论坛里有该书的一个帖子，但是给出的却是外部链接，而且无效。在这里重发一下，是我下载好的。
书名：Applied Choice Analysis A Primer
作者：David A. Hensher
The University of Sydney
John M. Rose
The University of Sydney
William H. Greene
New York University
出版社：Cambridge University Press
详细目录：Part I Basic topics
1 In the beginning 3
2 Basic notions of statistics
8
2.1 Introduction 8
2.2 Data 8
2.2.1 The importance of understanding
data 10
2.3 A note on mathematical notation 10
2.3.1 Summation 11
2.3.2
Product 12
2.4 Probability 12
2.4.1 Relative frequencies 13
2.4.2
Defining random variables 14
2.4.3 Probability distribution functions
14
2.4.4 Cumulative distribution functions 16
2.4.5 Multivariate
probability density functions 17
2.4.6 The multivariate probability function
18
2.4.7 Marginal probability density functions 21
2.4.8 Conditional
probability density functions 21
2.4.9 Defining statistical independence
23
2.5 Properties of random variables 23
2.5.1 Expected value
25
2.5.1.1 Properties of expected values 26
2.5.2 Variance 28
2.5.2.1
Properties of variance 28
v
vi Contents
2.5.3 Covariance 30
2.5.3.1
Properties of covariance 31
2.5.4 The variance–covariance matrix 32
2.5.5
Correlation 33
2.5.5.1 Properties of the correlation coefficient 34
2.5.6
Correlation and variances 36
2.6 Sample population statistics 36
2.6.1 The
sample mean 36
2.6.2 The sample variance 38
2.6.3 The sample covariance
38
2.6.4 The sample correlation coefficient 39
2.7 Sampling error and
sampling distributions 39
2.8 Hypothesis testing 41
2.8.1 Defining the
null and alternative hypotheses 42
2.8.2 Selecting the test-statistic
44
2.8.3 Significance of the test and alpha 45
2.8.4 Performing the test
51
2.8.5 Example hypothesis test: the one sample t -test 51
2.9 Matrix
algebra 52
2.9.1 Transposition 53
2.9.2 Matrix addition and subtraction
53
2.9.3 Matrix multiplication by a scalar 54
2.9.4 Matrix multiplication
54
2.9.5 Determinants of matrices 55
2.9.6 The identity matrix 56
2.9.7
The inverse of a matrix 57
2.9.8 Linear and quadratic forms 58
2.9.9
Positive definite and negative definite matrices 59
2.10 Conclusion
59
Appendix 2A Measures of correlation or similarity 59
3 Choosing
62
3.1 Introduction 62
3.2 Individuals have preferences, and they count
63
3.3 Using knowledge of preferences and constraints in choice analysis
71
3.4 Setting up a behavioral choice rule 74
3.5 Deriving a basic choice
model 82
3.6 Concluding overview 86
4 Paradigms of choice data 88
4.1
Introduction 88
4.2 Data consistent with choice 89
4.3 Revealed preference
data 92
4.3.1 Choice-based sampling 95
Contents vii
4.4 Stated
preference (or stated choice) data 96
4.5 Further comparisons 97
4.6 Why
not use both RP and SP data? 98
4.7 Socio-demographic characteristic data
98
5 Processes in setting up stated choice experiments 100
5.1
Introduction 100
5.2 What is an experimental design? 100
5.2.1 Stage 1:
Problem definition refinement 103
5.2.2 Stage 2: Stimuli refinement
104
5.2.2.1 Refining the list of alternatives 104
5.2.2.2 Refining the
list of attributes and attribute levels 105
5.2.3 Stage 3: Experimental
design considerations 109
5.2.3.1 Labeled versus unlabeled experiments
112
5.2.3.2 Reducing the number of levels 114
5.2.3.3 Reducing the size of
experimental designs 115
5.2.3.4 Dummy and effects coding 119
5.2.3.5
Calculating the degrees of freedom required 122
5.2.3.6 Blocking the design
126
5.2.4 Stage 4: Generating experimental designs 127
5.2.4.1 Assigning
an attribute as a blocking variable 130
5.2.5 Stage 5: Allocating attributes
to design columns 131
5.3 A note on unlabeled experimental designs 150
5.4
Optimal designs 152
Appendix 5A Designing nested attributes 154
Appendix
5B Assignment of quantitative attribute-level labels 156
6 Choices in data
collection 161
6.1 Introduction 161
6.2 General survey instrument
construction 161
6.3 Questionnaires for choice data 166
6.3.1 Stage 6:
Generation of choice sets 166
6.3.2 Stage 7: Randomizing choice sets
170
6.3.3 Stage 8: Survey construction 172
6.3.3.1 Choice context
173
6.3.3.2 Use an example 174
6.3.3.3 Independence of choice sets
174
6.3.3.4 More than one choice 175
6.3.3.5 The no-choice or delay-choice
alternative 176
6.4 Revealed preferences in questionnaires 177
6.5 Studies
involving both RP and SP data 177
6.6 Using RP data in SP experiments: the
“current alternative” 178
6.7 Sampling for choice data: the theory
184
6.7.1 Simple random samples 185
viii Contents
6.7.2 Stratified
random sampling 190
6.7.3 Conclusion to the theory of calculating sample
sizes 192
6.8 Sampling for choice data: the reality 193
7 NLOGIT for
applied choice analysis: a primer 197
7.1 Introduction 197
7.2 About the
software 197
7.2.1 About NLOGIT 197
7.2.2 About NLOGIT/ACA 198
7.2.3
Installing NLOGIT/ACA 198
7.3 Starting NLOGIT/ACA and exiting after a session
198
7.3.1 Starting the program 198
7.3.2 Inputting the data 198
7.3.3
Reading data 200
7.3.4 The project file 200
7.3.5 Leaving your session
201
7.4 Using NLOGIT 201
7.5 How to get NLOGIT to do what you want
202
7.5.1 Using the Text Editor 202
7.5.2 Command format 204
7.5.3
Commands 205
7.5.4 Using the Project File Box 206
7.6 Useful hints and
tips 206
7.6.1 Limitations in NLOGIT (and NLOGIT/ACA) 207
7.7 NLOGIT
software 207
7.7.1 Support 208
7.7.2 The program installed on your
computer 208
7.7.3 Using NLOGIT/ACA in the remainder of the book
208
Appendix 7A Diagnostic and error messages 208
8 Handling choice data
218
8.1 Introduction 218
8.2 The basic data setup 219
8.2.1 Entering
multiple data sets: stacking and melding 222
8.2.2 Handling data on the
non-chosen alternative in RP data 222
8.2.3 Combining sources of data
224
8.2.4 Weighting on an exogenous variable 226
8.2.5 Handling rejection:
the “no option” 227
8.3 Entering data into NLOGIT 230
8.3.1 Entering data
directly into NLOGIT 230
8.3.2 Importing data into NLOGIT 232
8.3.2.1 The
Text/Document Editor 232
8.3.3 Reading data into NLOGIT 232
8.3.4 Writing
data into NLOGIT 235
8.3.5 Saving data sets 235
Contents ix
8.3.6
Loading data into NLOGIT 236
8.3.6.1 Changing the maximum default size of
the
Data Editor 236
8.4 Data entered into a single line 237
8.5 Data
cleaning 241
8.5.1 Testing for multicollinearity using NLOGIT 246
Appendix
8A Design effects coding 248
Appendix 8B Converting single-line data commands
250
9 Case study: mode-choice data 254
9.1 Introduction 254
9.2 Study
objectives 254
9.3 The pilot study 256
9.3.1 Pilot sample collection
263
9.3.1.1 Interviewer briefing 263
9.3.1.2 Interviewing 264
9.3.1.3
Analysis of contacts 264
9.3.1.4 Interviewer debriefing 265
9.4 The main
survey 265
9.4.1 The mode-choice experiment 267
9.4.1.1 Detailed
description of attributes 274
9.4.1.2 Using the showcards 276
9.4.2 RP
data 276
9.4.3 The household questionnaire 277
9.4.4 The commuter
questionnaire 277
9.4.5 The sample 278
9.4.5.1 Screening respondents
282
9.4.5.2 Interviewer briefing 283
9.4.5.3 Interviewing 283
9.4.5.4
Analysis of total contacts 283
9.4.5.5 Questionnaire check edit
284
9.4.5.6 Coding and check edit 284
9.4.5.7 Data entry 286
9.4.5.8
SPSS setup 286
9.5 The case study data 286
9.5.1 Formatting data in NLOGIT
289
9.5.2 Getting to know and cleaning the data 292
Appendix 9A The
contextual statement associated with the travel
choice experiment
296
Appendix 9B Mode-choice case study data dictionary 298
Appendix 9C
Mode-choice case study variable labels 302
10 Getting started modeling: the
basic MNL model 308
10.1 Introduction 308
10.2 Modeling choice in NLOGIT:
the MNL command 308
x Contents
10.3 Interpreting the MNL model output
316
10.3.1 Maximum likelihood estimation 317
10.3.2 Determining the sample
size and weighting criteria used 323
10.3.3 Interpreting the number of
iterations to model convergence 324
10.3.4 Determining overall model
significance 326
10.3.5 Comparing two models 335
10.3.6 Determining model
fit: the pseudo-R2 337
10.3.7 Type of response and bad data 339
10.3.8
Obtaining estimates of the indirect utility functions 339
10.3.8.1 Matrix:
LastDsta/LastOutput 343
10.4 Interpreting parameters for effects and dummy
coded variables 344
10.5 Handling interactions in choice models 352
10.6
Measures of willingness to pay 357
10.7 Obtaining choice probabilities for
the sample 360
10.8 Obtaining the utility estimates for the sample
366
Appendix 10A Handling unlabelled experiments 371
11 Getting more from
your model 374

12 Practical issues in
the application of choice models 437

Part II Advanced topics
13
Allowing for similarity of alternatives 479

14 Nested logit estimation
518

15 The mixed logit model 605

16 Mixed logit estimation 623
由于字数限制，后面几章详细目录略去了。