Introduction

In recent years, there has been increasing recognition that traditional leadership tools fall short in capturing how students perceive their teachers’ influence on their personal growth, well-being, and motivation. While teachers are expected to promote both academic success and holistic student development, there remains a lack of psychometrically sound instruments that measure spiritual leadership from the direct perspective of students—a critical gap that limits both theoretical advancement and practical intervention.

In today's educational environment, teachers play a crucial role that goes beyond imparting academic knowledge; they are also responsible for fostering the overall well-being of their students (Zheng, 2022). This multifaceted responsibility aligns with Spiritual Leadership principles, which involve various approaches to inspire and motivate followers, leading to positive outcomes (Smith et al., 2018). According to Fry (2003), spiritual leadership theory emphasizes that authentic leadership is based on spiritual elements such as vision, hope/Faith, and altruistic Love.

Spiritual leadership in the classroom can significantly influence students’ motivation, engagement, and academic outcomes (Nurabadi et al., 2021), aligning with the broader evidence that workplace spirituality positively affects organizational commitment (Stephanou & Athanasiadou, 2020). For example, a teacher who fosters a shared vision and demonstrates unwavering belief in students’ potential may help disengaged learners regain confidence and commitment to their studies. Similarly, teachers who practice empathy and model moral integrity often build strong trust with students, leading to a more inclusive and supportive classroom environment. These practices not only enhance students’ academic performance but also contribute to their personal development and emotional resilience.

However, despite its importance, comprehensive tools are not designed to evaluate teachers' spiritual leadership from the students' viewpoint. Measuring spiritual leadership through students’ perspectives presents several challenges. Students may have varying interpretations of spiritual traits such as altruism, hope, and integrity, influenced by cultural, developmental, or personal factors. Furthermore, some students might conflate spiritual leadership with religious behavior, potentially affecting their responses. Capturing these nuanced perceptions requires a carefully designed and validated instrument that resonates with the students' lived classroom experiences.

Thus, the current study aims to develop and validate the Teachers' Spiritual Leadership Questionnaire (TSLQ), a new instrument based on students' perceptions. The TSLQ aims to capture the multifaceted dimensions of spiritual leadership in the classroom. This instrument assesses how teachers inspire, influence, and guide their students toward holistic growth and foster an environment that promotes spiritual well-being. By leveraging students' insights, the TSLQ offers a unique vantage point that reflects the direct impact of teachers' spiritual leadership on their primary beneficiaries.

In the domain of Vision, Fry (2003) identifies several qualities of a spiritual leader: broad appeal to key stakeholders, clear definition of both destination and journey, reflection of high ideals, encouragement of Hope and Faith, and establishment of a standard of excellence. Firstly, a spiritual teacher's vision should broadly appeal to all key stakeholders, including students, parents, and colleagues. This inclusive vision fosters a sense of shared purpose and collaboration within the educational community (Oude Groote Beverborg et al., 2020). Secondly, clearly defining both the destination and the journey is vital. In an educational context, teachers should articulate clear learning objectives and outline the steps students must take to achieve them, providing a structured and understandable pathway to success (Konrad et al., 2014). Thirdly, the vision should reflect high ideals, inspiring students to aim for excellence and ethical behavior. By upholding high standards, teachers encourage students to develop academically and morally, aligning their personal growth with broader societal values (Gui et al., 2020). Fourthly, a spiritual teacher's vision must encourage hope and faith in students. By fostering an optimistic outlook and confidence in their abilities, teachers can motivate students to persevere through challenges and believe in their potential to succeed (Thoonen et al., 2011). Lastly, the vision should establish a standard of excellence in the classroom, setting high expectations that drive students to strive for their best. By promoting a culture of excellence, teachers can enhance student achievement and foster a sense of pride and accomplishment in their educational journey (Qureshi & Niazi, 2012).

Similarly, in the domain of hope/Faith, Fry emphasizes the importance of endurance, perseverance, commitment to doing whatever it takes, setting stretch goals, and maintaining an expectation of reward and victory. These qualities inspire students and create a positive and resilient learning environment. Supporting this view, Korkmaz and Menge (2018) found that high-hope teachers exhibited more adaptive emotional and attributional responses, particularly during negative interactions, with agency thinking playing a more influential role than pathway thinking. Endurance is the ability to sustain effort and remain steadfast despite challenges and obstacles. A teacher's endurance is vital for maintaining consistent student engagement and support in a classroom setting. According to Day et al. (2005), when teachers demonstrate endurance, they model the importance of persistence and dedication to their students, encouraging them to stay committed to their learning goals even when faced with difficulties. Closely related to endurance, perseverance involves the continual effort to achieve goals despite setbacks (King, 2021). For teachers, this means not giving up on students who struggle or face significant barriers to learning. Perseverance ensures teachers look for new strategies and solutions to help students succeed. This trait inspires students to adopt a similar mindset, understanding that effort and resilience are key to overcoming challenges.

A commitment to doing whatever it takes underscores the dedication to student success. Teachers who embody this commitment are willing to go above and beyond the standard expectations to ensure their students achieve their full potential (Altun, 2017). This might involve extra tutoring sessions, creating individualized learning plans, or seeking additional resources. Such dedication shows students that their success is a priority and worth the extra effort. Setting stretch goals involves establishing ambitious but attainable objectives that push students to extend their capabilities (Benzaquen, 2023). For teachers, this means creating challenges that encourage students to go beyond their comfort zones and strive for higher levels of achievement. Stretch goals help build confidence and foster a growth mindset, showing students they can achieve more than they initially thought possible with effort and perseverance. Finally, maintaining an expectation of reward and victory fosters an optimistic outlook. According to Rubie-Davies et al. (2020), teachers who believe in their students' ability to succeed create an environment where success is seen as attainable and effort is rewarded. This expectation helps motivate students, as they understand their hard work will lead to positive outcomes. It also builds a sense of hope and faith in their abilities, reinforcing the idea that they can achieve their goals. Teachers can inspire and motivate their students by emphasizing endurance, perseverance, commitment, setting stretch goals, and maintaining a positive expectation of reward and victory. These qualities, central to Fry's concept of hope/faith in spiritual leadership, are essential for fostering a supportive and resilient learning environment where students are encouraged to reach their full potential.

In the realm of Altruistic Love, spiritual leadership in the classroom involves embodying qualities that foster a supportive, compassionate, and trusting learning environment. These qualities include forgiveness, kindness, integrity, empathy and compassion, honesty, patience, courage, trust, loyalty, and humility, each of which plays a crucial role in shaping the educational experience. Forgiveness is essential in a classroom where mistakes are an integral part of the learning process. When teachers practice forgiveness, they create a safe space for students to take risks and learn from their errors without fear of harsh judgment, encouraging a growth mindset where failure is seen as a stepping stone to success (Freedman, 2018). Furthermore, kindness forms the foundation of a positive classroom atmosphere. A kind teacher who treats all students with respect and consideration promotes a culture of mutual respect and cooperation, building strong relationships and fostering a sense of belonging among students (Gavrin, 2015). Integrity involves being consistent and fair in one's actions and decisions. A teacher with integrity sets a strong moral example for students, demonstrating the importance of doing the right thing even when it is difficult, thereby building a trustworthy and reliable classroom environment (Digennaro et al., 2007). Empathy and compassion enable teachers to understand and respond to their students' needs and feelings. By demonstrating empathy, teachers can better support struggling students, fostering an inclusive environment where every student feels valued and understood (McAllister & Irvine, 2002; Shapira et al., 2020). Honesty is crucial for building trust between teachers and students. When teachers are honest about expectations, feedback, and limitations, it encourages students to be honest, creating an open and transparent learning environment (Munif et al., 2021).

Patience is vital in addressing the diverse learning paces and needs within a classroom. A patient teacher who takes the time to explain concepts and allows students to learn at their own pace helps to reduce anxiety and build students' confidence in their abilities (Rogers, 2020). Courage in the classroom means standing up for what is right, even when it is unpopular. This might involve addressing unfair behavior, implementing innovative teaching methods, or advocating for students' needs. Courageous teachers inspire students to stand up for their beliefs and to face challenges head-on (Batra, 2019). Likewise, trust is the backbone of effective teacher-student relationships. When teachers trust their students and show that they believe in their potential, students are more likely to take ownership of their learning and strive to meet high expectations. According to Wang et al. (2021), teacher expectations influence student achievement, with higher expectations leading to higher achievement levels. Loyalty in a teaching context involves being consistently supportive and committed to students' well-being and success. Loyal teachers are advocates for their students, dedicated to helping them navigate academic and personal challenges. Dedicated teachers who faced and overcame academic challenges in their youth use teaching practices grounded in the professional disposition that all children can learn (Fry, 2015).

Moreover, humility allows teachers to acknowledge their mistakes and limitations, fostering a learning environment where everyone is a learner. Humble teachers are open to feedback and continuous improvement, setting an example of lifelong learning for their students. Student feedback is a valuable improvement tool and stimulus for teacher reflection, identifying areas for future professional learning, and enhancing teaching effectiveness (Mandouit, 2018). Teachers embody the qualities of altruistic love by integrating forgiveness, kindness, integrity, empathy, compassion, honesty, patience, courage, trust, loyalty, and humility into their practice. These qualities enhance the learning experience and help cultivate a classroom environment where students feel supported, valued, and empowered to achieve their full potential.

A review of existing literature reveals a significant gap in measuring spiritual leadership within educational settings. For instance, Tafreshi et al. (2017) developed a Persian version of the Spiritual Leadership Questionnaire to assess Iranian nurse managers' spiritual leadership (SL). Similarly, Beytell (2013) created and validated a Spiritual Leadership Questionnaire encompassing four key variables—spirituality, Vision, Hope/Faith, and altruism—to measure the SL of leaders in organizations within the South African context. In a comparable effort, Grobler and Sibanda (2024) designed a questionnaire to evaluate leadership in both public and private sectors within the African context. Furthermore, Cao et al. (2022) developed a 14-item Spiritual Leadership Questionnaire tailored to the Chinese context. While these questionnaires have been developed to assess spiritual leadership in corporate organizations, there is a conspicuous absence of similar instruments explicitly tailored for educational contexts, particularly from the perspective of students. This gap underscores the necessity of developing a specialized tool that can accurately reflect the unique dynamics of teacher-student interactions and the spiritual leadership role of teachers.

This study is structured to encompass the development and validation phases of the TSLQ. The conceptual framework underpinning spiritual leadership in educational settings will be initially delineated, emphasizing Fry's (2003) principles of Vision, Hope/Faith, and Altruistic Love. This will be followed by systematically generating questionnaire items that reflect these principles. Subsequently, the psychometric properties of the TSLQ will be rigorously evaluated through a series of statistical analyses, ensuring the tool's reliability and validity.

In summary, this research provides a validated, student-centered instrument for assessing teachers' spiritual leadership, thereby facilitating a deeper understanding of this critical yet underexplored dimension of educational leadership. By doing so, it aims to inform educational practices and policies that enhance the spiritual dimension of teaching, ultimately fostering environments where students can thrive academically and personally.

Methodology

Research Design

This study aimed to develop and validate a Teachers' Spiritual Leadership Questionnaire (TSLQ) based on students' observations of their teachers. The primary constructs of this study were teachers' attitudes, including vision, hope/Faith, and altruistic Love. The validated TSLQ underwent a rigorous process consisting of several steps. First, related literature was analyzed to identify gaps in the study. Second, interviews were conducted with three educational leaders and one English professor to determine the indicators for measuring the three constructs. Third, content validation was performed using the Lawshe method. Fourth, an Exploratory Factor Analysis (EFA) was conducted, followed by a Confirmatory Factor Analysis (CFA). Finally, Cronbach's alpha was applied to determine the reliability of the questionnaire. These steps ensured the development and validation of a robust TSLQ.

Item Development

The development of the Spiritual Leadership Questionnaire was guided by Fry's (2003) framework, which emphasizes the core constructs of Vision, Hope/Faith, and Altruistic Love. These constructs encompass the key qualities and behaviors essential for fostering spiritual motivation and meaningful leadership. Expert input was sought from three seasoned educational leaders and one English professor to refine and validate the indicators aligned with Fry's model. Their insights contributed to the formulation, enhancement, and validation of the questionnaire items, ensuring both theoretical soundness and clarity in language.

Review of Fry’s Qualities of Spiritual Leaders

According to Fry (2003), assertion spiritual leadership comprises the values, attitudes, and behaviors necessary to intrinsically motivate oneself and others intrinsically, fostering a sense of spiritual survival through calling and membership. Table 1 presents the qualities of spiritual leadership according to the three constructs: Vision, hope/faith, and altruistic love.

Table 1. The Qualities of Spiritual Leaders

Experts Review

To ensure the relevance, clarity, and comprehensiveness of the questionnaire items, the researcher consulted three educational leaders and one English professor. Their insights were instrumental in refining the indicators identified by Fry into clear, measurable statements and in validating the content based on their professional and academic expertise.

Interviews and Getting Ideas from Experts

Fry identified the constructs and potential indicators of the questionnaire. The next step was to refine these indicators into a statement format with the assistance of three educational leaders and one English professor. Additionally, these three educational leaders were asked to suggest other possible indicators beyond those provided by Fry.

The first educational leader has 25 years of service and extensive experience in various leadership and managerial roles, including university publication, secretary of the board of regents, and campus director. This educational leader holds a Doctor of Philosophy in Educational Management. The second educational leader, a Doctor of Education in Leadership and Management, has six years of service and has served as the chair of the College of Education. The third educational leader has 30 years of experience in service and different experiences in leadership and management, including being a dean of a particular college. This educational leader holds a Doctor of Philosophy in Educational Management. The English professor has 30 years of experience in the academe and has held several leadership positions, including chair of the Office of Student Affairs for ten years. The English professor was consulted for grammar and sentence construction expertise to ensure the questionnaire was clear and comprehensible. The researcher also engaged these experts to provide theoretical and face validity for the Spiritual Leadership Questionnaire, which focused on Vision, Hope/Faith, and altruistic Love.

Validity Testing

To ensure the accuracy, reliability, and appropriateness of the developed items measuring spiritual leadership, several validation procedures were conducted. These included content validation through expert review and statistical validation via Exploratory and Confirmatory Factor Analyses.

Content Validity

After constructing the 23-item questionnaire for Spiritual Leadership, the next step was content validation using the Lawshe method. The researcher sought the help of 15 educational leaders in NEUST and the Department of Education to determine the validity of the items in measuring the three constructs and whether these constructs could effectively measure Spiritual Leadership (Nikolopoulou, 2023).

Lawshe (1975) developed the formula for the Lawshe method, which is CVR = (ne - N/2) / (N/2), where CVR stands for Content Validity Ratio, indicating the validity of the questionnaire; ne is the number of faculty who rate the items as essential; and N is the total number of faculty, which is 15. The Content Validity Ratio is the validity index for each item, while the Content Validity Index (CVI) is the average of all CVRs in the construct. According to Lawshe, the minimum value for CVR and CVI for 15 raters is .49. Additionally, Ayre and Scally (2014) state that the minimum number of raters that must agree that an item is essential is 12.

Population and Sample Size

After conducting content validation, the instrument was distributed to 1,700 students of NEUST – Gabaldon Campus on July 8, 2024. After one month, 402 respondents had completed the questionnaires. To provide context and improve the interpretability of the findings, basic demographic data were collected. Among the respondents, 58% were female and 42% were male. The age range of participants was 18 to 25 years, with a mean age of 20.4 years (SD = 1.8). These demographic statistics help assess the representativeness of the sample and enhance the generalizability of the construct being measured.

According to MacCallum et al. (1999), commonalities greatly influence the appropriate number of samples in factorial analysis. When commonalities are high, the impact of sample size on the output of the factorial analysis is low. Conversely, when commonalities are low, a larger sample size is needed, ranging from 300 to 500 for worst-case scenarios. Additionally, Comrey and Lee (1992) suggest that, as a rule of thumb, 500 participants provide a very good basis for precise factorial analysis. Thus, the sample size in this study is deemed appropriate.

Data Collection

The data was collected using Google Forms, following permission from the NEUST – Gabaldon Campus director. With the assistance of class advisers, the questionnaire was distributed to 1,700 students, and after a month, 402 responses were received. This method ensured a comprehensive sample for the study.

Data Analysis

The exploratory factor analysis (EFA) was conducted to identify underlying factors in the dataset and validate the factors proposed by Fry. Additionally, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Test of Sphericity were calculated. According to Kaiser (1974) and Hair et al. (2019), KMO values between .60 and .80 are recommended. Bartlett's Test of Sphericity assessed whether the dataset significantly deviated from the identity matrix, which is necessary for factorial analysis (Bobbitt, 2019). Furthermore, oblique rotation, specifically Promax, was employed assuming correlated data (Abdi, 2003). Each item was required to have a minimum loading factor of .50, and factors needed Eigenvalues more significant than 1. Following EFA, Confirmatory Factor Analysis (CFA) was conducted to confirm discriminant and convergent validity, as well as to assess model fit using various indices.

These fit indices included: Normed Chi-squared value ideally between 1 and 3, with a Model Chi-Square p-value > .05 (Gaskin & Lim, 2016); Comparative Fit Index (CFI) ≥ .90 (Hu & Bentler, 1999); Root Mean Square of Approximation (RMSEA) ≤ .05 indicating good fit, and .05 – .08 indicating acceptable fit (Fabrigar et al., 1999); and Standardized Root Mean Square Residual (SRMR) < 0.08 (Gaskin & Lim, 2016).

Findings/Results

Content Validity Ratio and Content Validity Index of the Teacher's Spiritual Leadership Questionnaire

After analyzing the qualities of Spiritual Leaders, as stated by Fry (2003), and interviews with experts, 23 items were developed. Vision comprises six (6) items (VS1 to VS6) with CVR values ranging from .60 to .87. All items meet the minimum threshold for validity, with VS4 achieving the highest CVR (.87), indicating substantial agreement among experts regarding its relevance. The overall CVI for Vision is .73, which is interpreted as valid. These results demonstrate that the construct is adequate for capturing the intended concept of vision.

Hope/faith consists of seven (7) items (HF1 to HF7) with CVR values ranging from .60 to .87. HF2 achieved the highest CVR (.87), reflecting the most substantial consensus among experts. The overall CVI for Hope/Faith is .73, which validates its content as relevant and coherent. The construct strongly aligns with its theoretical basis, making it suitable for measuring aspects of Hope and Faith.

The altruistic Love construct includes ten (10) items (AL1 to AL10), with CVR values consistently meeting the validity threshold, ranging from .60 to .87. Four items (AL2, AL4, AL7, and AL8) obtained the highest CVR values (.87), indicating robust expert agreement. The CVI for Altruistic Love is .75, the highest among the three constructs, signifying its slightly greater level of consistency and refinement.

The results indicate that all three constructs—Vision, Hope/Faith, and Altruistic Love—demonstrate strong content validity based on expert evaluations. The CVI values exceeding .49 for all constructs confirm their adequacy for further research. Among these, Altruistic Love achieved the highest CVI, suggesting that it is the most refined and consistent construct regarding item relevance and clarity.

Table 2. Content Validity Ratio and Content Validity Index of Vision, Hope/Faith, and Altruistic Love

Kaiser-Meyer-Olkin (KMO) and Bartlett's Test

The results of the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity are presented in Table 3. These tests were conducted to determine the suitability of the data for factor analysis.

The KMO value is .976, which exceeds the recommended threshold of .50, indicating that the sampling is highly adequate for factor analysis. This suggests that the items included in the analysis are well-suited for revealing underlying structures (Nkansah, 2018).

Bartlett’s Test of Sphericity yielded a chi-square value of 7279.625, with 153 degrees of freedom (df) and a significance level (Sig.) of .000. The significance level indicates that the correlations between variables are sufficiently strong and significant for factor analysis, rejecting the null hypothesis of an identity matrix (Analysis INN, 2020).

Table 3. KMO and Bartlett's Test

Exploratory Factor Analysis

During the initial run of the EFA, certain items did not meet the minimum loading factor requirement of .50 (VS6, HF6, and HF7), while others exhibited cross-loadings (AL9 and AL10). Consequently, these items were excluded from further analysis. After five items had been deleted, the EFA was rerun (Samuels, 2017). The Exploratory Factor Analysis (EFA) results in Table 4 identified three distinct factors. Factor 1 exhibited high loadings from items related to Hope/Faith (HF4, HF3, HF1, HF2, and HF5), with factor loadings ranging from .663 to .784. For factor 2, Altruistic Love (AL1, AL3, AL5, AL6, AL2, AL4, AL8, and AL7), with loadings between .659 and .867. Factor 3 demonstrated strong associations with the Vision items (VS1, VS4, VS3, VS2, and VS5), with factor loadings ranging from .535 to .789. These loadings indicate the grouping of items under their respective factors.

Additionally, the uniqueness values for each item are presented in the final column. Uniqueness refers to the proportion of each item's variance not explained by the extracted factors. Lower uniqueness values (closer to 0) indicate that the factor model captures a large portion of the variance, while higher values (closer to 1) suggest that the item is less well-represented by the factors. In this analysis, uniqueness values ranged from .085 (AL1) to .298 (AL7), indicating that the model adequately explains most items.

Table 4. Exploratory Factor Analysis

Explained Variance by Each Factor

The exploratory factor analysis revealed a three-factor structure. The three extracted factors explained a total of 82.86% of the variance, indicating a strong factor solution. Specifically, Factor 1 accounted for 30.03%, Factor 2 for 27.57%, and Factor 3 for 25.25% of the total variance. These values exceed the commonly accepted threshold for social science research, supporting the robustness of the scale’s dimensionality.

The eigenvalues for the three factors after rotation were 4.425, 4.007, and 3.613, respectively, all exceeding the Kaiser criterion (eigenvalue > 1).

Table 5. Total Variance Explained

Model Fit Summary

The interpretation of model fit indices provides insights into the adequacy of the hypothesized model. Due to the sensitivity of the chi-square statistic to sample size, the normed chi-square (CMIN/DF) is also considered. The CMIN/DF value of 3.07 falls within the acceptable range of 1 to 3, indicating an acceptable balance between model fit and parsimony (Kline, 2018). The Comparative Fit Index (CFI) value of .962 exceeds the threshold of .95, reflecting an excellent fit and strong alignment of the model with the observed data (Fan et al., 1999; West et al., 2012). Similarly, the Standardized Root Mean Square Residual (SRMR) value of .026, being well below the threshold of .08, suggests minimal discrepancies between the observed and predicted correlations, further supporting excellent model fit (Diamantopoulos & Siguaw, 2000). Although the Root Mean Square Error of Approximation (RMSEA) value of .072 is slightly above the recommended threshold of .06, it still falls within an acceptable range, indicating a minor misfit that does not significantly compromise the model's overall adequacy (MacCallum et al., 1996). Additionally, the 90% Confidence Interval for RMSEA is [.063, .081], which provides further support that the misfit remains within an acceptable boundary. Finally, the PClose value of .06, greater than .05, indicates a reasonably well-fitting model (Joreskog & Sorbom, 1996). These indices collectively demonstrate that the model achieves an acceptable level of fit, balancing goodness-of-fit with theoretical parsimony.

Table 6. Model Fit Summary

Confirmatory Factor Analysis Loading Factors

The interpretation of factor loadings reveals insights into the relationships between observed variables and their respective latent factors. Many variables exhibit high factor loadings (e.g., HF4 = .898, VS3 = .858), signifying strong relationships and reliability as indicators of their constructs. However, some variables, like AL1 with a factor loading of .657, demonstrate moderate but acceptable contributions to their latent constructs.

In addition to the standardized loadings, all freely estimated items showed statistically significant Z-values (Critical Ratios), ranging from 14.424 to 27.949, with corresponding p-values less than .001, indicating that each observed variable significantly contributes to the underlying factor. HF4, AL3, and VS5 were fixed to 1.000 as reference indicators to identify the latent constructs; hence, no Z or p-values are reported for them.

Generally, factor loadings above .70 are considered strong (Statistics Solutions, 2013), those between .50 and .70 are moderate but acceptable (Hair et al., 2019), while loadings below .50 are often considered weak and may warrant exclusion. In this case, no factor loadings fall below the acceptable threshold, reinforcing the robustness of the measurement model.

Table 7. Confirmatory Factor Analysis

Master Validity Measures

The results of the validity measures confirm the strength of the measurement model (Gaskin & Lim, 2016). The Composite Reliability (CR) values for all constructs exceed the recommended threshold of .70, indicating strong internal consistency: Hope/Faith = .933, Altruistic love = .945, and Vision = .909. These high CR values suggest that the constructs are reliably measured.

Regarding convergent validity, the Average Variance Extracted (AVE) values for all constructs are above the .50 threshold, confirming that each construct effectively explains the variance in its indicators. Specifically, Hope/Faith and Altruistic Love have very high AVE values of .998, while vision has a slightly lower AVE of .888, still indicating good convergent validity.

For discriminant validity, the Maximum Shared Variance (MSV) values for all constructs are lower than their respective AVE values, supporting the distinctness of the constructs. Specifically, Hope/Faith (AVE = .998, MSV = .735), Altruistic Love (AVE = .998, MSV = .683), and Vision (AVE = .888, MSV = .668) show that the constructs are not overly correlated. Additionally, the diagonal values (square roots of AVE) in the correlation matrix are greater than the off-diagonal correlations, further confirming discriminant validity. The inter-construct correlations are as follows: Hope/Faith and Altruistic Love (.751), Hope/Faith and Vision (.792), and Altruistic Love and Vision (.761).

Finally, the Maximum Reliability (MaxR(H)) values are above .90 for all constructs, showing excellent reliability: Hope/Faith = .937, Altruistic love = .951, and Vision = .917. These values reinforce the robustness of the measurement model. In addition, the MaxR(H) is greater than CR, showing excellent construct validity (Nadeem et al., 2020).

Table 8. Master Validity Measures

Reliability Test

The Cronbach's Alpha values for the components of Spiritual Leadership demonstrate a high level of internal consistency, indicating reliable measurement tools for assessing each construct. For the Vision component, Cronbach's alpha value of .907 reflects a strong level of internal consistency among the items measuring this dimension. Similarly, the Altruistic Love component achieves an alpha value of .94, signifying excellent reliability and strong correlation among the items. The Hope/Faith component also exhibits excellent reliability, with a Cronbach's Alpha value of .92, confirming consistent measurement across its items.

Generally, a Cronbach's Alpha value of .70 or higher is considered acceptable in most research settings as a threshold for internal consistency (Cho & Kim, 2014). The fact that all three components exceed this benchmark underscores their high reliability. The measurement tools are dependable and can produce consistent results across repeated applications.

The term "reliable" implies that the instruments used to measure Vision, Altruistic Love, and Hope/Faith consistently capture these constructs and can be trusted to yield accurate and stable results in future testing.

Table 9. Reliability Test

Discussion

The findings demonstrate that the constructs Vision, Hope/Faith, and Altruistic Love exhibit strong content validity based on expert evaluations. Content Validity Indices (CVI) of 0.73, 0.73, and 0.75, respectively, were all interpreted as valid. Among the items, those with the highest Content Validity Ratios (CVR), such as VS4 (0.87), HF2 (0.87), and AL2, AL4, AL7, and AL8 (all 0.87), reflect a robust consensus among experts regarding their relevance. Overall, the instrument provides a solid foundation for measuring these constructs, supporting its use in further research, though additional psychometric testing is recommended to enhance its reliability and applicability.

Furthermore, the findings demonstrate that the dataset is appropriate for conducting factor analysis, as evidenced by the exceptionally high KMO value of 0.976 and the significant result of Bartlett's Test of Sphericity (p < .001) (Noor et al., 2015). The KMO value suggests a very high level of inter-item correlation, which supports the extraction of factors (Georgina & Yemisi, 2014). Similarly, Bartlett’s Test confirms that the data possess adequate relationships for factorization. These results provide robust statistical justification for proceeding with exploratory or confirmatory factor analysis to confirm the latent dimensions within the dataset. The EFA results confirm the multidimensionality of the measured constructs, with items clustering into three distinct factors. Factor 1 pertains to the construct of Hope/Faith, with all items exhibiting loadings surpassing the 0.5 threshold. Factor 2 represents Altruistic Love, characterized by uniformly strong item loadings above 0.5. Factor 3 corresponds to the Vision dimension, with most items demonstrating substantial loadings. On the other hand, according to Field (2013), the minimum loading factor for EFA is 0.3. Hence, the results validate the construct structure and provide evidence for the study's theoretical framework.

CFA was conducted to refine the result of EFA (Brown, 2015). The evaluation of model fit indices suggests that the hypothesized model demonstrates an acceptable and robust fit to the data. The normed chi-square (CMIN/DF) value of 3.07 indicates a satisfactory balance between fit and parsimony (Kline, 2023). The CFI (0.962) and SRMR (0.026) further confirm an excellent model fit, with both exceeding conventional thresholds (Bentler, 1990). Although the RMSEA (0.072) slightly exceeds the ideal cutoff, it remains within an acceptable range, indicating only a minor misfit. The PClose value of 0.06 supports this conclusion, suggesting that the model fits the data reasonably well (MacCallum et al., 1996). In summary, these indices provide strong evidence of the model's adequacy and theoretical soundness. In addition, the CFA results indicate that most observed variables exhibit strong loadings (>0.70), which supports the validity of the model (Statistics Solutions, 2013). It demonstrates that the indicators are suitable for measuring their respective latent constructs. Nevertheless, AL1's lower factor loading might suggest minor measurement issues or weaker alignment with its construct. While this variable is still within the acceptable range, further review and potential rewording or revision may strengthen its contribution.

The model's strength is highlighted by consistently high loadings across variables, particularly for HF4, AL6, and VS2, which are robust representations of their respective factors. These findings underline a well-specified model with reliable measures. Combined with the model fit indices confirming overall adequacy, the CFA results prove construct validity. While minor adjustments to specific variables, like AL1, may enhance reliability further, the observed variables generally function effectively as indicators of the latent constructs they represent (Kline, 2016).

The measurement model meets all the essential validity criteria, confirming the reliability and distinctiveness of the constructs. The high Composite Reliability (CR) values for all constructs indicate that the measures have strong internal consistency, ensuring that their indicators adequately represent the constructs (Analysis INN, 2020). The high Average Variance Extracted (AVE) values further affirm the convergent validity of the constructs, as they capture a significant amount of variance in their indicators (Mehmetoglu, 2015). Hope/Faith and Altruistic Love show the highest AVE values, reflecting their strong alignment with their respective indicators. Although vision has a slightly lower AVE, it remains within an acceptable range, ensuring good convergent validity.

Discriminant validity is well-established, as indicated by the MSV values being lower than the AVE values and the square root of AVE being greater than the inter-construct correlations. It confirms that each construct is distinct and measures a unique concept, which is essential for the robustness of the model (Gaskin & Lim, 2016).

TheMaximumReliability (MaxR(H)) values are allabovethe 0.70 threshold, demonstratingexcellentreliabilityforallconstructs (Matovu& Aluvala, 2022). It reinforces the stability of the measurement model and ensures that the constructs provide consistent measurements across different samples. The measurement model demonstrates strong reliability, convergent validity, and discriminant validity. The findings indicate that the constructs are adequately measured and distinct from one another, making them suitable for further structural analysis. No significant refinements are needed, as the values exceed the recommended thresholds, confirming the model's robustness.

The high reliability scores for all three components of Spiritual Leadership indicate that the constructs are measured with strong internal consistency. This robustness enhances the validity of subsequent analyses and ensures that interpretations involving these variables are based on reliable data. These findings underscore the effectiveness of the survey instrument used to measure Spiritual Leadership, with high Cronbach's Alpha values supporting its suitability for academic research and practical applications, such as leadership studies and organizational development programs.

Furthermore, these findings contribute meaningfully to the literature on spiritual leadership by aligning with and extending the theoretical framework outlined by Fry (2003). Compared to previous studies, the robust factor structure, high composite reliability, and solid convergent and discriminant validity not only support the multidimensional nature of spiritual leadership but also highlight the importance of a values-based approach in modern educational leadership research.

From a practical perspective, the validated instrument offers valuable applications for school administrators, teacher training programs, and policymakers. It can serve as a diagnostic tool to assess leadership qualities in educational settings and guide professional development initiatives aimed at nurturing transformational and spiritually grounded leadership practices. The tool’s applicability in teacher development programs is particularly promising, as it provides insights into aspects such as vision and altruistic love that are crucial for creating nurturing and effective learning environments.

While the results support the validity and reliability of the instrument, it is important to consider how cultural and educational contexts might influence its applicability and interpretation. Spiritual leadership constructs like altruistic love or hope may be perceived differently across cultures due to varying norms, values, and educational systems. For instance, in collectivist cultures, expressions of altruism may be more implicit or community-centered, while in individualist cultures, they may be more explicit and personal. Similarly, students from highly hierarchical educational systems may interpret vision and influence differently from those in more egalitarian classrooms. Therefore, further validation across diverse cultural and educational settings is essential to ensure the instrument's broader applicability and contextual relevance.

While the instrument's reliability is evident, future studies could explore additional psychometric properties, such as construct, convergent, and discriminant validity. Examining these aspects would further validate the scale's robustness and provide a more comprehensive understanding of its effectiveness in measuring Spiritual Leadership.

Conclusion

This study developed and validated a Spiritual Leadership Questionnaire grounded in Fry’s (2003) theoretical framework, demonstrating strong psychometric properties, including high content validity, internal consistency, and robust construct validity. The successful factor analyses confirm the multidimensional nature of the constructs—Vision, Hope/Faith, and Altruistic Love—and support the theoretical underpinnings of spiritual leadership. While most items loaded strongly on their respective factors, minor refinements to items such as AL1 may further improve precision.

Beyond statistical validation, this instrument offers a significant contribution to the Field of educational leadership by providing a reliable and context-specific tool for assessing spiritual leadership from the perspective of students. It fills a gap in measuring intangible leadership qualities that are often overlooked in traditional leadership assessments.

Practically, the tool can be used by school administrators to assess and strengthen leadership behaviors that foster purpose, compassion, and ethical grounding in educational settings. It may be integrated into teacher training and professional development programs to cultivate spiritually grounded educators and leaders. Furthermore, policymakers may find the instrument useful in evaluating leadership qualities aligned with holistic and values-driven education.

Future research should expand the scope of validation by testing the instrument across diverse cultural and institutional contexts, including different education levels or private institutions. Longitudinal studies could explore how spiritual leadership influences student outcomes, teacher motivation, and school climate over time. Additionally, teachers or administrators could adapt the instrument for self-assessment to offer further insights into leadership development.

In sum, the validated questionnaire not only advances scholarly work on spiritual leadership but also holds strong potential for meaningful application in both academic research and educational practice.

Recommendations

Further psychometric testing, including longitudinal stability and measurement invariance, is encouraged to confirm robustness across demographics. Testing the instrument in diverse educational and cultural settings is also suggested to validate its broader applicability. Future research could explore additional constructs like job satisfaction and teacher-student relationships to provide a more comprehensive understanding. Combining quantitative findings with qualitative insights and conducting longitudinal studies could establish causal relationships and deepen practical implications. Cross-cultural comparisons and further validation through confirmatory factor analyses are essential to ensure international applicability. Additionally, the findings should guide the development of training programs for educational leaders, emphasizing Vision, Hope/Faith, and Altruistic Love to improve organizational outcomes. Finally, investigating the long-term impacts of these constructs could offer insights for strategic planning and sustainable development in education.

Limitations

The study respondents were from a single university, which may limit the generalizability of the findings to other institutions. Future research could consider a broader sample across multiple universities to enhance the study’s applicability and robustness.

The author thanks the Campus Director of NEUST Gabaldon Campus for granting permission to conduct this study on campus. Sincere appreciation is also extended to the students who actively participated in the study, contributing valuable insights and data.

Conflict of interest

The author declares no conflict of interest.

Ethics Statement

All ethical standards for conducting research involving human participants were strictly observed throughout the study. Prior to data collection, approval was obtained from the NEUST – Gabaldon Campus administration. Informed consent was implied through voluntary participation, and students were assured of the confidentiality and anonymity of their responses. Participation was entirely voluntary, with no penalties for non-participation or withdrawal. The use of Google Forms ensured secure and private data submission, and no identifying information was collected. The study adhered to institutional and ethical guidelines to protect the rights and welfare of all respondents.

Generative AI Statement

The author of this article utilized Grammarly to review and correct grammatical errors. Following this process, I conducted a thorough review and verification of the final version. As the author, I take full responsibility for the content of the published work.