Akademik Veri Yönetim Sistemi
7. INTERNATIONAL ANTALYA Antalya, Turkiye 11-13 May 2024 SCIENTIFIC RESEARCH AND INNOVATIVE STUDIES CONGRESS, Antalya, Türkiye, 11 - 13 Mayıs 2024, ss.18
Introduction and Purpose: In this study, the relationship between the Human Development
Index (HDI) and the Happy Planet Index (HPI), which were created based on the idea that
people's happiness, welfare and development cannot be explained only by economic growth,
is tried to be measured by Canonical Correlation Analysis. Since both indices are like a data
set rather than a variable with their structure consisting of 3 sub-components each, it is aimed
to show how the relationship between them is misleading when measured by Classical
Pearson Correlation Analysis. This is because the Classical Pearson Correlation Analysis
considers the variables in pairs and cannot make a holistic inference. Canonical Correlation
Analysis, on the other hand, considers the variables as a whole with their sub-components and
measures the relationship through the canonical variables it creates. Materials and Methods:
The canonical relationships between HDI and HPI were measured for 34 OECD countries.
Korea, Costa Rica, Luxembourg and Turkey were excluded as some data were not available
for these 4 countries. The data of the study belongs to 2020, which is the most recent common
cluster for HDI and HPI. SPSS Syntax module was used in the application. Findings: Three
pairs of canonical variables were calculated between HDI and HPI variable sets, but only two
of them were found to be statistically significant. The first canonical correlation value is 0.976
(Wilks L=0.017) and the second canonical correlation value is 0.799 (Wilks L=0.357). The
first canonical variable of HDI is mainly composed of “life expectancy at birth index”,
secondly “GDP index” and thirdly “education index”. The second canonical variable of HDI
is mainly composed of “education index” (in negative direction), followed by “GDP index”
(in negative direction). The first canonical variable of the HPI is predominantly composed of
“life expectancy at birth” and secondly “welfare level”, the effect of “ecological footprint” is
not significant. The second canonical variable of the HPI is mainly composed of “ecological
footprint” (in negative direction) and secondly “welfare level” (in negative direction). “Life
expectancy at birth” was not significant in this canonical variable. Results: This study has
shown that the relationship between HDI and HPI can be as high as 0.976 when considered
holistically with Canonical Correlation Analysis. The Classic Pearson Correlation value
between HDI and HPI is 0.16. This shows that if the linear relationship between only two
variables is not to be measured directly, as in this study, and if the relationships between
variables with sub-components are to be measured, Canonical Correlation Analysis is a more
appropriate way than Classical Pearson Correlation Analysis.