Technical Appendix to NCHRP Report 571: Standardized Procedures for Personal Travel Surveys (2007) / Chapter Skim
Currently Skimming:
Pages 171-190
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 171... ...
165 CHAPTER 9 9. Data Analysis and Expansion 9.1 A-1: ASSESSING SAMPLE BIAS 9.1.1 Definition Sample bias is a systematic error in survey sample data.
|
From page 172... ...
166 Common causes of survey bias are coverage error, non-response, instrument error, and temporal and/or geographic bias. Coverage error is caused primarily by an inadequate sample frame resulting in omission of valid cases, inclusion of invalid cases, or duplication of valid cases within the frame.
|
From page 173... ...
167 Geographic bias occurs because the location of economic activities that prompt travel are constantly changing. The location and intensity of economic activity in an urban area change and expand into areas that were unoccupied at the time of the survey, resulting in different travel patterns to those observed in the travel survey.
|
From page 174... ...
168 100) (11 2 x r sr nn RMSEPercent i jin i n j ij ijij jii ∑ ∑ −= .......................................................
|
From page 175... ...
169 those from external sources, while the remaining two only used internal estimates of missing households and trip under-reporting to factor their data. Of the three that did not perform weighting, one did account for the disproportional sampling incorporated in the design of the study, another estimated bias but did not report any adjustment to the data to compensate for the bias, and the third made no mention of identifying bias or estimating weights at all.
|
From page 176... ...
170 results except when the distribution of the marginals is skewed, in which case the row-and-column balancing method produced more plausible results. They also noted that the least squares procedure which requires solution through the use of LaGrange multipliers, is considerably more labor-intensive than the row-and-column balancing method.
|
From page 177... ...
171 likely to be selected under this system than households with fewer lines. The same applies if the sample frame is dwelling units and multiple households occupy some dwelling units.
|
From page 178... ...
172 process employed here. The adjustments in stage 2 represent a further improvement in stage 1 weights, but, because cell totals are used in the process, individual weights are lost.
|
From page 179... ...
173 collected data falls within those ranges. Where possible, cases in which variable values fall outside the feasible range of values are identified, and the persons re-contacted to establish the correct value.
|
From page 180... ...
174 observations combined, or separate ratios can be established for individual classes of variables. Imputed values are derived as follows: where: kiy , = imputed value of the i th observation of variable y in class k.
|
From page 181... ...
175 Expectation Maximization Expectation Maximization is a general method of obtaining maximum likelihood estimates when missing data are present (McLachlan and Krishnan, 1997)
|
From page 182... ...
176 the head of the household, the occupation of the head of the household, and the presence of children may be used to further distinguish households. Other variables such as levels of mobility of the household, household structure, transit use, could also be used to distinguish households from each other.
|
From page 183... ...
177 better analysis, hence, more information derived from the data (Axhausen, 2000; ICPSR, 2002)
|
From page 184... ...
178 4. Descriptive information – information that helps users locate and access information of potential interest; this is distinct from PDI.
|
From page 185... ...
179 The Inter-university Consortium of Political and Social Research (ICPSR) proposed the following guidelines for the deposition of any social science database into an archive: 1.
|
From page 186... ...
180 o Accuracy; o Cost; o Assessment; o Responsibility; and o References. • Technical Report: o Specification of the sampling frame; o Design of the survey; o Personnel and equipment; o Statistical analysis and computational procedure; o Accuracy of the survey; o Accuracy, completeness and adequacy of the sampling frame; o Results and comparison of findings with findings from other sources; o Cost of project; o Efficiency; and o Conclusions drawn (Mayo, 2000)
|
From page 187... ...
181 Data Documentation Personnel working on certain projects usually are the only individuals who possess the critical information about the data. When these people leave the organization(s)
|
From page 188... ...
182 Coverage Management history History of use Use history Source: National Archives of Australia, 1999. Table 83: Preservation Metadata Elements and Description Layers No.
|
From page 189... ...
183 Layers No. Element Repeatable Description, Example 7.3 Relation Description Yes Additional description if 7.1 and 7.2 do not provide enough information 3 Title The name given to the record, e.g., "National Household Travel Survey 1995" 3.1 Scheme Type No Naming convention used to title the records 3.2 Scheme Name No Naming of standard used for naming 3.3 Title Words No The Title 3.4 Alternative Yes Alternative name by which the record is known 4 Subject Subject of topic that concisely or accurately describes the record's content 4.1 Keyword No Highest level of a subject weighted title 4.2 Second Level Keyword Yes Intermediate Level of a Subject Based Title 4.3 Third Level Keyword Yes Third level of a subject based title 5 Description No Free text description of the content and purpose of the dataset or record 6 Language No The language of the content or the record 8 Coverage The jurisdictional, spatial and/or temporal characteristics of the content of the record 8.1 Place Name Yes Locations, regions or geographical areas covered by/discussed in the content of the record 8.2 Period Name Yes Time period covered by and/or discussed in the record Content 15 Management History 15.1 Event Date/Time Yes E.g., date edited 15.2 Event Type Yes E.g., update records, add entries 15.3 Event Description Yes E.g., replacing outliers with data from another source… 16 Use History 16.1 Use Date/Time Yes E.g., access date 16.2 Use Type Yes E.g., extraction 16.3 Use Description Yes E.g., extraction of data for paper on… 21 Links to other documentation files Yes E.g., server2//data_documentation.doc History of Use 22 General Dataset Characteristics 22.1 Number of Records No E.g., 23455 22.2 Dataset Classification No E.g., random sample 22.3 Dataset Classification Description No E.g., random sample of 5% of the population 23 Field Identifiers 23.1 Table Name Yes E.g., survey.xls 23.2 Field Name Yes E.g., workers 23.3 Field Size Yes E.g., single, double 23.4 Field Format Yes E.g., integer, real, Boolean 23.5 Decimal Places Yes E.g., 3 23.6 Field Description Yes E.g., 3 For Databases 23.7 Primary Key Yes E.g., Yes/No Source: National Archives of Australia, 1999
|
From page 190... ...
184 Spatial Data Another type of database resulting from transportation research is the spatial database. Standards for documentation of spatial databases have been developed by the Federal Geographic Data Committee (FGDC)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.