Chapter 20: What do interviewers learn? Changes in interview length and interviewer behaviors over the field period. Appendix 20

Appendix 20A Full Model Coefficients and Standard Errors Predicting Count of Questions with Individual Interviewer Behaviors, Two-level Multilevel Poisson Models with Number of Questions Asked as Exposure Variable, WLT1 and WLT2 Analytic strategyTable A20A.1 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Exact Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.2 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Nondirective Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.3 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Adequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.4 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Clarification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.5 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.6 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Stuttering During Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.7 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Disfluencies with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.8 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Pleasant Talk with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.9 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Any Task-Related Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.10 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Laughter with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.11 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Minor Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.12 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Major Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.13 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Directive Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.14 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Inadequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.15 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Interruptions with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Appendix 20B Full Model Coefficients and Standard Errors Predicting Interview Length with Sets of Interviewer Behaviors, Two-level Multilevel Linear Models, WLT1 and WLT2 Table A20B.1 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, No Interviewer Behaviors, WLT1 and WLT2 Table A20B.2 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Standardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.3 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Inefficiency Interviewer Behaviors, WLT1 and WLT2 Table A20B.4 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Nonstandardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.5 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including All Interviewer Behaviors, WLT1 and WLT2 Appendix 20C Mediation Models for Each Individual Interviewer Behavior Table A20C.1 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today 1 Table A20C.2 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today

Data types

A Mathematical interpretation is given to the notion of a data type. The main novelty is in the generality of the mathematical treatment which allows procedural data types and circularly defined data types. What is meant by data type is pretty close to what any computer scientist would understand by this term or by data structure, type, mode, cluster, class. The mathematical treatment is the conjunction of the ideas of D. Scott on the solution of domain equations (Scott (71), (72) and (76)) and the initiality property noticed by the ADJ group (ADJ (75), ADJ (77)). The present work adds operations to the data types proposed by Scott and generalizes the data types of ADJ to procedural types and arbitrary circular type definitions. The advantages of a mathematical interpretation of data types are those of mathematical semantics in general : throwing light on some ill-understood constructs in high-level programming languages, easing the task of writing correct programs and making possible proofs of correctness for programs or implementations"

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Extended atmospheres of outer planet satellites and comets

An analysis of the extended atmospheres of outer planet satellites and comets is made. Primary emphasis is placed on cometary atmospheres because of the return of Comet P/Halley. As part of a collaborative effort with A.I.F. Stewart, observations of the hydrogen coma of Comet P/Giacobini-Zinner obtained from the Pioneer Venus Orbiter ultraviolet spectrometer (PVOUVS) were successfully analyzed at AER and are reported. In addition, significant pre-modeling and post-modeling activities to support and analyze the PVOUVS observations of Comet P/Halley successfully acquired in late 1985 and early 1986 are also discussed. Progress in model preparation for third-year analysis of the Voyager UVS Lyman-alpha brightness distribution emitted by hydrogen atoms in the Saturn system is also summarized

Extended atmospheres of outer planet satellites and comets

The new cometary hydrogen particle-trajectory model, completed last year, has been used successfully to analyze observations of Comet P/Giacobini-Zinner. The Pioneer Venus Orbiter Ultraviolet Spectrometer observed the comet at 1216 A (hydrogen Lyman-a) on 11 September 1985 when the comet was 1.03 AU from the Sun and 1.09 AU from Venus. The analysis implies a production rate at 1.03 AU 2.3 x 10 to the 28th power/sec of the water molecules which photodissociate to produce the observed hydrogen. An upper limit for the H2O production rate of Comet P/Halley of 5 x 10 to the 28th power/sec at 2.60 AU was also obtained from the Pioneer Venus instrument

Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Outer satellite atmospheres: Their extended nature and planetary interactions

Significant progress in model analysis of data for the directional features of the Io sodium cloud is reported and appears to provide some support for a satellite emission mechanism that is driven by a magnetospheric wind. A number of model calculations for the two dimensional intensity morphology of the Io sodium (region B) cloud are compared with six observations. Results of this comparison support tentative conclusions regarding the satellite emission conditions, the role of the plasma torus and the sodium atom escape flux. Progress in updating the Titan hydrogen torus model is also discussed

Outer satellite atmospheres: Their nature and planetary interactions

Significant insights regarding the nature and interactions of Io and the planetary magnetosphere were gained through modeling studies of the spatial morphology and brightness of the Io sodium cloud. East-west intensity asymmetries in Region A are consistent with an east-west electric field and the offset of the magnetic and planetary-spin axes. East-west orbital asymmetries and the absolute brightness of Region B suggest a low-velocity (3 km/sec) satellite source of 1 to 2 x 10(26) sodium atoms/sec. The time-varying spatial structure of the sodium directional features in near Region C provides direct evidence for a magnetospheric-wind-driven escape mechanism with a high-velocity (20 km/sec) source of 1 x 10(26) atoms/sec and a flux distribution enhanced at the equator relative to the poles. A model for the Io potassium cloud is presented and analysis of data suggests a low velocity source rate of 5 x 10(24) atoms/sec. To understand the role of Titan and non-Titan sources for H atoms in the Saturn system, the lifetime of hydrogen in the planetary magnetosphere was incorporated into the earlier Titan torus model of Smyth (1981) and its expected impact discussed. A particle trajectory model for cometary hydrogen is presented and applied to the Lyman-alpha distribution of Comet Kohoutek (1973XII)

Inferring an Indeterminate String from a Prefix Graph

An \itbf{indeterminate string} (or, more simply, just a \itbf{string}) \s{x} = \s{x}[1..n] on an alphabet $\Sigma$ is a sequence of nonempty subsets of $\Sigma$. We say that \s{x}[i_1] and \s{x}[i_2] \itbf{match} (written \s{x}[i_1] \match \s{x}[i_2]) if and only if \s{x}[i_1] \cap \s{x}[i_2] \ne \emptyset. A \itbf{feasible array} is an array \s{y} = \s{y}[1..n] of integers such that \s{y}[1] = n and for every $i \in 2..n$, \s{y}[i] \in 0..n\- i\+ 1. A \itbf{prefix table} of a string \s{x} is an array \s{\pi} = \s{\pi}[1..n] of integers such that, for every $i \in 1..n$, \s{\pi}[i] = j if and only if \s{x}[i..i\+ j\- 1] is the longest substring at position $i$ of \s{x} that matches a prefix of \s{x}. It is known from \cite{CRSW13} that every feasible array is a prefix table of some indetermintate string. A \itbf{prefix graph} \mathcal{P} = \mathcal{P}_{\s{y}} is a labelled simple graph whose structure is determined by a feasible array \s{y}. In this paper we show, given a feasible array \s{y}, how to use \mathcal{P}_{\s{y}} to construct a lexicographically least indeterminate string on a minimum alphabet whose prefix table \s{\pi} = \s{y}.Comment: 13 pages, 1 figur