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  • Produktbild: Osborne, J: Best Practices in Data Cleaning
  • Produktbild: Osborne, J: Best Practices in Data Cleaning

Osborne, J: Best Practices in Data Cleaning A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.01.2012

Verlag

Sage Publications

Seitenzahl

296

Maße (L/B/H)

22.9/15.2/1.7 cm

Gewicht

432 g

Sprache

Englisch

ISBN

978-1-4129-8801-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.01.2012

Verlag

Sage Publications

Seitenzahl

296

Maße (L/B/H)

22.9/15.2/1.7 cm

Gewicht

432 g

Sprache

Englisch

ISBN

978-1-4129-8801-8

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  • Produktbild: Osborne, J: Best Practices in Data Cleaning
  • Produktbild: Osborne, J: Best Practices in Data Cleaning

  • Chapter 1. Why Data Cleaning is Important: Debunking the Myth of Robustness
    Part 1. Best Practices as you Prepare for Data Collection
    Chapter 2. Power and Planning for Data Collection: Debunking the Myth of Adequate Power
    Chapter 3. Being True to the Target Population: Debunking the Myth of Representativeness
    Chapter 4. Using Large Data Sets with Probability Sampling Frameworks: Debunking the Myth of Equality
    Part 2. Best Practices in Data Cleaning and Screening
    Chapter 5. Screening your Data for Potential Problems: Debunking the Myth of Perfect Data
    Chapter 6. Dealing with Missing or Incomplete Data: Debunking the Myth of Emptiness
    Chapter 7. Extreme and Influential Data Points: Debunking the Myth of Equality
    Chapter 8. Improving the Normality of Variables through Box-Cox Transformation: Debunking the Myth of Distributional Irrelevance
    Chapter 9. Does Reliability Matter? Debunking the Myth of Perfect Measurement
    Part 3. Advanced Topics in Data Cleaning
    Chapter 10. Random Responding, Motivated Mis-Responding, and Response Sets: Debunking the Myth of the Motivated Participant
    Chapter 11. Why Dichotomizing Continuous Variables is Rarely a Good Practice: Debunking the Myth of Categorization
    Chapter 12. The Special Challenge of Cleaning Repeated Measures Data: Lots of Pits to Fall into
    Chapter 13. Now that the Myths are Debunked... Visions of Rational Quantitative Methodology for the 21st Century