quick word counts to laborious‚ in-depth‚ line-by-line scrutiny. Techniques are compared on six dimensions: (1) appropriateness for data types‚ (2) required labor‚ (3) required expertise‚ (4) stage of analysis‚ (5) number and types of themes to be generated‚ and (6) issues of reliability and validity. Keywords: theme identification; qualitative analysis; text analysis; open coding; qualitative research methods Analyzing text involves several tasks: (1) discovering themes and subthemes‚ (2) winnowing
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practices and cost performance is investigated. Construction project data for this research are derived from the Construction Industry Institute Benchmarking and Metrics database. Multiple one-way ANOVA and linear regression are performed to investigate the effectiveness of individual change management practices elements and overall change management practices implementation in controlling project change cost‚ respectively. The data analysis results show that individual change management practices elements
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4. Big Data 4.1 Introduction In 2004‚ Wal-Mart claimed to have the largest data warehouse with 500 terabytes storage (equivalent to 50 printed collections of the US Library of Congress). In 2009‚ eBay storage amounted to eight petabytes (think of 104 years of HD-TV video). Two years later‚ the Yahoo warehouse totalled 170 petabytes1 (8.5 times of all hard disk drives created in 1995)2. Since the rise of digitisation‚ enterprises from various verticals have amassed burgeoning amounts
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CHAPTER-1 INTRODUCTION 1.1 INTRODUCTION Ongoing and unrelenting economic‚ social and technological changes have spurred the need for flexible‚ skilled workers who can help their organizations succeed and sustain a competitive advantage. To be relevant within organizations and indispensable to clients and customers alike‚ workplace learning and performance professionals must continually reassess their competencies‚ update their skills and have the courage to make necessary changes. Businesses
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Professional Studies (MProfStuds) - Data Science. Since the day I graduated from my college‚ I always thought that with time‚ while attaining some professional experience‚ I would unveil the subject I want to be prowess. Now when I am sitting on a professional experience of more than 2 years‚ I realised I am somehow always working on the quantitative data in order to come out with a qualitative research. With a moderate amount of knowledge attained in data management in my academics‚ I embarked
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SPECIAL ISSUE: BUSINESS INTELLIGENCE RESEARCH BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management‚ University of Arizona‚ Tucson‚ AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business‚ University of Cincinnati‚ Cincinnati‚ OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business‚ Georgia State University‚ Atlanta‚ GA 30302-4015 U.S.A. {vstorey@gsu
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AN E-BOOK SERIES Publishers of Big Data DATA SCIENCE BIG DATA PREDICTIVE MODELING HARNESSING THE POWER OF BIG DATA THROUGH EDUCATION AND DATA-DRIVEN DECISION MAKING Sponsored by OPEN SOURCE DATA ANALYTICS Bigger. Faster. Better. Revolution R Enterprise is fast‚ powerful‚ and scales to fit your needs. Perform Big Data Analytics at speed and at scale. R-based analytics in-Hadoop and in-Database reduce latency and eliminate sampling. RRE is 100% R—get everything
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the system as reports. As long as the reports are from one source‚ and retrieved by a defined time‚ the data in these reports are truly the same all over the enterprise. c) Explain the difference between OLAP analysis and data mining / predictive analysis OLAP‚ Online Analytical Processing‚ analyst is to provide multidirectional data analysis data out of database(s). It analysis on operational questions‚ like average‚ summation‚ mid‚ median‚ maximum‚ minimum‚ variance‚ and etc. this
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interviews of the participants expressions of depression. 2. Are data collection and analysis techniques appropriate? How? Data collection techniques are questionable‚ because some of the subjects were interviewed at the health center and some at their home. They were questionable‚ due to the subjects at home may elicit more of an open and honest response versus the subjects interviewed at the healthcare center. The data analysis techniques were appropriate by how all of the interviews were
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future fears of the victims‚ as presented by the actual victims. The reader is able to garner the essence of the experience from the findings section of this article. Further‚ the readers are able to see that the theme of fear and safety I true to the data that is presented throughout this article They provide numbers for the reader to see how fear and safety were a common theme throughout all twenty-one narratives. The authors do not go into great discussion regarding existing literature on the topic
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