The third model, the residual invariance model model VI , has an equal factor loading, covariance, and residual show in Table 3 a. The comparative results are shown in Figure 3.
Lastly, the study compared the latent mean structural models of males and females, respectively, to determine their appropriate fit. In the residual invariance model of male college students, there is a positive significant correlation , between mobile phone addiction and Internet addiction. Likewise, in the residual invariance model of female college students, there is also a positive significant correlation , between mobile phone addiction and Internet addiction.
The aforementioned invariance models of factor loading, covariance, and residual are a fit. Afterwards, the study is going to examine the latent means of different genders. This study constrains the factor loading, covariance, variance, the error of means, and the intercepts of variables to all be 0 as the baseline model the invariance model of variable intercept, model VII. However, when comparing the invariance model of variable intercepts and the residual model, it was discovered that and , indicating that the intercepts were invariant.
In this case, the study makes model VIII a baseline model. For the time being, Internet addiction and mobile phone addiction are two of the most commonly discussed technological addictions. Beranuy et al. The reason for this may be that the two have similar personalities and lifestyles. It also supports the study result of Beranuy.
Moreover, this study adopted the latent means analysis of the structural equation model to compare the means difference between male and female college students in terms of Internet addiction and mobile phone addiction, in order to support the hypothesis that there may be a gender difference in Internet and mobile phone addiction.
Firstly, according to the results of the study, it was discovered that mobile phone addiction and Internet addiction are significantly positively correlated, which conforms to the study of Beranuy et al. In other words, the technological addiction of college students has gone beyond the Internet; they are addicted to mobile phones as well.
Because of their technological addiction, college students tend to indulge themselves on the Internet and on mobile phones, and they may experience depression and negative emotions if they do not have them. That is to say, technological addiction has a great impact on college students in terms of their mental health, time allocation and management, school performance, interpersonal relationships, and health.
For both male and female college students, the more addicted to the Internet they are, the higher the possibility of becoming addicted to mobile phones. The main reason may be that mobile phones share similar functions with the Internet. For example, both of them allow users to send messages as a means to interact with others via Internet services and provide users viz, college students with games to kill time. The correlation between Internet addiction and mobile phone addiction also suggests that the two may feed and fuel each other.
When college students cannot use computers, they are very likely to turn to mobile phones to satisfy their demands of Internet services and games. That is to say, the Internet and mobile phone can be substituted in terms of satisfaction. It was also discovered that there was a significant relation between the time which college students spend on the Internet and that spent on mobile phones.
Perhaps those with better tolerance of Internet addiction may select mobile phones to satisfy their yearning and thus develop the condition of substitute satisfaction. If this viewpoint is acceptable, it depends on future studies to discover substitute satisfaction between Internet addiction and mobile phone addiction in the manner of longitudinal studies.
This study adopted the latent means of multisample structural equation modelling, which is effective in controlling measurement errors and the group variance of measurement models as the analysis tool and concluded that Internet addiction does not show any difference because of gender. This study result is very similar to that of Beranuy et al.
Regarding online information, females favour online communication and personal messages, while males concern themselves with weather, sports, and games [ 49 , 50 ]. The boom of social network services attracts more females to use the Internet, and their daily lives may be seriously affected [ 51 ].
In this case, male and female college students show little difference in terms of Internet addiction. It was discovered that female college students tend to score higher than their male counterparts in the aspect of mobile phone addiction, which is in agreement with previous studies [ 9 , 28 , 40 — 42 ]. The reason may be that female college students are more likely to maintain their social relationships and communicate with the people they value via mobile phone.
Moreover, based on the study result, a high percentage of female college students like to make phone calls at night and text people daily. In other words, they prefer the manner of indirect communication [ 10 , 26 , 40 , 52 — 54 ]. In addition, female college students favour staying in touch with people and communicating with family members via email to establish close relationships [ 49 , 55 ].
Though this study has obtained two important results, namely, that the correlation between Internet addiction and mobile phone addiction is positive and female college students are more addicted to mobile phone than male ones, there are still some restrictions needed to be taken into consideration when interpreting the findings of the study. Also, there were more male participants than female ones, making this study somewhat weak in terms of representation.
Secondly, both the Internet addiction and mobile phone addiction surveyed by this study do not reach the extent of clinical level, and the correlation between the two types of addiction can only reflect the preliminary study on how college students use the Internet and mobile phone. As for the substitution of mobile phones for the Internet, it will still need further studies to conduct analysis in the manner of experiment and longitudinal studies. This study result is also a good reference for future studies.
On the other hand, it is also worth discussing if a college student has to have a certain personality to become both an Internet and a mobile phone addict, which can serve as a reference to identify technological addiction and guide college students.
Lastly, future studies are advised to design a technological addiction scale based on this study result to serve as a tool to identify the negative effects of technological development on adolescents in the future.
This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.
Read the winning articles. Academic Editor: G. Received 20 Jul Accepted 13 Aug Published 18 Sep Abstract This study is aimed at constructing a correlative model between Internet addiction and mobile phone addiction; the aim is to analyse the correlation if any between the two traits and to discuss the influence confirming that the gender has difference on this fascinating topic; taking gender into account opens a new world of scientific study to us.
Literary Review 2. Gender and Internet and Mobile Phone Addiction With regards to the literary reviews of previous studies on the gender differences between Internet addiction and mobile phone addiction, there is no consistent conclusion yet.
Materials and Methods 3. Participants The participants of this study were primarily selected from two colleges on the island for convenient sampling, Taipei College of Maritime Technology and Aletheia University. Data Analysis This study is aimed at discussing the relation between Internet addiction and mobile phone addiction, and how college students of different genders vary in the means of the two so as to construct a correlation model of Internet and mobile phone addiction.
Results 4. Table 1. Summary of the means, standard deviation, and range of variables. Table 2. Summary of the correlation coefficient of mobile addiction and Internet addiction ,.
Table 3. Figure 1. Note: inside the rectangle are the variables of measurement; all values are standardized parameter estimations. Figure 2. Figure 3.
References J. View at: Google Scholar M. Toda, S. Ezoe, A. Nishi, T. Mukai, M. Goto, and K. Beranuy, U. Oberst, X. Carbonell, and A. Toda, K. Monden, K. Kubo, and K. Yen, C. Ko, C. Chen, and C. A point-biserial correlation is used to measure the strength and direction of the association that exists between one continuous variable and one dichotomous variable. For example, you could use a point-biserial correlation to determine whether there is an association between salaries, measured in US dollars, and gender i.
This "quick start" guide shows you how to carry out a point-biserial correlation using SPSS Statistics, as well as how to interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a point-biserial correlation to give you a valid result.
We discuss these assumptions next. When you choose to analyse your data using a point-biserial correlation, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a point-biserial correlation. You need to do this because it is only appropriate to use a point-biserial correlation if your data "passes" five assumptions that are required for a point-biserial correlation to give you a valid result.
In practice, checking for these five assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task. Before we introduce you to these five assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated i.
This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a point-biserial correlation when everything goes well! Even when your data fails certain assumptions, there is often a solution to overcome this. Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running a point-biserial correlation might not be valid.
In the section, Procedure , we illustrate the SPSS Statistics procedure to perform a point-biserial correlation assuming that no assumptions have been violated.
First, we set out the example we use to explain the point-biserial correlation procedure in SPSS Statistics.
Active Oldest Votes. Improve this answer. So Spearman's rho is the rank analogon of the Point-biserial correlation. I don't see any problem in using Spearman's rho descriptively in this situation. It doesnt give information which is not contained in some difference of means! Even if so, would you call Spearman's rho wrong? Sad that we don't see the reviewers reasoning. It seems to be related to the test statistic of Wilcoxon's two-sample test, which is itself similar to Kendall's rank correlation between the numeric outcome and the binary group variable.
Show 4 more comments. Jon Jon 2, 1 1 gold badge 11 11 silver badges 27 27 bronze badges. However, assumptions listed are bit strong. The above statement is calulcated with the Area Under the Curve. Your answer is a bit too short, and it does not seem to help find: "the correlation between a continuous dependent variable and a categorical nominal: gender, independent variable variable". SriK SriK 1 1 silver badge 8 8 bronze badges. Sign up or log in Sign up using Google.
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