Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data
Autor Jason W. Osborneen Limba Engleză Paperback – 13 mar 2012
Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating, for each topic, the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible.
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Specificații
ISBN-13: 9781412988018
ISBN-10: 1412988012
Pagini: 296
Dimensiuni: 152 x 229 x 28 mm
Greutate: 0.34 kg
Ediția:First Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
ISBN-10: 1412988012
Pagini: 296
Dimensiuni: 152 x 229 x 28 mm
Greutate: 0.34 kg
Ediția:First Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
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Recenzii
“This
book
provides
the
perfect
bridge
between
the
formal
study
of
statistics
and
the
practice
of
statistics.
It
fills
the
gap
left
by
many
of
the
traditional
texts
that
focus
either
on
the
technical
presentation
or
recipe-driven
presentation
of
topics.”
“The first comprehensive and generally accessible text in this area.”
“The first comprehensive and generally accessible text in this area.”
Cuprins
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
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
Notă biografică
Descriere
This
book
provides
a
clear,
step-by-step
process
of
examining
and
cleaning
data
in
order
to
decrease
error
rates
and
increase
both
the
power
and
replicability
of
results.