JMA Journal
  • Twitter
  • Mail
Short Communication

Anxiety and Well-Being: A Factorial and Regression Analysis of Life Satisfaction Determinants in a Japanese Population

Haruka Ishii1, and Yoko Ishii2
1Academic Assembly Faculty of Medicine, University of Toyama, Toyama, Japan
2Graduate School of Health and Nutrition Sciences, The University of Nagano, Nagano, Japan

Corresponding author: Yoko Ishii, ishii.yoko@u-nagano.ac.jp

DOI: 10.31662/jmaj.2025-0383

Received: July 30, 2025
Accepted: November 15, 2025
Advance Publication: February 6, 2026
Published: March 16, 2026

Cite this article as:
Ishii H, Ishii Y. Anxiety and Well-Being: A Factorial and Regression Analysis of Life Satisfaction Determinants in a Japanese Population. JMA J. 2026;9(2):563-570.

Key words: subjective well-being, life satisfaction, anxiety-related factors

Introduction

Self-reports of psychological well-being have become a subject of intense debate in public policy and economics, with the improvement of population well-being emerging as a key societal goal. In its well-being survey How’s Life 2020?, the Organization for Economic Co-operation and Development (OECD) reported that Japanese respondents rated their overall life satisfaction at an average of 6.1 on a scale of 0 to 10―lower than the OECD average of 6.7 (1).

Previous frameworks such as the OECD Better Life Index and the Social Production Function theory have identified income and jobs, health and life satisfaction, and environmental quality as key components of well-being (2), (3).

Diener et al. (4) proposed theories suggesting that various components of subjective well-being―such as life satisfaction, positive affect, and negative affect―are influenced by different factors. A study on perceptions of past, present, and future life satisfaction among adults aged 33-79 years found that Japanese adults began to expect declines in life satisfaction at an earlier age than their United States counterparts (5).

Nishimura and Yagi conducted a factor analysis of the Oxford Happiness Questionnaire (6), a widely used measure of psychological well-being, to identify factors related to positive ideation and anxiety. In a sample of 20,000 Japanese individuals, they found that the negative correlation between anxiety and subjective well-being was stronger than the positive correlation with positive ideation (7). This suggests that anxiety-related factors have a more substantial impact on the subjective well-being of Japanese people than positive factors associated with satisfaction, contributing to lower overall life satisfaction.

Anxious temperament has also been identified as a significant risk factor for suicide-related ideation in the Japanese adult population (8). Despite its importance, research on anxiety-related determinants of subjective well-being in Japan remains limited (5), (9). In light of the limited empirical evidence, the present study employs factor analysis to identify latent psychosocial dimensions―including anxiety-related factors―and examines their relative contributions to life satisfaction in the Japanese population. By incorporating anxiety-related variables into a multidimensional framework, we aim to provide a more comprehensive understanding of the mechanisms underlying life satisfaction and to inform future research and policy development.

Materials and Methods

Data were obtained from the 2022 Survey on Satisfaction and Quality of Life. This survey was deposited by the Cabinet Office of Japan, conducted by Survey Research Center Co., Ltd. in February 2022, and provided through the Social Science Japan Data Archive, Center for Social Research and Data Archives, Institute of Social Science, The University of Tokyo. The target population consisted of internet panel registrants residing in Japan, aged between 15 and 89 years. Sampling was based on prefecture, gender, and age group composition ratios. A total of 10,631 respondents were included. Questions on life satisfaction and related factors were drawn from the survey instrument.

Life satisfaction was assessed with the question: “How satisfied are you with your current life overall?” Responses were given on a scale from 0 (“Not satisfied at all”) to 10 (“Very satisfied”).

Anxiety-related factors were assessed with the question: “When thinking about the future, how would you rate your level of anxiety in various aspects of life?” Responses were given on a scale from 0 (“Very anxious”) to 10 (“Not anxious at all”) for 13 categories For statistical processing, scores were converted from 0-10 to 1-11 for ease of calculation.

The Shapiro-Wilk test indicated that life satisfaction scores were not normally distributed. Therefore, non-parametric tests were applied: the Mann-Whitney U test for two-group comparisons and the Kruskal-Wallis test for three or more groups, with adjusted significance levels for pairwise comparisons. Inter-factor correlations were assessed using Spearman’s rank correlation coefficient. Internal consistency was evaluated using Cronbach’s alpha. Factor analysis was performed using the principal factor method with Promax rotation, and multiple regression analysis was conducted with extracted factors as independent variables.

All analyses were performed using SPSS version 28.0 (IBM Japan, Ltd., Tokyo, Japan), with a significance level of 0.05.

Ethics approval was not required because this study involved secondary analysis of publicly available data from the University of Tokyo’s Center for Social Research and Data Archives, Institute of Social Science. The microdata was used in full compliance with the pledge statement, ensuring adherence to all stipulated conditions regarding confidentiality, academic use, and data handling.

Results

Table 1 summarizes participants’ demographic characteristics.

Table 1. The Demographic Characteristics of the Participants.

Number %
The study participants Total 10631
Men 5296 49.8
Women 5335 50.2
Mean S.D. Median Minimum Maximum
Age Total 42.78 16.77 41 15 89
Men 42.80 16.84 41 15 88
Women 42.76 16.81 41 15 89
Number %
Age 15-19 323 3.0
20-29 2627 24.7
30-39 2133 20.1
40-49 1896 17.8
50-59 1345 12.7
60-69 1557 14.6
70-79 688 6.5
80-89 62 0.6
Number %
Age group Men 15-39 2506 23.6
40-59 1655 15.6
60-89 1135 10.7
Women 15-39 2577 24.2
40-59 1586 14.9
60-89 1172 11.0
Number of household members (including oneself) Men 1 (living alone) 1136 10.7
2 1283 12.1
3 1281 12.0
4 1031 9.7
5 or more 565 5.3
Women 1 (living alone) 960 9.0
2 1602 15.1
3 1277 12.0
4 997 9.4
5 or more 499 4.7
Highest education Junior high school 270 2.5
High school 2981 28.0
Vocational school 1251 11.8
2 or 3 years college 1134 10.7
University 4483 42.2
Graduate school 512 4.8
Annual household income less than 1 million yen
1~3 million yen 723 6.8
3~5 million yen 4784 45.0
5~10 million yen 4003 37.7
10~20 million yen 941 8.9
20~50 million yen 136 1.3
50~100 million yen 12 0.1
100 million yen or more 32 0.3
Number %
Residential prefecture Hokkaido 247 2.32
Aomori 215 2.02
Iwate 215 2.02
Miyagi 226 2.13
Akita 213 2.00
Yamagata 213 2.00
Fukushima 218 2.05
Ibaraki 228 2.14
Tochigi 219 2.06
Gunma 219 2.06
Saitama 264 2.48
Chiba 254 2.39
Tokyo 316 2.97
Kanagawa 279 2.62
Nigata 223 2.10
Toyama 212 1.99
Ishikawa 214 2.01
Fukui 212 1.99
Yamanashi 212 1.99
Nagano 220 2.07
Gifu 219 2.06
Shizuoka 235 2.21
Aichi 264 2.48
Mie 217 2.04
Shiga 215 2.02
Kyoto 227 2.14
Osaka 276 2.60
Hyogo 249 2.34
Nara 215 2.02
Wakayama 212 1.99
Tottori 211 1.98
Shimane 212 1.99
Okayama 219 2.06
Hiroshima 227 2.14
Yamaguchi 215 2.02
Tokushima 212 1.99
Kagawa 212 1.99
Ehime 216 2.03
Kochi 212 1.99
Fukuoka 245 2.30
Saga 212 1.99
Nagasaki 216 2.03
Kumamoto 217 2.04
Ohoita 214 2.01
Miyazaki 213 2.00
Kagoshima 216 2.03
Okinawa 214 2.01
» Full size table

Figure 1A shows the distribution of life satisfaction scores (mean = 5.76 ± 2.35; median = 6). Figure 1B indicates a significant gender difference (men: 5.63 ± 2.40; women: 5.88 ± 2.30; median = 6 for both; p < 0.001, Mann-Whitney U test). Figures 1C and D present mean and median scores by age group and household size.

Figure 1. Histograms of life satisfaction scores (A) and mean and median scores by gender (B), age group (C), and household size (D).
(A) Life satisfaction was assessed with the question: “How satisfied are you with your current life overall?” Responses were given on a scale from 0 to 10, where 0 means “Not satisfied at all” and 10 means “Very satisfied.”
(B-D) In the boxplots, median values are represented by a horizontal line and mean values by a cross mark. The y-axis extends to 12 to accommodate the confidence intervals; however, life satisfaction is rated on a scale from 0 to 10.
(B) a: p < 0.001 versus men (Mann-Whitney U test).
(C, D) b: p < 0.001 versus younger group (15-39 years); c: p < 0.001 versus middle-aged group (40-59 years); d: p < 0.001 versus single-person households; e: p < 0.01 versus two-person households; f: p < 0.05 versus two-person households (Kruskal-Wallis test).
» Full size image

Kruskal-Wallis tests revealed significant age-related differences for men across all comparisons (15-39 vs. 40-59 years: p = 0.001; 40-59 vs. 60+: p < 0.001; 15-39 vs. 60+: p < 0.001). For women, differences were significant between 60+ years and both younger groups (p < 0.001), but not between 15-39 and 40-59 years (p = 0.413).

Life satisfaction by household size was lowest for single-person households and highest for two-person households. Among men, most pairwise comparisons were significant (e.g., 1 vs. 2 persons: p < 0.001; 2 vs. 3 persons: p = 0.005). Among women, significant differences were mainly between one- and two-person households (p < 0.001) and between two- and five-or-more-person households (p = 0.040).

Spearman’s rank correlations showed all 13 anxiety factors were moderately and positively associated with life satisfaction (ρ = 0.44-0.62; p < 0.001; Table 2A). Cronbach’s alpha was 0.95, indicating high internal consistency. Variance Inflation Factor (VIF) values (1.99-3.84) suggested no multicollinearity.

Table 2A. Associations between 13 Anxiety-Related Factors and Current Life. Satisfaction: Spearman’s Rank Correlations.

“When thinking about the future, how anxious do you feel about various aspects of life (13 items)?
If 0 represents ‘very anxious’ and 10 represents ‘not anxious at all’, which number best reflects your level of anxiety? Please choose only one number.”		 Spearman’s rho for Life Satisfaction Cronbach’s alpha coefficient
Household finances and assets 0.620*** 0.95
Employment environment and wages 0.559***
Housing conditions 0.573***
Work-life balance 0.595***
Personal health status 0.548***
Educational level and environment 0.558***
Social connections, such as friendships and community involvement 0.558***
Trust in political institutions, public administration, and the judiciary 0.403***
Natural environment such as air and water 0.472***
Personal safety 0.495***
Ease of child-rearing 0.459***
Ease of caregiving (giving and receiving) 0.443***
Enjoyment and interest in life 0.681***
Anxiety-related factors were assessed using the question: When thinking about the future, how would you rate your level of anxiety in various aspects of life? Responses were given on a scale from 0 to 10, where 0 means “Very anxious” and 10 means “Not anxious at all.” Participants selected one number for each of the 13 categories. Spearman’s rho, ***p < 0.001.
» Full size table

Exploratory factor analysis (principal factor method, Promax rotation) extracted three factors explaining 66.74% of variance: Quality of Living Environment (α = 0.916), Personal Quality of Life (α = 0.874), and Economic Stability (α = 0.725). Inter-factor correlations were positive (0.67-0.78; Table 2B). To assess the suitability for factor analysis, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity were conducted. The KMO value was .957, indicating excellent sampling adequacy. Bartlett’s test of sphericity was significant, χ2 (78) = 105,728.07, p < .001, suggesting sufficient correlations among variables. Therefore, factor analysis was deemed appropriate.

Table 2B. Associations between 13 Anxiety-Related Factors and Current Life. Satisfaction: Exploratory Factor Analysis.

Factors The three extracted factors Cronbach’s alpha coefficient
1 2 3
The level of future anxiety about Enjoyment and interest in life 0.733 0.055 0.095 Personal Quality of Life 0.916
Social connections, such as friendships and community involvement 0.639 0.134 0.063
Personal health status 0.622 0.100 0.088
Educational level and environment 0.616 0.123 0.093
Work-life balance 0.516 0.002 0.390
Housing conditions 0.514 0.122 0.212
Natural environment such as air and water 0.251 0.733 -0.142 Quality of Living Environment 0.874
Personal safety 0.286 0.713 -0.129
Ease of child-rearing 0.040 0.597 0.183
Ease of caregiving (giving and receiving) -0.072 0.581 0.341
Trust in political institutions, public administration, and the judiciary -0.113 0.551 0.334
Employment environment and wages 0.100 -0.009 0.836 Economic Stability 0.725
Household finances and assets 0.239 0.010 0.663
Factor correlation matrix 1 2 3
1.000 0.776 0.746
1.000 0.676
1.000
An exploratory factor analysis was conducted on the 13 factors using the principal factor method with Promax rotation. The final factor pattern and inter-factor correlations after Promax rotation resulted in three extracted factors.
» Full size table

Multiple regression analysis (Table 2C) showed Personal Quality of Life had the strongest effect on life satisfaction (β = 0.68, p < 0.001), followed by Economic Stability (β = 0.21, p < 0.001). Quality of Living Environment was significant but weaker (β = 0.097, p < 0.001). The model fit was good (Adjusted R2 = 0.608), and ANOVA confirmed overall significance (F = 2359.532, p < 0.001).

Table 2C. Associations between 13 Anxiety-Related Factors and Current Life. Satisfaction: Multiple Regression Analysis.

B SE β p VIF
Anxiety factors Personal quality of life 0.895 0.013 0.678 < 0.001 1.068
Quality of living environment -0.136 0.014 -0.097 < 0.001 1.125
Economic stability 0.224 0.011 0.207 < 0.001 1.035
Control variable Household size 0.000 0.000 -0.013 0.030 1.034
Highest education -0.020 0.010 -0.012 0.058 1.050
Annual household income 0.051 0.010 0.032 < 0.001 2.809
Age group 0.000 0.000 0.032 < 0.001 2.604
Gender 0.200 0.029 0.042 < 0.001 2.666
Multiple regression analysis was performed to examine the influence on life satisfaction. The dependent variable was life satisfaction, and the independent variables were the three factors extracted from the factor analysis. Gender, age group, household size, total annual household income, and highest education level were included as covariates.
B: unstandardized coefficient; β: standardized coefficient; SE: standard error.
» Full size table

Discussion

Life satisfaction varied significantly by gender, age, and household size. The overall mean score (6.76; median = 7) indicates a moderately high level of satisfaction. Women reported slightly higher scores than men, consistent with previous findings on gender differences in well-being (10). Age-related differences were more pronounced among men, with all pairwise comparisons reaching significance. Among women, only comparisons involving the oldest age group (60+) were significant, aligning with prior research on aging and well-being (11).

Global surveys, such as the Gallup World Poll, have documented a U-shaped relationship between well-being and age, with the lowest levels around midlife (12).

Household size also influenced life satisfaction: individuals living alone reported the lowest scores, while those in two-person households reported the highest. These differences were more pronounced among men, whereas women exhibited fewer significant contrasts. This trend aligns with the findings of Hori and Kamo, who reported that marital status and household composition are more strongly associated with happiness among men, while women’s well-being is less dependent on these factors (13).

An analysis of 13 psychosocial and environmental factors revealed moderate positive correlations with life satisfaction, suggesting that multiple domains contribute to overall well-being. Factor analysis identified three key dimensions―Quality of Living Environment, Personal Quality of Life, and Economic Stability―which together explained 66.74% of the variance. High internal consistency and positive inter-factor correlations indicate that these domains are both reliable and interrelated.

Regression analysis confirmed Personal Quality of Life as the strongest predictor (β = 0.68), followed by Economic Stability (β = 0.21) and Quality of Living Environment (β = 0.097).

These findings align with the Social Production Function framework, which highlights personal experiences and financial security as central drivers of life satisfaction (3). Furthermore, cross-national data from the OECD Better Life Index suggest that while environmental quality contributes to well-being, its impact is often mediated by economic and social conditions―particularly in countries with lower income levels or less stable infrastructure (2). Swami et al. (14) similarly found that across 65 nations, financial security and relationship status were more strongly associated with life satisfaction than environmental factors, indicating a potentially universal pattern. The relatively smaller effect of environmental quality may reflect its indirect influence through personal and economic domains.

These findings also have important cultural and policy implications. For example, the Cabinet Office conducts the Survey on Satisfaction and Quality of Life to monitor subjective well-being and related indicators (15). Our multidimensional approach aligns with the Quality of Life framework adopted in policy evaluation and complements SDGs-related goals on health and social inclusion (16). By identifying psychosocial dimensions that influence life satisfaction, this study offers preliminary insights that may contribute to the development of targeted interventions and inform policy considerations aimed at enhancing population well-being in Japan.

Overall, these results underscore the importance of a holistic approach that integrates demographic, personal, and environmental factors. Future research should explore how these domains interact over time and across diverse populations to inform targeted interventions and policy development.

Limitations

This study used an online sample, which may limit generalizability, though regional distribution approximated national demographics. The cross-sectional design precludes causal inference; longitudinal studies are needed. Additionally, not all relevant factors could be assessed.

Conclusion

Life satisfaction is influenced by demographic and psychosocial factors. Older adults and individuals in two-person households reported higher well-being. Among the predictors, concerns about Personal Quality of Life and Economic Stability had the strongest impact, while environmental quality played a smaller but meaningful role. These findings suggest that effective interventions should address both structural and personal vulnerabilities, and future research should explore how these factors interact across different life stages.

Article Information

Acknowledgments

The data for this secondary analysis, “2022 Survey on Satisfaction and Quality of Life,” deposited by the Cabinet Office of Japan, was provided by the Social Science Japan Data Archive, Center for Social Research and Data Archives, Institute of Social Science, The University of Tokyo.

Author Contributions

Haruka Ishii: data analysis and manuscript writing; Yoko Ishii: project development, data management, and manuscript writing/editing.

Conflicts of Interest

None

Funding

This work was supported by JSPS KAKENHI grant number JP22K02139.

References

  1. 1.

    OECD. How’s life? 2020: measuring well-being [Internet]. Paris: OECD Publishing; 2020 [cited 2025 Nov 10]. Available from: https://www.oecd.org/content/dam/oecd/en/publications/reports/2020/03/how-s-life-2020_b547d82c/9870c393-en.pdf

  2. 2.

    OECD better life index. Environmental Quality as a Dimension of Well-Being [Internet]. Organisation for Economic Co-operation and Development. 2025 [cited 2025 Nov 10]. Available from: https://www.oecd.org/en/data/tools/oecd-better-life-index.html

  3. 3.

    Chowhan J, Samavatyan H, HakemZadeh F. Life satisfaction and the roles of work, family, and social factors in a social production function framework. J Happiness Stud. 2024;25(1-2):1-24.

  4. 4.

    Diener E, Suh EM, Lucas RE, et al. Subjective well-being: three decades of progress. Psychol Bull. 1999;125(2):276-302.

  5. 5.

    Hong JH, Charles ST, Lee S, et al. Perceived changes in life satisfaction from the past, present and to the future: a comparison of U.S. and Japan. Psychol Aging. 2019;34(3):317-29.

  6. 6.

    Hills P, Argyle M. The Oxford Happiness Questionnaire: a compact scale for the measurement of psychological well-being. Pers Individ Differ. 2002;33(7):1073-82.

  7. 7.

    Nishimura K, Yagi T. Happiness and self-determination - an empirical study in Japan. Rev Behav Econ. 2019;6(4):312-46.

  8. 8.

    Mitsui N, Nakai Y, Inoue T, et al. Association between suicide-related ideations and affective temperaments in the Japanese general adult population. PLoS One. 2017;12(6):e0179952.

  9. 9.

    Sulandari S, Coats RO, Miller A, et al. A systematic review and meta-analysis of the association between physical capability, social support, loneliness, depression, anxiety, and life satisfaction in older adults. Gerontologist. 2024;64(11):gnae128.

  10. 10.

    Joshanloo M, Jovanovic V. The relationship between gender and life satisfaction: analysis across demographic groups and global regions. Arch Womens Ment Health. 2019;23(3):331-8.

  11. 11.

    Calasanti T, Carr D, Homan P, et al. Gender disparities in life satisfaction after retirement: the roles of leisure, family, and finances. Gerontologist. 2021;61(8):1277-86.

  12. 12.

    Steptoe A, Deaton A, Stone AA. Subjective wellbeing, health, and ageing. Lancet. 2015;385(9968):640-8.

  13. 13.

    Hori M, Kamo Y. Gender differences in happiness: the effects of marriage, social roles, and social support in East Asia. Appl Res Qual Life. 2018;13(4):839-57.

  14. 14.

    Swami V, Stieger S, Voracek M, et al. Life satisfaction around the world: measurement invariance of the Satisfaction With Life Scale (SWLS) across 65 nations. PLoS One. 2025;22:20(1):e0313107.

  15. 15.

    Survey on satisfaction and quality of life [Internet]. Cabinet Office, Government of Japan. 2025 [cited 2025 Nov 10]. Japanese. Available from: https://www5.cao.go.jp/keizai2/wellbeing/manzoku/index.html

  16. 16.

    Sustainable development goals: the 17 goals [Internet]. United Nations. 2025 [cited 2025 Nov 10]. Available from: https://sdgs.un.org/goals

JMA Journal is an Open Access journal distributed under the Creative Commons Attribution 4.0 International License. To view the details of this license, please visit (https://creativecommons.org/licenses/by/4.0/).
PAGE TOP