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Best practices in data cleaning : a complete guide to everything you need to do before and after collecting your data / Jason W. Osborne.

By: Osborne, Jason W.
Material type: materialTypeLabelBookCall no.: H62 .O83 2013Publication: Thousand Oaks, Calif. : SAGE, c2013Description: xv, 275 p. : ill.ISBN: 9781412988018 (pbk.); 1412988012 (pbk.).Subject(s): Quantitative research | Social sciences -- Methodology
Contents:
Why data cleaning is important: debunking the myth of robustness -- Power and planning for data collection: debunking the myth of adequate power -- Being true to the target population: debunking the myth of representativeness -- Using large data sets with probability sampling frameworks: debunking the myth of equality -- Screening your data for potential problems: debunking the myth of perfect data -- Dealing with missing or incomplete data: debunking the myth of emptiness -- Extreme and influential data points: debunking the myth of equality -- Improving the normality of variables through box-cox transformation: debunking the myth of distributional irrelevance -- Does reliability matter? debunking the myth of perfect measurement -- Random responding, motivated misresponding, and response sets: debunking the myth of the motivated participant -- Why dichotomizing continuous variables is rarely a good practice: debunking the myth of categorization -- The special challenge of cleaning repeated measures data: lots of pits in which to fall -- Now that the myths are debunked: visions of rational quantitative methodology for the 21st century
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Book Book Professor Sangvian Indaravijaya Library
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General Books H62 .O83 2013 (See Similar Items) Available 31379013690483
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Includes bibliographical references and indexes.

Why data cleaning is important: debunking the myth of robustness -- Power and planning for data collection: debunking the myth of adequate power -- Being true to the target population: debunking the myth of representativeness -- Using large data sets with probability sampling frameworks: debunking the myth of equality -- Screening your data for potential problems: debunking the myth of perfect data -- Dealing with missing or incomplete data: debunking the myth of emptiness -- Extreme and influential data points: debunking the myth of equality -- Improving the normality of variables through box-cox transformation: debunking the myth of distributional irrelevance -- Does reliability matter? debunking the myth of perfect measurement -- Random responding, motivated misresponding, and response sets: debunking the myth of the motivated participant -- Why dichotomizing continuous variables is rarely a good practice: debunking the myth of categorization -- The special challenge of cleaning repeated measures data: lots of pits in which to fall -- Now that the myths are debunked: visions of rational quantitative methodology for the 21st century

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