The Relationship between Housing Problems and Overall Well-being

Introduction

Studies show that housing is significantly linked to a population’s quality of life and
satisfaction levels. Housing conditions are often applied in measuring a country’s economic
health, including its GDP and per capita income. Measuring housing conditions can provide
information about a population’s living conditions at the primary level (household). The leading
housing conditions used to measure satisfaction levels and quality of life are the size of the living
space, the condition of the toilets and the bathrooms, and the overall housing structure, including
roofing and walls.

Problem Statement and Hypothesis

In the previous years, a country’s socioeconomic well-being was only measured using its
GDP growth rate, which was problematic to many because it ignored the variabilities among the
different households. If a country or a community has a few wealthy individuals and a majority
are poor, using the Gross Domestic Product clusters everyone, ignoring the differences in
population factors like access to factors like education, health services, and quality housing
(Clair, 2019). Housing standards directly affect the overall satisfaction and material comfort
(Zhang et al., 2018). This study theorizes that people living in comfortable houses are more
satisfied and contented than those facing housing troubles. The two hypotheses below will be
used to test the correlation between housing conditions and overall satisfaction:
H 0 : overall satisfaction is not linked to the number of housing problems.
H 1 : overall satisfaction is linked to the number of housing problems.

Analysis

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The three independent variables used in this study are Two or More Housing Problems,
Two Housing Problems, and No Housing Problems. Dependent variables include overall material
well-being and satisfaction. The ANOVA test was used to calculate statistical differences
between and within the groups. To apply this technique, more than thirty observations are
needed, they must be independent of each other, the groups in observation must share the same
variance, and the data must have a normal distribution with errors having a mean of zero and a
constant variance (Gray & Grove, 2020). Additionally, a Tukey HSD (“honestly significant
difference”) test was used to establish whether the differences/variabilities between the
measurements were significant or just casual (Gurvich & Naumova, 2021).

Results

a. Descriptive Statistics
There was a total of 935 valid observations in all the groups included in the analysis. The
respondents without any housing group comprised 367 participants. The mean and standard
deviation of their overall satisfaction level and material well-being were 12.71 and 2.353,
respectively. The respondents with only a single housing problem constituted 264 participants.
The corresponding mean and SD of their overall satisfaction and material well-being were 11.97
and 2.588, respectively. Finally, respondents with two or more housing issues (n=304) had a
mean and SD of 10.57 and a and 2.594, in that order. The satisfaction scale ranged from 4 to 16.
b. ANOVA
The ANOVA results indicate significant differences between and within the three groups.
The between-groups and within-groups mean squares are 385.536 and 6.251, respectively. The
F-statistics is significantly larger than 1.000, and the p-value is 0.00, and this p-value is smaller

OVERALL WELL-BEING AND HOUSING PROBLEMS 4
than the set significant value of 0.05. Therefore, there is sufficient statistical evidence that the
satisfaction levels for people with different housing problems are not equal.
c. Multiple Comparisons
The results of the Turkey TSD analysis indicate that the values are statistically
significant, and the differences are not mere coincidences. The mean difference between “one
housing problem” and “no housing problem” is 0.739, and the p-value of this correlation is
0.001, indicating that the relationship is statistically significant. At the same time, the mean
difference between “two or more housing problems” and “no housing problem” is 2.139 with a
0.000 p-value, which also shows that the correlation between the two variables is statistically
significant. Finally, the p-value and mean difference of “two or more housing problems” and
“one housing problem” are 0.000 and 1.401, respectively. This p-value also shows that the
correlation between the variables is statistically significant. Overall, the fact that all the three
correlations have larger mean differences and p-values under 0.05 implies that they are
statistically significant.

Conclusion

The above analysis reveals that housing conditions are inversely linked to a population’s
overall satisfaction and material well-being. As housing problems surge, satisfaction level drops.
For that reason, it is necessary that stakeholders involved in planning pass or amend legislation
that directly improve the housing conditions of communities, not focusing on economic factors
only like employment.

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References

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research:
Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.
Clair, A. (2019). Housing: an under-explored influence on children’s well-being and
becoming. Child Indicators Research, 12(2), 609-626.
Zhang, F., Zhang, C., & Hudson, J. (2018). Housing conditions and life satisfaction in urban
China. Cities, 81, 35-44.
Gurvich, V., & Naumova, M. (2021). Logical Contradictions in the One-Way ANOVA and
Tukey–Kramer Multiple Comparisons Tests with More Than Two Groups of
Observations. Symmetry, 13(8), 1387.