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Open Access

Peer-reviewed

Research Article

Short version of the smartphone addiction scale: Measurement invariance across gender

  • Heng Yue,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation School of Psychology, Inner Mongolia Normal University, Hohhot, China

    ⨯
  • Xiwen Yue,

    Roles Investigation

    Affiliation Beidou College, Wuhan Qingchuan University, Wuhan, China

    ⨯
  • Bo Liu,

    Roles Investigation

    Affiliation School of Psychology, Inner Mongolia Normal University, Hohhot, China

    ⨯
  • Xueshan Li,

    Roles Investigation

    Affiliation School of Psychology, Inner Mongolia Normal University, Hohhot, China

    ⨯
  • Yaohua Dong,

    Roles Investigation

    Affiliation School of Psychology, Inner Mongolia Normal University, Hohhot, China

    ⨯
  • Hugejiletu Bao

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    baohuge@imnu.edu.cn

    Affiliation College of Physical Education, Inner Mongolia Normal University, Hohhot, China

    ⨯

Abstract

The Smartphone Addiction Scale Short Version (SAS-SV) has been widely used in research, but little is known about the measurement invariance across gender. The current study measured SAS-SV invariance between male and female college students in a sample of 1112 participants. Single- and multiple-group confirmatory factor analyses (CFAs) of smartphone addiction symptom ratings were conducted using R program with RStudio. SAS-SV was psychometrically robust in measuring the severity of smartphone addiction among college students, as well as the gender-based invariance. The differences in SAS-SV between male and female participants were likely to represent true gender differences, and meaningful comparisons could be made.

Citation: Yue H, Yue X, Liu B, Li X, Dong Y, Bao H (2023) Short version of the smartphone addiction scale: Measurement invariance across gender. PLoS ONE 18(3): e0283256. https://doi.org/10.1371/journal.pone.0283256

Editor: Juan-Luis Castillo-Navarrete, University of Concepcion Faculty of Medicine: Universidad de Concepcion Facultad de Medicina, CHILE

Received: July 11, 2022; Accepted: March 6, 2023; Published: March 22, 2023

Copyright: © 2023 Yue et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used to support the findings of this study are available with no restrictions. The data has been uploaded as one of the supporting information.

Funding: Authors: Hugejiletu BAO, Grant numbers: 2021MS03099, funder full name: Inner Mongolia Natural Science Foundation, Exercise improves executive function in patients with chronic pain, The role of the funder: study design, decision to publish.

Competing interests: NO authors have competing interests.

1. Introduction

Smartphones have led to dramatic changes in daily activities and behaviors. Applications allow communication with others, e-mail access, enjoyment of music/videos/films, game playing and schedule management. Smartphones may be beneficial, expanding horizons, promoting safety, alleviating stress, maintaining relationships and finding useful information [1–3] which has contributed to their indispensability [4]. However, improper smartphone use leads users towards unintentional time-wasting, and immoderate use carries the risk of smartphone addiction with an impact on physical and mental health.

Smartphone addiction describes a lack of control of smartphone use with negative consequences [5], and is considered a technological or behavioral addiction. Smartphone addicts may display the six core performances of addiction: salience, mood modification, tolerance, withdrawal, conflict and relapse [1, 6]. Smartphone addiction has been positively correlated with mental distress, such as depression, anxiety, loneliness, stress and boredom in empirical studies [7–10] and linked to poor sleep quality, impaired learning and acquisition and premature cognitive decline [11]. Adverse physical effects have also been reported, such as dry eye [12], musculoskeletal pain [13], hypertension [14], body dysfunction and weakened immunity [15]. These conditions are associated with a decrease in psychological well-being [16] and reduced life satisfaction [17]. Therefore, measures to prevent or treat this addictive behavior are needed, with the establishment of an accurate diagnostic criterion being the first step.

The Mobile Phone Addiction Index (MPAI) [18], Mobile Phone Problem Use Scale (MPPUS) [19], Problematic Mobile Phone Use Questionnaire [20], Smartphone Addiction Inventory (SPAI) [21], Smartphone Application-Based Addiction Scale (SABAS) [22] and Smartphone Addiction Scale (long version and short version) have all been introduced to assess the severity of smartphone addiction [23, 24] and the Smartphone Addiction Scale short version (SAS-SV) has been widely used. SAS-SV is considered simple, easy and efficient [23] with good reliability and validity in different cultural contexts. It has been translated into English [23], Turkish [25], Chinese [26], Italian [27], Iranian [28], Moroccan [29], Brazilian [30], Spanish and French [31]. An interesting outcome of SAS-SV-based research is the gender difference in smartphone addiction severity.

Gender differences were tested during SAS-SV development, but with controversial results. The mean scores of females were significantly higher than those of males [23], and females from Hong Kong were more likely to be addicted to smartphones than males [26]. Similar results were found among Japanese college students [32]. However, other studies have found higher scores for males than females and greater likelihood of addiction among the male population [30, 33, 34], and no gender difference has also been an outcome [35–38]. The last outcome indicated homogeneous symptoms among males and females and equivalent levels of smartphone addiction, although content viewed and motivation/justification might be varied.

The above controversy has arisen due to the assumption that “the measurement and the structure of the underlying construct are equivalent across groups” [39–41] and the failure to perform SAS-SV invariance tests. It remains unclear whether the functions of SAS-SV are identical across male and female participants, and thus, verification of the lack of cross-gender measurement bias is required. Measurement invariance of the SAS-SV across gender groups should be examined.

Measurement invariance refers to whether the same metric produces identical measurements under different conditions during observation and research [42]. For a given factorially defined construct, mathematical equality of corresponding measurement parameters, such as loadings and intercepts, across two or more groups would indicate measurement invariance [43]. Indeed, measurement invariance has been considered a prerequisite for the comparison of gender, age, culture and other dimensions [41]. The demonstration of measurement invariance across the cis genders would allow for the meaningful comparison of their mean scores [44]. Measurement invariance testing is often performed through an iterative process. A series of increasingly restrictive confirmatory factor analytic (CFA) models determine the extent to which measurement parameters (loadings and intercepts) are equivalent across different samples or time points [40, 45, 46]. Associations between manifest indicators and latent constructs, such as regression intercepts, factor loadings and residual (error/uniqueness) variances, are involved. In practice, measurement invariance includes four hierarchically nested parts: configural, metric, scalar and error variance invariance. Accordingly, the four aspects of SAS-SV should be tested to provide evidence for the gender-based measurement invariance of this scale.

Cross-culture and cross-time SAS-SV invariance have been investigated previously, and the measurement equivalence is verified [31, 47]. Cross-gender equivalence has been established for the revised Chinese Smartphone Addiction Scale (SAS-RC) [48]. However, this scale has been adapted from the Smartphone Addiction Scale long version [24] and lacks several items of the SAS-SV. University students engage in high levels of smartphone use [49], and addiction is prevalent in China [50, 51], as elsewhere. Therefore, the aim of the current study was to examine the cross-gender equivalence of SAS-SV in a population of Chinese university students.

2. Methods

2.1. Participants and procedure

Data about participants’ levels of smartphone addiction was collected by an anonymous online survey. A total of 1112 college students, 529 of whom were male and 583 female, with a mean age of 20.28 ± 1.43 years (range: 18–25), participated in the study.

Ethical approval was granted by the research ethics committee of the Inner Mongolia Normal University (College of Psychology). Verbal informed consent was given by all participants and their head teachers for the use of anonymized data for research and a link to the questionnaire sent via WeChat or QQ groups during participants’ spare time.

2.2. Measurements

SAS-SV is a self-reported measure for assessment of smartphone addiction severity. The scale consists of 10 items [23]. All participants were told that “please indicate to what extent you agree that this is true for you”, and were asked to answer the following question: “Is missing planned work due to smartphone use?”. Responses were recorded by a 6-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree). Cronbach’s alpha for the scale across the whole group was 0.883, with 0.895 and 0.872 for male for female participants, respectively. The construct validity of SAS-SV has been verified in various cultures and countries [23, 25–31], and it is correlated with numerous measures [52, 53], thus providing convergent and concurrent construct evidence for the validity of this scale.

2.3. Statistical analysis

SPSS 25.0 was used for descriptive statistics, normality evaluation and reliability assessment. “lavaan” [54] and “semTools” [55] in R programming language with RStudio [56] were used for confirmatory factor analysis (CFA) and evaluation of configural, metric, scalar and error variance invariance across genders. Data normality was assessed by skewness and kurtosis. Item scores were regarded as normally distributed if the absolute values of the two indices did not exceed 2 [57]. Weighted least squares means and variance adjusted (WLSMV) estimator was used for single- and multiple-group CFAs [58]. Multiple indices were adopted to examine the goodness of fit of CFA models, including the significance of χ2, ratio between χ2 and degrees of freedom (df), root mean square error of approximation (RMSEA), comparative fit index (CFI) and standardized root mean square residual (SRMR). A good fit of the CFA model to the data was indicated by a non-significant χ2, χ2/df < 5, RMSEA < 0.08, CFI > 0.95, TLI > 0.95, SRMR < 0.08 [59–62].

Two single-group CFAs were conducted to assess the fit of the model in the two gender groups separately. Multiple-group CFA was then used to analyze SAV-SV measurement invariance across the two groups. Four hierarchically nested models were constructed and compared in an iterative manner. The raw score means were compared between groups to test whether males and females had an equivalent levels of smartphone addiction.

SAS-SV measurement invariance was assessed via the overall fit for each model and comparisons between nested models. Measurement invariance was supported if fit indices were acceptable and nested model comparisons fulfilled the following criteria: (1) RMSEA change (ΔRMSEA) < 0.050; (2) CFI change (ΔCFI) < 0.004; (3) SRMR change (ΔSRMR) < 0.010 [63, 64]. Chi-square change was not a criterion due to its sensitivity to sample size and limited capacity to distinguish invariant from non-invariant models [41, 44, 45, 65] and results are reported merely for completeness.

3. Results

3.1. Descriptive statistics

Descriptive statistics for all male and female participants are shown in Table 1. Means (standard deviations) ranged from 3.27 to 3.88 (1.403 to 1.480). The mean for males ranged from 3.18 to 3.86 (1.371 to 1.460) and for females from 3.32 to 3.90 (1.397 to 1.500). The absolute values for skewness and kurtosis in all three groups were all less than 2, indicating normal distribution of all 10 items [65].

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Table 1. Descriptive statistics of the SAS-SV [total (male, female)].

https://doi.org/10.1371/journal.pone.0283256.t001

3.2. Single-group confirmatory factor analysis

Single-group CFAs for the whole group and for male and female participants are presented in Tables 2 and 3. SAS-SV yielded adequate model fit statistics of a significant χ2, χ2/df = 5.227, RMSEA = 0.062 CFI = 0.985, TLI = 0.980, SRMR = 0.029 with standardized factor loadings ranging from 0.581 to 0.782 for the whole sample. Although χ2 was significant. the value of χ2/df was slightly higher than the recommended cutoff value [60]. As the chi-square statistic was sensitive to sample size [41, 65] and all other model fit indices were within acceptable ranges, the model was considered to be appropriate.

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Table 2. Results of single-group confirmatory factor analysis (model fit indices).

https://doi.org/10.1371/journal.pone.0283256.t002

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Table 3. Results of single-group confirmatory factor analysis (standardized factor loadings).

https://doi.org/10.1371/journal.pone.0283256.t003

In order to test whether the unidimensional model is the best fitting model, according to the suggestions of Ronald F. Levant and his colleagues [66], variance composition of the SAS-SV was assessed based on the common factors, hierarchical, bifactor model. However, none of these models could be identified. Therefore, the unidimensional model of SAS-SV was considered acceptable and regarded as the best fitting model for this scale.

Fit indices indicated good data fitting to the model for the male sample with a significant χ2, χ2/df = 3.634, RMSEA = 0.071, CFI = 0.983, TLI = 0.978, SRMR = 0.032 and standardized factor loadings ranging from 0.586 to 0.802 and for the female sample with a significant χ2, χ2/df = 3.263, RMSEA = 0.062, CFI = 0.982, TLI = 0.977, SRMR = 0.034 and standardized factor loadings ranging from 0.575 to 0.792. According to the criteria mentioned above, the unidimensional model of SAS-SV was considered acceptable for both male and female groups.

3.3. Measurement invariance across gender

Multiple-group CFAs are presented in Table 4.

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Table 4. Model fit indices for measurement invariance tests.

https://doi.org/10.1371/journal.pone.0283256.t004

The configural invariance model (M1) was estimated without applying any equality constraint, and the model fit indices suggested good fitting of the data to the model (χ2/df = 3.426, RMSEA = 0.066, CFI = 0.983, SRMR = 0.033). Thus, configural invariance was supported.

The metric invariance model (M2) was tested by restricting factor loadings to be equivalent between the two gender groups. The model fit the data well (χ2/df = 2.534, RMSEA = 0.053, CFI = 0.983, SRMR = 0.033). ΔRMSEA was 0.013, ΔCFI 0.000 and ΔSRMR 0.000 between M1 and M2 which were all within the recommended guidelines. Thus, metric invariance of SAS-SV across gender was supported.

The scalar invariance model (M3) was assessed by constraining factor loadings and item intercepts to be identical across the two gender groups and good fitting of the data to the model was shown (χ2/df = 2.288, RMSEA = 0.048, CFI = 0.985, SRMR = 0.033). ΔRMSEA was 0.005, ΔCFI 0.002 and ΔSRMR 0.000 between M2 and M3 which were all within the recommended criteria. Therefore, SAS-SV scalar invariance across gender was confirmed.

Strict invariance of the scale (M4) was assessed by holding factor loadings, intercepts and residual item variance equal across male and female groups and good fitting of the data to the model was shown (χ2/df = 2.425, RMSEA = 0.051, CFI = 0.981, SRMR = 0.036). ΔRMSEA was 0.003, ΔCFI 0.004 and ΔSRMR 0.003 compared with M3, which were all within the recommended criteria. Therefore, strict SAS-SV invariance across gender was demonstrated.

Lastly, the raw score means were compared between the two groups. The results of the independent t test were: t (1110) = -1.01, p = 0.31, indicating that males and females had an equivalent level of smartphone addiction.

4. Discussion

The SAS-SV has been frequently used to assess the severity of smartphone addiction but without full characterization of cross-gender equivalence. The current study tested SAS-SV measurement invariance among male and female participants.

Single-group CFA indicated acceptable data fitting for the unidimensional model in the whole group and in both gender sub-groups, consistent with previous Chinese language and other studies [25–31]. However, the previous Chinese study was restricted to Hong Kong residents [26] and may be subject to some cultural differences from mainland China. Therefore, testing of SAS-SV factor structure in the mainland Chinese population was required. The present study suggests that this scale is a robust and useful tool for both male and female residents of mainland China. Scalar invariance is regarded as an imperative procedure for comparing latent means, which conforms to the identical operational definition, including equal intervals and zero points across the indicators between the two gender groups [67]. Thus, intercepts of items of the scale were equivalent across gender. In addition, multiple-group CFA demonstrated SAS-SV strict invariance, indicating homogeneous residual variances of manifest indicators (items) across male and female university students [68]. Thus, an observed variable (item) may be predicted from the underlying construct (smartphone addiction) with an identical degree of measurement error across gender [44]. Moreover, raw score means of the two gender groups were compared, and the results indicated that males and females had an equivalent level of smartphone addiction.

SAS-SV’s unidimensional structure was found to be equivalent for male and female university students. The configural, metric, scalar and strict invariance measurement invariance tests also demonstrated the gender invariance of the psychometric features. Identical SAS-SV scores from the two gender groups represented equal levels of smartphone addiction in male and female students. Any gender differences might reflect the true difference in the severity of smartphone addiction, but not measurement bias. Therefore, the SAS-SV may be used with confidence to measure and compare the degree of smartphone addiction across gender in Chinese university student samples. Previous research has identified different content preferences between male and female smartphone users, with males preferring games apps and females preferring social media apps [69], However, the current study suggests no significant gender differences in addictive symptoms. SAS-SV items were interpreted in a comparable way by male and female university students and the scale was robust. The current results support the usage of SAS-SV, free from measurement bias, in research and clinical contexts.

We acknowledge several limitations to the current study. First, only measurement invariance across two self-identified genders (male and female) was evaluated, and non-binary and transgender individuals were not taken into account, giving an incomplete view according to Ronald F. Levant and his colleagues [70]. Therefore, future studies are needed to investigate SAS-SV measurement invariance across different gender identities. Second, only college student samples were included in this study, and smartphone addiction is also prevalent in other populations, such as children, adolescents and adults. Thus, gender invariance of the scale should also be investigated in different sample populations. Third, the current study was conducted on a convenience sample, which could reduce the generalizability of the results. Future studies with different sampling methods are warranted.

5. Conclusions

The structural validity and measurement equivalence of the SAS-SV was assessed by single- and multiple-group CFAs. Empirical evidence is presented to support the unidimensional structure and full measurement equivalence of the SAS-SV across gender. Differences in SAS-SV scores between male and female college students indicated obvious gender differences in smartphone addiction, which were not due to measurement deviation of the scale items. The current study contributes to meaningful interpretations of gender comparisons of smartphone addiction. There are practical implications for researchers, clinicians and the general public.

Acknowledgments

We would like to express our great gratitude to Mr. Ronald F. Levant for his valuable comments and suggestions during the review process.

References

  1. 1. Panova T, Carbonell X. Is smartphone addiction really an addiction? Journal of Behavioral Addictions. 2018;7(2):252–9. pmid:29895183
  2. 2. Elhai JD, Dvorak RD, Levine JC, Hall BJ. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders. 2017; 207:251–9. pmid:27736736
  3. 3. Hong W, Liu R-D, Oei T-P, Zhen R, Jiang S, Sheng X. The mediating and moderating roles of social anxiety and relatedness need satisfaction on the relationship between shyness and problematic mobile phone use among adolescents. Computers in Human Behavior. 2019;93:301–8.
  4. 4. Hartanto A, Yang H. Is the smartphone a smart choice? The effect of smartphone separation on executive functions. Computers in human behavior. 2016;64:329–36.
  5. 5. Sunday OJ, Adesope OO, Maarhuis PL. The effects of smartphone addiction on learning: A meta-analysis. Computers in Human Behavior Reports. 2021;4:100114.
  6. 6. Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use. 2005;10(4):191–7.
  7. 7. Lapierre MA, Zhao P, Custer BE. Short-Term Longitudinal Relationships Between Smartphone Use/Dependency and Psychological Well-Being Among Late Adolescents. Journal of Adolescent Health. 2019;65(5):607–12. pmid:31477510
  8. 8. Zhang Y, Li S, Yu G. The longitudinal relationship between boredom proneness and mobile phone addiction: Evidence from a cross-lagged model. Current Psychology. 2021:1–8.
  9. 9. Karsay K, Schmuck D, Matthes J, Stevic A. Longitudinal effects of excessive smartphone use on stress and loneliness: the moderating role of self-disclosure. Cyberpsychology, Behavior, and Social Networking. 2019;22(11):706–13. pmid:31697600
  10. 10. Geng Y, Gu J, Wang J, Zhang R. Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. Journal of Affective Disorders. 2021;293:415–21. pmid:34246950
  11. 11. Neophytou E, Manwell LA, Eikelboom R. Effects of Excessive Screen Time on Neurodevelopment, Learning, Memory, Mental Health, and Neurodegeneration: a Scoping Review. International Journal of Mental Health and Addiction. 2019:1–21.
  12. 12. Inomata T, Iwagami M, Nakamura M, Shiang T, Yoshimura Y, Fujimoto K, et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA ophthalmology. 2020;138(1):58–68. pmid:31774457
  13. 13. Yang G, Cao J, Li Y, Cheng P, Liu B, Hao Z, et al. Association between Internet Addiction and the Risk of Musculoskeletal Pain among Chinese College Freshmen—A Cross-Sectional Study. Frontiers in psychology. 2019;10:1959. pmid:31551859
  14. 14. Zou Y, Xia N, Zou Y, Chen Z, Wen Y. Smartphone addiction may be associated with adolescent hypertension: a cross-sectional study among junior school students in China. BMC pediatrics. 2019;19(1):310. pmid:31484568
  15. 15. Xie X, Dong Y, Wang J. Sleep quality as a mediator of problematic smartphone use and clinical health symptoms. Journal of behavioral addictions. 2018;7(2):466–72. pmid:29788754
  16. 16. Yang Z, Asbury K, Griffiths MD. An exploration of problematic smartphone use among Chinese university students: Associations with academic anxiety, academic procrastination, self-regulation and subjective wellbeing. International Journal of Mental Health and Addiction. 2019;17(3):596–614.
  17. 17. Song S-M, Park B, Kim J-E, Kim JE, Park N-S. Examining the Relationship between Life Satisfaction, Smartphone Addiction, and Maternal Parenting Behavior: A South Korean Example of Mothers with Infants. Child Indicators Research. 2019;12(4):1221–41.
  18. 18. Leung L. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. Journal of Children and Media. 2008;2(2):93–113.
  19. 19. Bianchi A, Phillips JG. Psychological Predictors of Problem Mobile Phone Use. CyberPsychology & Behavior. 2005;8(1):39–51. pmid:15738692
  20. 20. Billieux J, Van der Linden M, Rochat L. The role of impulsivity in actual and problematic use of the mobile phone. Applied Cognitive Psychology. 2008;22(9):1195–210.
  21. 21. Lin Y-H, Chang L-R, Lee Y-H, Tseng H-W, Kuo TBJ, Chen S-H. Development and Validation of the Smartphone Addiction Inventory (SPAI). PloS one. 2014;9(6):e98312. pmid:24896252
  22. 22. Csibi S, Griffiths MD, Cook B, Demetrovics Z, Szabo A. The psychometric properties of the smartphone application-based addiction scale (SABAS). International Journal of Mental Health and Addiction. 2018;16(2):393–403. pmid:29670500
  23. 23. Kwon M, Kim D-J, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PloS one. 2013;8(12):e83558. pmid:24391787
  24. 24. Kwon M, Lee J-Y, Won W-Y, Park J-W, Min J-A, Hahn C, et al. Development and Validation of a Smartphone Addiction Scale (SAS). PloS one. 2013;8(2):e56936. pmid:23468893
  25. 25. Demirci K, Orhan H, Demirdas A, Akpinar A, Sert H. Validity and reliability of the Turkish Version of the Smartphone Addiction Scale in a younger population. Klinik Psikofarmakoloji Bülteni-Bulletin of Clinical Psychopharmacology. 2014;24(3):226–34.
  26. 26. Luk TT, Wang MP, Shen C, Wan A, Chau PH, Oliffe J, et al. Short version of the Smartphone Addiction Scale in Chinese adults: Psychometric properties, sociodemographic, and health behavioral correlates. Journal of behavioral addictions. 2018;7(4):1157–65. pmid:30418073
  27. 27. De Pasquale C, Sciacca F, Hichy Z. Italian validation of smartphone addiction scale short version for adolescents and young adults (SAS-SV). Psychology. 2017;8(10):1513–8.
  28. 28. Esmaeilpour F, Letafatkar A, Baker JS, Dutheil F, Khazaei O, Rabiei P, et al. Reliability and construct validity of the smartphone addiction scale short version (SAS-SV) in Iranian students. Journal of Public Health. 2021.
  29. 29. Sfendla A, Laita M, Nejjar B, Souirti Z, Touhami AAO, Senhaji M. Reliability of the Arabic smartphone addiction scale and smartphone addiction scale-short version in two different Moroccan samples. Cyberpsychology, Behavior,Social Networking. 2018;21(5):325–32. pmid:29762065
  30. 30. Andrade ALM, Kim D-J, Caricati VV, Martins GDG, Kirihara IK, Barbugli BC, et al. Validity and reliability of the Brazilian version of the Smartphone Addiction Scale-Short Version for university students and adult population. Estudos de Psicologia. 2020;37.
  31. 31. Lopez-Fernandez O. Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addictive behaviors. 2017;64:275–80. pmid:26685805
  32. 32. Tateno M, Kim D-J, Teo AR, Skokauskas N, Guerrero AP, Kato TA. Smartphone addiction in Japanese college students: usefulness of the Japanese version of the smartphone addiction scale as a screening tool for a new form of internet addiction. Psychiatry investigation. 2019;16(2):115. pmid:30808117
  33. 33. Yuchang J, Cuicui S, Junxiu A, Junyi L. Attachment styles and smartphone addiction in Chinese college students: The mediating roles of dysfunctional attitudes and self-esteem. International Journal of Mental Health Addiction. 2017;15(5):1122–34.
  34. 34. Chatterjee S, Kar SK. Smartphone addiction and quality of sleep among Indian medical students. Psychiatry. 2021;84(2):182–91. pmid:33856961
  35. 35. Mescollotto FF, Castro EMd, Pelai EB, Pertille A, Bigaton DR. Translation of the short version of the Smartphone Addiction Scale into Brazilian Portuguese: cross-cultural adaptation and testing of measurement properties. Brazilian Journal of Physical Therapy. 2019;23(3):250–6. pmid:30249438
  36. 36. Harris B, McCredie M, Fields S. Examining the psychometric properties of the Smartphone Addiction Scale and its short version for use with emerging adults in the U.S. Computers in Human Behavior Reports. 2020;1:100011.
  37. 37. Aker S, Sahin MK, Sezgin S, Oguz G. Psychosocial Factors Affecting Smartphone Addiction in University Students. Journal of Addictions Nursing. 2017;28(4):215–9. pmid:29200049-201710000-00008.
  38. 38. Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Industrial psychiatry journal. 2019;28(1):82. pmid:31879452
  39. 39. Byrne BM, Shavelson RJ, Muthén B. Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological bulletin. 1989;105(3):456.
  40. 40. Yue H, Zhang X, Cheng X, Liu B, Bao H. Measurement Invariance of the Bergen Social Media Addiction Scale Across Genders. Frontiers in Psychology. 2022;13:879259. pmid:35800944
  41. 41. Chen FF. Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal. 2007;14(3):464–504.
  42. 42. Houghton JD, Carnes A, Ellison CN. A cross-cultural examination of self-leadership: Testing for measurement invariance across four cultures. Journal of Leadership Organizational Studies. 2014;21(4):414–30.
  43. 43. Little TD. Mean and Covariance Structures (MACS) Analyses of Cross-Cultural Data: Practical and Theoretical Issues. Multivariate Behavioral Research. 1997;32(1):53–76. pmid:26751106
  44. 44. Cheung GW, Rensvold RB. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal. 2002;9(2):233–55.
  45. 45. Maiolatesi AJ, Clark KA, Pachankis JE. Rejection sensitivity across sex, sexual orientation, and age: Measurement invariance and latent mean differences. Psychological Assessment. 2022;34(5):1–12. pmid:35025579
  46. 46. Collison KL, South S, Vize CE, Miller JD, Lynam DR. Exploring gender differences in Machiavellianism using a measurement invariance approach. Journal of personality assessment. 2021;103(2):258–66. pmid:32130029
  47. 47. Su S, Larsen H, Cousijn J, Wiers RW, Van Den Eijnden RJJM. Problematic smartphone use and the quantity and quality of peer engagement among adolescents: A longitudinal study. Computers in Human Behavior. 2022;126:107025.
  48. 48. Li L, Niu Z, Griffiths MD, Mei S. The Smartphone Addiction Scale: Psychometric Properties, Invariance, Network Perspective, and Latent Profile Analysis Among a Sample of Chinese University Students. International Journal of Mental Health Addiction. 2022:1–23.
  49. 49. Aljomaa SS, Qudah MFA, Albursan IS, Bakhiet SF, Abduljabbar AS. Smartphone addiction among university students in the light of some variables. Computers in Human Behavior. 2016;61:155–64.
  50. 50. Alotaibi MS, Fox M, Coman R, Ratan ZA, Hosseinzadeh H. Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Umm Al-Qura University (UQU), Saudi Arabia. 2022;19(6):3710. pmid:35329397
  51. 51. Olson JA, Sandra DA, Colucci ÉS, Al Bikaii A, Chmoulevitch D, Nahas J, et al. Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Computers in Human Behavior. 2022;129:107138.
  52. 52. Yue H, Yue X, Zhang X, Liu B, Bao H. Exploring the relationship between social exclusion and smartphone addiction: The mediating roles of loneliness and self-control. Frontiers in Psychology. 2022;13:1–10. pmid:36046412
  53. 53. Yue H, Zhang X, Sun J, Liu M, Bao H. The relationships between negative emotions and latent classes of smartphone addiction. PloS one. 2021;16(3):e0248555. pmid:33720952
  54. 54. Rosseel Y. lavaan: An R package for structural equation modeling. Journal of statistical software. 2012;48:1–36.
  55. 55. Jorgensen TD, Pornprasertmanit S, Schoemann AM, Rosseel Y, Miller P, Quick C, et al. semTools: Useful tools for structural equation modeling. R package version 05–1. 2018.
  56. 56. RStudio Team UJR, Inc., Boston, MA URL http://www.rstudio.com. RStudio: integrated development for R. 2015;42:14.
  57. 57. Pituch KA, Stevens JP. Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS: Routledge; 2015.
    • 58. Wu H, Estabrook R. Identification of confirmatory factor analysis models of different levels of invariance for ordered categorical outcomes. Psychometrika. 2016;81(4):1014–45. pmid:27402166
    • 59. Lt Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1–55.
    • 60. Hoyle RH. Handbook of structural equation modeling: Guilford press; 2012.
      • 61. MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychological methods. 1996;1(2):130.
      • 62. Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociological methods & research. 1992;21(2):230–58.
      • 63. Rutkowski L, Svetina D. Measurement invariance in international surveys: Categorical indicators and fit measure performance. Applied Measurement in Education. 2017;30(1):39–51.
      • 64. Clark JC. Evaluating Model Fit for Longitudinal Measurement Invariance with Ordered Categorical Indicators: Brigham Young University; 2020.
        • 65. Kline RB. Principles and practice of structural equation modeling: Guilford publications; 2015.
          • 66. Levant R, Pryor S, Silver KE. The Woman’s Nontraditional Sexuality Questionnaire-Short Form (WNSQ-SF): Development, Variance Composition, Reliability, Validity, and Measurement Invariance. Gender Issues. 2022;39(4):409–36.
          • 67. Vlachopoulos SP. The basic psychological needs in exercise scale: measurement invariance over gender. Structural Equation Modeling: A Multidisciplinary Journal. 2008;15(1):114–35.
          • 68. Widaman KF, Grimm KJ. advanced psychometrics confirmatory factor analysis, item response theory, and the study of measurement invariance. In: Reis H T., Judd CM, editors. Handbook of research methods in social and personality psychology 2ed: Cambridge University Press; 2014. p. 534–70.
            • 69. Arpaci I. Relationships Between Early Maladaptive Schemas and Smartphone Addiction: the Moderating Role of Mindfulness. International Journal of Mental Health and Addiction. 2019;19:1–15.
            • 70. McDermott RC, Wolfe G, Levant RF, Alshabani N, Richmond K. Measurement invariance of three gender ideology scales across cis, trans, and nonbinary gender identities. Psychology of Men Masculinities. 2021;22(2):331.