MARC Record from marc_columbia

LEADER: 06846cam a2200349 a 4500
001 7976335
005 20221201050638.0
008 100106t20112011flua b 001 0 eng
010 $a 2010007126
020 $a9781420099966 (alk. paper)
020 $a1420099965 (alk. paper)
035 $a(OCoLC)ocn665064319
035 $a(OCoLC)665064319
035 $a(NNC)7976335
035 $a7976335
040 $aDLC$cDLC$dYDX$dYDXCP$dOrLoB-B
050 00 $aHF5415.2$b.R24 2011
082 00 $a658.8/343$222
100 1 $aRaghavarao, Damaraju.$0http://id.loc.gov/authorities/names/n87929006
245 10 $aChoice-based conjoint analysis :$bmodels and designs /$cDamaraju Raghavarao, James B. Wiley, Pallavi Chitturi.
260 $aBoca Raton, FL :$bChapman & Hall/CRC,$c[2011], ©2011.
300 $axi, 180 pages :$billustrations ;$c25 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
504 $aIncludes bibliographical references and index.
505 00 $g1.$tIntroduction -- $g1.1.$tConjoint Analysis -- $g1.2.$tDiscrete Choice Experimentation -- $g1.3.$tRandom Utility Models -- $g1.4.$tThe Logistic Model -- $g1.5.$tContributions of the Book -- $g2.$tSome Statistical Concepts -- $g2.1.$tPrinciples of Experimental Design -- $g2.2.$tExperimental versus Treatment Design -- $g2.3.$tBalanced Incomplete Block Designs and 3-Designs -- $g2.4.$tFactorial Experiments -- $g2.5.$tFractional Factorial Experiments -- $g2.6.$tHadamard Matrices and Orthogonal Arrays -- $g2.7.$tFoldover Designs -- $g2.8.$tMixture Experiments -- $g2.9.$tEstimation -- $g2.9.1.$tProbability Models -- $g2.9.2.$tLinear Estimation: Least Squares and Weighted Least Squares -- $g2.9.3.$tMaximum Likelihood Estimation -- $g2.10.$tTransformations of the Multinomial Distribution -- $g2.11.$tTesting Linear Hypotheses -- $g3.$tGeneric Designs -- $g3.1.$tIntroduction -- $g3.2.$tFour Linear Models Used in CA and DCE -- $g3.2.1.$tBrands-Only Models -- $g3.2.2.$tAttributes-Only Models -- $g3.2.3.$tBrand-Plus-Attributes Designs -- $g3.2.4.$tBrand-Plus-Attributes and Selected Two-Way Interactions Models -- $g3.3.$tBrands-Only Designs -- $g3.4.$tAttributes-Only Designs -- $g3.5.$tBrands-Plus-Attributes Designs -- $g3.6.$tBrands, Attributes, and Interaction Design -- $g3.7.$tEstimation and Hypothesis Testing -- $tAppendix 3A: Logit Analysis of Traditional Conjoint Rating Scale Data -- $g4.$tDesigns with Ordered Attributes -- $g4.1.$tIntroduction -- $g4.2.$tLinear, Quadratic, and Cubic Effects -- $g4.2.1.$tAttributes at Two Levels -- $g4.2.2.$tAttributes at Three Levels -- $g4.2.3.$tAttributes at Four Levels -- $g4.2.4.$tAttributes at Five Levels -- $g4.3.$tInteraction Components: Linear and Quadratic -- $g4.4.$tAn Illustration -- $g4.5.$tPareto Optimal Designs -- $g4.6.$tInferences on Main Effects -- $g4.7.$tInferences on Main Effects in 2m Experiments -- $g4.8.$tInferences on Interactions -- $g4.9.$tOrthogonal Polynomials -- $g4.10.$tSubstitution Rate of Attributes -- $g5.$tReducing Choice Set Sizes -- $g5.1.$tIntroduction -- $g5.2.$tSubsetting Choice Sets -- $g5.3.$tSubsetting Levels into Overlapping Sets -- $g5.4.$tSubsetting Attributes into Overlapping Sets -- $g5.5.$tDesigns Generated from a BIBD -- $g5.6.$tCyclic Construction: s Choice Sets of Size s Each for an ss Experiment -- $g5.7.$tEstimating a Subset of Interactions -- $g6.$tAvailability (Cross-Effects) Designs -- $g6.1.$tIntroduction -- $g6.2.$tBrands-Only Availability Designs -- $g6.2.1.$tRelationships between Availability Effects and Interactions -- $g6.2.2.$tGenerating the Design Matrix for an Availability Design -- $g6.3.$tPortfolio Designs -- $g6.3.1.$tRandom Utility Model for Portfolio Choice -- $g6.4.$tBrand and One (or More) Attributes -- $g6.4.1.$tBrands with One Attribute -- $g6.4.2.$tOne Attribute at Three or More Levels -- $g6.5.$tBrands and More than One Attribute -- $g6.5.1.$tGeneral Results for Two or More Attributes with Two or More Levels -- $g7.$tSequential Methods -- $g7.1.$tIntroduction -- $g7.2.$tSequential Experiment to Estimate All Two- and Three-Attribute Interactions -- $g7.3.$tSequential Methods to Estimate Main Effects and Interactions, Including a Common Attribute in 2m Experiments -- $g7.4.$tCA Testing Main Effects and a Two-Factor Interaction Sequentially -- $g7.5.$tInterim Analysis -- $g7.6.$tSome Sequential Plans for 3m Experiments -- $g8.$tMixture Designs -- $g8.1.$tIntroduction -- $g8.2.$tMixture Designs: CA Example -- $g8.3.$tMixture Designs: DCE Example -- $g8.4.$tMixture-Amount Designs -- $g8.5.$tOther Mixture Designs -- $g8.6.$tMixture Designs: Field Study illustration.
520 1 $a"Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used in marketing, economics, transportation, health, tourism, and other areas to develop and modify products, services, policies, and programs, specifically ones that can be described in terms of attributes. A specific combination of attributes is called a concept profile. Building on the authors' significant work in the field, Choice-Based Conjoint Analysis: Models and Designs explores the experimental design issues that occur when constructing concept profiles for DCE and CA studies. It shows how to modify commonly used designs and models as well as develop new types of designs for solving DCE and CA problems." "After reviewing the historical and statistical background, the book presents examples of "generic" experimental designs commonly used in CA and DCE. It then addresses designs appropriate for four classes of DOE problems: (1) attributes in CA and DCE studies are often ordered; (2) studies increasingly are computer assisted; (3) choice is often influenced by competition; and (4) constraints may exist on attribute levels. The designs covered include Pareto optimal designs, attribute/attribute level subsetting, orthogonal polynomials, sequential designs, availability and cross-effects designs, and mixture-amount designs. Mixture-amount designs are relevant to situations where constraints, such as budget or technological constraints, are imposed on the levels of attributes." "Features" "Extends existing DOE material to designs tailored to specific classes of choice experiments" "Explains experimental design concepts through illustrative examples" "Requires minimal mathematical background" "Presents examples of DCE and CA in mixture designs" "Provides detailed coding of design matrices for standard designs"--BOOK JACKET.
650 0 $aConjoint analysis (Marketing)$0http://id.loc.gov/authorities/subjects/sh94001705
650 0 $aConsumers' preferences.$0http://id.loc.gov/authorities/subjects/sh85031496
700 1 $aWiley, James B.$0http://id.loc.gov/authorities/names/n2010016588
700 1 $aChitturi, Pallavi.$0http://id.loc.gov/authorities/names/n2010016586
852 00 $boff,bus$hHF5415.2$i.R24 2011