Indiana University

The governance of natural resources used by many individuals in common is an issue of increasing concern to policy analysts. Both state control and privatisation of resources have been advocated, but neither the state nor the market have been uniformly successful in solving common pool resource problems. Offering a critique of the foundations of policy analysis as applied to natural resources, Elinor Ostrom here provides a unique body of empirical data to explore conditions under which common pool resource problems have been satisfactorily or unsatisfactorily solved. Dr Ostrom first describes three models most frequently used as the foundation for recommending state or market solutions. She then outlines theoretical and empirical alternatives to these models in order to illustrate the diversity of possible solutions. In the following chapters she uses institutional analysis to examine different ways - both successful and unsuccessful - of governing the commons. In contrast to the proposition of the tragedy of the commons argument, common pool problems sometimes are solved by voluntary organisations rather than by a coercive state. Among the cases considered are communal tenure in meadows and forests, irrigation communities and other water rights, and fisheries.

Self-reports figure prominently in organizational and management research, but there are several problems associated with their use. This article identifies six categories of self-reports and discusses such problems as common method variance, the consistency motif, and social desirability. Statistical and post hoc remedies and some procedural methods for dealing with artifactual bias are presented and evaluated. Recommendations for future research are also offered.

To facilitate a multidimensional approach to empathy the Interpersonal Reactivity Index (IRI) includes 4 subscales: Perspective-Taking (PT) Fantasy (FS) Empathic Concern (EC) and Personal Distress (PD). The aim of the present study was to establish the convergent and discriminant validity of these 4 subscales. Hypothesized relationships among the IRI subscales between the subscales and measures of other psychological constructs (social functioning self-esteem emotionality and sensitivity to others) and between the subscales and extant empathy measures were examined. Study subjects included 677 male and 667 female students enrolled in undergraduate psychology classes at the University of Texas. The IRI scales not only exhibited the predicted relationships among themselves but also were related in the expected manner to other measures. Higher PT scores were consistently associated with better social functioning and higher self-esteem; in contrast Fantasy scores were unrelated to these 2 characteristics. High EC scores were positively associated with shyness and anxiety but negatively linked to egotism. The most substantial relationships in the study involved the PD scale. PD scores were strongly linked with low self-esteem and poor interpersonal functioning as well as a constellation of vulnerability uncertainty and fearfulness. These findings support a multidimensional approach to empathy by providing evidence that the 4 qualities tapped by the IRI are indeed separate constructs each related in specific ways to other psychological measures.

BACKGROUND: Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. METHODS: In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. RESULTS: We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n < or = 15). For moderately sized samples (15 < n < or = 70), our simulations show that the formula range/4 is the best estimator for the standard deviation (variance). For large samples (n > 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. CONCLUSION: Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

As enrollments continue to decline, student retention is increasingly vital to the survival of most colleges and universities. In the second edition of this text, Tinto synthesizes far-ranging research on student attrition and on actions institutions can and should take to reduce it. The key to effective retention, Tinto demonstrates, is in a strong commitment to quality education and the building of a strong sense of inclusive educational and social community on campus. This revised and expanded edition incorporates the explosion of recent research and policy reports on why students leave higher education. Incorporating current data, Tinto applies his theory of student departure to the experiences of minority, adult and graduage students, and to the situation facing commuting institutions and two-year colleges. He has revised his theory, giving new emphasis to the central importance of the classroom experience and to the role of multiple college communities.

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

INTRODUCTION: The SCARE Guidelines were first published in 2016 and were last updated in 2018. They provide a structure for reporting surgical case reports and are used and endorsed by authors, journal editors and reviewers, in order to increase robustness and transparency in reporting surgical cases. They must be kept up to date in order to drive forwards reporting quality. As such, we have updated these guidelines via a DELPHI consensus exercise. METHODS: The updated guidelines were produced via a DELPHI consensus exercise. Members were invited from the previous DELPHI group, as well as editorial board members and peer reviewers of the International Journal of Surgery Case Reports. The expert group completed an online survey to indicate their agreement with proposed changes to the checklist items. RESULTS: A total of 54 surgical experts agreed to participate and 53 (98%) completed the survey. The responses and suggested modifications were incorporated into the new 2020 guideline. There was a high degree of agreement amongst the SCARE Group, with all modified SCARE items receiving over 70% scores 7-9. CONCLUSION: A DELPHI consensus exercise was completed and an updated and improved SCARE Checklist is now presented.

The rapid growth of research on organizational citizenship behaviors (OCBs) has resulted in some conceptual confusion about the nature of the construct, and made it difficult for all but the most avid readers to keep up with developments in this domain. This paper critically examines the literature on organizational citizenship behavior and other, related constructs. More specifically, it: (a) explores the conceptual similarities and differences between the various forms of “citizenship” behavior constructs identified in the literature; (b) summarizes the empirical findings of both the antecedents and consequences of OCBs; and (c) identifies several interesting directions for future research.

It is argued here that a category of performance called citizenship behavior is important in organizations and not easily explained by the same incentives that induce entry, conformity to contractual role prescriptions, or high production A study of 422 employees and their supervisors from 58 departments of two banks sought to elaborate on the nature and predictors of citizenship behavior Results suggest that citizenship behavior includes at least two separate dimensions Altruism, or helping specific persons, and Generalized Compliance, a more impersonal form of conscientious citizenship Job satisfaction, as a measure of chronic mood state, showed a direct predictive path to Altruism but not Generalized Compliance Rural background had direct effects on both dimensions of citizenship behavior The predictive power of other variables (e g , leader supportiveness as assessed independently by co-workers, personality measures) varied across the two dimensions of citizenship behavior

Abstract The emergence of a new paradigm of inquiry (naturalistic) has, unsurprisingly enough, led to a demand for rigorous criteria that meet traditional standards of inquiry. Two sets are suggested, one of which, the “trustworthiness” criteria, parallels conventional criteria, while the second, “authenticity” criteria, is implied directly by new paradigm assumptions.

The authors review the corporate social responsibility (CSR) literature based on 588 journal articles and 102 books and book chapters. They offer a multilevel and multidisciplinary theoretical framework that synthesizes and integrates the literature at the institutional, organizational, and individual levels of analysis. The framework includes reactive and proactive predictors of CSR actions and policies and the outcomes of such actions and policies, which they classify as primarily affecting internal (i.e., internal outcomes) or external (i.e., external outcomes) stakeholders. The framework includes variables that explain underlying mechanisms (i.e., relationship- and value-based mediator variables) of CSR–outcomes relationships and contingency effects (i.e., people-, place-, price-, and profile-based moderator variables) that explain conditions under which the relationship between CSR and its outcomes change. The authors’ review reveals important knowledge gaps related to the adoption of different theoretical orientations by researchers studying CSR at different levels of analysis, the need to understand underlying mechanisms linking CSR with outcomes, the need for research at micro levels of analysis (i.e., individuals and teams), and the need for methodological approaches that will help address these substantive knowledge gaps. Accordingly, they offer a detailed research agenda for the future, based on a multilevel perspective that aims to integrate diverse theoretical frameworks as well as develop an understanding of underlying mechanisms and microfoundations of CSR (i.e., foundations based on individual action and interactions). The authors also provide specific suggestions regarding research design, measurement, and data-analytic approaches that will be instrumental in carrying out their proposed research agenda.

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.

Single-subject research plays an important role in the development of evidence-based practice in special education. The defining features of single-subject research are presented, the contributions of single-subject research for special education are reviewed, and a specific proposal is offered for using single-subject research to document evidence-based practice. This article allows readers to determine if a specific study is a credible example of single-subject research and if a specific practice or procedure has been validated as “evidence-based” via single-subject research.

Transfer of training is of paramount concern for training researchers and practitioners. Despite research efforts, there is a growing concern over the “transfer problem.” The purpose of this paper is to provide a critique of the existing transfer research and to suggest directions for future research investigations. The conditions of transfer include both the generalization of learned material to the job and the maintenance of trained skills over a period of time on the job. The existing research examining the effects of training design, trainee, and work‐environment factors on conditions of transfer is reviewed and critiqued. Research gaps identified from the review include the need to (1) test various operationalizations of training design and work‐environment factors that have been posited as having an impact on transfer and (2) develop a framework for conducting research on the effects of trainee characteristics on transfer. Needed advancements in the conceptualization and operationalization of the criterion of transfer are also discussed.

A quantitative review of 55 studies supports the conclusion that job attitudes are robust predictors of organizational citizenship behavior (OCB). The relationship between job satisfaction and OCB is stronger than that between satisfaction and in‐role performance, at least among nonmanagerial and nonprofessional groups. Other attitudinal measures (perceived fairness, organizational commitment, leader supportiveness) correlate with OCB at roughly the same level as satisfaction. Dispositional measures do not correlate nearly as well with OCB (with the exception of conscientiousness). The most notable moderator of these correlations appears to be the use of self‐ versus other‐rating of OCB; self‐ratings are associated with higher correlations, suggesting spurious inflation due to common method variance, and much greater variance in correlation. Differences in subject groups and work settings do not account for much variance in the relationships. Implications are noted for theory, practice, and strategies for future research on OCB.

More than 40 years ago, Masahiro Mori, a robotics professor at the Tokyo Institute of Technology, wrote an essay [1] on how he envisioned people's reactions to robots that looked and acted almost like a human. In particular, he hypothesized that a person's response to a humanlike robot would abruptly shift from empathy to revulsion as it approached, but failed to attain, a lifelike appearance. This descent into eeriness is known as the uncanny valley. The essay appeared in an obscure Japanese journal called Energy in 1970, and in subsequent years, it received almost no attention. However, more recently, the concept of the uncanny valley has rapidly attracted interest in robotics and other scientific circles as well as in popular culture. Some researchers have explored its implications for human-robot interaction and computer-graphics animation, whereas others have investigated its biological and social roots. Now interest in the uncanny valley should only intensify, as technology evolves and researchers build robots that look human. Although copies of Mori's essay have circulated among researchers, a complete version hasn't been widely available. The following is the first publication of an English translation that has been authorized and reviewed by Mori. (See “Turning Point” in this issue for an interview with Mori.).

Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides a comprehensive account of models and methods to interpret such data. The authors have conducted research in the field for nearly fifteen years and in this work combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages.

By considering the amount of uncertainty perceived and the willingness to bear uncertainty concomitantly, we provide a more complete conceptual model of entrepreneurial action that allows for examination of entrepreneurial action at the individual level of analysis while remaining consistent with a rich legacy of system-level theories of the entrepreneur. Our model not only exposes limitations of existing theories of entrepreneurial action but also contributes to a deeper understanding of important conceptual issues, such as the nature of opportunity and the potential for philosophical reconciliation among entrepreneurship scholars.

this paper is to provide a clear link between the theoretical principles of constructivism and the practice of instructional design and the practice of teaching. We will begin with a basic characterization of constructivism identifying what we believe to be the central principles in learning and understanding. We will then identify and elaborate on eight instructional principles for the design of a constructivist learning environment. Finally, we will exam what we consider to be one of the best exemplars of a constructivist learning environment -- Problem Based Learning as described by Barrows (1985, 1986, 1992) at the Southern Illinois University Medical School and at the Problem Based Learning Institute for high school teachers .