A Symposium on Unemployment Statistics

Separate Poisson distributions were evaluated via several hypotheses tests for each of the 16 teams in the National Hockey League using 1973–74 goals for and goals against both home and away. After examining independence of goals for and goals against, they in turn were used to explain order of finish for the 16 teams. The results were quite surprising.     

Effects of data breaches from user-generated content: A corporate reputation analysis

A threat for a growing number of firms in various industries is the occurrence of different breaches of sensitive corporate data. These critical events represent vulnerability for firms' corporate reputations, whose multiple dimensions are affected by customers negative perceptions in various ways. Further, in the Industry 4.0 era, the redundancy of scandals on social media can exacerbate negative effects. This paper reports an investigation into the effects of data breaches on corporate reputation dimensions. In this context, the study conducted latent Dirichlet allocation analysis on social media user-generated content (UGC) for a sample of 35 firms in nine industries that suffered a data breach incident between 2013 and 2016. Incidents have been categorized into three categories: “intentional and internal,” “unintentional and internal,” and “intentional and external” data breaches. The aim of the study was to discover how reputational dimensions changed after these critical events and to identify the differences among the types of data breaches.Results reveal that after critical events, more reputational dimensions appear to be relevant. While before critical events, users typically discuss the “perceived quality” of a firm's offer, after all three types of data breaches, users also pay attention to “customer orientation” and “corporate performance” dimensions. Another key reputation dimension, especially after intentional and internal data breaches, is the “firm as employer,” particularly in the context of a lack of investment in training regarding these events.These findings provide key insights for academics and practitioners to understand large-scale data breaches effects and reputational drawbacks after such incidents.     

Performance evaluation and enrollment quota allocation for higher education institutions in China

Higher education has always played an important role in promoting economic and social progress. With the rapid development of economy, China's higher education has entered a new stage and made a series of extraordinary achievements. However, the uneven development of higher education has become a critical factor restricting China’s overall progress. In this paper, we propose Data Envelopment Analysis (DEA) approach to evaluate the performance of higher education resource utilization from the perspective of provincial level. In addition, we assess productivity changes of each province with the help of Malmquist Productivity Index (MPI). Further, by considering the enrollment quota as a special kind of resource, we construct a more reasonable allocation scheme, while keeping the total number of newly added enrollment quota unchanged in the next year. Finally, according to the results, some suggestions are put forward to help the Chinese government better solve the unbalanced development of higher education among provinces.     

Data envelopment analysis 1978–2010: A citation-based literature survey

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.     

Data craft: a theory/methods package for critical internet studies

ABSTRACT

Disinformation campaigns continue to thrive online, despite social media companies’ efforts at identifying and culling manipulation on their platforms. Framing these manipulation tactics as ‘coordinated inauthentic behavior,’ major platforms have banned culprits and deleted the evidence of their actions from social activity streams, making independent assessment and auditing impossible. While researchers, journalists, and civil society groups use multiple methods for discovering and tracking disinformation, platforms began to publish highly curated data archives of disinformation in 2016. When platform companies reframe manipulation campaigns, however, they downplay the importance of their products in spreading disinformation. We propose to treat social media metadata as a boundary object that supports research across platforms and use metadata as an entry point for investigating manipulation campaigns.

We illustrate how platform companies’ responses to disinformation campaigns are at odds with the interests of researchers, civil society, policy-makers, and journalists, limiting the capacity to audit the role that platforms play in political discourse. To show how platforms’ data archives of ‘coordinated inauthentic behavior’ prevent researchers from examining the contexts of manipulation, we present two case studies of disinformation campaigns related to the Black Lives Matter Movement. We demonstrate how data craft – the exploitation of metrics, metadata, and recommendation engines – played a prominent role attracting audiences to these disinformation campaigns. Additionally, we offer some investigative techniques for researchers to employ data craft in their own research of the disinformation. We conclude by proposing new avenues for research for the field of Critical Internet Studies.     

Estimation of weibull parameters for grouped data with competing risks

We consider the problem of estimating Weibull parameters for grouped data when competing risks are present. We propose two simple methods of estimation and derive their asymptotic properties. A Monte Carlo study was carried out to evaluate the performance of these two methods.     

A method for consideration of conditional dependencies in the Fellegi and Sunter model of record linkage

An objective of Record Linkage is to link two data files by identifying common elements. A popular model for doing the separation is the probabilistic one from Fellegi and Sunter. To estimate the parameters needed for the model usually a mixture model is constructed and the EM algorithm is applied. For simplification, the assumption of conditional independence is often made. This assumption says that if several attributes of elements in the data are compared, then the results of the comparisons regarding the several attributes are independent within the mixture classes. A mixture model constructed with this assumption has been often used. Within this article a straightforward extension of the model is introduced which allows for conditional dependencies but is heavily dependent on the choice of the starting value. Therefore also an estimation procedure for the EM algorithm starting value is proposed. The two models are compared empirically in a simulation study based on telephone book entries. Particularly the effect of different starting values and conditional dependencies on the matching results is investigated.     

Maximum likelihood estimation using the empirical fisher information matrix

In this paper the use of the empirical Fisher information matrix as an estimator of the information matrix is considered in the context of response models and incomplete data problems. The introduction of an additional stochastic component into such models is shown to greatly increase the range of situations in which the estimator can be employed. In particular the conditions for its use in incomplete data problems are shown to be the same as those needed to justify the use of the EM algorithm.     

Robust Methods—Some Examples of Their Use

We describe several analyses in which robustness considerations have proved relevant. These examples exhibit the importance of (a) graphic displays as aids to formulating a preliminary model; (b) summary statistics that reduce the influence of outliers, that (c) give added opportunities of detecting relationships, and (d) are not unduly sensitive to granularity in the observations; and (e) techniques that pay due attention to anomalies in the data that superficially may appear to be negligible but that can obscure important effects. Finally, we make some general comments on the advantages and disadvantages of robust methodology.     

Presenting DEA graphically

This paper introduces a methodology that permits presentation of the results of data envelopment analysis (DEA) graphically. A specialized form of multi-dimensional scaling, Co-Plot, enables presentation of the DEA results in a two-dimensional space, hence in a clear, understandable manner. When plotting ratios rather than original data, DEA efficient units can be visualized clearly, as well as their connections to specific variables and/or ratios. Furthermore, Co-Plot can be used in an exploratory data analysis to identify outliers, whose data require additional scrutiny, and potentially inconsequential variables that could be aggregated or removed from the analysis with little effect on the subsequent DEA results.