Pa ge 1 Pa ge 77 American Journal of Human Psychology (AJHP) Development of the Infidelity Tendency Comprehensive (ITCh) Scale Raven Kyle Gonzaga1*, Queen Mae Aguirre1, Novielen Jean Dacay1, Cristine Alegado1, Mark Luiz Demanawa1, Junna Predigosa1 Volume 3 Issue 1, Year 2025 ISSN: 2994-8878 (Online) DOI: https://doi.org/10.54536/ajhp.v3i1.4345 https://journals.e-palli.com/home/index.php/ajhp Article Information ABSTRACT Received: January 12, 2025 Accepted: February 11, 2025 Published: June 21, 2025 Infidelity is known to influence relationship dynamics negatively. However, it remains a societal issue in the Philippines. The purpose of this study was to develop an instrument that measures the likelihood of Filipino young adults to commit infidelity. The Infidelity Tendency Comprehensive (ITCh) Scale aims to generate sufficient data to aid the rising number of individuals committing infidelity. A sample of 500 Filipino young adults aged 18 to 25 was chosen using simple random sampling to ensure a representative population. The initial 80-item scale was refined through a pilot test with 50 respondents, resulting in a 45-item version. Principal Axis Factoring (PAF) was employed to identify the underlying factors, supported by the Kaiser-Meyer-Olkin (KMO) measure (0.954) and Barlette’s Test of Sphericity (p<0.05). Factor analysis revealed five key factors: sexual arousal, insecurity, extroversion, relationship dissatisfaction, and indifference in relationships. The final scale retained 43 items after removing them with low factor loadings. Reliability testing showed a high Cronbach’s alpha of 0. 956, and discriminant validity was confirmed, indicating the scale’s accuracy and relevance. The ITCh scale is a reliable and valid instrument that provides a basis for further research and interventions to build healthier relationships. Keywords Exploratory Factor Analysis, Infidelity Tendency Scale, Filipino Young Adults 1 UM Digos College, Digos City, Philippines * Corresponding author’s e-mail: r.gonzaga.60071.dc@umindanao.edu.ph INTRODUCTION Love is frequently a significant source of pleasure and fulfillment; however, transgressions in relationships, such as infidelity, can result in considerable stress, emotional pain, and, in some instances, trauma (Rokach & Chan, 2023). Infidelity is defined as the engagement in an intimate relationship with an individual outside of one’s marriage or relationship without consent (Lișman & Holman, 2021). This act encompasses breaches of a commitment to exclusivity that two partners in a relationship have explicitly made to each other (Lisman & Holman, 2021). Moreover, it often is interchanged with the act of adultery, however, infidelity differs distinctively in scope as it can manifest in various ways – such as financially or emotionally – even without the physical act of cheating, and it can happen in a committed relationship, not necessarily grounded on marriage (Kimber, 2024). Nonetheless, it is recognized as the most prevalent factor leading to the dissolution of relationships (Travers, 2024). Affiliated with the studies of Lee (2015) & Gono (2024) is the report from the National Commission on the Role of Filipino Women in 2009, which considers marital infidelity as one of the major causes of stress among Filipino couples. The account included a finding on 36% of men and 2% of women engaging in extramarital affairs. This precedence surfaced because of the causal connections found in the study and the present prevalence of infidelity. Moreover, the available data potentially infer the engender of the study, and the following literature on infidelity cases will be utilized to substantiate corresponding factors to further gauge the matter at hand. The proponents target a scale to determine an individual’s tendencies to commit infidelity, utilizing factors derived from the self-expansion theory (Aron & Aron, 1986) which offers a relevant theoretical framework for developing the Infidelity Tendency Comprehensive (ITCh) Scale. It suggests that individuals are driven to enhance their self-concept through new experiences, identities, and relationships (Aron et al., 2013). The identified factors include extraversion, arousal, relationship dissatisfaction, insecurity, and indifference. The factors are acquired from latent inclinations of the theory to relationships and are inquired about underlying dimensions that can be indicative factors of infidelity. The prospect factors concur with various found evidence in the Philippine context. In the essence of extraversion, according to the summary of Hedrih (2024), this characteristic is considered a significant predictor of infidelity reported in the Science of Infidelity. Extroversion is, by definition, a preference for social engagement and interaction with others (“Extroversion,” 2024). A contextual ground can be exemplified in the workplace within the busy streams of Business Process Outsourcing (BPO) companies in Quezon, Philippines (Avelino, 2024). In these settings, the demanding work hours often lead to close bonds and personal connections among employees, which the study articulates as a possible circumstance for employees to invest more energy to connect with their co-workers instead of their partners. Another identified factor is arousal, which can be categorized as either high or low and is associated with corresponding emotions (MSEd, 2023). Avelino (2024) expanded the circumstantial inferences of his study by considering other workplaces. He noted that arousal factors may lead individuals to engage in impulsive infidelity as a means of escaping the pressures and responsibilities of their daily lives. Pa ge 78 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 This behavior often seeks excitement or novelty beyond committed relationships as a form of relief from stress. Moreover, relationship dissatisfaction is highly predictive of infidelity tendencies discussed in Siguan and Cañete’s (2021) study which consequently leads to marital conflicts. Often, dissatisfaction manifests as negative behavior towards one’s partner, creating a distorted perception of each other and providing an opportunity to find fault in one another’s character. Also, insecurity is considered a determining factor, reported by Altgelt and Meltzer (2019), insecurely attached partners typically have more conflict and are less satisfied with their relationships. This is due to the concurring uncertainty, self-doubt, and fear that an insecure individual may feel when in a relationship (Browne, 2024). People who suffer from attachment anxiety worry that their partner will leave them or neglect them. Relationship instability and a decline in overall relationship satisfaction are associated with this attribute. Lastly, indifference causes individuals in a romantic relationship to be inclined to apathy rather than a disposition to what the other does (Howard, 2022). It was found that indifference is one of the most challenging hurdles in commitments, as the feeling leaves individuals unwilling to make an effort to make things work with the other person (Wanjiru et al., 2020), potentially incurring a sense of concern for someone else. Most studies on infidelity focus on Western contexts, leaving a gap in understanding this behavior within Filipino culture. Additionally, existing scales for measuring infidelity were developed primarily for married individuals, overlooking young adults. Infidelity has significantly impacted relationship disclosures over generations and is a prevalent issue today (Travers, 2024). The developing study aims to establish the ITCh which measures the likelihood of Filipino young adults to commit infidelity to be determined by a five-factor structure: extraversion, arousal, relationship dissatisfaction, insecurity, and indifference. The findings of the study will be beneficial in mitigating the high-cost consequences of infidelity (Belu & O’Sullivan, 2024) and in formulating preventive interventions for individuals who score high on the infidelity tendency scale. Mental health professionals may be able to develop strategies that can be implemented as a countermeasure after testing their infidelity tendency levels. Further implications from the significant predictors can also be a derivative for mediating problems arising from an individual’s daily life. This can either be their uninhibited socio-sexual identity regarding their extraversion, their hyper-arousal that may cause problems with different areas of their life, the relationship dissatisfaction that they may be experiencing, insecure attachment or insecurity in general, or unconsidered triggers to relational indifference that can still be mediated with the aid of interventions. Objectives of the Study This study primarily aimed to develop the Infidelity Tendency Comprehensive (ITCh) Scale that measures the likelihood of Filipino young adults committing infidelity. Moreover, it specifically sought the following: 1. To explore the factor structure of the Infidelity Tendency Comprehensive (ITCh) Scale; and, 2. To evaluate the instrument’s reliability and validity. Theoretical Framework The self-expansion theory (Aron & Aron, 1986) explains that individuals are naturally motivated to enhance their sense of self by seeking new experiences, forming new identities, and building relationships. For instance, extroverted individuals, who thrive on social interactions and novelty, may experience dissatisfaction if their relationship does not provide sufficient opportunities for growth or excitement. Arousal, or the desire for stimulation, can lead them to seek fulfillment outside the relationship. Similarly, relationship dissatisfaction, insecurity, and indifference in relationships stemming from unmet emotional needs or feelings of inadequacy may drive individuals to pursue external sources of validation (Cherry, 2020; Lewandowski & Ackerman, 2010). Together, these factors contribute to an increased susceptibility to infidelity when primary relationships fail to satisfy personal needs. This theory offers a relevant theoretical framework for developing the Infidelity Tendency Comprehensive (ITCh) Scale. MATERIALS AND METHODS Participants Filipino young individuals aged 18 to 25 from Davao del Sur were selected, with a sample size of 500 respondents chosen through simple random sampling. This approach ensured an equal chance of selection for every college student, resulting in a representative sample. Materials The Infidelity Tendency Comprehensive (ITCh) Scale, developed by the proponents, consisted of 45 items rated on a five-point Likert Scale: 1 (Strongly Disagree), 2 (Disagree), 3 (Neutral), 4 (Agree), and 5 (Strongly Agree). This scale measured the likelihood of young Filipino adults engaging in infidelity with their partners based on their responses. Procedure A methodical approach was followed to assure data accuracy and reliability. The proponents initially created an 80-item pool, tested in a pilot trial with 50 respondents. Preliminary results were analyzed using SPSS, resulting in adjustments and refinement of the scale to 45 items for the final survey. The finalized survey was administered to 500 respondents using pen-and-paper tests and Google Forms while ensuring anonymity and privacy, following ethical considerations. The respondents were also given informed consent before the survey. The dataset was then collected and thoroughly screened for errors. Pa ge 79 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 RESULTS AND DISCUSSION Data Screening The data was screened for potential multivariate outliers using the Mahalanobis distance. This method ensures that outliers are detected based on the multidimensional relationships between variables rather than solely considering each variable individually (Ghorbani, 2019). Data points with a Mahalanobis distance exceeding the critical value for the chi-square distribution at a significance level of α = 0.001 were flagged as potential outliers. Additionally, several items in the data exhibited Mahalanobis distances below this threshold, suggesting possible deviations from normality. To further assess the distribution of the data, a normality test was conducted using the Kolmogorov-Smirnov test, which is appropriate given that the sample size exceeds 50 (Chakravarti et al., 1967). The results indicated a significant value of less than 0.05, which supports the conclusion that the data does not follow a normal distribution. These methods ensured that the analysis accurately reflected the true underlying patterns in the data by effectively addressing outliers and distributional problems. Factor Analysis Table 1 shows the KMO and Bartlett’s test of sphericity results. The Kaiser-Meyer-Olkin (KMO) test was utilized to measure the sampling adequacy for factor analysis. This examines whether the partial correlations between variables are small, indicating that the variables share enough common variance to be grouped into factors (Kaiser, 1974). KMO values closer to 1.0 are considered ideal for factor analysis, while values below 0.5 suggest that the data may not be suitable. The KMO test with a value of 0.954 indicated that the data was acceptable for factor analysis. Additionally, Bartlett’s Test of Sphericity was conducted to assess the null hypothesis that the correlation matrix is an identity matrix, implying that the variables are unrelated, and factor analysis would not be appropriate (Bartlett, 1951). A p-value of less than 0.05 suggests that factor analysis can be performed. In this case, the results showed a p-value of 0.000, indicating sufficient correlations among the variables and confirming that factor analysis was appropriate. Both the KMO and Bartlett’s tests confirmed the suitability of the data for factor analysis. Table 1: KMO and Bartlett’s test of sphericity results KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .954 Bartlett's Test of Sphericity Approx. Chi-Square 13344.442 df 990 Sig. .000 Table 2: Total variance plot and criteria of eigenvalues ( >1.0) Initial Eigenvalues Extraction Sums of Squared Loadings Factor Total % of Variance Cumulative % Total % of Variance Cumulative % 1 16.142 35.871 35.871 15.676 34.836 34.836 2 3.480 7.733 43.604 3.055 6.788 41.624 3 2.734 6.076 49.680 2.289 5.086 46.710 4 1.791 3.979 53.659 1.275 2.832 49.542 5 1.248 2.774 56.433 .845 1.878 51.420 6 1.102 2.449 58.882 Furthermore, the Kaiser criterion was used to determine the number of factors with eigenvalues greater than 1.0 to retain in a factor analysis (Kaiser, 1960; Norman, 1988). Table 2 shows the total variance plot, revealing five (5) factors with eigenvalues greater than 1.0. Additionally, Figure 1 shows the scree plot representing the eigenvalues of extracted factors. Determining the eigenvalues alone was considered to be the least accurate method, hence, the scree plot was combined for better comparison (Cattell, 1966). The graph consists of eigenvalue plots (y-axis) against the components (x-axis), creating a slope with a point on the curve indicating the maximum number of components to retain (Ledesma, et al., 2015). In this case, the Kaiser criterion and scree plot revealed the same number of retained factors (5). The results from the data screening indicated that the data was not normally distributed. As a result, Principal Axis Factoring (PAF) was chosen as the extraction method, which identifies the underlying factors that explain the correlations among observed variables by focusing on common variance, or shared variance, across the variables (Taherdoost et al., 2014). Using PAF, the factor matrix revealed that six (6) factors are to be retained. However, other extraction methods were utilized for more accurate results. Parallel analysis and the minimum average partial criterion (MAP) were used as they provided a more accurate number of factors to retain. Both methods offer a more objective criterion based on simulated data, successfully detecting aspects that are statistically significant beyond random fluctuation in the data (Hayton et al., 2004). Pa ge 80 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 Parallel Analysis Parallel analysis is an approach that accurately determines the number of factors to retain (Horn, 1965). It was considered more accurate than other methods as it employs a simulation-based technique to produce random data with similar characteristics to the actual data, enabling a more objective assessment of whether aspects are genuinely relevant beyond random fluctuations (Cokluk & Kocak, 2016). Table 3 shows the eigenvalues of the total variance explained with eigenvalues of random data (parallel analysis). The eigenvalues of factors 1 to 5 from the total variance explained are greater than the eigenvalues of the random data, indicating that the parallel analysis determined five (5) factors to be extracted. according to the revised (2000) MAP Test is five (5). Rotation Method for Factor Loading In EFA, rotation is conceptually used to describe that the axes (x and y axes) are being rotated so that the clusters of items fall as closely as possible to them, which aids in clarifying and simplifying the results of factor analysis (Osborne, 2015). Oblique rotation, specifically the Promax method, was used in the study, allowing the factors to be correlated (Gaskin & Happell, 2014) as assumed through its sphericity (p=0.000 < 0.05). Through this method, the pattern matrix analyzed the relationship between items and factors, with a factor loading of 0.30 as an indication of a moderate correlation (Tavakol & Wetzel, 2020). With this, item 16 (“I feel judged when I open my needs to my significant other”) was removed from the item pool. Furthermore, the communalities further assessed the variables across factors to ameliorate the analytical process. These indicate the proportion of common variance associated with an item, relative to all the factors, thus, checking the relativity of items in comparison to others to generate a parsimonious set of factors. The communality cut-off value of < 0.40 was applied (Osborne et al., 2008), eliminating item 1 (“I can be attracted to someone else who is not my significant other”) which showcased 0.317 initial communality. Additionally, the factor correlation matrix revealed the correlation between factors (Osborne, 2015) showing a high association. Hence, 43 items were retained and 2 were removed from the questionnaire. Table 4 shows the factor loadings and communalities based on the PAF for the 43 items. Figure 1: Scree Plot Table 3: Eigenvalues of the total variance explained and random data from parallel analysis Total Variance Explained Random Data from Parallel Analysis 16.142 .777668 3.480 .702384 2.734 .645765 1.791 .598282 1.248 .555435 Minimum Average Partial Criteria The Minimum Average Partial (MAP) criterion provides an unambiguous concluding point for the number of factors by distinguishing common and unique variance and preserving only those components that are predominantly composed of common variance (Garrido et al., 2011). With this, the number of components Pa ge 81 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 Table 4: Factor loadings and communalities based on a Principal Axis Factoring (PAF) with oblique rotation for 43 items from the initial version of the Infidelity Tendency Comprehensive (ITCh) Scale (n= 500) Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Communalities I can imagine having sexual interactions with someone while I am in a relationship. .717 .541 I get excited when I engage in sexual interactions with someone who is already in a committed relationship. .903 .607 I get excited by the idea of sexual openness with individuals outside my relationship. .843 .608 I get excited when someone expresses love or sexual interest in me while being committed to someone else. .892 .638 I get excited when I imagine scenarios where platonic and romantic connections cross. .502 .455 The concept of being desired by many people while I am in a relationship simultaneously excites me. .630 .601 I feel a thrill imagining being close to someone I know I shouldn’t be with due to my relationship commitments. .767 .661 I feel excited about sharing intimate moments with someone in secret, despite the potential consequences of getting caught by my partner. .846 .714 I get thrilled when I think of someone secretly admiring me when they are in a relationship. .769 .674 I get proud when I think of having a relationship that others might not approve of. .497 .453 The secrecy of potential affairs stimulates or excites me physically and emotionally. .693 .572 I do not want my partner to be more accomplished than I am. .517 .438 I tend to seek attention from others when my significant other neglects me. .458 .484 I tend to entertain others to show my significant other how worthy of a person I am. .522 .566 I feel that my partner and I are better off without each other because I do not deserve his/her love. .485 .432 My significant other’s desire to leave me is something I frequently worry about. .652 .495 I am concerned that my significant other might start seeing someone else when they are not around. .806 .558 I need constant assurance that my significant other loves me. .764 .495 I overthink conversations with my partner to clarify if there are any underlying meanings to them. .618 .444 I am concerned about how others think of our relationship. .423 .416 I feel at ease when meeting new people regardless of my relationship status. .571 .469 I tend to go to social events on my own to meet new people and have new experiences. .855 .461 Pa ge 82 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 I am often the one to initiate social plans that involve people outside my immediate relationships. .622 .447 I find myself unintentionally spending time with people who may challenge the boundaries of my relationship. .571 .486 I am likely to explore emotional connections with others when given the chance. .569 .571 I sometimes feel tempted to share personal thoughts or feelings with people other than my partner. .444 .497 I naturally form close relationships with others, even when I’m in a committed relationship. .701 .495 I am likely to go out and socialize with people, even if it means having different plans from my partner. .646 .549 I sometimes feel excited about the possibility of developing a deep connection with someone new. .519 .541 I tend to be as available to others as I am to my partner. .598 .474 I enjoy receiving compliments or admiration from people other than my partner. .411 .477 I often feel a strong bond with new people, making me want to get to know them more. .636 .493 My significant other does not satisfy my sexual needs. .613 .617 My significant other does not satisfy my emotional needs. .884 .753 My significant other does not satisfy my physical needs. .874 .774 My significant other does not treat me as I expected them to. .705 .735 My significant other and I don’t share the same values. .627 .653 My significant other does not show interest in the things I do. .471 .592 I think I am better off with someone better than my significant other. .440 .633 I don’t think that this is the relationship I have dreamt of. .799 .657 I sometimes feel distant towards my significant other. .700 .631 I do not see my relationship with my significant other to last for years. .761 .650 I do not feel that my significant other and I are working together with a commitment to the success of our relationship. .747 .640 Through the pattern matrix, the factor analysis developed the measuring items classified by determinant factors of the scale. The exploratory analysis produced good results indicated by a minimum of four (4) retained items per factor (Fein et al., 2022); with Factor 1 featuring items 2-14, a total of 13 items; following is items 35-40 classified under Factor 2 with 6 total items; additionally, items 23 to 34 is sorted under Factor 3, in total of 12 items; then, Factor 4 have 5 total items namely, items 18- 22; lastly, Factor 5 have 6 total items which are items 17, and 41-45. These designations retained an overall total of 43 adequate items for use. The following objective is to establish the common semantic topic according to the measuring items per Pa ge 83 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 factor. For Factor 1, thorough deliberation of common words reveals characteristics associated with sexual arousal (Professional, 2024). The gist for the term “attraction” and repetitive reference to “sexual” engagements found in items 2-5, as well as terms such as “excite” and “thrill” in items 3-10 and 12 outlines the indicative meaning of the factor. What’s more, the statements in Factor 2 can be interpreted as reflecting the concurrent uncertainty and fear that an individual experiencing insecurity may feel (Browne, 2024). Items 35-40 collectively address a theme centered on “needs,” demonstrating that individuals with insecure attachments tend to experience greater levels of conflict and diminished satisfaction within their relationships (Altgelt & Meltzer, 2019). Moreover, a collection of statements under Factor 3 highlights the prevalent themes of a preference for social engagement and interaction with others (i.e. items 23-24, “meeting” and “initiating” social plans with other people; items 26-28, “spending time” and “exploring emotional connections” with others), which are characteristics of an individual’s extroversion (“Extroversion,” 2024). Another factor categorized items 18-22 under Factor 4 (My significant other’s desire to leave me is something I frequently worry about; I am concerned that my significant other might start seeing someone else when they are not around; I overthink conversations with my partner to clarify if there are underlying meaning to it; and I am concerned about how others think of our relationship) signifying of relationship dissatisfaction. According to Siguan & Cañete (2021), dissatisfaction is frequently reflected in negative behaviors directed toward one’s partner such as distorted perceptions of one another, leading individuals to focus on and identify faults in each other’s character. Lastly, Factor 5 explores statements from items 7 and 41-45 denoting apathy and lack of interest towards their relationship (I don’t think that this is the relationship I have dreamt of; I do not see my relationship with my significant other to last for years, I do not feel that my significant other and I are working together with a commitment to the success of our relationship) dominated with recognized feelings of indifference (“Indifferent,” 2025). Thematic categorization of each factor is contextualized on their respective operational definitions and locally related literature to substantiate the relevance of each variable (or statement) upon establishing discretion. The reliability and validity of the instrument were then evaluated. An instrument’s reliability is based on the dependability, consistency, and stability of the scores (McMillan, 2007). Tale 5 shows the descriptive statics for the five-factor Infidelity Tendency Comprehensive Scale. The 43-item instrument had a coefficient of .956, which is higher than the acceptable threshold of .70 (Blunch, 2008), indicating excellent internal consistency. Moreover, the factor correlation matrix was examined to evaluate discriminant validity. It revealed that no values exceeded .70, suggesting that the factors were distinct and not overly correlated, further supporting the construct validity of the instrument (Pillai & Rjumohan, 2020). Table 5: Descriptive Statistics for the five Infidelity Tendency Comprehensive Scale factors (n=500) No. of Items x̄ (SD) Cronbach’s α Sexual arousal 13 24.26 (9.721) .927 Insecurity 6 13.08 (5.441) .915 Extroversion 12 29.21 (8.917) .892 Relationship Dissatisfaction 5 14.25 (4.590) .802 Indifference 6 13.06 (5.275) .883 CONCLUSION The study primarily aimed to develop an Infidelity Tendency Comprehensive (ITCh) Scale that measures the likelihood of Filipino young adults committing infidelity in the future. The initial item pool consisted of 45 items, however, due to undervalued communality and factor correlation, items 1 and 16 were eliminated, retaining only 43 items for the final questionnaire. Moreover, the Minimum Average Partial (MAP) criterion revealed that the instrument consists of 5 factors, which were identified as sexual arousal, insecurity, extroversion, relationship dissatisfaction, and indifference. Both the reliability and validity of the instrument were then examined, revealing that the Infidelity Tendency Comprehensive (ITCh) Scale is a reliable (p=.956) and valid instrument based on the Filipino context that future researchers can use. REFERENCES Altgelt, E. E., & Meltzer, A. L. (2019). Mate-retention behaviors mediate the association between spouses’ attachment insecurity and subsequent partner satisfaction. Personality and Individual Differences, 151, 109534. https://doi.org/10.1016/j.paid.2019.109534 Aron, A., & Aron, E. (1986). Love and the expansion of self: Understanding attraction and satisfaction. Hemisphere Publishing Corp/Harper & Row Publishers. https:// psycnet.apa.org/record/1986-98255-000 Aron, A., Lewandowski, G. Jr., Mashek, D., & Aron, E. N. (2013). The self-expansion model of motivation and cognition in close relationships. In J. M. D. Clark & L. A. M. Feeney (Eds.), The Oxford handbook of close relationships (pp. 90-115). Oxford University Press. https://www.researchgate.net/ Pa ge 84 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 publication/284143380_The_self-expansion_ model_of_motivation_and_cognition_in_close_ relationships Avelino, E. J. G. (2024). Infidelity and stress in the workplace among selected call center agents in Quezon City. Academia. https://www.academia.edu/120235525/Infidelity_ and_Stress_in_the_Workplace_among_selected_ Call_Center_Agents_in_Quezon_City Bartlett, S. (1951). The effect of standardization on a Χ² approximation in factor analysis. Biometrika, 38(3), 337–344. Belu, C. F., & O’Sullivan, L. F. (2024). Predictors of infidelity among couples. The Journal of Sexual Medicine, 21(4), 270–272. https://doi.org/10.1093/jsxmed/ qdad165 Blunch, N. J. (2008). Introduction to structural equation modeling using SPSS and AMOS. SAGE Publications. https://doi.org/10.4135/9781446249345 Browne, H. (2024). Insecurities in relationships: Everything you need to know. Simply Psychology. https://www.simplypsychology.org/insecurities-in-a- relationship.html Chakravarti, I., Laha, G., & Roy, J. (1967). Handbook of methods of applied statistics. Biometrika, 1, 392–394. Cherry, K. (2022, March 27). Extroversion in personality. Verywell Mind. https://www.verywellmind.com/what- is-extroversion-2795994 Cokluk, O., & Kocak, D. (2016, March 30). Using Horn’s parallel analysis method in exploratory factor analysis for determining the number of factors. Education Science: Theory and Practice, 16(2), 537–551. https://doi. org/10.12738/estp.2016.2.0328 Extroversion. (2024). In Merriam-Webster Dictionary. https:// www.merriam-webster.com/dictionary/extroversion Fein, E. C., Gilmour, J., Machin, T., & Hendry, L. (2022). Section 8.4: EFA determining the number of factors. Retrieved from https://usq.pressbooks. pub/statisticsforresearchstudents/chapter/efa- assumptions/ Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71(3), 551– 570. https://doi.org/10.1177/0013164410389489 Gaskin, J., & Happell, B. (2014). Exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511– 521. https://doi.org/10.1016/j.ijnurstu.2013.10.005 Ghorbani, H. (2019). Mahalanobis distance and its application for detecting multivariate outliers. Facta Universitatis Series Mathematics and Informatics, 32(2), 583–595. https://doi.org/10.22190/fumi1903583g Gono, E. R., Jr. (2024). Model of cheating in a romantic relationship. European Journal of Social Sciences Studies, 9(5). https://doi.org/10.46827/ejsss.v9i5.1650 Hayton, J. C., Allen, D., & Scarpello, V. (2004, April). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205. https://doi. org/10.1177/1094428104263675 Hedrih, V. (2024). The science of infidelity: The key psychological and contextual factors that predict cheating. PsyPost-PsychologyNews. https://www.psypost. org/the-science-of-infidelity-the-key-psychological- and-contextual-factors-that-predict-cheating/ Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179– 185. https://doi.org/10.1007/BF02289447 Howard, A. (2022). What does indifference in a relationship look like? Psych Central. https:// psychcentral.com/blog/how-indifference-can-affect- a-relationship#what-is-indifference Indifferent. (2025). In Merriam-Webster Dictionary. https:// www.merriam-webster.com/dictionary/indifferent Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/ BF02291575 Kimber, A. (2024). Infidelity versus adultery — Is there a difference? Medium. https://medium.com/ the-infidelitist/infidelity-versus-adultery-is-there-a- difference-fbea3908611f Ledesma, R. D., Valero-Mora, P. M., & Macbeth, G. (2015, June). The scree test and the number of factors: A dynamic graphics approach. The Spanish Journal of Psychology, 18, E45. https://doi.org/10.1017/ sjp.2015.13 Lee, A. (2015, March 25). Have a break, have an affair? Manila Standard. https://manilastandard. net/?p=167613 Lewandowski, G., & Ackerman, R. (2010). Something’s missing: Need fulfillment and self-expansion as predictors of susceptibility to infidelity. The Journal of Social Psychology, 146(4), 389–403. https://doi. org/10.3200/SOCP.146.4.389-403 Lișman, C. G., & Holman, A. C. (2021). Cheating under the circumstances in marital relationships: The development and examination of the propensity towards infidelity scale. Social Sciences, 10(10), 392. https://doi.org/10.3390/socsci10100392 McMillan, J. H. (2007). Classroom assessment: Principles and practice for effective standards-based instruction (4th ed.). Pearson. MSEd, K. C. (2023). How the arousal theory of motivation works. Verywell Mind. https://www.verywellmind. com/the-arousal-theory-of-motivation-2795380 Norman, C. (1988, March). The eigenvalues-greater- than-one rule and the reliability of components. Psychological Bulletin, 103(2), 276–279. https://doi. org/10.1037/0033-2909.103.2.276 Osborne, J., Costello, A., & Kellow, J. (2008). Best practices in exploratory factor analysis. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 86–99). SAGE Publications. Osborne, J. (2015). What is rotating in exploratory factor analysis? Assessment, 20(1), 1–8. Pa ge 85 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 3(1) 77-85, 2025 Pillai, N. V., & Rjumohan, A. (2020). Reliability, validity, and uni-dimensionality: A primer. MPRA Paper. https://ideas.repec.org/p/pra/mprapa/101714.html Professional, C. C. M. (2024). Sexual response cycle. Cleveland Clinic. https://my.clevelandclinic.org/ health/articles/9119-sexual-response-cycle Rokach, A., & Chan, S. H. (2023). Love and infidelity: Causes and consequences. International Journal of Environmental Research and Public Health, 20(5), 3904. https://doi.org/10.3390/ijerph20053904 Siguan, A. N. D. R. E. W., Ong, M. F. T., & Cañete, S. I. M. (2021). The impact of infidelity on Filipino family dynamics and young adult Filipino’s self-concept. MALIM: Jurnal Pe Pengajian Umum Asia Tenggara, 22(2021), 14–37. Taherdoost, H., Sahibuddin, S., & Jalaliyoon, N. (2014). Exploratory factor analysis: Concepts and theory. HAL. https://hal.science/hal-02557344v1 Tavakol, M., & Wetzel, A. (2020). Factor analysis: A means for theory and instrument development in support of construct validity. International Journal of Medical Education, 11, 245–247. https://doi.org/10.5116/ ijme.5f96.0f4a Travers, M. (2024, June 29). A psychologist reveals 6 signs a marriage may end in divorce. Forbes. https:// www.forbes.com/sites/traversmark/2024/06/29/a- psychologist-reveals-the-6-signs-a-marriage-will-end- in-divorce/ Wanjiru, V., Ireri, N., & Menecha, J. (2020). An investigation of the factors contributing to infidelity among married couples in selected mainstream churches in Kikuyu Constituency, Kiambu County, Kenya. Journal of Sociology, Psychology & Religious Studies, 2(2), 1–8.