Interactive Effects of Major Response Facilitators

Practitioners and researchers have long been interested in achieving the highest response rate within resource constraints on their mailed surveys. Achieving this high response rate typically has required the selection of the right mix of response-facilitating techniques. The selection decisions were generally made under the assumption that the response-facilitating techniques act independently of one another. The authors have examined the validity of this independence assumption by identifying the major response facilitators in use and then analyzing the results of 531 independent mail survey studies. The authors conclude that major facilitators do not function independently. In fact, it was found that significant joint action interactive effects on response rates do exist. Practitioners and researchers who are faced with the survey design decisions of choosing response facilitators can receive assistance by reviewing the results of this diverse collection of reported experiences.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

DEGENERACY AND THE (MIS)INTERPRETATION OF SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING

Under conditions of degeneracy, sensitivity analysis information such as cost and right-hand-side ranges, which are produced by standard linear programming procedures used in commercial codes, can be misleading. From a managerial perspective, the interpretation of such information can be erroneous. In this paper we present these problems, explain their occurence with elementary examples, and discuss procedures for their resolution.     

DURATION MODELS FOR CREDIT RATING MIGRATION: EVIDENCE FROM THE FINANCIAL CRISIS

We introduce a specific duration model to analyze the prediction of the credit rating migration. We consider hazard rate processes based on multi‐state autoregressive conditional duration models. To take account of the economic context, we model the conditional mean of the duration between two ratings by means of a latent process. To this purpose, a dynamic‐ordered probit model is developed to describe the directions taken by the ratings in the presence of multiple states. As an illustration, we study the migration of credit rating during periods before and after the financial crisis. (JEL C14, C41, G24)     

Family Responsibilities,Productivity, and Earnings: A Study of Gender Differences Among Canadian Lawyers

This study examines whether men and women invest in different determinants of productivity and whether these investments affect productivity and salary in different ways. Hypotheses are tested from human and social capital theories that include more direct measures for family responsibilities and family-friendly firm arguments. Data from 670 law firm lawyers were used given they report a standardized measure of productivity in billable hours. Despite men investing more in their careers and women investing more in their families, both report similar productivity and their productivity is affected similarly by these factors. In addition, equally productive men and women are paid the same. The findings further our understanding of productivity and salary and the relevance of family responsibilities and family-friendly firms.

Housing interventions for homeless,pregnant/parenting women with addictions: a systematic review

Family homelessness is a growing problem in North America with most of these families headed by single women. Homeless women also experience high rates of pregnancy and addiction (drugs and alcohol). Housing interventions have been identified as key to addressing the complex needs of pregnant/early parenting, homeless women with addictions. The aim of this systematic review is to determine what housing models and programs for this population yield the best outcomes. We systematically searched 10 databases and retrieved eight articles describing four distinct studies that met the inclusion criteria for this review. Overall, improved outcomes were found for all intervention groups with the most recent and rigorous studies favoring models combining Housing First and case management. However, methodological limitations, study quality, and varied outcomes made comparison across studies difficult. Further research must be done in this area using standardized outcomes and rigorous designs to develop evidence-based best practice guidelines to address the unique needs of this population.     

College Students’ Perceptions of Media Portrayals of Divorce

This study takes an exploratory, qualitative approach to examine college students’ perceptions about divorce in the media. Findings suggest that participants perceived both positive and negative representations of divorced families in the media they consume, highlighting the complex nature of navigating and communicating through a divorce. Specifically, college students reported on aspects of the ex-spousal relationship, how children are affected, and the contrast between life pre- and postdivorce. An inductive theme analysis revealed several positive and negative media representations of divorce. Largely, college students perceive media messages about divorce to be accurate portrayals and impactful representations.     

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.