Foreword |
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xi | |
Preface |
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xiii | |
Acknowledgments |
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xvii | |
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I Quantitative descriptive approaches |
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1 | (106) |
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1 When panelists rate products according to a single list of attributes |
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5 | (30) |
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1.1 Data, sensory issues, and notations |
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5 | (2) |
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7 | (17) |
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1.2.1 What basic information can I draw from the data? |
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9 | (3) |
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1.2.2 How can I assess the performance of my panel? |
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12 | (8) |
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1.2.3 How can I assess the performance of my panelists? |
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20 | (4) |
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1.3 For experienced users: Measuring the impact of the presentation order on the perception of the products? |
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24 | (4) |
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28 | (4) |
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32 | (3) |
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2 When products are rated according to a single list of attributes |
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35 | (34) |
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2.1 Data, sensory issues, and notations |
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35 | (1) |
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36 | (20) |
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2.2.1 How can I get a list of the sensory attributes that structure the product space? |
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37 | (3) |
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2.2.2 How can I get a sensory profile for each product? |
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40 | (4) |
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2.2.3 How can I represent the product space on a map? |
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44 | (8) |
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2.2.4 How can I get homogeneous clusters of products? |
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52 | (4) |
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2.3 For experienced users: Adding supplementary information to the product space |
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56 | (7) |
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2.3.1 Introduction to supplementary information |
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57 | (2) |
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2.3.2 The panellipse function of the SensoMineR package |
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59 | (4) |
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63 | (4) |
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67 | (2) |
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3 When products are rated according to several lists of attributes |
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69 | (38) |
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3.1 Data, sensory issues, and notations |
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69 | (2) |
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71 | (19) |
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3.2.1 Why can't I analyze such a table in a classical way? |
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72 | (4) |
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3.2.2 How can I get a representation of the product space based on a consensus? |
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76 | (10) |
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3.2.3 How can I integrate the group structure in my interpretation? |
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86 | (4) |
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3.3 For experienced users: Comparing different panels with hierarchical multiple factor analysis (HMFA) |
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90 | (10) |
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100 | (2) |
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102 | (5) |
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II Qualitative descriptive approaches |
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107 | (122) |
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4 When products are depicted by comments |
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111 | (38) |
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4.1 Data, sensory issues, and notations |
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111 | (2) |
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113 | (24) |
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4.2.1 How can I approach textual data? |
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114 | (5) |
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4.2.2 How can I get an individual description of each product? |
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119 | (5) |
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4.2.3 How can I graphically represent the product space? |
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124 | (11) |
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4.2.4 How can I summarize the comments? |
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135 | (2) |
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4.3 For experienced users: Comparing free comments from different panels, the Rorschach test revisited |
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137 | (5) |
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142 | (5) |
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147 | (2) |
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5 When two different products are compared in various situations |
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149 | (24) |
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5.1 Data, sensory issues, and notations |
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149 | (2) |
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151 | (10) |
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5.2.1 How can I measure the distance between two products? |
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151 | (4) |
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5.2.2 How can I measure the inter-distance between products when compared in pairs? |
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155 | (6) |
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5.3 For experienced users: The Thurstonian approach |
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161 | (6) |
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167 | (3) |
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170 | (3) |
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6 When products are grouped into homogeneous clusters |
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173 | (28) |
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6.1 Data, sensory issues, and notations |
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173 | (2) |
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175 | (12) |
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6.2.1 How can I approach sorting data? |
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175 | (2) |
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6.2.2 How can I get a representation of the product space? |
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177 | (5) |
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6.2.3 How can I fully interpret the product space? |
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182 | (4) |
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6.2.4 How can I understand the data from a panel perspective? |
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186 | (1) |
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6.3 For experienced users: The hierarchical sorting task |
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187 | (9) |
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196 | (2) |
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198 | (3) |
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7 When products are positioned onto a projective map |
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201 | (28) |
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7.1 Data, sensory issues, and notations |
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201 | (3) |
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204 | (16) |
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7.2.1 How can I approach Napping® data? |
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204 | (5) |
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7.2.2 How can I represent the product space on a map? |
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209 | (2) |
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7.2.3 How can I interpret the product space with the verbalization data? |
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211 | (5) |
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7.2.4 How can I represent the consumers, and how can I explain the product representation through their individual rectangles? |
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216 | (4) |
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7.3 For experienced users: The sorted Napping® |
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220 | (3) |
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223 | (3) |
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226 | (3) |
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III Affective descriptive approaches |
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229 | (106) |
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8 When products are solely assessed by liking |
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233 | (38) |
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8.1 Data, sensory issues, and notations |
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233 | (2) |
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235 | (27) |
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8.2.1 How can I approach hedonic data? |
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235 | (11) |
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8.2.2 How can I identify the best product? |
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246 | (8) |
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8.2.3 How can I get homogeneous clusters of consumers? |
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254 | (8) |
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8.3 For experienced users: Dealing with multiple hedonic variables and supplementary consumer data |
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262 | (5) |
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8.3.1 Dealing with multiple hedonic variables |
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263 | (1) |
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8.3.2 Dealing with supplementary consumer data |
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264 | (3) |
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267 | (3) |
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270 | (1) |
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9 When products are described by both liking and external information "independently" |
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271 | (30) |
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9.1 Data, sensory issues, and notations |
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271 | (2) |
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273 | (13) |
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9.2.1 How can I explain the differences in preferences using sensory data? |
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273 | (5) |
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9.2.2 How can I evaluate the relationship between each sensory attribute and the hedonic scores, at different levels? |
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278 | (5) |
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9.2.3 How can I locate an optimum product within the product space? |
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283 | (3) |
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9.3 For experienced users: Finding the best correspondence between the sensory and hedonic matrices, using PrefMFA |
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286 | (8) |
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294 | (4) |
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298 | (3) |
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10 When products are described by a mix of liking and external information |
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301 | (34) |
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10.1 Data, sensory issues, and notations |
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301 | (3) |
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304 | (14) |
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10.2.1 How can I optimize products based on Just About Right data? |
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305 | (7) |
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10.2.2 How can I optimize products based on Ideal Profile Method data? |
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312 | (6) |
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10.3 For experienced users: Assessing the consistency of ideal data in IPM |
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318 | (10) |
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328 | (3) |
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10.5 Recommended readings |
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331 | (4) |
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335 | (18) |
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335 | (2) |
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335 | (1) |
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336 | (1) |
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337 | (1) |
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A.3 Running my first R function |
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337 | (5) |
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337 | (2) |
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339 | (2) |
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A.3.3 First operations on a data set |
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341 | (1) |
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342 | (2) |
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342 | (1) |
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343 | (1) |
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A.5 Non-exhaustive list of useful functions in R |
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344 | (6) |
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350 | (1) |
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351 | (2) |
Index |
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353 | |