The Relative Importance of Health

This article seeks to extend Michalos’ [Social indicators research and health-related quality of life (QoL) research. Social Indicators Research, 65, 27–72, 2004] discussion on bridging social indicators research and health-related QoL (HRQoL) research through an examination of (1) the relative importance of satisfaction with one’s own health to another common measure of QoL—Life satisfaction, and (2) the relative importance of health in relation to other major life domains. Using data from two surveys, this article found that individuals may perceive health as most important in relation to other major life domains but satisfaction with one’s own health may not necessarily be the most important determining factor (in relation to satisfaction with other major life domains) of QoL as measured by life satisfaction. These findings support Michalos’ (Social indicators research and HRQoL research. Social Indicators Research, 65, 27–72, 2004) call for caution regarding the interpretation of research results on HRQoL since many HRQoL measures are measures of satisfaction with one’s own health and should not be considered as measures of QoL.     

Improved estimation methods for unrelated question randomized response techniques

The randomized response technique (RRT) is an important tool, commonly used to avoid biased answers in survey on sensitive issues by preserving the respondents’ privacy. In this paper, we introduce a data collection method for survey on sensitive issues combining both the unrelated-question RRT and the direct question design. The direct questioning method is utilized to obtain responses to a non sensitive question that is related to the innocuous question from the unrelated-question RRT. These responses serve as additional information that can be used to improve the estimation of the prevalence of the sensitive behavior. Furthermore, we propose two new methods for the estimation of the proportion of respondents possessing the sensitive attribute under a missing data setup. More specifically, we develop the weighted estimator and the weighted conditional likelihood estimator. The performances of our estimators are studied numerically and compared with that of an existing one. Both proposed estimators are more efficient than the Greenberg's estimator. We illustrate our methods using real data from a survey study on illegal use of cable TV service in Taiwan.     

Development of an expert system for abnormal operating procedures in a main control room

The study was conducted from the perspective of human factors engineering in order to compare the process that operators originally used to diagnose potential and actual faults with a process that included an expert system for diagnosing faults. The results of the study indicated that the existence of an expert system for fault diagnosis makes the task of fault diagnosis easier and reduces errors by quickly suggesting likely Abnormal Operating Procedures (AOPs).     

Aquatic landscape change,extirpations, and introductions in the Chicago Region

Urban Ecosystems - Prior to European settlement, swamps, marshes, lakes, and streams were the major landscape features of the Chicago region. Much of this has been altered or lost in the past...     

A Comparison of Normalized and Non-Normalized Multiplicative Subjective Importance Weighting in Quality of Life Measurement

Social Indicators Research - In quality of life (QOL) studies, importance weighting generally refers to the incorporation of perceived importance as a weighting factor into measures of QOL....     

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure

ABSTRACT

Social media data are increasingly used by researchers to gain insights on individuals’ behaviors and opinions. Platforms like Twitter provide access to individuals’ postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents’ Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user’s friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents’ tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.     

The extended GWMA control chart

This study extends the generally weighted moving average (GWMA) control chart by imitating the double exponentially weighted moving average (DEWMA) technique. The proposed chart is called the double generally weighted moving average (DGWMA) control chart. Simulation is employed to evaluate the average run length characteristics of the GWMA, DEWMA and DGWMA control charts. An extensive comparison of these control charts reveals that the DGWMA control chart with time-varying control limits is more sensitive than the GWMA and the DEWMA control charts for detecting medium shifts in the mean of a process when the shifts are between 0.5 and 1.5 standard deviations. Additionally, the GWMA control chart performs better when the mean shifts are below the 0.5 standard deviation, and the DEWMA control performs better when the mean shifts are above the 1.5 standard deviation. The design of the DGWMA control chart is also discussed.     

Nonparametric tests for scale shift at an unknown time point

A class of linear rank tests is suggested for testing a shift in scale at an unknown time point in a sequence of independent observations. The tests,based on inverse normal scores and on ordered exponential scores,are shown to be asymptotically as efficient as their distribution-oriented competitors. Critical values and powers for these two rank tests are also discussed.     

A new method for finding hierarchical subgroups from networks

We present a new method for decomposing a social network into an optimal number of hierarchical subgroups. With a perfect hierarchical subgroup defined as one in which every member is automorphically equivalent to each other, the method uses the REGGE algorithm to measure the similarities among nodes and applies the k-means method to group the nodes that have congruent profiles of dissimilarities with other nodes into various numbers of hierarchical subgroups. The best number of subgroups is determined by minimizing the intra-cluster variance of dissimilarity subject to the constraint that the improvement in going to more subgroups is better than a network whose n nodes are maximally dispersed in the n-dimensional space would achieve. We also describe a decomposability metric that assesses the deviation of a real network from the ideal one that contains only perfect hierarchical subgroups. Four well known network data sets are used to demonstrate the method and metric. These demonstrations indicate the utility of our approach and suggest how it can be used in a complementary way to Generalized Blockmodeling for hierarchical decomposition.