LEADER 05370nam 2200661Ia 450 001 9910830940503321 005 20230207230453.0 010 $a1-282-18889-5 010 $a9786612188893 010 $a0-470-74691-2 010 $a0-470-74692-0 035 $a(CKB)1000000000794253 035 $a(EBL)454378 035 $a(OCoLC)609843881 035 $a(SSID)ssj0000354380 035 $a(PQKBManifestationID)11251813 035 $a(PQKBTitleCode)TC0000354380 035 $a(PQKBWorkID)10302637 035 $a(PQKB)10117377 035 $a(MiAaPQ)EBC454378 035 $a(EXLCZ)991000000000794253 100 $a20090306d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to optimal designs for social and biomedical research$b[electronic resource] /$fMartijn P.F. Berger, Weng Kee Wong 210 $aHoboken, NJ $cWiley$d2009 215 $a1 online resource (348 p.) 225 1 $aStatistics in Practice ;$vv.83 300 $aDescription based upon print version of record. 311 $a0-470-69450-5 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Optimal Designs for Social and Biomedical Research; Contents; Preface; Acknowledgements; 1 Introduction to designs; 1.1 Introduction; 1.2 Stages of the research process; 1.2.1 Choice of a 'good' design; 1.3 Research design; 1.3.1 Choice of independent variables and levels; 1.3.2 Units of analysis; 1.3.3 Variables; 1.3.4 Replication; 1.4 Types of research designs; 1.5 Requirements for a 'good' design; 1.5.1 Statistical conclusion validity; 1.5.2 Internal validity; 1.5.3 Control of (unwanted) variation; 1.6 Ethical aspects of design choice 327 $a1.7 Exact versus approximate designs1.8 Examples; 1.8.1 Radiation dosage example; 1.8.2 Designs for the Poggendorff and Ponzo illusion experiments; 1.8.3 Uncertainty about best .tting regression models; 1.8.4 Designs for a priori contrasts among composite faces; 1.8.5 Designs for calibration of item parameters in item response theory models; 1.9 Summary; 2 Designs for simple linear regression; 2.1 Design problem for a linear model; 2.1.1 The design; 2.1.2 The linear regression model; 2.1.3 Estimation of parameters and efficiency; 2.2 Designs for radiation-dosage example 327 $a2.3 Relative efficiency and sample size2.4 Simultaneous inference; 2.5 Optimality criteria; 2.5.1 D-optimality criterion; 2.5.2 A-optimality criterion; 2.5.3 G-optimality criterion; 2.5.4 E-optimality criterion; 2.5.5 Number of distinct design points; 2.6 Relative efficiency; 2.7 Matrix formulation of designs for linear regression; 2.8 Summary; 3 Designs for multiple linear regression analysis; 3.1 Design problem for multiple linear regression; 3.1.1 The design; 3.1.2 The multiple linear regression model; 3.1.3 Estimation of parameters and ef.ciency; 3.2 Designs for vocabulary-growth study 327 $a3.3 Relative efficiency and sample size3.4 Simultaneous inference; 3.5 Optimality criteria for a subset of parameters; 3.6 Relative efficiency; 3.7 Designs for polynomial regression model; 3.7.1 Exact D-optimal designs for a quadratic regression model; 3.7.2 Scale dependency of A- and E-optimality criteria; 3.8 The Poggendorff and Ponzo illusion study; 3.9 Uncertainty about best .tting regression models; 3.10 Matrix notation of designs for multiple regression models; 3.10.1 Design for regression models with two independent variables 327 $a3.10.2 Design for regression models with two non-additive independent variables3.11 Summary; 4 Designs for analysis of variance models; 4.1 A typical design problem for an analysis of variance model; 4.1.1 The design; 4.1.2 The analysis of variance model; 4.1.3 Formulation of an ANOVA model as a regression model; 4.2 Estimation of parameters and efficiency; 4.2.1 Measures of uncertainty; 4.3 Simultaneous inference and optimality criteria; 4.4 Designs for groups under stress study; 4.4.1 A priori planned unequal sample sizes; 4.4.2 Not planned unequal sample sizes 327 $a4.5 Specific hypotheses and contrasts 330 $aThe increasing cost of research means that scientists are in more urgent need of optimal design theory to increase the efficiency of parameter estimators and the statistical power of their tests. The objectives of a good design are to provide interpretable and accurate inference at minimal costs. Optimal design theory can help to identify a design with maximum power and maximum information for a statistical model and, at the same time, enable researchers to check on the model assumptions. This Book:Introduces optimal experimental design in an accessible format.Pro 410 0$aStatistics in Practice 606 $aSocial sciences$xResearch 606 $aBiology$xResearch 606 $aOptimal designs (Statistics) 615 0$aSocial sciences$xResearch. 615 0$aBiology$xResearch. 615 0$aOptimal designs (Statistics) 676 $a300.72 700 $aBerger$b Martijn P. F$01709287 701 $aWong$b Weng Kee$01659695 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830940503321 996 $aAn introduction to optimal designs for social and biomedical research$94098929 997 $aUNINA