Construction and Validation of the Binge-Watching Addiction Scale (BiWAS): Validity,Reliability,and Measurement Invariance in a Large Multinational Adult Sample From the Middle East

Abstract

Background:

Over the past decade, wide-spreading streaming platforms have given rise to new ways of consuming TV series, but also to the potential for harmful excessive use and the concept of binge-watching addiction. This highlights a clear need for developing a deeper understanding of this emerging behavioral phenomenon that threatens human physical and mental health. Our present research endeavor intended to investigate the psychometric characteristics of a brief measure of binge-watching addiction designed on the basis of components that theoretically reflect the 6 facets operationalizing the concept the behavioral addiction, i.e. the Binge-Watching Addiction Scale (BiWAS).

Method:

We carried out a multisite research study among 4092 participants from 5 Arab countries (i.e., Palestine, Jordan, Oman, Egypt, and Lebanon) during the period September-October 2025.

Results:

Factorial Structure Analysis showed that the single-factor model that was postulated was an acceptable fit, except for the RMSEA value. Internal consistency of the instrument’s global score was satisfactory, with a Cronbach’s alpha of .78. In addition, the BiWAS featured adequate configural, metric, and scalar invariance across the 2 sexes and the 5 country groups. Furthermore, BiWAS scores showed significant positive correlations with higher smartphone addiction, increased psychological distress, more severe insomnia, and greater fatigue levels.

Conclusion:

Results indicated that the BiWAS is a psychometrically valid and reliable scale in both sexes and all countries involved in this study. It is hoped that the new scale will open up possibilities for quantitatively based investigations of binge-watching thus contributing to deepen our knowledge of this prominent phenomenon and enhance understanding of the mechanisms involved in increased vulnerability to becoming addicted. Further studies are warranted to offer additional information and a more comprehensive understanding of the scale’s performance across different populations, cultures and societies across the globe.

Keywords

binge-watching addiction scale development validation psychometric properties

Introduction

Over the past decade, there has been a growing development of multiple on-demand streaming platforms such as Apple TV, Amazon Prime, Disney+, Hulu, Crunchyroll, and Netflix.¹ Due to the large availability of streaming services and digital video, a new pattern of TV viewing behavior has emerged, especially among young audience. Unlike traditional television where the watcher has to wait several days for the release of the next episode, the entire season of a TV series is made available at once by these technologies. A watcher can choose from diverse and extensive content and view as many episodes of a television show as desired. Watching TV series has become easily accessible anywhere including at home, workplace, or while traveling.² and via multiple devices.³ Following this new trend, a new phenomenon has increasingly gained prominence and popularity and has rapidly become an integral part of millions of people’s daily routine worldwide,⁴ which is called binge-watching. Binge-watching consists of the practice of watching multiple episodes (2-6) of a TV series sequentially in a single sitting.^5,6 However, various other definitions have been used by researchers to describe the problem, such as viewing 3 to 4 episodes more than half an hour lengthy, or viewing 1 to 3 episodes in 1 sitting.⁷ The prolonged involvement in this new normative way of watching TV series has led to problematic behavioral patterns of TV series viewing and subsequent detrimental effects on consumers’ health. Among the negative consequences of excessive binge-watching reported in the literature were an unhealthy, sedentary lifestyle,⁸ chronic fatigue and sleep disturbance,⁹ decrease of social networks,¹⁰ and negligence of other activities.⁵ Apart from these outcomes, 1 important line of research has called attention to the addictive potential of excessive binge-watching.¹¹

Despite the growing research on binge-watching, the increased awareness of its occurrence and potential deleterious impact, there is to date a lack of consensus on how to operationally define and measure it.¹² In the present research, binge-watching is approached as an addictive behavior because of a set of attributes reported in literature that accurately reflect and define behavioral addictions. Indeed, content of streaming media makes it particularly difficult and dissatisfying to stop watching at the end of 1 episode, which leads watchers to the desire to watch “just one more” and to feelings of “being drawn-in by a show.”^13,14 This often results in viewing more episodes than initially planned or anticipated, and subsequent distressing feelings of guilt.¹⁵ Therefore, people who engage in unhealthy patterns of binge-watching tend to lose time control and start to neglect their social, family, and school/work responsibilities, which may lead to psychosocial functioning disruption.¹⁶ In addition, a key factor that motivates people to binge watch is relaxation and pleasure-seeking,³ but also distraction and escapism from everyday distressing situations¹⁷; these motives are commonly seen in other addictive behaviors such as online gaming addiction.¹⁸ Researchers argued that binge-watching triggers the brain’s reward system and increases dopamine levels, as it allows watchers to meet their needs of instant gratification and immediate desire satisfaction.¹¹ Hence, the dopamine rush induced by binge-watching makes the individual engage in this behavior more and more. Furthermore, there is evidence that binge-watching episodes of TV series has a range of negative physical, psychological, and social consequences,¹⁹ which resemble consequences seen in other technology addictions like Internet, smartphone, social networking sites, and gaming addictions.²⁰ All that said, it is worth noting that the concept of binge-watching behavior as an addiction is still evolving. It is not yet recognized by international classifications^21,22 and is still in need for empirical and theoretical development. Based on these considerations, researchers called for further investigations of the addictive nature of binge-watching to clarify and better understand how a simple, rewarding daily habit can turn into an addictive, harmful behavior over time.^5,23

Measurement of Binge-Watching

The assessment of the extent of engagement in TV series watching has varied widely across studies, which is mainly due to large differences in the operationalization of the binge-watching construct.¹² The majority of previous studies used quantitative estimates, such as the average duration of 1 viewing session,^7,24 the average number of episodes watched per day or per viewing session,^25,26 the frequency of binge-watching over the last week or month,^27,28 the pace of watching a particular series,^29,30 the general tendency to binge-watching,³¹ or self-perceived binge-watching duration.³² Other studies used self-developed, non-psychometrically-validated measures of binge-watching-related tendency,³³ excessiveness,¹⁸ intention,³⁴ or cognitive/behavioral involvement.³⁵

Of note, there have been a few attempts to develop and validate psychometric tests to assess binge-watching. For instance, Orosz et al³⁶ created the problematic series watching scale based on the 6 component model of Griffiths.³⁷ The developers chose to call the measure “Problematic Series Watching” and not qualify the behavior as an addiction because there has been no evidence in 2016 regarding its health-related negative outcomes. In 2017, the Series Watching Engagement Scale (SWES) was designed to enable the differentiation between “highly engaged” and “problematic” viewers.³⁸ The scale is composed of 15 items and 5 factors assessing engagement (overuse, social interaction, identification, persistence, and self-development), a construct that is defined as a potential precursor for addiction.³⁸ However, the SWES has been critized because it involves dimensions that do not assess the behavior of binge watching per se, such as associated booster (“identification”) or motivational features (“social interaction”).³⁹ In 2019, the Binge-Watching Engagement and Symptoms Questionnaire (BWESQ) was developed, which comprises 40 items across 7 domains: Loss of control, Engagement, Dependency, Desire/Savoring, Positive Emotions, Binge-watching, and Pleasure preservation.³⁹ The BWESQ was defined by the developers as assessing a wide range of symptoms related to one’s involvement in TV series, including facets that reflect both positive expectancies (such as pleasure) and pathological high binge-watching engagement (such as loss of control and dependency). The authors argued that approach to conceptualization and measurement of binge watching was adopted to reflect the motive of binge watching as a “leisure habit,” and avoid overpathologization such a recreational behavior. However, such an approach poses the risk of overlooking the documented harmful effects addictive binge-watching may have on a person’s health.

Watching TV series can be regarded as an expanding technology addiction that mirrors other well-known addictions that apply to other technology-related online activities such as gaming, social networking sites, online shopping, online pornography, or smartphone use. Because these potential behavioural addictions are still relatively new for researchers and practitioners, there can be resistance to consider the term “addiction” as applying to these entities (e.g., de Alarcón et al⁴⁰). The reluctance to admit the obvious addictive potentials of technology-related activities was analogized to the era when alcohol addiction was regarded as a “personality problem.”⁴¹ Hence, while most binge-watchers enjoy watching TV series as a leisure habit and are healthy, minimizing or disregarding the addictive risk potential of binge watching will not help unhealthy binge-watcher addicts in need for care. In this line of reasoning, Forte et al⁴² developed the Binge-Watching Addiction Questionnaire (BWAQ) consisting of 20 items and 4-factor dimensions (i.e., “Dependency,” “Craving,” “Avoidance,” and “Anticipation”), which demonstrated good psychometric properties among Italian-speaking individuals. However, because it is relatively lengthy, the BWAQ can have limited utility in resource- and time-pressured real-life contexts. Overall, the plurality of definitions of the binge-watching construct and the ambiguity surrounding its conceptualization pose a major challenge to an adequate understanding of this behavior. In addition, the heterogeneity and lack of robustness of assessment of binge-watching impedes accuracy and consistency across existing research and hampers the replication and comparisons of findings of empirical studies.¹²

Rationale and Purpose of This Study

In today’s technological landscape, traditional TV is experiencing a steady viewership decline while binge-watching is becoming an increasingly prominent behavior in Arab societies.⁴³ A study conducted among Egyptian Youth showed that 93.2% reported binge-watching (defined as viewing more than 2 episodes in 1-sitting), among them 49% reported that they either always or often binge-watch.⁴⁴ A cross-cultural study on binge-watching showed that 18.7% of Arabic-speaking participants from Egypt reported watching more than 6 episodes in 1 session, which is much higher than that reported by participants from other countries (ranging from 6.1% among Spanish-speakers to 15.4% among Chinese-speakers), and 24.6% reported a frequency of watching of once to several times a day.⁴⁵ One important barrier to advancing knowledge in this field is the lack of valid, reliable, yet convenient-to-use instruments to measure the binge-watching construct among Arabic-speakers. Echoing earlier research that has introduced brief measurement tools to the field of technology addiction, such as the Bergen Social Media Addiction Scale (BSMAS⁴⁶), the Bergen Facebook Addiction Scale (BFAS⁴⁷), and the TikTok Addiction Test (TAT,⁴⁸ a short Binge-watching addiction instrument may be particularly useful to capture core features of the construct while preserving sound psychometric properties.

Therefore, the main goal of this study was to create and validate a brief scale measuring the binge-watching construct based on the 6 key components of addiction via 1 item each. The newly developed measure is labelled Binge-Watching Addiction Scale (BiWAS). A multinational sample of Arabic-speakers is used to increase the cross-cultural generalizability of the reported findings. Our study sought to test whether the BiWAS was reliable and valid. Another aim of this work was to test and establish measurement invariance of the new scale across sex and countries. It is proposed to test the following hypotheses: (1) results from factor analysis will indicate a 1-factor structure of the BiWAS representing a unique dimension of binge-watching addiction with adequate internal consistency (Cronbach’s alpha >.70); (2) factor loadings connecting the items to the single dimension will be invariant across country and sex, indicating measurement equivalence; and (3) the BiWAS will positively correlate with relevant measures including smartphone addiction, insomnia, fatigue, and psychological distress.

Methods

Procedure and Sample

We carried out a multisite research study in 5 Arab countries (i.e., Palestine, Jordan, Oman, Egypt, and Lebanon) during the period September to October 2025. Our inclusion criteria encompassed adults from the general population, who were aged at least 18 years, who were able to read and understand Arabic, who had access to the internet, and who have been TV series viewers for at least the past 12 months. Eligible individuals who agreed voluntarily to take part in the study were administered an online survey anonymously and under conditions of confidentiality. The questionnaire was designed in Arabic using Google Forms application, and distributed to potential participants using a link shared via multiple social networking sites. Convenience and snowball sampling approaches have been adopted to collect data. Specific information about the research purpose and processes was provided to participants in the first section of the questionnaire, which was also designed to gather informed consent before allowing access to the rest of sections. Participants were also informed that they can withdraw from the study at any time without penalty and without giving reasons. The research protocol of this study was reviewed and approved by the Scientific Research Ethics Committee of Isra University, Jordan (Reference: SREC/25/09/158, Date: 15/9/2025). A total of 299 individuals ticked “No” to the informed consent statement and were asked to submit the form without going any further into the questionnaire. There were 4092 valid responses obtained and included in the analysis (Palestine: N = 520; Jordan: N = 552; Oman: N = 398; Egypt: N = 1892; and Lebanon: N = 730).

Minimum Sample Size

According to the suggestion of 20 times the number of BiWAS items, we estimated a minimum sample size of 120 participants based on the recommendation of per scale’s variables.⁴⁹

Measures

The Binge-Watching Addiction Scale (BiWAS)

The BiWAS has been designed in the Arabic language. The same approach and criteria used to develop several established measurements of behavioral addictions (such as sex addiction⁵⁰) were applied to develop the new scale. The items were constructed based on core criteria fitting behavioral addiction frameworks that have been established as relevant for content validity.^21,37,51,52 The specific wording of items and response options were formulated on the basis of those used in scales assessing other technology addictions (such as social media,⁴⁶ TikTok addiction,⁴⁸ and conversational artificial intelligence⁵³), and were modified for the BiWAS to use only binge-watching-related terminology. For example, the modifications involve using the words “TV series” instead of the words “Social media”⁴⁶ “TikTok,”⁴⁸ “conversational artificial intelligence,”⁵³ or “sex.”⁵⁰

Similar to the above-mentioned measures, the BiWAS operationalizes binge-watching addiction according to the 6 components that define behavioral addictions – more particularly Gambling Disorder – following the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria.²¹ The components consist of: (a) tolerance, reflecting important increase over time in TV series viewing to attain acceptable levels of satisfaction; (b) salience, designating excessive attention and preoccupation with TV series viewing; (c) mood change, reflecting improvements in mood as a result TV series viewing, (d) withdrawal, defined as unpleasant sensations (emotional or physical) that a person can experience when TV series viewing is abruptly interrupted, (e) relapse/loss of control, reflecting failure in attempts to reduce TV series viewing, uninstall, or block the app, and (f) conflict, indicating negative impacts of excessive TV series viewing on interpersonal relationships. For each criterion, 1 item was created that can be scored on a 5-point scale as follows: very rarely (1), rarely (2), sometimes (3), often (4), and very often (5). The Likert-type scale was chosen to allow for clearer evaluation of risk degrees and facilitate capturing of at-risk users. The time frame was set at the past 12-month period preceding the completion of the scale. After developing the scale, a pilot study of the Arabic version using a sample of 30 general population adults for clearer comprehensibility and cultural suitability. After pilot testing, no changes were deemed necessary.

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

The SAS-SV consists of a self-administered instrument composed of 10 items measuring levels of smartphone addiction.⁵⁴ Each item can be rated on a 6-point from 1 (Strongly disagree) to 6 (Strongly agree), with total scores ranging from 6 to 60. A higher total score indicated greater severity of smartphone addiction. The version of the SAS-SV validated in Arabic was used,⁵⁵ which showed a Cronbach’s alpha of .93 in this study.

The Insomnia Severity Index (ISI)

The ISI is a self-rating scale consisting of 7 items that evaluate the severity of insomnia symptoms across the following domains: severity of sleep onset, sleep maintenance, early morning awakening problems, interference of sleep difficulties with daytime functioning, sleep dissatisfaction, distress caused by the sleep difficulties, and noticeability of sleep problems by others.⁵⁶ Items are scored on a 5-point Likert scale. Total scores range between 0 and 28, with greater total scores reflecting more severe insomnia. The version of the ISI validated in Arabic was used,⁵⁷ which showed a Cronbach’s alpha of .79 in this study.

The 11-Item Chalder Fatigue Scale (CFS)

The CFS is a self-report measure consisting of 11 items that assess physical and mental fatigue.⁵⁸ Response options are as follows: “much worse than usual,” “worse than usual,” “no more than usual,” and “better than usual.” The version of the CFS validated in Arabic was used,⁵⁹ which showed a Cronbach’s alpha of .90 in the present sample.

The Patient Health Questionnaire (PHQ-4)

The PHQ-4 is a short self-completed scale composed of 4 items measuring depression (2 items) and anxiety (2 items) over the past 15 days.⁶⁰ Respondents can answer each item on a scale from 0 (not at all) to 3 (almost every day), leading to a total score that ranges from 0 to 12. The version of the PHQ-4 validated in Arabic was used in our study,⁶¹ which showed a Cronbach’s alpha of .85.

Sociodemographic Data

Participants were requested to provide information about their sex, age, country, educational level, occupation, and marital status. Financial burden level was assessed using the Single-Item Financial Stress Scale (SIFiS),⁶² which consists of the following: “How stressed do you feel about your personal finances in general?”. The item can be answered on a 10-point scale ranging from 1 (“No Stress at All”) to 10 (“Overwhelming Stress”).

Data Analysis

We conducted the Exploratory Factor Analysis (EFA) and calculated reliability indices using FACTOR 12.04.01,⁶³ followed by Confirmatory Factor Analysis (CFA) with R software. To evaluate the internal structure of the scale, the full sample was randomly divided into 2 groups; the EFA was performed on the first group, representing one-third of the total sample (1349 participants), while the CFA was run on the second group (2743 participants). Prior to the EFA, data adequacy was assessed with the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity. Additionally, item-level suitability was examined through the Measure of Sampling Adequacy (MSA)⁶⁴ and the Anti-Image Correlation.⁶⁵ Items with MSA values below 0.50 were excluded, as such values indicate poor adequacy.⁶⁴ The Expected Residual correlation direct Change (EREC) index was further employed to evaluate residual associations between item pairs after accounting for common factors, where values should approximate zero. Strongly correlated item pairs (doublets) were identified and items repeatedly appearing in different doublets were removed.⁶⁶ Since the variables were ordinal and several items presented skewness and kurtosis beyond |1|, the EFA was based on a polychoric correlation matrix given the ordinal nature of the variables.⁶⁷ The method of estimation was Unweighted Least Squares (ULS) according the recommendations of the current literature.⁶⁸ Factor retention was determined through the Optimal Implementation of Parallel Analysis.^69,70

The choice of FACTOR for EFA was motivated by its superior handling or ordinal Likert-type data. FACTOR allows the computation of polychoric correlation matrices and supports robust extraction methods and optimal factor retention procedures such as parallel analysis. In contrast, AMOS does not implement polychoric correlations for EFA and relies primarily on Pearson correlations, which may underestimate factor loadings when applied to ordinal data. Second, FACTOR provides reliability estimates that are directly aligned with latent variable modeling, including McDonald’s omega, Cronbach’s alpha, and factor score determinacy indices, which are more appropriate than classical Cronbach’s alpha in the context of ordinal constructs. CFA was subsequently conducted in R using a structural equation modeling framework, which allows flexible model specification, and the computation of standard fit indices, and average variance extracted. This separation of analyses follows established psychometric conventions. The use of different softwares does not compromise comparability of interpretability of the results.

We conducted a confirmatory factor analysis in R software (lavaan package). We used the Weighted Least Squares Mean and Variance Adjusted (WLSMV) estimator, the most appropriate for ordinal data. Model fit was assessed with several indices, including Standardized Root Mean Squared Residual (SRMR), root mean square error of approximation (RMSEA), Tucker-Lewis Index (TLI), and comparative fit index (CFI). Adequate model fit was considered for SRMR values ≤0.05, RMSEA ≤0.08, and CFI and TLI ≥0.90.⁷¹ Multivariable normality was not verified as shown by the skewness (=2094.12; P < .001) and kurtosis (59.46; P < .001) values.

Prior to EFA/CFA, data were screened for careless responding using straight-lining, within-person response variance, and long-string response patter analyses; sensitivity analyses excluding flagged cases yielded comparable structures. Furthermore, multi-group CFA was applied on the second subsample to test measurement invariance across sex and countries.⁷² Configural, metric, and scalar invariance were evaluated, with ΔCFI ≤0.010 and ΔRMSEA ≤0.015 or ΔSRMR ≤0.010 serving as evidence of invariance.^73,74 Group differences in binge watching scores were examined with the Mann-Whitney U and Kruskal-Wallis tests.

Internal consistency was estimated using Cronbach’s α coefficient and McDonald’s ω, while validity evidence was explored through Spearman correlations between the new scale and related constructs.

Results

Participants’ characteristics are depicted in Table 1. The most frequent streaming platforms used were Chahed (35%) and Netflix (29%). The most common device used for binge-watching was the smartphone (71%), followed by the television (55%), tablet (38%), streaming player (6%), and game console (6%).

Table 1.

Sample Characteristics (n = 4092).

Variables	N (%) or mean ± SD
Sex
Males	947 (23.1)
Females	3145 (76.9)
Education level
Primary	23 (0.6)
Secondary	386 (9.4)
University	3683 (90.0)
Occupation
Full-timer	636 (15.5)
Part-timer	336 (8.2)
Unemployed	711 (17.4)
Student	2409 (58.9)
Age (years)	23.05 ± 7.19
Financial burden	5.47 ± 2.84
Binge-Watching addiction scores	7.52 ± 4.70
Insomnia	10.47 ± 5.50
Fatigue	14.68 ± 7.03
Psychological distress	4.41 ± 3.23
Smartphone addiction	30.58 ± 11.67

A total of 387 (9.5%) participants showed floor effect, whereas 6 (0.1%) showed ceiling effect. The 2 groups did not differ by sex (χ² (1) = 0.93, P = .336), by country (χ² (4) = 0.89, p = 0.927), or by age (t(4087) = −1.04, P = .301) (Supplemental Table 1).

Factorial Validity

Item relevance was first examined using the MSA index, which showed values above 0.50 for all items, indicating that each contributed to the same underlying construct. The EREC analysis did not identify any doublets. The analysis confirmed the adequacy of the data for EFA, as reflected by the KMO value of 0.787) and a significant Bartlett’s test (P ≤ .001). The results supported a unidimensional factor structure that explained 53.63% of the variance, with satisfactory indices: GFI = .972 (>.95) and UniCo = .970 (>.95). Parallel analysis further corroborated the 1-factor solution.

The unidimensional model was then validated through CFA on the second subsample. Initial model fit was acceptable (SRMR = 0.098, RMSEA = 0.173 [90% CI 0.163-0.184], CFI = 0.957, and TLI = 0.928). Internal consistency of the binge-watching score was good (α = .78/ω = .78) [95% CI 0.77; 0.79 for both]. The description of the items can be found in Table 2.

Table 2.

English Items of the Binge-watching Addiction Scale (BiWAS), Their Frequency, and Standardized Estimates of Factor Loadings from the Exploratory (EFA) and Confirmatory Factor Analysis (CFA).

Items		Frequency of each item					EFA	CFA
Items		Never (%)	Rarely (%)	Sometimes (%)	Most of the times (%)	Always (%)	EFA	[95% CI]
1	Spent a lot of time thinking about TV series, or planning to watch TV series?	1280 (31.3)	926 (22.6)	1328 (32.5)	412 (10.1)	146 (3.6)	0.75	0.78 [0.76-0.79]
2	Felt an urge to watch episodes of TV series more and more?	805 (19.7)	876 (21.4)	1402 (34.3)	725 (17.7)	284 (6.9)	0.71	0.81 [0.79-0.83]
3	Watched TV series to relieve negative feelings or forget about personal problems?	1028 (25.1)	770 (18.8)	1165 (28.5)	722 (17.6)	407 (9.9)	0.68	0.69 [0.67-0.71]
4	Tried to cut down on the number of episodes you watch in 1 sitting, or uninstall the TV series app without success?	1437 (35.1)	1069 (26.1)	934 (22.8)	454 (11.1)	198 (4.8)	0.47	0.45 [0.42-0.48]
5	Become depressed, anxious, restless, or irritable if you could not watch TV series?	2224 (54.3)	909 (22.2)	640 (15.6)	235 (5.7)	84 (2.1)	0.70	0.72 [0.70-0.75]
6	Watched TV series so much that they have had a negative effect on your personal relationships, work, or academic performance?	2242 (54.8)	950 (23.2)	584 (14.3)	236 (5.8)	80 (2.0)	0.67	0.72 [0.69-0.74]

Sex Invariance (Subsample 2)

The invariance across sexes and countries was established at all levels (Table 3). Higher binge-watching was found in females (Median = 8; IQR = 7) compared to males (Median = 6; IQR = 7), Mann-Whitney U = 1 266 959; Z = −6.99; P < .001.

Table 3.

Measurement Invariance of the Binge-watching Addiction Scale (BiWAS) Using the Total Sample.

Model	CFI	RMSEA	SRMR	Model comparison	ΔCFI	ΔRMSEA	ΔSRMR
Model 1: Across sex
Configural	0.959	0.174	0.099
Metric	0.955	0.152	0.103	Configural vs metric	0.004	0.022	0.004
Scalar	0.959	0.110	0.100	Metric vs scalar	0.004	0.042	0.003
Strict	0.959	0.110	0.100	Scalar vs strict	<0.001	<0.001	<0.001
Model 2: Across countries
Configural	0.959	0.174	0.099
Metric	0.955	0.152	0.103	Configural vs metric	0.004	0.022	0.004
Scalar	0.959	0.110	0.100	Metric vs scalar	0.004	0.042	0.003
Strict	0.959	0.110	0.100	Scalar vs strict	<0.001	<0.001	<0.001

Abbreviations: CFI, comparative fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual.

No significant difference was found between countries: Egypt (Median = 7; IQR = 7), Oman (Median = 8; IQR = 7), Palestine (Median = 7; IQR = 7), Jordan (Median = 7; IQR = 6), and Lebanon (Median = 7; IQR = 6), Kruskal-Wallis H = 8.67; P = .070.

Concurrent Validity (Total Sample)

Higher binge watching was significantly associated with higher smartphone addiction, increased psychological distress, more severe insomnia, and greater fatigue (Table 4).

Table 4.

Correlation Matrix Between Continuous Variables.

Variables	1	2	3	4
1. Binge watching	1
2. Smartphone addiction	0.37***	1
3. Psychological distress	0.31***	0.29***	1
4. Insomnia	0.27***	0.27***	0.50***	1
5. Fatigue	0.28***	0.37***	0.57***	0.52***

Numbers reflect Spearman correlation coefficients (rho).

***

P < .001.

Discussion

Over the past decade, wide-spreading streaming platforms have given rise to new ways of consuming TV series, but also to the potential for harmful excessive use and the concept of binge-watching addiction.¹¹ This highlights a clear need for developing a deeper understanding of this emerging behavioral phenomenon that threatens human physical and mental health. Our present research endeavor intended to investigate the psychometric characteristics of a brief measure of binge-watching addiction designed on the basis of components that theoretically reflect the 6 facets operationalizing the concept the behavioral addiction.²¹ The BiWAS was found to be unidimensional, invariant across sex and country, reliable, and valid in Arabic-speaking people residing in 5 Arab, Middle-Eastern nations. These findings are of substantial practical importance.

Both CFA and EFA were used to uncover and confirm the underlying structure of the BiWAS in our multi-country sample of community adult Arabic-speakers. Factorial Structure Analysis showed that the single-factor model that was postulated was an acceptable fit. In addition, internal consistency of the instrument’s global score was satisfactory, with a Cronbach’s alpha of .78, supporting that the 6 items analyzed had a good level of homogeneity among them. This suggests that the BiWAS may be structured on 1 dimension and that a global score might be a good indicator of binge-watching addiction. The BiWAS adequately and meaningfully measures the binge-watching addiction construct it intends to measure. Similar result was reported in previous brief, 6-item technology addiction measures that consistently produced a unidimensional construct, with the single dimension comprising the 6 components of behavioral addiction, namely withdrawal, relapse, tolerance, salience, mood modification, and conflict.^46,48,53 This study designed the BiWAS to be brief enough to be practical and applicable for use among the target population for assessing binge-watching addiction. Nowadays technology consumers may tend to prefer activities that are less demanding and time-consuming, especially as increased screen time was shown to be associated with more distractibility, more inability to finish tasks, heightened impatience and waiting difficulty for rewards.⁷⁵

Regarding the fit indices, the CFI and TLI values were within acceptable ranges, whereas the SRMR was borderline but within the upper acceptable limit (<0.10), indicating that the standardized residuals did not diverge substantially from the observed data. However, the RMSEA value was substantially above the conventional cutoff suggesting poor absolute fit. Given the very small degrees of freedom (df = 9) and the short scale, the RMSEA is expected to be inflated. It is well documented that RMSEA overestimates misfit in models with low df, even when the model is correctly specified.⁷⁶ Therefore, greater weight should be given to incremental indices (CFI/TLI) and residual-based indices (SRMR) in this context. Considering these limitations, and given the theoretical justification for unidimensionality and the overall acceptable performance of CFI, TLI, and SRMR, the model can be considered to show adequate fit. It is noteworthy that the loading factor values of item 4 in both EFA and CFA was low (=0.45) but exceeded the cut-off value of 0.40 recommended by previous authors.⁷⁷ Its retention was justified by its theoretical relevance to loss-of-control processes in binge-watching addiction and by meeting established psychometric threshold for acceptable item performance, and was consequently kept in the analysis. Item 4, i.e. “Tried to cut down on the number of episodes you watch in 1 sitting, or uninstall the TV series app without success?”, reflects the relapse/loss of control component of behavioral addiction, which represents 1 of the 6 components model of addiction outlined earlier in the paper and largely supported by theory.^21,37,51,52

A series of multi-group CFA were conducted to verify and establish measurement invariance across sex and country groups. In particular, 3 levels of measurement invariance were tested: (1) configural (i.e., items load onto the same factor across groups, (2) metric (i.e., item factor loadings are equal across groups), and (3) scalar (i.e., item intercepts are equal across groups). Findings indicated that the scale replicated adequate adjustments for a unifactorial model across the 2 sexes and the 5 country groups based on fit indices. In other terms, the BiWAS items are interpreted in a conceptually similar manner by male and female TV series binge-watchers, and whichever the country is. Invariance is considered a prerequisite to research and an important step to ensure that the interpretation of cross-group differences is not misleading and that the instrument performs equivalently across different groups or settings. After ensuring measurement invariance and soundness comparisons of BiWAS mean levels, results indicated more severe binge-watching addiction symptoms among females, whereas no significant differences were found based on the country of the participants. These findings build on prior evidence showing that females are more inclined towards binge-watching for longer periods of time⁷ and are more likely to exhibit binge-watching addiction symptoms⁷⁸ than males.

Another objective of our study was to examine the concurrent validity of the BiWAS. To investigate this psychometric property, we correlated respondents’ BiWAS mean scores with variables that have empirically established relationships with binge-watching, namely smartphone addiction, fatigue, insomnia, and psychological distress. Findings showed that BiWAS scores correlated with these variables in the expected direction, thus supporting its practical usefulness as a construct. Binge-watching scores showed significant positive correlations with smartphone addiction scores. Notably, One’s smartphone is the handiest gadget, and was found to be the most common streaming device used to binge watch TV series in our sample. Consistently, smartphones and laptops were found to be the most frequently used devices to binge-watch among Arab people from the United Arab Emirates.⁷⁹ In addition, prior findings indicated that, in both males and females, smartphones are an important motivating factor and a major source behind binge watching.³³ Furthermore, addictive patterns of binge-watching may lead to both physical and psychological negative effects on the individual.¹¹ Physical impacts include sleep disruption and fatigability,⁸⁰ while psychological effects encompass depression and anxiety symptoms.⁸¹

Practical Implications and Future Research Directions

An attempt conceptualization of binge-watching as a behavioral addiction, and the creation of a new instrument that allows its assessment based on core criteria of addiction will generate more consistency and coherence at theoretical and methodological levels in how the construct is measured. Results indicated that the BiWAS is a psychometrically valid and reliable scale in both sexes and all countries involved in this study. As a youthful region with increasing trends of binge-watching, the Middle East provides a fertile environment to study and understand the factors that relate to this behavioral problem. Users of the scale should bear in mind that although the binge-watching behavior can have a profound impact on individuals’ lives, this entity is not formally recognized as an addiction in many diagnostic frameworks and international classifications.^21,22 Therefore, the main function of the BiWAS should be for screening rather than diagnostic classification.

It is hoped that the new scale will open up possibilities for quantitatively based investigations of binge-watching thus contributing to deepen our knowledge of this prominent phenomenon and enhance understanding of the mechanisms involved in increased vulnerability to becoming addicted. The brevity of the BiWAS makes it a valuable asset for use among binge-watchers in future clinical and research applications, especially in Arab countries with limited resources where researchers and practitioners often work under time and financial pressure.

Study Limitations

Although this study provides sufficient evidence to indicate the good validity and reliability of the BiWAS based on a large multisite sample, some limitations need to be discussed. The psychometric properties of the scale have only been examined in the adult general population, and their testing should be expanded to other age groups (such as adolescents) and other settings (including clinical populations). The study relied on cross-sectional data, precluding the examination of test-retest reliability. Future research with a longitudinal design still needs to verify the stability of the BiWAS over time to guarantee that differences observed across time in binge-watching scores reflect actual changes in the latent construct measured by the BiWAS. In addition, participants were recruited using a snowball sampling approach, which may have introduced selection bias by overrepresenting individuals who are socially connected, digitally active, or more interested in binge-watching behaviors. As a result, our sample may not be fully representative of the broader adult population, thereby limiting the generalizability of the findings. The cross-sectional design used in this study precludes the assessment of test-retest reliability and temporal stability of the BiWAS. Without longitudinal data, it is not possible to determine whether observed scores remain stable over time. Although data was collected across 5 Arabic-speaking countries, the age and country distributions seem broadly comparable between subsamples, whereas the sex distribution shows a noticeable imbalance, with a higher proportion of males in the CFA sample; therefore, cultural representation remains limited and does not capture the full sociocultural diversity of the Arab world. Finally, to improve the generalizability of the findings, there is a need to replicate the validation study with more culturally diverse samples from various regions within Arabic-speaking countries.

Conclusion

There is a clear need for the field to reach a consensus regarding the conceptualization and assessment of binge-watching addiction. This study is meant to contribute to this area by designing and validating a new measure of problematic behavioral patterns of binge-watching. The BiWAS showed good psychometric qualities, suggesting that it can have widespread clinical and research applicability among adults in Arabic-speaking communities. It is our hope that the BiWAS will help both researchers and practitioners to generate reliable information derived from accurate data that can be used to guide prevention and intervention strategies. Further studies are warranted to offer additional information and a more comprehensive understanding of the scale’s performance across different populations, cultures, and societies across the globe. Longitudinal studies are needed to evaluate the scale’s stability and sensitivity to change and should include more diverse samples from additional Arab countries.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319261422127 – Supplemental material for Construction and Validation of the Binge-Watching Addiction Scale (BiWAS): Validity, Reliability, and Measurement Invariance in a Large Multinational Adult Sample From the Middle East

Supplemental material, sj-docx-1-jpc-10.1177_21501319261422127 for Construction and Validation of the Binge-Watching Addiction Scale (BiWAS): Validity, Reliability, and Measurement Invariance in a Large Multinational Adult Sample From the Middle East by Feten Fekih-Romdhane, Mai Helmy, Hanaa Ahmed Mohamed Shuwiekh, Abdallah Y. Naser, Mirna Fawaz, Kamel Jebreen, Diana Malaeb, Muna Barakat, Esra’ O. Taybeh, Eqbal Radwan, Sahar Obeid and Souheil Hallit in Journal of Primary Care & Community Health

Footnotes

Acknowledgements

Author KJ would like to acknowledge support through the ICTP-Arab Fund Associates Program (2024-2026).

ORCID iDs

Feten Fekih-Romdhane

Muna Barakat

Souheil Hallit

Ethical Considerations

The research protocol of this study was reviewed and approved by the Scientific Research Ethics Committee of Isra University, Jordan (Reference: SREC/25/09/158, Date: 15/9/2025). All methods were performed in accordance with the relevant guidelines and regulations.

Consent to Participate

Written informed consent was obtained from all subjects; the online submission of the soft copy was considered equivalent to receiving a written informed consent.

Author Contributions

FFR and SH designed the study; FFR drafted the manuscript; SH carried out the analysis and interpreted the results; MH, HAMS, AYN, KJ, ER, and MF collected the data; DM, MB, and SO reviewed the paper for intellectual content; all authors reviewed the final manuscript and gave their consent.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

All data generated or analyzed during this study are not publicly available due the restrictions from the ethics committee but are available upon a reasonable request from the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

References

Winland

An exploration of binge-watching and its effects on college academic. Pediatrics. 2015;75(5):807-812.

Starosta

Izydorczyk

Sitnik-Warchulska

Lizińczyk

Impulsivity and difficulties in emotional regulation as predictors of binge-watching behaviours. Front Psychiatry. 2021;12:743870.

Starosta

Izydorczyk

Dobrowolska

Personality traits and motivation as factors associated with symptoms of problematic binge-watching. Sustainability. 2020;12(14):5810.

Westcott

Loucks

Downs

Watson

Digital Media Trends Survey: A New World of Choice for Digital Consumers. Deloitte Insight-The Center for Technology, Media & Telecommunications ILMU; 2018.

Rubenking

Bracken

Sandoval

Rister

Defining new viewing behaviours: what makes and motivates TV binge-watching?

Int J Digit Telev. 2018;9(1):69-85.

Netflix

Netflix Declares Binge Watching is the New Normal. PR Newswire; 2013.

Merrill

Jr Rubenking

Go long or go often: influences on binge watching frequency and duration among college students. Soc Sci. 2019;8(1):10.

Vaterlaus

Spruance

Frantz

Kruger

JS.

College student television binge watching: conceptualization, gratifications, and perceived consequences. Soc Sci J. 2019;56(4):470-479.

Brookes

Ellithorpe

Good for your mood, bad for your health: narrative involvement, health behaviors, and binge watching. Paper presented at: 67th ICA annual conference; May 25-29, 2017; San Diego, CA. 2017.

10.

Pérez

JFH

Díaz

MÁM

. Nuevos modelos de consumo audiovisual: los efectos del binge-watching sobre los jóvenes universitarios. AdComunica. 2017;13:201-221.

11.

Ilyas

Maqsood

Ahsan

Afzal

Binge-watching as behavioral addiction: a systematic review. Binge-Watch Behav Addict Syst Rev. 2023;2(2):211-65.

12.

Flayelle

Maurage

Di Lorenzo

Vögele

Gainsbury

Billieux

Binge-watching: what do we know so far? A first systematic review of the evidence. Curr Addict Rep. 2020;7(1):44-60.

13.

Pierce-Grove

Just one more: how journalists frame binge watching. First Monday. 2016;22:1-2.

14.

Merikivi

Bragge

Scornavacca

Verhagen

Binge-watching serialized video content: a transdisciplinary review. Telev New Media. 2020;21(7):697-711.

15.

Wagner

CN.

“Glued to the Sofa”: Exploring Guilt and Television Binge-Watching Behaviors. Trinity University; 2016.

16.

Chambliss

Gartenberg

Honrychs

, et al. Distracted by binge-watching: sources of academic and social disruption in students. ARC J Pediatr. 2017;3(1):14-17.

17.

Sung

Kang

Lee

W-N.

Why do we indulge? Exploring motivations for binge watching. J Broadcast Electron Media. 2018;62(3):408-426.

18.

Starosta

Izydorczyk

Lizińczyk

Characteristics of people’s binge-watching behavior in the “entering into early adulthood” period of life. Health Psychol Rep. 2019;7(2):149-164.

19.

Gangadharbatla

Ackerman

Bamford

Antecedents and Consequences of Binge-Watching for College Students. First Monday; 2019.

20.

Uslu

Causes and consequences of technology addiction: a review of information systems and information technology studies. Int J Soc Sci Educ Res. 2022;8(3):299-306.

21.

American Psychiatric Association A. Diagnostic and Statistical Manual of Mental Disorders (5th Edition, Text revision): DSM–5–TR. American Psychiatric Association; 2022.

22.

World Health Organization. ICD-11, the 11th Revision of the International Classification of Diseases. World Health Organization; 2022.

23.

LaRose

Lin

Eastin

MS.

Unregulated internet usage: addiction, habit, or deficient self-regulation?

Media Psychol. 2003;5(3):225-253.

24.

Rubenking

Bracken

CC.

Binge-watching: a suspenseful, emotional, habit. Commun Res Rep. 2018;35(5):381-391.

25.

Tukachinsky

Eyal

The psychology of marathon television viewing: antecedents and viewer involvement. Mass Commun Soc. 2018;21(3):275-295.

26.

Walton-Pattison

Dombrowski

Presseau

‘Just one more episode’: frequency and theoretical correlates of television binge watching. Journal of health psychology. 2018;23(1):17-24.

27.

Riddle

Peebles

Davis

Schroeder

The addictive potential of television binge watching: comparing intentional and unintentional binges. Psychology of Popular Media Culture. 2018;7(4):589.

28.

Pittman

Steiner

Transportation or narrative completion? Attentiveness during binge-watching moderates regret. Soc Sci. 2019;8(3):99.

29.

Conlin

Auverset

Billings

Time-shifting

vs.

Appointment viewing: the role of fear of missing out within TV consumption behaviors. Comun Soc. 2016;29(4):151-164.

30.

Tefertiller

Maxwell

LC.

Depression, emotional states, and the experience of binge-watching narrative television. Atl J Commun. 2018;26(5):278-290.

31.

Shim

Lim

Jung

Shin

I hate binge-watching but I can’t help doing it: the moderating effect of immediate gratification and need for cognition on binge-watching attitude-behavior relation. Telemat. Inform. 2018;35(7):1971-1979.

32.

Spruance

Karmakar

Kruger

Vaterlaus

. Are you still watching?” Correlations between binge TV watching, diet and physical activity. J Obes Weight Manage. 2017:1-8.

33.

Granow

Reinecke

Ziegele

Binge-watching and psychological well-being: media use between lack of control and perceived autonomy. Commun Res Rep. 2018;35(5):392-401.

34.

Panda

Pandey

SC.

Binge watching and college students: motivations and outcomes. Young Consum. 2017;18(4):425-438.

35.

Merikivi

Salovaara

Mäntymäki

Zhang

On the way to understanding binge watching behavior: the over-estimated role of involvement. Electron Mark. 2018;28(1):111-122.

36.

Orosz

Bőthe

Toth-Kiraly

The development of the problematic series WatchingScale (PSWS). J Behav Addict. 2016;5(1):144-150.

37.

Griffiths

A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use. 2005;10(4):191-197.

38.

Tóth-Király

Bőthe

Tóth-Fáber

Hága

Orosz

Connected to TV series: quantifying series watching engagement. J Behav Addict. 2017;6(4):472-489.

39.

Flayelle

Canale

Vögele

Karila

Maurage

Billieux

Assessing binge-watching behaviors: development and validation of the “Watching TV Series Motives” and “Binge-watching Engagement and Symptoms” questionnaires. Comput Hum Behav. 2019;90:26-36.

40.

de Alarcón

Iglesia

Casado

Montejo

AL.

Online porn addiction: what we know and what we don’t-a systematic review. J Clin Med. 2019;8(1):91. doi:10.3390/jcm8010091

41.

Lee

Choo

Lee

HK.

Balancing between prejudice and fact for Gaming Disorder: does the existence of alcohol use disorder stigmatize healthy drinkers or impede scientific research?

J Behav Addict. 2017;6(3):302-305. doi:10.1556/2006.6.2017.047

42.

Forte

Favieri

Tedeschi

Casagrande

Binge-watching: development and validation of the binge-watching addiction questionnaire. Behav Sci. 2021;11(2):27.

43.

Dennis

Martin

Hassan

Media Use in the Middle East, 2019: A Seven-nation Survey. Northwestern University in Qatar; 2019.

44.

Umara

LS.

Binge-watching Effect on Egyptian Youth Perceptions: The Case of Netflix. The American University in Cairo (Egypt); 2022.

45.

Flayelle

Castro-Calvo

Vögele

, et al. Towards a cross-cultural assessment of binge-watching: psychometric evaluation of the “watching TV series motives” and “binge-watching engagement and symptoms” questionnaires across nine languages. Comput Hum Behav. 2020;111:106410.

46.

Andreassen

Pallesen

Griffiths

MD.

The relationship between addictive use of social media, narcissism, and self-esteem: findings from a large national survey. Addict Behav. 2017;64:287-293.

47.

Andreassen

Torsheim

Brunborg

Pallesen

Development of a Facebook addiction scale. Psychol Rep. 2012;110(2):501-517.

48.

Fekih-Romdhane

Ali

Shuwiekh

HAM

, et al. Development and initial validation of a brief measure of TikTok addiction in a multi-country sample. J Prim Care Community Health. 2025;16:21501319251352917. doi:10.1177/21501319251352917

49.

Mundfrom

Shaw

TL.

Minimum sample size recommendations for conducting factor analyses. Int J Test. 2005;5(2):159-168.

50.

Andreassen

Pallesen

Griffiths

Torsheim

Sinha

The development and validation of the Bergen–Yale Sex Addiction Scale with a large national sample. Front Psychol. 2018;9:293148.

51.

Brown

RIF

. Some contributions of the study of gambling to the study of other addictions. In: Gambling Behavior and Problem Gambling, vol. 1. University of Nevada Press; 1993: 241-272.

52.

Andreassen

Griffiths

Gjertsen

Krossbakken

Kvam

Pallesen

The relationships between behavioral addictions and the five-factor model of personality. J Behav Addict. 2013;2(2):90-99.

53.

Mao

Kim

KJ.

How social anxiety leads to problematic use of conversational AI: the roles of loneliness, rumination, and mind perception. Computers in Human Behavior. 2023;145:107760.

54.

Kwon

Kim

Cho

Yang

The smartphone addiction scale: development and validation of a short version for adolescents. PLoS ONE. 2013;8(12):e83558. doi:10.1371/journal.pone.0083558

55.

Zeidan

Hallit

Akel

Louragli

Obeid

Problematic smartphone use and affective temperaments among Lebanese young adults: scale validation and mediating role of self-esteem. BMC Psychol. 2021;9(1):136. doi:10.1186/s40359-021-00638-y

56.

Bastien

Vallières

Morin

CM.

Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2(4):297-307. doi:10.1016/s1389-9457(00)00065-4

57.

Hallit

Haddad

Hallit

, et al. Validation of selected sleeping disorders related scales in Arabic among the Lebanese population. Sleep Biol Rhythms. 2019;17:183-189.

58.

Chalder

Berelowitz

Pawlikowska

, et al. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147-153. doi:10.1016/0022-3999(93)90081-P

59.

McIlvenny

Ahmed

Dunn

Swadi

Balshie

The translation into Arabic and revalidation of a fatigue questionnaire. East Mediterr Health J. 1999;5(3):503-514.

60.

Kroenke

Spitzer

Williams

Löwe

An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics. 2009;50(6):613-621. doi:10.1176/appi.psy.50.6.613

61.

Obeid

Hemade

Malaeb

, et al. Psychometric properties of the ultra-brief self-report Patient Health Questionnaire-4 (PHQ-4) to assess anxiety and depression in Arabic-speaking adults. BMC Psychiatry. 2024;24(1):537. doi:10.1186/s12888-024-05978-8

62.

El Zouki

C-J

Chahine

Hallit

, et al. Arabic validation of the incharge financial distress/financial well-being scale and the new single-item financial stress scale. Front Public Health. 2025;13:1570404.

63.

Lorenzo-Seva

Ferrando

PJ.

FACTOR: a computer program to fit the exploratory factor analysis model. Behav Res Methods. 2006;38(1):88-91.

64.

Kaiser

Rice

Little Jiffy, Mark IV. Educat Psychol Meas. 1974;34(1):111-117.

65.

Mulaik

SA.

Foundations of Factor Analysis. CRC Press; 2009.

66.

Ferrando

Lorenzo-Seva

Hernández-Dorado

Muñiz

Decalogue for the factor analysis of test items. Psicothema. 2022;34(1):7.

67.

Muthén

Kaplan

A comparison of some methodologies for the factor analysis of non-normal Likert variables. Br J Math Stat Psychol. 1985;38(2):171-189.

68.

Lloret-Segura

Ferreres-Traver

Hernández-Baeza

Tomás-Marco

El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada. Ann Psychol. 2014;30(3):1151-1169.

69.

Calderón Garrido

Navarro González

Lorenzo Seva

Ferrando Piera

. Multidimensional or Essentially Unidimensional? A Multi-faceted Factoranalytic Approach for Assessing the Dimensionality of Tests and Items. Psicothema; 2019.

70.

Timmerman

Lorenzo-Seva

Dimensionality assessment of ordered polytomous items with parallel analysis. Psychol Methods. 2011;16(2):209.

71.

Byrne

BM.

Structural Equation Modeling With Mplus: Basic Concepts, Applications, and Programming. Routledge; 2013.

72.

Chen

FF.

Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct Equ Model. 2007;14(3):464-504.

73.

Vadenberg

Lance

A review and synthesis of the measurement in variance literature: suggestions, practices, and recommendations for organizational research. Organ Res Methods. 2000;3:4-70.

74.

Swami

Todd

Azzi

, et al. Psychometric properties of an Arabic translation of the Functionality Appreciation Scale (FAS) in Lebanese adults. Body Image. 2022;42:361-369. doi:10.1016/j.bodyim.2022.07.008

75.

Twenge

Campbell

WK.

Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep. 2018;12:271-283.

76.

Kenny

Kaniskan

McCoach

DB.

The performance of RMSEA in models with small degrees of freedom. Sociol Methods Res. 2015;44(3):486-507.

77.

Worthington

Whittaker

TA.

Scale development research: a content analysis and recommendations for best practices. Couns Psychol. 2006;34(6):806-838.

78.

Özkent

Akdoğan

Relationships between problematic binge-watching behavior with psychological and social states. BMC Public Health. 2025;25(1):2309. doi:10.1186/s12889-025-23422-8

79.

Ahmed

AA-AM

. New era of TV-watching behavior: binge watching and its psychological effects. Media Watch. 2017;8(2):192-207.

80.

Exelmans

Van den Bulck

Binge viewing, sleep, and the role of pre-sleep arousal. J Clin Sleep Med. 2017;13(8):1001-1008.

81.

Sun

J-J

Chang

Y-J.

Associations of problematic binge-watching with depression, social interaction anxiety, and loneliness. Int J Environ Res Public Health. 2021;18(3):1168.

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