Predictors of Mobile Phone and Social Networking Site Dependency in Adulthood

Abstract

The present study explored social and psychological predictors of social networking site (SNS) and mobile phone dependency in a sample of emerging adults (ages 18–25, n = 159, M = 21.87, SD = 2.08) and young adults (ages 26–40, n = 97, M = 31.21, SD = 4.11). Path analysis revealed that SNS dependency mediated the relationship of social comparison, SNS support, and impulsivity on mobile phone dependency. Impulsivity also showed direct links to mobile phone dependency. The present findings suggest that individuals with a strong orientation toward social comparison, who perceive a strong sense of support through SNS networks, or who show difficulty with self-regulation may be at risk for SNS and mobile phone dependency.

Introduction

Eighty-five percent of adults aged 18–29 years and 79 percent of those aged 30–39 years own mobile phones with Internet capacities, known as Smartphones.¹ Likewise, 91 percent and 77 percent of 18–29- and 30–39-year-old adults, respectively, use social networking sites (SNSs) on their mobile phones.¹ The availability of mobile phones and SNSs provides important benefits, such as the ability to maintain close contact with faraway friends and family; however, some individuals develop problematic patterns of mobile phone and SNS use characteristic of dependency. Dependency occurs when SNS or mobile phone use impairs one's work, academic life, or interpersonal relationships and involves greater use than desired, unsuccessful attempts to reduce use, or complaints from friends and family about use.² The present study examined social and psychological factors as predictors of SNS and mobile phone dependency in adults.

Impulsivity, difficulty with self-regulation, is a defining feature of behavioral dependency,^3,4 including SNS^5,6 and mobile phone dependency.^7,8 A related concept, sensation seeking, the drive to seek novelty,⁹ has also been implicated in SNS¹⁰ and mobile phone dependency¹¹ and other addictive behaviors.¹² SNS and mobile phone dependency may be fueled by a desire to experience new exciting stimuli, coupled with poor impulse control.

Social factors play an undeniable role in mobile phone and SNS use. For example, getting support from others is the most important reason young people list for using SNSs.¹³ Communicating with friends and family on SNSs is associated positively with perceived social support.^14,15 SNSs offer the unique opportunity to seek social support from people who are known only virtually and who are otherwise strangers, as well as to maintain contact with faraway friends and acquaintances. Similar to findings regarding social support, perceived support received through online social relationships is associated with self-esteem and reduced feelings of depression and loneliness.¹⁶ Individuals who are lonely and find face-to-face interaction challenging may be more likely to use online means of communication, perhaps viewing it as easier.¹⁷ They may find social support online, devote more time to SNS interactions, and may find it difficult to regulate their use.^18–20 Perceptions of social support through SNSs may uniquely predict problematic SNS use, apart from traditional measures of social support.

Participation in SNSs offers opportunities for social comparison. Individuals are naturally driven to compare themselves with others to understand themselves and their place in the world.²¹ SNSs promote social comparison as they enable users to create profiles and view others' profiles, allowing the comparison of experiences. SNS participation involves exploring and presenting different aspects of the self²² and may play a role in how people understand and define themselves.^23,24

To date, research on SNS and mobile phone dependency has focused nearly exclusively on samples of high school and, more often, college students.^6,25 Although recent prevalence surveys show that adults of all ages use mobile phones and SNSs,^26,27 it is unclear whether the research findings based largely on college students, emerging adults, can be extended to adults of other ages. Emerging adulthood is a unique period of development spanning roughly from age 18 to 25, distinct from young, middle, and older adulthood and characterized by exploration, self-focus, and the subjective sense of being in-between, neither adolescent nor adult.^28,29 Some of the predictors of mobile phone and SNS dependency show developmental changes during emerging adulthood. For example, sensation seeking and impulsivity tend to decline over adolescence into emerging adulthood,³⁰ potentially influencing patterns of SNS dependency. Emerging adults may show a distinct pattern of predictors of SNS and mobile phone dependency compared with young adults over the age of 25, who may, in turn, show greater psychosocial maturity. Research has shown that adults show lower rates of SNS and phone use and dependency than adolescents.^2,31 However, it is unclear whether predictors and patterns of SNS and mobile phone dependency vary among adults, whether the likelihood of dependency shifts from emerging adulthood to young adulthood. The purpose of this study was to examine predictors of SNS and mobile phone dependency in a sample of emerging adults, aged 18–25 years, and young adults, aged 26–40 years. The present study addressed two broad research questions.

RQ1: Do emerging adults and adults differ in SNS and mobile phone use and dependency and in the proposed social and psychological predictors?

Prior research would suggest age differences in nearly all of the variables, with emerging adults also reporting higher rates of SNS and mobile phone use and dependency.

RQ2: Do psychological and social variables mediate the relationship between age and SNS dependency, and is SNS dependency a mediator of relationships among psychological and social variables and mobile phone dependency?

Figure 1 illustrates the proposed relationships among age, psychological and social variables, and SNS and mobile phone dependency. Age is expected to predict impulsivity, sensation seeking, and self-esteem. Social comparison is a means of learning about oneself, a task of emerging adulthood²⁹; therefore, age is expected to predict the use of social comparison. The few studies to date that have examined SNS and mobile phone dependency together have suggested that it is a dependence on SNSs that drives mobile phone dependency.^32,33 SNS dependency, therefore, is proposed to mediate the relationships between both social and psychological variables and phone dependency. Specifically, social comparison, social support, and SNS support are expected to positively predict SNS dependency, extending prior findings.^{14,15,18–20,34,35} Similar to prior work, impulsivity and sensation seeking are expected to positively predict SNS dependency^5–8,10 and are also permitted direct links to mobile phone dependency.¹¹

FIG. 1.

Hypothesized relationships among age and social and psychological predictors of SNS and mobile phone dependency. SNS, social networking site.

Methods

Participants

Three hundred ten U.S. adults aged 18–68 years were recruited through Amazon Mechanical Turk (MTurk). Given our interest in examining distinctions among emerging and young adults, who comprised 83 percent of the sample (51 participants were between the ages of 41 and 64 and 3 were over the age of 65), the analyses included only adults age 40 and under. The resulting sample consisted of 256 U.S. adults ranging in age from 18 to 40 (M = 25.41, 62 percent female). Sixty-seven percent identified as white, 14 percent black/African American, 8 percent Asian/Pacific Islander, 7 percent Hispanic/Latino, and 4 percent other. Nearly all participants reported having a cell phone (98 percent) and using SNSs (98 percent). Participants were divided by age group to represent emerging adults (ages 18–25, n = 159; M = 21.87, SD 2.08) and young adults (ages 26–40, n = 97, M = 31.21, SD 4.11).

Measures

SNS and mobile phone use

Participants reported daily mobile phone use on a 5-point scale representing usage of less than 1 hour each day, between 1 and 2 hours, between 3 and 4 hours, or 5 hours or more each day, including an option to indicate if they did not own a phone. SNS use was rated on the same scale. Table 1 presents the response scale, sample items, means, standard deviations, and Cronbach's alpha for all measures.

Table 1.

Response Scale, Sample Items, Means, Standard Deviations, and Cronbach's Alpha for All Measures

Scale	Response scale	Sample items	M	SD	α
Mobile phone use	1–5	—	3.57	1.04	—
SNS use	1–5	—	3.10	0.97	—
Mobile phone dependency	1–6 Likert	“I feel anxious if I have not checked for messages or switched on my mobile phone for some time”	26.66	8.38	0.85
		“I find myself engaged on the mobile phone for longer periods of time than intended.”
SNS dependency	1–6 Likert	“I often find myself using social media longer than intended”	21.2	7.00	0.81
		“I will be upset if I had to cut down the amount of time I spend using social media”
Impulsivity	1–6 Likert	“I plan tasks carefully”	21.35	5.83	0.75
		“I am self-controlled”
Sensation seeking	1–6 Likert	“I like doing things just for the thrill of it”	21.80	6.53	0.86
		“I'll try anything once”
Self-esteem	1–6 Likert	“I feel I do not have much to be proud of”	30.57	6.62	0.85
		“I certainly feel useless at times”
Social support	1–6 Likert	“There is a special person who is around when I am in need”	30.76	7.00	0.86
		“I can count on my friends when things go wrong”
SNS support	1–6 Likert	“When I am feeling down or in a difficult situation, I can find the emotional help and support that I need on social networking sites”	22.78	5.48	0.91
		“When I am feeling down or in a difficult situation, I can find someone on social networking sites who helps me make decisions”
Social comparison	1–6 Likert	“I am not the type of person who compares often with others”	22.78	5.54	0.76
		“I always pay a lot of attention to how I do things compared with how others do things”

SNS, social networking site.

Mobile phone dependency

Participants responded to 8 items from the Mobile Phone Problem Use Scale.²

SNS dependency

Seven items from the Social Media Addiction Scale³⁶ measured SNS dependency.

Impulsivity

Participants responded to 7 items from the Barratt Impulsivity Scale.³⁷

Sensation seeking

A short 6-item version of the Zuckerman Sensation Seeking Scale³⁰ measured sensation seeking.

Self-esteem

Self-esteem was measured with 7 items from the Rosenberg Self-Esteem Scale.³⁸

Social support

Seven items from the Multidimensional Scale of Perceived Social Support³⁹ measured perceived social support.

SNS support

A 4-item scale measured the degree of support participants perceive from their SNS networks.⁴⁰

Social comparison

A shortened 6-item version of the Social Comparison Orientation Scale⁴¹ examined participants' orientation toward social comparison.

Procedure

Participants were recruited online through the Amazon Mechanical Turk (MTurk) Web site, completed the survey on Survey Monkey, and received a small monetary compensation (10 cents) upon completion. MTurk is a platform through which registered users can complete computerized tasks, including surveys, for a nominal financial incentive. Research has suggested that samples recruited by MTurk yield data comparable with traditional samples of college students and community members.^42,43 MTurk workers show similar patterns of responses to measures of personality characteristics,^44,45 political orientation,⁴⁶ risk behavior,⁴⁷ and body image⁴⁸ and responses to classic psychological experiments such as the prisoner's dilemma⁴⁹ and measures of basic biases in decision-making.⁵⁰

Results

Table 2 lists the bivariate correlations. A 2 × 2 (gender × age group) multivariate analysis of variance (MANOVA) explored age and gender differences in each of the variables. There was a main effect on gender, F(10, 243) = 3.166, p = 0.001. Table 3 presents the significant gender differences as per the univariate analyses. There was no main effect of age, F(10, 243) = 1.385, p > 0.05. The interaction was not significant, F(10, 243) = 0.790, p > 0.05.

Table 2.

Correlations Among Social Networking Site, Mobile Phone, and Predictor Variables

	1	2	3	4	5	6	7	8	9	10	11
1. Age	—	−0.06	−0.10	−0.10	−0.02	−0.16^*	−0.21^**	0.12	0.07	0.10	−0.07
2. Phone use		—	0.47^**	0.36^**	0.23^**	0.04	0.09	0.03	0.07	0.11	0.11
3. SNS use			—	0.36^**	0.47^**	0.15^*	0.16^**	−0.06	−0.04	0.28^**	0.07
4. Phone dependency				—	0.76^**	0.45^**	0.29^**	−0.32^**	0.02	0.33^**	0.28^**
5. SNS dependency					—	0.36^**	0.20^**	−0.26^**	−0.04	0.47^**	0.26^**
6. Impulsivity						—	0.35^**	−0.58^**	−0.18^**	0.08	0.09
7. Sensation seeking							—	0.01	0.10	0.16^*	0.28^**
8. Self-esteem								—	0.49^**	0.02	0.00
9. Social support									—	0.24^**	0.16^*
10. SNS social support										—	0.17^**
11. Social comparison											—

p < 0.01; ^*p < 0.05.

Table 3.

Univariate Results for Gender Main Effect

	Male M (SD)	Female M (SD)	F
Sensation seeking	23.52 (6.22)	20.76 (6.51)	F(1, 252) = 12.209, p = 0.001
Perceived SNS support	13.69 (5.54)	11.52 (5.30)	F(1, 252) = 9.183, p < 0.01
Social comparison	23.60 (4.97)	22.28 (5.82)	F(1, 252) = 4.557, p < 0.05

A path model tested the proposed relationships described in Figure 1. Initial analyses suggested that non-normality and multicollinearity were not a concern. Given that the MANOVA indicated a nonsignificant main effect for age, it was excluded from the path model. Amos 23 was used to obtain maximum likelihood estimates of the model parameters.⁵¹ The path model's goodness of fit was judged with the following criteria: comparative fit index (CFI) of 0.95 or greater, adjusted goodness-of-fit index (AGFI) of 0.90 or greater, root mean squared error of approximation (RMSEA) of less than or equal to 0.08, and standardized root mean square residual (SRMR) of less than 0.05.^52–55 The results of this analysis are shown in Figure 2. The χ² global fit index was significant, meaning that the sample data are a poor fit to the theoretical model (χ² = 9.88, df = 3, p = 0.02). Other indices also suggest an inadequate fit (CFI = 0.98; AGFI = 0.89; RMSEA = 0.10; SRMR = 0.02).

FIG. 2.

Path analysis testing the hypothesized relationships among SNS and mobile phone dependency. **p < 0.001; *p < 0.01.

As suggested by Jöreskog and Sörbom,⁵³ the model was adjusted by removing nonsignificant paths. Although this method is data driven, it remains theoretically meaningful and is consistent with the hypothesized model. Figure 3 illustrates the final model. The χ² global fit index was not significant, suggesting good model fit (χ² = 5.35, df = 2, p = 0.07). Given that chi-square estimates are sensitive to sample size, Hu and Bentler⁵² suggest a χ²/df ratio of 5 or less as indicating good model fit (χ²/df = 2.58 for the present data). Other indices suggested a good fit (CFI = 0.99; AGFI = 0.94; RMSEA = 0.08, SRMR = 0.02). As hypothesized, SNS dependency predicted phone dependency; SNS dependency mediated the relationship of SNS support, social comparison, and impulsivity on phone dependency; and impulsivity showed direct links to phone dependency. The model accounted for 35 percent of the variance in SNS dependency and 61 percent of the variance in mobile phone dependency.

FIG. 3.

Final path model illustrating mediational relationship of SNS and mobile phone dependency. **p < 0.001; *p < 0.01.

Discussion

The present study examined psychological and social predictors of SNS and mobile phone dependency in a sample of emerging adults and young adults. Although prior research has suggested that with age, adults are less likely to display symptoms of SNS and phone dependency,² the present findings indicate no age differences. Prior studies sampled a wider age range of adults than the present sample, spanning from emerging adulthood (age 18) to older adulthood (up to 85 years of age). The lack of age differences in the present sample may be due to the smaller age range examined, emerging adults and young adult ages, and who tend to share similarities in social technology use. For example, in 2009, 73 percent and 72 percent of adolescents, age 12–17, and adults, age 18–29, (who today are emerging and young adults, respectively) reported participating in SNSs.⁵⁶

The present findings of no age differences in self-esteem, sensation seeking, and impulsivity contrast with prior research, indicating that all three show substantial development from adolescence to emerging adulthood.^30,57 However, developmental changes from emerging adulthood to young adulthood, as examined in this study, may be smaller in magnitude and more gradual. Future research should sample a wider age range to examine patterns of change in SNS and mobile phone dependency and their predictors over the course of adulthood rather than emphasizing differences between adolescents and adults, or emerging adults and young adults.

As hypothesized, SNS dependency predicted mobile phone dependency and mediated relationships among the psychological and social predictors and mobile phone dependency. Specifically, impulsivity, social comparison, and perceptions of SNS support predicted SNS dependency, which, in turn, predicted mobile phone dependency. Impulsivity also showed a direct link to mobile phone dependency, suggesting that difficulties in behavioral regulation may place individuals at risk for problematic use of social technology. This is consistent with findings that impulsivity is a feature in common with behavioral addictions, such as gambling and Internet use.⁵⁸

Unique to this study are findings regarding the predictive value of social comparison for SNS dependency. Individuals with a strong orientation toward social comparison tend to be highly sensitive to others and may experience more uncertainty and instability in their self-concept, determining their self-worth through comparisons with others.^35,41 Individuals with a strong orientation toward social comparison may turn to SNSs to explore their emerging selves, share their profiles, and draw comparisons with others, similar to the offline experimentation essential to establishing a sense of identity.^23,24 The present findings suggest that individuals with a strong orientation toward social comparison may be at risk for SNS dependency, and in turn, mobile phone dependency, relying on SNSs to satisfy their drive to learn about and evaluate themselves through comparisons with others. Further research is needed to understand this process by gathering information about the nature of SNS participation and consumption, how these interactions reflect and contribute to self-conceptions, and what role SNS participation plays in identity development. Are individuals with strong orientations toward social comparison more likely to explore through SNS use? Do patterns among social comparison, SNS sharing, and SNS dependency change with age as identity issues are revisited?

Prior research has suggested that those who lack social support in everyday life tend to turn to online sources of support to replace contact with family and friends.^17,19 The present findings are inconsistent with this perspective as SNS dependency was predicted only by perceptions of social support received through SNSs, not everyday social support from family and friends. Individuals may not necessarily trade one type of support for the other.^59,60 For example, in one study, despite substantial overlap between college students' online and offline friends, many indicated that their closest SNS friends were different than their closest offline friends.⁶¹ Individuals may use SNSs to maintain and strengthen connections with offline friends that they do not encounter often in their day-to-day lives. Further research should examine the degree and nature of support that places individuals at risk for SNS dependency. What types of interactions and relationships pose risks for problematic SNS use? When does perceived social support through SNSs cross the threshold from predicting well-being to SNS dependency?

Several limitations must be considered in drawing conclusions from the present study. Participants were solicited from an online job board and therefore may be more technologically savvy than, and not representative of, their peers. According to the Pew Research Center, SNS use is ubiquitous with 90 percent of adults, age 18–29, reporting using SNSs²⁷ compared with 99 percent in our sample. The use of community samples can improve the generalizability of future work. A second limitation concerns the use of self-report measures, which are sensitive to biases such as social desirability that may influence participants' disclosure. Future research might pair self-report measures with other methods, such as observations of SNS behavior. Third, conclusions about development in this study were based on cross-sectional data. Longitudinal data are needed to determine whether our inferences about development, in this case, the lack of age differences, are supported. Finally, although path analysis permitted us to examine proposed relationships among the variables, causality cannot be inferred as the data are correlational. Future research might experimentally manipulate variables, such as priming social comparison tendencies, to determine their influence on SNS and mobile phone dependency.

In conclusion, the present study offers several unique contributions to understanding SNS and mobile phone dependency. First, emerging adults and young adults report similar levels of SNS and mobile phone dependency. Second, difficulties in self-regulation predict mobile phone dependency directly and indirectly, through SNS dependency. Finally, SNS dependency motivates mobile phone dependency and mediates the effects of social comparison and perceived SNS support on mobile phone dependency. That is, the tendency to engage in social comparison, to self-evaluate in reference to others, and perceiving supportive SNS relationships predict problematic use of SNSs and, in turn, mobile phone dependency. Further work is needed to understand how patterns of interactions on SNSs (what is shared and consumed and by whom) influence users' sense of support and how support, along with tendencies toward social comparison, relates to problematic use of SNSs.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

References

Anderson

. (2015) Technology device ownership: 2015. Pew Research Center. www.pewinternet.org/2015/10/29/technology-device-ownership-2015/ (accessed August 8, 2016).

Bianchi

, Phillips

DJG

. Psychological predictors of problem mobile phone use. CyberPsychology & Behavior, 2005; 8:39–51.

Billieux

. Problematic use of the mobile phone: a literature review and a pathways model. Current Psychiatry Reviews, 2012; 8:299–307.

Billieux

, Van der Linden

, d'Acremont

, et al. Does impulsivity relate to perceived dependence on and actual use of the mobile phone?. Applied Cognitive Psychology, 2007; 21:527–537.

AMS

, Cheung

, Ku

, et al. Psychological risk factors of addiction to social networking sites among Chinese smartphone users. Journal of Behavioral Addiction, 2013; 2:160–166.

Andreassen

. Online social network site addiction: a comprehensive review. Current Addiction Reports, 2015; 2:175–184.

Billieux

, Maurage

, Lopez-Fernandez

, et al. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2015; 2:156–162.

Roberts

, Pullig

, Manolis

. I need my smartphone: a hierarchical model of personality and cell-phone addiction. Personality and Individual Differences, 2015; 79:13–19.

Zuckerman

. (1994) Behavioral expressions and biosocial bases of sensation seeking. New York: Cambridge University Press.

10.

Wang

J-L

, Jackson

, Zhang

D-J

, et al. The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students' uses of social networking sites (SNSs). Computers in Human Behavior, 2012; 28:2313–2319.

11.

Moreau

, Laconi

, Delfour

, et al. Psychopathological profiles of adolescent and young adult problematic Facebook users. Computers in Human Behavior, 2015; 44:64–69.

12.

Leung

. (2008) Leisure boredom, sensation seeking, self-esteem, and addiction. In Konijn

, Utz

, Tanis

, Barnes

, eds. Mediated interpersonal communication. New York: Routledge, pp. 359–381.

13.

Park

, Kee

, Valenzuela

. Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 2009; 12:729–733.

14.

Kraut

, Kiesler

, Boneva

, et al. Internet paradox revisited. Journal of Social Issues, 2002; 58:49–74.

15.

, Ozkaya

, LaRose

. How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior, 2014; 30:69–78.

16.

Shaw

, Gant

. In defense of the internet: the relationship between internet communication and depression, loneliness, self-esteem, and perceived social support. CyberPsychology & Behavior, 2002; 5:157–171.

17.

Davis

, Flett

, Besser

. Validation of a new scale for measuring problematic internet use: implications for pre-employment screening. CyberPsychology & Behavior, 2002; 5:331–345.

18.

Kim

, LaRose

, Peng

. Loneliness as the cause and the effect of problematic internet use: the relationship between internet use and psychological well-being. CyberPsychology & Behavior, 2009; 12:451–455.

19.

Caplan

. Preference for online social interaction: a theory of problematic internet use and psychosocial well-being. Communication Research, 2003; 30:625–648.

20.

Caplan

. A social skill account of problematic internet use. Journal of Communication, 2005; 55:721–736.

21.

Festinger

. A theory of social comparison processes. Human Relations, 1954; 7:117–140.

22.

Manago

, Graham

, Salimkhan

. Self-presentation and gender on MySpace. Journal of Applied Developmental Psychology, 2008; 29:446–458.

23.

Michikyan

, Dennis

, Subrahmanyam

. Can you guess who I am? Real, ideal, and false self-presentation on facebook among emerging adults. Emerging Adulthood, 2015; 3:55–64.

24.

Subrahmanyam

, Šmahel

. (2011) Constructing identity online: identity exploration and self-presentation. In Subrahmanyam

, Šmahel

, eds. Digital youth. New York: Springer, pp. 59–80.

25.

Kuss

, Griffiths

. Online social networking and addiction—a review of the psychological literature. International Journal of Environmental Research and Public Health, 2011; 8:3528–3552.

26.

Smith

. (2015) U.S. Smartphone use in 2015. Pew Research Center. www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/ (accessed August 8, 2016).

27.

Perrin

. (2015) Social media usage: 2005–2015. Pew Research Center. www.pewinternet.org/2015/10/08/social-networking-usage-2005/ (accessed August 8, 2016).

28.

Arnett

. Emerging adulthood: a theory of development from the late teens through the twenties. The American Psychologist, 2000; 55:469–480.

29.

Arnett

. (2004) Emerging adulthood: the winding road from the late teens through the twenties. New York: Oxford University Press.

30.

Steinberg

, Albert

, Cauffman

, et al. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model. Developmental Psychology, 2008; 44:1764–1778.

31.

Sheldon

. Profiling the non-users: examination of life-position indicators, sensation seeking, shyness, and loneliness among users and non-users of social network sites. Computers in Human Behavior, 2012; 28:1960–1965.

32.

Roberts

, Yaya

LHP

, Manolis

. The invisible addiction: cell-phone activities and addiction among male and female college students. The Journal of Behavioral Addictions, 2014; 3:254–265.

33.

Salehan

, Negahban

. Social networking on smartphones: when mobile phones become addictive. Computers in Human Behavior, 2013; 29:2632–2639.

34.

Valkenburg

, Peter

, Schouten

. Friend networking sites and their relationship to adolescents' well-being and social self-esteem. CyberPsychology & Behavior, 2006; 9:584–590.

35.

Vogel

, Rose

, Okdie

, et al. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Personality and Individual Differences, 2015; 86:249–256.

36.

Al-Menayes

. Psychometric properties and validation of the Arabic social media addiction scale. Journal of Addiction, 2015; 2015:291743.

37.

Spinella

. Normative data and a short form of the Barratt Impulsiveness Scale. The International Journal of Neuroscience, 2007; 117:359–368.

38.

Rosenberg

. (1965) Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

39.

Zimet

, Dahlem

, Zimet

, Farley

. The multidimensional scale of perceived social support. Journal of Personality Assessment, 1988; 52:30–41.

40.

Frison

, Eggermont

. Exploring the relationships between different types of facebook use, perceived online social support, and adolescents depressed mood. Social Science Computer Review, 2016; 34:153–171.

41.

Gibbons

, Buunk

. Individual differences in social comparison: development of a scale of social comparison orientation. Journal of Personality and Social Psychology, 1999; 76:129–142.

42.

Casler

, Bickel

, Hackett

. Separate but equal? A comparison of participants and data gathered via Amazon's MTurk, social media, and face-to-face behavioral testing. Computers in Human Behaviour, 2013; 29:2156–2160.

43.

Chandler

, Shapiro

. Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 2016; 12:53–81.

44.

Feitosa

, Joseph

, Newman

. Crowdsourcing and personality measurement equivalence: a warning about countries whose primary language is not English. Personality and Individual Differences, 2015; 75:47–52.

45.

Buhrmester

, Kwang

, Gosling

. Amazon's mechanical turk: a new source of inexpensive, yet high-quality, data?. Perspectives on Psychological Science, 2011; 6:3–5.

46.

Berinsky

, Huber

, Lenz

. Evaluating online labor markets for experimental research: Amazon.com's mechanical turk. Political Analysis, 2012; 20:351–368.

47.

Eriksson

, Simpson

. Emotional reactions to losing explain gender differences in entering a risky lottery. Judgment and Decision Making, 2010; 5:159–163.

48.

Tylka

, Wood-Barcalow

. The Body Appreciation Scale-2: item refinement and psychometric evaluation. Body Image, 2015; 12:53–67.

49.

Horton

, Rand

, Zeckhauser

. The online laboratory: conducting experiments in a real labor market. Experimental Economics, 2011; 14:399–425.

50.

Paolacci

, Chandler

, Stern

. Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 2010; 5:411–419.

51.

Arbuckle

. (2014) IBM SPSS Amos 23 User's Guide. ftp://public.dhe.ibm.com/software/analytics/spss/documentation/amos/23.0/en/Manuals/IBM_SPSS_Amos_User_Guide.pdf (accessed August 8, 2016).

52.

, Bentler

. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equations Modeling: A Multidisciplinary Journal, 1999; 6:1–55.

53.

Jöreskog

, Sörbom

. (1996) LISREL 8: user's reference guide. Chicago, IL: Scientific Software International, Inc.

54.

Tabachnick

, Fidell

. (2007) Using multivariate statistics, 5th ed. New York: Allyn and Bacon.

55.

Diamantopoulos

, Siguaw

. (2000) Introducing LISREL. London: Sage.

56.

Lenhart

, Purcell

, Smith

, et al. (2010) Social media and young adults. www.pewinternet.org/2010/02/03/social-media-and-young-adults/ (accessed August 8, 2016).

57.

Orth

, Robins

, Widaman

. Life-span development of self-esteem and its effects on important life outcomes. Journal of Personality and Social Psychology, 2012; 102:1271–1288.

58.

Lee

, Choi

J-S

, Shin

Y-C

, et al. Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychology, Behavior, and Social Networking, 2012; 15:373–377.

59.

Grieve

, Indian

, Witteveen

, et al. Face-to-face or Facebook: can social connectedness be derived online?. Computers in Human Behavior, 2013; 29:604–609.

60.

Indian

, Grieve

. When Facebook is easier than face-to-face: social support derived from Facebook in socially anxious individuals. Personality and Individual Differences, 2014; 59:102–106.

61.

Subrahmanyam

, Reich

, Waechter

, et al. Online and offline social networks: use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 2008; 29:420–433.