Psychometric Properties of the Smartphone Addiction Scale – Short Version (SAS-SV) in a Sample of Iranian Adolescents

Abstract

Smartphone addiction among adolescents has become a major concern. The present study aims to investigate the psychometric properties of the Persian Smartphone Addiction Scale – Short Version (SAS-SV). A sample of 398 adolescents (52% girls and 48% boys) completed the SAS-SV, the Kutcher Adolescent Depression Scale (KADS), and the Young Internet Addiction Test (IAT). Factor analysis showed that the Persian SAS-SV consisted of one factor and is a reliable (Cronbach’s α= 0.82) and valid (CFA model fit: RMSEA = 0.7, CFI = .95, AGFI = .92) measure. Strong correlations with depressive symptoms (r = 0.41) and internet addiction scores (r = 0.57) were also found. A high prevalence of smartphone addiction in adolescents and the correlation with depression and internet addiction signals that more attention should be given to this issue. The Persian SAS-SV is a valid and reliable scale to be used for Persian speaking samples.

Keywords

Smartphone addiction internet addiction adolescence measurement Persian SAS-SV

Smartphone Addiction as an Emerging Problem

There are nearly 2.6 billion smartphone users all over the world and it is assumed that this number would go up to 6.1 billion by 2020 (Ericson Mobility, 2017). This means that in a couple of years, 70% of the people all over the world will be smartphone users. In Iran, official reports claim that nearly 47 million people use smartphones daily (MCIT, 2017). Mobile phones are especially attractive for adolescents all over the world – and also in Iran: According to MCIT (2017), 88% of adolescents and young adults (age range 15–25 years) use mobile phones.

The popularity of smartphone use is a consequence of its efficiency in meeting the needs of users. A smartphone can be easily carried and used almost everywhere and in nearly all situations. Moreover, there are many smartphone applications that can help people do their jobs faster, stay in contact with friends and family, or get amused (e.g. by games). Besides all these benefits, some possible harms are also associated with smartphone usage. Such harms are usually caused by one being too attracted to these devices and result in overusing or getting addicted to them. Turel and Serenko (2010) stated that smartphone addiction can be a type of non-substance addiction known as a behavioral addiction. In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013), behavioral addiction is defined as “a type of addiction in which the behavioral and cognitive symptoms such as an increased decrease in the control over the toleration and rehabilitation symptoms are observed similar to substance addictions” (Grant & Black, 2013, p. 481). There is still debate about the behaviors that constitute behavioral addictions because some of these behaviors should be classified as the constituents of impulse control disorders (Grant, Potenza, & Weinstein, 2010). Moreover, Billieux, Maurage, Lopez-Fernandez, Kuss, and Griffiths (2015) concluded that given the lack of adequate research evidence on the neurobiological and behavioral similarities between smartphone addiction and the common addictive behaviors, perhaps it is better not to refer to such behaviors as “addiction”. Hence, these authors recommended the use of the term “problematic use”. Kim, Lee, Nam, and Chung (2014) suggested the term “addiction proneness” for this purpose. Kim (2013) also considered excessive Internet use as a possible sign of “smartphone addiction”. However, there is considerable evidence suggesting that the problematic use of the Internet should be regarded as a “behavioral addiction” (Aboujaoude, 2010; Spada, 2014; Weinstein & Lejoyeux, 2010). Moreover, Lee, Chang, Lin, and Chang, (2014) observed some of the constituents of substance addictions in smartphone addiction such as tolerance, rehabilitation, compulsive symptoms, and serious impairment of personal performance. However, research on (heavy) smartphone use and its effect on the lives of people, especially adolescents, is still in its early stages.

Tindell and Bohlander (2012) found that most students use smartphones in class. The excessive use of smartphones can have various destructive physical and mental consequences. Smartphone addiction can lead to some physical harms on fingers, arms, the neck vertebrae, and the spine (Binning, 2010; Cha & Seo, 2018; Ming, Pietikainen, & Hanninen, 2006) and also cause sleep disturbance (Kauderer & Randler, 2013). Smartphone addiction can cause some psychological problems as well. Numerous studies have found depression, anxiety, criminal behavior, aggression, attention deficit, irritability, low self-esteem, and academic failure (Cha & Seo, 2018; Ko, Hsiao, Liu, YenYang, & Yen, 2010; Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015; Takao, Talahashi, & Kitamura, 2009; Turel & Serenko, 2010), as being the most prevalent psychological consequences of such behavioral addiction. The damage caused by smartphone addiction is not limited to adolescents – these negative consequences can extend to the subsequent periods of life, with the persistence of these behaviors. Depression has been reported to correlate strongly with internet addiction and smartphone addiction (Billieux et al., 2015; De-Sola Gutiérrez, de Fonseca, & Rubio, 2016; Hussain, Griffiths, & Sheffield, 2017; Park & Choi, 2017) which is the result of a lack of energy for getting involved in social activities, and also of a tendency to avoid daily responsibilities in one’s life (Elhai, Dvorak, Levine, & Hall, 2017).

The Need to Study the Psychometric Properties of the Persian Translation of the SAS-SV

As adolescents are quite vulnerable to smartphone addiction and its consequences (Salehan & Negahban, 2013), it proves necessary to study this phenomenon and to design appropriate measurement instruments. The short version of the Smartphone Addiction Scale (SAS-SV) is among the most widely used instruments in the field. The SAS-SV has been validated in Turkish (Noyan, Darçín, Nurmedov, Yílmaz, & Dilbaz, 2015), Spanish and French (Lopez–Fernandez, 2017), Italian (De Pasquale, Sciacca, & Hichy, 2017), Arabic (Sfendla et al., 2018), and Chinese (Luk et al., 2018) populations, while the psychometric properties of the Persian version have not yet been investigated and reported. The present study has two aims: 1) to examine the psychometric properties of the Persian version of SAS-SV; and 2) to explore the correlations between smartphone addiction, Internet addiction, and depression in adolescents.

Methods

Participants and Data Collection

The present study is based on a convenience sample of students aged 12 to 18 years. To obtain adequate data from participants, the authors first had to gain permission from the Tehran Department of Education to conduct the study with adolescents at schools. After receiving the official permission, two girls and two boys schools were randomly selected from each part of Tehran (i.e. the north, south, west, and east of Tehran) resulting in eight boys and eight girls schools. In the first step, research staff members visited the selected schools and met the respective principal. The principals had already been informed about the study and its aims by the Education Department of Tehran and – accordingly – had already asked the respective school counselor to collaborate. At a certain time for each class, students were informed about the study aims and procedures. Students were also told that they were not obliged to participate in the study and if they would, their questionnaires would be unnamed and kept only for the researcher for the study aims. For students aged 17 and younger who were willing to participate in the study, their parents’ consent was needed. Therefore, parents of these students were informed about the aims and procedures of the study and those students whose parents gave consent could participate in the study. A total of 398 male (n = 190) and female (n = 208) students aged 12 to 18 years participated in this study. The descriptive statistics of the study groups are given in Table 1. Data were statistically analyzed using SPSS, version 24.0, and LISREL, version 8.8. The study procedures were carried out in accordance with the Declaration of Helsinki. All subjects were informed about the study and provided informed consent.

Table 1

Descriptive Statistics

		Min	Max	Range	Total	Mean	SD
Age	Female	12	18	7	208	15.22	1.49
	Male	12	18	7	190	15.77	1.81
	Total	12	18	7	398	15.5	1.79

Note. Min = Minimum; Max = Maximum; SD = Standard Deviation.

Measures

The Smartphone Addiction Scale-Short Version (SAS-SV)

The original smartphone addiction scale (SAS) was developed by Kwon, Lee et al. (2013); and a 10-item short version of this scale (SAS-SV) was developed for adolescents (Kwon, Kim, Cho, & Yang, 2013). This scale is rated in a Likert type from 1 (“completely disagree”) to 6 (“completely agree”). The overall score of this scale varies between 10 and 60, and the highest score suggests the highest level of addiction to smartphones in the past year (sample item: “Missing planned work due to smartphone use”). The SAS-SV was translated into Persian and then it was re-translated into English. The English translation was compared to the original English version by an English expert. Then, the Persian scale was administered to 20 adolescents to assess the comprehensiveness and clarity of the statements: adolescents were asked about the meaning of each item to check whether the items conveyed the right meaning or not. The items were finally approved after some modifications. The resulted Persian translation of SAS-SV was then used in the present study. Cronbach’s alpha coefficients obtained in the original and the present study were .91 and .82, respectively.

Kutcher Adolescent Depression Scale (KADS-11)

This scale, which was developed to measure depression in adolescents (LeBlanc, Almudevar, Brooks, & Kutcher, 2002), is a self-report, 11-item instrument. Respondents are asked to answer each question concerning their mood on a four-point Likert scale (from 0 for “minimum agreement” to 3 for “maximum agreement”), with a total score ranging from 0 to 33. It consists of two subscales: the major depression and the suicide subscales (Bravo, Mayoral, Laorden, & Moreno, 2014). For the Persian version of KADS-11, Habibi, Hamedinia, Asgarinezhad, and Kholghi (2015) found three scales and reported Cronbach’s alpha coefficients of .79, .82, and .83 for the major depression, physical factor, and suicide subscales, respectively.

Young Internet Addiction Test (IAT)

This test was developed by Young and Rogers (1998). It consists of 20 items and is rated on a five-point Likert scale. A higher score reflects a higher level of addiction to the Internet. Young assessed the reliability of this test in several studies and reported Cronbach’s alpha coefficients between 0.74 and 0.89. Alavi, Islami, Mer’ati, Najafi, Janatifard, and Rezapour (2010) also reported Cronbach’s alpha coefficient of .88 for the Persian version of this test. They also reported an optimum cut-off score of 46.

Results

Psychometric Properties of the Persian SAS-SV

The following assumptions were examined before assessing the fit of the confirmatory factor analysis model against the data: 1) assumption of normality of variables distribution; 2) assumption of a linear relationship between the variables; 3) multiple observed variables (having at least two observed variables for each endogenous and exogenous latent variable); 4) over-identified model; 5) multicollinearity among the variables ruled out; and 7) interval scale assumption (Curran, West, & Finch, 1996). Performance on the SAS-SV was normally distributed (skewness = –0.11 [SE = 0.09]; kurtosis = 0.07 [SE = 0.16]), with no evidence of univariate or multivariate outliers.

Model Fit

The fit of the measurement model was examined using LISREL, version 8.8. This model was a single-dimensional model in which 10 questions were loaded. The method of robust maximum likelihood to non-normality of distribution was used to estimate the model and the following indices were used to assess the fitness of the model. As shown in Table 2, confirmatory factor analysis displayed that the factor structure provided a good fit to the data.

Table 2

Model Fit Index

	X²	Df	RMR	SRMR	CFI	NFI	PNFI	IFI	RFI	GFI	AGFI	RMSEA
1 Factor	96.00**	33	0.052	0.052	0.97	0.95	0.70	0.97	0.94	0.95	0.92	0.071

Note. RMR = Root Mean Square Residual, SRMR = Standardized RMR, CFI = Comparative Fit Index, NFI = Normed Fit Index, PNFI = Parsimony Normed Fit Index, IFI = Incremental Fit Index, GFI = Goodness of Fit Index, AGFI = Adjusted Goodness of Fit Index, RMSEA = Root Mean Square Error of Approximation.

Analysis of the difference in the given model fit indicated a good model fit. In other words, the results supported the single-factor model. Moreover, the analysis of the model fit indices showed a relatively satisfactory fit between the given model and the data. The CFI, NFI, PNFI, IFI, RFI, AGFI, and RMSEA and SRMR indices suggested a highly satisfactory and a good fit (Tabachnick & Fidell, 2013). In addition, the fit was acceptable based on the Chi-Square to the degree-of-freedom ratio (Table 2 and Fig. 1).

Figure 1

The standardized statistics of the items of the SAS-SV using the single-factor model.

Description of Loadings

Results from the Confirmatory Factor Analysis (CFA) for a single-factor structure are presented in Table 3: factor loadings, the parameter estimation standard errors, the results of the t-test carried out to determine the significance of the parameters, and the coefficient of determination of the parameters. Except for item 3, the factor loadings were higher than.50. For the corrected item-total correlations, only 1 item was below.40 (item 3). All alpha values remained >.79 if any item was deleted (see Table 2 and Fig. 1).

Table 3

Standardized Factor Loadings for the Single-Factor Solution of the SAS-SV Identified Using Confirmatory Factor Analysis and Descriptive Statistics for all SAS-SV Items

	Items statistics			Item-total statistics
Items	M	SD	F.L.	V	I.T.	C.D.
ITEM 1	3.31	1.674	0.65	95.98	0.419	0.813
ITEM 2	2.61	1.616	0.78	95.91	0.443	0.810
ITEM 3	2.86	1.743	0.33	102.45	0.200	0.835
ITEM 4	3.90	1.860	0.56	91.53	0.492	0.806
ITEM 5	3.14	1.724	0.73	89.06	0.630	0.791
ITEM 6	2.91	1.627	0.77	89.94	0.646	0.790
ITEM 7	2.62	1.693	0.64	92.36	0.531	0.801
ITEM 8	2.93	1.696	0.68	90.49	0.594	0.795
ITEM 9	3.55	1.669	0.62	90.77	0.597	0.795
ITEM 10	3.24	1.838	0.51	91.16	0.512	0.803

Note. M = Mean, SD = Standard Deviation, FL = Factor Loadings, V = Scale Variance if item deleted, I.T. = Corrected Item-Total Correlations, C.D. = Cronbach’s Alpha if item deleted.

As it is shown in Table 4, additional findings revealed a slightly higher score of the SAS-SV for girls (M = 31.70) than boys (M = 29.53). This difference was not significant though.

Table 4

SAS-SV Scores for Girls and Boys

Variable		Min	Max	Range	Total	Mean	SD
SAS-SV	Female	11	58	47	208	31.70	10.57
	Male	10	60	50	190	29.53	10.72
	Total	10	60	50	398	30.85	10.67

Note. Min = Minimum, Max = Maximum, SD = Standard Deviation.

Reliability and Convergent Validity

As presented in Table 3, Cronbach’s α indicated good internal reliability for a single factor, α= 0.82; the Guttmann Split-Half coefficient was.79. The correlation between smartphone addiction and other psychological variables demonstrated that there was a significant positive relationship between smartphone addiction and Internet addiction (r = .57, p < 0.001) and a significant positive relationship between smartphone addiction and depression (r = .41, p < 0.001). However, no significant relationship was observed between age and smartphone addiction (r = .027, p = 0.1).

Discussion

The primary aim of this study was to examine the psychometric properties of the Persian version of the SAS-SV. In accordance with the initial version of the SAS-SV (Kwon et al., 2013), the findings of the present study revealed that the Persian version loaded on one scale with high validity and a Cronbach’s alpha coefficient of.82. The only item with a factor loading <.50 was Item 3 (“Feeling pain in the wrists or at the back of the neck while using a smartphone”). This might be due to what this item is about (physical pain). Since adolescents are usually in better health and suffer from physical pains in the neck or wrist less frequently than older individuals, the physical consequences of smartphone addiction might not be revealed so quickly. This item might bear higher loading if the scale is carried out on a sample of older individuals.

The second aim of the present study was to investigate the relationship between smartphone addiction and depression as well as internet addiction among adolescents. The positive significant correlation observed in the present study between Internet addiction and smartphone addiction is in line with recent research findings (Kim, 2015; Kim & Hwang, 2016; To, Liao, & Huang, 2017; Thorsteinsson & Davey, 2014; Young & Rogers, 1998). This correlation can be understood as follows. On one hand, Internet usage is widespread among adolescents and of all kinds of devices used to connect to the Internet (like PCs, tablets, and laptops), smartphones are one of the most appealing ones. This is due to the fact that smartphones are normally more lightweight, cheaper, and more user-friendly than other devices. On the other hand, as statistics show, one of the main features of smartphones is that they allow the users to stay connected with their friends and families via social media, which requires them to stay online. This can lead to a more intense use of online services and may lead to or exacerbate Internet addiction.

The strong significant relationship found in the present study between adolescents’ depression and addiction to smartphones is in line with many other previous studies on internet addiction (Choi & Kim, 2013; Kim, 2015; Kim & Hwang, 2016; Thorsteinsson & Davey, 2014; Young & Rogers, 1998). Smartphone addiction and depression can trap adolescents in a vicious cycle. In other words, adolescents suffering from depression who probably suffer from inadequate social support and a lack of mental and physical energy required for mobility and enjoyable relationships rely on a smartphone to overcome these deficiencies, meet these needs more easily and quickly, and avoid the outside world unpleasant experiences. However, given the lack of joyfulness of the outside world and the immediate joy associated with social networks, gaming, and other features of smartphones, adolescents overspend time on these networks. As a result, adolescents spend too much time on cyberspace using a smartphone to forget the actual world and real-world issues and problems.

The lower prevalence of smartphone addiction among adolescents below the age of 14 as compared to the other two groups is in line with findings by Park and Park (2014), who reported a lower level of Internet addiction among younger adolescents and children. They found that children do not become easily addicted to mobile phones because they are not old enough to make rational decisions. According to the report published on the Official Portal of Measuring Information Society of Iran (2017), the most attractive feature the Internet offers to users, especially adolescents, is the possibility of using social media to communicate with others. Compared with younger ones, older adolescents usually have wider social connections and therefore may use their smartphones more frequently (than younger ones) in order to connect with others. Dissatisfaction with family also arises at the end of adolescence (Lewinsohn, Rohde, Klein, & Seeley, 1999) and thus increases the tendency of adolescents to stay aloof from the family by playing with their smartphones. Another reason for the rise of smartphone addiction might be the increase of depression, as one of the most common factors in smartphone addiction. With the cumulative probability of depression rising from around 5% in early adolescence to as high as 20% by the end of that time (Hankin et al., 1998) smartphone addiction can also rise in late adolescence.

The present study revealed no significant difference between the scores of girls and boys in smartphone addiction. This is in line with the findings regarding this issue. Although some studies have reported higher prevalence rates of smartphone addiction among females (Pugh, 2017) or males (Öztunc, 2013), several studies have found no significant gender differences (Demirci, Orhan, Demirdas, Akpunar, & Sert, 2014). On one side, higher prevalence rates of depression (known as a predictor of smartphone addiction) and a more pronounced need to socialize (also known as a predictor of smartphone addiction) among girls can increase the likelihood of smartphone addiction among girls (compared with boys). On the other side, more intense use of online gaming in boys compared with girls (Lopez, 2018) makes boys more prone to smartphone addiction.

Conclusion

This is the first published study investigating the psychometric properties of the Persian translation of the SAS-SV. The current findings proved this instrument to be a reliable tool for measuring smartphone addiction in adolescents. We found a significant correlation between depression, internet addiction, and smartphone addiction. Older adolescents were more inclined to smartphone addiction than younger ones. Given the potential for smartphone addiction to affect health behaviors and academic performance (e.g. physical activity), this instrument can also be used in health promotion settings.

Conflict of Interest

All authors declare that they have no conflicts of interest.

Bio Sketches

Sadeq Fallahtafti, Ph.D. candidate of psychology, Tarbiat Modares University, Tehran, Iran. Research interests: behavioral addiction, adolescence, and risk behaviors. He is also interested in studying romantic relationships.

Nikzad Ghanbaripirkashani, Ph.D. candidate of clinical psychology, Shahid Beheshti University, Tehran, Iran. Research interests: behavioral addiction, adolescents’ problems, and risk behaviors.

Seyed Shahram Alizadeh, Master of child and adolescent clinical psychology, Shahid Beheshti University, Tehran, Iran. Research interests: behavioral addiction, adolescents’ problems and risk behaviors, sensory processing model, and attachment theory.

Ramin Safiyari Rovoshi, M.A student of child and adolescent clinical psychology, Shahid Beheshti University, Tehran, Iran. Research interests: adolescent developmental problems.

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