Extending systems thinking in planning and evaluation using group concept mapping and system dynamics to tackle complex problems

Group concept mapping (GCM) has been successfully employed in program planning and evaluation for over 25 years. The broader set of systems thinking methodologies (of which GCM is one), have only recently found their way into the field. We present an overview of systems thinking emerging from a system dynamics (SD) perspective, and illustrate the potential synergy between GCM and SD. As with GCM, participatory processes are frequently employed when building SD models; however, it can be challenging to engage a large and diverse group of stakeholders in the iterative cycles of divergent thinking and consensus building required, while maintaining a broad perspective on the issue being studied. GCM provides a compelling resource for overcoming this challenge, by richly engaging a diverse set of stakeholders in broad exploration, structuring, and prioritization. SD provides an opportunity to extend GCM findings by embedding constructs in a testable hypothesis (SD model) describing how system structure and changes in constructs affect outcomes over time. SD can be used to simulate the hypothesized dynamics inherent in GCM concept maps. We illustrate the potential of the marriage of these methodologies in a case study of BECOMING, a federally-funded program aimed at strengthening the cross-sector system of care for youth with severe emotional disturbances.     

Pseudo-Bayes and pseudo-empirical Bayes estimators in randomized response sampling

In this article, new pseudo-Bayes and pseudo-empirical Bayes estimators for estimating the proportion of a potentially sensitive attribute in a survey sampling have been introduced. The proposed estimators are compared with the recent estimator proposed by Odumade and Singh [Efficient use of two decks of cards in randomized response sampling, Comm. Statist. Theory Methods 38 (2009), pp. 439–446] and Warner [Randomized response: A survey technique for eliminating evasive answer bias, J. Amer. Statist. Assoc. 60 (1965), pp. 63–69].     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.     

Examining the interplay of an organization's prior reputation,CEO's visibility,and immediate response to a crisis

In one of only a few crisis communication research studies taking a relational approach, examining the effects of a company's prior reputation in publics’ responses to a given crisis situation, Lyons and Cameron (2004) found that both reputation and response profoundly affected publics’ attitude and behavioral intentions toward an organization involved in a crisis situation, using hypothetical, fictitious organizations and crises. Using actual organizations and crises, our research team designed a 2 (reputation: good vs. bad) × 2 (crisis response: apologetic vs. defensive) × 2 (CEO visibility in immediate crisis response: visible vs. invisible) within-subjects experiment (N = 102) to examine the variances in stakeholders’ attitudes and behavioral intentions toward a company after being exposed to online video that delivered a corporate crisis response. Findings were counter-intuitive: a defensive response to a crisis is as acceptable to crisis stakeholders as an apologetic response if the CEO is visible (or audible) in the response and if the pre-crisis company-stakeholder reputation is positive. Good reputation, defensive crisis response and CEO visibility in immediate response to a crisis resulted in the best stakeholder attitudes and purchase intentions.     

Testing for parameter constancy in the time series direction in panel data models

We propose tests for parameter constancy in the time series direction in panel data models. We construct a locally best invariant test based on Tanaka [Time series analysis: nonstationary and noninvertible distribution theory. New York: Wiley; 1996] and an asymptotically point optimal test based on Elliott and Müller [Efficient tests for general persistent time variation in regression coefficients. Rev Econ Stud. 2006;73:907–940]. We derive the limiting distributions of the test statistics as T→∞ while N is fixed, and calculate the critical values by applying numerical integration and response surface regression. Simulation results show that the proposed tests perform well if we apply them appropriately.     

Seeing the system: Systemic gender knowledge to support transformational change towards gender equality in science

Scholars agree that gender inequality is systemic and that participants in gender equality interventions need knowledge on gender inequality processes. However, a detailed view on the specific characteristics of this knowledge is as yet missing. This article aims to contribute to gender equality interventions by conceptualizing and visualizing systemic gender knowledge as an important condition for transformational change. Combining gender and participatory system dynamics literature, this article first introduces the concept of systemic gender knowledge. This concept captures two main characteristics that make gender knowledge systemic: knowledge on the interaction of gender inequality processes and endogenous thinking, here implying a focus on the organization as the relevant level of analysis. In addition to this conceptual contribution, the research contributes methodologically to the gender inequality intervention literature by designing a visualization process, translating written texts into system dynamics models which enable exploration of systemic gender knowledge. Finally, the research contributes empirically by exploring the systemic gender knowledge of participants in two science research institutes of a Dutch university, finding shifts in both characteristics of systemic gender knowledge. This enables researchers to discern whether gender equality interventions lead to increases in systemic gender knowledge, thus supporting transformational change.     

A stratified unrelated question randomized response model using Neyman allocation

This paper applies stratified random sampling using Neyman allocation to Mangat et al. (1992 Mangat, N.S., Singh, R., Singh, S. (1992). An improved unrelated question randomized response strategy. Cal. Stat. Assoc. Bull. 42:277–281.[Crossref] , [Google Scholar]) unrelated question randomized response (RR) strategy for both completely truthful reporting and less than completely truthful reporting. It is shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) and mean square error (in terms of less than completely truthful reporting) than Kim and Elam's (2007 Kim, J.M., Elam, M.E. (2007). A stratified unrelated question randomized response model. Stat. Papers 48:215–233.[Crossref], [Web of Science ®] , [Google Scholar]) model. Numerical illustrations and graphs are also given in support of the present study.     

Bayesian estimation of rare sensitive attribute

Randomized response models have been used to estimate a population proportion of a sensitive attribute. A randomized device is typically employed to protect respondent's privacy in a survey. In addition, an unrelated question is asked to improve the statistical efficiency. In this article, we propose Bayesian estimation of rare sensitive attribute using randomized response technique, which includes a rare unrelated attribute. Two cases are considered, the proportion of a rare unrelated attribute is known and unknown. A simulation study is conducted to assess the performance of the models using mean absolute error and coverage probability. The results show that the performance depends on the parameters and is robust to priors.     

Multiple-class classification: Ordinal and categorical labels

We study multiple-class classification problems. Both ordinal and categorical labeled cases are discussed. The common approaches for multiple-class classification are built on binary classifiers, in which one-versus-one and one-versus-rest are typical approaches. When the number of classes is large, then these binary-classifier-based methods may suffer from either computational costs or the highly imbalanced sample sizes in their training stage. In order to alleviate the computational burden and the imbalanced training data issue in multiple-class classification problems, we propose a method that has competitive performance and retains the ease of model interpretation, which is essential for a prognostic/predictive model.     

Impact of an Overt Response System on Staff Performance and Maintenance of Computer-Based Instruction

Interactive Computer Based Instruction (CBI) systems involve teaching strategies to facilitate greater response opportunities during training in an effort to improve learner performance. The current study investigated the effect of online staff training videos with and without overt response requirements on posttest and maintenance test scores across six block-randomized instructional modules for four employees of a university located in the southeastern region of the United States. The overt response consisted of multiple-choice questions administered throughout select teaching modules using an alternating treatment design. Findings suggest that learning occurred as a result of the CBI training; however, the effectiveness of the overt response system varied across individuals, with no discernible differentiation between learning gains associated with response requirements. Other areas of research for expanding the efficacy of CBI and overt response systems are highlighted.