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  <titleInfo>
    <title>Advanced techniques for collecting statistical data</title>
  </titleInfo>
  <name type="personal">
    <namePart>Moreira, Olga</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
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    <dateIssued encoding="marc">2023</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
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    <extent>xxiv, 385 pages : illustrations ; 25 cm.</extent>
  </physicalDescription>
  <abstract>"“Advanced Techniques for Collecting Statistical Data" is an edited book consisting of 17 contemporaneous open-access articles focused on data collection methods, from qualitative research techniques to automated data collection systems. The first chapters include a practical guide to designing, sampling, and collecting qualitative research data. The second part of the book is devoted to data mining of information collected from clinical and social studies surveys, as well as from social media. The final chapters reflect on current efforts to optimize and automate data collection procedures. This book also includes new methodologies based on automatic data collection and analysis systems based on smartphone technology and artificial intelligence, as well as their application in clinical research, sociology, stock market prediction and other fields. This book is intended to reach an academic audience ranging from undergraduate students to junior researchers"-- Back cover.</abstract>
  <tableOfContents>Series: practical guidance to qualitative research. Part 1: introduction -- Series: practical guidance to qualitative research. Part 2: context, research questions and designs -- Series: practical guidance to qualitative research. Part 3: sampling, data collection and analysis -- Series: practical guidance to qualitative research. Part 4: trustworthiness and publishing -- Participant observation as a data collection method -- Attitudes towards participation in a passive data collection experiment -- An integrative review on methodological considerations in mental health research - design, sampling, data collection procedure and quality assurance -- Wiki surveys: open and quantifiable social data collection -- Towards a standard sampling methodology on online social networks: collecting global trends on twitter -- mobile data collection: smart, but not (yet) smart enough -- Comparing a mobile phone automated system with a paper and email data collection system: substudy within a randomized controlled trial -- Big data collection and object participation willingness: an analytical framework from the perspective of value balance -- Research on computer simulation big data intelligent collection and analysis system -- Development of a mobile application for smart clinical trial subject data collection and management -- The CoronaSurveys system for COVID-19 incidence data collection and processing -- Artificial intelligence based body sensor network framework - narrative review: proposing and end-to-end framework using wearable sensors, real-time location systems and artificial intelligence/machine learning algorithms for data collection, data mining and knowledge discovery in sports and healthcare -- DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction.</tableOfContents>
  <note type="statement of responsibility">edited by Olga Moreira.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Data collection</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Research methodology</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Qualitative research</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Sampling (Statistics)</topic>
  </subject>
  <classification authority="lcc">Q180.55.M4 .A38 2023</classification>
  <classification authority="ddc" edition="23">001.422 Ad951</classification>
  <identifier type="isbn">9781774694978</identifier>
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    <recordCreationDate encoding="marc">260929</recordCreationDate>
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      <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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