Can risky behaviors, gaming addiction, and family sense of coherence accurately classify gender among university students?

The present study examined whether risky behaviors, gaming addiction, and family sense of coherence classify female male university students correctly. For this purpose, Logistic Regression Analysis (LRA) was used. main purpose LRA is to estimate which group the individual a member of. widely used predict categorical data (i.e., female, addicted non-addicted, single married). comprised 281 (148 females 133 males) who had been playing digital videogames for at least six months. measures included Risk Behaviors Scale, Digital Game Addiction Family Sense Coherence Scale-Short Form. demonstrated that behavior, predicted gender with 76.5% accuracy. In other words, scores can high probability an or female. More specifically, behaviors (school dropout, antisocial alcohol use, smoking, eating habits) addiction contributed significantly classification being biggest contribution in addiction. such as substance use suicide tendency did not contribute classification. provided important resulted terms demonstrating risk factors related gender.