000			01887nam a22002657a 4500
005			20251211164304.0
008			250627b |||||||| |||| 00| 0 eng d
020			_a9780367634315
040			_aCSL _cCSL
041			_2eng _aeng
084			_aB28 R3 _qCSL
100			_aTan , Frans E.S _eauthor. _9814859
245			_aApplied Linear Regression for Longitudinal Data _b: With an Emphasis on Missing Observations
260			_aBoca Raton : _bCRC Press/Taylor & Francis, _c2023.
300			_axxi, 226p. _b: ill. _c; 24 cm.
490			_aChapman & Hall/CRC Texts in Statistical Science Series
500			_aIncludes references and index.
520			_aThis book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Provides datasets and examples onlineGives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysisConceptualises the analysis of comparative (experimental and observational) studiesIt is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.
650			_aLongitudinal data. _9814860
650			_aLinear regression. _9451157
650			_aScientific framework. _9814861
650			_aMissing observations. _9814862
700			_aJolani, Shahab _eco-author. _9814863
942			_2CC _cTEXL _hB28 R3 _n0
999			_c1433091 _d1433091