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E-raamat: Collecting, Managing, and Assessing Data Using Sample Surveys

(University of Sydney)
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  • Ilmumisaeg: 19-Jan-2012
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139209434
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 19-Jan-2012
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139209434
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Collecting, Managing, and Assessing Data Using Sample Surveys provides a thorough, step-by-step guide to the design and implementation of surveys. Beginning with a primer on basic statistics, the first half of the book takes readers on a comprehensive tour through the basics of survey design. Topics covered include the ethics of surveys, the design of survey procedures, the design of the survey instrument, how to write questions and how to draw representative samples. Having shown readers how to design surveys, the second half of the book discusses a number of issues surrounding their implementation, including repetitive surveys, the economics of surveys, web-based surveys, coding and data entry, data expansion and weighting, the issue of non-response, and the documenting and archiving of survey data. The book is an excellent introduction to the use of surveys for graduate students as well as a useful reference work for scholars and professionals.

Requiring no prior knowledge of statistics or surveys, this book provides a thorough, step-by-step guide to the design and implementation of surveys. It is an excellent introduction to the use of surveys for graduate students as well as a useful reference work for scholars and professionals.

Arvustused

'Drawing on the author's wealth of knowledge and experience, this excellent book provides a comprehensive treatment of every aspect involved in preparing for, carrying out, cleaning and archiving data from a population survey. It is very clearly written, fully illustrated and enables the reader to jump the learning curve.' Peter Jones, University College London 'Data are fundamental to our understanding and stewardship of the world around us. Peter Stopher's new book is an extensive, accessible and highly informative reference work for those engaged in data acquisition from human subjects. Covering all aspects of survey design and distribution, data management and archiving, this unique treatise is more comprehensive than one can find elsewhere. Offering something new for the most accomplished of data gatherers, this book serves as a remarkable reference, as well as a textbook.' Kara Kockelman, University of Texas, Austin 'This is a 'must have' reference for all people who want to design, conduct and/or communicate market research. It addresses material that builds on more traditional texts but adds value in many ways. In particular, it sheds light on the critical importance of correct sampling and valid questionnaire design as the core of good research. Peter Stopher leaves no stone unturned in his quest to ensure that researchers understand what is needed in their planning, execution and delivery. He provides a comprehensive, rigorous and evidence-based coverage of the basics of survey methodology, with enough nuggets of more advanced and often-neglected topics to be of interest to the experienced practitioner.' Philip Mitchell-Taverner, Taverner Research

Muu info

A step-by-step guide to the design and implementation of surveys.
List of figures
xix
List of tables
xxii
Acknowledgements xxv
1 Introduction
1(5)
1.1 The purpose of this book
1(1)
1.2 Scope of the book
2(2)
1.3 Survey statistics
4(2)
2 Basic statistics and probability
6(58)
2.1 Some definitions in statistics
6(2)
2.1.1 Censuses and surveys
7(1)
2.2 Describing data
8(56)
2.2.1 Types of scales
8(1)
Nominal scales
8(1)
Ordinal scales
9(1)
Interval scales
9(1)
Ratio scales
10(1)
Measurement scales
10(1)
2.2.2 Data presentation: graphics
11(5)
2.2.3 Data presentation: non-graphical
16(1)
Measures of magnitude
17(1)
Frequencies and proportions
17(4)
Central measures of data
21(13)
Measures of dispersion
34(11)
The normal distribution
45(1)
Some useful properties of variances and standard deviations
46(1)
Proportions or probabilities
47(1)
Data transformations
48(2)
Covariance and correlation
50(1)
Coefficient of variation
51(2)
Other measures of variability
53(9)
Alternatives to Sturges' rule
62(2)
3 Basic issues in surveys
64(17)
3.1 Need for survey methods
64(1)
3.1.1 A definition of sampling methodology
65(1)
3.2 Surveys and censuses
65(3)
3.2.1 Costs
66(1)
3.2.2 Time
67(1)
3.3 Representativeness
68(3)
3.3.1 Randomness
69(1)
3.3.2 Probability sampling
70(1)
Sources of random numbers
71(1)
3.4 Errors and bias
71(7)
3.4.1 Sample design and sampling error
73(1)
3.4.2 Bias
74(4)
3.4.3 Avoiding bias
78(1)
3.5 Some important definitions
78(3)
4 Ethics of surveys of human populations
81(10)
4.1 Why ethics?
81(1)
4.2 Codes of ethics or practice
82(2)
4.3 Potential threats to confidentiality
84(2)
4.3.1 Retaining detail and confidentiality
85(1)
4.4 Informed consent
86(3)
4.5 Conclusions
89(2)
5 Designing a survey
91(13)
5.1 Components of survey design
91(2)
5.2 Defining the survey purpose
93(9)
5.2.1 Components of survey purpose
94(1)
Data needs
94(3)
Comparability or innovation
97(2)
Defining data needs
99(1)
Data needs in human subject surveys
99(1)
Survey timing
100(1)
Geographic bounds for the survey
101(1)
5.3 Trade-offs in survey design
102(2)
6 Methods for conducting surveys of human populations
104(23)
6.1 Overview
104(1)
6.2 Face-to-face interviews
105(2)
6.3 Postal surveys
107(1)
6.4 Telephone surveys
108(3)
6.5 Internet surveys
111(1)
6.6 Compound survey methods
112(8)
6.6.1 Pre-recruitment contact
112(1)
6.6.2 Recruitment
113(2)
Random digit dialling
115(2)
6.6.3 Survey delivery
117(1)
6.6.4 Data collection
118(1)
6.6.5 An example
119(1)
6.7 Mixed-mode surveys
120(5)
6.7.1 Increasing response and reducing bias
123(2)
6.8 Observational surveys
125(2)
7 Focus groups
127(10)
7.1 Introduction
127(1)
7.2 Definition of a focus group
128(4)
7.2.1 The size and number of focus groups
128(1)
7.2.2 How a focus group functions
129(2)
7.2.3 Analysing the focus group discussions
131(1)
7.2.4 Some disadvantages of focus groups
131(1)
7.3 Using focus groups to design a survey
132(2)
7.4 Using focus groups to evaluate a survey
134(1)
7.5 Summary
135(2)
8 Design of survey instruments
137(40)
8.1 Scope of this chapter
137(1)
8.2 Question type
137(8)
8.2.1 Classification and behaviour questions
138(1)
Mitigating threatening questions
139(3)
8.2.2 Memory or recall error
142(3)
8.3 Question format
145(5)
8.3.1 Open questions
145(1)
8.3.2 Field-coded questions
146(1)
8.3.3 Closed questions
147(3)
8.4 Physical layout of the survey instrument
150(27)
8.4.1 Introduction
150(3)
8.4.2 Question ordering
153(1)
Opening questions
153(1)
Body of the survey
154(4)
The end of the questionnaire
158(1)
8.4.3 Some general issues on question layout
159(1)
Overall format
160(1)
Appearance of the survey
161(1)
Front cover
162(1)
Spatial layout
163(1)
Choice of typeface
164(2)
Use of colour and graphics
166(3)
Question numbering
169(1)
Page breaks
170(1)
Repeated questions
171(1)
Instructions
172(2)
Show cards
174(1)
Time of the interview
174(1)
Precoding
174(1)
End of the survey
175(1)
Some final comments on questionnaire layout
176(1)
9 Design of questions and question wording
177(22)
9.1 Introduction
177(1)
9.2 Issues in writing questions
178(10)
9.2.1 Requiring an answer
178(2)
9.2.2 Ready answers
180(1)
9.2.3 Accurate recall and reporting
181(1)
9.2.4 Revealing the data
182(1)
9.2.5 Motivation to answer
183(1)
9.2.6 Influences on response categories
184(1)
9.2.7 Use of categories and other responses
185(2)
Ordered and unordered categories
187(1)
9.3 Principles for writing questions
188(9)
9.3.1 Use simple language
189(1)
9.3.2 Number of words
190(1)
9.3.3 Avoid using vague words
191(2)
9.3.4 Avoid using `Tick all that apply' formats
193(1)
9.3.5 Develop response categories that are mutually exclusive and exhaustive
193(2)
9.3.6 Make sure that questions are technically correct
195(1)
9.3.7 Do not ask respondents to say `Yes' in order to say `No'
196(1)
9.3.8 Avoid double-barrelled questions
196(1)
9.4 Conclusion
197(2)
10 Special issues for qualitative and preference surveys
199(12)
10.1 Introduction
199(1)
10.2 Designing qualitative questions
199(7)
10.2.1 Scaling questions
200(6)
10.3 Stated response questions
206(4)
10.3.1 The hypothetical situation
206(1)
10.3.2 Determining attribute levels
207(1)
10.3.3 Number of choice alternatives or scenarios
207(1)
10.3.4 Other issues of concern
208(1)
Data inconsistency
208(1)
Lexicographic responses
209(1)
Random responses
209(1)
10.4 Some concluding comments on stated response survey design
210(1)
11 Design of data collection procedures
211(40)
11.1 Introduction
211(1)
11.2 Contacting respondents
211(10)
11.2.1 Pre-notification contacts
211(2)
11.2.2 Number and type of contacts
213(1)
Nature of reminder contacts
213(2)
Postal surveys
215(1)
Postal surveys with telephone recruitment
216(1)
Telephone interviews
217(2)
Face-to-face interviews
219(1)
Internet surveys
220(1)
11.3 Who should respond to the survey?
221(4)
11.3.1 Targeted person
221(2)
11.3.2 Full household surveys
223(1)
Proxy reporting
224(1)
11.4 Defining a complete response
225(4)
11.4.1 Completeness of the data items
226(2)
11.4.2 Completeness of aggregate sampling units
228(1)
11.5 Sample replacement
229(6)
11.5.1 When to replace a sample unit
229(4)
11.5.2 How to replace a sample
233(2)
11.6 Incentives
235(5)
11.6.1 Recommendations on incentives
236(4)
11.7 Respondent burden
240(10)
11.7.1 Past experience
241(1)
11.7.2 Appropriate moment
242(1)
11.7.3 Perceived relevance
242(1)
11.7.4 Difficulty
243(1)
Physical difficulty
243(1)
Intellectual difficulty
244(1)
Emotional difficulty
245(1)
Reducing difficulty
246(1)
11.7.5 External factors
246(1)
Attitudes and opinions of others
246(1)
The `feel good' effect
247(1)
Appropriateness of the medium
248(1)
11.7.6 Mitigating respondent burden
248(2)
11.8 Concluding comments
250(1)
12 Pilot surveys and pretests
251(14)
12.1 Introduction
251(1)
12.2 Definitions
252(3)
12.3 Selecting respondents for pretests and pilot surveys
255(7)
12.3.1 Selecting respondents
255(3)
12.3.2 Sample size
258(1)
Pilot surveys
258(3)
Pretests
261(1)
12.4 Costs and time requirements of pretests and pilot surveys
262(2)
12.5 Concluding comments
264(1)
13 Sample design and sampling
265(72)
13.1 Introduction
265(1)
13.2 Sampling frames
266(2)
13.3 Random sampling procedures
268(2)
13.3.1 Initial considerations
268(1)
13.3.2 The normal law of error
269(1)
13.4 Random sampling methods
270(44)
13.4.1 Simple random sampling
271(1)
Drawing the sample
271(2)
Estimating population statistics and sampling errors
273(3)
Example
276(3)
Sampling from a finite population
279(1)
Sampling error of ratios and proportions
279(2)
Defining the sample size
281(2)
Examples
283(2)
13.4.2 Stratified sampling
285(1)
Types of stratified samples
285(2)
Study domains and strata
287(1)
Weighted means and variances
287(2)
Stratified sampling with a uniform sampling fraction
289(1)
Drawing the sample
289(1)
Estimating population statistics and sampling errors
290(1)
Pre- and post-stratification
291(2)
Example
293(1)
Equal allocation
294(1)
Summary of proportionate sampling
295(1)
Stratified sampling with variable sampling fraction
295(1)
Drawing the sample
295(1)
Estimating population statistics and sampling errors
296(1)
Non-coincident study domains and strata
296(1)
Optimum allocation and economic design
297(1)
Example
298(2)
Survey costs differing by stratum
300(1)
Example
301(2)
Practical issues in drawing disproportionate samples
303(2)
Concluding comments on disproportionate sampling
305(1)
13.4.3 Multistage sampling
305(1)
Drawing a multistage sample
306(1)
Requirements for multistage sampling
307(1)
Estimating population values and sampling statistics
308(1)
Example
309(5)
Concluding comments on multistage sampling
314(1)
13.5 Quasi-random sampling methods
314(20)
13.5.1 Cluster sampling
316(1)
Equal clusters: population values and standard errors
317(2)
Example
319(2)
The effects of clustering
321(1)
Unequal clusters: population values and standard errors
322(2)
Random selection of unequal clusters
324(1)
Example
325(1)
Stratified sampling of unequal clusters
326(1)
Paired selection of unequal-sized clusters
327(1)
13.5.2 Systematic sampling
328(1)
Population values and standard errors in a systematic sample
328(1)
Simple random model
329(1)
Stratified random model
329(1)
Paired selection model
329(1)
Successive difference model
330(1)
Example
330(3)
13.5.3 Choice-based sampling
333(1)
13.6 Non-random sampling methods
334(2)
13.6.1 Quota sampling
334(1)
13.6.2 Intentional, judgemental, or expert samples
335(1)
13.6.3 Haphazard samples
335(1)
13.6.4 Convenience samples
336(1)
13.7 Summary
336(1)
14 Repetitive surveys
337(19)
14.1 Introduction
337(1)
14.2 Non-overlapping samples
338(1)
14.3 Incomplete overlap
339(2)
14.4 Subsampling on the second and subsequent occasions
341(1)
14.5 Complete overlap: a panel
342(1)
14.6 Practical issues in designing and conducting panel surveys
343(5)
14.6.1 Attrition
344(1)
Replacement of panel members lost by attrition
345(1)
Reducing losses due to attrition
346(1)
14.6.2 Contamination
347(1)
14.6.3 Conditioning
348(1)
14.7 Advantages and disadvantages of panels
348(1)
14.8 Methods for administering practical panel surveys
349(3)
14.9 Continuous surveys
352(4)
15 Survey economics
356(9)
15.1 Introduction
356(1)
15.2 Cost elements in survey design
357(2)
15.3 Trade-offs in survey design
359(4)
15.3.1 Postal surveys
360(1)
15.3.2 Telephone recruitment with a postal survey with or without telephone retrieval
361(1)
15.3.3 Face-to-face interview
362(1)
15.3.4 More on potential trade-offs
362(1)
15.4 Concluding comments
363(2)
16 Survey implementation
365(20)
16.1 Introduction
365(1)
16.2 Interviewer selection and training
365(5)
16.2.1 Interviewer selection
365(3)
16.2.2 Interviewer training
368(1)
16.2.3 Interviewer monitoring
369(1)
16.3 Record keeping
370(2)
16.4 Survey supervision
372(1)
16.5 Survey publicity
373(1)
16.5.1 Frequently asked questions, fact sheet, or brochure
374(1)
16.6 Storage of survey forms
374(3)
16.6.1 Identification numbers
375(2)
16.7 Issues for surveys using posted materials
377(1)
16.8 Issues for surveys using telephone contact
377(4)
16.8.1 Caller ID
378(1)
16.8.2 Answering machines
378(2)
16.8.3 Repeated requests for callback
380(1)
16.9 Data on incomplete responses
381(1)
16.10 Checking survey responses
382(1)
16.11 Times to avoid data collection
383(1)
16.12 Summary comments on survey implementation
383(2)
17 Web-based surveys
385(16)
17.1 Introduction
385(3)
17.2 The internet as an optional response mechanism
388(1)
17.3 Some design issues for Web surveys
389(9)
17.3.1 Differences between paper and internet surveys
389(1)
17.3.2 Question and response
390(2)
17.3.3 Ability to fill in the Web survey in multiple sittings
392(1)
17.3.4 Progress tracking
393(1)
17.3.5 Pre-filled responses
394(1)
17.3.6 Confidentiality in Web-based surveys
395(1)
17.3.7 Pictures, maps, etc. on Web surveys
395(1)
Animation in survey pictures and maps
396(1)
17.3.8 Browser software
396(1)
User interface design
396(1)
Creating mock-ups
397(1)
Page loading time
398(1)
17.4 Some design principles for Web surveys
398(1)
17.5 Concluding comments
399(2)
18 Coding and data entry
401(17)
18.1 Introduction
401(1)
18.2 Coding
402(11)
18.2.1 Coding of missing values
402(1)
18.2.2 Use of zeros and blanks in coding
403(1)
18.2.3 Coding consistency
404(1)
Binary variables
404(1)
Numeric variables
404(1)
18.2.4 Coding complex variables
405(1)
18.2.5 Geocoding
406(2)
Requesting address details for other places than home
408(1)
Pre-coding of buildings
409(1)
Interactive gazetteers
410(1)
Other forms of geocoding assistance
410(1)
Locating by mapping software
411(1)
18.2.6 Methods for creating codes
412(1)
18.3 Data entry
413(3)
18.4 Data repair
416(2)
19 Data expansion and weighting
418(13)
19.1 Introduction
418(1)
19.2 Data expansion
419(2)
19.2.1 Simple random sampling
419(1)
19.2.2 Stratified sampling
419(1)
19.2.3 Multistage sampling
420(1)
19.2.4 Cluster samples
420(1)
19.2.5 Other sampling methods
421(1)
19.3 Data weighting
421(8)
19.3.1 Weighting with unknown population totals
422(1)
An example
423(1)
A second example
424(2)
19.3.2 Weighting with known populations
426(1)
An example
427(2)
19.4 Summary
429(2)
20 Nonresponse
431(33)
20.1 Introduction
431(1)
20.2 Unit nonresponse
432(18)
20.2.1 Calculating response rates
432(1)
Classifying responses to a survey
433(2)
Calculating response rates
435(5)
20.2.2 Reducing nonresponse and increasing response rates
440(1)
Design issues affecting nonresponse
440(2)
Survey publicity
442(1)
Use of incentives
442(1)
Use of reminders and repeat contacts
443(1)
Personalisation
444(1)
Summary
445(1)
20.2.3 Nonresponse surveys
445(5)
20.3 Item nonresponse
450(14)
20.3.1 Data repair
450(1)
Flagging repaired variables
451(1)
Inference
452(1)
Imputation
452(1)
Historical imputation
453(1)
Average imputation
454(1)
Ratio imputation
454(1)
Regression imputation
455(1)
Cold-deck imputation
456(1)
Hot-deck imputation
457(1)
Expectation maximisation
457(1)
Multiple imputation
458(1)
Imputation using neural networks
458(2)
Summary of imputation methods
460(1)
20.3.2 A final note on item nonresponse
460(1)
Strategies to obtain age and income
461(1)
Age
461(1)
Income
462(2)
21 Measuring data quality
464(14)
21.1 Introduction
464(1)
21.2 General measures of data quality
464(5)
21.2.1 Missing value statistic
465(1)
21.2.2 Data cleaning statistic
466(1)
21.2.3 Coverage error
467(1)
21.2.4 Sample bias
468(1)
21.3 Specific measures of data quality
469(3)
21.3.1 Non-mobility rates
469(1)
21.3.2 Trip rates and activity rates
470(1)
21.3.3 Proxy reporting
471(1)
21.4 Validation surveys
472(4)
21.4.1 Follow-up questions
473(2)
21.4.2 Independent measurement
475(1)
21.5 Adherence to quality measures and guidance
476(2)
22 Future directions in survey procedures
478(21)
22.1 Dangers of forecasting new directions
478(1)
22.2 Some current issues
478(11)
22.2.1 Reliance on telephones
478(1)
Threats to the use of telephone surveys
479(2)
Conclusions on reliance on telephones
481(1)
22.2.2 Language and literacy
481(1)
Language
481(2)
Literacy
483(3)
22.2.3 Mixed-mode surveys
486(1)
22.2.4 Use of administrative data
487(1)
22.2.5 Proxy reporting
488(1)
22.3 Some possible future directions
489(10)
22.3.1 A GPS survey as a potential substitute for a household travel survey
493(2)
The effect of multiple observations of each respondent on sample size
495(4)
23 Documenting and archiving
499(12)
23.1 Introduction
499(1)
23.2 Documentation or the creation of metadata
499(7)
23.2.1 Descriptive metadata
500(3)
23.2.2 Preservation metadata
503(1)
23.2.3 Geospatial metadata
503(3)
23.3 Archiving of data
506(5)
References 511(14)
Index 525
Peter Stopher is Professor of Transport Planning at the Institute of Transport and Logistics Studies at the University of Sydney. He has also been a professor at Northwestern University, Cornell University, McMaster University and Louisiana State University. Professor Stopher has developed a substantial reputation in the field of data collection, particularly for the support of travel forecasting and analysis. He pioneered the development of travel and activity diaries as a data-collection mechanism, and has written extensively on issues of sample design, data expansion, nonresponse biases and measurement issues.