Pa ge 1 Pa ge 12 7 American Journal of Human Psychology (AJHP) Joy of Missing Out (JOMO): Developing a Multi-Factorial Scale for the FOMO Antithesis in Filipino Emerging Adults Jelyn D. Canonigo1*, Angela Marie H. Mendez1, Greg Adrian D. Cañamo1, Gerlie Mae V. Subido1, Roselle Daireen L. Guinto1, Rose Ann L. Bautista1, Jenelyn C. Abrenilla1, Mayumi A. Castaños1 Volume 3 Issue 1, Year 2025 ISSN: 2994-8878 (Online) DOI: https://doi.org/10.54536/ajhp.v3i1.4408 https://journals.e-palli.com/home/index.php/ajhp Article Information ABSTRACT Received: January 09, 2025 Accepted: February 17, 2025 Published: July 09, 2025 The social media’s recent growth has brought about a constant need for engagement which led people to Fear Of Missing Out (FOMO). In line with this, a novel concept, which is the Joy of Missing Out (JOMO), has emerged. JOMO encourages people to embrace solitude, prioritize self-care, and enjoy both online and physical disconnection. Given the increasing relevance of JOMO in the modern age, this study aims to develop a scale that measures its multidimensional aspects. For the first step of the development, the study employed a quantitative research design utilizing Exploratory Factor Analysis (EFA). An 83-item questionnaire, having a 7-point Likert scale, was constructed and distributed to 520 emerging adults (ages 18-30) residing in the province of Davao del Sur. A Shapiro-Wilk test was conducted, and results showed that the data is not normally distributed (p < 0.001). Thus, the Principal Axis Factoring (PAF) was used for factor extraction. Results revealed a five- factor structure which consists of Mindfulness (14 items), Social Media Usage (17 items), Contentment of Solitude (15 items), Social Detachment (5 items), and Social Comparison (5 items). Meanwhile, the Kaiser-Meyer-Olkin (KMO) value of .974 and a significant Bartlett’s Test (p < 0.001) confirmed sampling adequacy. The final scale demonstrated a satisfactory internal consistency (α = .978). Aside from the methodological findings, results also highlighted that mindful individuals who embrace solitude often experience JOMO, while those stuck in social comparison and social media use have a hard time attaining this state. It is recommended to conduct further studies on JOMO in various settings and age groups to raise the findings’ generalizability. It is also recommended to conduct a Confirmatory Factor Analysis to fortify the results of this study. Keywords Digital Well-Being, Emerging Adults, FOMO, Joy of Missing Out, Mental Health, Social Media Use 1 Department of Arts and Sciences, UM Digos College, Philippines * Corresponding author’s e-mail: j.canonigo.59823.dc@umindanao.edu.ph INTRODUCTION The growth of digitalization and social media has created a constant need for engagement, leading individuals to feel pressured not to miss out on anything, which can negatively affect their well-being (Eitan & Gazit, 2024). This phenomenon is known as FOMO, or Fear of Missing Out, which is characterized by the urge to remain constantly connected to what others are doing (Gupta & Sharma, 2021). In response to this, a relatively new concept has emerged as a counterbalance: the Joy of Missing Out (JOMO). The term “JOMO,” coined by Anil Danish in 2012, refers to the concept of finding joy and contentment by intentionally disconnecting from the constant pressure of social engagements and digital distractions, emphasizing the importance of prioritizing self-care by consciously choosing what you want to participate in, rather than feeling obligated to engage in activities driven by external pressures (Cleveland Clinic, 2024; Kaswa & Kolapkar, 2024). The concept of JOMO is becoming increasingly relevant in today’s digital era, where the constant demand for connectivity often contributes to heightened feelings of stress, anxiety, and feelings of missing out (FOMO) (Arfan, 2024). Unlike FOMO, the joy of missing out encourages individuals to embrace solitude, prioritize self- care, and enjoy both online and physical disconnection (Albers, 2023). This emphasizes the importance of intentional living, stepping away from the social pressure of being included every time, and focusing on one’s well- being. Several studies have explored the correlations and antecedents of JOMO, offering valuable insights into this emerging concept. Some studies have linked JOMO to positive outcomes, such as reduced social media use, higher self-esteem, and improved mental health. Kaswa and Kolapkar (2024) specifically found that individuals with higher levels of JOMO tend to exhibit beneficial characteristics, including reduced anxiety and depression. This suggests that JOMO encourages a healthier appreciation for solitude and more intentional living, which helps counteract the negative impacts of FOMO (Fear of Missing Out) and excessive social media use, leading to better overall well-being (Reid & Clark, 2025). Similarly, Eitan and Gazit (2024) found that individuals with stronger psychological well-being and lower social media engagement tend to experience higher levels of JOMO, further supporting the idea of JOMO as a supportive factor against social comparison. Conversely, a study by Barry et al. (2023) revealed that social media use did not generally moderate the relationship between JOMO and self-perception or adjustment variables. However, participants with high JOMO and high social media use showed the highest levels of social anxiety. This suggests that individuals Pa ge 12 8 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 with high JOMO may avoid direct social interactions due to anxiety but still seek connection through social media, which provides a less direct form of engagement. Furthermore, the interplay between FOMO and JOMO has been examined. Rautela and Sharma (2022) found that excessive social media use worsens mental health, increases FOMO, and causes social media fatigue, which subsequently drives individuals to disconnect and embrace JOMO. This transition from FOMO to JOMO can be facilitated through mindfulness practices, as suggested by the “Social Media FOMO to JOMO” framework proposed by Chan et al., (2022), which posits that mindful social media use can reduce FOMO and promote JOMO, leading to improved mental health. This connection between FOMO and social media use is strengthened by the findings from a study by Servidio et al. (2024), which linked higher levels of social comparison to increased FOMO, which negatively impacts self-esteem and can contribute to problematic social media use. However, despite these insights, a comprehensive understanding of the factors contributing to JOMO remains limited. Therefore, further research across diverse contexts is warranted to explore the multifaceted nature of JOMO and the various factors that shape its individual experiences. To serve as a guide for the researchers, this study is grounded on two key theories: Social Comparison Theory (Festinger, 1954) and Self-Determination Theory (Deci & Ryan, 1985). Social Comparison Theory suggests that individuals tend to compare themselves with others to fulfill their need to know themselves (Crusius et al., 2022). Since JOMO centers on joy and contentment without making these comparisons, this theory can provide a framework for a better understanding of why some people feel fulfilled and unaffected by other people’s achievements while others may struggle with constant comparison. By developing questions around this concept, the researchers can measure an individual’s independence from social influence, which directly aligns with the goals of the JOMO scale (Parveiz et al., 2023). Meanwhile, Self-Determination Theory (SDT) explores the motivation behind people’s choices in the absence of external influences. This theory emphasizes three psychological needs: autonomy, competence, and relatedness. According to the theory, when these needs are met, individuals experience a greater sense of well- being and intrinsic motivation (Ackerman, 2018). This study will incorporate the concept of autonomy, focusing on autonomous motivation (the act of doing something for self-determined reasons) and its contrary, controlled motivation (performing tasks for externally referenced conditions) in developing the JOMO scale (McAnally & Hagger, 2024). JOMO reflects a sense of autonomy and intrinsic satisfaction, as people find joy in choosing their own paths without feeling pressured by social expectations or trends. It may help in explaining why some individuals feel content with their own choices and do not seek validation from others. Using SDT as a basis, the JOMO scale can assess autonomy, focusing on whether individuals are motivated by their internal standards rather than external pressures (Lemay et al., 2019) Development of the Survey Question Initially, the researchers created a set of potential items for the item pool. The items were guided by existing literature related to the construct, particularly fear of missing out (FOMO), mindfulness, and social comparison (Barry et al., 2023; Gupta & Sharma, 2021; Chan et al., 2022) to ensure that the generated statements measured what it was intended to measure. The items were thoroughly reviewed and tested in a pilot study. Feedback from this pilot test prompted revisions to improve the instrument’s accuracy, clarity, and sentence structure. Consequently, an 83-item questionnaire was developed, utilizing a 7-point Likert scale, and was prepared for the main study. MATERIALS AND METHODS Study Design This study employed a quantitative research design and Exploratory Factor Analysis (EFA) to explore the underlying dimensions for developing a scale that measures the Joy of Missing Out (JOMO). Study Setting and Population The researchers gathered responses from 520 individuals aged 18 to 30 from Davao del Sur, Philippines, through a simple random sampling method. Data were collected using a blended approach, combining pen-and-paper surveys with Google Forms for online responses. Data Analysis Exploratory factor analysis (EFA) was used to analyze the Joy of Missing Out (JOMO) to assess the internal consistency of the components and identify the number of its underlying dimensions, EFA is a multivariate statistical tool that identifies the smallest number of hypothetical factors that can concisely explain the connections between factors (Watkins, 2018). Principal Axis Factoring (PAF), a type of EFA, was used in this study as an extraction method to identify the factor structure of the JOMO scale’s set of variables. Principal Axis Factoring (PAF) is an appropriate method for non normally distributed data, as it does not rely on distributional assumptions about the observed variable (Finch & French, 2018). The reliability of each set item in every factor was analyzed using the Cronbach Alpha, a widely used reliability estimator (Amirrudin et al., 2020) RESULTS AND DISCUSSION Data Screening To ensure the suitability of the dataset for Exploratory Factor Analysis, the researchers conducted a data screening. Two measures are used during this step; the Mahalanobis Distance and the Shapiro-Wilk Test. Mahalanobis distance is used to determine whether there Pa ge 12 9 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 are outliers in multivariate statistics. It takes into account the correlation between variables, making it suitable for datasets where variables are interrelated (Brereton, 2015). In this study, cases with p-values less than 0.001 (Hair et al., 1998) were flagged as outliers and removed using listwise deletion to prevent distortion in Exploratory Factor Analysis (EFA). Meanwhile, the researchers also utilized a Shapiro-Wilk test to determine the normality of the data (King & Eckersley, 2019). The result shows that the data is not normally distributed (p < 0.001), which prompted the researchers to use the Principal Axis Factoring as this study’s factor extraction method. This extraction method is ideal for data that are not normally distributed because it does not rely on distributional assumptions about the indicators (Finch & French, 2018). Factor Analysis The factorability of the 82 Likert scale items was assessed using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. Table 1 presents the results of both tests. The KMO test is used to determine whether they are suitable for identifying underlying factors. The Kaiser-Meyer-Olkin (KMO) result was .97 which is above the desired threshold of .70 (Hoelzle & Meyer, Table 1: KMO and Bartlett’s Test Result Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .974 Bartlett’s Test of Sphericity Approx. Chi-Square 34627.692 df 3403 Sig. .000 Table 2: The Communalities of items of the JOMO scale Item No. Extracted Item No. Extracted Item No. Extracted Item No. Extracted 1 .458 23 .642 45 .632 *67 .146 2 .616 24 .517 46 .584 68 .668 3 .648 25 .578 47 .665 69 .654 4 .639 26 .568 48 .684 70 .634 5 .523 27 .596 49 .725 71 .665 6 .664 28 .605 50 .729 72 .636 7 .578 *29 .320 51 .666 73 .686 *8 .203 30 .655 52 .623 74 .667 9 .602 31 .666 53 .670 75 .593 10 .615 32 .762 54 .643 76 .662 11 .572 33 .641 55 .566 77 .654 12 .590 34 .656 56 .610 78 .614 13 .594 35 .586 57 .713 79 .645 14 .585 36 .605 58 .597 80 .711 15 .630 *37 .143 59 .666 81 .609 16 .551 38 .732 60 .624 82 .703 17 .572 39 .724 61 .668 83 .620 18 .440 40 .614 62 .622 19 .640 *41 .146 63 .752 20 .676 42 .456 64 .614 21 .589 43 .607 65 .622 22 .610 44 .655 66 .634 Note. Extraction Method: Principal Axis Factoring * indicates h2<.40 and is, therefore, to be deleted The minimum acceptable commonality threshold for questionnaire items is typically above 0.5 (Arfin, 2018), though some studies consider values between 0.3 and 0.4 acceptable (Eaton et al., 2019). In this study, items 8, 29, 37, 41, and 67 were removed because their commonalities were below 0.40, indicating insufficient correlation and unsuitability for further analysis. 2013; Lloret et al., 2017). This indicates that sampling is highly suitable for factor analysis, which suggests a strong degree of shared variance among the variables. Bartlett’s test of sphericity was utilized to examine if the variables are unrelated, which indicates they are not suitable for finding factors. The results reveal a significant p-value of <0.05, indicating that the variables are correlated and appropriate for factor analysis. Pa ge 13 0 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 As shown in Figure 1, the scree plot in this study revealed an elbow, where the curve began to flatten. A study by Del Giudice (2022) examined the relationship between scree plots and eigenvalues, identifying inconsistencies in their interpretation. The study found that eigenvalues, which represent the variance explained by factors or components, can distort the perceived importance of these factors by amplifying dominant effects while diminishing smaller ones. Similarly, research by Onatski and Wang (2021) highlighted inconsistencies in high- dimensional, nonstationary data, where a few principal components may absorb most of the variation, falsely suggesting the presence of multiple factors. This phenomenon can create the illusion of significant eigenvalues, even when the data may lack true factors. These findings contrast with traditional scree plots, which often exaggerate differences between larger and smaller factors, leading to a flattened visual representation, similar Figure 1: Scree Plot to what was observed in this study. This suggests that factors beyond this point contributed very little to the variance. Therefore, retaining only the first few factors for further analysis was deemed appropriate. To achieve a more accurate factor retention, Parallel analysis was utilized in this study. It is a method to find out how many factors to retain in an exploratory factor analysis. It compares the eigenvalues from the exploratory factor analysis with some obtained from random data. (Horn, 1965). Parallel Analysis improves factor selection by accounting for sampling variability, making it particularly useful for small datasets (Xia & Zhou, 2024). It helps prevent the inclusion of unnecessary factors (Holubova, 2023). In this study, the parallel analysis showed that items 1 to 83 have higher initial eigenvalues compared to the percentiles from random data eigenvalues. This suggests that all factors are retained. To further refine the factor selection, the Minimum Average Partial (MAP) analysis was also utilized. In a Rigamonti and Lučivjanská (2022) study, Velicer (1976) introduced the MAP criterion, which is used to determine the optimal number of factors for dimensionality reduction in portfolio optimization. This was achieved by computing the average squared partial correlations in the partial correlation matrix, effectively addressing parameter uncertainty without relying on sample size. The revised MAP test (2000) indicates that four factors, on average, should be retained for this study for a more robust and reliable factor structure (Caron, 2018). By utilizing the MAP analysis in this study, from all factors that were retained in the parallel analysis, it narrowed down to 8. Table 3: MAP analysis results Smallest average squared partial correlation Smallest average 4th power partial correlation Number of components MAP (1976) Number of components MAP (2000) .0061 .0001 8 8 Table 3: MAP analysis results Factor Items 1 2 3 4 5 6 7 8 I enjoy spending time offline because it allows me to focus on my hobbies and interests.** .625 Being offline gives me more space to engage in meaningful connections with people around me.** .728 Disconnecting from social media helps me stay focused on my personal goals.** .795 I feel more in control of my time when I limit my social media use.** .815 In this study, rotation, a statistical technique that simplifies factor interpretation, was employed. This method mathematically transforms factor loadings to achieve a simpler structure where each variable primarily loads on one factor, thus facilitating interpretation (Fabrigar et al., 1999). The analysis used principal axis factoring (PAF) with a promax rotation, an oblique method chosen to allow for factor correlations (Jackson, 2014). This study yielded an eight-factor solution. Table 5 presents the rotated component matrix, showing variable loadings on the extracted factors. For clarity, loadings below 0.40 have been suppressed. Pa ge 13 1 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 I prefer real-life conversations over virtual interactions.** .683 I feel a sense of accomplishment when I stay off social media for a while.** .860 I appreciate not being constantly exposed to others' opinions and updates on social media.** .597 Taking a break from social media helps me feel more connected with myself.** .758 I enjoy keeping my personal life private instead of sharing it on social media. .469 .423 .469 I am happy in knowing that I don’t need to conform to online expectations or trends.** .512 When I disconnect from social media, I feel more grateful for the small things in my life.** .658 I enjoy discovering new interests that aren’t influenced by social media trends.** .589 I experience more joy from engaging in real-life activities than from using social media.** .655 I feel more authentic when I don't compare my life to others on social media.** .556 I notice my self-esteem improves when I limit my social media use.** .618 Skipping social media use improves my mood and reduces stress.** .590 Not checking social media before bed improves the quality of my sleep.** .512 I feel free and satisfied when I can enjoy moments without the need to document them for social media.** .509 I feel relieved when I don't need to keep up with other posts or stories online. .423 .443 .423 I don’t feel pressure to constantly post on social media. I am not concerned with the number of likes and comments I receive on social media.**** .426 I don't feel left out when I skip updates or events on social media platforms.**** .588 I feel comfortable engaging in activities by myself. I’m not afraid of feeling left out in group conversations.**** .611 I am not upset that other people are having fun without me.**** .758 I am fine with not being fully aware of what my social circle is doing.**** .511 I appreciate the simplicity of spending time alone without any external distractions.*** .497 Being alone after a stressful day helps me recharge emotionally and mentally.*** .752 I feel comfort in quiet moments.*** .883 I find it rewarding to spend time alone enjoying nature, music, or self-care.*** .839 Pa ge 13 2 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 I feel happy choosing to spend time on what I enjoy, rather than joining in just to be part of a group.*** .746 I experience satisfaction knowing that I am true to myself, even if it means saying “no” to social invitations.*** .675 I feel fulfilled by my own interests and don’t need to participate in everything to feel satisfied.*** .504 I trust my decisions to prioritize myself over what others are doing or expecting.*** .551 I am comfortable spending time alone and don’t feel the need to explain my choices to others.*** .573 I enjoy spending time alone, even when there are other people around me.*** .474 I value quiet time for reflection and introspection.*** .618 I am not afraid to be seen alone in public places.*** I don’t feel like I’m missing out when I choose to stay home rather than go out and socialize.*** .495 I don’t feel pressure to keep up with others’ achievements. When I see others’ successes, it doesn’t make me feel less satisfied with my own progress. I rarely feel the need to alter my choices based on what others are doing. I can appreciate others’ experiences without feeling the need to be part of them.*** .466 I find value in what I’m doing, even if it doesn’t align with popular trends.*** .451 I am comfortable with having different priorities than the people around me. .534 .402 I am comfortable with having different interests than the people around me. .510 .420 I feel satisfied with pursuing my own goals, even if they look different from what others are doing.*** .447 I don’t feel anxious when I see others are succeeding in areas I haven’t pursued. I feel proud of myself regardless of how my accomplishments measure up to others. .495 I don’t feel pressure to keep up with others’ lifestyles. I am confident in expressing my opinions, even if they differ from the majority. .525 I am comfortable with my own body image. .432 .600 I am confident in my own abilities and skills. .416 .628 I prefer planning my weekends based on my own interests. I enjoy hobbies that matter to me, even without recognition.***** .438 I don’t need to share personal moments to enjoy them.***** .592 Pa ge 13 3 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 I’m fine with my relationships being different from others.***** .593 I don’t need approval to pursue unique career paths.***** .596 I don’t need approval to pursue unique hobbies.***** .639 I find satisfaction in focusing on my current experience rather than worrying about what I might be missing elsewhere. I am fully present in my own activities even when I know others are engaged in different things. I am at peace with my choice to spend time alone and do not feel the need to be elsewhere. I feel content with my decisions and focus on appreciating the present, even if it means missing out on other events.* .489 I am able to let go of my comparison and stay centered on my own experiences.* .576 I focus on what brings me joy in the present. I feel at ease with my choices and do not let thoughts of missing out affect my peace of mind.* .411 I am content with a slower pace in life and don’t feel to constantly engage in social activities.* .429 I feel grateful for my own experiences and don’t feel regret about not participating in every opportunity.* .455 I value being fully immersed in what I’m doing, without concern for what others might be up to.* .489 I feel no urgency to follow the crowd, preferring to stay grounded in my own decisions. I trust my choices and let go of second-guessing about other possibilities.* .760 I avoid distractions that pull me away from the simplicity of the present.* .752 I find richness in doing less, without craving the busyness of others.* .763 I let go of the need to compare, focusing instead on what feels right for me.* .713 I take pleasure in the calm of stepping back, while others move forward.* .919 I enjoy choosing rest over rushing, without feeling I need to justify it.* .822 I resist the pull of constant engagement, valuing my time as my own.* .855 I trust that stepping away from the crowd brings clarity and calm.* .602 Note. * indicates that the item belongs to factor 1, “Mindfulness” ** indicates that the item belongs to factor 2, “Social Media Usage” *** indicates that the item belongs to factor 3, “Contentment of Solitude” **** indicates that the item belongs to factor 4, “Social Detachment” ***** indicates that the item belongs to factor 5, “Social Comparison” Pa ge 13 4 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 Factor 1, designated “Mindfulness,” demonstrated strong loadings on fourteen items: items 68, 69, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, and 83. Seventeen items exhibited strong loadings on Factor 2, labeled “Social Media Usage”: items 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, and 19. Factor 3, named “Contentment of Solitude,” showed positive loadings on fifteen items: items 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 43, 47, 48, and 51. The following five items loaded positively on Factor 4, designated “Social Detachment”: items 22, 23, 25, 26, and 27. Finally, five items demonstrated positive loadings on Factor 6, identified “Social Comparison”: items 59, 60, 61, 62, and 63. However, several instances of cross-loading were observed among the items. Specifically, items 10 and 20 loaded on Factors 1, 2, and 8; items 48 and 49 loaded on both Factors 3 and 7; and items 54 and 58 loaded on Factors 1 and 5. Because cross-loading items do not exclusively represent a single construct, they can complicate factor interpretation and obscure the underlying dimensions being measured (Güvendir & Özkan, 2022). Therefore, these items were excluded from the interpretation of their respective factors. Additionally, Factor 5 is represented by only two items, which necessitates additional supporting evidence to confidently associate these items with the factor (Fabrigar & Wegener, 2011). Finally, Factor 7 and 8 each lacked distinct defining items, as the two items associated with each factor also exhibited cross-loadings with other factors. Table 5: Factor Correlation Matrix Factor 1 2 3 4 5 6 7 8 1 1.000 2 .520 1.000 3 .635 .664 1.000 4 .550 .575 .559 1.000 5 .585 .484 .546 .492 1.000 6 .657 .595 .647 .537 .593 1.000 7 .472 .402 .452 .543 .455 .501 1.000 8 .194 .281 .425 .289 .234 .167 .305 1.000 The table above presents the factor correlation matrix, detailing the interrelationships among the eight factors extracted through principal axis factoring with a promax rotation. The correlation coefficients indicate the extent to which each pair of factors are linearly related. Moderate positive correlations are evident among most factors. Factor 3 exhibits moderate positive correlations with all other factors, most notably with Factor 2 (.664). Factors 1, 2, 4, 5, 6, and 7 also demonstrate moderate positive intercorrelations, with coefficients ranging from .4 to .7. In contrast, Factor 8 shows weak correlations with most other factors, with the exception of moderate correlations observed with Factor 3. Reliability Analysis Cronbach’s alpha was used to evaluate the internal consistency reliability of each factor measuring the JOMO construct. Internal consistency evaluates the Table 6: Reliability Analysis for every factor measuring JOMO Name of Factor No. of items Cronbach’s Alpha 1 Mindfulness 14 .951 2 Social Media Usage 17 .947 3 Contentment of Solitude 15 .951 4 Social Detachment 5 .857 5 Social Comparison 5 .898 consistency of results across factors within a test and determines how all factors on the test relate to all other factors (Hajjar, 2018). The JOMO scale’s Cronbach’s alpha for the 58 items was found to have an excellent level of internal reliability with .978. As shown in Table 7, there are five factors assessing the JOMO construct—14 items for “Mindfulness,” 17 items for “Social Media Usage,” 15 items for “Contentment of Solitude,” 5 items for “Social Detachment,” and 5 items for “Social Comparison.” The Cronbach’s alpha for each factor showed a high- reliability standard with 0.951, 0.947, 0.951, 0.857, and 0.898 for factors one through five, respectively. These results show that all the reliability measures for the five dimensions of JOMO have exceeded the required value of more than 0.70. As a result, the extracted dimensions with their respective items are reliable and appropriate for measuring the Joy of Missing Out construct. CONCLUSION The objective of this study is to establish a test that measures the concept of Joy of Missing Out (JOMO). Through careful analysis, the questionnaire was refined into 58 items, which include five key factors: Mindfulness, Social Media Usage, Contentment of Solitude, Social Detachment, and Social Comparison. These factors represent the important dimension of JOMO, which reflects different aspects of an individual’s psychological and emotional state. Beyond the methodological findings, this scale also highlights how digital habits and mindset influence the attainment of Joy Pa ge 13 5 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 127-136, 2025 of Missing Out (JOMO). People who practice mindfulness and enjoy solitude tend to experience JOMO, while those who are constantly comparing themselves with others and are heavy social media users may struggle to attain this state. This suggests that further research on how JOMO helps with mental health and how it can ease the stress caused by FOMO is needed. Limitation of the Study Although the results of the study are satisfactory, the researchers have found limitations that should be taken into account. One of which is the sample- dependent approach of this study, which made it lack generalizability. Since the target respondents of this study are emerging adults of Davao del Sur, the results cannot be generalized to either people outside of the target age group or those with different cultures. Additionally, as this is an Exploratory Factor Analysis study, the factors extracted may not hold much meaning unless a Confirmatory Factor Analysis is conducted. In line with these limitations, it is recommended to conduct further studies on JOMO in various settings and age groups. Furthermore, a Confirmatory Factor Analysis on this questionnaire is necessary to further fortify the findings of this study. REFERENCES Ackerman, C. (2018, June 21). 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