Neighborhood Perception and Happiness among University Students: Moderating Effects of Gender and Socio-economic Status

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Kamal Uddin, Mushrat Alam Shama, Toma Adhikary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7132642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Perceptions about the residential neighborhood environment significantly influence individuals’ emotional well-being and life satisfaction. For university students, who often experience transitions in living situations and social environments, understanding how these perceptions relate to happiness is essential. This study investigates the associations between students’ neighborhood perceptions and their happiness levels, examining the moderating effect of gender and socio-economic status. A cross-sectional survey of 400 students (56.3% female, 43.7% male; average age 22.54 years) was conducted using standardized measures of environmental perception and happiness. Correlational, regression, and moderation analyses revealed a positive association between neighborhood perception and happiness. Gender moderated this relationship in a crossover pattern: males reported greater happiness at lower neighborhood perception levels, whereas females showed higher happiness at more positive perceptions. Also, socio-economic status functioned as a significant moderator between perceived neighborhood and perceived happiness. Upper-class residents are less responsive to perceived happiness with a corresponding increase in neighborhood positivity unlike the lower-class individuals who manifested a greater subjective well-being comparatively in response to the enhancement of neighborhood quality. These findings underscore the dynamic interaction between environmental perceptions, gender and socio-economic status in shaping happiness, with important implications for designing residential spaces and mental health initiatives that support diverse populations. Gender Studies Psychology Behavioral Geography Environmental Economics neighborhood perception happiness gender differences socio-economic status health geography subjective well-being Figures Figure 1 Figure 2 Figure 3 Introduction Neighborhood is denoted as an area where people live and interact with each other but often the boundary of neighborhood is fuzzy to define since it is hard to delimit the starting and ending points (National Geography Society, 2024). Happiness is a positive construct which indicates the presence of positive affect, and absence of negative affect. Although, in hedonic perspective, happiness and emotional well-being are synonymous, the eudaimonic paradigm suggested that, emotional well-being is comprised of happiness and a meaning of life (Snyder & Shane, 2007 ). Growing evidences indicate a strong association between neighborhood perception and status of mental health and well-being. Environment of a neighborhood, whether it contains a therapeutic landscape or has the potential to cause neighborhood disorder, obviously holds a power to stimulate the dwellers which in turn is reflected via the outcomes of mental health, often physical health also. Deprived neighborhood is a determinant of poor mental well-being whereas therapeutic neighborhood has a healing power. Outcomes of perceived neighborhood can be juxtaposed with thoughts and affections; which in turn can have an effect on happiness, a component of subjective well-being. Various neighborhood backgrounds (socio-economic and environmental) can have their respective effects on mental and emotional well-beings. Therefore, current study would link to cognitive as well as affective components with residential environment of both urban and rural. Existing literatures supported the theme. Perceived neighborhood characteristics like neighborhood stressors are significant indicators of emotional well-being. Study was conducted showing the link between such stressors and mental health disorders among young adults in USA. It reflected mental health outcomes of neighborhood stressors pointing out three risk factors— depressed affect, hopelessness, and anger as components of poor emotional well-being (Snedker & Hooven, 2013 ). Another related study focused an association between cognition and neighborhood‑related factors like social isolation, social interactions, access to green, environmental pollution, safety, traffic, noise, physical inactivity etc. The qualitative study showed that, cognitive health and brain functioning are dependent on the nature of perception toward neighborhood (Stevens et al., 2023 ). A geoepidemiological study centering the mental health status of the slum dwellers in Dhaka megacity of Bangladesh found that, mental well-being of the dwellers is closely associated with the neighborhood perception of slum settlements. The study confirmed, there is a link between physiological well-being and socio-physical environment (Gruebner et al., 2012 ). That neighborhood perception has a strong connection with mental well-being, has also been revealed by a study of UK regarding neighborhood disorder. Negative perceptions of neighborhood mean high level of neighborhood disorder and poorer mental well-being. The opposite is true for positive perceptions (Toma et al., 2015 ). Systematic review research also confirmed that, neighborhood deprivation or disorder can lead to poorer mental health and well-being (Visser et al., 2021 ). Perceived neighborhood safety is another vital component that has significant correlation with psychological health (Choi & Matz-Costa, 2018 ). Another study of Netherlands showed that, social and physical neighborhoods are significant determinants of depression (Helbich et al., 2020 ). A qualitative study centering impacts of urban neighborhood environment on mental well-being in Belgium found neighbor diversity, social security and neighbor ties as crucial components that have associations regulating mental well-being (Lauwers et al., 2021 ). Migrant’s mental state and well-being are also affected by perceived neighborhood. A Chinese cross-sectional study presented comparative analysis of migrants’ risk of mental illness prior to and after the migration to host country. Neighborhood safety, aesthetics, and access to green space were the factors assumed to be correlated with higher level of well-being and the decline of these led to mental distress. The study indicated neighborhood safety as the most influential variable in this regard (Yang et al., 2021 ). A Swiss experimental study comparing disadvantaged neighborhood with affluent neighborhood found that, disadvantaged neighborhood is liable for worse health even early mortality. Such environment elicits stress and emotional responses which in turn threat both mental and physical health (Hackman et al., 2019 ). An American study predicted depression and anxiety due to ‘ Neighborhood Danger ’ among older adults. Therefore, poor or unwelcomed neighborhood is a potential risk factor for mental health (Adaralegbe et al., 2021 ). Not only neighborhood conditions affect mental health, it also deteriorates cognitive functioning. Perceived neighborhood stressors can lessen working memory, cause lower performance in spatial abilities, and disturb executive functioning of the brain during adulthood. Lower safety, higher rate of crime, lower aesthetic quality, less social cohesion etc. were labelled as neighborhood stressors (Muñoz et al., 2020 ). Multiple outcomes of mental health like depression, anxiety etc. have been identified because of unsatisfactory neighborhood conditions of natural, social, and built environments (Sui et al., 2022 ). Often the effects of poor neighborhoods can be liable for the generation of psychiatric diseases beyond mere mental health symptoms like depression and anxiety. A Finish study dealt with the effects of childhood neighborhood context and its after effects on the psychiatric issues during adolescence (Vaalavuo et al., 2021 ). Characteristics of perceived neighborhood regulates the mechanisms behind physical and psychological health status and life satisfaction. A Chinese study drew attention focusing on affluent neighborhood which promote healthy life styles and adequate physical activity. It also bears a healing power that can enhance life satisfaction (Liu et al., 2022 ). Again, evidences suggested that, favorable neighborhood environments can be characterized as having the quality of reducing sedentary behaviours like smoking, drinking, gambling etc. (Chang et al., 2021 ). Neighborhood perception also varies based on gender differences (Stafford et al., 2005 ). A Polish study showed differential safety perception of urban neighborhood between male and female (Polko & Kimic, 2022 ). Life satisfaction, positive emotions and psychological well-being which combinedly measure happiness— are also regulated by gender differences, according to literatures (Daffon, 2017 ). According to the literatures reviewed, a research gap has been found regarding the effects of perceived neighborhoods on level of happiness among young adults in the context of Bangladesh. This study intends to capture responses from the students of the University of Dhaka, as it is a place of wide diversity based on regional differences. Environment and mental health both of the domains are seeking much more attention today. Issues of mental health and happiness can better be configured if environmental impacts are to be investigated. Perceived neighborhood (environmental- therapeutic, favorable, congested, polluted; socio-economic- affluent, deprived, safe, unsafe etc.) and mental health are strongly associated with according to the research evidences. Also, urban planning and development can be impacted by the consequences derived from the relations between emotional well-being specially happiness among dwellers and neighborhood perception. Such multidisciplinary research is a desideratum of current world, indeed. The objective of this study is to investigate the relationships between neighborhood perception and happiness among university students considering the roles of gender and socio-economic status. In order to reach the objectives, following research questions were outlined— Is there any significant association between neighborhood perception and happiness? Does neighborhood perception significantly predict happiness among university students? Does gender moderate the relationship between neighborhood perception and happiness? Does socio-economic status moderate the relationship between neighborhood perception and happiness? Methods Study Design This quantitative study was conducted following a correlational design. Sample Size and Sampling Participants were recruited based on specific characteristics relevant to the research questions. The only inclusion criterion is that participants are the university students age ranging from 18 to 28 years (both male and female). Purposive sampling is methodologically appropriate in constrained settings when subgroup diversity is essential for analysis (Etikan et al., 2016 ). Therefore, purposive sampling was applied. A pre-test was run as a preliminary data collection for this research taking a sample of 30 from the target population (Perneger et al., 2015 ). Results of pre-test ensured the face validity of the scales and allowed the study to go ahead. To determine an appropriate sample size from a finite population of approximately 46,000 (Wikipedia, 2025), the current students of the target organization (University of Dhaka), Cochran’s formula (1977) was employed. According to the formula, minimum required sample size for this study is 382. The figure was calculated using the finite population correction (FPC) formula: n = n₀ / [1 + (n₀ − 1)/N] where N = 46,000. Thus, n = 385 / [1 + (384 / 46000)] ≈ 385 / 1.00835 ≈ 382. Therefore, a minimum of 382 participants was required to achieve a representative sample with a 95% confidence level and ± 5% margin of error. The current study employed a sample of 400 students, which exceeds the minimum requirement, thereby ensuring sufficient precision, statistical power, and representativeness of the target population. Although a purposive sampling technique was employed in this study due to practical and contextual considerations, Cochran’s ( 1977 ) formula was applied to ensure adequacy and approximate representativeness of the selected participants. Moreover, it would guarantee maintaining statistical power, reducing sampling bias, and enhancing the credibility of the findings. Measuring Tools A structured questionnaire survey was conducted for data collection. Two translated (Bangla version) psychometric questionnaires were used to quantify environmental perception, as well as the level of happiness—respectively named as Environmental Perception Scale (EPS) (Ho & Au, 2020 ), Oxford Happiness Questionnaire (OHQ) (Hills & Argyle, 2002 ). The multidimensional Environmental Perception Scale (EPS) is constituted of 3 domains named as cognitive, affective and environmental preferences. Each domains have their respective sub-scales under which there are several items. The cognitive domain contains 6 factors; affective domain has 2 factors and environmental perception domain bears 3 factors—all together the whole scale has 11 factors and 42 items. The affective domain has 8 items dividing themselves into 2 factors— comfort and activity , with a scoring pattern of semantic deferential scale with a bipolar response format ranging from 1 to 6 denoting worst to best respectively. Again, there are 6 factors in the cognitive domain named as— legibility, enclosure, complexity, crime potential, wildlife , and lighting . These factors include 22 items in total. The remaining domain environmental preferences includes 3 factors like— Perceived Restorativeness, Perceived Safety and Perceived Visitability which together have 12 items. The last two domains used 6-point Likert-type scale for the responses where strongly agree and strongly disagree were the two extremes. The Oxford Happiness Questionnaire (OHQ) is unidimensional having 29 items. Response format is 6-point Likert-type scale ranging from strongly disagree to strongly agree . The Oxford Happiness Questionnaire (OHQ) measured happiness numerically coded from 1 to 6 where, 1 denotes the least happy state and 6 indicates highest level of happiness. Like the construct itself, all the items are worded positively; hence, higher the total score, higher the happiness. Similar norm was applicable for the Environmental Perception Scale (EPS), higher the composite score (for each domain), higher the positive attitude of the residents (of the particular neighborhood) toward their hosting environment. Results Descriptive Statistics Demographic data were subjected to statistical analysis calculating Mean, SD, frequency and range. Among the 400 university students, 56.3% were female and male grabbed 43.7% of the sample. Age of the participants ranged from 18 years to 28 years having a mean of 22.54 years and mode of 24 years. Among the participants, 49.2% belong to middle class, 26.5% were from upper class and 24.3% reported as residents of lower class. Table 1. Descriptive statistics of environmental perception, and level of happiness Variables N of Items Possible Range Observed Range M SD Coefficient Alpha Environmental Perception 42 42─252 88─240 179.35 32.24 .938 Affective Domain 8 8-48 8-48 36.54 7.96 .831 Cognitive Domain 22 22-132 42-122 91.41 14.45 .560 Environmental Preferences 12 12-72 12-72 51.40 14.08 .854 Happiness 29 29-174 44-168 113.17 19.77 .880 Environmental perception was rated with 6-point rating scale (semantic differential and likert-type) where higher the score, higher the perception towards environment and vice versa in terms of cognition, affection and preferences. In cases of happiness, the scenarios are similar—higher the scores, higher the happiness and lower the scores, lower the happiness respectively. Environmental perception scale consists of three different domains/factors—affective, cognitive and environmental preferences. For all the factors, it indicates higher the scores, higher the positive perceptions toward residential neighborhood. Two psychometric scales used in the study showed satisfactory reliability. Internal consistency reliability was estimated via SPSS calculating Coefficient Alpha. Inferential Statistics Independent sample t-test was conducted to seek whether there is any gender difference in neighborhood perception, and happiness among the participants. Table 2. Gender differences in neighborhood perception and happiness Variables Male Female t p M SD M SD Neighborhood Perception 179.98 30.26 178.87 33.76 -.338 .735 Happiness 115.02 18.02 111.74 20.95 -1.67 .095 Study found no significant gender difference in university students’ perception towards either neighborhood or happiness. In both cases, p value is much higher than .05 (.735, and .095 respectively; at 95% confidence level). Result indicates no variation between male and female students regarding their perception of neighborhood, and level of happiness. Table 3. Correlation among neighborhood perception and happiness **Correlation is significant at the 0.01 level (2-tailed) According to the correlation matrix, neighborhood perception (measured with cognitive, affective and environmental preferences) is significantly correlated with happiness (r =.43). All the domains of neighborhood perception showed a moderate positive correlation with happiness implying construct validity (convergent validity). Theoretically, all the constructs are positive therefore, correlations within the sub-scales and between the scales denote convergent validity. Simple linear regression was conducted considering the assumptions of regression—linearity, homoscedasticity, multicollinearity, normality and independence of errors (Tabachnick & Fidell, 2019). While the first three are essential for this study, testing normality and independence of errors are not so. The study conducted moderation analysis through Process SPSS Macro Hayas which supports bootstrapping; hence, normality of residuals is not a strict requirement (Hayes, 2018). Again, checking independence of errors is required when the study is longitudinal by nature unlike this cross-sectional study. Tests of linearity and homoscedasticity ensured that linearity (p = .000) is not deviated and the spread of data points around the regression line is roughly equal. Linearity is significant and deviation of linearity is not statistically significant (p = .113). Moreover, homoscedasticity is not violated also. Tests of multicollinearity found acceptable multicollinearity level (where VIF = 1.006, 1.084 & 1.054; tolerance = .994, .954, & .949). VIF values should be below 5 and tolerance be above 0.2 indicate for the acceptance level. Table 4. Regression of neighborhood perception on happiness among university students Model R 2 B β t p Neighborhood Perception .188 .266 .434 9.55 .000 Note: Happiness=dependent variable According to the regression table, neighborhood perception significantly predicts happiness (p value is much less than .05) though R 2 is .188 which indicates, independent variable, neighborhood perception can cause only 18% changes of the dependent variable, happiness. Coefficient results showed that, β value is .434 meaning that, one unit change of neighborhood perception would bring a change of happiness by .434 units or 43%. Moreover, a positive beta value denotes a positive correlation between predictor and outcome variables. In another word, a unit increase of neighborhood perception would accompany .434 units increase of happiness and vice versa. Moderation Analysis Moderation analysis was performed using centered variables by the Process SPSS Macro Hayas (Model 2). According to the analysis, altogether 23.23% of the variability in happiness ( p =.0000) was predicted by all the variables—neighborhood perception (NP), gender (G) and socio-economic status (SES) and the associations are significant. Table 5. Variability in happiness predicted by NP, G and SES Moderation Analysis R R 2 p Happiness .4820 .2323 .0000 To examine whether gender and SES moderate the relationship between neighborhood perception and happiness, a multiple linear regression analysis was conducted using interaction terms. Suppose, NP = X, Gender = M 1 , SES = M 2 , Happiness = Y (DV). So, the regression equation would be— Y= b₀ + b₁X + b₂M 1 + b 3 M 2 + b 4 (X × M 1 ) + b 5 (X × M 2 ) + e It can be expressed more vividly and practically as— Happiness = b₀ + b₁ (Neighborhood Perception) + b₂ (Gender) + b₃ (Socio-economic Status) + b 4 (Neighborhood Perception × Gender) + b 5 (Neighborhood Perception × Socio-economic Status) + error Here, multiple linear regression equation with two moderators (G and SES) included main effects of neighborhood perception (IV), and gender & socio-economic status (MODs), along with the interaction terms between neighborhood perception and the moderators. Neighborhood perception and happiness were treated as continuous variables, whereas gender (coded 0 for men and 1 for female) and SES (coded 1 for upper class, 2 for middle class and 3 for lower class) were included as categorical variables. Table 6. Effects of G and SES as moderators on the relationship between NP and Happ Results of moderation analysis showed that the interaction between NP and gender (p = .0393) and interaction between NP and SES (p = .0149) are statistically significant. Gender and socio-economic status both are functioning as moderators between the association of perceived neighborhood quality and perceived happiness among individuals. f-square effect size for gender as moderator is .0084 and according to the Cohen’s formula (f² = ΔR² / (1 − R²) of moderation effect size (small = 0.02, medium = 0.15, large = 0.35) a low moderating effect contributes significantly the association between the IV and DV (Lakens, 2013). This is an indicator of strong difference between two groups of gender (male vs. female). For SES as moderator, the effect size is .0118 implying a weak difference between the three social classes (upper, middle and lower). The interaction terms explained an additional 5% of the variance in happiness (ΔR² = .05), with a small effect size (f² = .0084 & .0118). Table 7. Simple slope analysis of the effects of moderators Note : LC = lower class, MC = middle class, UP = upper class According to the hypothesis, there exists a two-way linear relationship; gender and socio-economic status both was functioning as moderators on the association between perceived neighborhood and perceived happiness. Further, simple slope analysis was performed to better configure the nature of the moderating effect. Figure 2 presents the moderating role of gender in the association between neighborhood perception and perceived happiness. The interaction plot reveals that both male and female individuals experience increased happiness with improved neighborhood perception; however, the pattern of increase slightly differs between genders. At low levels of neighborhood perception, males reported notably higher happiness than females. This gender gap appears to narrow as neighborhood perception improves, suggesting that females may derive relatively greater psychological benefit from positive neighborhood characteristics. This aligns with gender-based frameworks in community psychology, which highlighted women often value communal support and environmental safety more than men. The interaction plot also exhibited a disordinal interaction between gender (coded as 0 = male, 1 = female) and perceived neighborhood quality in predicting happiness. While happiness increases for both males and females with higher neighborhood perception, the rate of increase is steeper for females. Males report higher happiness at low levels of neighborhood perception, but this trend reverses at higher levels where females report greater happiness than males. This crossover at the upper end suggests that perceived neighborhood quality may have a stronger positive impact on females’ happiness than on males. The pattern resembles a converging or reverse fan effect or partial cross over effect, and aligns with moderation literature on disordinal interactions (at low neighborhood perception, males are happier than females, at high neighborhood perception, females are happier than males). Figure 3 illustrates the moderating role of socio-economic status (SES) in the relationship between neighborhood perception and perceived happiness. A positive association is observed across all SES groups—happiness increases with better neighborhood perception. However, the strength of this association varies by SES. The slope is steepest for individuals from lower SES backgrounds, suggesting that they derive greater psychological benefit from improved neighborhood conditions, likely due to fewer buffering resources. In contrast, the relationship is weaker among upper SES holders, implying that their happiness is less dependent on neighborhood quality—possibly due to greater access to compensatory assets. These patterns indicate a significant interaction effect, supporting the contextual model of emotional well-being and highlight the relevance of environmental equity in promoting psychological outcomes. Discussion Current study aspired to understand the relationship between neighborhood perception and happiness considering the roles gender and socio-economic conditions on the relationship. A significant moderate positive association was found between neighborhood perception happiness (table 3). Simple linear regression analysis found that, neighborhood perception (which is comprised of cognitive and affective components along with environmental preferences) significantly predicted happiness among the university students (table 4). This indicates when individuals perceive their positive neighborhood characteristics such as comfort, safety, restorativeness, enclosure etc. highly, happiness also increases with the increase of positive perception. The opposite is true for lower level of perception, where happiness decreases as individuals poorly perceive their neighborhood characteristics. Such outcomes are aligned with the study which suggested the influence of neighborhood on cognitive and emotional dimensions of life. Study by Snedker & Hooven in 2013 revealed that neighborhood stress (perception) was a significant predictor for emotional well-being. Findings also fitted with the study by Stevens et al. (2023) where cognitive performance was depended on neighborhood perception and Gruebner et al. (2012) which reported that, mental well-being was dictated by perception of neighborhood. Findings parallel to the research conducted by Lauwers et al. (2021) denoted neighborhood aspects like security, coherence, and diversity had a regulating power for ensuring mental well-being. Neighborhood safety was reported as a significant predictor for well-being according to the study conducted by Yang et al. in 2021. and Choi & Matz-Costa in 2018. Similar facts were noted in the study by Toma et al. (2015) for the relationship between neighborhood disorder and poorer mental well-being—neighborhood threats diminished mental health and well-being. Two-way linear model of moderation analysis found gender as a significant moderator between the association of neighborhood perception and happiness (table 5). When individuals hold a lower-level of perception of their neighborhood qualities—safety, closure, comfort, activity, restorativeness etc. happiness is comparatively higher for males than females. The reverse happens when the score of perceived neighborhoods is high; denoting that females are happier than males at higher order perception toward Neighborhood (figure 2). Findings are consistent with the study by Daffon (2017) where there is an indication of gender as a significant predictor for happiness. Research conducted by Polko & Kimic (2022) also predicted the role of gender (male vs female) in differential perception of neighborhood safety. Statistically significant association was also evident between gender and neighborhood aspects like trust, political climate, residential environment etc. (Stafford et al., 2005). One interpretation is that women may initially experience greater vulnerability or dissatisfaction in low-quality neighborhoods due to concerns over safety, social trust, or environmental stressors (Stafford et al., 2005). However, as neighborhood perception becomes more positive—reflecting better infrastructure, aesthetics, and social cohesion—their well-being catches up with that of men, possibly due to heightened sensitivity to environmental quality and social connectedness (Morrison et al., 2003; Diener et al., 2003). The converging slopes at the high end suggest that while men consistently report higher happiness levels, women benefit more steeply from positive environmental evaluations. This has meaningful implications for urban planning and policy, indicating that gender-responsive strategies may be warranted when designing community environments that aim to enhance overall well-being. Study also found socio-economic status as a significant moderator on the association between perceived neighborhood and perceived happiness (table 6). The interaction effect displayed in Figure 3 provides compelling insight into how socio-economic status (SES) moderates the relationship between neighborhood perception and perceived happiness. Consistent with ecological and contextual models of well-being (Bronfenbrenner, 1979; Lent, 2004), individuals from lower SES backgrounds appear more sensitive to changes in neighborhood conditions. The steep positive slope for this group suggests that improvements in neighborhood perception may significantly enhance their happiness, possibly because these individuals lack other structural or psychological buffers (Evans, 2004). Middle SES individuals also show a positive association, but with a moderate gradient, indicating a balanced yet meaningful response to contextual quality. Interestingly, upper SES individuals exhibit the weakest slope—implying that their well-being is comparatively less contingent on neighborhood factors. This aligns with the resource substitution theory (Ross & Mirowsky, 2006), which posits that higher SES groups may rely on alternative sources of well-being, such as financial stability or access to leisure and healthcare services. These findings underscore the importance of socio-structural moderators in psychological outcomes and have policy implications. Specifically, targeted urban development and neighborhood improvement efforts may yield disproportionately greater psychological returns for lower-income populations, thereby promoting equity in subjective well-being (Marmot et al., 2008). Findings are consistent with the studies by Hackman et al. (2019) and Adaralegbe et al. (2021) which highlighted a connection between neighborhood affluence and mental health and well-being. Well-being significantly differs based on affluent and poor neighborhoods (Sui et al., 2022). Also, study by Liu et al. (2022) confirmed that, socio-economic situation od residential neighborhood had a significant impact on subjective well-being—especially affluent neighborhood promotes healthy life-styles that in turn enable well-being. Declarations Conflict of Interest Authors declared no conflict of interest. Funding Information A fund was awarded by the second author of this paper granted by the Centre for Advanced Study and Research in Biological Science located in the University of Dhaka, Dhaka, Bangladesh. Acknowledgement Special gratitude to all who somehow associated with the study and contributed to. Data Availability Statement Data associated with the research are available and can be shared upon request. Author’s Contribution Conceptualization, literature review, data collection, data processing and preparation of initial draft were done by the 1 st author (Khadiza Ahsan) of this paper. Research design was prepared and analysis was done jointly by the 1 st author and 2 nd author (Muhammad Kamal Uddin). Parallelly, 2 nd author supervised the whole paper. Third author (Mushrat Alam Shama) and 4 th author (Toma Adhikary) contributed during data collection and proof reading of the draft. References Adaralegbe, A. A., Adaralegbe, N. J., Moore, A., Iyanda, A. E., Olawaye, A., Aroyewun, O., Ezeani, E., Okunola, O. & Ayeni, O. (2021). Neighborhood Predictors of Mental Health of Older Americans: Evidence from a 5-year Longitudinal Study. Journal of Geriatric Medicine and Gerontology , 7 (3), 1-10. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design . Harvard University Press. Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons. Chang, S-H., Rutherford, R., Hsueh, M-C., Yu, Y-C., Park, J-H., Wang, S. & Liao, Y. (2021). 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Morrison, D., Petticrew, M., & Thomson, H. (2003). What are the most effective ways of improving population health through transport interventions? Evidence-based Public Health, 7 (1), 17–24. Muñoz, E., Scott, S. B., Corley, R., Wadsworth, S. J., Sliwinski, M. J., & Reynolds, C. A. (2020). The Role of Neighborhood Stressors on Cognitive Function: A coordinated Analysis. Health Place , 66:102442. Perneger, T. V., Courvoisier, D. S., Hudelson, P. M. & Gayet-Ageron, A. (2015). Sample size for pre-tests of questionnaires. Quality of Life Research, 24, 147–151. https://doi.org/10.1007/s11136-014-0752-2 Polko, P. & Kimic, K. (2022). Gender as a factor differentiating the perceptions of safety in urban parks. Ain Shams Engineering Journal , 13, 1-12. https://doi.org/10.1016/j.asej.2021.09.032 Ross, C. E., & Mirowsky, J. (2006). Sex differences in the effect of education on depression: resource multiplication or resource substitution? Social science & medicine, 63 (5), 1400–1413. https://doi.org/10.1016/j.socscimed.2006.03.013 Stafford, M., Cummins, S., Macintyre, S., Ellaway, A., Marmota, M. (2005). Gender differences in the associations between health and Neighborhood environment. Social Science & Medicine , 60, 1681–1692. Stafford, M., Chandola, T., & Marmot, M. (2005). Association between fear of crime and mental health and physical functioning. American Journal of Public Health, 97 (11), 2076–2081. https://doi.org/10.2105/AJPH.2006.097154 Snedker, K. & Hooven, C. (2013). Neighborhood Perceptions and Emotional Well-Being in Young Adulthood. Journal of Child and Adolescent Psychiatric Nursing , 26, 62–73. Stevens, M., Matosevic, T., Pinilla, M. S., Pais, S., Rossor, M. & Knapp, M. (2023). The link between cognitive health and Neighborhood: perceptions of the public, and of policy-makers, about problems and solutions. BMC Public Health , 23:1694, https://doi.org/10.1186/s12889-023-16592-w Sui, Y., Ettema, D. & Helbich, M. (2022). Longitudinal associations between the neighborhood social, natural, and built environment and mental health: A systematic review with meta-analyses. Health and Place , 77: 102893 https://doi.org/10.1016/j.healthplace.2022.102893 Snyder, C. R. & Shane, J. L. (2007). Positive Psychology: The Scientific and Practical Explorations of Human Strengths . Sage Publications. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7 th ed.). Pearson. Toma, A., Hamer, M. & Shankar, A. (2015). Associations between neighborhood perceptions and mental well-being among older adults. Health and Place. Vaalavuo, M., Kailaheimo-Lonnqvist, S., Kauppinen, T. M., & Sirnio, O. (2021). Neighborhood effects on psychiatric disorders among Finnish adolescents: The moderating impact of family background. Health and Place , 71: 102671, https://doi.org/10.1016/j.healthplace.2021.102671 Visser, K., Bolt, G., Finkenauer, C., Jonker, M., Weinberg, D. & Stevens, G. W. J. M. (2021). Neighbourhood deprivation effects on young people’s mental health and well-being: A systematic review of the literature. Social Science & Medicine , 270:113542, https://doi.org/10.1016/j.socscimed.2020.113542 Yang, M., Hagenaue, J., Dijst, M. & Helbich, M. (2021). Assessing the perceived changes in neighborhood physical and social environments and how they are associated with Chinese internal migrants’ mental health. BMC Public Health , 21:1240, https://doi.org/10.1186/s12889-021-11289-4 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7132642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485893022,"identity":"311be6bd-b68f-4ce7-bd34-8c3ee1657454","order_by":0,"name":"Khadiza Ahsan","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Khadiza","middleName":"","lastName":"Ahsan","suffix":""},{"id":485893023,"identity":"060a98c7-4c60-491c-bb0d-e4750b229f76","order_by":1,"name":"Dr. Md. Kamal Uddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYPACG8YGEMXYwEy0ljTStRwmQYvujAS2hz/bzstuOMD88APjDmvCWsxuJLAb87bdNt5wgM1YgvFMOlFa2KQZ224nbjjAYMbA2HaYOC2SP9vOAbWwfyNeiwRv2wGgFh5ibTnzgN2Y51yy8czDPMUSiUT55TgwxH6U2cn2HW/f+OEjMSHGwMAP9AIbkAbFSAIxGoAAqP4PkUpHwSgYBaNgZAIAFB06xN09wvcAAAAASUVORK5CYII=","orcid":"","institution":"University of Dhaka","correspondingAuthor":true,"prefix":"Dr.","firstName":"Md.","middleName":"Kamal","lastName":"Uddin","suffix":""},{"id":485893024,"identity":"0bfed137-1ea2-41e1-9052-5e40d7956254","order_by":2,"name":"Mushrat Alam Shama","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Mushrat","middleName":"Alam","lastName":"Shama","suffix":""},{"id":485893025,"identity":"edd81fde-7f6e-46cd-80cd-9a58d96d257b","order_by":3,"name":"Toma Adhikary","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Toma","middleName":"","lastName":"Adhikary","suffix":""}],"badges":[],"createdAt":"2025-07-15 16:23:27","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7132642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7132642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86829784,"identity":"664c0987-9397-4788-acf8-42d90727c595","added_by":"auto","created_at":"2025-07-16 05:53:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGender and socio-economic status as moderator on the interaction between perceived Neighborhood and the state of being happy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7132642/v1/e00a3799113b47365a815bcc.png"},{"id":86830708,"identity":"cbd12163-b3e5-46e6-9f03-7340487816af","added_by":"auto","created_at":"2025-07-16 06:01:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating effect of gender on the association between neighborhood perception and perceived happiness\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7132642/v1/85c69e0904cadfded0867f5d.png"},{"id":86829775,"identity":"88932d3f-9473-4321-8aee-859c84a4073a","added_by":"auto","created_at":"2025-07-16 05:53:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating effect of socio-economic status on the association between neighborhood perception and perceived happiness\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7132642/v1/0560d4cd479484061cdd81b7.png"},{"id":86830709,"identity":"b15826b1-1256-4489-a658-c5e28bbe50e8","added_by":"auto","created_at":"2025-07-16 06:01:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":952537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7132642/v1/16875e07-29d3-436f-83a9-e26822d389c5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNeighborhood Perception and Happiness among University Students: Moderating Effects of Gender and Socio-economic Status\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeighborhood is denoted as an area where people live and interact with each other but often the boundary of neighborhood is fuzzy to define since it is hard to delimit the starting and ending points (National Geography Society, 2024). Happiness is a positive construct which indicates the presence of positive affect, and absence of negative affect. Although, in hedonic perspective, happiness and emotional well-being are synonymous, the eudaimonic paradigm suggested that, emotional well-being is comprised of happiness and a meaning of life (Snyder \u0026amp; Shane, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrowing evidences indicate a strong association between neighborhood perception and status of mental health and well-being. Environment of a neighborhood, whether it contains a therapeutic landscape or has the potential to cause neighborhood disorder, obviously holds a power to stimulate the dwellers which in turn is reflected via the outcomes of mental health, often physical health also. Deprived neighborhood is a determinant of poor mental well-being whereas therapeutic neighborhood has a healing power. Outcomes of perceived neighborhood can be juxtaposed with thoughts and affections; which in turn can have an effect on happiness, a component of subjective well-being. Various neighborhood backgrounds (socio-economic and environmental) can have their respective effects on mental and emotional well-beings. Therefore, current study would link to cognitive as well as affective components with residential environment of both urban and rural.\u003c/p\u003e\u003cp\u003eExisting literatures supported the theme. Perceived neighborhood characteristics like neighborhood stressors are significant indicators of emotional well-being. Study was conducted showing the link between such stressors and mental health disorders among young adults in USA. It reflected mental health outcomes of neighborhood stressors pointing out three risk factors— depressed affect, hopelessness, and anger as components of poor emotional well-being (Snedker \u0026amp; Hooven, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Another related study focused an association between cognition and neighborhood‑related factors like social isolation, social interactions, access to green, environmental pollution, safety, traffic, noise, physical inactivity etc. The qualitative study showed that, cognitive health and brain functioning are dependent on the nature of perception toward neighborhood (Stevens et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA geoepidemiological study centering the mental health status of the slum dwellers in Dhaka megacity of Bangladesh found that, mental well-being of the dwellers is closely associated with the neighborhood perception of slum settlements. The study confirmed, there is a link between physiological well-being and socio-physical environment (Gruebner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). That neighborhood perception has a strong connection with mental well-being, has also been revealed by a study of UK regarding neighborhood disorder. Negative perceptions of neighborhood mean high level of neighborhood disorder and poorer mental well-being. The opposite is true for positive perceptions (Toma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Systematic review research also confirmed that, neighborhood deprivation or disorder can lead to poorer mental health and well-being (Visser et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePerceived neighborhood safety is another vital component that has significant correlation with psychological health (Choi \u0026amp; Matz-Costa, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another study of Netherlands showed that, social and physical neighborhoods are significant determinants of depression (Helbich et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A qualitative study centering impacts of urban neighborhood environment on mental well-being in Belgium found neighbor diversity, social security and neighbor ties as crucial components that have associations regulating mental well-being (Lauwers et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Migrant’s mental state and well-being are also affected by perceived neighborhood.\u003c/p\u003e\u003cp\u003eA Chinese cross-sectional study presented comparative analysis of migrants’ risk of mental illness prior to and after the migration to host country. Neighborhood safety, aesthetics, and access to green space were the factors assumed to be correlated with higher level of well-being and the decline of these led to mental distress. The study indicated neighborhood safety as the most influential variable in this regard (Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA Swiss experimental study comparing disadvantaged neighborhood with affluent neighborhood found that, disadvantaged neighborhood is liable for worse health even early mortality. Such environment elicits stress and emotional responses which in turn threat both mental and physical health (Hackman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An American study predicted depression and anxiety due to ‘\u003cem\u003eNeighborhood Danger\u003c/em\u003e’ among older adults. Therefore, poor or unwelcomed neighborhood is a potential risk factor for mental health (Adaralegbe et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Not only neighborhood conditions affect mental health, it also deteriorates cognitive functioning. Perceived neighborhood stressors can lessen working memory, cause lower performance in spatial abilities, and disturb executive functioning of the brain during adulthood. Lower safety, higher rate of crime, lower aesthetic quality, less social cohesion etc. were labelled as neighborhood stressors (Muñoz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple outcomes of mental health like depression, anxiety etc. have been identified because of unsatisfactory neighborhood conditions of natural, social, and built environments (Sui et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Often the effects of poor neighborhoods can be liable for the generation of psychiatric diseases beyond mere mental health symptoms like depression and anxiety. A Finish study dealt with the effects of childhood neighborhood context and its after effects on the psychiatric issues during adolescence (Vaalavuo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Characteristics of perceived neighborhood regulates the mechanisms behind physical and psychological health status and life satisfaction.\u003c/p\u003e\u003cp\u003eA Chinese study drew attention focusing on affluent neighborhood which promote healthy life styles and adequate physical activity. It also bears a healing power that can enhance life satisfaction (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Again, evidences suggested that, favorable neighborhood environments can be characterized as having the quality of reducing sedentary behaviours like smoking, drinking, gambling etc. (Chang et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Neighborhood perception also varies based on gender differences (Stafford et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A Polish study showed differential safety perception of urban neighborhood between male and female (Polko \u0026amp; Kimic, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Life satisfaction, positive emotions and psychological well-being which combinedly measure happiness— are also regulated by gender differences, according to literatures (Daffon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the literatures reviewed, a research gap has been found regarding the effects of perceived neighborhoods on level of happiness among young adults in the context of Bangladesh. This study intends to capture responses from the students of the University of Dhaka, as it is a place of wide diversity based on regional differences. Environment and mental health both of the domains are seeking much more attention today. Issues of mental health and happiness can better be configured if environmental impacts are to be investigated. Perceived neighborhood (environmental- therapeutic, favorable, congested, polluted; socio-economic- affluent, deprived, safe, unsafe etc.) and mental health are strongly associated with according to the research evidences. Also, urban planning and development can be impacted by the consequences derived from the relations between emotional well-being specially happiness among dwellers and neighborhood perception. Such multidisciplinary research is a desideratum of current world, indeed.\u003c/p\u003e\u003cp\u003eThe objective of this study is to investigate the relationships between neighborhood perception and happiness among university students considering the roles of gender and socio-economic status. In order to reach the objectives, following research questions were outlined—\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIs there any significant association between neighborhood perception and happiness?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDoes neighborhood perception significantly predict happiness among university students?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDoes gender moderate the relationship between neighborhood perception and happiness?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDoes socio-economic status moderate the relationship between neighborhood perception and happiness?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis quantitative study was conducted following a correlational design.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Size and Sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants were recruited based on specific characteristics relevant to the research questions. The only inclusion criterion is that participants are the university students age ranging from 18 to 28 years (both male and female). Purposive sampling is methodologically appropriate in constrained settings when subgroup diversity is essential for analysis (Etikan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, purposive sampling was applied. A pre-test was run as a preliminary data collection for this research taking a sample of 30 from the target population (Perneger et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Results of pre-test ensured the face validity of the scales and allowed the study to go ahead. To determine an appropriate sample size from a finite population of approximately 46,000 (Wikipedia, 2025), the current students of the target organization (University of Dhaka), Cochran’s formula (1977) was employed. According to the formula, minimum required sample size for this study is 382.\u003c/p\u003e\u003cp\u003eThe figure was calculated using the finite population correction (FPC) formula: n = n₀ / [1 + (n₀ − 1)/N] where N = 46,000. Thus, n = 385 / [1 + (384 / 46000)] ≈ 385 / 1.00835 ≈ 382. Therefore, a minimum of 382 participants was required to achieve a representative sample with a 95% confidence level and ± 5% margin of error. The current study employed a sample of 400 students, which exceeds the minimum requirement, thereby ensuring sufficient precision, statistical power, and representativeness of the target population. Although a purposive sampling technique was employed in this study due to practical and contextual considerations, Cochran’s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) formula was applied to ensure adequacy and approximate representativeness of the selected participants. Moreover, it would guarantee maintaining statistical power, reducing sampling bias, and enhancing the credibility of the findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasuring Tools\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA structured questionnaire survey was conducted for data collection. Two translated (Bangla version) psychometric questionnaires were used to quantify environmental perception, as well as the level of happiness—respectively named as Environmental Perception Scale (EPS) (Ho \u0026amp; Au, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Oxford Happiness Questionnaire (OHQ) (Hills \u0026amp; Argyle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The multidimensional Environmental Perception Scale (EPS) is constituted of 3 domains named as cognitive, affective and environmental preferences. Each domains have their respective sub-scales under which there are several items. The cognitive domain contains 6 factors; affective domain has 2 factors and environmental perception domain bears 3 factors—all together the whole scale has 11 factors and 42 items.\u003c/p\u003e\u003cp\u003eThe affective domain has 8 items dividing themselves into 2 factors—\u003cem\u003ecomfort\u003c/em\u003e and \u003cem\u003eactivity\u003c/em\u003e, with a scoring pattern of semantic deferential scale with a bipolar response format ranging from 1 to 6 denoting worst to best respectively. Again, there are 6 factors in the cognitive domain named as—\u003cem\u003elegibility, enclosure, complexity, crime potential, wildlife\u003c/em\u003e, and \u003cem\u003elighting\u003c/em\u003e. These factors include 22 items in total. The remaining domain environmental preferences includes 3 factors like—\u003cem\u003ePerceived Restorativeness, Perceived Safety\u003c/em\u003e and \u003cem\u003ePerceived Visitability\u003c/em\u003e which together have 12 items. The last two domains used 6-point Likert-type scale for the responses where \u003cem\u003estrongly agree\u003c/em\u003e and \u003cem\u003estrongly disagree\u003c/em\u003e were the two extremes.\u003c/p\u003e\u003cp\u003eThe Oxford Happiness Questionnaire (OHQ) is unidimensional having 29 items. Response format is 6-point Likert-type scale ranging from \u003cem\u003estrongly disagree to strongly agree\u003c/em\u003e. The Oxford Happiness Questionnaire (OHQ) measured happiness numerically coded from 1 to 6 where, 1 denotes the least happy state and 6 indicates highest level of happiness. Like the construct itself, all the items are worded positively; hence, higher the total score, higher the happiness. Similar norm was applicable for the Environmental Perception Scale (EPS), higher the composite score (for each domain), higher the positive attitude of the residents (of the particular neighborhood) toward their hosting environment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic data were subjected to statistical analysis calculating Mean, SD, frequency and range. Among the 400 university students, 56.3% were female and male grabbed 43.7% of the sample. Age of the participants ranged from 18 years to 28 years having a mean of 22.54 years and mode of 24 years. Among the participants, 49.2% belong to middle class, 26.5% were from upper class and 24.3% reported as residents of lower class. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescriptive statistics of environmental perception, and level of happiness\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN of Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9515%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePossible Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\u003cbr\u003e\u003cstrong\u003eObserved Range\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\u003cbr\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003eEnvironmental Perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4146%;\"\u003e\n \u003cp\u003e42─252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\n \u003cp\u003e88─240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\n \u003cp\u003e179.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e32.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; .938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003eAffective Domain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4146%;\"\u003e\n \u003cp\u003e8-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\n \u003cp\u003e8-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\n \u003cp\u003e36.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003eCognitive Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4146%;\"\u003e\n \u003cp\u003e22-132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\n \u003cp\u003e42-122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\n \u003cp\u003e91.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;.560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003e\u0026nbsp; Environmental Preferences \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4146%;\"\u003e\n \u003cp\u003e12-72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\n \u003cp\u003e12-72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\n \u003cp\u003e51.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6782%;\"\u003e\n \u003cp\u003eHappiness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.655%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4146%;\"\u003e\n \u003cp\u003e29-174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1278%;\"\u003e\n \u003cp\u003e44-168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.3391%;\"\u003e\n \u003cp\u003e113.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.8895%;\"\u003e\n \u003cp\u003e19.77 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4728%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEnvironmental perception was rated with 6-point rating scale (semantic differential and likert-type) where higher the score, higher the perception towards environment and vice versa in terms of cognition, affection and preferences. In cases of happiness, the scenarios are similar\u0026mdash;higher the scores, higher the happiness and lower the scores, lower the happiness respectively. Environmental perception scale consists of three different domains/factors\u0026mdash;affective, cognitive and environmental preferences. For all the factors, it indicates higher the scores, higher the positive perceptions toward residential neighborhood. Two psychometric scales used in the study showed satisfactory reliability. Internal consistency reliability was estimated via SPSS calculating Coefficient Alpha.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInferential Statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent sample t-test was conducted to seek whether there is any gender difference in neighborhood perception, and happiness among the participants. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Gender differences in neighborhood perception and happiness\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eNeighborhood Perception\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 179.98 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e30.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e178.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;33.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; -.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 115.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e111.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; -1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStudy found no significant gender difference in university students\u0026rsquo; perception towards either neighborhood or happiness. In both cases, p\u003cem\u003e\u0026nbsp;\u003c/em\u003evalue is much higher than .05 (.735, and .095 respectively; at 95% confidence level). Result indicates no variation between male and female students regarding their perception of neighborhood, and level of happiness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Correlation among neighborhood perception and happiness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 635px; height: 235.122px;\" width=\"635\" height=\"235.122\"\u003e\u003c/p\u003e\n\u003cp\u003e**Correlation is significant at the 0.01 level (2-tailed) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the correlation matrix, neighborhood perception (measured with cognitive, affective and environmental preferences) is significantly correlated with happiness (r =.43). All the domains of neighborhood perception showed a moderate positive correlation with happiness implying construct validity (convergent validity). Theoretically, all the constructs are positive therefore, correlations within the sub-scales and between the scales denote convergent validity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimple linear regression was conducted considering the assumptions of regression\u0026mdash;linearity, homoscedasticity, multicollinearity, normality and independence of errors (Tabachnick \u0026amp; Fidell, 2019). While the first three are essential for this study, testing normality and independence of errors are not so. The study conducted moderation analysis through Process SPSS Macro Hayas which supports bootstrapping; hence, normality of residuals is not a strict requirement (Hayes, 2018). Again, checking independence of errors is required when the study is longitudinal by nature unlike this cross-sectional study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTests of linearity and homoscedasticity ensured that linearity (p = .000) is not deviated and the spread of data points around the regression line is roughly equal. Linearity is significant and deviation of linearity is not statistically significant (p = .113). Moreover, homoscedasticity is not violated also. Tests of multicollinearity found acceptable multicollinearity level (where VIF = 1.006, 1.084 \u0026amp; 1.054; tolerance = .994, .954, \u0026amp; .949). VIF values should be below 5 and tolerance be above 0.2 indicate for the acceptance level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eRegression of neighborhood perception on happiness among university students \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;t\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;p\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNeighborhood Perception\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Happiness=dependent variable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the regression table, neighborhood perception significantly predicts happiness (p value is much less than .05) though R\u003csup\u003e2\u003c/sup\u003e is .188 which indicates, independent variable, neighborhood perception can cause only 18% changes of the dependent variable, happiness. Coefficient results showed that, \u0026beta; value is .434 meaning that, one unit change of neighborhood perception would bring a change of happiness by .434 units or 43%. Moreover, a positive beta value denotes a positive correlation between predictor and outcome variables. In another word, a unit increase of neighborhood perception would accompany .434 units increase of happiness and vice versa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModeration Analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModeration analysis was performed using centered variables by the Process SPSS Macro Hayas (Model 2). According to the analysis, altogether 23.23% of the variability in happiness (\u003cem\u003ep\u003c/em\u003e=.0000) was predicted by all the variables\u0026mdash;neighborhood perception (NP), gender (G) and socio-economic status (SES) and the associations are significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eVariability in happiness predicted by NP, G and SES\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModeration Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;p\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHappiness \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e.4820 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; .2323 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo examine whether gender and SES moderate the relationship between neighborhood perception and happiness, a multiple linear regression analysis was conducted using interaction terms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSuppose, NP = X, Gender = M\u003csub\u003e1\u003c/sub\u003e, SES = M\u003csub\u003e2\u003c/sub\u003e, Happiness = Y (DV). So, the regression equation would be\u0026mdash; Y= b₀ + b₁X + b₂M\u003csub\u003e1\u003c/sub\u003e + b\u003csub\u003e3\u003c/sub\u003eM\u003csub\u003e2\u003c/sub\u003e + b\u003csub\u003e4\u0026nbsp;\u003c/sub\u003e(X \u0026times; M\u003csub\u003e1\u003c/sub\u003e) + b\u003csub\u003e5\u0026nbsp;\u003c/sub\u003e(X \u0026times; M\u003csub\u003e2\u003c/sub\u003e) + e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt can be expressed more vividly and practically as\u0026mdash; Happiness = b₀ + b₁ (Neighborhood Perception) + b₂ (Gender) + b₃ (Socio-economic Status) + b\u003csub\u003e4\u0026nbsp;\u003c/sub\u003e(Neighborhood Perception \u0026times; Gender) + b\u003csub\u003e5\u0026nbsp;\u003c/sub\u003e(Neighborhood Perception \u0026times; Socio-economic Status) + error \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, multiple linear regression equation with two moderators (G and SES) included main effects of neighborhood perception (IV), and gender \u0026amp; socio-economic status (MODs), along with the interaction terms between neighborhood perception and the moderators.\u003c/p\u003e\n\u003cp\u003eNeighborhood perception and happiness were treated as continuous variables, whereas gender (coded 0 for men and 1 for female) and SES (coded 1 for upper class, 2 for middle class and 3 for lower class) were included as categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eEffects of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eG and SES as moderators on the relationship between NP and Happ\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"745\" height=\"274\"\u003e\u003c/p\u003e\n\u003cp\u003eResults of moderation analysis showed that the interaction between NP and gender (p = .0393) and interaction between NP and SES (p = .0149) are statistically significant. Gender and socio-economic status both are functioning as moderators between the association of perceived neighborhood quality and perceived happiness among individuals. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ef-square effect size for gender as moderator is .0084 and according to the Cohen\u0026rsquo;s formula (f\u0026sup2; = \u0026Delta;R\u0026sup2; / (1 \u0026minus; R\u0026sup2;) of moderation effect size (small = 0.02, medium = 0.15, large = 0.35) a low moderating effect contributes significantly the association between the IV and DV (Lakens, 2013). This is an indicator of strong difference between two groups of gender (male vs. female). For SES as moderator, the effect size is .0118 implying a weak difference between the three social classes (upper, middle and lower). The interaction terms explained an additional 5% of the variance in happiness (\u0026Delta;R\u0026sup2; = .05), with a small effect size (f\u0026sup2; = .0084 \u0026amp; .0118). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u0026nbsp;\u003c/strong\u003eSimple slope analysis of the effects of moderators\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"668\" height=\"317\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: LC = lower class, MC = middle class, UP = upper class\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the hypothesis, there exists a two-way linear relationship; gender and socio-economic status both was functioning as moderators on the association between perceived neighborhood and perceived happiness. Further, simple slope analysis was performed to better configure the nature of the moderating effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 presents the moderating role of gender in the association between neighborhood perception and perceived happiness. The interaction plot reveals that both male and female individuals experience increased happiness with improved neighborhood perception; however, the pattern of increase slightly differs between genders.\u003c/p\u003e\u003cp\u003eAt low levels of neighborhood perception, males reported notably higher happiness than females. This gender gap appears to narrow as neighborhood perception improves, suggesting that females may derive relatively greater psychological benefit from positive neighborhood characteristics. This aligns with gender-based frameworks in community psychology, which highlighted women often value communal support and environmental safety more than men. \u003c/p\u003e\n\u003cp\u003eThe interaction plot also exhibited a disordinal interaction between gender (coded as 0 = male, 1 = female) and perceived neighborhood quality in predicting happiness. While happiness increases for both males and females with higher neighborhood perception, the rate of increase is steeper for females. Males report higher happiness at low levels of neighborhood perception, but this trend reverses at higher levels where females report greater happiness than males. This crossover at the upper end suggests that perceived neighborhood quality may have a stronger positive impact on females\u0026rsquo; happiness than on males. The pattern resembles a converging or reverse fan effect or partial cross over effect, and aligns with moderation literature on disordinal interactions (at low neighborhood perception, males are happier than females, at high neighborhood perception, females are happier than males). \u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the moderating role of socio-economic status (SES) in the relationship between neighborhood perception and perceived happiness. A positive association is observed across all SES groups\u0026mdash;happiness increases with better neighborhood perception. However, the strength of this association varies by SES. The slope is steepest for individuals from lower SES backgrounds, suggesting that they derive greater psychological benefit from improved neighborhood conditions, likely due to fewer buffering resources. In contrast, the relationship is weaker among upper SES holders, implying that their happiness is less dependent on neighborhood quality\u0026mdash;possibly due to greater access to compensatory assets. These patterns indicate a significant interaction effect, supporting the contextual model of emotional well-being and highlight the relevance of environmental equity in promoting psychological outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrent study aspired to understand the relationship between neighborhood perception and happiness considering the roles gender and socio-economic conditions on the relationship. A significant moderate positive association was found between neighborhood perception happiness (table 3). Simple linear regression analysis found that, neighborhood perception (which is comprised of cognitive and affective components along with environmental preferences) significantly predicted happiness among the university students (table 4). This indicates when individuals perceive their positive neighborhood characteristics such as comfort, safety, restorativeness, enclosure etc. highly, happiness also increases with the increase of positive perception. The opposite is true for lower level of perception, where happiness decreases as individuals poorly perceive their neighborhood characteristics. Such outcomes are aligned with the study which suggested the influence of neighborhood on cognitive and emotional dimensions of life. \u003c/p\u003e\n\u003cp\u003eStudy by Snedker \u0026amp; Hooven in 2013 revealed that neighborhood stress (perception) was a significant predictor for emotional well-being. Findings also fitted with the study by Stevens et al. (2023) where cognitive performance was depended on neighborhood perception and Gruebner et al. (2012) which reported that, mental well-being was dictated by perception of neighborhood. Findings parallel to the research conducted by Lauwers et al. (2021) denoted neighborhood aspects like security, coherence, and diversity had a regulating power for ensuring mental well-being. Neighborhood safety was reported as a significant predictor for well-being according to the study conducted by Yang et al. in 2021. and Choi \u0026amp; Matz-Costa in 2018. Similar facts were noted in the study by Toma et al. (2015) for the relationship between neighborhood disorder and poorer mental well-being—neighborhood threats diminished mental health and well-being. \u003c/p\u003e\n\u003cp\u003eTwo-way linear model of moderation analysis found gender as a significant moderator between the association of neighborhood perception and happiness (table 5). When individuals hold a lower-level of perception of their neighborhood qualities—safety, closure, comfort, activity, restorativeness etc. happiness is comparatively higher for males than females. The reverse happens when the score of perceived neighborhoods is high; denoting that females are happier than males at higher order perception toward Neighborhood (figure 2). Findings are consistent with the study by Daffon (2017) where there is an indication of gender as a significant predictor for happiness. Research conducted by Polko \u0026amp; Kimic (2022) also predicted the role of gender (male vs female) in differential perception of neighborhood safety. Statistically significant association was also evident between gender and neighborhood aspects like trust, political climate, residential environment etc. (Stafford et al., 2005). \u003c/p\u003e\n\u003cp\u003eOne interpretation is that women may initially experience greater vulnerability or dissatisfaction in low-quality neighborhoods due to concerns over safety, social trust, or environmental stressors (Stafford et al., 2005). However, as neighborhood perception becomes more positive—reflecting better infrastructure, aesthetics, and social cohesion—their well-being catches up with that of men, possibly due to heightened sensitivity to environmental quality and social connectedness (Morrison et al., 2003; Diener et al., 2003). The converging slopes at the high end suggest that while men consistently report higher happiness levels, women benefit more steeply from positive environmental evaluations. This has meaningful implications for urban planning and policy, indicating that gender-responsive strategies may be warranted when designing community environments that aim to enhance overall well-being.\u003c/p\u003e\n\u003cp\u003eStudy also found socio-economic status as a significant moderator on the association between perceived neighborhood and perceived happiness (table 6). The interaction effect displayed in Figure 3 provides compelling insight into how socio-economic status (SES) moderates the relationship between neighborhood perception and perceived happiness. Consistent with ecological and contextual models of well-being (Bronfenbrenner, 1979; Lent, 2004), individuals from lower SES backgrounds appear more sensitive to changes in neighborhood conditions. The steep positive slope for this group suggests that improvements in neighborhood perception may significantly enhance their happiness, possibly because these individuals lack other structural or psychological buffers (Evans, 2004).\u003c/p\u003e\n\u003cp\u003eMiddle SES individuals also show a positive association, but with a moderate gradient, indicating a balanced yet meaningful response to contextual quality. Interestingly, upper SES individuals exhibit the weakest slope—implying that their well-being is comparatively less contingent on neighborhood factors. This aligns with the resource substitution theory (Ross \u0026amp; Mirowsky, 2006), which posits that higher SES groups may rely on alternative sources of well-being, such as financial stability or access to leisure and healthcare services. These findings underscore the importance of socio-structural moderators in psychological outcomes and have policy implications. Specifically, targeted urban development and neighborhood improvement efforts may yield disproportionately greater psychological returns for lower-income populations, thereby promoting equity in subjective well-being (Marmot et al., 2008). \u003c/p\u003e\n\u003cp\u003eFindings are consistent with the studies by Hackman et al. (2019) and Adaralegbe et al. (2021) which highlighted a connection between neighborhood affluence and mental health and well-being. Well-being significantly differs based on affluent and poor neighborhoods (Sui et al., 2022). Also, study by Liu et al. (2022) confirmed that, socio-economic situation od residential neighborhood had a significant impact on subjective well-being—especially affluent neighborhood promotes healthy life-styles that in turn enable well-being. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA fund was awarded by the second author of this paper granted by the Centre for Advanced Study and Research in Biological Science located in the University of Dhaka, Dhaka, Bangladesh. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial gratitude to all who somehow associated with the study and contributed to. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData associated with the research are available and can be shared upon request. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, literature review, data collection, data processing and preparation of initial draft were done by the 1\u003csup\u003est\u003c/sup\u003e author (Khadiza Ahsan) of this paper. Research design was prepared and analysis was done jointly by the 1\u003csup\u003est\u003c/sup\u003e author and 2\u003csup\u003end\u003c/sup\u003e author (Muhammad Kamal Uddin). Parallelly, 2\u003csup\u003end\u003c/sup\u003e author supervised the whole paper. Third author (Mushrat Alam Shama) and 4\u003csup\u003eth\u003c/sup\u003e author (Toma Adhikary) contributed during data collection and proof reading of the draft. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdaralegbe, A. A., Adaralegbe, N. J., Moore, A., Iyanda, A. E., Olawaye, A., Aroyewun, O., Ezeani, E., Okunola, O. \u0026amp; Ayeni, O. (2021). Neighborhood Predictors of Mental Health of Older Americans: Evidence from a 5-year Longitudinal Study. \u003cem\u003eJournal of Geriatric Medicine and Gerontology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 1-10. \u003c/li\u003e\n\u003cli\u003eBronfenbrenner, U. 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Assessing the perceived changes in neighborhood physical and social environments and how they are associated with Chinese internal migrants\u0026rsquo; mental health. \u003cem\u003eBMC Public Health\u003c/em\u003e, 21:1240, https://doi.org/10.1186/s12889-021-11289-4 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Dhaka","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neighborhood perception, happiness, gender differences, socio-economic status, health geography, subjective well-being ","lastPublishedDoi":"10.21203/rs.3.rs-7132642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7132642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePerceptions about the residential neighborhood environment significantly influence individuals\u0026rsquo; emotional well-being and life satisfaction. For university students, who often experience transitions in living situations and social environments, understanding how these perceptions relate to happiness is essential. This study investigates the associations between students\u0026rsquo; neighborhood perceptions and their happiness levels, examining the moderating effect of gender and socio-economic status. A cross-sectional survey of 400 students (56.3% female, 43.7% male; average age 22.54 years) was conducted using standardized measures of environmental perception and happiness. Correlational, regression, and moderation analyses revealed a positive association between neighborhood perception and happiness. Gender moderated this relationship in a crossover pattern: males reported greater happiness at lower neighborhood perception levels, whereas females showed higher happiness at more positive perceptions. Also, socio-economic status functioned as a significant moderator between perceived neighborhood and perceived happiness. Upper-class residents are less responsive to perceived happiness with a corresponding increase in neighborhood positivity unlike the lower-class individuals who manifested a greater subjective well-being comparatively in response to the enhancement of neighborhood quality. These findings underscore the dynamic interaction between environmental perceptions, gender and socio-economic status in shaping happiness, with important implications for designing residential spaces and mental health initiatives that support diverse populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Neighborhood Perception and Happiness among University Students: Moderating Effects of Gender and Socio-economic Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 05:53:08","doi":"10.21203/rs.3.rs-7132642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ed6293d-c0ad-44ee-a24e-640375b8aff5","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51602014,"name":"Gender Studies"},{"id":51602015,"name":"Psychology"},{"id":51602016,"name":"Behavioral Geography"},{"id":51602017,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2025-07-16T05:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-16 05:53:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7132642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7132642","identity":"rs-7132642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}