The Structural Validity of the Employee Performance Scale for Filipino Workers (Eps-Fw)

The Structural Validity of the Employee Performance Scale for Filipino Workers (Eps-Fw)

Alvin M. Nieva, Annabel D. Quilon, Josephine E. Prudente, Esther R. Cuaresma

San Beda University, Manila, Philippines

DOI: https://dx.doi.org/10.47772/IJRISS.2025.917PSY0025

Received: 07 April 2024; Accepted: 14 April 2025; Published: 15 May 2025

ABSTRACT

This study assessed the structural validity of the Employee Performance Scale among Filipino workers, a scale originally developed and validated by Pradhan and Jena (2017). Specifically, 546 Filipino workers, encompassing 236 males (43.22%) and 310 females (56.78%), participated from both government and private sectors. Additionally, their ages spanned from 18 to 63, with a mean of 38 and a standard deviation of 10.40. Consequently, convenience sampling was employed to select these participants, and a cross-sectional, explanatory nonexperimental design was utilized. Importantly, the resulting Employee Performance Scale for Filipino Workers (EPS-FW) exhibited robust psychometric properties, establishing its validity and reliability for measuring Filipino employee performance. Furthermore, within-network construct validation supported the refined 20-item scale, as opposed to the original 23-item version. Finally, confirmatory factor analysis confirmed a strong three-factor structure, providing further evidence of its construct validity.

Keywords: Employee Performance, Filipino Workers, Construct Validation, Confirmatory Factor Analysis

INTRODUCTION

Research on employee performance is diverse, with management focusing on maximizing productivity, occupational health on preventing health-related productivity loss, and work/organizational psychology examining the impact of factors like engagement and personality (Koopmans et al., 2011).

Notably, in the Philippines, many studies on employee performance rely on researcher-developed instruments. For instance, Galvez (2019) measured employee performance within the Municipal Government of Catubig, and similarly, Alvarado and Padilla (2022) assessed job performance in a Local Government Unit. In contrast, some studies utilize existing company performance appraisal tools, such as the Performance Appraisal Evaluation used by Getz Pharma (Silvestre, 2022). Furthermore, Pacut (2024) adapted and revised Linda Koopmans’s (2015) scale, but utilized only two dimensions of Employee Performance: task performance and contextual performance.

In order to come up with a parsimonious scale to assess Filipino employee performance, this study aimed to evaluate the structural validity of the Employee Performance Scale among Filipino workers, addressing the suitability of psychological tests for use in new cultural contexts. The scale, originally developed and validated by Pradhan and Jena (2017) within Indian manufacturing and service industries, was identified by its authors as needing further refinement to enhance reliability and to better account for variance in measured constructs across diverse situations. This highlights the importance of validation when adapting scales for different cultural contexts.

Validation’s Role in Scale Adaptation

For confident utilization, all tests, scales, and tools applied in clinical, educational, or research settings must possess essential measurement properties—including validity, reliability, utility, fairness, clinical utility, and responsiveness (Brown et al., 2019; Nieva, 2023; Nieva, 2024; Quilon & Kurniawan, 2023; Rada & Nieva, 2024). Furthermore, they need to be developed with a high degree of scientific rigor to ensure adequate outcomes, support relevant conclusions about the instrument’s measurement characteristics (Echevaria-Guanilo et al., 2017; Nieva, 2024; Quilon & Kurniawan, 2023; Rada & Nieva, 2024), and maximize the research’s overall usefulness.

When an existing measure is adopted, evidence of its reliability and validity from previous research may be referenced for the current investigation; however, establishing proof of reliability within the new context remains necessary, even if new validity evidence is not the primary focus (Shodiya & Adekunle, 2022).

The importance of evaluating the psychometric properties of adopted instruments prior to their use in diverse contexts is well-established in the literature. Indeed, Maneesriwongul and Dixon (2004) highlight the necessity of cross-cultural validation before applying measures to populations outside their original design, as scales may function differently across cultures (Clark & Watson, 2019; Fischer, 2004; Hambleton, 2001; Nieva, 2023). Moreover, utilizing foreign psychological measures without prior validation is not only premature (Bernardo, 2011), but also potentially detrimental. Specifically, unvalidated instruments can yield unreliable results and, consequently, lead to flawed research findings (Clark & Watson, 2019; Flake, Pek, & Hehman, 2017; Goni et al., 2020; Nieva, 2023; Nieva, 2024; Rada & Nieva, 2024; Vazire, Schiavone, & Bottesini, 2022). Therefore, thorough validation is crucial to ensure the accuracy and applicability of psychological measures in new cultural settings.

 Employee Performance

While the terms employee performance and job performance are often used interchangeably, a subtle nuance distinguishes their meanings. Job performance, described as individual behavior, is something that people do and that is visible, adding value to the organization and advancing its objectives. It is the degree to which a person satisfies overall organizational performance standards and can also be viewed as an achievement-related behavior with an evaluation component (Lopez-Cabarcos, et al., 2022). While job performance focuses on individual tasks and outputs, employee performance encompasses a broader scope, being a key factor in achieving organizational goals. Productive and efficient employees can help companies increase output and quality of products or services produced, enhance customer and employee satisfaction, and build a positive reputation for the company (Triansyah, et al., 2023). Additionally, technology, internal and external boundaries, the structure of hierarchical tiers, and formalization all significantly improve employee performance (Shabbir, 2017). Furthermore, transformational leadership methods have a favorable effect on job performance, while family-work conflict has a detrimental influence (Adil & Vapur, 2023). Lastly, numerous elements, such as the workplace’s human resource management system, leadership style, working conditions, opportunities for advancement and compensation, employee communication patterns, and job motivation, can affect an employee’s performance (Rokim & Tentama, 2020).

In addition, employee performance is a measure of the extent to which an employee is able to fulfill their duties and responsibilities properly and effectively. Employee performance is assessed through output, efficiency, quality, initiative, and attitude. Effective employee performance helps companies achieve organizational goals more efficiently, while poor employee performance can cause losses for the company (Triansyah, et al., 2023). Consequently, the new definition of individual performance, according to Lopez-Cabarcos, et al. (2022), includes in-role performance, adaptive performance, proactive performance, and citizenship behaviors. This new conceptualization of job performance is explored through an integrative performance model that breaks down ‘the three levels at which role behaviors can contribute to effectiveness (individual, team, and organization) and the three forms of behavior (proficiency, adaptivity, and proactivity) into subdimensions of work role performance’ (Lopez-Cabarcos, et al., 2022).

Building upon the work of Pradhan & Jena (2017), numerous other measures of employee performance exist. For instance, Platania et al. (2023) highlight that the Individual Work Performance Questionnaire (IWPQ) assesses individual behavior that contributes to organizational value and competitive advantage. More specifically, the IWPQ, as adapted into Indonesian by Widyastuti and Hidayat, comprises three key indicators: Task Performance, Contextual Performance, and Counterproductive Work Behavior (Juariyah, et al., 2023). In particular, Task Performance encompasses behaviors directly related to the production of goods or services. This dimension includes completing work tasks, maintaining current knowledge, ensuring accuracy and neatness, planning, organizing, and problem-solving. Moreover, it aligns with behaviors prescribed by the role and outlined in the job description, representing the individual’s competence in performing core job tasks (Platania, et al., 2023).

Furthermore, Caliskan & Koroglu (2022) developed and validated a Job Performance Scale that includes two sub-dimensions: Task Performance and Contextual Performance, measured across 11 items. Conversely, Rokim & Tentama (2020) proposed an Employee Performance scale with three aspects: work results, defined as the quantity and quality of completed work; discipline, which involves timely task completion; and employee responsibility, reflecting the ability to perform effectively under various conditions, with or without supervision.

Conceptual Dimensions of Employee Performance

Koopmans et al. (2011; 2014) systematically analyzed various conceptual frameworks related to individual work performance, ultimately concluding that certain dimensions were recurrent. Specifically, they highlighted task performance, contextual performance, counterproductive work behavior, and adaptive performance as frequently employed descriptors of employee effectiveness. This analysis not only consolidated existing knowledge but also provided a clear, operational framework for future empirical studies. Moreover, the findings underscored the necessity of employing multi-dimensional performance measures in organizational settings to achieve a more accurate and comprehensive understanding of employee contributions.

Figure 1 illustrates Pradhan and Jena’s (2017) Triarchy Model of Employee Performance. This model conceptualizes employee performance as being comprised of three distinct components: task performance, contextual performance, and adaptive performance.

Task performance, a key aspect of employee effectiveness, involves job-specific actions aligned with official duties. This form of performance is significantly influenced by cognitive aptitude and is primarily supported by: technical expertise and the capacity to manage diverse tasks (task knowledge), the practical application of this expertise with minimal oversight (task skill), and inherent tendencies that either enhance or hinder task completion (task habits) (Pradhan & Jena, 2017).

Figure 1. The Triarchy Model of Employee Performance (Pradhan & Jena, 2017).

Adaptive performance refers to an individual’s capacity to adjust and provide necessary support within a fluctuating professional context (Pradhan & Jena, 2017). As employees gain expertise in their assigned duties, they tend to modify their attitudes and behaviors to accommodate diverse job requirements. Effective adaptive performance necessitates the ability to manage unpredictable workplace circumstances.

Contextual performance encompasses prosocial behaviors exhibited by individuals within a professional environment (Pradhan & Jena, 2017). These behaviors, though anticipated, are not explicitly stipulated in formal job descriptions. Such unstated expectations are also designated as prosocial or extra-role behaviors.

METHOD

Participants

This study included 546 Filipino workers from both government and private companies. Specifically, the participants were both male and female, comprising 236 males (43.22%) and 310 females (56.78%). Furthermore, their ages ranged from 18 to 63, with a mean age of 38 and a standard deviation of 10.40. To select these individuals, convenience sampling was used. In essence, this method, as described by Urdan (cited in Nieva, 2023), involves selecting readily available and willing individuals.

Measures

Employee Performance Scale.  Developed by Pradhan and Jena (2017), this assessment serves as a concise tool for evaluating employee performance. This 23-item scale covers three dimensions: task performance, adaptive performance, and contextual performance. Respondents indicate their agreement using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree).

Some statements of the original scale were modified to ensure their suitability for Filipino participants. For instance, the original statement, ‘I extend my sympathy and empathy to my co-workers when they are in trouble,’ was revised to ‘I empathize with my co-workers when they are in trouble.’ Similarly, ‘I used to extend help to my co-workers when asked or needed‘ was changed to ‘I help my co-workers when asked or needed.’ The need to alter survey questions, as highlighted by Nieva (2023, 2024), stems from the desire to guarantee participants’ complete understanding and the validity of their responses.

The scale’s reliability, assessed through Cronbach’s alpha (α) and McDonald’s omega (ω) for its three dimensions, yielded the following results: task performance (α = 0.78, ω = 0.78), adaptive performance (α = 0.68, ω = 0.68), and contextual performance (α = 0.86, ω = 0.86). Cronbach’s alpha, a measure of internal consistency, is generally considered sufficient when it reaches .70 or higher, as suggested by Nunnally (1978). Nevertheless, some researchers propose that values between 0.6 and 0.8 can also be deemed acceptable (Raharjanti et al., 2022; Shi et al., 2012).

Research Design and Procedure

This study employed a quantitative approach, specifically a cross-sectional, explanatory nonexperimental design (Johnson, 2001; Nieva & Prudente, 2022). As Nieva (2024, p. 156) states, “Cross-sectional as a research dimension means that data were collected from participants only once, at a single point in time,” while the explanatory objective aimed to explore and explain relationships between variables in real-world settings where experimental manipulation is not feasible.

Subsequently, the survey was administered online using Microsoft Forms. Prior to participation, participants were provided with a consent form, which outlined the study’s purpose, emphasized the voluntary nature of their involvement, and guaranteed their rights as participants. Moreover, participant anonymity was ensured. Specifically, their consent was indicated by completing the entire survey. Following data collection, the data were processed using JASP 0.19.3.

Data Analysis

The analytical procedures employed in this study comprised the following: calculation of descriptive statistics, specifically means, standard deviations, zero-order correlations, and normality assessments (skewness and kurtosis); determination of scale reliability using Cronbach’s alpha and McDonald’s omega; and evaluation of the measurement model’s construct validity through Confirmatory Factor Analysis (CFA). The assessment of CFA model fit was based on a variety of goodness-of-fit indices, as prescribed by Hu and Bentler (1999), including the Chi-square test statistic, Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Bentler-Bonett Normed Fit Index (NFI), and Root Mean Square Error of Approximation (RMSEA).

RESULTS AND DISCUSSION

 For the Employee Performance Scale, normality testing of its factors produced skewness values ranging from -0.167 to -1.033 and kurtosis values ranging from 0.038 to 1.695. These values are within the acceptable range, as defined by George and Mallery (2010), where skewness and kurtosis values between -2 and +2 are indicative of a normal univariate distribution.

Table 1 presents the means and standard deviations for the three dimensions of the Employee Performance Scale, as part of the preliminary data analyses. Employing a median split as the basis for score interpretation, the calculated mean scores were observed to be slightly above the midpoint. This suggests that the participants, on average, demonstrated a relatively high level of employee performance. Furthermore, the low standard deviations indicate that the participants’ scores were highly consistent and similar, reflecting a high degree of uniformity and low variability. Consequently, these findings collectively indicate that a significant proportion of the participants exhibited high employee performance.

Table 1. Mean and Standard Deviation of the Employee Performance Scale

To determine the relationships between the three dimensions of the scale, zero-order correlations were performed. The resulting correlation coefficients are presented in Table 2. Specifically, the results indicate that the three factors are significantly and positively correlated. Furthermore, these correlation coefficient values are within the acceptable limits for conducting Confirmatory Factor Analysis (CFA). Indeed, the three dimensions did not exhibit high correlations with the four factors of the Online Student Engagement Scale, thus indicating the absence of multicollinearity (Nieva, 2022; Nieva et al., 2024). As Kline (2023) explains, ‘High correlations between latent variables can lead to multicollinearity, which affects the stability and interpretability of CFA results. Correlations above 0.8 or 0.9 between latent variables are often considered problematic in CFA, as they suggest that the variables may not be distinct constructs.

Table 2. Zero-Order Correlations of the Employee Performance Scale

Confirmatory factor analysis (CFA) was conducted to evaluate the within-network construct validity of the Employee Performance Scale. This involved testing a predefined model with three latent factors: task performance, adaptive performance, and contextual performance. The aim was to determine if the observed data fit the hypothesized three-factor structure derived from the theoretical framework. This analysis provides evidence for the scale’s ability to accurately measure the intended constructs.

The confirmatory factor analysis (CFA) indicated a lack of adequate fit between the a priori three-factor model and the empirical data, as demonstrated by several items with factor loadings below .34 and standardized residuals exceeding 2.58 (Stevens, 2002). To enhance model fit, these items were removed. However, the desired fit indices were not achieved. Subsequently, modification indices were inspected, leading to the removal of redundant items with high modification indices (above 15, Awang, 2015) and low factor loadings.

An improved model fit was observed after three poorly fitting items were identified and discarded. However, this did not result in a reduction of the number of factors; the three factors were retained. The summary of the CFA fit indices is presented in Table 2. To interpret the Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Bentler-Bonett Normed Fit Index (NFI), values greater than .90 are considered acceptable. Therefore, these results indicate an acceptable fit, with the exception of the NFI. Regarding the Root Mean Square Error of Approximation (RMSEA), a value less than .08 indicates a close fit (Matsunaga, 2010). Consequently, the RMSEA result suggests a close fit. Finally, because chi-square fit statistics are sensitive to large sample sizes, and as West, Taylor, and Wu (2012, p. 211) state, the chi-square test is ‘not always the final word in assessing fit,’ the ratio of the chi-square statistic to its degrees of freedom was chosen as an additional measure. Ideally, this ratio should be close to 1.0. However, achieving perfect fit is rare in real-world data. Hu and Bentler (1999) suggest a chi-square/df ratio of less than 5 as a sign of reasonable fit, with adjustments for model complexity and sample size. Bentler (1990) supports this, indicating that ratios between 2 and 5 are acceptable, with lower values preferred.

Table 3. Summary of Goodness of Fit Indices of the CFA

Figure 1. CFA model plot, illustrating the three latent factors of the Employee Performance Scale for Filipino Workers (EPS-FW)

The model plot of the three factors of the Employee Performance Scale is shown in Figure 1, along with the standardized factor loadings and error terms. Factor 1 (Task Performance) includes 5 items, Factor 2 (Adaptive Performance) includes 6 items, and Factor 3 (Contextual Performance) includes 9 items. For a complete list of item questions per factor, refer to Appendix A.

CONCLUSION

The Employee Performance Scale for Filipino Workers (EPS-FW) demonstrates strong psychometric properties, thus establishing its validity and reliability for measuring Filipino employee performance. Furthermore, within-network construct validation supports the use of the refined 20-item scale, rather than the original 23-item version (see Appendix for the final scale details). Moreover, confirmatory factor analysis confirms a robust three-factor structure, further validating its construct.

However, this study has limitations. Firstly, the reliance on self-report data introduces the potential for common method variance, as described by Tehseen, Ramayah, and Sajilan (2017), whereby relationships between variables may be inflated or deflated due to the shared data source. Secondly, while English is widely understood in the Philippines, translating the scale into Filipino, the participants’ native language, could enhance accuracy and cultural relevance.

REFERENCES

1. Adil, B. & Vpaur, M. (2023). The effects of transformational leadership practices in organizations on family-work conflict, work-family conflict and job performance. International Journal of Management Economics and Business, Vol. 19, No. 4. http://dx.doi.org/10.17130/ijmeb.1288234
2. Alvarado, A., & Padilla, J. G. A. (2022). Work ethics and job performance of employees in local government unit. Multidisciplinary International Journal of Research and Development, 1(5), 41-53.
3. Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publication.
4. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.
5. Bernardo, A. B. I. (2011). Lost in translation? Challenges in using psychological tests in the Philippines. Silliman Journal, 52, 19-42.
6. Brown, T., Bonsaksen, T. (2019). An examination of the structural validity of the physical self-description questionnaire-short form using the rasch measurement model. Cogent Education, 6(1). https://doi.org/10.1080/2331186X.2019.1571146
7. Çalışkan, A., & Köroğlu, E. Ö. (2022). Job performance, task performance, contextual performance: development and validation of a new scale. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8(2), 180-201. DOI: 10.29131/uiibd.1201880
8. Clark, L. A., & Watson, D. (2019). Constructing validity: New developments in creating objective measuring instruments. Psychological assessment, 31(12), 1412. https://www.doi.org/ 10.1037/pas0000626
9. Echevarria-Guanilo, M., Goncalves, N., & Romanoski, P. (2017). Psychometric properties of measurement instruments: conceptual bases and evaluation methods – part 1. Texto Contexto Enferm, 26(4).  https://doi.org/10.1590/0104-07072017001600017
10. Fischer, R. (2004). Standardization to account for cross-cultural response bias: A classification of score adjustment procedures and review of research in JCCP. Journal of Cross-Cultural Psychology, 35(3), 263–282. https://www.doi.org/10.1177/0022022104264122
11. Flake, J. K., Pek, J., & Hehman, E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science, 8(4), 370-378. https://www.doi.org/10.1177/1948550617693063
12. Galvez, G. D. (2019). The Level of Performance of the Employees in the Municipal Government of Catubig. SSRG International Journal of Economics and Management Studies, vol. 6, no. 2, pp. 124-126. https://doi.org/10.14445/23939125/IJEMS-V6I2P115
13. George D, & Mallery P. (2010). SPSS for Windows Step by Step. A Simple Study Guide and Reference (10^th). GEN. Boston, MA: Pearson Education, Inc.
14. Goni, M. D., Naing, N. N., Hasan, H., Wan-Arfah, N., Deris, Z. Z., Arifin, W. N., Hussin, T. M. A. R., Abdulrahman, A. S., Baaba, A. A., & Arshad, M. R. (2020). Development and validation of knowledge, attitude and practice questionnaire for prevention of respiratory tract infections among Malaysian Hajj pilgrims. BMC Public Health, 20(1), 189. https://www.doi.org/10.1186/s12889-020-8269-9
15. Juariyah, L., Putra, D., & Syihabudhin (2023). Employee performance in aviation industry based on koopman’s individual work performance questionnaire (IWPQ). International journal of multidisciplinary research and analysis, Volume 06 Issue12. DOI: 10.47191/ijmra/v6-i12-28
16. Hambleton, R. K. (2001). The Next Generation of the ITC Test Translation and Adaptation Guidelines. European Journal of Psychological Assessment, 17(3), 164–172. https://www. doi.org/10.1027//1015-5759.17.3.164
17. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://www.doi.org/10.1080/10705519909540118
18. Kline, R. B. (2023). Principles and practice of structural equation modeling (5^th). Guilford publications.
19. Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet Henrica, C. W., & Van Der Beek, A. J. (2011). Conceptual frameworks of individual work performance: A systematic review. Journal of occupational and environmental medicine, 53(8), 856-866. DOI: 10.1097/JOM.0b013e318226a763
20. Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., de Vet, H. C. W., & van der Beek, A. J. (2014). Measuring individual work performance: Identifying and selecting indicators. WORK, 48(2), 229-238. https://doi.org/10.3233/WOR-131659
21. Lopez-Cabarcos, M., Vazquez-Rodriguez, P. & Quiñoa-Piñeiro, L. (2022). An approach to employees’ job performance through work environmental variables and leadership behaviors. Journal of Business Research 140, 361-369. https://doi.org/10.1016/j.jbusres.2021.11.006
22. Nieva, A. (2022). The Relationship between Career Interests and Academic Achievements in English, Mathematics, and Science of Grade 10 Students. International Journal of Arts, Sciences and Education, 3(2 June Issue). Retrieved from https://www.mail.ijase.org/index.php/ijase/article/view/122
23. Nieva, A., & Prudente, J. (2022). Online self-regulated learning, academic performance, and well-being of Senior High School Students in the NCR: A mediation analysis. Bedan Research Journal, 7(1), 48–62. https://doi.org/10.58870/berj.v7i1.32
24. Nieva, A. (2023). Construct validation of the teacher attitude to inclusion scale for Filipino pre-service teachers. Bedan Research Journal, 8(1), 305–329. https://doi.org/10.58870/berj.v8i1.56
25. Nieva, A. (2024). Personality Traits as Predictors of Pro-Environmental Behavior: Evidence from the Philippines. Bedan Research Journal, 9(1), 145–173. https://doi.org/10.58870/berj.v9i1.68
26. Nieva, A. M., Quilon, A. D., Butac, S. R., & Beltran, R. M. (2024). The Predictive Role of Hope on the Online Student Engagement of Filipino Pre-Service Teachers. International Journal of Research and Innovation in Social Science, 8(3s), 5465-5473. https://dx.doi.org/10.47772/IJRISS.2024.803411S
27. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
28. Maneesriwongul, W., & Dixon, J. K. (2004). Instrument translation process: a methods review. Journal of Advanced Nursing, 48(2), 175–186. https://www.doi.org/10.1111/j.1365-2648.2004.03185.x
29. Matsunaga, M. (2010). How to Factor-Analyze Your Data Right: Do’s, Don’ts, and How-To’s. International journal of psychological research, 3(1), 97-110.
30. Pacut, H. A. (2024). Total Quality Management and Employee Performance among Motorcycle Dealers: Basis for TQM Initiatives. International Journal of Scientific Research and Management (IJSRM), 12(03), 6204-6219. DOI: 10.18535/ijsrm/v12i03.em21
31. Platania, S., Morando, M., Gruttadauria, S., & Koopmans, L. (2023). The individual work performance questionnaire: psychometric properties of the italian version.
32. Pradhan, R. K., & Jena, L. K. (2017). Employee performance at workplace: Conceptual model and empirical validation. Business perspectives and research, 5(1), 69-85. https://doi.org/10.1177/2278533716671630
33. Quilon, A., & Kurniawan, Y. (2023). Online learning environment and mental health among university students. Bedan Research Journal, 8(1), 259–284. https://doi.org/10.58870/berj.v8i1.54
34. Rada, E., & Nieva, A. (2024). Translation and Validation of the Filipino Sustainability Consciousness Questionnaire. Bedan Research Journal, 9(1), 53–82. https://doi.org/10.58870/berj.v9i1.65
35. Raharjanti, N. W., Wiguna, T., Purwadianto, A., Soemantri, D., Indriatmi, W., Poerwandari, E. K., … & Levania, M. K. (2022). Translation, validity and reliability of decision style scale in forensic psychiatric setting in Indonesia. Heliyon, 8(7). DOI: 10.1016/j.heliyon.2022.e09810
36. Rokim, M. & Tentama, F. (2020). The employee’s performance: a study of construct validity and reliability. American Scientific Research Journal for Engineering, Technology, and Sciences, Volume 72, No. 1, pp 90-100.
37. Shabbir, M. (2017). Organizational structure and employee’s performance: a study of brewing frims in nigeria. American Research Journal of Business and Management, V3 I1;pp:1-16.
38. Shi, J., Mo, X., & Sun, Z. (2012). Content validity index in scale development. Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical sciences, 37(2), 152-155. https://doi.org/10.3969/j.issn.1672-7347.2012.02.007
39. Shodiya, O. & Adekunle, T. (2022). Reliability of research instruments in management sciences research: an explanatory perspective. Silesian University of Technology Publishing House, Scientific Papers of Silesian University of Technology Organization and Management No. 166. https://dx.doi.org/10.29119/1641-3466.2022.166.46
40. Silvestre, A. A. (2022). Personality Traits, Motivation, and Performance of Employees in a Pharmaceutical Company in the Philippines: Basis for Performance Improvement Plan. Psychology and Education: A Multidisciplinary Journal, 2(2), 1-1. doi: 10.5281/zenodo.6547994
41. Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum. Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://www.doi.org/10.1007/s11165-016-9602-2
42. Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of management sciences, 4(2), 142-168. https://www.doi.org/10.20547/jms.2014.1704202
43. Triansyah, F., Hejin, W., & Stefania, S. (2023). Factors affecting employee performance: a systematic review. Journal Markcount Finance, 1(3). https://doi.org/10.55849/jmf.v1i2.102
44. Vazire, S., Schiavone, S. R., & Bottesini, J. G. (2022). Credibility beyond replicability: Improving the four validities in psychological science. Current Directions in Psychological Science, 31(2), 162-168. https://www.doi.org/10.1177/09637214211067779
45. West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209– 231). New York, NY: The Guilford Press.

Appendix A: Items of the Employee Performance Scale for Filipino Workers (EPS-FW)

N.B.: Test scoring and interpretation instructions are available by email request to the first author at