Happiness Engineering: impact of hope-based intervention on life satisfaction, self-worth, mental health, and academic achievement of Indian school students

Abstract

This research article explores the impact of an intervention aimed at enhancing hope among senior secondary school students. The investigation was carried out in the Bokaro district of Jharkhand, India. The study focuses on the changes observed in scores on the Children Hope Scale (CHS), Students’ Life Satisfaction Scale (SLSS), and Self-Worth Sub-Scale (SWS) to assess the effectiveness of this intervention. The results reveal significant increases in hope, life satisfaction, and self-worth among the participants, aligning with prior research emphasizing the positive outcomes of interventions that foster goal-directed thinking and hope. The intervention’s strengths-oriented approach empowers students to identify and pursue meaningful goals, which leads to increased life satisfaction. The limitations of this investigation emphasize the need for future research with larger and more diverse samples to enhance the generalizability of the findings. The study encourages educators and clinicians to consider strengths-based approaches for fostering positive development among school students and creating a more conducive learning environment. The research emphasizes the importance of addressing psychological constructs in educational settings, as fostering hope, life satisfaction, and self-worth can have long-lasting effects on students’ well-being and academic performance. The study serves as a valuable contribution to the field of positive education, paving the way for further research on the effectiveness of similar interventions in diverse educational contexts. Additionally, understanding how different stakeholders perceive the benefits of such interventions can guide the design of future studies, making a significant impact on students’ psychological development.

Keywords:

• Strengths-based approach
• student well-being
• positive psychology intervention
• goal-directed thinking
• positive education
• psychological development

Reviewing Editor:

• John Chiang, Communication and Media, State University of New York, United States

Subjects:

• Teacher Education & Training
• School Classroom Management
• Teaching & Learning
• Middle School Education
• Educational Change & School Reform
• School Effectiveness & Improvement
• Teacher Training
• Teachers & Teacher Education
• Classroom Practice
• Schools & Schooling

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Correction

1. Introduction

Snyder’s hope theory goes beyond mere wishful thinking and delves into a profound understanding of how our cognitive processes influence our behaviors in a positive and adaptive way (Rand & Cheavens, 2009; Snyder et al., 2002). According to Snyder and his colleagues, hope is a human strength that can be observed through three crucial capacities: goals thinking, pathways thinking, and agency thinking (Snyder et al., 2000, 2003). Goals thinking refers to the ability to clearly conceptualize and set meaningful objectives for ourselves (Snyder, 2002; Snyder et al., 2005, 2002). It is about identifying what we want to achieve in life, whether they are big life goals or smaller daily targets (Chang, 2003; Lopez et al., 2004; Steen, 2004). While young people often have goals in mind, they might not always be fully defined or well-refined (C. Snyder et al., 2008; C. R. Snyder, 2005). Pathways thinking is the capacity to devise specific strategies and plans to achieve those goals (Snyder et al., 2002). It involves strategic planning and thinking about the various routes, alternatives, and action steps that can lead to goal attainment (Cheavens et al., 2019). By visualizing these pathways, individuals gain a sense of direction and purpose, which fuels their determination to move forward despite facing obstacles along the way (Munoz et al., 2020). Agency thinking is the motivation and willpower to initiate and sustain the use of those strategies (Dursun, 2021). It encompasses self-efficacy, which is the belief in one’s ability to carry out the necessary actions to reach the desired goals (Cheavens et al., 2019). It also involves self-determination and a sense of control over one’s actions and destiny (Corrigan & Schutte, 2023). Agency thinking empowers individuals to overcome challenges, maintain focus, and persistently work towards their aspirations (Duncan et al., 2022). The interesting thing is that pathways thinking and agency thinking are interdependent and positively correlated, but they are distinct and do not have the same meaning (Cheavens et al., 2019). When combined, they form the essential elements of hopeful thinking. The presence of both components fosters a sense of hopefulness that propels individuals forward in their pursuits (Munoz et al., 2020). Hope, in this context, refers to the relatively stable and enduring subjective evaluations of one’s capabilities to achieve their goals across different situations (Chan et al., 2019). It is a profound optimism that motivates individuals to persevere through challenges and empowers them to believe in their ability to shape their future through purposeful actions (Corn et al., 2020). Snyder’s hope theory emphasizes the intrinsic motivation and proactive mindset that individuals naturally embrace when approaching their objectives with hopeful thinking (Bryce et al., 2020). By cultivating hopeful thinking, people become more resilient and open to embracing new opportunities, turning obstacles into stepping stones towards their aspirations (Ozyilmaz, 2020). Importantly, hope is not restricted to specific circumstances or fleeting moments (Todorov et al., 2019). Instead, it represents a relatively stable and consistent evaluation of one’s capabilities across various life situations (Sulimani-Aidan et al., 2019). It is a positive mindset that can have a transformative impact on how individuals approach challenges and opportunities in their lives (S. C. Marques & Lopez, 2017). Snyder’s hope theory highlights the significance of hopeful thinking, which includes setting clear goals, strategizing effective pathways to achieve those goals, and having the motivation and determination to take action (Gallagher & Lopez, 2018). It is a powerful force that can drive individuals to overcome obstacles and pursue their aspirations with confidence (L. M. Edwards & McClintock, 2018). Hope is not limited to particular moments; instead, it represents an enduring evaluation of one’s capabilities in various situations (Peila-Shuster, 2016). Understanding and cultivating hopeful thinking can lead to personal growth, resilience, and success in diverse areas of life (Lee & Gallagher, 2018). Hope represents a fundamental aspect of human thinking and behavior that deeply influences how people approach challenges and pursue their life’s ambitions (Dixson et al., 2017). When individuals cultivate hope, it can lead to a more positive outlook on life and develop the resilience needed to overcome adversities and work towards achieving their dreams (C. R. Snyder, 2000). Snyder’s hope theory explores the enduring nature of hope in human cognition, which significantly impacts how individuals tackle challenges and perceive opportunities across various aspects of life (Chang & DeSimone, 2001). People with high levels of hope are more likely to embrace challenges with a sense of optimism, view setbacks as temporary obstacles, and persistently strive towards their goals (C. R. Snyder, 2000). Snyder’s hope theory goes beyond wishful thoughts and emphasizes the importance of intentional cognitive processes that result in adaptive actions (Onwuegbuzie & Snyder, 2000). By understanding the interplay between goals-thinking, pathways thinking, and agency thinking, individuals can cultivate hope and resilience in their pursuit of meaningful objectives (C. R. Snyder et al., 2002). Hope is not just an abstract concept; instead, it acts as a driving force that empowers individuals to navigate challenges and seize opportunities with confidence (Klausner et al., 2000). It leads to a more fulfilled and purposeful life, as individuals become more open to new possibilities and are better equipped to deal with life’s ups and downs (S. J. Lopez et al., 2003). Empirical research consistently demonstrates that hope is a significant predictor of critical outcomes in various domains, such as physical and mental health, academic achievements, and athletic accomplishments (Gallagher et al., 2019). People with elevated hope levels tend to achieve their goals more successfully and experience more positive emotions (Valle et al., 2006). On the other hand, those with low hope levels may encounter difficulties in overcoming obstacles and are more likely to experience negative emotions (C. R. Snyder, 1995). Studies show that hopeful thinking in children and adolescents positively correlates with feelings of competence, self-esteem, and self-worth, while being negatively associated with symptoms of depression (C. Snyder et al., 2006). Moreover, higher levels of hope are linked to enhanced scholastic and social competence, further underscoring the positive effects of hope on various aspects of human functioning (Cheavens et al., 2006). Recent investigations suggest that hope can even act as a buffer against the adverse effects of acute negative life events, highlighting its significance in fostering emotional well-being and overall life satisfaction (C. R. Snyder & Rand, 2003). Understanding and nurturing hope as a vital psychological resource can offer valuable insights and strategies for promoting positive development and resilience in individuals of diverse age groups and life contexts (Idan & Margalit, 2013). By understanding and applying hope theory, educators, psychologists, and policymakers can foster hope in individuals, enhancing their positive outlook and empowering them to confront challenges with tenacity and determination (Stobart, 2012). This approach has profound implications for promoting resilience and success across diverse contexts and age groups, ultimately fostering a society that embraces hope as a potent force for personal growth and thriving (Juntunen & Wettersten, 2006). To impart hopeful thinking and its transformative potential to students, various studies and interventions have been conducted. These sources, such as works by S. J. Lopez et al. (2009), McDermott and Snyder (1999), McDermott and Snyder (2000), C. Snyder et al. (2002), and C. R. Snyder and Lopez (2001), offer valuable guidance in empowering students with the cognitive tools necessary for developing hopeful thinking. Research on hope-centered interventions has shown promising results across diverse samples (C. Snyder, 2004). These interventions have led to significant increases in hope, purpose in life, self-esteem, and subjective well-being, along with decreases in symptoms of depression and anxiety (C. R. Snyder, 2014). Additionally, interventions targeting children and adolescents have successfully increased their levels of hope, demonstrating the effectiveness of such approaches in different contexts (Helland & Winston, 2005). The current study aims to evaluate the effectiveness of a 12-week hope-based intervention in senior secondary school students hailing from Bokaro district of Jharkhand, India; and its impact on hope, life satisfaction, self-worth, mental health, and academic achievement. One of the authors of this papers have designed and developed a subject – Happiness Engineering (https://www.ashrafalam.co.in/Happiness-Engineering), where Hope is one of its modules. That module was used as an intervention in this study. What makes this study unique is its comprehensive, broad-scale approach to hope intervention, which actively involves key stakeholders such as parents, teachers, and school peers. Recognizing their pivotal role in shaping children’s hope, the study acknowledges the importance of ecological factors within their social-familial and institutional context. This collaborative approach seeks to create a more holistic and enduring impact on students’ hope and related behaviors. By modifying the broader educational environment and involving parents, teachers, and school peers, the study aims to establish a supportive, nurturing setting that fosters the development and sustenance of hope among senior secondary school students of India. Overall, the study contributes to the growing body of research on hope theory and its application in educational contexts. It emphasizes the importance of intentional cognitive processes and the role of various ecological factors in cultivating hopeful thinking among students. The findings have implications for designing interventions that promote positive development and resilience, ultimately leading to more fulfilled and purposeful lives for young individuals. This is a replicative study originally carried out by S. C. Marques et al. (2011) in Portuguese community school setting. This study is greatly inspired by the work of S. C. Marques et al. (2011). This investigation is an attempt of discovering whether the findings of S. C. Marques et al. (2011) is still relevant, and whether will it be the same or may show a slight or totally opposite findings. This study has a different setting, different sample, different hope intervention, and different demographics. However, what is same is the methodology of conduction of the research. Same measures/tools have been used to keep this research as close to the original research carried out by S. C. Marques et al. (2011) as possible so that the findings can be compared. It is intended that the outcome of this research will add newer dimensions to the existing body of knowledge on hope-based intervention in educational settings.

2. Methods

2.1. Participants

This research aimed to investigate the impact of hope-based intervention (S. C. Marques et al., 2011) on senior secondary school students hailing from Bokaro district of Jharkhand, India. Convenient sampling technique was used to select the schools. Only those schools that consented to perform this intervention were part of this investigation. A total of 146 participants were included in the study, and they were evenly divided into two groups: a hope group (HG) consisting of 73 students and a matched comparison group (CG) with 73 students. All participants were either in standard 11 or 12. The majority of the participants were male, accounting for 69% of the sample. The mean age of the participants was 17.03 years, with a range between 16 to 19 years, and a standard deviation of 1.29. This indicates that the sample was relatively homogeneous in terms of age. The study employed a quasi-experimental design with a matched comparison group. From a larger pool of 215 students, the researchers carefully selected the participants to ensure that the two groups were closely matched. The matching process aimed to minimize any confounding variables that could potentially affect the results. The researchers considered various important variables such as age, gender, school year, hope, mental health, life satisfaction, self-worth, and academic achievement. The goal was to make sure that the two groups were as similar as possible at the beginning of the study.

To evaluate the effectiveness of the intervention and its long-term effects, the participants completed assessments at four different time points. These time points were: pre-intervention (baseline assessment) (Time-1/T-1), post-intervention (right after the 12-week intervention) (Time-2/T-2), 1-month follow-up (Time-3/T-3), and 2-month follow-up (Time-4/T-4). The researchers used these assessments to measure changes in hope, life satisfaction, self-worth, mental health, and academic achievement over time. Remarkably, there was no drop-out of participants at the post-assessment stage, meaning that all 146 participants completed the assessments after the intervention was completed. However, as time passed and the follow-up assessments were conducted, some participants did not complete the later assessments, leading to attrition rates. At the 1-month follow-up, the intervention group had 68 students remaining (attrition rate of 6.85%), while the comparison group had 69 students remaining (attrition rate of 5.48%). By the 2-month follow-up, the intervention group had 64 students left (attrition rate of 12.33%), and the comparison group had 61 students left (attrition rate of 16.44%). The attrition rates indicate the percentage of participants who dropped out of the study over time (Goertzen, 2017). It is common for longitudinal studies to experience attrition as participants may have various reasons for not continuing with the study, such as moving away or personal circumstances (Lazaraton, 2005). The study aimed to evaluate the effects of the hope-based intervention (S. C. Marques et al., 2011) on senior secondary school students and tracked their progress over time through multiple assessments. The researchers took great care in ensuring the matching of the comparison group and analyzing the data despite the attrition rates at later follow-ups to draw meaningful conclusions about the intervention’s effectiveness. In the research study, some eligible students did not participate at the 2-month follow-up assessment. The researchers investigated the reasons for this non-participation to understand potential sample attrition. Among the students who did not participate from the intervention group, it was found that 54.53% had either changed the school or dropped out of the school. Similarly, in the comparison group, 83.21% of the students who did not participate at the 2-month follow-up had changed the school or dropped out of the school. The remaining 16.79% were absent on the data collection dates. To address the potential effects of sample attrition, the researchers conducted t-tests to compare the mean scores on hope, life satisfaction, self-worth, mental health, and academic achievement between the students who completed all four assessments and those who were lost to attrition (Time 1–4). The t-tests are statistical analyses that help determine if there are significant differences between two groups (Oflazoglu, 2017). The results of the t-tests showed no statistically significant differences between the two groups, suggesting that attrition did not have a significant impact on the variables of interest. This is a crucial finding as it indicates that the loss of some participants over time did not bias the results of the study. In addition to the student participants, the study also involved two secondary groups: parents and teachers associated with the intervention group. The researchers collected data from 48 parents and 13 teachers. Among the parents, the majority (81.3%) were female. All teachers in the study were married, and 73.2% of them were female. The researchers’ inclusion of parents and teachers in the study added valuable perspectives to the research. Gathering data from these stakeholders provided insights into the impact of the hope-based intervention from multiple angles. The involvement of parents helped shed light on how the intervention might have influenced students’ home environments and family dynamics. On the other hand, input from teachers offered valuable information about the intervention’s effects within the school setting and its potential implications for classroom dynamics and academic performance. Overall, the study employed rigorous methods to ensure sample comparability and accounted for sample attrition in its analysis. The inclusion of parents and teachers enhanced the understanding of hope-based interventions in the educational context and contributed to the comprehensive evaluation of the intervention’s effectiveness and potential long-term impacts. By considering multiple perspectives and addressing potential biases, the researchers strengthened the validity and reliability of their findings.

2.2. Measures

In this investigation, the researchers focused on understanding the impact of a hope-based intervention (S. C. Marques et al., 2011) on students of standards 11 and 12. To do so, they utilized measures of several crucial psychological constructs, including hope, life satisfaction, self-worth, and mental health. These constructs have been extensively studied in the existing literature and are known to significantly influence the well-being and development of young individuals (C. R. Snyder, Feldman, Taylor, Schroeder, & Adams III, 2000; Huebner, 2004; Riesen & Porath, 2004). By incorporating these constructs in the study, the researchers aimed to gain a comprehensive understanding of positive thinking and its effects on psychological development during this critical stage of life. Hope, in particular, played a central role in the investigation. It represents a key psychological construct that influences individuals’ attitudes towards goal pursuit and resilience in the face of challenges. The concept of hope, as studied in this context, goes beyond mere wishful thinking. It involves a cognitive understanding of deliberate processes that lead to adaptive behaviors (C. R. Snyder et al., 2000). By focusing on hope, the researchers sought to explore its effects on various psychological outcomes in the context of the hope-based intervention. Life satisfaction, another crucial construct examined in the study, has been extensively linked to overall positive development (Huebner, 2004; Proctor et al., 2009; Veenhoven, 1996). It refers to an individual’s cognitive evaluation of their life as a whole, encompassing various domains such as family life, friendships, academic achievements, and self-perception (Kapteyn et al., 2010; Proctor et al., 2017). Research consistently shows that higher levels of life satisfaction are associated with favorable adaptive outcomes and overall psychological well-being (J. N. Edwards & Klemmack, 1973; Huebner, 1991a; Rojas, 2006). By including life satisfaction in the study, the researchers aimed to understand its potential relationship with the hope-based intervention and its role in fostering positive development among the participating students. Self-worth is another important construct under investigation. It pertains to how individuals perceive and value themselves, their abilities, and their worth as individuals (Crocker & Wolfe, 2001). A positive self-worth (Crocker et al., 2006; Pelham & Swann, 1989) is associated with enhanced self-esteem (Orth & Robins, 2014; Zeigler-Hill, 2013) and confidence (Möbius et al., 2014), which can have positive effects on various aspects of a person’s life. In this study, understanding the changes in self-worth among the participants would provide valuable insights into how the hope-based intervention impacts their self-perception (Bem, 1972; Fazio, 2014) and confidence (Benabou & Tirole, 2000). Mental health (Prince et al., 2007) is yet another critical construct examined in the investigation. It encompasses an individual’s emotional, psychological, and social well-being. Positive mental health (Keyes & Lopez, 2009) is associated with higher levels of resilience, the ability to cope with stress, and overall life satisfaction. By assessing mental health in the study, the researchers aimed to gain a comprehensive understanding of the intervention’s effects on participants’ emotional wellbeing (Barry et al., 2017; Charles, 2010) and overall mental health (Katz & Davison, 2014). The decision to incorporate these constructs in the study was well-founded, given their significance in the literature and their potential to shed light on the positive outcomes of the hope-based intervention. By measuring and analyzing hope (Idan & Margalit, 2013), life satisfaction (Kapteyn et al., 2010), self-worth (Crocker & Wolfe, 2001), and mental health (Katz & Davison, 2014) in the context of the intervention, the researchers aimed to provide a robust and comprehensive evaluation of its effects on the participating students. Such investigations are essential to further our understanding of positive development (Zarrett & Lerner, 2008) in young individuals and to identify effective strategies for fostering their well-being (Pollard & Lee, 2003; Stiglic & Viner, 2019) and resilience (Ager, 2013; Condly, 2006; Masten, 2014). By focusing on these constructs, the researchers contributed to the growing body of knowledge on hope theory (Cheavens, Feldman, Woodward, et al., 2006; Cheavens et al., 2019; Corn et al., 2020) and its application in educational settings, potentially providing valuable insights for future interventions and programs aimed at enhancing positive psychological development among youth. Self-worth, also commonly referred to as self-esteem (Randal et al., 2015), is a fundamental aspect of social and cognitive maturation during adolescence (Harter, 2015). It pertains to how individuals evaluate their own worth and value, influencing their perceptions of themselves and their abilities (Crocker et al., 2004). In the context of this study, self-worth represents an important psychological construct that can significantly impact participants’ responses to the hope-based intervention. Adolescence is a critical period of identity development (Ragelienė, 2016), where individuals are actively shaping their self-concept (Shavelson et al., 1976) and self-perceptions (Horn, 2004). High self-worth has been consistently associated with various positive outcomes, such as better academic achievements, healthier relationships, and improved psychological well-being. By assessing self-worth in this study, the researchers aim to explore its potential connection with hopeful thinking and investigate its role in shaping how participants respond to the intervention. Mental health, another focal construct in the study, encompasses various dimensions of individual adjustment and functioning. Positive indicators of mental health are linked to enhanced psychological well-being and overall positive development (Ware, 1993). This construct takes into account a person’s emotional, psychological, and social well-being, reflecting their ability to cope with challenges and maintain a positive outlook on life. By examining mental health in the context of this study, the researchers seek to gain valuable insights into how the hope-based intervention may influence participants’ emotional well-being (Schutte et al., 2002) and adaptive coping mechanisms (Aldwin & Revenson, 1987). Mental health is particularly relevant during adolescence, as it is a period of increased vulnerability to various stressors and emotional challenges. By understanding the effects of the intervention on mental health, the study aims to shed light on how hopeful thinking can positively impact participants’ emotional resilience and overall well-being. The inclusion of self-worth and mental health as focal constructs in the investigation is well-grounded in previous research and theoretical frameworks. Numerous studies have demonstrated the importance of self-worth in shaping adolescents’ behaviors, attitudes, and academic achievements. Likewise, mental health has been recognized as a critical aspect of overall well-being and positive development. By focusing on these constructs, the researchers aim to contribute to the existing body of knowledge concerning positive psychological development (Luthans et al., 2010) in early adolescence and the potential benefits of targeted interventions in this domain. To ensure the accuracy and reliability of the data collected, the study employs translated and validated metrics for assessing self-worth and mental health. Translated measures ensure that participants can provide responses in their native language, reducing potential language-related biases and misunderstandings. Validated metrics have been rigorously tested and demonstrated to be reliable indicators of self-worth and mental health, ensuring the robustness of the data collected. By using such measures, the study ensures that the assessment of these constructs is both comprehensive and culturally sensitive. The decision to include self-worth and mental health as focal constructs in the investigation is based on a solid foundation of empirical evidence and theoretical frameworks.

2.3. Children Hope Scale

The Children Hope Scale (CHS) is a psychometric tool developed by C. R. Snyder et al. (1997) with the specific purpose of assessing hopeful thinking in children. This instrument is designed to measure what is known as “dispositional hope,” which refers to an individual’s enduring tendency to engage in positive thinking and maintain optimistic attitudes towards their goals and aspirations (Dixson, 2017; Valle et al., 2004). The CHS consists of six items, each presented as an affirmative statement. Participants are asked to rate their responses to each item on a 6-point scale, where 1 indicates “none of the time” and 6 represents “all of the time.” This scaling system allows for nuanced responses, enabling participants to express the extent to which they experience hopeful thoughts and attitudes. The scale is built to capture two main components of hopeful thinking: pathways thinking and agentic thinking. Pathways thinking centers on a person’s belief in their ability to generate multiple ways or strategies to achieve the things they value in life. For instance, one of the items assessing pathways thinking is “I can think of many ways to get the things in life that are most important to me.” This component reflects the idea that hopeful individuals can envision creative and diverse paths towards their goals. They have a sense of resourcefulness and adaptability, allowing them to explore different avenues in their pursuit of what matters most to them. On the other hand, agentic thinking focuses on a person’s sense of agency and self-belief in their capabilities to perform competently and achieve their objectives. An example of an item assessing agentic thinking is “I am doing just as well as other kids of my age.” This aspect of hopeful thinking reflects an individual’s confidence in their abilities to take effective actions to pursue their goals successfully. It highlights their belief in their competence and capacity to make a difference in their own life and in achieving their goals. Each participant’s total score on the CHS can range from 6 to 36, with higher scores indicating higher levels of hope. In other words, a higher score suggests that the individual tends to engage in more hopeful thinking and displays greater optimism in their approach to goal pursuit. The CHS has been specifically tailored for students to ensure that it is age-appropriate and relevant to their developmental stage. By utilizing this psychometric tool, researchers and educators can gain valuable insights into the hopefulness of young individuals, which can be instrumental in understanding their attitudes towards their aspirations and the challenges they encounter in life. The Children Hope Scale (CHS) used in this study is labelled as “Questions About Your Goals,” indicating that it prompts respondents to provide answers based on their general responses across various life situations. This approach allows researchers to gauge the overall dispositional hope of the participants, considering how they approach goal-directed thinking in different contexts. In other words, the CHS is designed to assess the participants’ enduring tendency to engage in hopeful thinking and maintain optimistic attitudes towards their goals and aspirations, not just in specific situations but in their overall outlook on life. To ensure that the scale is a valid and reliable measure of hope in adolescents, previous research has investigated its psychometric properties. These studies have shown satisfactory internal consistencies, which refer to the extent to which the items in the scale are consistent in measuring the construct of interest. The internal consistency for the total score of the CHS has been reported to range from .72 to .86 in different studies (S. J. Lopez et al., 2003; C. R. Snyder et al., 2003; C. R. Snyder & Rand, 2003). These values indicate that the scale is reliable and consistent in measuring hopeful thinking among adolescents. The higher the internal consistency value, the more reliable the scale is in assessing the construct it aims to measure. Moreover, the CHS has undergone validation for use with Portuguese children, demonstrating its applicability and reliability in different cultural contexts. In this validation study, the scale yielded a Cronbach’s alpha of .81 for the total score (S. C. Marques, Pais-Ribeiro, & Lopez, 2009). Cronbach’s alpha is a statistical measure used to assess the internal consistency of a scale, with values closer to 1.0 indicating higher reliability (Tavakol & Dennick, 2011). The Cronbach’s alpha of .81 for the CHS in the context of Portuguese children further supports the robustness of the scale as a measure of hope in diverse populations.

2.4 Students’ Life Satisfaction Scale

The Students’ Life Satisfaction Scale (SLSS) is a self-report instrument developed by Huebner (1991c), specifically designed to evaluate individuals’ overall satisfaction with life (Seligson et al., 2003). It is tailored to assess life satisfaction among young individuals, encompassing various domains such as family life, friendships, school experiences, and general well-being (Huebner, 1991b). The scale prompts respondents to reflect on their recent thoughts and feelings over the past few weeks, offering insights into their overall level of life satisfaction during that specific period (Huebner, 1994). The SLSS comprises seven items, each presented as an affirmative statement. Participants are asked to rate their level of agreement with each statement on a 6-point scale, where 1 represents “strongly disagree” and 6 signifies “strongly agree.” By providing their responses to each item, respondents indicate the extent to which they agree or disagree with the presented statements regarding their life satisfaction. The scale covers a diverse range of aspects that contribute to overall life satisfaction, allowing researchers to gain a comprehensive understanding of participants’ subjective well-being. After collecting responses from participants, researchers sum the scores of the seven items to obtain a comprehensive index of life satisfaction for each individual. The resulting scale scores can range from 7 to 42, with higher values indicating elevated levels of overall life satisfaction. This scale provides a numerical representation of life satisfaction, allowing for easy comparison and analysis across different groups and time points. One noteworthy feature of the SLSS is its robust internal consistency. Internal consistency refers to the extent to which the items within the scale consistently measure the same underlying construct (Boyle, 1991; Streiner, 2003; Vaske et al., 2017). It ensures that the items in the scale are cohesive and effectively capture the concept of life satisfaction. In the original study conducted by Huebner in 1991, the reported internal consistency for the SLSS was .82, indicating a strong reliability of the scale in assessing life satisfaction among adolescents. Subsequently, in a follow-up exploratory study conducted by Dew and Huebner (1994), the internal consistency was further supported with a reported value of .86, reaffirming the scale’s reliability in subsequent research. These high internal consistency values provide confidence in the scale’s ability to accurately measure life satisfaction. In a study conducted by Marques et al. in 2007, the internal consistency was assessed (S. C. Marques et al., 2007). The results of this validation study reported a Cronbach’s alpha of .89, which further confirms the scale’s appropriateness and reliability when applied to different cultural contexts. Cronbach’s alpha is a statistical measure used to assess the internal consistency of a scale, with values closer to 1.0 indicating higher reliability (Connelly, 2011). The high Cronbach’s alpha value of .89 suggests that the items within the SLSS effectively measure the same underlying construct of life satisfaction, regardless of the participants’ cultural background. This cross-cultural validation supports the notion that the scale is applicable and reliable in assessing life satisfaction across diverse populations.

2.5 Global self-worth Sub-scale

The Self-Worth Sub-Scale (SWS) is one of the six sub-scales that constitute Harter’s Self Perception Profile for Children (SPPC), a highly regarded self-report measure designed to assess children’s domain-specific evaluations of their competence, as well as their global perception of self-worth (Harter, 1985; S. C. Marques et al., 2011). The SWS aims to capture the extent to which adolescents holds positive sentiments towards themselves as an individual, providing a comprehensive appraisal of their personal value and self-worth (Granleese & Joseph, 1994b; Muris et al., 2003). The SWS is designed to assess self-worth, which refers to their overall evaluation of themselves as individuals and their perception of their own worthiness and value (Granleese & Joseph, 1993; Van Dongen-Melman et al., 1993). It encompasses how youths see themselves in terms of their abilities, attributes, and personal characteristics (Boivin et al., 1992). By focusing on self-worth, the SWS provides valuable insights into an adolescent’s self-esteem and self-concept, which are crucial aspects of their psychological well-being and development (Meijer et al., 2008). One of the key advantages of the SWS is its versatility in administration, as it can be employed both in group settings and on an individual basis (Granleese & Joseph, 1994a). This flexibility makes it a valuable tool for researchers, educators, and clinicians working with children in various contexts, such as schools, clinical settings, or research studies (Eapen et al., 2000). To ensure the accuracy and validity of the responses, the developers of the SWS (Harter, 1985) adopted a novel approach to its design. They aimed to minimize the potential confounding impact of socially desirable responses that can influence self-concept scales. Social desirability bias refers to the tendency of individuals to respond in a way they believe is socially acceptable or desirable, rather than providing honest and accurate answers. In the SWS, the adolescent is initially presented with two sentences that reflect different self-perceptions. These statements are carefully constructed to represent opposing views about self-worth. For example, one item might read: “Some individuals admire the person they are, but others often wish they were someone else.” The individual is then required to select the sentence that more closely aligns with their own self-perception. This forced-choice format encourages the adolescent to consider their self-concept more deeply and make a choice between the two contrasting statements. Subsequently, the chosen sentence is further assessed for its degree of truthfulness, and the adolescent is asked to categorize it as either “sort of true” or “really true.” This additional step provides a fine-grained assessment of the adolescent’s self-perception, allowing for a nuanced understanding of their self-worth. Each item in the SWS is scored on a 4-point scale. A score of 4 indicates a high level of self-worth, reflecting an adolescent who generally holds positive feelings about themselves and has a strong sense of personal value. On the other hand, a score of 1 denotes a low level of self-worth, suggesting that the adolescent may struggle with self-esteem and have less positive feelings about their value as an individual. The use of a 4-point scale allows for a range of responses, offering a more sensitive measurement of self-worth. It avoids a simple binary choice, providing more depth to the adolescent’s self-reported feelings of self-worth. The scale’s design takes into account the developmental stage of the participants, ensuring that it is age-appropriate and comprehensible for adolescents. One critical aspect of any psychometric instrument is its internal consistency, which refers to the extent to which the items within a sub-scale consistently measure the same underlying construct. In the context of the SWS, internal consistency ensures that the items within the sub-scale are tapping into the same concept of self-worth. Harter’s earlier data from 1985 provides evidence of acceptable internal consistency for the SWS (Harter, 1985). The reported internal consistency values for the SWS ranged from .78 to .84. These values suggest a high degree of reliability in assessing individual’s self-worth, indicating that the items within the sub-scale are highly consistent in measuring the construct of interest. The SPPC, which includes the SWS as one of its sub-scales, has undergone validation for use with Portuguese children in a study conducted by Alves-Martins et al. (1995). The purpose of this validation study was to examine the appropriateness and reliability of the SPPC as a self-report measure for assessing the self-concept. The findings of the study revealed a Cronbach’s alpha of .62 for the entire Self Perception Profile for Children, which includes the SWS. The reported Cronbach’s alpha value provides an estimate of internal consistency for the entire scale and is a measure of how closely related the items are to each other within the scale. The value of .62 indicates a moderate level of internal consistency for the entire scale, which is generally acceptable for research purposes. While the Cronbach’s alpha for the entire Self Perception Profile for Children is reported, it can also be inferred that the individual sub-scales, including the SWS, would contribute to this overall level of internal consistency. Since the SWS is a part of the Self Perception Profile for Children, it is reasonable to assume that it shares in contributing to the overall internal consistency of the instrument in this study. This finding confirms the reliability and suitability of the SWS for use and affirms that the items within the sub-scale consistently measure the same underlying construct of self-worth.

2.6 Mental health Inventory—5

The Mental Health Inventory-5 (MHI-5) is a concise version of the Mental Health Inventory, initially developed in 1975 as part of the “Rand Health Insurance Experiment”. It has been included in two versions of the Medical Outcome Study (MOS) questionnaires: the MOS Short Form 20 (SF-20) developed by Stewart, Hays, and Ware Jr, (1988), and the MOS Short Form 36 (SF-36) created by Ware Jr and Sherbourne, (1992). The MHI-5 is a crucial component of the SF-36 Health Survey questionnaire, which is a valuable generic instrument used to assess respondents’ perceived health state and life quality, making the MHI-5 particularly significant in understanding mental well-being. The main objective behind formulating the MHI-5 was to improve upon previous instruments by incorporating aspects that specifically gauge psychological well-being. The creators, Veit and Ware (1983), aimed to develop an inventory suitable for utilization in the general population, enabling quick and efficient assessment of mental health (Veit & Ware, 1983). The MHI-5 comprises a succinct set of five inquiries, each designed to probe the respondent’s mood and emotional state over the preceding month. For instance, one of the questions is “How much of the time, during the last month, have you been a happy person?” These five questions effectively assess psychological well-being and the absence of psychological distress, offering valuable insights into an individual’s mental health. To complete the MHI-5, respondents provide their answers on a 6-point rating scale, ranging from “all of the time” to “none of the time.” This scale allows individuals to indicate the frequency with which they experience certain emotions or feelings over the specified time frame. The scores that can be attained on the MHI-5 range from 6 to 30, with higher values indicating enhanced mental health and greater psychological well-being. A higher score on the MHI-5 suggests that the individual has experienced more positive emotions and is generally in a better mental state. In terms of reliability, the MHI-5 has shown satisfactory internal consistency reliability coefficients for the five items encompassing the SF-36 scale. Internal consistency reliability refers to the extent to which the items within a scale consistently measure the same underlying construct. The MHI-5 has been found to have reliable and consistent responses among respondents, indicating that the items effectively measure the construct of psychological well-being. The MHI-5 is a valuable tool in the assessment of mental health, as it provides a concise and quick means of gauging psychological well-being and the absence of psychological distress. Its inclusion in the SF-36 questionnaire allows for a comprehensive evaluation of both physical and mental aspects of health, contributing to a more comprehensive understanding of an individual’s overall well-being and quality of life. Due to its brevity and efficiency, the MHI-5 is well-suited for use in research studies and clinical settings, enabling practitioners to gain valuable insights into the mental health status of individuals and identify potential areas for intervention and support. The coefficients reported by "SF-36 health survey. Manual and interpretation guide," (1993) and Ware et al. (1996) for the Mental Health Inventory-5 (MHI-5) range from .67 to .95. These coefficients represent the internal consistency reliability of the MHI-5, which indicates the extent to which the items in the inventory consistently measure the same underlying construct of mental health. Internal consistency and reliability assess the degree to which the items in the MHI-5 are measuring a single, cohesive concept of mental health. Coefficients closer to 1.0 indicate higher internal consistency, suggesting that the items in the inventory are highly related and effectively measure the same construct. On the other hand, coefficients closer to 0.0 indicate lower internal consistency, implying that the items may not be strongly related and may not be effectively measuring the intended construct. In the case of the MHI-5, the coefficients ranging from .67 to .95 indicate that the items are sufficiently related and reliably measure mental health. Furthermore, the MHI-5 has undergone validation in a study conducted by S. C. Marques et al. (2009). During this validation study, the researchers calculated Cronbach’s alpha for the MHI-5, resulting in a value of 0.82. Cronbach’s alpha is another measure of internal consistency, similar to the coefficients reported by Ware (1993; Ware et al., 1996). A Cronbach’s alpha of .82 for the MHI-5 in the study by S. Marques, Ribeiro, and Lopez et al. (2009) indicates a high level of internal consistency within the inventory. The high Cronbach’s alpha value (.82) obtained in the validation study further confirms the reliability and appropriateness of the MHI-5 for assessing mental health in children. It suggests that the items in the MHI-5 are internally consistent, and together, they effectively measure the construct of mental health.

2.7. Academic achievement

The academic achievement (AA) data for the students were collected from their school transcripts. School transcripts are official records that contain detailed information about a student’s academic performance over a specific period, typically a school year or a semester (Buckley, 2011; Perkins, 2004). These records include grades received in various subjects and provide a comprehensive overview of the student’s scholastic progress and performance (Kim et al., 2015). The grades recorded in the school transcripts represent the student’s performance in different subjects, such as Mathematics, Physics, History, Political Science, Chemistry, and Geography, and other subjects like Technological, Visual, Physical, and Musical Education (Sanchez & Buddin, 2015). Each subject is typically graded on a numerical scale or letter grade system, depending on the educational institution’s grading policy (Rosen et al., 2017). To calculate the academic achievement score for each student, the numerical values of their grades in all the subjects are added up (Boleslavsky & Cotton, 2015). For instance, if a student receives an 80 in History, 90 in Mathematics, 85 in Physics, and so on, the scores from all the subjects are summed together. Once the sum of the scores from all subjects is obtained, it is then divided by the total number of subjects to calculate the average score for that student. This average score serves as a representation of the student’s overall academic performance throughout the current grade or academic year. The academic achievement score typically falls within a possible range, which is determined by the grading system used by the school. In the explanation provided, the range is specified to be from 1 to 5. Different schools or educational systems may have varying grading scales, but in this context, the score can be interpreted such that a score of 1 indicates the lowest level of academic achievement, suggesting that the student’s performance in their subjects is significantly below the expected standard. It implies that the student may be struggling or facing challenges in their studies. A score of 5 signifies the highest level of academic accomplishment, indicating that the student’s performance is outstanding and well above the expected standard. A score of 5 suggests that the student has excelled in their studies and has achieved excellent grades in various subjects. Scores between 1 and 5 represent varying degrees of academic performance, with higher scores indicating higher levels of achievement and lower scores indicating lower levels of achievement. The academic achievement data obtained from the students’ school transcripts provide valuable information about their scholastic performance in various subjects throughout the current grade or academic year. By analyzing this data, researchers and educators gain insights into the students’ academic progress and performance in different subject areas, helping them identify areas of strength and areas that may require additional support or attention. This information can be used to tailor educational interventions and support strategies to meet the individual needs of students and promote their academic success.

2.8. Procedure

The process of obtaining approval and consent for data collection in the study involved several important steps to ensure ethical and legal compliance, as well as the voluntary participation of both students and their parents. The researchers sought approval from authorities of the two participating schools. This step is crucial as it ensures that the research is conducted in accordance with the school’s policies and guidelines, and it helps to establish a positive working relationship between the researchers and the schools. Explicit consent was obtained from the parents of potential participants before their child could take part in the research. To secure parental consent, a detailed letter was sent to the parents, explaining the objectives of the study and the procedures involved. This letter provided all the necessary information for the parents to make an informed decision about their child’s participation. Parents were asked to sign and return a consent form indicating their willingness to allow their child to take part in the study. In addition to parental consent, the researchers sought explicit consent from the students themselves to participate in the study. This step is essential, especially for older students who have the capacity to understand the purpose of the research and make a voluntary decision to participate. Students were informed about the study, its objectives, and what would be required of them if they chose to participate. They were also assured that their participation was voluntary and that they could withdraw at any time without facing any negative consequences. Once consent was obtained from both parents and students, the participants were organized into groups consisting of 30 to 45 students. The size of the groups was determined based on practical considerations such as the available space within each school and the presence of adult facilitators to ensure that the students clearly understood the instructions and could complete the measures confidentially. The data collection process involved administering various psychological scales on all the participants. Before administering the scales, a demographic survey was completed to gather basic information about the participants, such as age, gender, and caste. The psychological scales were then administered in a counterbalanced order to avoid potential bias. Counterbalancing ensures fairness by rotating the order in which the measures are presented to different participants. Research assistants were present during the administration sessions to address any questions or concerns that the students might have and to ensure the confidentiality of their responses. Research assistants played a crucial role in creating a comfortable and supportive environment for the participants, which contributed to more accurate and reliable data collection. The data collection process was not limited to a single time point. Instead, the researchers collected data at four time points to assess changes over time and the long-term effects of the intervention. At each subsequent time point, consent was sought again from the students to continue their participation in the study. The study included intervention groups that met after school for a series of five-days-a-week sessions, each lasting 60 minutes. The intervention program aimed to provide students with specific tools and strategies to foster hopeful thinking and positive psychological development. During the first week of the intervention, teachers and parents of the intervention group also participated in a one-hour session. Involving teachers and parents in the intervention process enhanced the effectiveness of the intervention and promoted consistent support for the students. To maintain the integrity of the study, there was no communication between the parents and the teachers of the intervention group with those of the comparison group. This measure was implemented to prevent any potential influence or contamination between the two groups, ensuring that any observed differences in outcomes could be attributed to the intervention itself. Obtaining approval and consent for data collection in the study involved a comprehensive and systematic approach. Ethical considerations were given top priority to ensure that all participants, both students and their parents, were fully informed about the study and provided voluntary consent to participate. The use of research assistants, counterbalancing of measures, and inclusion of teacher and parents’ involvement all contributed to the robustness and validity of the study’s findings. By following these rigorous procedures, the researchers conducted a well-designed and ethical study that provided valuable insights into the impact of the intervention on students’ psychological well-being and academic achievement.

2.9. About the intervention program

The intervention program was meticulously designed and conducted in a group format, aimed to equip students with essential skills to achieve four key objectives. The first objective of the intervention program was to help students set clear and well-defined goals for themselves. Clear goals provide a sense of direction and purpose, giving students a better understanding of what they want to achieve in their lives. By clarifying their goals, students become more focused and motivated in the pursuit of their life’s objectives. The second objective was to encourage students to generate a wide range of potential pathways to reach their goals. The program aimed to foster creative and flexible thinking, allowing students to explore various strategies and approaches to achieve their goals. By considering multiple pathways, students become more adaptable and resilient when faced with challenges or obstacles. The third objective focused on helping students develop the cognitive and emotional strength required to sustain their pursuit of goals. This involves instilling a sense of self-belief and self-efficacy in students, empowering them to take initiative and persevere despite setbacks or difficulties. By cultivating cognitive and emotional strength, students are more likely to remain hopeful and resilient in the face of adversity. The fourth objective aimed to assist students in reframing perceived insurmountable obstacles as challenges that can be overcome. Often, individuals may encounter obstacles that seem overwhelming or impossible to overcome. By helping students reframe these obstacles as challenges, the program sought to promote a positive mindset that fosters problem-solving and determination. The decision to implement the intervention in a group setting was rooted in the theoretical premise that it provides opportunities for students to interact, share experiences, and support each other, which can significantly impact their behaviors. There was a total of 60 sessions in this intervention program. The intervention program was concluded in 12 weeks. Each session was of one hour in length. The hope-based intervention was greatly inspired by the ‘Hope’ module of the subject of ‘Happiness Engineering’ (https://www.ashrafalam.co.in/Happiness-Engineering) designed and developed by the first author of this article. The program started with an introduction to the concept of hope theory and its implications for positive outcomes in students’ lives. Engaging activities were employed to familiarize the students with the vocabulary and concepts used in the hope model. These activities included role-playing hope-filled scenarios, discussions, and group exercises, which allowed students to gain a deeper understanding of hope and its relevance to their lives. Focus of the hope-based intervention program was on helping participants develop a comprehensive understanding of the components of hope, namely goals, pathways, and agency. Goals represent the desired outcomes that students aim to achieve, pathways refer to the different strategies and routes they can take to reach their goals, and agency involves their belief in their ability to initiate and sustain goal-directed actions. The ensuing sessions addressed the identification and handling of potential obstacles that students might encounter on their journey towards their goals. By addressing obstacles, the program aimed to equip students with problem-solving skills and a proactive approach to overcoming challenges. During the different sessions, students were encouraged to identify their own personal goals that were meaningful and achievable. Personalizing the goals allowed students to connect more deeply with the intervention and foster a sense of ownership and responsibility for their aspirations. The program’s group format, engaging activities, and step-by-step approach created a supportive and empowering environment for students. By fostering a sense of hope and providing students with valuable tools and strategies, the intervention sought to contribute positively to their psychological well-being, academic achievement, and overall development. Towards last 7-8 sessions, participants practiced applying the hope model further. The discussions centered around refining personal goals to make them more specific, positive, and clearly defined. By refining the goals, students gained a better understanding of what they wanted to achieve and how to approach their aspirations. Additionally, students created multiple pathways and identified agency thoughts for each goal. The concept of pathways refers to the various strategies and routes students can take to reach their goals, while agency involves their belief in their ability to initiate and sustain goal-directed actions. The “goal enhancer worksheet” was used as a tool to aid this process, guiding students in exploring different pathways and fostering agency in their thinking. In the entire intervention program, the focus was on helping students identify and cultivate “hopeful talk.” The hope model was reinforced during these sessions, and the students’ personal workable goals were reviewed. Each participant’s progress was tracked and integrated into a personal hope story, which they recorded in a “Hope Buddy Journal.” The idea of a “Hope Buddy Journal” allowed students to reflect on their experiences and progress over time, reinforcing hopeful thinking and self-efficacy. Towards the end, participants were given the opportunity to share their personal hope stories with the group and collaboratively plan for their future steps. Through evaluation and discussion with their “hope buddy” and the rest of the group, the students explored potential next steps in their journey of hopeful thinking. These sessions allowed for the integration and application of the learned concepts into real-life scenarios, promoting the transfer of skills and strategies to their everyday experiences. This session was designed to allow students to select personal goals that were relevant to their lives. These goals were diverse and could include enhancing academic performance, improving interpersonal relationships, participating in extracurricular activities, or pursuing personal interests and passions. By allowing students to choose their goals, this session promoted a sense of ownership and investment in their journey of hopeful thinking. The foundation of the intervention program was rooted in theoretical work by C. R. Snyder (1994) and supported by applied research from various sources, including McDermott and Snyder (1999), Cook et al. (2002), C. Snyder et al. (2002), S. Lopez et al. (2000), S. J. Lopez et al. (2000), Alam and Mohanty (2023a, 2023b, 2023c), Alam and Mohanty (2023), and S. C. Marques et al. (2011). This theoretical grounding ensured that the program’s content was based on empirical evidence and established principles in the field of hopeful thinking. To create a comprehensive and impactful approach, the session effectively integrated solution-focused, narrative, and cognitive-behavioral techniques. Solution-focused techniques emphasized identifying solutions and building on strengths, narrative techniques helped students create and understand their personal stories, and cognitive-behavioral techniques promoted cognitive restructuring and positive thinking patterns. The session also utilized structured activities, role-playing, and guided discussions to engage students and facilitate learning. These activities were thoughtfully incorporated into the program’s design to ensure a holistic and impactful approach to fostering hopeful thinking in the students. Moreover, it carefully accounted for and controlled various factors that could potentially influence its outcomes. These factors included the amount of attention received, the level of group cohesion and social support, the discussions centered around hope components, the opportunity to share thoughts and feelings with peers, and the active involvement of participants in the activities during the session. By considering and managing these factors, the program aimed to create a supportive and empowering environment for students, conducive to the development of hopeful thinking and positive psychological outcomes. There was a total of 60 sessions in this intervention program. Each of the 60 sessions commenced with a 10-minute segment dedicated to modeling and cultivating enthusiasm for the program, while reinforcing the ideas and concepts previously learned. This brief period helped set the tone for the session and motivated students to actively participate in the activities and discussions. The intervention group attended a series of sixty 1-hour sessions, each led by either of the four trained psychologists that was recruited for conducting the hope-based intervention program. The four hired psychologists received a detailed and structured manual from the researchers along with 10 hours of didactic training that was conducted over a 14-day period to ensure consistency and standardization in delivering the intervention. Assessment measures were administered to the participants in the intervention group at the beginning of the program (1^st session) and at the end of the 12-week period (60^th session). This allowed researchers to evaluate the impact of the program on the students’ levels of psychological well-being. In contrast, the comparison group only received the same assessment measures at the beginning and end of the 12-week period without any intervention during this time. By comparing the results of the intervention and comparison groups, researchers determined whether the intervention program had a significant effect on the students’ positive psychological outcomes. The impact of the intervention program was not limited to the students alone. Thoughtfully developed manuals were also provided for parents and teachers. By involving parents and teachers in the process, the program aimed to create a collaborative and consistent approach to nurturing happiness in the students’ lives. In three separate 2-hour sessions, the content of the manuals was presented to the parents of the intervention group participants. Each group consisted of 12 to 14 individuals, allowing for an interactive and personalized approach to understanding the importance of learning the subject of Happiness Engineering and its relevance in the students’ lives. Participants utilized the Hope Scale developed by Snyder to assess their own levels of hope. This exercise allowed parents and teachers to gain insights into their own hope levels and served as a basis for further exploration of hopeful thinking. The second segment focused on fostering hopeful relationships within the family or classroom environment. This segment emphasized the importance of supportive and positive relationships in promoting hopeful thinking among students. The final segment of the manual provided basic strategies to enhance hope in everyday life. These strategies were practical and easy to implement, allowing parents and teachers to actively cultivate hope in themselves and in their children or students respectively. Overall, the intervention program was carefully constructed to ensure standardization in the delivery of the intervention and to consider the impact on participants’ positive psychological outcomes.

3. Results

In this study, the statistical analyses were conducted using SPSS version 16, a widely used software for statistical analysis. Before proceeding with the analyses, the researchers conducted some preliminary checks on the data to ensure its quality and suitability for statistical testing. The first step was to examine the distribution of the data to determine if it followed a normal distribution. A normal distribution is a bell-shaped curve where most data points cluster around the mean, and the distribution is symmetrical. This is an essential assumption for many statistical tests. If the data did not meet this assumption, the researchers might have needed to consider using non-parametric tests or transformations to ensure the validity of the results. Fortunately, after examining the data, researchers found that it approximately followed a normal distribution, which was ideal for the planned statistical analyses. Next, the researchers checked for significant outliers in the data. An outlier is a data point that is significantly different from the rest of the data, and it can have a considerable influence on statistical results. The researchers wanted to ensure that no outliers were present in the data that could potentially bias the findings. Upon conducting this check, they found no significant outliers, which meant they did not have to apply any data transformations to account for extreme values. Another critical check the researchers conducted was to assess the effectiveness of the matching procedure used to create the intervention and comparison groups. The matching procedure aimed to ensure that both groups were comparable at the beginning of the study, minimizing any initial differences that could confound the results. The researchers compared the two groups on all the variables of interest at the initial time point (Time 1) and found no significant differences between them. This outcome indicated that the matching procedure was successful, and any subsequent group differences observed during the study were more likely to be attributed to the effects of the intervention rather than initial group disparities. To provide an overview of the data, the researchers calculated the means and standard deviations for the dependent variables (hope, life satisfaction, self-worth, mental health, and academic achievement) across different groups and time points. Means represent the average score of a variable within each group, while standard deviations indicate the variability or spread of scores around the mean. These descriptive statistics are compiled in Table 1, which serves as a reference to understand the central tendencies and dispersion of the data for each variable. To investigate the relationships between hope and the other variables of interest at the initial time point (Time 1), the researchers conducted correlation analyses.

Table 1. M and SD on DVs by groups (I and C) and times (T-1 through T-4).

Measure		T-1		T-2		T-3		T-4
	Group	M	SD	M	SD	M	SD	M	SD
Children Hope Scale	I	19.49	4.87	27.17	3.98	27.19	3.97	26.58	3.24
	C	19.17	4.21	21.92	4.07	21.13	4.24	20.21	3.21
Students’ Life Satisfaction Scale	I	28.56	5.78	33.43	4.39	33.83	5.13	32.42	4.56
	C	28.13	5.24	28.29	4.75	29.51	4.89	29.28	4.82
Self-Worth Sub-Scale	I	16.34	2.87	20.69	1.83	20.06	2.46	19.58	2.16
	C	16.98	2.14	17.02	2.03	17.79	2.62	18.34	2.68
Mental Health Inventory-5	I	21.89	4.05	23.94	3.38	24.34	3.27	22.39	3.12
	C	22.03	4.18	22.08	3.79	23.23	3.54	21.49	2.96
Academic Achievement	I	4.01	.72	3.96	.69	4.17	.70	4.34	.69
	C	3.98	.81	3.59	.62	4.02	.69	4.17	.57

M: Mean; SD: Standard Deviation; DV: Dependent Variable; I: Intervention; C: Comparison.

Correlation analyses assess the degree and direction of association between two or more variables. The researchers found significant positive correlations between hope and various measures: life satisfaction [r(146) = .54, p < .01], self-worth [r(146) = .53, p < .01], mental health [r(146) = .48, p < .01], and academic achievement [r(146) = .36, p < .01]. These correlation coefficients indicate the strength and direction of the relationship between hope and each of the other variables. In this case, higher levels of hope were associated with higher levels of life satisfaction, self-worth, mental health, and academic achievement. The significance levels (p-values) indicate that these correlations were unlikely to occur by chance and suggest meaningful associations between hope and the other variables. In this phase of the study, the researchers aimed to explore potential group differences in hope, life satisfaction, self-worth, mental health, and academic achievement over time. To achieve this, they employed several statistical techniques to analyze the data obtained from the intervention and comparison groups at different time points (T-1 through T-4). One of the main statistical tests used was repeated measures ANOVA (Analysis of Variance). This test is suitable when measuring the same participants at multiple time points or under different conditions. In this study, the assessment points (T-1 through T-4, i.e., pre-test, post-test, 1-month follow-up, and 2-month follow-up) were treated as the within-subjects dependent variables, while the treatment condition (I vs. C, i.e., intervention vs. comparison) was considered the between-subject factor. The repeated measures ANOVA allowed the researchers to compare the two groups at different time points (T-1 through T-4) to examine if there were any significant group differences over time in hope, life satisfaction, self-worth, mental health, and academic achievement. By using this test, they could determine if the intervention had an impact on these variables over time and if there were any significant changes within each group. Additionally, to identify specific time points where there might be significant differences between the intervention and comparison groups, the researchers conducted independent sample t-tests. These tests compared the means of the two groups at each measurement occasion, allowing the researchers to detect any significant group differences at individual time points. Furthermore, to assess changes within each group from the initial baseline, the researchers employed paired sample t-tests. These tests allowed them to compare the means of the intervention and comparison groups at time T-1 with their respective means at post-test, 1-month follow-up, and 2-month follow-up. By conducting paired sample t-tests, the researchers could determine if there were any significant changes within each group over time. Setting the significance level (alpha) at .05 is a standard practice in statistical analysis to control for Type I errors. Type I errors occur when a researcher falsely rejects a true null hypothesis (i.e., when they incorrectly identify a difference as statistically significant). By setting the alpha level at .05, the researchers ensured that there was a 95% probability that the observed results were not due to chance. In other words, to establish statistical significance, the probability of obtaining the observed results by chance had to be less than 5%. This helps minimize the risk of reporting false-positive results and provides greater confidence in the study’s findings.

3.1 Children hope scale (CHS)

The results of the statistical analysis using repeated measures ANOVA provided valuable insights into the impact of the intervention on the Children Hope Scale (CHS) scores over time. We here break down the findings and their significance. The interaction effect between the group (intervention vs. comparison) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up) was found to be statistically significant. An interaction effect occurs when the effect of one independent variable (in this case, the group, i.e., intervention or comparison) on the dependent variable (CHS scores) is influenced by the levels of another independent variable (time points). In this study, the significant interaction effect suggests that the two groups (intervention and comparison) showed different patterns of change in hope levels as the study progressed over time. This implies that the intervention had a notable impact on the participants’ hope levels compared to the comparison group. Wilks’ Lambda is a multivariate test statistic that indicates the degree of effect of the independent variable (group) on the dependent variable (CHS scores) across the different time points. It ranges from 0 to 1, where a value closer to 0 indicates a stronger effect. In this case, the Wilks’ Lambda value of .81 is considered significant, as it is not close to 1, indicating that the intervention had a substantial impact on the participants’ hope levels (Figure 1).

Figure 1. Means of Children Hope Scale by group at times T-1 through T4.

A smaller Wilks’ Lambda value suggests that the intervention had a significant effect on the participants’ hope levels at different time points. The F-value is another test statistic used to determine whether there are significant differences between the groups at different time points. It compares the variance between the groups (due to the intervention) to the variance within the groups (individual differences and random variability). A higher F-value indicates a greater effect of the independent variable (group) on the dependent variable (CHS scores). In this case, the F-value of 3.00 is significant, confirming that there are indeed differences in hope levels between the intervention and comparison groups at various time points. This further supports the notion that the intervention had a meaningful impact on the participants’ hope levels. The p-value indicates the probability of obtaining the observed results by chance alone. It is a crucial measure in statistical analysis to assess the significance of the results. A p-value of .04 means that there is a 4% chance of obtaining the observed differences in hope levels between the intervention and comparison groups purely due to random chance. Since the p-value is less than the predetermined significance level (alpha = .05), the results are considered statistically significant. In other words, the observed differences in hope levels between the groups are unlikely to have occurred by chance alone, providing strong evidence for the effectiveness of the intervention. Thus, the results of the repeated measures ANOVA indicate that the intervention had a significant and positive impact on the participants’ hope levels over time. The interaction effect, Wilks’ Lambda value, F-value, and p-value all point to the intervention’s effectiveness in fostering hopeful thinking among the participants. These findings support the hypothesis that the program positively influenced the participants’ hope, which is an essential factor in shaping positive psychological development and overall well-being among adolescents. The p-value of less than .05 suggests that the results are statistically significant. In hypothesis testing, the significance level (alpha) is set to .05, which means that if the p-value is less than .05, the observed differences are unlikely to be due to random chance. In this case, the p-value being lower than .05 indicates that the differences in hope levels between the intervention and comparison groups at different time points are likely due to the intervention’s effect and not random variability. The effect size (partial eta squared) value of .08 is a measure of effect size that indicates the proportion of variance in the CHS scores that can be attributed to the group and time interaction. A larger partial eta squared value indicates a stronger effect. In this study, the value of .08 suggests that 8% of the variance in hope scores can be explained by the interaction between the group (intervention vs. comparison) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up). Effect sizes of .01 are generally considered small, .06 as moderate, and .14 as large. Therefore, the effect size of .08 is considered moderate, indicating that the intervention had a meaningful impact on the participants’ hope levels. The post-test analysis compared the intervention group’s hope scores after the intervention with their pre-assessment scores. The t-value of -4.33 and the p-value of less than .001 (two-tailed) indicate that the intervention group demonstrated a significant increase in hope levels after the intervention compared to their initial scores. The negative t-value indicates that the mean hope score of the intervention group significantly increased after the intervention. The researchers also conducted analyses at the 1-month and 2-month follow-up time points to assess whether the changes in hope levels observed immediately after the intervention were sustained over time. The t-values of -4.12 and -3.41, along with the corresponding p-values of .001 and .003 (both two-tailed), indicate that the intervention group continued to show significant increases in hope compared to their initial scores at both follow-up time points. These results suggest that the positive impact of the intervention on hope levels was maintained over time. In contrast to the intervention group, the comparison group, which did not receive the intervention, did not exhibit any significant changes in hope levels over time. The lack of significant changes in hope in the comparison group further supports the notion that the improvements in hope levels observed in the intervention group were indeed due to the effects of the intervention and not because of other external factors. In conclusion, the results of the statistical analysis demonstrate that the intervention had a positive and lasting impact on the participants’ hope levels. The intervention group showed significant increases in hope at multiple time points, while the comparison group remained relatively stable. These findings provide strong evidence for the effectiveness of the program in fostering hopeful thinking and enhancing the participants’ sense of optimism and agency.

3.2. Students’ life satisfaction scale (SLSS)

Wilks’ Lambda is a multivariate test statistic used in repeated measures ANOVA. It measures the effect of the independent variable (group: intervention vs. comparison) on the dependent variable (SLSS scores) across different time points (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up). A lower Wilks’ Lambda (Λ) value indicates a more robust effect. In this study, the value of .79 is considered statistically significant, indicating that the intervention had a noteworthy impact on the participants’ life satisfaction levels over time. The F-value of 2.72 is the test statistic used to determine whether there are significant differences in life satisfaction levels between the intervention and comparison groups at different time points. In this case, the F-value of 2.72 is statistically significant. The p-value of .05 indicates the probability of obtaining the observed results by chance alone. Since it is less than the standard significance level of .05, it suggests that the differences in life satisfaction levels between the groups at different time points are statistically significant. The observed differences are unlikely to be due to random chance, and there is a high probability that they are a result of the intervention. The partial eta squared value of .07 is a measure of effect size. It indicates the proportion of variance in the SLSS scores that can be attributed to the interaction between the group and time. A larger partial eta squared value suggests a stronger effect. In this case, the value of .07 suggests that 7% of the variance in life satisfaction scores can be explained by the interaction between the group and time. This effect size is considered moderate, indicating that the intervention had a meaningful impact on students’ life satisfaction levels. Thus, the repeated measures ANOVA provided significant insights into the impact of the intervention on students’ life satisfaction over time. The significant interaction effect indicates that the two groups (intervention and comparison) showed different patterns of change in life satisfaction throughout the study.

The statistically significant F-value and p-value further support the notion that there were indeed differences in life satisfaction levels between the intervention and comparison groups at various time points. Additionally, the moderate effect size indicates that the intervention had a meaningful impact on students’ life satisfaction, further highlighting the importance of the intervention in fostering positive changes in their well-being. The p-value of .05 indicates the probability of obtaining the observed results by chance alone. In hypothesis testing, researchers set a significance level (alpha) to determine if the results are statistically significant. The standard significance level is often set at .05, which means that to establish statistical significance, the probability of obtaining the observed results by chance should be less than 5%. In this study, the p-value is less than the standard significance level, suggesting that the results are statistically significant. Therefore, the differences in life satisfaction levels between the intervention and comparison groups at different time points are unlikely to be due to random chance. The partial eta squared (η²) value of .07 is a measure of effect size. It indicates the proportion of variance in the SLSS scores that can be attributed to the interaction between the group (intervention vs. comparison) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up). A larger partial eta squared value signifies a stronger effect. In this case, the value of .07 suggests that 7% of the variance in life satisfaction scores can be explained by the interaction between the group and time. An effect size of .07 is considered a moderate effect size, indicating that the intervention had a meaningful impact on students’ life satisfaction levels. The subsequent analysis examines the specific time points to identify significant differences in life satisfaction between the intervention and comparison groups. At post-test, the intervention group demonstrated a substantial increase in life satisfaction compared to their pre-assessment scores. The t-value of -4.51 and the p-value of less than .001 (two-tailed) confirm the statistical significance of this improvement. This means that the observed increase in life satisfaction in the intervention group is highly unlikely to be due to random chance. Similarly, at the 1-month follow-up and 2-month follow-up, the intervention group continued to show significant increases in life satisfaction compared to their initial scores. The t-values of -3.91 and -3.93, along with the corresponding p-values of less than .001 (two-tailed), further confirm the statistical significance of these improvements. In contrast, the comparison group did not exhibit any significant change in life satisfaction over time, suggesting that the comparison group, which did not receive the intervention, did not experience any noteworthy improvements in life satisfaction during the study. The findings regarding the changes in life satisfaction scores between the intervention and comparison groups at different time points are summarized in Figure 2. This figure presents a graphical representation of the data, illustrating the trends in life satisfaction over time for both groups. It visually demonstrates the significant improvements in life satisfaction observed in the intervention group compared to the relatively stable scores in the comparison group. The results of the statistical analysis provide significant insights into the impact of the intervention program on students’ life satisfaction. The statistical significance, as indicated by the p-value, and the moderate effect size, as indicated by the partial eta squared value, suggest that the intervention had a positive and lasting impact on students’ life satisfaction levels. The analysis of specific time points further confirms the significant improvements in life satisfaction in the intervention group, while the comparison group did not show any significant changes over time. These findings highlight the effectiveness of the intervention in fostering positive changes in students’ well-being and life satisfaction.

Figure 2. Means of Students’ Life Satisfaction Scale by group at times T-1 through T4.

3.3. Self-Worth Sub-scale (SWS)

Repeated measures ANOVA is used when measuring the same participants at multiple time points. In this study, it was employed to explore how the intervention (group) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up) interacted to influence students’ self-worth (SWS scores). The statistical analysis revealed a significant interaction effect between the group and time on the SWS scores, as indicated by Wilks’ Lambda value of .78, F = 2.62, p = .05, and a partial eta squared value of .07. A significant interaction effect suggests that the two groups (intervention and comparison) showed different patterns of change in self-worth over the course of the study. Wilks’ Lambda value of .78 reflects the strength of the impact of the independent variable (group) on the dependent variable (SWS scores) across the different time points. A lower Wilks’ Lambda value indicates a more robust effect. In this case, the value of .78 is considered statistically significant, indicating that the intervention had a notable impact on the participants’ self-worth levels. The F-value of 2.62 is the test statistic used to determine whether there are significant differences between the groups at different time points. A higher F-value suggests a more substantial effect of the independent variable. In this case, the F-value of 2.62 is statistically significant, confirming that there are indeed differences in self-worth levels between the intervention and comparison groups at various time points. The p-value of .05 indicates the probability of obtaining the observed results by chance alone. In hypothesis testing, researchers set a significance level (alpha) to determine if the results are statistically significant. The standard significance level is often set at .05, which means that to establish statistical significance, the probability of obtaining the observed results by chance should be less than 5%. In this study, the p-value of .05 is less than the standard significance level, suggesting that the results are statistically significant. Therefore, the differences in self-worth levels between the intervention and comparison groups at different time points are unlikely to be due to random chance. Here, the results of the repeated measures ANOVA provide important insights into the impact of the intervention on students’ self-worth as measured by the SWS over time. The significant interaction effect indicates that the intervention and comparison groups showed different patterns of change in self-worth throughout the study. The statistical significance, as indicated by the p-value and F-value, further confirms that the intervention had a notable impact on the participants’ self-worth levels. The findings suggest that the program was effective in fostering positive changes in students’ self-perception and self-worth, which can contribute to their overall well-being and development (Figure 3).

Figure 3. Means of Self-Worth Sub-Scale by group at times T-1 through T4.

The partial eta squared value of .07 is a measure of effect size, which quantifies the proportion of variance in the SWS scores (Self-Worth Sub-Scale scores) that can be attributed to the interaction between the group (intervention vs. comparison) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up). Effect size is important because it provides information about the practical significance and magnitude of the observed effects beyond statistical significance. In this case, the partial eta squared value of .07 suggests that approximately 7% of the variance in self-worth scores can be explained by the interaction between the group and time. A larger partial eta squared value signifies a stronger effect, and 7% is considered a moderate effect size, indicating that the intervention had a meaningful impact on students’ self-worth. Further analysis of specific time points in the study revealed significant differences between the intervention and comparison groups. At the post-test assessment, the intervention group demonstrated a substantial increase in self-worth compared to their pre-assessment scores. This increase was statistically significant, as indicated by the t-value of -5.31 and the p-value of less than .001 (two-tailed). The t-value represents the magnitude of the difference between the intervention group’s mean post-test self-worth scores and their mean pre-assessment self-worth scores, while the p-value reflects the probability of obtaining such results by chance alone. In this case, the low p-value indicates that the improvement in self-worth observed in the intervention group was highly unlikely to be due to chance. Similarly, at the 1-month follow-up and 2-month follow-up, the intervention group maintained the significant increase in self-worth compared to their initial scores. The t-values of -2.47 and the corresponding p-value of .02 (two-tailed) confirm the statistical significance of these improvements. These findings suggest that the positive effect of the intervention on students’ self-worth persisted over time, with the intervention group continuing to exhibit higher self-worth levels at both follow-up assessments. In contrast, the comparison group, which did not receive the intervention, did not experience any significant change in self-worth over time. This finding suggests that the comparison group did not show any noteworthy improvements in self-worth during the course of the study. Overall, the results demonstrate the positive and enduring impact of the intervention on students’ self-worth, leading to significant improvements in this crucial aspect of their psychological well-being. The effect size analysis highlights that the intervention had a meaningful effect on self-worth, and the statistical significance at various time points supports the effectiveness of the program in fostering positive changes in students’ self-perception and self-worth. These findings contribute valuable insights to the field of psychological intervention research, emphasizing the importance of promoting hope and positive psychological outcomes in educational settings.

3.4. Mental health Inventory-5 (MHI-5)

The repeated measures ANOVA for the Mental Health Inventory (MHI) did not yield statistically significant findings for the interaction between the group (intervention vs. comparison) and time (pre-assessment, post-assessment, 1-month follow-up, and 2-month follow-up). The results suggest that the intervention did not have a substantial effect on participants’ mental health scores over time, as measured by the MHI. The Wilks’ Lambda value of .78 is a multivariate test statistic that indicates the strength of the impact of the independent variables (group and time) on the dependent variable (MHI scores). In this case, the value of .78 suggests that the interaction between the group (intervention vs. comparison) and time did not exert a substantial effect on participants’ mental health scores. A lower Wilks’ Lambda value would indicate a more significant effect of the independent variables on the dependent variable. The F-value of 1.49 is another test statistic used to determine whether there are significant differences between the groups at different time points for the MHI scores. A higher F-value would indicate a more substantial effect of the independent variables on the dependent variable. However, in this instance, the F-value of 1.49 was not statistically significant, as the p-value of .23 exceeds the standard significance level of .05. This suggests that any observed differences in mental health scores between the intervention and comparison groups at various time points are likely due to chance and are not statistically meaningful. The partial eta squared value of .04 is a measure of effect size, representing the proportion of variance in the MHI scores that can be attributed to the interaction between group and time. In this case, the partial eta squared value of .04 indicates a small effect size, suggesting that the interaction between group and time has limited explanatory power in relation to the variation in mental health scores. Effect size provides information about the practical significance of the observed effects beyond statistical significance. Regarding the main effect for time, the Wilks’ Lambda value of .91 represents the impact of time on the MHI scores across all participants, regardless of group assignment. The Wilks’ Lambda value for the main effect of time is higher than the value for the interaction effect between group and time, indicating that time alone had a relatively weaker impact on participants’ mental health scores compared to the combined impact of group and time. The lack of statistical significance and the small effect size suggest that the intervention did not have a substantial impact on participants’ mental health, as measured by the MHI. It is important to interpret these results cautiously and consider other factors that may have influenced mental health outcomes in the study. The findings may also inform future research and intervention development to better address mental health needs in educational settings. The main effect for time investigates the overall impact of time on the MHI scores across all participants, regardless of their group assignment (intervention vs. comparison). The Wilks’ Lambda value of .91 represents the effect of time on the MHI scores. A Wilks’ Lambda value closer to 1 indicates a weaker effect of time, whereas a value closer to 0 indicates a stronger effect. In this case, the value of .91 suggests that time alone had a relatively weak impact on participants’ mental health scores. The F-value of 1.49 is the test statistic used to determine whether the main effect for time is statistically significant. A higher F-value would indicate a more substantial effect of time on the MHI scores. However, in this instance, the F-value of 0.49 was not statistically significant, as the p-value of .97 exceeds the standard significance level of .05. This suggests that there were no significant changes in mental health scores over time for all participants, irrespective of whether they were in the intervention or comparison group. The main effect for group investigates the overall impact of group assignment (intervention vs. comparison) on the MHI scores, disregarding time. The F-value of 2.27 represents the effect of group on the MHI scores. A higher F-value would indicate a more substantial effect of group assignment on the MHI scores. However, in this case, the F-value of 2.27 was not statistically significant, as the p-value of .15 exceeds the standard significance level of .05. The lack of statistical significance for the main effect of group suggests that the intervention did not lead to notable changes in mental health scores compared to the comparison group.

3.5. Academic achievement (AA)

The interaction effect between group and time investigates whether the intervention had a different impact on academic achievement scores compared to the comparison group over time. The Wilks’ Lambda value of .07 represents the strength of the impact of the independent variables (group and time) on the dependent variable (AA scores). A lower Wilks’ Lambda value suggests a stronger effect. In this case, the small value of .07 indicates that the interaction between group and time did not have a substantial effect on participants’ academic achievement scores. The F-value of .91 is the test statistic used to determine whether there are significant differences between the intervention and comparison groups at different time points for academic achievement. A higher F-value would indicate a more substantial effect of group assignment on the AA scores. However, in this instance, the F-value of .91 was not statistically significant, as the p-value of .52 exceeds the standard significance level of .05. This suggests that the observed differences in academic achievement scores between the two groups at various time points are likely due to chance and are not statistically meaningful. The partial eta squared value of .03 is a measure of effect size, representing the proportion of variance in the academic achievement scores that can be attributed to the interaction between group and time. In this case, the value of .03 suggests a small effect size, indicating that the interaction between group and time has limited explanatory power in relation to the variation in academic achievement scores. The main effect for time investigates the overall impact of time on the AA scores across all participants, regardless of their group assignment. The Wilks’ Lambda value of .57 represents the effect of time on the academic achievement scores. A lower Wilks’ Lambda value indicates a stronger effect of time. In this case, the value of .57 suggests a moderate impact of time on the academic achievement scores across all participants. The F-value of 6.67 and the p-value of .00 indicate that there were significant changes in academic achievement over the course of the study. The p-value of .00 indicates that the probability of obtaining the observed results by chance alone is very low, making the results statistically significant. This suggests that academic achievement scores changed significantly over time for all participants, regardless of whether they were in the intervention or comparison group. The partial eta squared value of .17 represents a relatively large effect size, suggesting that the passage of time has a meaningful influence on the variation in academic achievement scores. This indicates that 17% of the variance in academic achievement scores can be attributed to the passage of time. Thus, the repeated measures ANOVA did not reveal a statistically significant interaction between group and time for academic achievement scores. This suggests that the intervention did not have a substantial effect on academic achievement compared to the comparison group over time. However, the analysis did show a statistically significant main effect for time, indicating that academic achievement scores changed significantly over the course of the study for all participants. The effect size for the main effect of time was relatively large, suggesting that the passage of time had a meaningful influence on the variation in academic achievement scores. It is important to consider these findings in the context of the study’s design and sample characteristics to draw meaningful conclusions about the impact of the intervention on academic achievement. The main effect for group investigates whether there are significant differences in academic achievement scores between the intervention and comparison groups when considering all time points together. The F-value of .37 is the test statistic used to determine the significance of the main effect. A higher F-value would indicate a more substantial effect of group assignment on the AA scores. However, in this case, the F-value of .37 was not statistically significant, as the p-value of .61 exceeds the standard significance level of .05. The p-value of .61 indicates the probability of obtaining the observed results by chance alone. Since it is higher than the standard significance level of .05, it suggests that the main effect for group is not statistically significant. This means that there were no significant differences in academic achievement scores between the intervention and comparison groups when considering all time points together. The partial eta squared value of .00 reinforces this finding, suggesting that the group assignment did not have a substantial impact on the variation in academic achievement scores. The partial eta squared value of .00 indicates that 0% of the variance in academic achievement scores can be attributed to the group assignment. In other words, the intervention and comparison groups showed similar academic achievement scores on average across all time points. The main effect for time investigates the overall impact of time on the AA scores across all participants, regardless of their group assignment. The Wilks’ Lambda value of .57 from the previous analysis represents the effect of time on the academic achievement scores. A lower Wilks’ Lambda value indicates a stronger effect of time. In this case, the value of .57 suggests a moderate impact of time on the academic achievement scores across all participants. The F-value of 6.67 and the p-value of .00 from the previous analysis indicate that there were significant changes in academic achievement over the course of the study. The p-value of .00 suggests that the probability of obtaining the observed results by chance alone is very low, making the main effect for time statistically significant. This means that academic achievement scores changed significantly over time for all participants, regardless of whether they were in the intervention or comparison group. The repeated measures ANOVA thus did not yield a significant interaction between group and time for Academic Achievement (AA). This suggests that the intervention did not have a substantial effect on academic achievement compared to the comparison group over time. However, there was a significant main effect for time, indicating changes in academic achievement over the course of the study for all participants. The main effect for group was not significant, suggesting that there were no significant differences in academic achievement between the intervention and comparison groups when considering all time points together. Interpreting these findings requires caution, as various factors related to the study’s design, sample size, or external influences may have influenced the results. It is essential to consider these factors when interpreting the lack of significant effects in the study and to explore possible explanations for the observed patterns in academic achievement scores. Further research and analysis may be necessary to better understand the complex interplay between the intervention, group assignment, and academic achievement outcomes.

4. Discussion

This research highlights the significant changes observed in the scores on the Children Hope Scale (CHS), Students’ Life Satisfaction Scale (SLSS), and Self-Worth Sub-Scale (SWS) as a result of the intervention aimed at enhancing students’ strengths, particularly hope. The intervention in this study focused on enhancing hope, which is the belief in one’s ability to create pathways to achieve desired goals and the motivation to pursue those pathways. The significant increase in hope scores, as measured by the Children Hope Scale, provides compelling evidence that the intervention was effective in fostering hope among the participants. This aligns with previous research that has demonstrated the positive impact of interventions aimed at fostering goal-directed thinking and hope. Studies conducted by Klausner et al. (1998), Curry et al. (1999), S. C. Marques et al. (2011), S. Lopez et al. (2000), Klausner et al. (2000), MacLeod et al. (2008), and Cheavens et al. (2006) have also reported similar findings. These studies have shown that interventions focusing on goal-setting and hope have resulted in increased hope levels among participants. The findings from these studies support the notion that interventions can positively influence individuals’ hopeful thinking, leading to increased motivation, agency, and positive attitudes towards goal pursuit. The intervention’s positive impact on hope is also related to the observed increase in life satisfaction among the participants, as measured by the Students’ Life Satisfaction Scale (SLSS). When individuals experience an enhancement in their hope levels, they are more likely to set and pursue meaningful goals, which in turn can lead to increased life satisfaction. Previous studies, such as the work of MacLeod et al. (2008), have shown that goal-directed thinking and hope are positively associated with life satisfaction. The intervention in this study followed a strengths-oriented approach, empowering students to identify and pursue their goals for a more fulfilling life. This aligns with the findings of S. Lopez et al. (2000), who have suggested that focusing on strengths and positive goal-setting behavior can contribute to greater life satisfaction. The increase in life satisfaction observed in this study can be seen as an outcome of the enhanced goal-directed thinking and hope fostered by the intervention. By empowering students to believe in their ability to achieve their desired goals, the intervention likely contributed to the development of a positive self-attitude, resulting in higher life satisfaction levels. In addition to hope and life satisfaction, the intervention also had a significant impact on students’ self-worth, as indicated by the Self-Worth Sub-Scale (SWS) scores. The rise in self-worth can be attributed to the positive changes in hope and goal-directed thinking. When individuals have increased hope and set and pursue meaningful goals, they are more likely to experience a sense of competence and accomplishment. This positive reinforcement of their abilities and accomplishments can lead to an increase in self-worth. The findings from this study are consistent with the work of MacLeod et al. (2008), who demonstrated a positive association between hope and self-worth. By empowering students to believe in their potential to achieve their goals, the intervention likely bolstered their self-worth, contributing to a more positive self-perception. Thus, the significant changes observed in hope, life satisfaction, and self-worth following the intervention provide compelling evidence that interventions aimed at enhancing students’ strengths, particularly hope, can exert a positive influence on other psychological constructs. The results align with previous research highlighting the importance of fostering goal-directed thinking and hope to promote well-being and positive psychological outcomes. The strengths-oriented approach of the intervention, coupled with empowering students to pursue meaningful goals, likely contributed to the observed improvements in life satisfaction and self-worth. These findings underscore the potential benefits of interventions that focus on building hope and positive goal-setting behavior for enhancing students’ overall psychological well-being and development. These findings are consistent with the results reported by Curry et al. (1999), which showed that interventions targeting hope and positive goal-directed thinking can lead to improvements in these psychological constructs. The comparison group, which did not receive the strengths intervention, did not exhibit significant changes in hope, life satisfaction, or self-worth during the designated time frame. This emphasizes the importance of the intervention in fostering positive changes in these constructs. The study included the Mental Health Inventory (MHI-5) as an exploratory measure. While previous research has used mental-health indicators as outcome variables ((Cheavens et al., 2006; Cheavens, Feldman, Woodward, et al., 2006; Cheavens et al., 2019) and mediators between parenting styles and mental health outcomes in youth (Shorey et al., 2003), there were limited existing longitudinal intervention data for this particular measure in the context of strengths-oriented interventions. The researchers hypothesized that mental health would improve specifically for the strengths intervention group. While the results showed a trend in the predicted direction, they lacked statistical significance. Further research incorporating mental health measures is recommended to explore this relationship more comprehensively and understand the potential impact of strengths interventions on mental health outcomes. Similarly, the study hypothesized that academic achievement would show improvement in the intervention group compared to the comparison group. However, although there was a trend in the predicted direction, the result did not reach statistical significance. Academic achievement is known to be relatively stable over time (S. C. Marques et al., 2011), which may make it less amenable to immediate change. Additionally, while hope has been found to predict academic achievement at a single time point (S. C. Marques et al., 2011; C. R. Snyder et al., 1997), its contribution to the prediction of students’ academic achievement one or two years later, when initial academic achievement was controlled for, appears to be less significant (S. C. Marques et al., 2011). Alternatively, these results may be attributed to the need for a longer intervention duration to observe a meaningful shift in academic achievement. Future studies are warranted to delve further into this matter and explore potential long-term effects of the intervention on academic performance. The study demonstrates that interventions focusing on students’ strengths, particularly hope, can lead to significant improvements in hope, life satisfaction, and self-worth. These findings contribute to the growing body of research on positive psychology interventions and underscore the importance of promoting strengths-based approaches in educational and developmental settings. However, the impact on mental health and academic achievement was not as prominent within the timeframe of the study. This highlights the need for further investigation to fully understand the effects of strengths-oriented interventions on different psychological constructs over varying time periods. Overall, the study provides valuable evidence of the potential benefits of strengths-based interventions and encourages future research to explore and refine these approaches to promote the well-being and development of students and individuals in various settings.

5. Conclusions and implications

The study found that implementing an intervention aimed at fostering hope among senior secondary school students resulted in significant increases in hope, life satisfaction, and self-worth. These findings are consistent with prior research on interventions that focus on cultivating goal-directed thinking. The positive impact on hope, life satisfaction, and self-worth highlights the potential benefits of adopting group-based approaches to promote hopeful thinking among all students. The study suggests that enhancing the curriculum and school environment to support hopeful thinking could be advantageous in various ways. By incorporating interventions that promote hopeful thinking, schools may address issues related to efficacy, accessibility, and sustainability. Efficacy refers to the effectiveness of the intervention in producing positive outcomes, while accessibility ensures that the intervention is readily available and applicable to all students, teachers, and parents. Sustainability involves implementing low-cost interventions within a group setting over a 3-month period, making it more feasible for educational institutions to adopt such interventions. The intervention in this study is noteworthy because it actively involves key stakeholders, such as parents and teachers, in promoting hope in students. This comprehensive approach recognizes the important roles parents and teachers play in shaping students’ attitudes and beliefs. By including parents and teachers in the intervention process, the study emphasizes the significance of collaborative efforts in fostering positive development and enhancing educational contexts for students. The study is a significant stepping stone towards encouraging further comprehensive research in this area. By examining the impact of the intervention on multiple psychological constructs, the study lays the groundwork for future studies to explore the effectiveness of similar interventions in different contexts. This research investigation suggests that gathering feedback on the perceived advantages of the comprehensive intervention approach, including its impact on parents and teachers’ hope, would be insightful for guiding future studies. Understanding how different stakeholders perceive the benefits of the intervention can inform the design and implementation of future interventions. One notable limitation of the study is its inability to utilize a randomized control trial (RCT) design due to the 5-month duration of intervention and follow-up. Randomized control trials involve randomly assigning participants to either an intervention group or a comparison group to ensure that any observed effects are indeed due to the intervention and not other factors. Furthermore, the study lacked a placebo or competing treatment group, which could have provided more depth to the observation of the intervention’s effects. Having such comparison groups helps researchers rule out alternative explanations for the observed results. The absence of control groups in the study presents challenges to the interpretation of the findings. Without control groups, it is challenging to determine whether the observed changes in hope, life satisfaction, and self-worth are solely attributable to the intervention or if other factors may have influenced the results. While the results are promising, the lack of control groups limits the study’s ability to make strong causal claims about the effects of the intervention. In conclusion, the study suggests that implementing an intervention focused on cultivating hope among senior secondary school students can lead to notable psychological benefits, particularly in hope, life satisfaction, and self-worth. However, the study also highlights the importance of addressing limitations, such as the absence of control groups, to ensure robust research findings and advance the understanding of fostering positive development in educational settings. The study’s sample size was relatively small, which could limit the generalizability of the findings. A small sample size may not adequately represent the broader population and may not capture the full diversity of responses to the intervention. Moreover, the sample predominantly consisted of upper caste participants, which may introduce biases in the results. Different cultural backgrounds and experiences can influence how individuals respond to interventions, and having a more diverse sample would enhance the study’s external validity. Additionally, the overrepresentation of female students might also impact the generalizability of the findings, as gender can influence psychological outcomes and responses to interventions. Given the limitations in sample size and demographic representation, the study’s findings may not be fully generalizable to broader and more diverse populations. To enhance the validity and generalizability of the results, future research should aim to include larger and more diverse samples, encompassing individuals from various cultural backgrounds, genders, and socio-economic statuses. A more comprehensive understanding of the intervention’s impact across different groups would provide more robust evidence of its effectiveness. Despite the limitations, the study’s findings have significant scientific and social implications. They contribute valuable information to the growing body of scientific knowledge surrounding students’ hope and other positive psychology variables. By shedding light on the potential of purposeful actions in fostering hopeful traits, the study advances the field of positive psychology. Understanding how interventions can positively impact students’ psychological strengths is crucial for promoting their overall well-being and success in various aspects of life. The study provides valuable insights for educators and clinicians seeking to nurture psychological strengths in students. By demonstrating the effectiveness of interventions aimed at cultivating hope, the study highlights the importance of adopting strengths-based approaches in educational settings. Educators and clinicians can use this knowledge to design and implement interventions that foster hopeful thinking and other positive psychological traits in students, creating a more conducive learning environment. By further exploring and refining interventions that nurture hopeful thinking, educators and clinicians can make substantial strides in promoting positive development among students. Fostering hope, life satisfaction, and self-worth in students can have long-lasting effects on their psychological well-being and academic performance. A strengths-based approach can empower students to overcome challenges, build resilience, and develop a positive outlook on their future. In conclusion, the study’s results underscore the importance and effectiveness of interventions aimed at cultivating hope among senior secondary school students. While acknowledging the limitations, the findings contribute valuable information to the field of positive psychology and advocate for the adoption of strengths-based approaches in educational settings. By addressing the limitations and continuing to explore and refine interventions, educators and clinicians can play a significant role in fostering positive development and creating a nurturing learning environment for students.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Research involving human and/or animals

The authors confirm that ethical considerations have been taken into account and that the study was conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are not available due to the sensitive nature of the research. Moreover, the participants of this study did not give written consent for their data to be shared publicly. Research ethics thus prohibit supporting data to be made publicly available.

Additional information

Notes on contributors

Ashraf Alam

Ashraf Alam is a Ph.D. Scholar at IIT Kharagpur. He holds a master’s degree in education and a bachelor’s degree in computer science and engineering from the University of Delhi. Over the course of Ashraf’s academic journey, his research and teaching interest has inclined towards the philosophy and sociology of education, and in areas of research ethics, educational psychology, and educational technology. He works at the crossroads of research and action for sustainable development, focusing on policies that impact the vulnerable. Currently, he is researching the different facets of ‘Positive Education’ at the Rekhi Centre of Excellence for the Science of Happiness at IIT Kharagpur under the esteemed guidance and patronage of Prof. Atasi Mohanty.

Atasi Mohanty

Atasi Mohanty, PhD, is an Assistant Professor at the Rekhi Centre of Excellence for the Science of Happiness, Indian Institute of Technology Kharagpur, India. Her research interests include positive youth development, social psychology, youth mental health, the science of happiness, prosocial behavior, happiness at the workplace, sustainable health and wellbeing, positive psychology, organizational behavior, Indian psychology, and sustainable entrepreneurship. She is currently guiding 14 Ph.D. research scholars.