(PDF) Clustering technique for analysing environmental attitude among undergraduate students in Purulia district, West Bengal

Journal of Social Studies (JSS), ISSN: 1858-2656 (p); 2721-4036 (e) Vol. 21. No. 1 (2025), pp. 1-12 doi: 10.21831/jss.v20i1.70279 Clustering technique for analysing environmental attitude among undergraduate students in Purulia district, West Bengal SUBIR SEN Sidho-Kanho-Birsha University, India Email:

SURAJIT MAHATO Sidho-Kanho-Birsha University, India Email:

RAMESH CHANDRA MAHATO Sidho-Kanho-Birsha University, India Email:

BISHAL DAS Sidho-Kanho-Birsha University, India Email:

ABSTRACT This study aims to explore inter-dimension rela5onships in the environment, iden5fying clusters based on independent variables (Stream, gender, loca5on) and their consequences on Environmental Behaviour (EB), Environmental Opinion (EO), and Environmental Emo5on (EE) among undergraduates in Purulia district, West Bengal. Using a descrip5ve survey method with randomly sampled par5cipants, the study employs the “Environmental AJtude Scale” developed by Uzun et al. (2019) and sta5s5cal techniques such as the Product Moment Method for calculate and Two-Step Clustering techniques. The results reveal significant correla5ons between Environmental Behaviour (EB), Environmental Emo5on (EE), Environmental AJtude (EA), and Environmental Opinion (EO). Factors like Stream, gender, and loca5on contribute to dis5nct clusters, emphasizing their influence on environmental aspects. Overall, the study provides valuable insights into the mul5faceted dynamics of environmental aJtudes and behaviours, underscoring the importance of considering various factors in addressing environmental issues. Keywords: Environmental AJtude, cluster analysis, Environmental behaviour, Predictors, Environmental opinion, Undergraduate students, Environmental emo5on, Simple random sampling. INTRODUCTION We all are very closely associated with the modern technology, therefore we erosion our awareness towards our environment day by day. In a world grappling with the escala5ng challenges of climate change, pollu5on, and resource deple5on, the significance of individual and collec5ve environmental aJtudes cannot be overstated. The term "environmental aJtude" encapsulates the complex web of beliefs, values, and behaviours that shape our rela5onship with the natural world. 1 Journal of Social Studies (JSS), Volume 21, Number 1, 2025: 1-12 Environmental factors significantly shape human health outcomes, including air and water quality, exposure to toxins, access to green spaces, and clean energy sources. Conversely, human and societal health directly affects the environment, with unhealthy prac5ces like excessive consump5on, pollu5on, and deforesta5on hastening environmental degrada5on and endangering sustainability (Das et al., 2023). It advocates for the safeguarding of the environment, the establishment of social fairness, the maintenance of economic stability, the conserva5on of resources, efforts to mi5gate climate change, and the preserva5on of cultural heritage (Das et al., 2023). Naviga5ng the complexi5es of this new era requires a crucial understanding of the mul5faceted dimensions of 21st-century lifestyle (Adhikari, 2023). Clustering, a core sta5s5cal method, plays a crucial role in data analysis, pa\ern recogni5on, and machine learning (Das, 2023). Employing specific traits, this technique clusters akin data points, unveiling concealed pa\erns within a dataset (Das et al., 2023). In clustering, the main objec5ve is to group data into clusters where data points within the same cluster are more similar to each other than to those in other clusters. This simplifies large datasets, aiding knowledge of sta5s5cs and data science in be\er understanding and decision-making (Mahato et al., 2023). Various studies have employed this technique such as Mahato et al. (2024), Das et al. (2024) etc. LITERATURE REVIEW Uzun et al. (2019) carried out “environmental aJtude scale for secondary school, high school and undergraduate (UG) students: validity and reliability study.” Major goal of the study is to create a valid and trustworthy Environmental Attitude Scale. The findings thereafter demonstrate that the scale may be used to gauge students’ environmental attitudes at various levels. Islam & Bhuiyan (2018) proposed a comprehensive approach for the Sundarbans mangrove forest’s preserva5on, addressing threats from human ac5vi5es and natural factors. Salloum et al. (2021) explored the impact of Cancer Centre Cessa5on Ini5a5ve (C3I) on na5onal tobacco treatment. Their study revealed that C3I played a pivotal role in catalysing tobacco treatment, providing con5nuous learning, adap5ve programs, sustainability assessment, implementa5on strategy, health impact sustainability, research advancement, policy alignment, and SWG knowledge dissemina5on for cancer care organiza5ons within and beyond C3I. Dian et al. (2022) explored the interplay between green human resources management, green supply chain, environmental sustainability, and green businesses in Central Java. Their conceptual model provided prac5cal recommenda5ons for enhancing manufacturing company performance, contribu5ng significantly to HRM and supply chain management literature. Kumari (2022) studied the impact of lifestyle choices on the environment, emphasizing sustainable prac5ces in food, transporta5on, and daily life for an ecofriendlier future. Roberts et al. (2022) discovered a link between posi5ve lifestyle behaviours, emo5onal health factors, and low back pain resilience, highligh5ng the importance of op5mal choices for maintaining high func5on despite pain. S5llwell et al. (2023) delved into resource consump5on and sustainability in the built environment, examining infrastructure through various lenses. Their study addressed local and global factors, urban dependencies, and digital technology's role, emphasizing adap5ve planning, climate neutrality, equity, and well-being. Hendriyani et al. (2023) inves5gated the Independent Curriculum's implementa5on through a project on sustainable lifestyle for Grade 10 students. Results indicated a 70.7% posi5ve impact on environmental awareness, showcasing the effec5veness of the curriculum. Duarte et al. (2023) focused on lifestyle entrepreneurship as a vehicle for sustainable tourism, promo5ng economic progress, environmental balance, public health, and social context through decision tools and renewable biomass energy. Scozzese & Gelli (2023) discussed lifestyle branding as a sustainable strategy, fostering eco-consciousness through climate solu5ons and environmental stewardship advocacy. Dey & Bairagya 2 Clustering technique for analysing environmental aPtude among undergraduate students in Purulia district, West Bengal (Surajit Mahato, Subir Sen, Ramesh Chandra Mahato, Bishal Das) (2023) emphasized the green economy as a roadmap for a sustainable lifestyle, transforming quality of life, promo5ng sustainability, and driving economic and social development through a carbon-free environment and non-conven5onal energy. Saha et al. (2021) inves5gated Yoga AJtudes among College Students using Clustering Techniques. They found the college loca5on to be a significant factor, forming clusters, especially among rural male and female students who shared similar opinions on yoga prac5ces. Gorain et al. (2022) examined the “Rela5onship and Cluster Analysis among Internet Dependency, Social Isola5on, and Personality.” They found low to mediocre correla5ons in arts and science learners, revealing three dis5nct clusters: separate male and female arts clusters and a dis5nct science cluster. Ansary et al. (2023) delved into “AJtudes towards Value-oriented Educa5on among UG Students using Clustering Techniques.” They iden5fied loca5on as the most significant predictor, no5ng no correla5on between academic achievement and aJtudes toward value-oriented educa5on. Mohanta et al. (2023) explored Ins5tu5onal Commitment using Cluster Analysis, revealing clusters (Female and Male, Rural and Urban Ins5tu5ons) that posi5vely impacted Predictor influence. Professional Commitment emerged as the most influen5al dimension in cluster forma5on. In a study by Sen et al. (2023a), they explored Leadership Style in Ins5tu5ons using Clustering Techniques. As cluster count increased, so did predictors, with loca5on consistently being the most crucial predictor. Similar leadership styles were observed based on ins5tu5on loca5on. Mahalanobis Distance gauges the strength of Cluster Analysis in educa5onal contexts (Adhikari, 2023; Adhikari et al., 2023a; 2023b; Mahato & Sen, 2021; Sen & Pal, 2020; Sen et al., 2023a; 2023b; 2023c; Ahamed et al., 2020; 2021; 2022a). Adhikari & Sen (2023a) focused on Cluster Analysis of Ins5tu5onal Commitment and Organiza5onal Climate. Across gender and rural-urban seJngs, teachers' views on ins5tu5onal commitment and organiza5onal climate remained similar. In another study by Adhikari & Sen (2023b) on Recent Trends of Cluster Analysis in Educa5on, they highlighted the role of predictor counts, their rela5on to socio-psychological variables, and the increase in predictor values with cluster count. Present study explores various clusters using dichotomous variables steam, gender and locality on Environmental AJtude with its dimensions Environmental Behaviour (EB), Environmental Opinion (EO), and Environmental Emo5on (EE). Different number of clusters are considered for analysing cluster forma5on and respec5ve predictors with their degree of predic5on (high, mediocre, low and very low). Two step clustering technique use for detec5on of clusters. OBJECTIVES OF THE STUDY • • • To Explore inter-dimension rela5onships in the Environment. To Iden5fy clusters based on independent variables (Stream, gender, loca5on) and their consequences on Environmental Behaviour (EB), Environmental Opinion (EO), and Environmental Emo5on (EE). To Assess predictor significance in cluster forma5on. METHODOLOGY Method: Descrip5ve survey is u5lized in this study. Popula3on: All undergraduates of Purulia district in West Bengal. Sample: 149 undergraduates were randomly sampled for the research. Sampling procedure: Sampling was done using simple random sampling technique. Tool used: “Environmental AJtude Scale developed by Uzun et al. (2019) used in this study to collect the data from samples. 3 Journal of Social Studies (JSS), Volume 21, Number 1, 2025: 1-12 Sta3s3cal technique used: The study applies the Product Moment Method to calculate correla5on coefficients and employs a Two-Step Clustering technique to categorize the en5re sample into dis5nct clusters. RESULT AND DISCUSSION Table 1 value of r for Environmental A3tude and its component Correla'ons EB EO EE EA 𝑟 EB EO EE EA 1 -.005 .494** .843** Level of significance .951 .000 .000 1 -.350** .178* .000 .030 1 .756** 𝑟 -.005 Level of significance .951 𝑟 .494** -.350** Level of significance .000 .000 𝑟 .843** .178* .756** Level of significance .000 .030 .000 .000 1 .01 level significance is detected .05 level significance is detected From table 1 Environmental behaviour is correlated (.01 level of significance) with Environmental Emo5on and Environmental AJtude. Environmental Opinion (EO) is significantly correlated (.01 and .05 level of significance) with Environmental Emo5on and Environmental AJtude. Environmental Emo5on is significantly correlated (.01 level of significance) with Environmental AJtude. Above men5oned results showed that pair wise rela5onships are significant at .01 and .05 level of significances. This is the conclusion about objec5ve 1 which states “Explore inter-dimension rela5onships in the environment”. Table 2: forma:on of 2 clusters 4 Clustering technique for analysing environmental aPtude among undergraduate students in Purulia district, West Bengal (Surajit Mahato, Subir Sen, Ramesh Chandra Mahato, Bishal Das) Urban students (75.2%) mainly formed by the stream arts (100%) and male UG students (65.0%) formed cluster 1 (represented in table 2), with 78.5% of the total sample size. Cluster 2 is consis5ng of urban UG students (78.1%), dominated by science UG students (100.0%), and male UG students (78.1%) and made up of 21.5% of total sample size. Figure 1: Clusters according to impotence of predicters (from table 2) From figure 1, it is clear that stream is the major predictor of clusters, where Environmental AJtude (EA), Environmental Behaviour (EB), Environmental Emo5on (EE) and gender are low and environmental opinion and locality are negligible predictors. Table 3: forma:on of 3 clusters 5 Journal of Social Studies (JSS), Volume 21, Number 1, 2025: 1-12 Rural students (50.9%) mainly formed by the stream arts (100%) and female UG students (71.9%) formed cluster 1 (represented in table 3), with 38.3% of the total sample size. Cluster 2 is consis5ng of urban UG students (78.1%), dominated by science UG students (100.0%), and male UG students (78.1%) and made up of 21.5% of total sample size. Cluster 3 consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%) and is total sample size 40.3%. Figure 2: Clusters according to impotence of predicters (from table 3) From figure 2 it is clear that stream, shows us major predictor of clusters, mediocre in gender and locality is low predictor. where Environmental AJtude (EA), Environmental Behaviour (EB) and Environmental Emo5on (EE) Environmental Opinion (EO) are the low predictors of the cluster men5oned in Table 3. Table 4: forma:on of 5 clusters Rural students (100%) mainly formed by the stream arts (100%) and male UG students (55.2%) formed cluster 1 (represented in table 4), with 19.5% of the total sample size. Cluster 2 is consis5ng of urban UG students (78.1%), dominated by science UG students (100.0%), and UG students of male category (78.1%) and coun5ng 21.5% of total sample size. Cluster 3 consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and UG students of male category (100.0%) and is total sample size 26.8%. cluster 4 is UG students of urban category (100.0%), dominated by arts UG students (100.0%), and UG students of male category (100.0%), and is total sample size 13.4%. cluster 5 is UG students of urban 6 Clustering technique for analysing environmental aPtude among undergraduate students in Purulia district, West Bengal (Surajit Mahato, Subir Sen, Ramesh Chandra Mahato, Bishal Das) category (100.0%), dominated by arts UG students (100.0%), and female UG students (100.0%), and is total sample size 18.8%. Figure 3: Clusters according to impotence of predicters (from table 4) From figure 3 it is clear that stream, and locality are major predictor and gender is mediocre of clusters, where Environmental AJtude (EA), Environmental Behaviour (EB) and Environmental Emo5on (EE) are low predictors and Environmental Opinion (EO) is very low predictors of the cluster men5oned in Table 4. Table 5: forma:on of 7 clusters Rural students (100%) mainly formed by the stream arts (100%) and female UG students (100%) formed cluster 1 (represented in table 5), with 8.7% of the total sample size. Cluster 2 is consis5ng of urban UG students (85.7%), dominated by science UG students (100.0%), and female UG students (100.0%) and made up of 4.7% of total sample size. Cluster 3 consis5ng of rural UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%) and is total sample size 10.7%. cluster 4 is consis5ng of urban UG students (76.0%), dominated by science UG students (100.0%), and male UG students (100.0%), and is total sample size 16.8%. cluster 5 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%), and is total sample size 26.8%. cluster 6 is 7 Journal of Social Studies (JSS), Volume 21, Number 1, 2025: 1-12 consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%), and is total sample size 13.4%. cluster 7 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and female UG students (100.0%), and is sample size 18.8% of total sample. Figure 4: Clusters according to impotence of predicters (from table 5) From figure 2 it is clear that stream, gender and locality are major predictor of clusters, where Environmental AJtude (EA), Environmental Behaviour (EB) and Environmental Emo5on (EE) are low predictors and Environmental Opinion (EO) is very low predictors of the cluster men5oned in Table 5. Table 6: forma:on of 10 clusters Rural students (100%) mainly formed by the stream arts (100%) and female UG students (100%) formed cluster 1 (represented in table 5), with 8.7% of the total sample size. Cluster 2 is consis5ng of urban UG students (85.7%), dominated by science UG students (100.0%), and female UG students (100.0%) and made up of 4.7% of total sample size. Cluster 3 consis5ng of rural UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%) and is total sample size 10.7%. cluster 4 is consis5ng of rural UG students (100.0%), dominated by science UG students (100.0%), and male UG students 8 Clustering technique for analysing environmental aPtude among undergraduate students in Purulia district, West Bengal (Surajit Mahato, Subir Sen, Ramesh Chandra Mahato, Bishal Das) (100.0%), and is total sample size 4.0%. cluster 5 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%), and is total sample size 25.5%. cluster 6 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%), and is total sample size 2.0%. cluster 7 is consis5ng of urban UG students (100.0%), dominated by science UG students (100.0%), and male UG students (100.0%), and is total sample size 12.8%. cluster 8 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and male UG students (100.0%), and is total sample size 12.8%. cluster 9 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and female UG students (100.0%), and is total sample size 8.7%. cluster 10 is consis5ng of urban UG students (100.0%), dominated by arts UG students (100.0%), and female UG students (100.0%), and is total sample size 10.1%. Figure 5: Clusters according to impotence of predicters (from table 6) From figure 5 it is clear that stream, gender and locality, are major predictor of clusters, where Environmental AJtude (EA), Environmental Opinion (EO) and Environmental Emo5on (EE) are mediocre predictors and Environmental behaviour is very low predictors of the cluster men5oned in Table 6. To achieve objec5ves 2 and 3, refer to the table below: Table 7: Cluster and Predictor summary Number of clusters High predictor Mediocre predictor 2 Stream 3 Stream Gender and Locality 5 Stream Gender 7 Stream and Locality Gender 10 Stream, Gender and Locality Environmental A3tude (EA) and Environmental Emo:on (EE) Low predictor Environmental A3tude (EA), Environmental Behaviour (EB), Environmental Emo:on (EE) and Gender Environmental Opinion (EO) and Locality Environmental A3tude (EA), Environmental Behaviour (EB) and Environmental Emo:on (EE) Environmental Opinion (EO) Locality, Environmental A3tude (EA), Environmental Behaviour (EB) and Environmental Emo:on (EE) Environmental Opinion (EO) Environmental A3tude (EA), Environmental Behaviour (EB) and Environmental Emo:on (EE) Environmental Opinion (EO) Environmental Opinion (EO) Environmental Behaviour (EB) 9 Journal of Social Studies (JSS), Volume 21, Number 1, 2025: 1-12 Table 7 displays the clusters and their predictors, revealing varia5ons in cluster size and predictors, especially notable when examining clusters with 3, 5, 7, and 10 elements. Remarkably, the smallest cluster (2.0%) remains consistent across these numbers. Addressing objec5ve 2, focusing on iden5fying clusters based on independent variables (Stream, gender, loca5on) and their influence on Environmental Behaviour (EB), Environmental Opinion (EO), and Environmental Emo5on (EE), suggests that specific dependent and independent variables contribute to cluster forma5on. Moving to objec5ve 3, which involves evalua5ng predictor significance in cluster forma5on, it was observed that with two clusters, Stream emerges as a crucial predictor. As the number of clusters increases, Stream, Gender, and Locality become significant predictors, emphasizing their role in cluster forma5on, par5cularly with ten clusters. CONCLUSION In summary, this study successfully achieved its objec5ves by establishing significant correla5ons between environmental behaviour, emo5on, aJtude, and opinion. The intricate inter-dimension rela5onships within the environmental context were highlighted, and the explora5on of clusters revealed varia5ons in size and predictors. Factors such as Stream, gender, and loca5on were iden5fied as contributors to dis5nct clusters, shedding light on their influence on environmental behaviour, opinion, and emo5on. The study's findings provide valuable insights into the mul5faceted dynamics of environmental aJtudes and behaviours, emphasizing the importance of considering various factors in understanding and addressing Environmental issues. REFERENCE 1. Adhikari, A. & Sen, S. (2023a). Cluster Analysis on Ins5tu5onal commitment and organiza5onal climate. InternaEonal Journal of Research publicaEon and reviews, 4(5), 4974-4988. 2. Adhikari, A. & Sen, S. (2023b). Recent Trends of Cluster Analysis in Educa5on. InternaEonal Research Journal of ModernizaEon in Engineering Technology and Science, 5(8), 1858-1861. 3. Adhikari, A. (2023). 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