Internet gaming disorder as a formative construct : implications for conceptualization and measurement

Background. Some people have serious problems controlling their internet and video game use. The DSM-5 now includes a proposal for ‘Internet Gaming Disorder’ as a condition in need of further study. Various studies aim to validate the proposed diagnostic criteria for Internet Gaming Disorder and multiple new scales have been introduced that cover the suggested criteria. Approach. Using a structured approach, we demonstrate that Internet Gaming Disorder might be better interpreted as a formative construct, as opposed to the current practice of conceptualizing it as a reflective construct. Incorrectly approaching a formative construct as a reflective one causes serious problems in scale development including (a) incorrect reliance on item-to-total scale correlation to exclude items and incorrectly relying on indices of inter-item reliability that do not fit the measurement model (e.g., Cronbach’s α) (b) incorrect interpretation of composite or mean scores that assume all items are equal in contributing value to a sum score, and (c) biased estimation of model parameters in statistical models. Implications. We show that these issues are impacting current validation efforts through two recent examples. A reinterpretation of Internet Gaming Disorder as a formative construct has broad consequences for current validation efforts and provides opportunities to reanalyze existing data. We discuss three broad implications for current research: (1) Composite latent constructs should be defined and used in models, (2) Item exclusion and selection should not rely on item-to-total scale correlations, and (3) Existing definitions of Internet Gaming Disorder should be enriched further

Course developers as students: a designer perspective of the experience of learning online

Academic developers of online courses may not have experienced this mode of learning and teaching from the learner perspective. This article makes a comparison between suggestions for online course design from research literature and user perspectives from a focus group, responses to questions on the most and least effective aspects of online study and lasting impressions, and from reflective diaries kept by two of the authors while they were engaged in study from online courses. This direct evidence is used to highlight key issues in the literature from the viewpoint of the learner

Determinants of harassment in online multiplayer games

Objective. Online multiplayer games allow large numbers of participants to play simultaneously online. Unfortunately, this has also given rise to new forms of harassment and abuse. The current study used the criminological framework of Routine Activity Theory to identify possible circumstantial and individual risk factors that predict both general and sexual harassment victimization in this online context. Method. An online survey of online multiplayer gamers (N = 883) was conducted. Measures included harassment exposure, guardianship, and target suitability. These determinants were used to predict general and sexual harassment victimization in multiple regression analyses. Analyses controlled for social desirability responding. Results. Both sexual harassment victimization (R² = 63%) and general harassment victimization (R² = 57%) were successfully predicted using the determinants. The gender of the gamer is associated with the type of harassment received; women are more likely to encounter sexual harassment, while men are more likely to be harassed in the general sense. Gaming for fewer hours per week, with mostly or exclusively female avatars, and sharing personal information (e.g. gender) predicts sexual harassment victimization, whereas playing in Player-versus-Player game modes predicts general harassment victimization. Harassing other gamers and associating with harassers predicts both general and sexual harassment. Conclusions. Our models explained a sizable percentage of the variation in harassment, indicating for the first time that the Routine Activity Theory can be applied to understand online harassment in gaming. Specifically, it was found that different types of in-game exposure predict different types of victimization, that lacking personal self-guardianship predicts sexual harassment and that gender (or revealing gender) is associated with changes in harassment

Comprehensible credit scoring models using rule extraction from support vector machines.

In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformation of the original problem to a high-dimensional (possibly infinite) feature space in which a linear decision hyperplane is constructed that yields a nonlinear classifier in the input space. However, since the classifier is described as a complex mathematical function, it is rather incomprehensible for humans. This opacity property prevents them from being used in many real- life applications where both accuracy and comprehensibility are required, such as medical diagnosis and credit risk evaluation. To overcome this limitation, rules can be extracted from the trained SVM that are interpretable by humans and keep as much of the accuracy of the SVM as possible. In this paper, we will provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX. The described techniques are compared using publicly avail- able datasets, such as Ripley's synthetic dataset and the multi-class iris dataset. We will also look at medical diagnosis and credit scoring where comprehensibility is a key requirement and even a regulatory recommendation. Our experiments show that the SVM rule extraction techniques lose only a small percentage in performance compared to SVMs and therefore rank at the top of comprehensible classification techniques.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research;

The curriculum and society

Through curricula, society expresses and determines its identity. The curriculum is always a more or less successful picture of what society was in the past, what it is now and what it wants to be in the future. A number of stakeholders and individuals cannot be indifferent to questions like: why, what and how to do it. The basic purpose of determinations (why), content (what) and methods (how) are a kind of a pedagogical vision of the future of a society and determine-that members of a society can be and what human potential can be developed. It largely depends on a successfully prepared curriculum. The question of which we will indulge in this paper is the culture of excellence in university teaching. Whether the quality of university teaching came to the level of metaphysics and became the essence of herself? Bologna process as a modern European trend is designed to cover effective models for teaching the student clearly knows what he teaches and learns why. Do we use models of motivation for academic achievement of students and teachers at universities in Macedonia? Is there a culture of quality? The results of the survey which included students in their final years and graduates as well as business community, showed that more than 60% of graduates have deficiencies in key professional working skills. More than 51% of students said they have not gained any practical skills during their studies. Young people in the region (1) does not possess the appropriate skills for employment, mainly skills that are listed by employers, and (2) there is no relationship between universities, students and society

Country corruption analysis with self organizing maps and support vector machines.

During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models' predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models' behavior.cross-country;

Pattern matching

Pattern matching is comparing two patterns in order to determine whether they match (i.e., that they are the same) or do not match (i.e., that they differ). Pattern matching is the core procedure of theory-testing with cases. Testing consists of matching an “observed pattern” (a pattern of measured values) with an “expected pattern” (a hypothesis), and deciding whether these patterns match (resulting in a confirmation of the hypothesis) or do not match (resulting in a disconfirmation). Essential to pattern matching (as opposed to pattern recognition, which is a procedure by which theory is built) is that the expected pattern is precisely specified before the matching takes place

Theory-Testing With Cases

Theory-testing with cases is ascertaining whether the empirical evidence in a case or in a sample of cases either supports or does not support the theory. There are two methodologies for theory-testing with cases, (a) testing in a single case (‘theory-testing single case study’), and (b) testing in a sample of cases (‘theory-testing sample case study’). The functional form of the proposition that is tested determines which of these two methodologies should be used

Theory-Building With Cases

Theory-building with cases is (a) formulating new propositions that emerge from the empirical evidence in a sample of cases and (b) testing them in the same sample. The main difference with most other forms of generating new propositions (such as analyzing the theoretical literature, brainstorming, etc.) is its empirical character. The main difference with other forms of discovering new propositions in empirical evidence (such as in ‘exploratory’ research) is that only those theoretical formulations are accepted as a result of the theory-building study that are confirmed in a test in the sample from which the proposition was built. It is possible that a proposition about a relationship between two variables emerges from an exploratory single case study (e.g., when both variables have extreme values in that case), but it is not possible to test that new proposition in the same study because this would require a comparison in a sample of cases. The term theory-building study (as distinct from an exploratory study) is used here only for studies in which a proper test of the new proposition has been conducted