rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Researching big data skills using a mixed-methods approach
  name:
    name part: McGuire, Alana
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Case studies
  origin info:
    place:
      place term: enk
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations.
  abstract: My PhD explores the impact of big data on skill requirements for employers in Scotland, asking the following question: How is big data changing skill demands for employers? The main question of sociological interest is as follows: Has big data created a new skill divide? This case study explains my mixed-methods design and examines the first strand of my data analysis which is quantitative and uses regression analysis. So far, I have addressed one aspect of big data skill inequalities using the British Cohort Study and the 1986 arithmetic data from this study to investigate social inequalities. The main statistical analysis that has been conducted thus far uses data from the Employer Skill Survey in regression models. This case will explain my overall mixed-methods approach and discuss the practical aspects and challenges of my statistical analysis.
  target audience: specialized
  note: Alana McGuire.
  note: Includes bibliographical references and index.
  subject:
    geographic code: e-uk-st
  subject:
    topic: Big data
    topic: Social aspects
    geographic: Scotland
    genre: Case studies
  subject:
    topic: Business intelligence
    geographic: Scotland
    topic: Data processing
    genre: Case studies
  subject:
    topic: Business planning
    geographic: Scotland
    topic: Statistical methods
    genre: Case studies
  subject:
    topic: Données volumineuses
    topic: Aspect social
    geographic: Écosse
    genre: Études de cas
  subject:
    topic: Big data
    topic: Social aspects
  subject:
    geographic: Scotland
  classification: QA76.9.B45 M34 2017
  classification: 005.7
  related item:
    title info:
      title: SAGE Research Methods. Cases
  identifier: 9781526419620
  identifier: 1526419629
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UAB
    record creation date: 170109
    record change date: 20240923170339.9
    record identifier: in00005494732
    language of cataloging:
      language term: eng