Professionalization through attrition? An event history analysis of mortalities in citizen journalism

ABSTRACT

Despite both scholarly and popular claims that citizen journalism (CJ) represents a growing democratizing force in the journalistic field, recent scholarship in the area has noted the decline of the organizational population of CJ. In this paper, we investigate how individual characteristics of sites and the dynamics of larger organizational population affect a CJ site’s risk of experiencing a mortality. Drawing on the largest sample to date of US-based English-language CJ sites, this study examines the risk of site mortality through an event history framework. Findings indicate that the strongest predictor of a site’s mortality is the age of the site, consistent with organizational population theory’s ‘liability of newness.’ We also find that for-profit and community-based sites have lower rates of site mortality, indicating that adopting legitimate conventions of journalism may serve as a protective buffer to site death. The results offer mixed evidence on whether CJ has become more professionalized via attrition.     

Psychometric properties of the “parent-child relationship inventory” (PCRI) in a portuguese forensic sample

In Portugal, there is a gap regarding psychological tests validated for forensic contexts, particularly those related to child custody issues. The Parent–Child Relationship Inventory (PCRI) is one of the most used instruments in child custody contexts. This study aimed to analyze the psychometric properties of PCRI in a Portuguese forensic sample. PCRI factorial structure and psychometric properties were analyzed in a sample of 144 parents involved in child custody assessments. The questionnaire showed good internal consistency, except for the parental support, autonomy, and role orientation scales. It also showed good discriminant capacity. The confirmatory factor analysis did not replicate the 7-factor model proposed by Gerard. The results were discussed based on the use of PCRI in the context of child custody assessment.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

Estimates of subgroup treatment effects in overall nonsignificant trials: To what extent should we believe in them?

In drug development, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population but appears to be beneficial in a relevant subgroup. In case the subgroup of interest was not part of a confirmatory testing strategy, the inflation of the overall type I error is substantial and therefore such a subgroup analysis finding can only be seen as exploratory at best. To support such exploratory findings, an appropriate replication of the subgroup finding should be undertaken in a new trial. We should, however, be reasonably confident in the observed treatment effect size to be able to use this estimate in a replication trial in the subpopulation of interest. We were therefore interested in evaluating the bias of the estimate of the subgroup treatment effect, after selection based on significance for the subgroup in an overall “failed” trial. Different scenarios, involving continuous as well as dichotomous outcomes, were investigated via simulation studies. It is shown that the bias associated with subgroup findings in overall nonsignificant clinical trials is on average large and varies substantially across plausible scenarios. This renders the subgroup treatment estimate from the original trial of limited value to design the replication trial. An empirical Bayesian shrinkage method is suggested to minimize this overestimation. The proposed estimator appears to offer either a good or a conservative correction to the observed subgroup treatment effect hence provides a more reliable subgroup treatment effect estimate for adequate planning of future studies.     

A Bayesian sequential design with binary outcome

Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha‐spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha‐spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.     

Uniform in bandwidth consistency for various kernel estimators involving functional data

The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.     

Multiphase experiments in practice: A look back

Multiphase experiments are introduced and an overview of their design and analysis as it is currently practised is given via an account of their development since 1955 and a literature survey. Methods that are available for designing and analysing them are outlined, with an emphasis on making explicit the role of the model in their design. The availability of software and its use is described in detail. Overall, while multiphase designs have been applied in areas such as plant breeding, plant pathology, greenhouse experimentation, product storage, gene expression studies, and sensory evaluation, their deployment has been limited.     

Structuring composite local governance indicators

The importance of good governance is praised by many academics and practitioners. The prominence of the subject suggests that measurement is important. However, setting out to measure the quality of governance empirically is controversial. Doubts regarding the feasibility and meaningfulness of this undertaking are widespread in the literature. Recognizing the potential caveats, the current article discusses a set of guidelines for structuring a theoretically sound local governance assessment tool based on the Decision Analysis and Operational Research literatures. The authors argue that using a multi-criteria model which employs several objective (quantitative and qualitative) indicators and relies on a participatory method to aggregate them is a suitable way of developing sensible Local Governance Indicators. The purpose here is to provide a detailed roadmap for any country (or region, or locality) willing to engage in the assessment of the quality of local governance. The real-world implementation of a model developed according to these guidelines could help raise awareness, promote good practices and increase the ‘governance literacy’ of citizens. By operationalizing good governance, analysts may also be able to further investigate the relationships between local governance practices and several socio-economic factors.     

Bayesian quantile regression for skew-normal linear mixed models

Linear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations. Here, quantile regression in the Bayesian framework for the linear mixed models is described to carry out the robust inferences. We also relax the normality assumption for the random effects by using a multivariate skew-normal distribution, which includes the normal ones as a special case and provides robust estimation in the linear mixed models. For posterior inference, we propose a Gibbs sampling algorithm based on a mixture representation of the asymmetric Laplace distribution and multivariate skew-normal distribution. The procedures are demonstrated by both simulated and real data examples.     

Decomposition of the main effects and interaction term by using orthogonal polynomials in multiple non symmetrical correspondence analysis

The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials.