic location and can identify the foremost Army-related concerns that potential recruits have. Moreover, by analyzing how search terms correlate across time, it is possible to predict with reasonable accuracy what non-Army related terms people are searching for in the months before or after an Army query. These queries serve as leading and lagging indicators of army-related searches and can offer a glimpse into the concerns of individuals near the time period when they are considering joining. The results suggest that search terms can serve as an indicator of propensity and can be incorporated into models to predict highly qualified Army accessions"--Publisher's web site
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|a Online resource; title from PDF title page (RAND, viewed March 9, 2016).
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|a JSTOR
|b Books at JSTOR All Purchased
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|a JSTOR
|b Books at JSTOR Open Access
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1 |
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|a United States.
|b Army
|x Recruiting, enlistment, etc.
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|a United States.
|b Army
|x Data processing.
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|a États-Unis.
|b Army
|x Informatique.
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|a United States.
|b Army.
|2 fast
|0 (OCoLC)fst00533532
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|a Recruiting and enlistment
|x Data processing.
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|a Big data.
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|a Recrutement des armées
|x Informatique.
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|a Données volumineuses.
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|a COMPUTERS
|x Internet
|x General.
|2 bisacsh
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|a Armed Forces
|x Data processing.
|2 fast
|0 (OCoLC)fst01351761
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|a Big data.
|2 fast
|0 (OCoLC)fst01892965
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|a Recruiting and enlistment.
|2 fast
|0 (OCoLC)fst01091944
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|a Recruiting and enlistment
|x Data processing.
|2 fast
|0 (OCoLC)fst01431999
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1 |
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|a Wenger, Jennie W.,
|e author.
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|a Yeung, Douglas,
|e author.
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|a Arroyo Center,
|e issuing body.
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|a Rand Corporation,
|e publisher.
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|t Books at JSTOR: Open Access
|d JSTOR
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776 |
0 |
8 |
|i Print version:
|a Jahedi, Salar.
|t Searching for information online.
|d Santa Monica, Calif. : RAND Corporation, [2016]
|w (DLC) 2020303225
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830 |
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|a Research report (Rand Corporation) ;
|v RR-1197-A.
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856 |
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|u https://jstor.uam.elogim.com/stable/10.7249/j.ctt1b67wpk
|z Texto completo
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|a YBP Library Services
|b YANK
|n 12899498
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|a 92
|b IZTAP
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