Design and Validation of the Brief Self Online Scale (SO-8) in Early Adolescence: An Exploratory Study

Abstract

For the Z-Generation, the Internet has become a very important experimentation laboratory for the discovery and validation of their identity. Despite the importance of the process of building the self in the adolescent, there are hardly any validated instruments that measure the self online. The aim of this research was to design and validate the Brief Self Online Scale (SO-8). A total of 843 students (384 boys, 45.6%), with an age range of 10 to 14 years participated. Confirmatory factor analysis (CFA) confirmed the hypothesized model of two correlated factors (Online Self-Perception and Online Idealized Projection), previously obtained through exploratory factor analysis (EFA). The reliability coefficients of Self Online dimensions were adequate. Indicators of convergent validity were obtained, finding significant correlations with self-concept, problematic Internet use, and online emotional intelligence. The SO-8 has adequate psychometric properties to be considered a reliable and valid tool to measure the construct of the Self Online in adolescents.

Keywords

self-concept measurement/validation gender/gender differences identity

Introduction

The technological advancement of recent decades and the transcendence of the Internet in our daily lives have generated a new way of understanding the world and relating to other people (Author et al., 2018; Belk, 2016; Emanuel et al., 2014; Iranzo, Buelga, Cava, & Ortega-Barón, 2019). This rise and importance of Internet and social media connectivity in adolescence is largely justified by the characteristics of adolescence. This developmental stage is a period of many changes at the biopsychosocial level in which the peer group acquires central relevance in the development of identity. During adolescence, minors wonder who they are, what they are like, and what their place is concerning others (Poletti & Rak, 2014).

The definition of self (the conception of oneself) has been a source of controversy and debate throughout the scientific literature because of the multiple approaches to it according to different psychological currents (Sassenfeld, 2011). Rosenberg (1979) defined the self-concept as “the totality of thoughts and feelings of an individual that refer to oneself” (p. 7). Research has consistently demonstrated the influence that the self-concept has on the adolescent’s psychosocial adaptation, social support, or emotional intelligence (Calero, Barreyro, & Injoque-Ricle, 2018). From this perspective, the self is a very important psychosocial variable to understand adolescents in the different contexts in which they relate, both online and offline (Belk, 2016).

According to the co-construction model, which indicates the existence of parallelism and connection between the offline and online settings (Subrahmanyam & Smahel, 2011), research has focused on the study of different aspects of the traditional self in the online environment. Thus, it has been found that the use of technologies, especially social networks, allows adolescents to experiment with identity and decide what information and image about themselves they want to present to others (Poletti & Rak, 2014).

The self online refers to people’s conception of themselves based on the use and interactions they conduct over the Internet. In this regard, authors like Ching and Foley (2014) and Poletti and Rak (2014) suggest that the self online allows adolescents to construct themselves in different ways and offer a version of themselves according to their desires and preferences. In this line, Knutzen and Kennedy (2012) conclude that social interaction in a virtual environment can change the way adolescents perceive themselves. Moreover, as many researchers have shown, in each distinct cultural context, individuals adapt the technologies to identities that are culturally relevant in the online context (Campbell & Haynes, 2012).

Other investigations have been concerned with the clarity and continuity of the self-concept in online and offline settings (Merdin-Uygur, Sarial-Abi, Gurhan-Canli, & Hesapci, 2018; Valkenburg & Peter, 2008). In this regard, some studies find that the self-presentation is quite consistent in both contexts (Back et al., 2010; Marriott & Buchanan, 2014). However, other studies find that, in the online setting, people tend to idealize their self or modify certain aspects of themselves (Fullwood, James, & Chen-Wilson, 2016; Michikyan, Subrahmanyam, & Dennis, 2014). For this reason, adolescents can build and rebuild their self through electronic devices to project to others any desired image of their self (Fullwood et al., 2016; Strimbu & O’Connell, 2019). Considering that social validation is very important in the development of the adolescent’s identity, we can understand this need to project an idealized image of the self to obtain social recognition also in the online environment (Buelga et al., 2012).

Concerning the self online measurement tools, many investigations use traditional self offline scales to study and relate the self online to other online variables and behaviors (Delgado, Escortell, Martínez-Monteagudo, Ferrández-Ferrer, & Sanmartín, 2019; Mathew & Raman, 2020). Other investigations have developed surveys, interviews, or experiments designed ad hoc to evaluate different aspects of the self in the online environment (Emanuel et al., 2014; Fox, Bacile, Nakhata, & Weible, 2018). Hardly any questionnaires have been designed and validated to evaluate this construct, except for the Presentation of Online Self Scale (POSS) of Fullwood et al. (2016). However, this scale does not evaluate other important aspects of the self, such as the perception and assessment of the self in the online setting, closely associated with the online self-concept.

Taking into account the importance of the self online in adolescence and that there are very few instruments to measure this new construct, the main objective of this study has been to design the Brief Self Online Scale (SO-8) for adolescents and to establish its psychometric properties. Also, to establish convergent validity indicators, the associations between scores on the SO-8 with other variables, we studied the offline self-concept, problematic Internet use, and online emotional intelligence.

Method

Design and Participants

The instrumental validation study was conducted during October and November 2019. The initial number of participants was 863 students, although the final sample was made up of 843 participants of whom 384 were boys (45.6%) and 459 girls (54.4%). The mean and standard deviation of age was 12.16 ± 1.02, with a range of 10 to 14 years. The sample was obtained in five schools of three autonomous communities of Spain. The distribution of students by grades was 250 students in sixth grade of Primary Education (110 boys and 140 girls), 304 students in first grade of Compulsory Secondary Education (CSE) (129 boys and 175 girls), and 289 students in second grade of CSE (145 boys and 144 girls). Sampling was non-probabilistic and incidental.

Instruments

The participants provided information about demographic variables such as sex, age, grade, and school. Also, the following Spanish version instruments were used.

SO-8

This instrument was designed following national and international guidelines and standards for the design of questionnaires (Eignor, 2014). The self online is defined as the beliefs, concepts, and subjective representations that people have of themselves as a result of the use and interactions they carry out through the Internet. This definition is based on the online adequacy of the classic theories and definitions of the traditional self (Poletti & Rak, 2014; Sassenfeld, 2011). The Online Self-Perception dimension evaluates people’s perception of themselves in the online setting. The Idealized Online Projection dimension evaluates adolescents’ tendency or preference to show, invent, or exaggerate positive aspects of themselves to others over the Internet. Three experts in the field of study participated in the content validation process, reaching high inter-judge reliability throughout the process (>0.8) in the different elements shown in the specification table of the questionnaire (Eignor, 2014). We used a 4-point Likert-type response scale, ranging from 0 (strongly disagree) to 3 (strongly agree). The items are shown in Table 1.

Table 1.

Means, Standard Deviations, and Item-Total Correlation of the Items of the Brief Online-Self Scale (SO-8) (n = 883).

Brief Online-Self Scale (SO-8)	$\bar{X}$	SD	IT
1. I feel like my contacts and followers like me.	1.46	1.09	.66
2. In my profiles and publications, I show that I have good qualities.	1.47	1.11	.73
3. I can do things on the Internet as well as most people.	1.37	1.08	.67
4. I have a positive attitude toward myself when I post something or talk to someone on the Internet.	1.56	1.14	.65
5. I wish I could feel more respect for myself, and that what they say about me on the Internet wouldn’t affect me.	1.32	1.24	.43
6. I try to show myself as better than I am on the Internet with my posts, photos, and/or videos.	0.96	1.13	.65
7. I think it’s important to give a good image of myself on the Internet, even if it doesn’t match reality.	0.91	1.09	.63
8. Having many likes and followers is important for me to feel at ease with myself.	0.67	1.00	.55

Note. IT = corrected item-total correlation.

Self-Concept Questionnaire Form 5 (AF-5; García & Musitu, 2001)

This questionnaire is based on the theoretical model of the multidimensional self-concept and consists of five subscales: Academic, Social, Emotional, Family, and Physical Self-Concept (e.g., “I’m a good student”; “I’m a friendly person”; “I’m nervous”; “I feel loved by my parents”; and “I’m an attractive person”). Each dimension has six items. Participants are requested to respond to items on a scale ranging between 1 and 99 points, where 1 = completely disagree and 99 = completely agree. The reliability obtained for this study is presented in Table 3.

Generalized and Problematic Internet Use Scale (GPIUS2; Caplan, 2010; Spanish version by Gámez-Guadix, Orue, & Calvete, 2013)

It presents 15 items divided into five factors: Preference for Online Social Interaction, Mood Regulation, Negative Consequences, Cognitive Preoccupation, and Compulsive Use (e.g., “I prefer to interact with other people through the Internet rather than communicating face to face”; “I’ve used the Internet to talk to others when I’ve felt lonely”; “My use of the Internet has hindered the control of my life”; “I would feel lost if I couldn’t connect to the Internet”; “When I’m not on the Internet, it’s hard to resist the urge to connect”). Responses are rated on a 6-point Likert-type scale ranging 0 (completely disagree) to 5 (completely agree). The reliability obtained for this study is shown in Table 3.

Internet Emotional Intelligence Scale (EIEI; González-Cabrera, Pérez-Sancho, & Calvete, 2016)

This scale is based on the meta-knowledge traits of emotional states in online settings. It presents 15 items in three dimensions with five items for each one: Online Emotional Attention, Online Emotional Clarity, and Online Emotion Regulation (e.g., “I pay attention to my emotions when something happens to me on the Internet”; “I can tell someone else about my emotions when I’m on the Internet”; “On the Internet, if something negative happens, I try to control myself before I act”). The items have five response options ranging from 1 (strongly disagree) to 5 (strongly agree). The reliability obtained for this study is presented in Table 3.

Procedure

The battery of questionnaires was applied at school in an online format through the Survey Monkey platform©. Participants completed the questionnaires in their computer classrooms or on tablets, coordinated by the orientation departments of each school and under the supervision of the classroom tutor. Prior to completing the questionnaires, the tutor gave the following instructions to the students: answer the questions sincerely about how you perceive yourself and how you feel and act on the Internet. The time needed to fill out the Spanish version of these questionnaires ranged between 15 and 25 minutes, depending on students’ age and reading comprehension. No questions were answered by the tutors in the classrooms during the questionnaire application.

Ethical Considerations

The study was conducted with the informed consent of the participants and the directors of the schools. Through official communication channels, the educational leaders sent an informed consent model to the families. The acceptance rate to participate in the study was more than 98%. This study was approved by the Research Ethics Committee of the Universidad Internacional de la Rioja (PI:004/2019). There were no exclusion criteria.

Data Analysis

Statistical analyses were carried out using the Statistical Package for the Social Sciences program (SPSS; IBM Corp, 2015), the R software (R Core Team, 2015), the psych package (Revelle, 2018), and the Lavaan package (Rosseel, 2012). First, an analysis of the psychometric properties of each item was performed, indicating the arithmetic mean, the standard deviation, the item-total correlation, and the factorial loads of each item.

The structure of the SO-8 scale was initially examined through exploratory factor analysis (EFA) of the items on the scale. After verification of the assumptions (Kaiser-Meyer-Olkin index and Bartlett’s sphericity test), the factor extraction method used was principal components with Oblimin Rotation. Confirmatory factor analysis (CFA) was subsequently performed using the Weighted Least Squared Mean and Variance adjusted (WLSMV) estimate. The hypothesized model was two correlated dimensions in which four items (Items 1, 2, 3, and 4) load on the factor (Online Self-Perception), and Items 5, 6, 7, and 8 load on the other factor (Idealized Online Projection). Following the recommendations of Hu and Bentler (1999), goodness of fit was assessed with the Satorra-Bentler χ², the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Also, we determined directly whether the more restricted model had a significant fit compared with the less constrained models, following the indications of Satorra and Bentler (2001). In general, CFI and non-normed fit index (NNFI) values of .95 or higher reflect a good fit. RMSEA values between .06 and .08 indicate an acceptable fit. SRMR values lower than .08 indicate good fit.

To determine the internal consistency of the instruments, we employed Cronbach’s alpha and McDonald’s omega coefficients. The following analyses were also performed: (a) frequency analysis, central tendency, and dispersion measures; (b) chi-square analysis and analysis of adjusted standardized residuals; (c) t-test for independent samples; (d) calculation of the effect size, using Cohen’s d statistic; (e) analysis of variance with post hoc Games-Howell comparisons; and (f) Pearson’s bivariate correlations. A p-value of less than .05 was considered significant.

Results

Evidence of Validity of the SO-8 Scores

Table 1 depicts various psychometric indicators for each of the items of the SO-8, specifically the mean, standard deviation, and the item-total correlation. At the psychometric level, the scores obtained in the items reveal problems in the means of some items, although the item-total correlations are satisfactory in all of them, and the factorial loads are equal to or greater than .35 in all cases.

Factorial Structure

Concerning the EFA, the Kaiser-Meyer-Olkin index and Bartlett’s sphericity test data reflected values of .874 and χ² = 2,265.021, p < .001, respectively. The intercorrelation matrix of the items was appropriate for EFA. The results indicated two factors that explained 63.91% of the total sample variance. The first factor (Items 1-4) explains 50.2%, and the second factor (Items 5-8) explains 13.9%. The factorial loads range from .66 to .87 in the first factor, and between .34 and .84 in the second one. A CFA was then performed on a model with two correlated factors, obtaining adequate fit indexes: S-Bχ² (19, n =735) = 66.795, p < .001; RMSEA = .059, 95% confidence interval (CI) = [.044, .074], CFI = .971, TLI = .958, and SRMR = .034. Table 2 presents the comparisons with the other models and the fit indexes of each one. The most appropriate model was the model with two correlated factors. The standardized factor loadings (see Figure 1) were high in all items, ranging from .56 to .81. The Cronbach alpha coefficients and the omega coefficient were .84 and .84 for Online Self-Perception and .76 and .77 for Idealized Online Projection, respectively. Both dimensions correlated positively and directly (r = .56, p < .001).

Table 2.

Fit Indexes for the Models (n = 863).

Model	S-Bχ²	RMSEA	95% CI	CFI	TLI	SRMR
Model 1: Two correlated factors (hypothesized model)	χ² (19, n = 735) = 66.795	.059	[.044, .074]	.971	.958	. 034
Model 2: Two uncorrelated factors	χ² (20, n = 735) = 841.065	.236	[.223, .250]	.507	.310	.241
Model 3: One factor	χ² (20, n = 735) = 271.541	.131	[.117, .145]	.849	.789	.076

Note. S-Bχ² = Satorra-Bentler χ²; RMSEA = root mean square error of approximation; CI = confidence interval; TLI = Tucker-Lewis Index; SRMR = standardized root mean squared residual.

Figure 1.

Estimated model for Brief Online-Self Scale (SO-8).

SO-8 Associations With AF5, GPIUS2, and EIEI

Regarding convergent validity, correlations were obtained between the total Online Self-Perception score and the traditional self-concept, and the correlation with the Academic dimension was particularly relevant (r = .12, p = .002). The Idealized Online Projection only correlated with the Academic Self-Concept (r = −.076, p = .039). Correlations were also found between the Online Self-Perception and the Idealized Online Projection and almost all dimensions of the GPIUS (except between Online Self-Perception and Negative Consequences). The relationship between Idealized Online Projection and Negative Consequences (r = .22, p < .001), Online Social Preference (r = .30, p < .001), and Compulsive Use (r = .24, p < .001) was noteworthy. There were also significant and positive correlations between Online Self-Perception and Idealized Online Projection and Online Emotional Attention, Clarity, and Regulation (see all the correlations in Table 3).

Table 3.

Pearson’s Bivariate Correlations Between Online Self-Perception, Idealized Online Projection, and the Dimensions of the AF5, and the Dimensions of the GPIUS2 and the EIEI.

		1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.	14.	15.	α	ω
SO-8	Online self-perception	—															.84	.84
SO-8	Online idealized projection		—														.76	.77
AF5	Academic	.12*	−.08*	—													.90	.90
	Social	.09*	−.05	.45**	—												.60	.70
	Family	.06	−.04	.36**	.41**	—											.85	.86
	Physical	.10**	.01	.46**	.48**	.30**	—										.79	.80
	Emotional	.05	−.05	.08*	.38**	.19**	.07*	—									.71	.71
GPIUS2	Preference Online Interaction	.11**	.29**	−.10*	−.12**	−.18**	−.13**	−.07	—								.80	.81
	Mood Regulation	.18**	.26**	−.17**	−.14**	−.14**	−.14**	−.11**	.46**	—							.80	.81
	Negative Consequences	.07	.22**	−.20**	−.23**	−.19**	−.14**	−.16**	.42**	.36**	—						.75	.75
	Cognitive Preoccupation	.15**	.29**	−.15**	−.14	−.18**	−.11**	−.10**	.47**	.50**	.58**	—					.80	.80
	Compulsive Use	.13*	.24**	−.17**	−.21**	−.12**	−.15**	−.16**	.44**	.50**	.62**	.70**	—				.80	.80
EIEI	Online Emotional Attention	.25**	.39**	−.05	−.06	−.07	−.05	−.13**	.28**	.35**	.23**	.37**	.38**	—			.83	.84
	Online Emotional Clarity	.31**	.26**	.06	−.03	−.01	−.04	−.07	.18**	.27**	.12**	.22**	.24**	.66**	—		.87	.87
	Online Emotion Regulation	.31**	.25**	.10*	−.03	−.04	−.05	−.08*	.16**	.25**	.13**	.22**	.24**	.58**	.71**	—	.87	.87

Note. Reliability indexes of all dimensions are also included (n = 843). AF5 = Self-Concept Questionnaire Form 5; GPIUS2 = Generalized and Problematic Internet Use Scale; EIEI = Internet Emotional Intelligence Scale; α = Cronbach alpha; ω = Omega coefficient.

p < .05. **p < .001.

Discussion

This study addressed the lack of existing instruments for the self online, presenting the development and validation of the SO-8 scale. This instrument evaluates with conceptual and methodological rigor a new psychological reality. For its design, we used the conjunction of the classic frameworks on the traditional self and the new perspectives of the self online (Poletti & Rak, 2014; Sassenfeld, 2011). Adolescents’ reality is made up of the joint interaction of offline and online experiences (Subrahmanyam & Smahel, 2011).

Concerning the factorial structure of the SO-8, the results of this study show a structure comprising two factors: Online Self-Perception and Idealized Online Projection, which explain 63.91% of the variance. The CFA confirmed the validity of this structure of two correlated factors, through the appropriate factorial loads of all the items and the internal consistency of both dimensions.

The results of this work showed that Online Self-Perception and Idealized Online Projection correlate significantly and positively with each other. This finding is in line with Higgins’s (1987) theory of self-discrepancy, which suggests the idea of congruence between the real self and the ideal self. In this way, the Internet and particularly the social networks enable adolescents to experiment with, reconstruct, and publicly validate their online perception of the Self and their idealized projection of the self (Belk, 2016; Fullwood et al., 2016; Valkenburg & Peter, 2008). Other interesting results of the SO-8 suggest its convergent validity with the traditional self-concept, problematic and generalized use of the Internet, and online emotional intelligence.

Concerning the dimension of Online Self-Perception, significant correlations were observed between this aspect and the Social, Academic, and Physical Self-Concept. These results partially support the perspective of the co-construction model in terms of parallelism and continuity between what happens to the adolescent in the online and offline environments (Belk, 2016; Subrahmanyam & Smahel, 2011). In this line, other research reveals the existence of some correspondence between self-concept in the two environments, offline and online (Back et al., 2010; Marriott & Buchanan, 2014).

Regarding the Idealized Online Projection, the results obtained indicate a single significant and inverse correlation with Academic Self-Concept. This result could explain how adolescents want to compensate for a negative academic self-perception (González-Cabrera et al., 2016) by idealizing their image on the Internet. Other results have indicated the existence of positive correlations between Idealized Online Projection and all the dimensions of problematic Internet use. Thus, concerning Online Social Preference, having a good image and projecting it in an idealized way to others probably depends on how and what information people decide to post in their online interaction (Belk, 2016). Regarding mood regulation, the asynchrony in Internet interactions allows having time to regulate emotions and reflect on which response, information, or images are most appropriate to show at all times (Berger, 2013). The association found between this dimension and Cognitive Preoccupation is also consistent with the idea of Fullwood et al. (2016), which indicates that idealized projecting in the online environment is a reflexive activity that takes time and cognitive effort. In this sense, the possible influence of the cognition, subjectivity, and emotions of each person can be taken into account when constructing and showing their self in the online context (González Rey, 2019).

Regarding the relationships with emotional intelligence on the Internet, our results revealed positive correlations between Online Self-Perception and the Idealized Online Projection with Online Attention, Clarity, and Emotion Regulation. Thus, adolescents with better self-perception and emotional intelligence tend to better understand how others feel (González-Cabrera et al., 2016). Bar-On (2001) reported the relation between emotional intelligence and self-actualization, which could be an important aspect in the construction of the self in the online context.

This study also has some limitations. First, the results are based on self-reports with the entailed response bias. We propose that future research should use additional measures with hetero reports (peers, parents, teachers). Second, although evidence of the psychometric properties of the instrument (reliability, construct validity, factorial and convergent validity) is provided, other analyses could be performed in future works that could include predictive validity and test-retest reliability. However, in future applications of the SO-8 scale, it would be advisable to modify Item 5 as follows to improve its understanding: “I wish that what they say about me on the Internet wouldn’t affect me.” Finally, although the sample of early adolescents was obtained in different regions of Spain, we must be cautious about its generalization to other ages and cultures, as the sampling was not probabilistic, and the orientation of the study is exploratory because it is a novel construct.

Future lines of research would be of interest to delve into how self-perception and idealized online projection relate to specific Internet risks such as nomophobia, Internet gambling disorder, sexting, or online grooming, among others. It would also be relevant to incorporate the SO-8 to assess the potential impact of this construct on prevention programs for abusive and dysfunctional Internet use (Author et al., 2018; Caplan, 2010).

Conclusion

In conclusion, the results of this study suggest that the validity and reliability of the SO-8 allows evaluating in early adolescence the beliefs, concepts, and subjective representations individuals have of themselves over the Internet, providing insight into their self-perception and idealized online projection.

Footnotes

Appendix

Brief Self Online Scale (SO-8).

	Muy en desacuerdo	En desacuerdo	Deacuerdo	Muyde acuerdo
1. Siento que mis contactos y seguidores me aprecian.
2. Creo que en mis perfiles y en mis publicaciones muestro que tengo cualidades buenas.
3. Creo que soy capaz de hacer las cosas tan bien en Internet como la mayoría de gente.
4. Tengo una actitud positiva hacia mí mismo/a cuando publico algo o hablo con alguien a través de Internet.
5. Me gustaría poder sentir más respeto por mí mismo/a, y que no me afectara lo que dicen de mí en Internet.
6. Intento mostrarme mejor de lo que soy en Internet con mis publicaciones, fotos y/o videos.
7. Creo que es importante dar una buena imagen de mí mismo en Internet, aunque no corresponda con la realidad.
8. Para mí es importante tener muchos likes y seguidores para estar a gusto conmigo mismo/a.

Note. It would be advisable to modify the item 5 to improve its understanding in this way: “Me gustaría que no me afectara lo que dicen de mi en internet.”

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Spanish Ministry of Economy, Industry and Competitiveness, RTI2018-094212-B-I00: (CIBER-AACC), and by the International University of la Rioja, Project “Cyberpsychology (Trienio 2017-2020).”

ORCID iDs

J. Ortega-Barón

J. M. Machimbarrena

I. Montiel

J. González-Cabrera

Author Biographies

J. Ortega-Barón is an assistant professor and researcher in the Cyberpsychology group at International University of la Rioja (UNIR), Spain. Her research interest is mainly focused on the prevention of Internet risks among adolescents and the influence of psychosocial variables in the adolescent’s behavior.

J. M. Machimbarrena is an assistant professor of psychology at the University of the Basque Country (UPV/EHU), Spain. His research interests focus on the use of new technologies by adolescents and their effect on health and health-related quality of life.

I. Montiel is an assistant professor and researcher in the Cyberpsychology group at International University of la Rioja (UNIR), Spain. Her research interests are mainly focused on Internet risks and online risky behavior among children and adolescents (online gambling, online grooming, and sexting).

S. Buelga is a professor of psychology at the University of Valencia (UV), Spain. Her scientific work has been mainly focused on the analysis and prevention of risk behaviors for adolescents. Nowadays, her main research is focused on the problem of cyberbullying during the adolescence.

A. Basterra-González is a predoctoral researcher in the Cyberpsychology group at International University of la Rioja (UNIR), Spain. Her research interest is mainly focused on the analysis and prevention of the dysfunctional Internet risks as abuse of Internet, nomophobia, or Internet gaming disorder.

J. González-Cabrera is a professor and the director of the Cyberpsychology research group at International University of la Rioja (UNIR), Spain. His research interests focus on school violence and Internet risks (especially nomophobia, problematic Internet use, etc.).

References

Back

M. D.

Stopfer

J. M.

Vazire

Gaddis

Schmukle

S. C.

Egloff

Gosling

S. D.

(2010). Facebook profiles reflect actual personality, not self-idealization. Psychological Science, 21, 372-374. doi:10.1177/0956797609360756

Bar-On

(2001). Emotional intelligence and self-actualization. In Ciarrochi

Forgas

J. P.

Mayer

J. D.

(Eds.), Emotional intelligence in everyday life: A scientific inquiry (pp. 82-97). New York, NY: Psychology Press.

Belk

(2016). Extended self and the digital world. Current Opinion in Psychology, 10, 50-54. doi:10.1016/j.copsyc.2015.11.003

Berger

(2013). Beyond viral: Interpersonal communication in the internet age. Psychological Inquiry, 24, 293-296. doi:10.1080/1047840X.2013.842203

Buelga

Cava

M. J.

Musitu

(2012). Social reputation, psychosocial adjustment and victimization among adolescents in the school context. Anales de Psicologia, 28(1), 180-187.

Calero

A. D.

Barreyro

J. P.

Injoque-Ricle

(2018). Emotional intelligence and self-perception in adolescents. Europe’s Journal of Psychology, 14, 632-643. doi:10.5964/ejop.v14i3.1506

Campbell

Haynes

(2012). Constructing the digital self in the Global South. Journal of Language and Sexuality, 9, 1-13.

Caplan

S. E.

(2010). Theory and measurement of generalized problematic Internet use: A two-step approach. Computers in Human Behavior, 26, 1089-1097. doi:10.1016/j.chb.2010.03.012

Ching

C. C.

Foley

B. J.

(2014). Constructing the self in a digital world. Cambridge, UK: Cambridge University Press.

10.

Delgado

Escortell

Martínez-Monteagudo

M. C.

Ferrández-Ferrer

Sanmartín

(2019). Cyberbullying, self-concept and academic goals in childhood. The Spanish Journal of Psychology, 22, E46. doi:10.1017/sjp.2019.46

11.

Eignor

(2014). The standards for educational and psychological testing. In APA handbook of testing and assessment in psychology (Vol. 1, pp. 245-250). doi:10.1037/14047-013

12.

Emanuel

Neil

G. J.

Bevan

Fraser

D. S.

Stevenage

S. V.

Whitty

M. T.

Jamison-Powell

(2014). Who am I? Representing the self offline and in different online contexts. Computers in Human Behavior, 41, 146-152. doi:10.1016/j.chb.2014.09.018

13.

Fox

A. K.

Bacile

T. J.

Nakhata

Weible

(2018). Selfie-marketing: Exploring narcissism and self-concept in visual user-generated content on social media. Journal of Consumer Marketing, 35, 11-21. doi:10.1108/JCM-03-2016-1752

14.

Fullwood

James

B. M.

Chen-Wilson

(2016). Self-concept clarity and online self-presentation in adolescents. Cyberpsychology, Behavior, and Social Networking, 19, 716-720. doi:10.1089/cyber.2015.0623

15.

Gámez-Guadix

Orue

Calvete

(2013). Evaluation of the cognitive-behavioral model of generalized and problematic Internet use in Spanish adolescents. Psicothema, 25, 299-306. doi:10.7334/psicothema2012.274

16.

García

Musitu

(2001). Cuestionario Autoconcepto Forma 5 (AF5) (Vol. 5). Retrieved from https://www.researchgate.net/publication/238727916_AF5_AUTOCONCEPTO_FORMA_5_AF5_Self-concept_form_5

17.

González-Cabrera

Pérez-Sancho

Calvete

(2016). Diseño y validación de la “Escala de inteligencia emocional en internet” (EIEI) para adolescentes. Behavioral Psychology, 24(1), 93-105.

18.

González Rey

F. L

. (2019). Subjectivity and discourse: Complementary topics for a critical psychology. Culture & Psychology, 25, 178-194. doi:10.1177/1354067X18754338

19.

Higgins

E. T.

(1987). Self-discrepancy: A theory relating to self and affect. Psychological Review, 94, 319-340. Retrieved from https://pdfs.semanticscholar.org/a59e/1e54374a90b854e0d1d443168325a26bef48.pdf

20.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. doi:10.1080/10705519909540118

21.

IBM Corp. (2015). Statistical Package for the Social Sciences for Windows (No. 23).

22.

Iranzo

Buelga

Cava

M. J.

Ortega-Barón

(2019). Cyberbullying, psychosocial adjustment, and suicidal ideation in adolescence. Psychosocial Intervention, 28, 75-81.

23.

Knutzen

K. B.

Kennedy

D. M.

(2012). Designing the self: The transformation of the relational self-concept through social encounters in a virtual immersive environment. Interactive Learning Environments, 20, 271-292. doi:10.1080/10494820.2011.641680

24.

Marriott

T. C.

Buchanan

(2014). The true self online: Personality correlates of preference for self-expression online, and observer ratings of personality online and offline. Computers in Human Behavior, 32, 171-177. doi:10.1016/j.chb.2013.11.014

25.

Mathew

Raman

(2020). Impact of problematic internet use on the self-esteem of adolescents in the selected school, Kerala, India. Archives of Psychiatric Nursing, 34, P122-P128. doi:10.1016/j.apnu.2020.02.008

26.

Merdin-Uygur

Sarial-Abi

Gurhan-Canli

Hesapci

(2018). How does self-concept clarity influence happiness in social settings? The role of strangers versus friends. Self and Identity, 18, 443-467. https://doi.org/10.1080/15298868.2018.1470563

27.

Michikyan

Subrahmanyam

Dennis

(2014). Can you tell who i am? Neuroticism, extraversion, and online self-presentation among young adults. Computers in Human Behavior, 33, 179-183. https://doi.org/10.1016/j.chb.2014.01.010

28.

Poletti

Rak

(2014). Identity technologies: Constructing the self online. Madison: University of Wisconsin-Madison.

29.

R Core Team. (2015). R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

30.

Revelle

(2018). psych: Procedures for Personality and Psychological Research (1.8.12). Evanston, IL: Northwestern University.

31.

Rosenberg

(1979). Conceiving the self. New York, NY: Basic Book.

32.

Rosseel

(2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1-36. Retrieved from https://www.jstatsoft.org/article/view/v048i02

33.

Sassenfeld

(2011). Afecto, vínculo y desarrollo del self. Clínica e Investigación Relacional, 5, 261-294. Retrieved from https://www.psicoterapiarelacional.es/Portals/0/eJournalCeIR/V5N2_2011/3_A-Sassenfeld_Afecto-vinculo-desarrollo-Self_CeIR_V5N2.pdf

34.

Satorra

Bentler

(2001). Is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models AdO and Adl. As in the case of the goodness-of-fit test, it is of interest to scale the statistic. Psychometrika, 66, 507-514. Retrieved from https://link-springer-com-443.web.bisu.edu.cn/content/pdf/10.1007%2FBF02296192.pdf

35.

Strimbu

O’Connell

(2019). The relationship between self-concept and online self-presentation in adults. Cyberpsychology, Behavior, and Social Networking, 22, 804-807. doi:10.1089/cyber.2019.0328

36.

Subrahmanyam

Smahel

(2011). Connecting online behavior to adolescent development: A theoretical framework. In Digital youth: The role of media in development (pp. 27-40). Springer-Verlag. doi:10.1007/978-1-4419-6278-2

37.

Valkenburg

P. M.

Peter

(2008). Adolescents’ identity experiments on the internet: Consequences for social competence and self-concept unity. Communication Research, 35, 208-231. doi:10.1177/0093650207313164