Chengdu Urban And Rural Mobile Phone Use Behavior Study

As mobile phones in people's daily life and overall mobile phone popularity growing functional integration, cell phone use behavior has drawn increasing attention. The act of defining the concept of mobile phone use on the basis of analysis of variance, linear regression of technology on urban and rural residents in Chengdu and its contributing factors using mobile phones and mobile phone usage behavior dependence on the impact of the depth.First of all, the author of Chengdu urban and rural residents use the phone a brief overview of descriptive statistics. Mobile phone usage and mobile phone holder into the situation and phone usage. Mobile phone holders were divided into the number of years, the Department of the number of mobile phone use, the reasons for using mobile phones, mobile phone access to the source of the purchase price and the mobile phone brand mobile phone selection of six areas; phone usage is divided into the number of phone calls, send short messages every day number and the monthly mobile phone charges and other three aspects.Secondly, I know degrees from the phone functions, phone functions and importance of frequency of use evaluation of three dimensions of the phone features an overall analysis of the use of behavior analysis found that the three dimensions, in general, positively correlated, that is, the more survey What are aware of their mobile phone functions (phone features on the amount of subjective), then the frequency of his use of these features will be higher, subsequently he also believes that the more important of these features. Then I used the phone acts grouped into five areas: communication links, network applications, entertainment, tools, extensions and personal assistant. Then, I used multivariate analysis of variance methods were discussed in detail in this population attributes of mobile phone use behavior of five different effects. The results showed that age and education level of mobile phone use the 5 aspects of behavior have a significant impact, age and phone use behavior was negatively correlated with educational level was positively correlated with mobile phone use behavior.Again, the author of the urban and rural areas rely on mobile phones Scale questionnaire were analyzed to determine its reliability and validity in compliance, based on the survey calculated the scores, and rely on the results is divided into mild, moderate dependence highly dependent on three levels. Then the author discusses the gender, age, occupation, status and educational level in the industry attribute characteristics such as population, dependence on the phone if there is a significant difference. It was found that dependence on the phone sex was no significant difference.Finally, I built a mobile phone model dependent, dependent on the mobile phone dependence and life into work depends, and then further divided into communication links rely on, rely on network applications, entertainment depend, tools, extended dependency, dependent on personal assistants. With the exception of recreational and personal assistant to rely on is dependent on life depends, the other three belong to both the dependence of working life depends. Subsequently, the author uses a multiple linear regression model of mobile phone use behavior (communication links, network applications, entertainment, tools, extensions, personal assistants) dependence on the impact of mobile phones. The results showed that personal assistants (P = 0.077> 0.05) on the phone there is no significant dependence on impact. Scores of mobile phones rely on the overall distribution is significantly non-normality, so before doing the multiple linear regression, but also its data conversion.