Evaluación de la calidad de vida urbana: estudio
exhaustivo del distrito 12 de la Ciudad de Ho Chi
Minh, Vietnam
Hoang Thi Viet Ha 1
, Nguyen Thi Binh2
1
Department of Geography, Dong Thap University, Cao Lanh city, Vietnam.
Email: Corresponding author: htvha@dthu.edu.vn
ORCID: https://orcid.org/0009-0009-6145-7382
2
Faculty of Geography, Ho Chi Minh City University of Education, Ho Chi Minh City,
Vietnam. Email: binhnt@hcmue.edu.vn; ORCID: https://orcid.org/0009-0007-0315-9286
Resumen. Este estudio investiga los determinantes de la calidad de vida de los
habitantes del distrito 12 de Ciudad Ho Chi Minh. Empleando el análisis factorial ex-
ploratorio (AFE) y el análisis de regresión, la investigación desarrolla un modelo para
evaluar el impacto de diversos factores en el nivel de vida de los habitantes. El análisis
identificó cinco factores principales, clasificados según su influencia: condiciones de
vida, educación y formación, salud y servicios sanitarios, estabilidad vital, empleo e
ingresos, relaciones familiares y sociales, e infraestructuras. A partir de estos resultados,
el estudio sugiere estrategias prioritarias destinadas a mejorar la calidad de vida en el
Distrito 12. Estas recomendaciones se adaptan para abordar las necesidades y retos
específicos identificados a través de la investigación. El estudio aporta valiosas ideas a
la planificación urbana y la formulación de políticas, sobre todo en zonas urbanas en
rápido desarrollo.
Palabras clave: calidad de vida, urbano, análisis de regresión, análisis factorial exploratorio
(AFE), Ciudad Ho Chi Minh.
Recibido: 01/05/2024 ~ Aceptado: 28/09/2024
INTERACCIÓN Y PERSPECTIVA
Revista de Trabajo Social
ISSN 2244-808X ~ Dep. Legal pp 201002Z43506
DOI: https://doi.org/10.5281/zenodo.14031799
Vol. 15 (1): 239 - 248 pp, 2025
exhaustivo del distrito 12 de la Ciudad de Ho Chi
Minh, Vietnam
Hoang Thi Viet Ha 1
, Nguyen Thi Binh2
1
Department of Geography, Dong Thap University, Cao Lanh city, Vietnam.
Email: Corresponding author: htvha@dthu.edu.vn
ORCID: https://orcid.org/0009-0009-6145-7382
2
Faculty of Geography, Ho Chi Minh City University of Education, Ho Chi Minh City,
Vietnam. Email: binhnt@hcmue.edu.vn; ORCID: https://orcid.org/0009-0007-0315-9286
Resumen. Este estudio investiga los determinantes de la calidad de vida de los
habitantes del distrito 12 de Ciudad Ho Chi Minh. Empleando el análisis factorial ex-
ploratorio (AFE) y el análisis de regresión, la investigación desarrolla un modelo para
evaluar el impacto de diversos factores en el nivel de vida de los habitantes. El análisis
identificó cinco factores principales, clasificados según su influencia: condiciones de
vida, educación y formación, salud y servicios sanitarios, estabilidad vital, empleo e
ingresos, relaciones familiares y sociales, e infraestructuras. A partir de estos resultados,
el estudio sugiere estrategias prioritarias destinadas a mejorar la calidad de vida en el
Distrito 12. Estas recomendaciones se adaptan para abordar las necesidades y retos
específicos identificados a través de la investigación. El estudio aporta valiosas ideas a
la planificación urbana y la formulación de políticas, sobre todo en zonas urbanas en
rápido desarrollo.
Palabras clave: calidad de vida, urbano, análisis de regresión, análisis factorial exploratorio
(AFE), Ciudad Ho Chi Minh.
Recibido: 01/05/2024 ~ Aceptado: 28/09/2024
INTERACCIÓN Y PERSPECTIVA
Revista de Trabajo Social
ISSN 2244-808X ~ Dep. Legal pp 201002Z43506
DOI: https://doi.org/10.5281/zenodo.14031799
Vol. 15 (1): 239 - 248 pp, 2025
240 Thi Viet Ha, Thi BinhInteracción y P erspectiva. R evista de Trabajo S ocial Vol. 15(1): 2025
Evaluating urban quality of life: a comprehensive study
of District 12, Ho Chi Minh City, Vietnam
Abstract. This study investigates the determinants of quality of life among re-
sidents of District 12 in Ho Chi Minh City. Employing exploratory factor analysis
(EFA) and regression analysis, the research develops a model to evaluate the impact
of various factors on the inhabitants’ living standards. The analysis identified five pri-
mary factors, ranked according to their influence: living conditions, education and
training, health and healthcare services, life stability, employment and income, family
and social relationships, and infrastructure. Based on these findings, the study suggests
prioritized strategies aimed at enhancing the quality of life in District 12. These recom-
mendations are tailored to address the specific needs and challenges identified through
the research. The study contributes valuable insights into urban planning and policy-
making, particularly in rapidly developing urban areas.
Keywords: quality of life, urban, regression analysis, exploratory factor analysis (EFA), Ho Chi
Minh City.
INTRODUCTION
Quality of life encompasses feelings of happiness and satisfaction with life’s essential elements,
varying per individual. According to P. Beohnke, quality of life relates to individual happiness en-
compassing a broad, multidimensional spectrum of emotions. In Europe, it is often associated with
societal goals such as equal life opportunities, a guaranteed minimum living standard, employment
opportunities, and social assistance. Thus, quality of life extends beyond income, education, and
material possessions to encompass health care, family issues, and social relations (P. Boehnke, 2005).
The World Health Organization defines quality of life as an individual’s perception of their position
in life within their cultural context, influenced by goals, expectations, values, and concerns. It is a
multifaceted concept affected by physical health, psychological state, independence, social relation-
ships, personal beliefs, and the surrounding environment (WHO, 2000).
Currently, Vietnam is undergoing an economic transition, where quality of life is influenced by
various factors, including politics, economy, society, environment, transportation, health care, and
education. Although material living standards have improved, issues like environmental pollution
and urbanization challenges, such as slums, flooding, healthcare, and traffic safety, are affecting life
quality. District 12, originally part of Hoc Mon district and primarily agricultural land, faces chal-
lenges like unclear development planning, infrastructure inadequacies, and a lack of green spaces,
all of which pressure the residents’ quality of life.
LITERATURE REVIEW
The concept of quality of life has garnered significant attention in urban studies, particularly in
understanding how various factors contribute to residents’ overall well-being. Previous studies have
identified key elements impacting urban quality of life, including economic conditions, social rela-
tionships, health and well-being, education, and the environment (Sirgy, M. J., Rahtz, D. R., Cicic,
M., & Underwood, R, 2000); (Marans, R. W, 2012). Focusing on Vietnam, several researchers have
Evaluating urban quality of life: a comprehensive study
of District 12, Ho Chi Minh City, Vietnam
Abstract. This study investigates the determinants of quality of life among re-
sidents of District 12 in Ho Chi Minh City. Employing exploratory factor analysis
(EFA) and regression analysis, the research develops a model to evaluate the impact
of various factors on the inhabitants’ living standards. The analysis identified five pri-
mary factors, ranked according to their influence: living conditions, education and
training, health and healthcare services, life stability, employment and income, family
and social relationships, and infrastructure. Based on these findings, the study suggests
prioritized strategies aimed at enhancing the quality of life in District 12. These recom-
mendations are tailored to address the specific needs and challenges identified through
the research. The study contributes valuable insights into urban planning and policy-
making, particularly in rapidly developing urban areas.
Keywords: quality of life, urban, regression analysis, exploratory factor analysis (EFA), Ho Chi
Minh City.
INTRODUCTION
Quality of life encompasses feelings of happiness and satisfaction with life’s essential elements,
varying per individual. According to P. Beohnke, quality of life relates to individual happiness en-
compassing a broad, multidimensional spectrum of emotions. In Europe, it is often associated with
societal goals such as equal life opportunities, a guaranteed minimum living standard, employment
opportunities, and social assistance. Thus, quality of life extends beyond income, education, and
material possessions to encompass health care, family issues, and social relations (P. Boehnke, 2005).
The World Health Organization defines quality of life as an individual’s perception of their position
in life within their cultural context, influenced by goals, expectations, values, and concerns. It is a
multifaceted concept affected by physical health, psychological state, independence, social relation-
ships, personal beliefs, and the surrounding environment (WHO, 2000).
Currently, Vietnam is undergoing an economic transition, where quality of life is influenced by
various factors, including politics, economy, society, environment, transportation, health care, and
education. Although material living standards have improved, issues like environmental pollution
and urbanization challenges, such as slums, flooding, healthcare, and traffic safety, are affecting life
quality. District 12, originally part of Hoc Mon district and primarily agricultural land, faces chal-
lenges like unclear development planning, infrastructure inadequacies, and a lack of green spaces,
all of which pressure the residents’ quality of life.
LITERATURE REVIEW
The concept of quality of life has garnered significant attention in urban studies, particularly in
understanding how various factors contribute to residents’ overall well-being. Previous studies have
identified key elements impacting urban quality of life, including economic conditions, social rela-
tionships, health and well-being, education, and the environment (Sirgy, M. J., Rahtz, D. R., Cicic,
M., & Underwood, R, 2000); (Marans, R. W, 2012). Focusing on Vietnam, several researchers have
Evaluación de la calidad de vida urbana: estudio exhaustivo del distrito
12 de la Ciudad de Ho Chi Minh, Vietnam 241Vol. 15(1) enero-marzo 2025/ 239 - 248
emphasized the importance of infrastructure development, economic growth, and social policies in
enhancing the quality of life in urban settings (World Bank, 2011); (Nguyen, T.C., Nguyen, H.D.,
Le, H.T. and Kaneko, S, 2022). Notably, the transformation of urban areas in Vietnam, character-
ized by rapid development and modernization, presents unique challenges and opportunities in this
context.
The quality of life in urban areas is intrinsically linked to the urban development process (Ma-
rans, R. W, 2012). Marans and Stimson (2011) highlight that urban development influences resi-
dents’ quality of life through changes in economic opportunities, social dynamics, environmental
conditions, and infrastructure. These aspects collectively shape residents’ perceptions of their living
conditions and overall satisfaction with urban life.
In the context of District 12, Ho Chi Minh City, the rapid urbanization and subsequent socio-
economic transformations have brought a significant shift in the quality of life of its residents. This
study aims to delve deeper into these changes, exploring how different factors contribute to the
residents’ perceptions of their quality of life and identifying areas for improvement.
METHODOLOGY AND DATA COLLECTION
Methodology
The research paper employs the sociological survey approach; data is gathered using 5-point
Likert scale questionnaires. The analysis of the information gathered after the survey will be done
in five steps. First, broad descriptive statistics will be run in order to generalize the characteristics of
the sample that is being studied. Second, Cronbach’s alpha will be used to assess the scale’s depend-
ability. Research can retain meaningful observable variables and eliminate irrelevant ones in this
way. Third, an exploratory factor analysis (EFA) will be used to reduce the observed variables to a
subset of more important components. To determine whether the actual number of components
combined together agrees with theory, EFA analysis will be performed. Fourth, a Pearson correlation
coefficient test was performed to determine whether there was a linear link between the independent
and dependent variables before beginning the regression. Finally, the influence of independent fac-
tors on the dependent variable is determined using multiple linear regression analysis, which aids in
testing the study hypothesis.
Research Data
Hair et al. (2010) suggest that for effective EFA, a sample size should be at least five times the
number of observed variables. In this study, the survey sample comprised 34 observations, with 30
observations for each of the 6 independent variables and 4 observations for each dependent variable.
The observed factors were ranked on a 5-level scale, ranging from “not at all important” to “very
important.” Consequently, a minimum sample size of 170 (34 x 5) was required. A total of 250
questionnaires were distributed to residents of select wards in District 12, Ho Chi Minh City. Out
of these, 46 responses were deemed invalid and excluded from analysis, leading to a final sample
size of 204. The survey was conducted from mid-January to mid-March 2023, a period marking the
beginning of a new year and the Lunar New Year holiday. The respondents primarily consisted of
long-term residents of District 12, Ho Chi Minh City, Vietnam.
12 de la Ciudad de Ho Chi Minh, Vietnam 241Vol. 15(1) enero-marzo 2025/ 239 - 248
emphasized the importance of infrastructure development, economic growth, and social policies in
enhancing the quality of life in urban settings (World Bank, 2011); (Nguyen, T.C., Nguyen, H.D.,
Le, H.T. and Kaneko, S, 2022). Notably, the transformation of urban areas in Vietnam, character-
ized by rapid development and modernization, presents unique challenges and opportunities in this
context.
The quality of life in urban areas is intrinsically linked to the urban development process (Ma-
rans, R. W, 2012). Marans and Stimson (2011) highlight that urban development influences resi-
dents’ quality of life through changes in economic opportunities, social dynamics, environmental
conditions, and infrastructure. These aspects collectively shape residents’ perceptions of their living
conditions and overall satisfaction with urban life.
In the context of District 12, Ho Chi Minh City, the rapid urbanization and subsequent socio-
economic transformations have brought a significant shift in the quality of life of its residents. This
study aims to delve deeper into these changes, exploring how different factors contribute to the
residents’ perceptions of their quality of life and identifying areas for improvement.
METHODOLOGY AND DATA COLLECTION
Methodology
The research paper employs the sociological survey approach; data is gathered using 5-point
Likert scale questionnaires. The analysis of the information gathered after the survey will be done
in five steps. First, broad descriptive statistics will be run in order to generalize the characteristics of
the sample that is being studied. Second, Cronbach’s alpha will be used to assess the scale’s depend-
ability. Research can retain meaningful observable variables and eliminate irrelevant ones in this
way. Third, an exploratory factor analysis (EFA) will be used to reduce the observed variables to a
subset of more important components. To determine whether the actual number of components
combined together agrees with theory, EFA analysis will be performed. Fourth, a Pearson correlation
coefficient test was performed to determine whether there was a linear link between the independent
and dependent variables before beginning the regression. Finally, the influence of independent fac-
tors on the dependent variable is determined using multiple linear regression analysis, which aids in
testing the study hypothesis.
Research Data
Hair et al. (2010) suggest that for effective EFA, a sample size should be at least five times the
number of observed variables. In this study, the survey sample comprised 34 observations, with 30
observations for each of the 6 independent variables and 4 observations for each dependent variable.
The observed factors were ranked on a 5-level scale, ranging from “not at all important” to “very
important.” Consequently, a minimum sample size of 170 (34 x 5) was required. A total of 250
questionnaires were distributed to residents of select wards in District 12, Ho Chi Minh City. Out
of these, 46 responses were deemed invalid and excluded from analysis, leading to a final sample
size of 204. The survey was conducted from mid-January to mid-March 2023, a period marking the
beginning of a new year and the Lunar New Year holiday. The respondents primarily consisted of
long-term residents of District 12, Ho Chi Minh City, Vietnam.
242 Thi Viet Ha, Thi BinhInteracción y P erspectiva. R evista de Trabajo S ocial Vol. 15(1): 2025
Theoretical Framework
Christopher et al. (2010) observed that individuals with higher incomes, who can afford more
life-related goods, tend to report higher standards of living and satisfaction. The Treasury Board
of the Secretariat of Canada also noted that financial factors significantly influence quality of life
(Treasury Board of Canada Secretariat, 2000).
Infrastructure, as asserted by Jonathan Brooks (2021), plays a pivotal role in socio-economic
development. A modern infrastructure system enhances production efficiency, supports economic
growth, and aids in resolving social issues. Quality of life, according to this research, is influenced
by factors like quality roads, traffic flow, and safety.
Health services and healthcare directly impact individuals’ well-being. The World Health Or-
ganization defines it as the prevention, treatment, and control of disease, as well as the promotion of
health through services offered by healthcare organizations and professionals (WHO, 2000).
Education’s impact on quality of life is highlighted by Boehnke (2005). In addition, research
by Made Nyandra et al. (2018) found that university professors nearing retirement are more suscep-
tible to depression. Their study suggests that education and training, particularly for older adults,
can enhance quality of life and reduce depression. Family and social interactions are crucial in cop-
ing with adverse situations, as indicated by Boeknke (2005). Zhao (2004) emphasizes the significant
impact of having friendly neighbors on one’s quality of life. Furthermore, housing is identified as a
critical factor, reflecting societal progress and living standars.
Katherine Ka Pik Chang et al. (2020) found that living conditions and health behaviors signif-
icantly affect overall well-being. Environmental factors, particularly when interacting with personal
factors like stress and sleep, play a crucial role in determining quality of life.
Drawing from the works of Pastha Eva et al. (2011), Michael Douglass, the Treasury Board
of the Canadian Secretariat, Jonathan Brooks et al. (2021), Boeknke (2003), and Katherine Ka Pik
Chang (2020), and observations of residential life in District 12, the author proposes a research
model to analyze factors affecting the quality of life in District 12. These include: (1) employment
and income factors; (2) infrastructure factors; (3) health and healthcare factors; (4) education and
training factors; (5) family and social relationship factors; and (6) environmental factors. The theo-
retical framework is depicted in Figure 1 below.
Figure 1. Theoretical framework of a research proposal
Theoretical Framework
Christopher et al. (2010) observed that individuals with higher incomes, who can afford more
life-related goods, tend to report higher standards of living and satisfaction. The Treasury Board
of the Secretariat of Canada also noted that financial factors significantly influence quality of life
(Treasury Board of Canada Secretariat, 2000).
Infrastructure, as asserted by Jonathan Brooks (2021), plays a pivotal role in socio-economic
development. A modern infrastructure system enhances production efficiency, supports economic
growth, and aids in resolving social issues. Quality of life, according to this research, is influenced
by factors like quality roads, traffic flow, and safety.
Health services and healthcare directly impact individuals’ well-being. The World Health Or-
ganization defines it as the prevention, treatment, and control of disease, as well as the promotion of
health through services offered by healthcare organizations and professionals (WHO, 2000).
Education’s impact on quality of life is highlighted by Boehnke (2005). In addition, research
by Made Nyandra et al. (2018) found that university professors nearing retirement are more suscep-
tible to depression. Their study suggests that education and training, particularly for older adults,
can enhance quality of life and reduce depression. Family and social interactions are crucial in cop-
ing with adverse situations, as indicated by Boeknke (2005). Zhao (2004) emphasizes the significant
impact of having friendly neighbors on one’s quality of life. Furthermore, housing is identified as a
critical factor, reflecting societal progress and living standars.
Katherine Ka Pik Chang et al. (2020) found that living conditions and health behaviors signif-
icantly affect overall well-being. Environmental factors, particularly when interacting with personal
factors like stress and sleep, play a crucial role in determining quality of life.
Drawing from the works of Pastha Eva et al. (2011), Michael Douglass, the Treasury Board
of the Canadian Secretariat, Jonathan Brooks et al. (2021), Boeknke (2003), and Katherine Ka Pik
Chang (2020), and observations of residential life in District 12, the author proposes a research
model to analyze factors affecting the quality of life in District 12. These include: (1) employment
and income factors; (2) infrastructure factors; (3) health and healthcare factors; (4) education and
training factors; (5) family and social relationship factors; and (6) environmental factors. The theo-
retical framework is depicted in Figure 1 below.
Figure 1. Theoretical framework of a research proposal
Evaluación de la calidad de vida urbana: estudio exhaustivo del distrito
12 de la Ciudad de Ho Chi Minh, Vietnam 243Vol. 15(1) enero-marzo 2025/ 239 - 248
RESULTS AND DISCUSSION
Utilizing Cronbach’s Alpha to evaluate the scale’s reliability
The reliability of the scale, a crucial step in ensuring the accuracy of the Exploratory Fac-
tor Analysis (EFA), was assessed using Cronbach’s Alpha reliability coefficient analysis. (Hoang.
T, Chu. M,N, 2017) suggested that for a scale to be considered reliable, the correlation coefficient
of the entire variable must be less than 0.3 and the Cronbach’s Alpha coefficient must exceed 0.6.
In this study, six independent variables with 30 observations and one dependent variable with 4
observations were formulated. Adequate observations per variable allowed for a reliable analysis of
Cronbach’s Alpha. The reliability coefficient for each variable was calculated, and any observation
or variable not meeting the criteria was reassessed. Table 1 presents the analysis results of the scales’
Cronbach’s Alpha coefficients. Both Cronbach’s Alpha and the total variable’s correlation coefficient
met the theoretical requirements, indicating overall reliability.
TABLE 1. Evaluation of Cronbach’s Alpha Coefficient.
Factors Number of observed
variables
Corrected Item-Total
Correlation
Cronbach’s
Alpha
Employment and income 5 .445 .756
Infrastructure 5 .597 .849
Health and health care 5 .770 .925
Education and training 5 .627 .874
Family and social relationships 4 .634 .732
The living environment 4 .740 .906
The quality of the population 4 .627 .797
The motivation scale’s Cronbach’s Alpha coefficient was the lowest among the evaluated scales,
at 0.906, with the smallest total correlation coefficient at 0.345. All observed variables’ total correla-
tion coefficients exceeded 0.3. Consequently, 32 observed variables met the reliability criteria and
were utilized for further analysis.
Results from Exploratory Factor Analysis (EFA)
Subsequent to the Cronbach’s Alpha analysis, 28 observations across 6 independent variables
and 4 observations of 1 dependent variable were deemed reliable. These observations were struc-
tured using a theoretical framework of variables influencing urban residents’ quality of life, focusing
on District 12 in Ho Chi Minh City, Vietnam.
TABLE 2. KMO and Bartlett’s Testa
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .796
Bartlett’s Test of Sphericity Approx. Chi-Square 4867.215
df 378
Sig. .000
a. Based on correlations.
12 de la Ciudad de Ho Chi Minh, Vietnam 243Vol. 15(1) enero-marzo 2025/ 239 - 248
RESULTS AND DISCUSSION
Utilizing Cronbach’s Alpha to evaluate the scale’s reliability
The reliability of the scale, a crucial step in ensuring the accuracy of the Exploratory Fac-
tor Analysis (EFA), was assessed using Cronbach’s Alpha reliability coefficient analysis. (Hoang.
T, Chu. M,N, 2017) suggested that for a scale to be considered reliable, the correlation coefficient
of the entire variable must be less than 0.3 and the Cronbach’s Alpha coefficient must exceed 0.6.
In this study, six independent variables with 30 observations and one dependent variable with 4
observations were formulated. Adequate observations per variable allowed for a reliable analysis of
Cronbach’s Alpha. The reliability coefficient for each variable was calculated, and any observation
or variable not meeting the criteria was reassessed. Table 1 presents the analysis results of the scales’
Cronbach’s Alpha coefficients. Both Cronbach’s Alpha and the total variable’s correlation coefficient
met the theoretical requirements, indicating overall reliability.
TABLE 1. Evaluation of Cronbach’s Alpha Coefficient.
Factors Number of observed
variables
Corrected Item-Total
Correlation
Cronbach’s
Alpha
Employment and income 5 .445 .756
Infrastructure 5 .597 .849
Health and health care 5 .770 .925
Education and training 5 .627 .874
Family and social relationships 4 .634 .732
The living environment 4 .740 .906
The quality of the population 4 .627 .797
The motivation scale’s Cronbach’s Alpha coefficient was the lowest among the evaluated scales,
at 0.906, with the smallest total correlation coefficient at 0.345. All observed variables’ total correla-
tion coefficients exceeded 0.3. Consequently, 32 observed variables met the reliability criteria and
were utilized for further analysis.
Results from Exploratory Factor Analysis (EFA)
Subsequent to the Cronbach’s Alpha analysis, 28 observations across 6 independent variables
and 4 observations of 1 dependent variable were deemed reliable. These observations were struc-
tured using a theoretical framework of variables influencing urban residents’ quality of life, focusing
on District 12 in Ho Chi Minh City, Vietnam.
TABLE 2. KMO and Bartlett’s Testa
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .796
Bartlett’s Test of Sphericity Approx. Chi-Square 4867.215
df 378
Sig. .000
a. Based on correlations.
244 Thi Viet Ha, Thi BinhInteracción y P erspectiva. R evista de Trabajo S ocial Vol. 15(1): 2025
For effective EFA, the Kaiser-Meyer-Olkin (KMO) coefficient should range between 0.5
and 1, with higher KMO values indicating stronger correlations with survey data. The results
of the Barlett test shown in Table 2 show that the variables in the population are correlated
with each other with Sig. = 0.00 < 0.05. At the same time, the KMO coefficient = 0.796 >
0.05, proving that factor analysis to group variables together is appropriate and the data is
suitable for factor analysis.
TABLE 3. Total Variance Explained.
Component
Initial Eigenvalues Extraction Sums
of Squared Loadings
Rotation Sums
of Squared Loadings
Total % of
Variance
Cumulative
% Total % of
Variance
Cumulative
% Total % of
Variance
Cumulative
%
1 10.954 39.122 39.122 10.954 39.122 39.122 4.076 14.558 14.558
2 2.429 8.676 47.798 2.429 8.676 47.798 3.996 14.270 28.828
3 2.081 7.434 55.232 2.081 7.434 55.232 3.636 12.984 41.813
4 1.956 6.987 62.218 1.956 6.987 62.218 3.164 11.302 53.114
5 1.778 6.349 68.567 1.778 6.349 68.567 2.446 8.735 61.849
6 1.226 4.378 72.945 1.226 4.378 72.945 2.204 7.873 69.722
7 1.175 4.196 77.141 1.175 4.196 77.141 2.077 7.420 77.141
8 .922 3.294 80.435
9 .755 2.697 83.132
10 .637 2.276 85.408
11 .574 2.051 87.459
12 .515 1.838 89.297
13 .407 1.455 90.752
14 .381 1.359 92.111
15 .327 1.167 93.278
Extraction Method: Principal Component Analysis.
Table 3 shows that all 5 factors have Eigenvalues > 1. The extracted variance is 77.141% >
50%, which is satisfactory. With the Principal Components Analysis extraction method and Vari-
max rotation, there are 5 factors extracted from the observed variables. This shows, solvability ex-
plains 77.141% of the change in the dependent variable in the population.
Rotated matrix demonstrates that all variables have factor loadings greater than 0.5, the scale
is acceptable, and there are seven groups of factors influencing the standard of living of District 12
residents in Ho Chi Minh City. The following will be the names of the newly adjusted factor groups:
(1) Employment and income; (2) Infrastructure; (3) Medicine and Health care; (4) Education and
training; (5) Family and social relationships; (6) Living environment; and (7) Stability in life. Con-
sequently, the new model will be reorganized as shown in Figure 2.
For effective EFA, the Kaiser-Meyer-Olkin (KMO) coefficient should range between 0.5
and 1, with higher KMO values indicating stronger correlations with survey data. The results
of the Barlett test shown in Table 2 show that the variables in the population are correlated
with each other with Sig. = 0.00 < 0.05. At the same time, the KMO coefficient = 0.796 >
0.05, proving that factor analysis to group variables together is appropriate and the data is
suitable for factor analysis.
TABLE 3. Total Variance Explained.
Component
Initial Eigenvalues Extraction Sums
of Squared Loadings
Rotation Sums
of Squared Loadings
Total % of
Variance
Cumulative
% Total % of
Variance
Cumulative
% Total % of
Variance
Cumulative
%
1 10.954 39.122 39.122 10.954 39.122 39.122 4.076 14.558 14.558
2 2.429 8.676 47.798 2.429 8.676 47.798 3.996 14.270 28.828
3 2.081 7.434 55.232 2.081 7.434 55.232 3.636 12.984 41.813
4 1.956 6.987 62.218 1.956 6.987 62.218 3.164 11.302 53.114
5 1.778 6.349 68.567 1.778 6.349 68.567 2.446 8.735 61.849
6 1.226 4.378 72.945 1.226 4.378 72.945 2.204 7.873 69.722
7 1.175 4.196 77.141 1.175 4.196 77.141 2.077 7.420 77.141
8 .922 3.294 80.435
9 .755 2.697 83.132
10 .637 2.276 85.408
11 .574 2.051 87.459
12 .515 1.838 89.297
13 .407 1.455 90.752
14 .381 1.359 92.111
15 .327 1.167 93.278
Extraction Method: Principal Component Analysis.
Table 3 shows that all 5 factors have Eigenvalues > 1. The extracted variance is 77.141% >
50%, which is satisfactory. With the Principal Components Analysis extraction method and Vari-
max rotation, there are 5 factors extracted from the observed variables. This shows, solvability ex-
plains 77.141% of the change in the dependent variable in the population.
Rotated matrix demonstrates that all variables have factor loadings greater than 0.5, the scale
is acceptable, and there are seven groups of factors influencing the standard of living of District 12
residents in Ho Chi Minh City. The following will be the names of the newly adjusted factor groups:
(1) Employment and income; (2) Infrastructure; (3) Medicine and Health care; (4) Education and
training; (5) Family and social relationships; (6) Living environment; and (7) Stability in life. Con-
sequently, the new model will be reorganized as shown in Figure 2.
Evaluación de la calidad de vida urbana: estudio exhaustivo del distrito
12 de la Ciudad de Ho Chi Minh, Vietnam 245Vol. 15(1) enero-marzo 2025/ 239 - 248
Figure 2. Proposed model to analyze factors affecting the quality of life in District 12,
Ho Chi Minh City
Results of Pearson correlation analysis
Before progressing to regression analysis, Pearson correlation was used to assess the linear cor-
relation between the dependent and independent variables.
TABLE 4. Correlation between independent and dependent variables
F_SF F_EI F_IS F_MH F_ET F_FS F_LE F_SL
Satisfied
(SF)
Pearson
Correlation
1 .704 ** .811** .809** .779** .549** .656** .689**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 204 204 204 204 204 204 204 204
The analysis of Pearson correlation coefficients revealed that all factors in the model had Sig
values less than 0.05, indicating significant statistical correlations. Consequently, the regression
model incorporated these statistically significant variables.
Linear regression analysis
The regression model’s test value F = 933.931 and Sig significance level of 0.000 demonstrated
a strong fit with the collected data. The independent variables in the regression analysis accounted
for 87% of the variation in the dependent variable, as indicated by an adjusted R2 value of 0.87. The
remaining 13% was attributed to errors and variables outside the model. Durbin-Watson values,
assessing first-order series autocorrelation, showed Durbin-Watson = 1.849, within the acceptable
range of 1.5 to 2.5.
12 de la Ciudad de Ho Chi Minh, Vietnam 245Vol. 15(1) enero-marzo 2025/ 239 - 248
Figure 2. Proposed model to analyze factors affecting the quality of life in District 12,
Ho Chi Minh City
Results of Pearson correlation analysis
Before progressing to regression analysis, Pearson correlation was used to assess the linear cor-
relation between the dependent and independent variables.
TABLE 4. Correlation between independent and dependent variables
F_SF F_EI F_IS F_MH F_ET F_FS F_LE F_SL
Satisfied
(SF)
Pearson
Correlation
1 .704 ** .811** .809** .779** .549** .656** .689**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 204 204 204 204 204 204 204 204
The analysis of Pearson correlation coefficients revealed that all factors in the model had Sig
values less than 0.05, indicating significant statistical correlations. Consequently, the regression
model incorporated these statistically significant variables.
Linear regression analysis
The regression model’s test value F = 933.931 and Sig significance level of 0.000 demonstrated
a strong fit with the collected data. The independent variables in the regression analysis accounted
for 87% of the variation in the dependent variable, as indicated by an adjusted R2 value of 0.87. The
remaining 13% was attributed to errors and variables outside the model. Durbin-Watson values,
assessing first-order series autocorrelation, showed Durbin-Watson = 1.849, within the acceptable
range of 1.5 to 2.5.
246 Thi Viet Ha, Thi BinhInteracción y P erspectiva. R evista de Trabajo S ocial Vol. 15(1): 2025
TABLE 5. Regression Coefficients
Coefficientsa
Model
Unstandardized
Coefficients Standardized
Coefficients t Sig. Collinearily Statistics
B Std. Error Tolerance VIF
1
(Constant) .060 .053 1.149 .252
EI .097 .008 .163 12.436 .000 .865 1.156
SL .137 .013 .172 10.355 .000 .537 1.862
LE .227 .011 .348 21.086 .000 .544 1.838
MH .173 .013 .243 13.212 .000 .439 1.280
ET .207 .014 .258 15.337 .000 .527 1.898
FS .108 .012 .128 9.119 .000 .752 1.330
IS .024 .010 .037 2.277 .004 .550 1.818
a. Dependent variable: Y_SF
The analysis of the variance inflation factor (VIF) for independent variables revealed no mul-
ticollinearity, with all VIF values below 2. The regression model identified the variables influencing
the quality of life in District 12 of Ho Chi Minh City After normalization, the regression equation
is expressed as follows:
Y=β0 + β1⋅X1+β2⋅X2+β3⋅X3+β4⋅X4+β5⋅X5+β6⋅X6+β7⋅X6+ε
Here, the standardized beta coefficients of the seven independent variables (X1, X2, X3, X4,
X5, X6, X7) all have positive values; as a result, the magnitude of beta indicates a positive relation-
ship with the standard of living in District 12. Coefficients of beta in factors The living environment
(LE) is the largest standardized factor; education and training (ET) is the second; medicine and
health care (MH) is the third; stability in life (SL) is the fourth; employment and income (EI) is the
fifth; family and social relationships (FS) is the sixth; and infrastructure (IS) is the last. From there,
the specific formulation of the regression equation used in this investigation is as follows:
Y =0.060+ 0.348*LE + 0.258*ET + 0.243 *MH + 0.172*SL + 0.163*EI + 0.128*FS + 0.037*IS + 𝜀
The above analysis and testing results show seven factors, including: living environment; edu-
cation and training; health and health care; life stability; employment, and income; family and
social relationships; and infrastructure. All have a certain impact on the quality of life of District 12
residents. These factors have a descending order based on the relative importance of each element, in
addition to being influenced by particular observed variables. For the purpose of suggesting ideas to
raise the standard of living for those living in the research area, this will be a crucial starting point.
Furthermore, the research methodology can be extended to other regions that bear resemblances to
Ho Chi Minh City’s District 12.
TABLE 5. Regression Coefficients
Coefficientsa
Model
Unstandardized
Coefficients Standardized
Coefficients t Sig. Collinearily Statistics
B Std. Error Tolerance VIF
1
(Constant) .060 .053 1.149 .252
EI .097 .008 .163 12.436 .000 .865 1.156
SL .137 .013 .172 10.355 .000 .537 1.862
LE .227 .011 .348 21.086 .000 .544 1.838
MH .173 .013 .243 13.212 .000 .439 1.280
ET .207 .014 .258 15.337 .000 .527 1.898
FS .108 .012 .128 9.119 .000 .752 1.330
IS .024 .010 .037 2.277 .004 .550 1.818
a. Dependent variable: Y_SF
The analysis of the variance inflation factor (VIF) for independent variables revealed no mul-
ticollinearity, with all VIF values below 2. The regression model identified the variables influencing
the quality of life in District 12 of Ho Chi Minh City After normalization, the regression equation
is expressed as follows:
Y=β0 + β1⋅X1+β2⋅X2+β3⋅X3+β4⋅X4+β5⋅X5+β6⋅X6+β7⋅X6+ε
Here, the standardized beta coefficients of the seven independent variables (X1, X2, X3, X4,
X5, X6, X7) all have positive values; as a result, the magnitude of beta indicates a positive relation-
ship with the standard of living in District 12. Coefficients of beta in factors The living environment
(LE) is the largest standardized factor; education and training (ET) is the second; medicine and
health care (MH) is the third; stability in life (SL) is the fourth; employment and income (EI) is the
fifth; family and social relationships (FS) is the sixth; and infrastructure (IS) is the last. From there,
the specific formulation of the regression equation used in this investigation is as follows:
Y =0.060+ 0.348*LE + 0.258*ET + 0.243 *MH + 0.172*SL + 0.163*EI + 0.128*FS + 0.037*IS + 𝜀
The above analysis and testing results show seven factors, including: living environment; edu-
cation and training; health and health care; life stability; employment, and income; family and
social relationships; and infrastructure. All have a certain impact on the quality of life of District 12
residents. These factors have a descending order based on the relative importance of each element, in
addition to being influenced by particular observed variables. For the purpose of suggesting ideas to
raise the standard of living for those living in the research area, this will be a crucial starting point.
Furthermore, the research methodology can be extended to other regions that bear resemblances to
Ho Chi Minh City’s District 12.
Evaluación de la calidad de vida urbana: estudio exhaustivo del distrito
12 de la Ciudad de Ho Chi Minh, Vietnam 247Vol. 15(1) enero-marzo 2025/ 239 - 248
CONCLUSION
The survey findings indicate that the living environment is the most significant factor affect-
ing an individual’s quality of life, followed closely by education and training. Other factors such as
health and medical care, stability in life, employment and income, family and social ties, and infra-
structure also impact quality of life, albeit to a lesser extent.
Despite the rapid survey execution and small sample size, the results hold statistical and practi-
cal significance, validating the research. However, this limitation suggests that the study might not
comprehensively cover all factors influencing quality of life. Future research should aim to expand
the study area, extend the survey period, increase the sample size, and refine the sampling technique
to address these constraints.
RECOMMENDATIONS
Environmental quality directly affects every individual, particularly in urban areas where pol-
lution levels have recently escalated alarmingly. The survey indicates that the environmental factor,
with the highest Beta coefficient, significantly impacts the standard of living in District 12, which
suffers from environmental degradation and flooding issues. The following are pragmatic sugges-
tions to address these challenges:
Addressing natural landfills: Prioritize the removal of natural landfills in District 12 to mitigate
their negative impact on the environment and public health.
Promoting eco-friendly interior decor: Encourage the use of sustainable materials like wood
and bamboo for interior decoration. Avoid the trap of fleeting design trends by opting for antique
wooden furniture.
Reducing insecticide use: Gradually phase out the use of insecticides linked to diseases like
Parkinson’s and cancer. Alternatives should be explored to minimize health risks.
Encouraging reusable bags in stores: Implement policies in convenience stores to promote the
use of reusable bags. The environmental cost of plastic bags is significant; alternatives like cloth bags,
paper, or leaves are preferable.
Government and local authority actions: The government should focus on analyzing and ad-
dressing the root causes of flooding issues. Local authorities need to implement supportive policies
for infrastructure improvement, such as enhancing internal roadways and renovating buildings to
prevent flood damage. Job settlement should be part of these redevelopment efforts.
BIBLIOGRAPHIC REFERENCES
• Buhalis, D. (2000). Marketing the competitive destination of the future. . Tourism Manage-
ment Vol 21, 97-116.
• Christopher J. Boyce, Gordon D.A. Brown and Simon C. Moore. (2010). Money and Happi-
ness: Rank of Income, Not Income, Affects Life Satisfaction. Psychological Science Vol. 21, No.
4 , pp. 471-475 .
• Gao, J & Jiao, Y.B. (2014). Differentiating Brand Positioning of Tourism Destinations: A
Brand Personality Perspective . Tourism Tribune, Vol 29, 49-57.
12 de la Ciudad de Ho Chi Minh, Vietnam 247Vol. 15(1) enero-marzo 2025/ 239 - 248
CONCLUSION
The survey findings indicate that the living environment is the most significant factor affect-
ing an individual’s quality of life, followed closely by education and training. Other factors such as
health and medical care, stability in life, employment and income, family and social ties, and infra-
structure also impact quality of life, albeit to a lesser extent.
Despite the rapid survey execution and small sample size, the results hold statistical and practi-
cal significance, validating the research. However, this limitation suggests that the study might not
comprehensively cover all factors influencing quality of life. Future research should aim to expand
the study area, extend the survey period, increase the sample size, and refine the sampling technique
to address these constraints.
RECOMMENDATIONS
Environmental quality directly affects every individual, particularly in urban areas where pol-
lution levels have recently escalated alarmingly. The survey indicates that the environmental factor,
with the highest Beta coefficient, significantly impacts the standard of living in District 12, which
suffers from environmental degradation and flooding issues. The following are pragmatic sugges-
tions to address these challenges:
Addressing natural landfills: Prioritize the removal of natural landfills in District 12 to mitigate
their negative impact on the environment and public health.
Promoting eco-friendly interior decor: Encourage the use of sustainable materials like wood
and bamboo for interior decoration. Avoid the trap of fleeting design trends by opting for antique
wooden furniture.
Reducing insecticide use: Gradually phase out the use of insecticides linked to diseases like
Parkinson’s and cancer. Alternatives should be explored to minimize health risks.
Encouraging reusable bags in stores: Implement policies in convenience stores to promote the
use of reusable bags. The environmental cost of plastic bags is significant; alternatives like cloth bags,
paper, or leaves are preferable.
Government and local authority actions: The government should focus on analyzing and ad-
dressing the root causes of flooding issues. Local authorities need to implement supportive policies
for infrastructure improvement, such as enhancing internal roadways and renovating buildings to
prevent flood damage. Job settlement should be part of these redevelopment efforts.
BIBLIOGRAPHIC REFERENCES
• Buhalis, D. (2000). Marketing the competitive destination of the future. . Tourism Manage-
ment Vol 21, 97-116.
• Christopher J. Boyce, Gordon D.A. Brown and Simon C. Moore. (2010). Money and Happi-
ness: Rank of Income, Not Income, Affects Life Satisfaction. Psychological Science Vol. 21, No.
4 , pp. 471-475 .
• Gao, J & Jiao, Y.B. (2014). Differentiating Brand Positioning of Tourism Destinations: A
Brand Personality Perspective . Tourism Tribune, Vol 29, 49-57.
248 Thi Viet Ha, Thi BinhInteracción y P erspectiva. R evista de Trabajo S ocial Vol. 15(1): 2025
• Hair, J. B. (2010). Multivarite data analysis. Prentice Hall, Upper saddle river, NJ.
• Hoang. T, Chu. M,N. (2017). Statistics applied in economy and society. Economic Publishing
House. Ho Chi Minh.
• Jonathan Brooks, R. G. (2021). Transportation, community quality of life, and life satisfac-
tion in metro and non-metro areas of the United States Jeremy Mattson. Wellbeing, Space and
Social, Vol 2.
• Katherine Ka Pik Chang, F. K.-m. (2020). The Impact of the Environment on the Quality
of Life and the Mediating Effects of Sleep and Stress. International Journal of invironmental
research and public health.
• Made Nyandra, B. H. (2018). Education and Training Improve Quality ofLife and Decrease
Depression Score in Elderly Population . Eurasian Journal of Analytical Chemistry, Vol 13 (2),
371 377.
• Marans, R. W. (2012). Quality of urban life studies: An overview and implications for environ-
ment-behaviour research. . Procedia - Social and Behavioral Sciences, 35, 9-22.
• Marans, R. W., & Stimson, R. J. (2011). Investigating Quality of Urban Life: Theory, Meth-
ods, and Empirical Research. Social Indicators Research Series, Vol. 45. Springer Netherlands.
• Nguyen, T.C., Nguyen, H.D., Le, H.T. and Kaneko, S. (2022). “Residents’ preferred measures
and willingness-to-pay for improving urban air quality: A case study of Hanoi city, Vietnam”.
Journal of Economics and Development, Vol. 24 No. 3, pp. 262-275 https://doi.org/10.1108/
JED-03-2021-0036.
• P. Boehnke. (2005). First European Quality of Life Survey: Life satisfaction, happiness and
sense of belonging Office for Official Publications of the European Communities. Luxem-
bourg: Belonging Office for Official Publications of the European Communities.
• Pastha. Eva, D. A. (2011). Defining the quality of urban life: Which factors should be consid-
ered? Eupean Regional Science Association 51st European Congress Barcelona, Spain.
• Sirgy, M. J., Rahtz, D. R., Cicic, M., & Underwood, R. (2000). A method for assessing resi-
dents’ satisfaction with community-based services: A quality-of-life perspective. Social Indica-
tors Research, 49(3), 279-316.
• Treasury Board of Canada Secretariat. (2000). Defining, Measuring and Reporting Quality of
Life for Canadians. See also website: https://www.tbs-sct.canada.ca/pubs_pol/dcgpubs/pubs-
disc/qol01-eng.asp.
• WHO. (2000). Why do health systems matter?
• World Bank. (2011). Vietnam Urbanization Review. Retrieved at: https://documents.world-
bank.org/curated/en/225041468177548577/pdf/669160ESW0P1130Review000Full0repo
rt.pdf: Technical Assistance Report.
• Zhao B. (2004). Perceptions of Quality of Life and Use of Human Sevices by Households: A
Model . University of Kentucky doctoral dissertations.
• Hair, J. B. (2010). Multivarite data analysis. Prentice Hall, Upper saddle river, NJ.
• Hoang. T, Chu. M,N. (2017). Statistics applied in economy and society. Economic Publishing
House. Ho Chi Minh.
• Jonathan Brooks, R. G. (2021). Transportation, community quality of life, and life satisfac-
tion in metro and non-metro areas of the United States Jeremy Mattson. Wellbeing, Space and
Social, Vol 2.
• Katherine Ka Pik Chang, F. K.-m. (2020). The Impact of the Environment on the Quality
of Life and the Mediating Effects of Sleep and Stress. International Journal of invironmental
research and public health.
• Made Nyandra, B. H. (2018). Education and Training Improve Quality ofLife and Decrease
Depression Score in Elderly Population . Eurasian Journal of Analytical Chemistry, Vol 13 (2),
371 377.
• Marans, R. W. (2012). Quality of urban life studies: An overview and implications for environ-
ment-behaviour research. . Procedia - Social and Behavioral Sciences, 35, 9-22.
• Marans, R. W., & Stimson, R. J. (2011). Investigating Quality of Urban Life: Theory, Meth-
ods, and Empirical Research. Social Indicators Research Series, Vol. 45. Springer Netherlands.
• Nguyen, T.C., Nguyen, H.D., Le, H.T. and Kaneko, S. (2022). “Residents’ preferred measures
and willingness-to-pay for improving urban air quality: A case study of Hanoi city, Vietnam”.
Journal of Economics and Development, Vol. 24 No. 3, pp. 262-275 https://doi.org/10.1108/
JED-03-2021-0036.
• P. Boehnke. (2005). First European Quality of Life Survey: Life satisfaction, happiness and
sense of belonging Office for Official Publications of the European Communities. Luxem-
bourg: Belonging Office for Official Publications of the European Communities.
• Pastha. Eva, D. A. (2011). Defining the quality of urban life: Which factors should be consid-
ered? Eupean Regional Science Association 51st European Congress Barcelona, Spain.
• Sirgy, M. J., Rahtz, D. R., Cicic, M., & Underwood, R. (2000). A method for assessing resi-
dents’ satisfaction with community-based services: A quality-of-life perspective. Social Indica-
tors Research, 49(3), 279-316.
• Treasury Board of Canada Secretariat. (2000). Defining, Measuring and Reporting Quality of
Life for Canadians. See also website: https://www.tbs-sct.canada.ca/pubs_pol/dcgpubs/pubs-
disc/qol01-eng.asp.
• WHO. (2000). Why do health systems matter?
• World Bank. (2011). Vietnam Urbanization Review. Retrieved at: https://documents.world-
bank.org/curated/en/225041468177548577/pdf/669160ESW0P1130Review000Full0repo
rt.pdf: Technical Assistance Report.
• Zhao B. (2004). Perceptions of Quality of Life and Use of Human Sevices by Households: A
Model . University of Kentucky doctoral dissertations.