Pa ge 1 Pa ge 34 American Journal of Human Psychology (AJHP) Development of A Multi-factorial Fear of Happiness Scale for Young Adults Leonora Kyla Uy1*, Hannah Dela Pena1, Claudine Jade Abellana1, Honie Mae Matutis1, Eden Grace Laurente1, Marc Loyd Dumanhog1, Mayumi Castanos1 Volume 3 Issue 1, Year 2025 ISSN: 2994-8878 (Online) DOI: https://doi.org/10.54536/ajhp.v3i1.4317 https://journals.e-palli.com/home/index.php/ajhp Article Information ABSTRACT Received: December 30, 2024 Accepted: February 02, 2025 Published: March 22, 2025 This study developed and tested the Fear of Happiness Scale for Young Adults (FHS-YA) to understand how young adults aged 18–26 experience fear of happiness. A total of 501 respondents from Davao del Sur, Philippines completed the survey. The data was analyzed using Exploratory Factor Analysis (EFA), and methods like Principal Axis Factoring (PAF) were used due to the non-normal data distribution. Four key factors were identified: Social and Emotional Avoidance (SEA), Future-Oriented Anxiety or Guilt (FOAG), Cognitive Distortion (CD), and Emotional Discomfort (ED). The scale showed excellent reliability with a Cronbach’s Alpha of 0.975. This alternative tool provides a reliable way to explore fear of happiness in young adults and can help guide future research and interventions aimed at improving emotional well-being. Keywords Exploratory Factor Analysis, Fear of Happiness, Young Adults 1 Bachelor of Arts in Psychology, UM Digos College, Philippines * Corresponding author’s e-mail: l.uy.59951.dc@umindanao.edu.ph INTRODUCTION Fear of happiness has emerged as a growing concern, particularly among young adults who are increasingly experiencing mental health challenges. Recent trends highlight the rise in well-being issues within this population, often associated with negative beliefs and attitudes toward happiness (Westberg et al., 2022). Individuals with a high fear of happiness may suppress positive emotions, avoiding the pursuit of happiness to prevent perceived negative consequences of positive emotional states (Yildirim, 2019). There is increasing research to suggest that fears of, and resistances to, affiliative and positive emotions are linked to self-criticism and a range of psychopathologies (Gilbert et al., 2011). This avoidance can contribute to broader mental health struggles, making it critical to understand and address this phenomenon to promote psychological well-being. there is a lack of research about predictors that might lead to fear of happiness particularly where both happiness and fear of happiness are studied (Dobos et al., 2024). Fear of happiness is the belief that experiencing happiness may lead to unfavorable and adverse outcomes, suggesting that it is something to be avoided (Sar et al., 2019). This phenomenon, also referred to as cherophobia, involves absolute opposition to the idea of feeling happiness to any extent (Chakraborty & Pandey, 2024). To assess this concept, two primary tools have been developed. The first, the Fear of Happiness Scale (FHS), originally developed by Joshanloo (2013) and recently validated in Polish by Janus (2023), has demonstrated strong psychometric properties, with a Cronbach’s α of .85 indicating excellent internal consistency, yet it remains unifactorial and consists of only five items. Similarly, an exploratory factor analysis of the second scale assessing fear of happiness, developed by Gilbert et al. (2011), initially suggested a two-factor solution. Due to cross- loadings, a one factor model was ultimately adopted. This newly developed scale originally comprised 10 items but was later reduced to nine and demonstrated strong internal consistency with a Cronbach’s α of .90. Despite their reliability, both scales are limited by their unifactorial structures and item exclusions, which may hinder a comprehensive understanding of the complexity of fear of happiness. Several studies have investigated the concept of Fear of Happiness, its potential underlying factors, and its profound impact for mental health. However, existing assessment tools, despite their strong psychometric sound, fail to capture the multifaceted nature of this phenomenon, particularly among young adults who are increasingly vulnerable to mental health challenges. This highlights the need for a tailored instrument, such as a “Fear of Happiness for Young Adults Scale” to better understand the intricate attitudes toward happiness that impact mental health in this group. Developing a more comprehensive measure could provide valuable insights and support the creation of effective interventions to address and mitigate the effects of fear of happiness on young adults’ well-being. Development of the Survey Questionnaire Initially, the researchers drafted a large pool of items guided by the literature and the researcher’s observation to people. The items were carefully reviewed, finalized and then tested in a pilot study. A total of 55 respondents, selected based on the study’s inclusion criteria, completed the questionnaire. Feedback from the pilot test led to revisions in the instrument. Consequently, a refined questionnaire with 68 items, using 7-point Likert scale (1 - Strongly Disagree , 2- Disagree, 3- Somewhat Disagree, 4- Neutral, 5- Somewhat Agree, 6- Agree, 7- Strongly Agree), was developed and is ready for use in the main study. Pa ge 35 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 MATERIALS AND METHODS Study design The study employed a quantitative Exploratory Factor Analysis (EFA) research design to identify the underlying structure of the data and to assess the dimensionality of the construct being measured. Study setting and population The researchers gathered a total of 501 young adults from Davao del Sur, Philippines, aged between 18 and 26, as respondents for the study. According to Sürücü et al. (2024), a sample size exceeding 400 is considered excellent for conducting EFA. Therefore, the sample size utilized in this study is more than enough to ensure reliable and accurate results. Among the age groups analyzed, individuals aged 20 showed the highest frequency (N=145), while those aged 26 had the fewest respondents (N=9). Data analysis To analyze the Fear of Happiness Scale for Young Adults (FHS-YA), an Exploratory Factor Analysis (EFA) was conducted to identify the underlying structure of the scale and evaluate its reliability (Watkins, 2018). Principal Axis Factoring (PAF) was employed as the extraction method due to the non-normal distribution of the data, ensuring accuracy under such conditions (Costello & Osborne, 2005). The analysis used multiple techniques to determine the number of factors, including eigenvalues, scree plots, parallel analysis, and MAP analysis. Additionally, the reliability of the scale was assessed using Cronbach’s Alpha. RESULTS AND DISCUSSION The researchers created a total of 68 items to measure the fear of happiness, covering different aspects of the construct. These items were based on ideas from past studies, Cognitive-behavioral theory and personal observations of the researchers. Each item was carefully reviewed to make sure they were relevant and suitable for the respondents. Data Screening Data screening was conducted to ensure the dataset’s suitability for Exploratory Factor Analysis (EFA). One critical step involved identifying and addressing multivariate outliers using Mahalanobis distance, a statistical measure that considers correlations between variables and their multivariate mean. Mahalanobis distance calculates how far each data point is from the center of the distribution (Etherington, 2021). In this study, cases with probability values less than 0.001, as suggested by Hair et al. (1998), were flagged as multivariate outliers. These outliers were subsequently removed using listwise deletion to prevent them from distorting the results of the analysis. Another important step was performing a normality check to assess whether the data followed a normal distribution. The Shapiro-Wilk test was applied to each group of items, testing the null hypothesis that the data was normally distributed. Results revealed that the data violated the assumption of normality, as the p-values were less than 0.05. This indicated non-normality and necessitated the use of Principal Axis Factoring (PAF) for EFA. PAF is a robust factor extraction method that performs well with non-normally distributed data, making it an appropriate choice for this study (Andrew, 2023). Addressing normality through the Shapiro-Wilk test ensured that the analysis was based on methods suited to the dataset’s characteristics. Factor Analysis Factor analysis was conducted to examine the underlying structure of the group of items for the Fear of Happiness Scale for young adults. Table 1 shows the results of the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity, which assess the dataset’s suitability for Exploratory Factor Analysis (EFA). KMO is a test conducted to examine the strength of the partial correlation (how the factors explain each other) between the variables. KMO values closer to 1.0 are considered ideal while values less than 0.5 are unacceptable (Analysis INN, 2020). The KMO value of 0.977 indicates excellent sampling adequacy, meaning the variables share enough common variance for factor analysis. Bartlett’s test of Sphericity is used to test the null hypothesis that the correlation matrix is an identity matrix (Analysis INN, 2020). Bartlett’s Test of Sphericity, with a Chi-Square value of 25489.984, 2278 degrees of freedom (df), and a significance level of 0.000, confirms that the variables are significantly related. These results demonstrate that the dataset is appropriate for factor analysis. Table 1: KMO and Bartlett’s Test Result Kaiser-Meyer-Olkin Adequacy Measure of Sampling .977 Bartlett’s Test of Sphericity Approx. Chi- Square 25489.984 df 2278 Sig. .000 To determine the number of factors affecting fear of happiness constructs, several methods were employed, including the total variance plot, scree plot, and parallel analysis. Table 2 shows the total variance explained, which identifies how much of the dataset’s variance is captured by each factor. Using eigenvalues greater than 1.0 is a common criterion, as factors with eigenvalues above this threshold explain more variance than a single variable (UCLA, 2021). Moreover, the scree plot helps visualize the number of factors by identifying the “elbow,” where the number of meaningful factors becomes apparent. (Reijer et al., 2022). This table 2 shows the initial eigenvalues and their corresponding percentages of total variance explained by different factors. The first five factors accounted for substantial proportions Pa ge 36 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 of the total variance—31.29, 3.39, 2.21, 1.50, and 1.29, respectively. However, relying solely on these eigenvalues is insufficient, as they can provide a partial view of the factor contributions. The total variance explained cannot stand alone because higher-order factors tend to explain smaller proportions of variance. To address this limitation, a scree plot is used in combination with eigenvalues to visually assess the point at which additional factors contribute minimally to the total variance. This approach helps in identifying the optimal number of factors to retain for a more accurate and meaningful interpretation of the data. Table 2: Total Variance and Scree Plot Factor Eigenvalues Variance Explained 1 31.290 46. 01% 2 3.393 4.99% 3 2.213 3.25% 4 1.496 2.20% 5 1.291 1.89% 6 1.095 1.610 7 1.041 1.530 8 1.009 1.484 Table 3: Total Variance and Parallel Analysis Total Variance/ Initial Eigenvalues Parallel Analysis 31.290 1.018030 3.393 .928374 2.213 .881356 1.496 .828677 1.291 .791112 1.095 .753348 1.041 .715271 1.009 .686636 Table 4: MAP analysis The smallest average partial correlation The smallest average 4rth power partial correlation The Number of Components According to the Original (1976) MAP Test The Number of Components According to the Revised (2000) MAP Test .0057 .0001 5 5 Figure 1: Scree plot The study employed principal axis factoring as the extraction method because it does not rely on distributional assumptions (Grieder & Steiner, 2021), making it An additional procedure was conducted to confirm the findings of parallel analysis. Specifically, the researchers used the MAP analysis, as shown in table 4, which involves performing detailed principal component tests and analyzing matrices of partial correlations (Watkins, 2018). According to Zwick and Velicer (1986), the MAP analysis is considered more reliable than both the eigenvalue-greater-than-1 rule and the visual scree test. Consequently, the final number of factors retained is based on the results of the Revised MAP test (2000), which indicated that 5 factors are retained. appropriate for the study’s non-normally distributed data. The Kaiser criterion and scree plot usually align to each other (Çokluk & Koçak, 2016), but inconsistencies in the results were observed. Moreover, according to Sheytanova (2015), the Kaiser criterion has the poorest performance in determining a number of factors. While the scree plot does not substantially provide accurate and clear data as seen above. Therefore, another method was used to ensure accurate results, which is the parallel analysis. According to Horn (1965), parallel analysis uses the population’s correlation matrix to determine which factors are significant, with eigenvalues greater than 1. Table 3 presents a comparison of eigenvalues from the total variance explained and those derived from random data. Initially, the results suggested that 8 factors were extracted from the dataset. The table 5 shows the results, that most of the communalities are high, indicating that the majority of items share a significant proportion of variance with the extracted factors. Specifically, the Initial communalities reflect the variance explained by each item before extraction, and many of these values are already relatively high, with several above 0.70. After extraction, the Extraction communalities exceed 0.70 for most items, with several items showing values above 0.80, demonstrating strong relationships with the factors. This suggests that these items are highly relevant in defining the constructs being measured. Items with high communalities contribute effectively to the overall model and help ensure the clarity and reliability of the factor structure. Conversely, items with lower extraction values (below 0.40) show weak associations and may need to be Pa ge 37 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 reconsidered or excluded to improve the factor structure. Notably, there are no communalities below 0.40, reinforcing the strength of the factor structure, as all items show strong associations with the extracted factors. During factor loadings, a total of 18 items were removed due to cross-loading, meaning they did not strongly align with a single factor and were found to be associated with other factors. The items removed were numbers 7, 8, 11, 12, 14, 16, 19, 35, 36, 40, 42, 44, 50, 53, 54, 61, 63, and 66. This removal was necessary to improve the clarity of the factors, ensuring that each item was strongly associated with only one factor. Table 5: Factor Loadings and Communalities for Promax Rotation Factor Loading 1 2 3 4 5 Communalities 1. I feel uncomfortable after experiencing happiness. .559 .483 2. I worry that people might judge me for being too happy. .506 .562 3. I avoid getting too excited about good news. .633 .418 4.Happiness makes me feel vulnerable. .528 .664 5. I feel uneasy about planning for happiness in the future. .612 .698 6. I worry that others might be jealous of me if I express my happiness. .499 .676 7. I avoid activities that might make me too happy. .438 .538 .665 8. I feel like being too happy might attract problems. .373 .551 .526 9. Happiness feels overwhelming. .497 .649 10. Expressing happiness feels like bragging to me. .493 .659 11. I hesitate to try new activities that might bring me happiness. .534 .402 .693 12. I fear that my happiness will be taken away. .534 .320 .640 13. I associate happiness with feelings of guilt. .468 .661 14. I hold back my happiness to avoid drawing attention to myself. .355 .422 .713 15. I limit my expression of happiness. .461 .743 16. I worry that enjoying happiness will lead totears soon after. .593 . .352 .643 17. I feel a sense of fear when I am in a good mood. .372 .672 18. I hesitate to express positive feelings to others. .660 .617 19. When I’m happy, I try to quickly shift my focus to something else. .616 .359 .546 20. Happiness might make me ignore reality. .433 .552 21. I experience more fear than relief after achieving something good. .590 .614 22. I avoid posting or sharing good news about myself publicly. .691 23. I tend to overanalyze moments of happiness. .663 24. I think being happy will lead to higher expectations I can’t meet. .488 25. I find myself questioning whether I deserve happiness. .650 26. I find excuses to leave when others are celebrating something. .713 .486 27. I avoid reflecting on my positive achievements. .580 .661 28. Happiness made me worry about what’s to come. .630 Pa ge 38 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 29. Happiness feels undeserved if others around me are suffering. .704 30. I think happiness will make others think I have no problems. .660 31. I distance myself from people to avoid feeling too happy. .695 .654 32. I fear that happiness is just a brief escape from reality. .616 33. Being happy feels like betraying my pastpain. .615 .663 34. I worry that expressing happiness will make others upset. .423 .650 35. I find myself preparing for failure rather than success. .465 .544 36. Happiness feels like an illusion that will soon fade away. .307 .626 37. Happiness feels empty to me. .627 .600 38. I feel uneasy when I see someone smiling or laughing. .730 .639 39. I associate happiness with punishment. .625 .722 40. I feel disconnected from myself when I am truly happy. .522 .689 41. I avoid optimistic people. .819 .684 42. I think it is safer to feel neutral than to feel happy. .622 43. I associate moments of joy with potential future regret. .438 .570 44. Happiness is too good to last for someone like me. .577 45. I fear that feeling happy will make me seem gullible to people. .458 .596 46. I resist engaging in hobbies or activities Used to enjoy. .569 .644 47. I feel like happiness is a mask people wear to hide their struggles. .709 .698 48. I find myself worrying even during happy moments. .534 .700 49. Being overly happy might make me ignore the needs or feelings of others. .421 .601 50. I think being overly happy is a sign of weakness. .499 .410 .490 51. I am afraid that happiness might make me selfish. .660 .642 52. Being overly happy might make others see me as immature. .655 .532 53. I avoid planning for happiness in the future. .349 .439 .698 54. I think happiness is a distraction from what’s important. .301 .647 .548 55. I second-guess my moments of happiness. .489 .687 56. I worry that others will expect too much from me if I am happy. .602 .707 57. I avoid happiness because it might make me irresponsible. .749 .654 58. I think happiness requires too much energy to sustain. .641 .684 59. I feel anxious when things are going well. .373 .662 Pa ge 39 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 60. Being overly happy will distract me from what's important. .635 .684 61. It feels wrong to be happy when things aren’t perfect. .403 .365 .596 62. I fear that being happy will make me miss the things I’ve lost. .492 .696 63. I believe that happiness is a luxury I can't afford to enjoy. .468 .304 .654 64. When something good happens, I tend to look for hidden problems. .451 .661 65. I might lose control when I feel happy. .561 .623 66. I feel uneasy when others are happier than me. .551 .350 .652 67. I hold back from smiling or laughing in public settings. .492 .676 68. I think happiness is superficial. .331 .584 Note: Factor loadings < .3 are suppressed Table 6 shows the Factor Correlation Matrix, which shows how five factors are related. Factors 1 to 4 have strong correlations, meaning they are closely connected and measure similar things. For example, Factor 1 and Factor 3 have a correlation of 0.735, and Factor 2 and Factor 3 have a correlation of 0.655. On the other hand, Factor 5 has weak correlations with the other factors, like 0.142 with Factor 1 and 0.168 with Factor 2, because it only has one item. Since Factor 5 does not meet the standard of having at least three items, it is excluded from further analysis. Factors 1 to 4 are reliable and show strong relationships, while Factor 5 is not considered valid due to its limited number of items. Therefore, item number 68 was removed. Table 6: Factor Correlation Matrix Factor 1 2 3 4 5 1 1.000 .607 .735 .698 .142 2 .607 1.000 .655 .649 .168 3 .735 .655 1.000 .622 .170 4 .698 .622 .622 1.000 .76 5 .142 .168 .170 .76 1.000 Table 7 presents the items along with their factor loadings for each of the four factors (F1, F2, F3, F4) derived from factor analysis. For Factor 1 (F1), which represents Social and Emotional Avoidance (SEA), items demonstrate significant factor loadings, with SEA4, “I find excuses to leave when others are celebrating something,” having the highest loading of .713. These items reflect individuals’ tendencies to avoid emotional engagement or social interactions that trigger happiness, suggesting a discomfort with positive emotional expression in social contexts. Factor 2 (F2), corresponding to Future-Oriented Anxiety or Guilt (FOAG), exhibits high factor loadings for items related to anticipatory anxiety or guilt about happiness, such as FOAG19, “I think being happy will lead to higher expectations I can’t meet,” and FOAG22, “Happiness feels undeserved if others around me are suffering,” with loadings of .704 and .696, respectively. These items highlight individuals’ future- oriented concerns, including fears that happiness may result in negative consequences, such as increased pressure or guilt. Factor 3 (F3), representing Cognitive Distortion (CD), includes items reflecting irrational thoughts and beliefs about happiness, such as CD29, “I am afraid that happiness might make me selfish,” and CD33, “I avoid happiness because it might make me irresponsible,” with loadings of .660 and .749, respectively. These items reflect cognitive distortions related to happiness, where individuals hold negative or unrealistic views about the emotional and social consequences of feeling happy. Finally, Factor 4 (F4), corresponding to Emotional Discomfort (ED), captures items that express emotional unease or vulnerability associated with experiencing happiness, such as ED40, “I feel uncomfortable after experiencing happiness,” and ED42, “I avoid getting too excited about good news,” with loadings of .559 and .633, respectively. These items suggest an emotional discomfort with happiness, where individuals may feel vulnerable, overwhelmed, or unable to handle the emotional intensity associated with positive experiences. Table 7: Factor Loading of items SEA FOAG CD ED 1. I limit my expression of happiness .461 2. I hesitate to express positive feelings to others. .660 3. I experience more fear than relief after achieving something good. .590 Pa ge 40 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 4.I find excuses to leave when others are celebrating something. .713 5. I avoid reflecting on my positive achievements. .580 6. I distance myself from people to avoid feeling too happy. .695 7. Being happy feels like betraying my past pain. .615 8. I worry that expressing happiness will make others upset. .423 9. Happiness feels empty to me. .627 10. I feel uneasy when I see someone smiling or laughing. .730 11. I associate happiness with punishment. .625 12. I avoid optimistic people. .819 13. I associate moments of joy with potential future regret. .438 14. I resist engaging in hobbies or activities I used to enjoy. .569 15. I hold back from smiling or laughing in public settings. .492 16. Happiness might make me ignore reality. .433 17. I avoid posting or sharing good news about myself publicly. .614 18. I tend to overanalyze moments of happiness. .655 19. I think being happy will lead to higher expectations I can’t meet. .704 20. I find myself questioning whether I deserve happiness. .678 21. Happiness made me worry about what’s to come. .596 22. Happiness feels undeserved if others around me are suffering. .696 23. I think happiness will make others think I have no problems. .659 24. I fear that happiness is just a brief escape from reality. .671 25. I feel like happiness is a mask people wear to hide their struggles. .709 26. I find myself worrying even during happy moments. .534 27. I fear that feeling happy will make me seem gullible to people. .458 28. Being overly happy might make me ignore the needs or feelings of others. .421 29. I am afraid that happiness might make me selfish. .660 30. Being overly happy might make others see me as immature. .655 31. I second guess my moments of happiness. .489 32. I worry that others will expect too much from me if I am happy. .602 33. I avoid happiness because it might make me irresponsible. .749 34. I think happiness requires too much energy to sustain. .641 35. I feel anxious when things are going well. .373 36. Being overly happy will distract me from what’s important. .635 37. I fear that being happy will make me miss the things I’ve lost. .492 38. When something good happens, I tend to look for hidden problems. .451 39. I might lose control when I feel happy. .561 40. I feel uncomfortable after experiencing happiness. .559 41. I worry that people might judge me for being too happy. .506 42. I avoid getting too excited about good news. .633 43. Happiness makes me feel vulnerable. .528 44. I feel uneasy about planning for happiness in the future. .612 45. I worry that others might be jealous of me if I express my happiness. .499 46. Happiness feels overwhelming. .497 47. Expressing happiness feels like bragging to me. .493 48. I associate happiness with feelings of guilt. .468 49. I feel a sense of fear when I am in a good mood. .372 1 - Strongly Disagree , 2- Disagree, 3- Somewhat Disagree, 4- Neutral, 5- Somewhat Agree, 6- Agree, 7- Strongly Agree Pa ge 41 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 34-42, 2025 The table 8 presents the reliability statistics for a questionnaire consisting of 49 items, completed by 501 participants. The key metric, Cronbach’s Alpha, is reported as 0.975, indicating excellent internal consistency. Cronbach’s Alpha ranges from 0 to 1, with higher values reflecting greater reliability. Generally, a value above 0.7 is considered acceptable, while values above 0.9, like the one in this study, suggest that the items in the questionnaire are highly consistent in measuring the same underlying concept. The inclusion of 49 items means that the survey comprehensively covers the construct it aims to assess, and the large sample size of 501 participants strengthens the reliability analysis by ensuring the results are stable and representative. The high Cronbach’s Alpha value indicates that participants responded to the items in a consistent manner, meaning the questionnaire is highly reliable. This level of reliability suggests that if the questionnaire were administered again to a similar group, it would likely produce consistent results. However, it is important to note that while high reliability ensures consistency, it does not guarantee that the questionnaire is valid or measure what it is intended to measure. Additional tests for validity would be needed to confirm this. Nonetheless, the high reliability score provides confidence in the data collected and ensures that the findings based on this survey are dependable. Table 8: Reliability Statistics Cronbach’s Alpha N of Items N .975 49 501 CONCLUSION The focus of the study was to provide an alternative instrument for measuring fear of happiness. After thorough analysis, the questionnaire was refined to 49 items that capture four key factors: Social and Emotional Avoidance, Future-Oriented Anxiety or Guilt, Cognitive Distortion, and Emotional Discomfort. These factors represent the different ways fear of happiness can manifest in thoughts, emotions, and behaviors. The scale demonstrated excellent reliability, with a Cronbach’s Alpha of 0.975, indicating strong internal consistency. This high reliability reinforces the FHS-YA’s ability to measure fear of happiness accurately and consistently. The reduction of items from 68 to 49 ensured the scale’s efficiency while maintaining its focus on capturing the most relevant aspects of the construct. The application of Exploratory Factor Analysis (EFA) and Principal Axis Factoring (PAF) was pivotal in validating the scale, particularly given the non-normal data distribution. The study also highlighted the possible influence of cultural and social norms on fear of happiness. For example, some young adults may associate happiness with guilt, vulnerability, or social judgment, which can discourage them from embracing positive emotions. These findings suggest the need for culturally sensitive approaches to help individuals overcome these fears. While the FHS-YA has shown strong reliability and validity, future research is needed to test its applicability across diverse populations and cultural contexts. Further studies could also explore the relationship between fear of happiness and other psychological factors, such as resilience, self-esteem, and coping strategies.This study provides a valuable tool for researchers and mental health professionals by identifying the key factors contributing to the fear of happiness. The FHS-YA can support the development of interventions aimed at helping young adults build healthier relationships with happiness and improve their overall emotional well-being. Limitations of the study This study has several limitations. First, the sample was limited to young adults from Davao del Sur, which may not be representative of other geographic regions, limiting the generalizability of the findings. Second, the study relied on self-reported data, which is subject to response biases such as social desirability or misunderstanding of the questions. This reliance on self-reporting may limit the accuracy of the findings, as participants may not always express their true feelings or may be influenced by external factors, such as current emotional states or social pressures. Additionally, the exclusion of Factor 5 from the final analysis due to its weak correlation with other factors raises concerns about the stability of the factor model and suggests that the construct of fear of happiness might not be fully captured in this study. Future revisions of the scale could address this issue by refining the model or adding new items to improve its comprehensiveness. Another limitation is the study’s reliance on a single theoretical framework, Cognitive Behavioral Theory (CBT). While CBT provides a useful basis for understanding fear of happiness, the scale might not fully capture other psychological processes or theories, such as social or existential factors, which could be relevant for understanding this phenomenon more holistically. 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