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作者 Osborne, Jason W
書名 Best practices in data cleaning : a complete guide to everything you need to do before and after collecting your data / Jason W. Osborne
出版項 Thousand Oaks, Calif. : SAGE, ©2013
國際標準書號 9781412988018 (paperback)
1412988012 (paperback)
國際標準號碼 99965643975
book jacket
館藏地 索書號 處理狀態 OPAC 訊息 條碼
 歐美所圖書館3F西文書區  001.42 Os16 2013    在架上  -  30500101432618
 統計所圖書館圖書區I  H62 O82 2013    在架上    30570000128708
 人文社會聯圖  H62 .O82 2013    在架上    30610020371266
說明 xv, 275 pages : illustrations ; 23 cm
text txt rdacontent
unmediated n rdamedia
volume nc rdacarrier
附註 "Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process to examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. 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 is indispensible."--Publisher's website
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
主題 Quantitative research
Social sciences -- Methodology
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