1. Can risky behaviors, gaming addiction, and family sense of coherence accurately classify gender among university students?
- Author
-
Mustafa Ercengiz, Mustafa Savci, Banu Yildiz, Ferda Aysan, Mark D. Griffiths, Belirlenecek, ALKÜ, 0-belirlenecek, and ALKÜ, Fakülteler, Eğitim Fakültesi, Eğitim Bilimleri Bölümü
- Subjects
Turkish Version ,media_common.quotation_subject ,050109 social psychology ,Game Addiction ,Logistic regression ,Gaming addiction ,050105 experimental psychology ,Developmental psychology ,Age ,mental disorders ,Risky behaviors ,0501 psychology and cognitive sciences ,Family sense of coherence ,Eating habits ,Categorical variable ,General Psychology ,media_common ,Addiction ,05 social sciences ,Risk behavior ,Gender ,Childhood ,University students ,Scale ,Adolescence ,Video Games ,Health ,Scale (social sciences) ,Substance use ,Psychology ,Stability ,Sense of coherence ,Personality - Abstract
WOS: 000604544800006 The present study examined whether risky behaviors, gaming addiction, and family sense of coherence classify female and male university students correctly. For this purpose, Logistic Regression Analysis (LRA) was used. The main purpose of LRA is to estimate which group the individual is a member of. LRA is widely used to predict categorical data (i.e., male and female, addicted and non-addicted, single and married). The present study comprised 281 university students (148 females and 133 males) who had been playing digital videogames for at least six months. The measures used included the Risk Behaviors Scale, Digital Game Addiction Scale, and Family Sense of Coherence Scale-Short Form. Analysis demonstrated that risky behavior, gaming addiction, and family sense of coherence predicted gender with 76.5% accuracy. In other words, risky behaviors, gaming addiction, and family sense of coherence scores can predict a high probability that an individual is male or female. More specifically, risky behaviors (school dropout, antisocial behaviors, alcohol use, smoking, and eating habits) and gaming addiction contributed significantly to the classification of an individual being male or female. The biggest contribution in the classification of gender was digital gaming addiction. Family sense of coherence and risky behaviors such as substance use and suicide tendency did not contribute significantly to gender classification. The present study provided important resulted in terms of demonstrating risk factors related to gender.
- Published
- 2022