Peer assessment as a method for measuring harmful internet use.

Harmful Internet use (HIU) describes unintended use of the Internet. It could be both self-harm and harming others. Our research goal is to develop a more accurate method for measuring HIU by this novel peer assessment. As such, it may become, with our call for more research, a paradigm shift supplementing every rating scale or other type of Internet use assessment. In addition to classic statistical analysis, structural equations have been employed. Results indicate that the true positive rate (TPR) is substantially higher than assessed in other studies.•Peer assessment improvement.•AUC for ROC was computed to establish cut-off points for the used scale.•Results obtained by the Structural Equation model indicate that parental care has a moderate influence on subjects' attempts to fight HIU.

1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic.

We forecast 1,000,000 COVID-19 cases outside of China by March 31st, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic; we model only the number of cases. The proposed heuristic is based on a simple observation that the plot of the given data is well approximated by an exponential curve. The exponential curve is used for forecasting the growth of new cases. It has been tested for the last situation report of the last day. Its accuracy has been 1.29% for the last day added and predicted by the 57 previous WHO situation reports (the date 18 March 2020). Prediction, forecast, pandemic, COVID-19, coronavirus, exponential growth curve parameter, heuristic, epidemiology, extrapolation, abductive reasoning, WHO situation report.

How to reduce the number of rating scale items without predictability loss?

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The "area under the receiver operator curve method" (AUC ROC) is used. The presented method has reduced the number of rating scale items (variables) to 28.57% (from 21 to 6) making over 70% of collected data unnecessary. Results have been verified by two methods of analysis: Graded Response Model (GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method differentiates observations of high and middle scores. CFA proved that the reliability of the rating scale has not deteriorated by the scale item reduction. Both statistical analysis evidenced usefulness of the AUC ROC reduction method.