The Development of the Game Engagement Questionnaire: A Measure of Engagement in Video Game Playing: Response to Reviews
This paper begins with an argument that most measure development in the social sciences, with its reliance on correlational techniques as a tool, falls short of the requirements for constructing meaningful, unidimensional measures of human attributes. By demonstrating how rating scales are ordinal-level data, we argue the necessity of converting these to equal-interval units to develop a measure that is both qualitatively and quantitatively defensible. This requires that the empirical results and theoretical explanation are questioned and adjusted at each step of the process. In our response to the reviewers, we describe how this approach was used to develop the Game Engagement Questionnaire (GEQ), including its emphasis on examining a continuum of involvement in violent video games. The GEQ is an empirically sound measure focused on one player characteristic that may be important in determining game influence.
Traffic information systems based on mobile, in-car sensor technology are a challenge for data management systems as a huge amount of data has to be processed in real-time. Data mining methods must be adapted to cope with these challenges in handling streaming data. Although several data stream mining methods have been proposed, an evaluation of such methods in the context of traffic applications is yet missing. In this paper, we present an evaluation framework for data stream mining for traffic applications. We apply a traffic simulation software to emulate the generation of traffic data by mobile probes. The framework is evaluated in a first case study, namely queue-end detection. We show first results of the evaluation of a data stream mining method, varying multiple parameters for the traffic simulation. The goal of our work is to identify parameter settings for which the data stream mining methods produce useful results for the traffic application at hand.