Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark

Traditional survey design draws a representative sample and implements post-survey weighting adjustments to compensate for nonresponse. When survey participation decline renders respondents nonrepresentative, the effectiveness of post-survey weighting adjustment becomes uncertain. Recent developments to improve respondent representativeness via adaptive data collection design have delivered promising results on bias reduction. This dissertation develops a new adaptive design to improve survey data quality, by capitalizing on a benchmark data which captures the target population. The basic idea is to adaptively draw samples that lead to representative respondents; and to compensate for nonrespondents by benchmarked imputation procedures. Respondent representativeness is enhanced by the sampling procedure as opposed to data collection, eliminating costs of nonresponse follow-up and inferential complexity due to varying data collection protocols. The new adaptive design consists of benchmarked sequential sampling (BSS) and benchmarked multiple imputation (B-MI) procedures. The new design first improves respondent representativeness by BSS, which conforms either the frame variables alone (BSS-Z) or both frame and survey covariate information (BSS-X) to those of the benchmark. With improved respondent representativeness, the benchmarked multiple imputation recovers the population information, leading to better quality survey estimates that are less susceptible to the unknown nonresponse pattern. This design applies to surveys with rich micro-level auxiliary data and surveys that use respondents of other surveys as sampling frame. The BSS-Z method is demonstrated using the National Health Interview Survey and Behavior Risk Factor Surveillance System; the BSS-X and the benchmarked MI methods are demonstrated using the American Community Survey, the Current Population Survey, and the Census Planning Database. An evaluation is done between the new design of adaptive sampling and imputation and the traditional design of fixed sampling and weighting (generalized regression estimator). To assess respondent representativeness, data from the new design is compared to those of the benchmark in marginal, conditional, and descriptive statistics. To assess the quality of the survey inference, a sample mean is calculated along with its root mean square error (RMSE), bias and coverage rate. To assess whether a design is of better value, a cost-effectiveness measure is derived from RMSE and a new cost model.PHDSurvey MethodologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137173/1/julialee_1.pd

Improving Neural Question Generation using Answer Separation

Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer. By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used. We also propose a new module termed keyword-net, which helps the model better capture the key information in the target answer and generate an appropriate question. Experimental results demonstrate that our answer separation method significantly reduces the number of improper questions which include answers. Consequently, our model significantly outperforms previous state-of-the-art NQG models.Comment: The paper is accepted to AAAI 201

Changes in Academics’ Identity, Job Satisfaction, and Job Stress between 1992 and 2018 in South Korea

This study analyzed the association between academic identity, job satisfaction and stress over the last three decades. This study found that most Korean academics have a dual identity as researcher and teacher but tend to prefer research to teaching. In addition, we found that performance-based management practices at universities have strengthened the researcher identity during last three decades. Finally, this study found that while academic identity is not associated with academics’ job satisfaction, those who identify as researchers are likely to experience higher stress. This study has implications for studying academic identity in relation to the critical topics of job satisfaction and job stress, which are core concepts of organizational studies.This research is supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2017021672)

Physical Activity Recognition based on Rotated Acceleration Data using Orientation Filter

The purpose of the study was to examine the accuracy of physical activity (PA) classification algorithms using a rotational analysis

The Language of Histone Crosstalk

It has been suggested that a specific pattern of histone posttranslational modifications and their crosstalk may constitute a code that determines transcriptional outcomes. However, recent studies indicate that histone modifications have context-dependent effects, making their interplay more like a language within the chromatin signaling pathway than a code