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

Osborne, Jason W
Call no.: H62 .O83 2013Publication: Thousand Oaks, Calif. : SAGE, c2013Description: xv, 275 p. : illISBN: 9781412988018 (pbk.); 1412988012 (pbk.)Subject(s): Quantitative researchSocial sciences -- MethodologyLOC classification: H62 | .O83 2013
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
แท็ก: ไม่มีแท็กจากห้องสมุดสำหรับชื่อเรื่องนี้ เข้าสู่ระบบเพื่อเพิ่มแท็ก
ประเภททรัพยากร ตำแหน่งปัจจุบัน กลุ่มข้อมูล ตำแหน่งชั้นหนังสือ เลขเรียกหนังสือ สถานะ วันกำหนดส่ง บาร์โค้ด การจองรายการ
Book Book Professor Sangvian Indaravijaya Library
General Books General Stacks H62 .O83 2013 (เรียกดูชั้นหนังสือ) พร้อมให้บริการ
31379013690483
รายการจองทั้งหมด: 0

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

There are no comments on this title.

เพื่อโพสต์ความคิดเห็น

คลิกที่รูปภาพเพื่อดูในตัวแสดงภาพ

ห้องสมุด:

Thammasat University Library, 2 Prachan Road, Phranakorn, Bangkok 10200

Puey Ungphakorn Library (Rangsit Campus), Circulation Desk 662 564-4444 ext. 1305

Pridi Banomyong Library, Circulation Desk 662 613-3544