Quality of Life and Perception of Aging in Institutionalized and Non-Institutionalized Elderly in the South Canara Region in India

Faculty of Health, Medicine and Life Sciences (FHML)
Master’s degree programme in Global Health
05.05.2014 – 31.08.2014
T.M. Kafczyk
tom.kafczyk@hotmail.de
Master’s thesis:
Quality of Life and Perception of Aging in Institutionalized and
Non-Institutionalized Elderly in the South Canara Region in India
I am truly grateful and humbled by the reliable and outstanding supervision, both in a
didactic and inter-personal way, of my first supervisor, Prof. Dr. G.I.J.M. Kempen,
Maastricht University, Dept. of Health Services Research. My thanks also go to my
second supervisor, Dr. J.S.M. Krumeich, Maastricht University, Dept. of Health, Ethics
and Society, who was especially at the beginning of the thesis project of priceless help in
pulling the strings to allow me to work on this important topic in Manipal, Karnataka
state, India. Another word of thanks which I absolutely want to express to my field
supervisor in Manipal, T. Andrews J, Manipal University, Dept. of Public Health, who was
of significant help, a source of great advice and field wisdom and who had always an
open ear for my problems.
Maastricht, August 26 2014
Note: this study was conducted in cooperation with the Dept. of Public Health, Manipal
University, Manipal 576 104, Karnataka, India. The placement took place from May 5 to
June 20 2014.

II
Abstract
Introduction
India will soon have the second largest population over 60 after China. Amplified by
globalization, older people face now a decline in traditional values that is reflected in a
breakdown of traditional family structures and a rise in the number of old age homes. In
light of these changes, a better understanding of how different living arrangements affect
quality of life and the self-perception of aging (SPA) is urgently warranted. This study
investigated if the living arrangement (community versus old age home) determined
differences in quality of life and if these differences are partly explained by the SPA
among older adults in India.
Methods
Purposive sampling was applied to collect data in India in the South Canara region in
Karnataka state in non-institutionalized and institutionalized older people ≥ 55 years old.
The mean age of the final sample of 139 participants was 69.75 (SD = 8.83) years.
Quality of life was assessed with the WHOQOL-BREF instrument, SPA was assessed with
the Attitudes Toward Own Aging subscale of the Lawton Philadelphia Geriatric Centre
Moral Scale. The statistical analysis was based on multiple regression analysis.
Results
Institutionalized elderly people showed lower quality of life scores in the domains physical
health, psychological health, social relationships and environment. The SPA did not vary
between the living arrangements and did not seem to explain the relationship between
living arrangement and quality of life. Instead, SPA changed the relationship between
living arrangement and quality of life. Possible explanations and implications are
discussed.
Conclusion
The results highlight the need to improve quality of life particularly in institutionalized
older people in the South Canara region. However, definite conclusions about whether
this should be done through the improvement of SPA cannot be drawn due to limitations
of the present study. A follow-up study is warranted in order to shed more light on this
important topic.

III
Table of contents
Abstract ...............................................................................................................II
Table of contents..................................................................................................III
List of figures........................................................................................................ V
List of tables ........................................................................................................ VI
List of abbreviations.............................................................................................VII
1. Introduction ...................................................................................................... 1
1.1 Aging .......................................................................................................... 1
1.1.1 Demographic shift ................................................................................... 2
1.1.2 Health status.......................................................................................... 3
1.1.3 Perception of aging.................................................................................. 4
1.2 Caring structures .......................................................................................... 6
1.2.1 Impact of globalization ............................................................................ 6
1.2.2 Old age homes ....................................................................................... 7
1.2.3 Quality of life.......................................................................................... 8
1.3 Rationale and aim of the study ....................................................................... 9
1.4 Research question and hypotheses.................................................................10
1.4.1 Research question..................................................................................10
1.4.2 Hypotheses ...........................................................................................10
2. Methods ..........................................................................................................12
2.1 Data collection.............................................................................................12
2.1.1 Sampling ..............................................................................................12
2.1.2 Procedure .............................................................................................15
2.2 Variables and measurement instruments ........................................................16
2.2.1 Independent variable: living arrangement .................................................16
2.2.2 Dependent variable: quality of life............................................................16
2.2.3 Mediating variable: self-perception of aging...............................................17
2.2.4 Covariates.............................................................................................18
2.2.5 Validity and reliability .............................................................................18
2.3 Statistical analysis .......................................................................................19
2.4 Ethical considerations ...................................................................................20
3. Results ............................................................................................................21
3.1 Descriptive statistics ....................................................................................21
3.2 Hypotheses .................................................................................................22
3.2.1 Hypothesis one: living arrangement and quality of life ................................22

IV
3.2.2 Hypothesis two: living arrangement and self-perception of aging .................24
3.2.3 Hypothesis three: self-perception of aging and quality of life .......................24
3.2.4 Hypothesis four: mediating role of self-perception of aging .........................26
4. Discussion and conclusion..................................................................................29
4.1 Discussion...................................................................................................29
4.2 Study limitations..........................................................................................34
4.3 Future directions..........................................................................................36
4.3.1 Research...............................................................................................36
4.3.2 Policy implications..................................................................................37
4.4 Conclusion ..................................................................................................39
References ..........................................................................................................40
Appendices ..........................................................................................................45
Appendix 1 Global AgeWatch domains and indicators .............................................45
Appendix 2 Demographics in Karnataka ...............................................................46
Appendix 3 Informed consent template ................................................................47
Appendix 4 WHOQOL-BREF and domain facets ......................................................51
Appendix 5 WHOQOL-BREF user agreement..........................................................55
Appendix 6 Self-perception of aging questionnaire.................................................58
Appendix 7 Translation of the self-perception of aging questionnaire........................59
Appendix 8 Ethical clearance certificate ................................................................64
Appendix 9 Correlations among study variables.....................................................66
Appendix 10 Study summary: statistical diagrams.................................................67
Appendix 11 Regression with interaction term.......................................................68

V
List of figures
Figure 1: Population pyramids in India for 1950, 2000 and 2050 ................................. 3
Figure 2: Conceptual diagram of the study model .....................................................11
Figure 3: Recruitment and data collection procedure.................................................16
Figure 4: Statistical diagram of the study model with effect terms ..............................19
Figure 5: Quality of life domains as a function of self-perception of aging per living
arrangement........................................................................................................33

VI
List of tables
Table 1: Global AgeWatch health status domain ........................................................ 4
Table 2: Characteristics of old age homes................................................................13
Table 3: Characteristics of study areas and participants per living arrangement ...........14
Table 4: Descriptive characteristics of the study sample ............................................22
Table 5: Regression results for living arrangement on quality of life controlling for
covariates............................................................................................................23
Table 6: Regression results for living arrangement on self-perception of aging controlling
for covariates.......................................................................................................24
Table 7: Regression results for self-perception of aging on quality of life controlling for
covariates............................................................................................................25
Table 8: Regression results for living arrangement on quality of life controlling for self-
perception of aging and covariates .........................................................................28

VII
List of abbreviations
BCa Bias-corrected and accelerated bootstrap
CI Confidence interval
DALYs Disability Adjusted Life-Years
GDP Gross domestic product
H1-4 Hypothesis 1-4
SPA Self-perception of aging
SPSS Statistical Package for the Social Sciences
Note: elderly people living in the community are described in the present study as “non-
institutionalized” and elderly people living in old age homes are described as
“institutionalized”. Both formulations (e.g. old age home and institution) are used
interchangeable.

Maastricht University | FHML | Tom Kafczyk | Master’s thesis | Chapter: 1. Introduction
1
1. Introduction
In this first chapter, the background of the study is described. First, the topic “aging” is
introduced. Here, an emphasis is put on the demographic shift and the health status and
the self-perception of aging (SPA) of older people in India. Next, caring structures in
India are described, followed by the rationale and aim of the study. Finally, the research
question and hypotheses of the present study are stated.
1.1 Aging
“Population ageing is unprecedented, without parallel in human history—and the twenty-
first century will witness even more rapid ageing than did the century just past.” (United
Nations, 2001, second paragraph).
Aging is a normal phenomenon and means the effects of older age (Devi & Roopa, 2013;
Mahapatra, 2010). It is often associated with a decline in vitality (Sinclair & Guarente,
1997). More precise, aging has three aspects: biological, psychological and social
(Mahapatra, 2010). Age-related changes include a decline in physical and cognitive
abilities such as abilities in daily living activities or learning (Lockenhoff et al., 2009).
Research is undertaken to reverse or mitigate these biological changes and to increase
longevity (e.g. Baur et al., 2006; de Grey et al., 2004). However, the causes of aging, on
a molecular level, have yet to be determined (Sinclair & Guarente, 1997). Age-related
socio-emotional aspects of aging include wisdom, knowledge, received respect, life
satisfaction and family authority (Lockenhoff et al., 2009). Especially the perceived socio-
emotional characteristics of aging vary across cultures. The proportion of older persons in
the society or cultural aspects such as status differences, were found to affect societal
views on the socio-emotional aspects of aging. Basic biological age-related changes are
perceived to be similar across cultures (Lockenhoff et al., 2009).
Aging can lead to an increased vulnerability due to, for example, financial insecurity or
neglect in the society (Devi & Roopa, 2013; Mahapatra, 2010). In India, a non-universal
social pension scheme might contribute to financial insecurity (Pension watch, 2014).
Currently, about 19% of the population over 60 are covered by the pension scheme
(Pension watch, 2014). States as well as the children have a moral and legal obligation to

2
care and provide security for older persons in India while the extent of this liability
varies; for instance, Hindu, Muslim or Christian common law describes different
obligations. The Code of Criminal Procedure 1973 governs all religions and communities.
It obliges children to care for their parents. Furthermore, the Maintenance and Welfare of
Parents and Senior Citizen Act 2007 obliges the family (including relatives) and the state
to care for the needs based maintenance and the welfare of elderly (National Human
Rights Commission, 2011). The Act has to be brought to action on a state level; not all
states notified the act (National Human Rights Commission, 2011).
1.1.1 Demographic shift
Population aging has become a major global demographic trend. Underlying population
aging is a reduction in mortality followed by a reduction in fertility along with a
lengthening of life expectancy (United Nations, 2001, 2013), whereas not all extra life
years are necessarily spend free of disabilities (Robine et al., 2005). The process is called
“demographic transition”. Developed countries have gone through the demographic
transition already. Developing countries such as India are presently in the transition
(United Nations, 2001); but developing countries are ageing now at faster speed than
more developed countries did (HelpAge International, 2000; United Nations, 2001).
Soon, there will be more older people than children in the world (World Health
Organization, 2014a). The major demographic shift has economic, social and political
consequences for societies. While developed countries faced the challenges of population
ageing earlier, developing countries are only starting to tackle some of the challenges
(e.g. housing or health facilities for older people). Due to its undeniable importance, the
World Health Organization devoted the World Health Day 2012 to the topic “ageing and
health” (World Health Organization, 2014a). Moreover, the Madrid International Strategic
Plan of Action on Aging, adopted during the World Assembly on Ageing in 2002,
emphasized the well-being of older persons as a priority for action (United Nations, 2002,
2013).
Demographic shift in India
In India, declining fertility and mortality rates, accompanied by decreasing child mortality
rates and an increasing life expectancy led to an increase in the number of elderly
persons (Lena, Ashok, Padma, Kamath, & Kamath, 2009). At the present point in time,
approximately 8% of the population are over 60 years old. The population in India aged
60 and older will double in the next 40 years to over 220 million in 2050 (Chatterji et al.,

3
2008; Global AgeWatch, 2013; The Lancet, 2013). India will then have the second
largest population of people over 60 after China (Global AgeWatch, 2013). The societal
aging structure will change dramatically (see Figure 1), leading to major implications for
policy makers (Chatterji et al., 2008).
Figure 1: Population pyramids in India for 1950, 2000 and 2050
Note: from United Nations (2001, p. 271)
1.1.2 Health status
The Global AgeWatch report from 2013 compared 91 countries representing about 89%
of the world’s population aged 60 and older according to their economic and social well-
being; India ranked 73rd
in total and Sweden ranked 1st
showing the best results (Global
AgeWatch, 2013). Four domains were assessed, namely income security, health status,
employment and education and enabling societies and environment (see “Appendix 1
Global AgeWatch domains and indicators”). Table 1 summarizes the results for the health
status domain in comparison with Sweden. It shows, for example, that only 77.70% of
people in India over 50 years of age indicate that their life has meaning compared to
95.60% in Sweden (Global AgeWatch, 2013). This is similar to what Lena et al. (2009)
reported, who found that 48.00% of people aged 60 and older in South India were not
happy in life.

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Table 1: Global AgeWatch health status domain
Domain and indicators Description indicator
India
Total rank: 73
Sweden
Total rank: 1
Health status Rank: 85 Rank: 7
Life expectancy
at age 60
Average number of years a person
aged 60 can expect to live
17.00 24.00
Healthy life expectancy
at age 60
The average number of years a
person aged 60 can expect to live in
good health
12.60 18.20
Relative
psychological/mental
wellbeing
% of people over 50 who feel their
life has meaning compared with
people aged 35-49 who feel the same
77.70 95.60
Note: data from Global AgeWatch (2013), rank number 1 is the best rank and rank number 91 is the poorest
rank
Despite advancing technologies and increasing efforts by the government and non-
governmental organizations, elderly grow unhealthier (Chatterji et al., 2008). The
perceived health of the elderly in India has declined between 1995-96 and 2004 (Husain
& Ghosh, 2010). By 2030, over 45.76% of the Indian health burden measured in
Disability Adjusted Life-Years (DALYs) is projected to be caused by adults aged 45 and
older although this age group will account for only approximately 30% of the total
population. In 2004, 25.52% of DALYs were attributable to the age group 45 and older.
There is conclusive evidence indicating that India has an “elderly (health) problem”
resulting in various challenges such as the adaptation to the needs of the elderly (Global
AgeWatch, 2013; Raju, 2011). Nevertheless, there is a lack of studies exploring the
needs such as health needs of the elderly in India making it hard to precisely define them
(Boralingaiah, Bettappa, & Kashyap, 2012; Husain & Ghosh, 2010; Raju, 2011).
Contributing to the elderly (health) problem could be the share of the gross domestic
product (GDP) devoted to health. If India wants to deliver true universal health care to
its ageing population, the share of the GDP needs to be increased (The Lancet, 2013).
India spends currently about 2% of the GDP on health (The Lancet, 2013). The World
Bank listed in 2012 for India a total health expenditure of 4.00% and a public health
expenditure of 1.30% of the GDP (The World Bank, 2014); similar numbers are reported
by Spinaci, Currat, Shetty, Crowell, and Kehler (2006).
1.1.3 Perception of aging
SPA has been defined as a lens through which age-related changes are interpreted
(Sargent-Cox, Anstey, & Luszcz, 2014; Steverink, Westerhof, Bode, & Dittmann-Kohli,
2001). It is a dynamic concept (i.e. the perception of aging can change over time)

5
(Sargent-Cox et al., 2014). In the context of aging, SPA is considered an important
concept because it affects future health, health behaviours and help-seeking behaviour
(Cheng, Yip, Jim, & Hui, 2012; Kleinspehn-Ammerlahn, Kotter-Gruhn, & Smith, 2008;
Sargent-Cox et al., 2014). For example, SPA is a significant predictor of mortality (Levy,
Slade, Kunkel, & Kasl, 2002; Uotinen, Rantanen, & Suutama, 2005). It has a greater
impact on survival than gender, socioeconomic status, functional health and loneliness
(Levy, Slade, Kunkel, et al., 2002) and can alternate the cardiovascular response to
stress and a wide array of other functions such as cognitive and behavioural outcomes,
handwriting, mathematical performance, physical performance, memory or views of
other older people (Levy, 1996, 2000, 2003; Levy, Hausdorff, Hencke, & Wei, 2000; Levy
& Myers, 2004; Levy, Slade, & Kasl, 2002; Levy, Slade, Kunkel, et al., 2002; Moser,
Spagnoli, & Santos-Eggimann, 2011). Moreover, SPA contributes to the interpretation of
illness and quality of life (Low, Molzahn, & Schopflocher, 2013).
SPA seems to develop by internalization and depends to some degree on common
stereotypes (Kotter-Gruhn & Hess, 2012; Levy, Slade, Kunkel, et al., 2002). In
accordance with this point are findings after which the frequency of contacts of younger
individuals with older people diminishes stereotypes (Lockenhoff et al., 2009). One
contributing factor to the SPA was described as the “societally sanctioned denigration of
the aged” (Levy, Slade, Kunkel, et al., 2002, p. 268). Stigmatized groups share the same
stereotypes that influence their self-perception (Levy, Slade, Kunkel, et al., 2002). This
could mean, for example, that the attitude of younger people towards old people living in
old age homes affects the perception of aging of older people (Dubey, Bhasin, Gupta, &
Sharma, 2011).
The aforementioned studies on SPA barely studied the variability across different settings
such as institutions, little attention has been paid to it (Kleinspehn-Ammerlahn et al.,
2008). Particularly in India, with changing caring structures and the breakdown of strong
traditional cultural systems after which the family needs to care for the elderly (see next
section “1.2 Caring structures”), the perception of aging has not been studied yet.
We may conclude that India is in a demographic transition with a growing older
population that will soon be the second largest in the world. Elderly people are compared
to younger people facing more problems such as health problems. Furthermore, SPA is
an important variable. It has shown to be a significant predictor of mortality and
contributes to quality of life. In light of societal changes in India (that are discussed in
the next subsection), a better understanding of the concept of SPA is needed.

6
1.2 Caring structures
This section is subdivided into three subsections. First, aging in the context of
globalization and consequences for caring structures is discussed. Next, the rise of old
age homes in India is described. Lastly, quality of life in older people is elaborated.
1.2.1 Impact of globalization
Traditionally, older persons played an important role in the family and the community in
India, their opinions carried weight (Mahapatra, 2010). Informal support systems (e.g.
values, kinship) provided support to the elderly. According to Dubey et al. (2011) old age
has never been a problem for India, just since the disintegration of the joint family old
people face now a problem. “The older generation is caught between the decline in
traditional values on one hand and the absence of an adequate social security system on
the other hand thus, finding it difficult to adjust in the family.” (Dubey et al., 2011, p.
98). Nowadays in India, amplified by globalization due to economic development, a
gradual vanishing of the traditional societal structures and social and economic values
takes place. This trend is exemplified in the transition from joint families to nuclear
families. Informal support systems are breaking down leading to increasing economic
insecurity, social isolation and abuse and neglect of the elderly within the society (Dubey
et al., 2011; Ingle & Nath, 2008; Lena et al., 2009; Mahapatra, 2010; Raju, 2011).
There is evidence, that the modernization of societies is associated with unfavourable
attitudes towards aging and a lower status of elderly within the society (Bengtson, Dowd,
Smith, & Inkeles, 1975; Lockenhoff et al., 2009). As well, with rising proportions of
elderly, as expected for India (Chatterji et al., 2008), the views of the society of aging
become less favourable and authority within families depends less on age (Lockenhoff et
al., 2009). At present, India with a young population and high status differences was
found to have a favourable view on socio-emotional aspects of aging (Lockenhoff et al.,
2009). Westernization might play a role as well. Generally, Eastern cultures hold more
positive views of aging than western cultures (e.g. Italy or France). One reason might be
higher status differences in Eastern cultures (Lockenhoff et al., 2009). This holds true for
India that has shown more positive views on aging (Lockenhoff et al., 2009).
Nevertheless, “westernization” is progressing in India (Stigler et al., 2010), what could
affect the societal views on aging.
The aforementioned changes contributed to a change in the care for the elderly towards
institutionalized care (see next subsection). Mahapatra (2010, p. 116) wrote in this

7
context: “The rapid increase in the elderly population, the changes in the family system,
the lifestyle of the younger generation have led to changes in the living arrangements of
elderly both in developed countries and developing ones.”
1.2.2 Old age homes
“India is at a crossroads and has to decide whether to go the family care way or the
institutional/ community care way.” (Raju, 2011, p. 13).
In order to give elderly people without social security system and without someone to
care for them a place where they get attention and care, old age homes were created
(Mahapatra, 2010). Depending on the context, old age homes are defined differently. The
National Centre for Health and Statistics defined it as “[…] a residence facility with three
or more beds that provided nursing and personal care to the aged who are chronically ill
or destitute or needy persons”.” (in Mahapatra, 2010, p. 120). Devi and Roopa (2013, p.
7) add, old age homes “are needed to take care of the lonely and forsaken elderly in the
evening of their lives”.
The demand for old age homes in India is increasing. An important reason is that less
adult children are caring for their parents in India (Liebig, 2003; Mahapatra, 2010). As a
result, old age homes “have sprung up” to meet the needs of the elderly that have not
been recognized earlier; consequently the population living in old age homes is rising
(Lena et al., 2009; Singh, Kumar, & Reddy, 2012).
Major reasons to shift to an old age home in India include the maladjustment of the
elderly in the family, poverty of the elderly and the migration of children in search of
employment opportunities (Devi & Roopa, 2013). In addition, Mahapatra (2010)
mentioned the following reasons: feeling of loneliness at home (e.g. no kids, loss of
husband/wife, loss of control), having no one to care for them (e.g. family might not be
willing to care for their older family members at home), decline of physical and mental
functioning or seeking a change from the urban hype.
The societal change towards nuclear families driven by factors associated with
globalization is reflected to some degree in the rising demand of old age homes. The new
form of institutionalized care for older family members in the Indian society has been
considered by Devi and Roopa (2013) as borrowed from the “West”. Since the idea of old
age homes in India is relatively new, the adjustment and well-being of residents in old
age homes is an important field of study. However, there is a scarcity of research

8
investigating the results of these changes from non-institutional to institutional care in
the Indian context. “There is much research on the problem of the institutionalized old
people abroad but in India, very little organized information is available about the
problem of the aged living the families and in old age homes.” (Devi & Roopa, 2013, p.
7).
1.2.3 Quality of life
The World Health Organization defines quality of life as “an individual’s perception of
his/her position in life in the context of the culture and value systems in which he/she
lives, and in relation to his/her goals expectations, standards and concerns. It is a broad-
ranging concept, incorporating in a comparing way the person’s physical health,
psychological state, level of independence, social relationships, and their relationship to
salient features of their environment.” (in Devi & Roopa, 2013, p. 8).1
Mathew, George, and Paniyadi (2009) reported that institutionalized older people in
Kerala, India, have more stress and report lower quality of life than their non-
institutionalized counterparts. Dongre and Deshmukh (2012) identified factors that could
explain why elderly in India in old age homes have a lower quality of life. They identified
the working status, not being neglected, involvement in social activities and the
relationship with family as factors that determine quality of life. These factors can be
expected to be lower in residents in old age homes and could therefore explain why
Mathew et al. (2009) found a lower quality of life. This is in line with Raju (2011, p. 8)
who stated that non-institutionalized elderly people are better adjusted because of the
“deep rooted” tradition in the Indian society that it is the duty of the family to “look after
the elderly”. The family is perceived to be the main provider of elderly care and the
better environment (Dongre & Deshmukh, 2012). Dubey et al. (2011) bring in another
factor that could partly explain why institutionalized elderly people demonstrate lower
quality of life. They reported that women in the state Jammu and Kashmir, India, living
with their families in contrast to institutionalized older people had a more positive
perception of aging while the attitude of younger people towards old people living in old
age homes was unsatisfactorily (see subsection “1.1.3 Perception of aging”). Antonelli,
Rubini, and Fassone (2000) reported results for elderly in Italy that are similar to what
1
Leventhal and Colman (2007, p. 756) add: “Quality may be better in one domain (e.g., social relationships)
than another (e.g., ambulatory ability), but whatever and wherever it differs, the judgment of quality is a
product of both the individual’s assessment of his or her personal experience within a variety of domains (i.e.,
data) and the integration of these observations into an overall judgment using a decision rule yet unspecified.”

9
Dubey et al. (2011) found in India: institutionalized elderly people compared to their
non-institutionalized counterparts had a more negative self-concept. Contrary to what
has been mentioned above, Devi and Roopa (2013) reported lower quality of life in non-
institutionalized elderly in Karnataka in the age group 65-70 compared to elderly that are
71-76 years old. However, Devi and Roopa (2013) did not offer an explanation.
Ultimately, there is a lack of studies in quality of life research in this respect, making it
hard define the needs of the elderly and consequently to enable the conditions or
circumstances in which successive elderly cohorts grow older with more quality in their
life.
To summarize what has been mentioned so far, India’s population is growing and aging.
Due to processes associated with globalization, older people face now a decline in
traditional values that is leading to a breakdown of family structures and a rise in the
number of old age homes. Not the family but institutions look now more and more after
the elderly. Non-institutionalized older people living among their family might be better
adjusted and hence show a higher quality of life. The perception of aging could be one
factor that explains this relationship. In light of the societal changes in India, a better
understanding of different living arrangements and hence caring structures and their
impact on quality of life could be beneficial. Interesting is furthermore, if or how the SPA
is involved in this relationship.
1.3 Rationale and aim of the study
The present study assumes that processes associated with globalization such as
modernization changed traditional family dynamics. With fewer children caring for their
parents and a breakdown in traditional norms, more and more elderly have to move to
an old age home. Accordingly, old age homes reflect changing caring-structures in India
towards institutionalization. India will soon have the second largest population of older
people in the world and yet, the impact of this change from traditional caring structures
within the family to institutions is not well understood. This holds particularly true for the
impact on quality of life. Spending the last years in an old age home and not as
traditionally dominant among the family in the community may negatively affect quality
of life (Mathew et al., 2009). Moreover, the SPA could mediate this relationship as the
experience associated with moving to an old age home could affect the SPA (Dubey et
al., 2011). The SPA can explain why the external event of living in an old age home leads

10
to differences in quality of life. In light of the ever growing population in India over 60
(Global AgeWatch, 2013), factors affecting quality of life is an urgent issue (Low et al.,
2013) and should have priority in research.
The aim of the present study is to shed light on potential differences in quality of life of
elderly people in India living in the community versus living in old age homes; and to
shed light on the SPA of elderly people as a possible concept that partly explains the
difference. This understanding could help policy-makers to design better strategies to
enhance the quality of life of older persons in late life.
1.4 Research question and hypotheses
In this section, the research question and stipulated hypotheses are introduced followed
by a conceptual diagram of the study model.
1.4.1 Research question
The research question is: Does living arrangement (community versus old age home)
determines differences in quality of life and is this difference partly explained by the
perception of aging among older adults over 55? As sex, age and health may influence
living arrangement, the perception of aging and quality of life, the influence of these
variables will be taken into account.
1.4.2 Hypotheses
Hypothesis 1 (H1): Older people in old age homes have a more negative quality of life
compared to older people living in the community.
Hypothesis 2 (H2): Older people in old age homes have a more negative SPA compared
to older people living in the community.
Hypothesis 3 (H3): Older people with a more negative SPA report a more negative
quality of life compared to older people with a more positive SPA.
From these three hypotheses, a final fourth hypothesis can be formulated:
Hypothesis 4 (H4): The association between living arrangement and quality of life is
partly explained by SPA.

11
A summary of the proposed model is depicted in Figure 2 below.
Figure 2: Conceptual diagram of the study model
H= Hypothesis

Maastricht University | FHML | Tom Kafczyk | Master’s thesis | Chapter: 2. Methods
12
2. Methods
In this second chapter the proposed methods of the present study will be described.
First, the data collection is elaborated. Next, the variables that are used are introduced.
Finally, the statistical analysis of the data is described followed by ethical considerations.
2.1 Data collection
In this section, first, the sampling is described with details about the inclusion- and
exclusion criteria, the recruitment procedure, the sampling area and the sample size.
Second, the data collection procedure in the field is outlined.
The data collection took place from May 24 to June 10 2014.
2.1.1 Sampling
Inclusion criteria
Three inclusion criteria for the present study were defined: (1) participants were ≥ 55
years old, (2) participants gave an informed consent and (3) the participants lived either
in the community or in an old age home at the time of data collection.
Worldwide, there is no standard numerical criterion to define an older person (World
Health Organization, 2014b). Often the pension age of ≥ 60 is used (e.g. Mathew et al.,
2009; World Health Organization, 2014b); in other contexts, for example Africa, the age
of ≥ 50 is considered as old (World Health Organization, 2014b). The present study has
adopted the age of ≥ 55 to define an older person for three reasons. First, to account for
a lower life expectancy in India; life expectancy at age 60 is 17-18 years in India (Global
AgeWatch, 2013; Jeyalakshmi, Chakrabarti, & Gupta, 2011) compared to approximately
23 in more developed regions such as Europe and as high as 26 in Japan (Global
AgeWatch, 2013; United Nations, 2013). Second, most Indians seem to consider
themselves as old before the age of 60 (Dubey et al., 2011). And lastly, in view of
practical issues such as resource constraints in the present study, the age of ≥ 55
enabled to collect more data in an easy way. Indirectly through more data more
statistical power was achieved.

13
Exclusion criteria
Two exclusion criteria were defined: (1) the participant was unable to communicate
intended answers (e.g. participant is unable to speak or give answers otherwise) and (2)
the participant did not understand instructions (written and verbally) (e.g. due to severe
dementia, visual and hearing impairment). Eventually, the exclusion of participants was
subject to the researchers’ interpretation in a case to case manner in consultation with
the translator (see below) and employees (e.g. manager) within the old age homes or
relatives in the community.
Two different sampling methods were employed for the different living arrangements and
are described in the following.
Recruitment: old age home
There is no record of old age homes in Karnataka state hence, a convenient sample had
to be deployed to recruit participants from institutions (T. Andrews, personal
communication, March 12, 2014). Participants were recruited from six old age homes
(see Table 2 for a description of the old age homes). Table 3 lists the number of
participants that were sampled from each old age home.
Table 2: Characteristics of old age homes
OAH
Religious
affiliation
Residents in total
(Female/male)
Payment
(Free vs. for-pay home)
Bedrooms
(Single vs. shared bedroom)
OAH1 Hindu 7 (5/2)
For-pay home
(approx. 7000Rps/mo)
Shared bedrooms
(≤ 5 people)
OAH2 Christian 34 (25/9)
For-pay home
(approx. 2000Rps/mo)
Single bedrooms and shared
bedrooms (≤ 5 people)
OAH3 Hindu 35 (23/12)
Free-home or
for-pay homea
Shared bedrooms
(≤ 4 people)
OAH4 Hindu 10 (6/4) Free-home
Shared bedrooms
(≤ 3 people)
OAH5 Hindu 12 (12/0) Free-home
Single shared bedroom
(12 people)
OAH6 Christian 180 (138/42) Free-homeb Shared bedrooms
(≤ 25 people)
Note: information was obtained from old age home (OAH) managers, Rps= Indian Rupees (1000 Rupees are
approximately 12.50 Euro), a
depending on the economic status residents pay approximately 6000Rps/mo,
b
some residents do pay a little bit if they economically can
Recruitment: community
A purposive sampling procedure was applied to recruit elderly from the community.
Community participants were recruited (with a door-to-door survey) within the same
area (i.e. the same town) of the old age home. This had two reasons. First, to sample

14
participants from the same area reduces extraneous variables such as access to
transport or noise pollution and in this way the influence of other (not controlled)
environmental variables on the study outcome was kept as minimal as possible; and
second, to collect data in the same town was practically more feasible with regards to the
time- and resource constraints of the present study work.
Sampling area
Data was collected in the South Canara region in Karnataka state, India, in the Dakshina
Kannada and Udupi district; both districts bound to the west with the Arabian Sea. From
1.2 billion people in India, 61.1 million lives in the state of Karnataka. Within Karnataka,
about 2.1 million lives in Dakshina Kannada district and about 1.2 million lives in Udupi
district (see “Appendix 2 Demographics in Karnataka”). Kannada is the official language
and most widely spoken in Karnataka. The vast majority is Hindu, with fewer Muslims
and Christians (Ministry of Home Affairs, 2014a). The percentage of urban population in
Karnataka has increased from 34.00% in 2001 to 38.67% in 2011. In Dakshina Kannada
district 47.67% lived in urban areas in 2011 and in Udupi 28.37% (Indian Census, 2011).
Urbanization in Karnataka and South Canara is rapidly increasing.
In Dakshina Kannada district, data was collected in the city Mangalore. In Udupi district,
data was collected in the village Thekatte, the city Udupi and the towns Brahmavar,
Kundapura, Santhekatte and Manipal. Table 3 shows how many participants were
sampled from each settlement area per living arrangement.
Table 3: Characteristics of study areas and participants per living arrangement
Living arrangement
Area
Non-
Institutionalized
Institutionalized
Settlementc Urban
vs. rural
Income
Participants
(Female/male)
OAH
Participants
(Female/male)
Total
Manipal and
Udupia Urban
Middle to
lower-
income
15 (10/5) OAH1 6 (4/2) 21
Santhekattea
Rural
Middle-
income
12 (7/5) OAH2 12 (8/4) 24
Kundapuraa, +
Urban
Middle-
income
7 (5/2) OAH3 10 (6/4) 17
Thekattea
Rural
Lower-
income
10 (5/5) OAH4 7 (4/3) 17
Brahmavara
Rural
Middle-
income
9 (5/4) OAH5 12 (12/0) 21
Mangaloreb
Urban
Middle-
income
24 (16/8) OAH6 25 (18/7) 49
Total 77 (48/29) 72 (52/20) 149
Note: information about areas was obtained from community residents, OAH= old age home, a
Udupi district,
b
Dakshina Kannada district, c
Setttlement refers to either a city, town or village

15
Target sample size
The general rule of thumb was applied, that at least 10 participants per variable are
needed in order to obtain adequate statistical results. The target sample size was set at a
minimum of approximately 120 participants, i.e. six (variables) multiplied with 10
(participants) multiplied by two (living arrangements).
2.1.2 Procedure
Each participant was invited to take part in the study individually. The study details were
explained and the possibility to ask questions was given. A subject information sheet was
handed over to the participant along with contact addresses in case of any further
questions, doubts or requests. If the participant agreed to participate, an informed
consent had to be signed stating for instance that the participation is voluntary and
confidential. If the participant was not able to sign, an oral consent was obtained and
confirmed by the signature of a witness (see “Appendix 3 Informed consent template”).
If the participant had no further questions, the questionnaires were filled in. Because
some elderly were not able to do the paper and pencil task or did not want to self-
administer the questionnaires, assistance in filling in the questionnaires was provided.
Reasons for assistance included illiteracy2
, visual impairments or writing limitations. Each
participant was asked if she or he could or wants to fill in the questionnaire by her- or
himself. Two modes of questionnaire administration were hence used: (1) interview-style
(interviewer reads the questions and writes down the answers) and (2) questionnaire-
form (self-administered). English and Kannada version questionnaires were employed. If
the participant was bilingual in both languages, the participant was asked which language
she or he preferred. The procedure is summarized in Figure 3 (along with the frequency
of the language and interview-form).
Translator
Because the principal investigator was not fluent in Kannada, a translator was employed
for this study. The translator was a Master of Science student at Manipal University,
fluent in Kannada (native) and English.
2
In 2011, Dakshina Kannada district had a literacy rate of 88.57%, Udupi district of 86.24% (Indian Census,
2011).

16
Figure 3: Recruitment and data collection procedure
a
number in total, b
number non-institutionalized, c
number institutionalized
2.2 Variables and measurement instruments
In the following subsections, the variables used in this study are introduced. Hereafter,
the validity and reliability of the methods is elaborated.
2.2.1 Independent variable: living arrangement
Living arrangement is the independent variable. It is a dichotomous variable (1=
community, 2= old age home).
2.2.2 Dependent variable: quality of life
Quality of life is the dependent variable. The WHOQOL-BREF was used to collect data
(WHOQOL Group, 1998). It consists of 26 items and measures the domains physical
health (7 items), psychological health (6 items), social relationships (3 items) and
environment (8 items). For each domain, up to 20% of missing items were tolerated.
Missing items were substituted with the mean scores of the other items in the domain of

17
the respective participant. Two general items measure health satisfaction and overall
quality of life. Answers were given on a 5-point Likert scale in relation to the last two
weeks (see “Appendix 4 WHOQOL-BREF and domain facets”). The domain scores ranged
on a scale from 0 to 100, higher scores indicate a “better” quality of life.3
Some items
had to be recoded. A translated version in Kannada was available from the World Health
Organization (Chandra, Deepthivarma, Jairam, & Thomas, 2003). Skevington, Lotfy, and
O'Connell (2004) found a Cronbach’s alpha in a field trial in India (Delhi) of 0.76 for the
physical domain, 0.80 for the psychological domain, 0.63 for the social domain and 0.84
for the environmental domain. Hwang, Liang, Chiu, and Lin (2003) confirmed these
results for older people but found a higher Cronbach’s alpha for the social domain with
0.73. In the present study, the Cronbach’s alpha for the physical domain was 0.80, for
the psychological domain 0.62, for the social domain 0.75 and for the environmental
domain 0.76. Permission to use the English and Kannada version of the WHOQOL-BREF
for the purpose of this study was obtained prior the study start (see “Appendix 5
WHOQOL-BREF user agreement”).
2.2.3 Mediating variable: self-perception of aging
Self-perception of aging (SPA) is considered the mediating variable. SPA was measured
with the Attitudes Toward Own Aging subscale from the Lawton Philadelphia Geriatric
Centre Morale Scale (Lawton, 1975). The scale has been used in different studies (e.g.
Jang, Poon, Kim, & Shin, 2004; Levy, Slade, & Kasl, 2002; Sargent-Cox et al., 2014) and
consists of five items. Respondents answered in a yes/no or better/worse format for each
item (see “Appendix 6 Self-perception of aging questionnaire”). Two items had to be re-
coded. The score in sum ranges theoretically from 0 to 5. For each high morale response
a “1” was given, each low morale response received a “0”. For example, if “Do things
keep getting worse as you get older?” was answered with a “yes” then a “0” was given.
Higher scores indicate a more positive SPA. A translated version into Kannada language
was not found in the literature. The English version was hence translated in Kannada
following the validated two-phase translation procedure from Sperber (2004). The reader
is referred to “Appendix 7 Translation of the self-perception of aging questionnaire” for a
detailed description of the procedure. Jang, Poon, Kim, and Shin (2004) established a of
Cronbach’s alpha of 0.71. In the present study, a Cronbach’s alpha of 0.70 was found.
Permission to use the scale is granted (Lawton, 2003).
3
At first, a sum score for each of the domains was calculated. The scores range theoretically from 7 to 35 for
the physical domain, 6 to 30 for the psychological domain, from 3 to 15 for the social relationships domain and
from 8 to 40 for the environmental domain. These raw scores were then converted to a 0-100 scale.

18
2.2.4 Covariates
Covariates were obtained to statistically control for variables that could confound the
outcome. For practical reasons and reasons of feasibility it has been decided to control
for three variables. These are sex (0= female, 1= male), age (numerical in years) and
health (1= very dissatisfied, 2= dissatisfied, 3= neither satisfied nor dissatisfied, 4=
satisfied, 5= very dissatisfied). For the latter, the question “How satisfied are you with
your health?” was asked, one general question from the WHOQOL-BREF questionnaire
(WHOQOL Group, 1998). Age and health have been shown to influence quality of life
(Dongre & Deshmukh, 2012; Kumar, Majumdar, & G, 2014). A less consistent finding in
the literature is that the sex of elderly people influences quality of life (Barua, Mangesh,
Kumar, & Saajan, 2005; Devi & Roopa, 2013). As well, age and health have been shown
to affect the perception of aging (Jang et al., 2004; Kleinspehn-Ammerlahn et al., 2008).
2.2.5 Validity and reliability
Validity and reliability has been ensured by using already existing and validated
measurement instruments that are frequently used in the literature. The internal
consistency of the instruments (in English) was found to be acceptable to good in
previous studies what has been confirmed by the internal consistency measures obtained
in this study. The translated versions of the instruments (in Kannada language) were
either validated by the World Health Organization (WHOQOL-BREF) or were translated in
the present study with a strict and validated translation procedure (SPA scale, see
“Appendix 7 Translation of the self-perception of aging questionnaire”). Furthermore, to
reduce the influence of extraneous variables on the study outcome, a purposive sampling
was applied to sample elderly from both the community and institutions from the same
area. The principal investigator was moreover aware of potential biases due to his own
role in the study and tried to minimize these biases (e.g. interviewer bias4
) as much as
possible.
4
The interviewer bias means that the interviewer influences the respondents’ answer; the interviewer is not
completely neutral (Bowling, 2005).

19
2.3 Statistical analysis
The Statistical Package for the Social Sciences (SPSS, v. 20) was used to analyze the
data. In order to avoid data entry errors to SPSS, a double entry verification method was
used using the update syntax of SPSS to identify mismatches.
The model, terms and relation to hypotheses
Before the analytical strategy is introduced a few general terms and abbreviations are
described that will be used in this study. Figure 4 shows the paths (a, b, c, c’) and effects
that are tested here. The paths are equivalent to the tested hypotheses. “X” refers to the
predictor variable, “Y” to the outcome variable and “M” to the proposed intermediate
variable. The total effect (X on Y) is equivalent to H1. The total effect can be portioned in
a direct and indirect effect. The direct effect is the effect of X on Y when M is in the
model. The indirect effect is the amount of mediation or path a plus b (X  M  Y). The
indirect effect is equivalent to H4. Path a and b are equivalent to H2 and H3.
Figure 4: Statistical diagram of the study model with effect terms
e=error term, SPA= self-perception of aging, a, b, c, c’= paths
Analytical strategy
At first, descriptive statistics are reported accompanied by bivariate statistics to test for
differences between the living arrangements. Hereafter, each stipulated hypothesis is
tested with a regression-based approach, controlling for the covariates (see Figure 4).
Covariates were entered to partial out their association with the main variables of
interest (Hayes, 2013). The statistical analysis follows in general the four steps (i.e. the

20
four hypotheses) of the mediation analysis described by Baron and Kenny (1986).
However, the procedure from Baron and Kenny (1986) seems to be in some aspects
outdated and restrictive (e.g. Hayes, 2009, 2013; Rucker, Preacher, Tormala, & Petty,
2011; Shrout & Bolger, 2002). For example, the procedure by Baron and Kenny (1986)
was shown to have less power in detecting mediation effects than newer methods (e.g.
Hayes, 2009; Shrout & Bolger, 2002). In addition, the four step approach requires that
each path in the model is significant. Although one path is not significant, the indirect
effect should always be tested (Hayes, 2013). To test hypothesis four, consequently, a
bootstrapping procedure was performed that is the recommended method to test the
indirect effects (Hayes, 2009, 2013; Preacher & Hayes, 2004; Shrout & Bolger, 2002).5
Supplementary, a Sobel test, i.e. a normal theory test for the indirect effect, was
performed. Assumptions to run the statistical tests were tested.6
There were no extreme
violations. Outliers of greater than three standard deviations of the residuals from the
mean were removed if their influence on the test outcome was of serious concern.
Statistical significance was set at an alpha level of 0.05. In addition, to account for
multiple comparisons, a Bonferroni correction was applied (e.g. Zhang, Quan, Ng, &
Stepanavage, 1997). The alpha of 0.05 was divided by the number of comparisons (i.e.
four WHOQOL-BREF domains). Hence, an alpha of 0.0125 was considered as significant
each time quality of life was the outcome variable (in H1, H3 and H4). To take into
account multiple comparison for the bootstrapping procedure in H4, a 99% confidence
interval (CI) was used to infer significance. The 95% CI is reported nevertheless, for
reasons of familiarity, completeness and to increase replicability.7
2.4 Ethical considerations
This study was reviewed by the Institutional Ethics Committee, Kasturba Hospital,
Manipal, India. The study protocol received approval May 13 2014 (no. IEC 300/2014)
(see “Appendix 8 Ethical clearance certificate”).
5
Bootstrapping estimates the indirect effect by resampling the original sample with replacement (here 10000
bootstrap samples were used). It generates a representation of the sampling distribution of the indirect effect
to compute a confidence interval (CI). If the CI for the indirect effect does not include zero, it suggests a
significance of the indirect effect (i.e. mediation) (Hayes, 2013).
6
For bivariate statistics, the assumptions normality and homogeneity of variance were tested. For multiple
regression, the assumptions independence of residuals, linear relationship, homoscedasticity, multicolinearity
and normal distribution of residuals were tested.
7
In the following text, two asterisks highlight a Bonferonni corrected significance (**p < 0.0124), one asterisk
highlights a non-corrected significance (*p < 0.05).

Maastricht University | FHML | Tom Kafczyk | Master’s thesis | Chapter: 3. Results
21
3. Results
In this third chapter, first descriptive statistics are reported. Secondly, results are
presented to test each stipulated hypothesis. Additionally, from “Appendix 9 Correlations
among study variables” correlations between the study variables can be obtained.
From 149 initially conducted interviews, 10 were excluded from the data analysis to not
affect the trustworthiness of the data. During the data collection, while the interviews
were conducted, it became clear that three participants from the community and four
from old age homes did not understand the instructions. As well, during two interviews in
the community relatives influenced or answered in the name of the participants. The
participants answered therefore not entirely independent. One participant’s age in an old
age home turned out to be under 55 years of age. These participants were excluded from
the analysis. The analysis is therefore based on a sample of 139 participants.
3.1 Descriptive statistics
From the final sample of 139 participants, 72 were from the community and 67 from old
age homes. The majority (67%) of the sample was female and there were more females
(73%) in institutions compared to the community (61%). However, the difference was
not significant. Participants in institutions were with a mean age of 71.64 (SD = 9.40)
significantly older than participants in the community with a mean age of 68.04 (SD =
8.00). The self-perception of aging (SPA) did not differ significantly between participants
in the community (M = 3.24, SD = 1.60) and institutions (M = 2.88, SD = 1.65).
Significant differences in quality of life between elderly people in the community and old
age homes were found. Elderly people in old age homes reported significantly lower
scores in all quality of life domains compared to elderly people in the community (see
Table 4).

22
Table 4: Descriptive characteristics of the study sample
Living arrangement
Characteristic
Non-
institutionalized
(N= 72)
Institutionalized
(N= 67)
Total
(N= 139)
Statistical comparison
Sex
Female (N (%))
Male (N (%))
44 (61.11%)
28 (38.89%)
49 (73.13%)
18 (26.87%)
93 (66.91%)
46 (33.09%)
X2
(1, N= 139)= 2.27,
p= 0.152
Age (Mean ± SD) 68.04 ± 8.00 71.64 ± 9.40 69.65 ± 8.83 t(137) = -2.26, p = 0.025*
Health (Mean ± SD) 3.26 ± 1.05 3.34 ± 0.96 3.30 ± 1.00 t(137) = -0.46, p = 0.643
SPA (Mean ± SD) 3.24 ± 1.60 2.88 ± 1.65 3.06 ± 1.63 t(137) = 1.29, p = 0.199
Domains of QOL (Mean ± SD)
Physical 61.86 ± 18.61 51.60 ± 16.78 56.88 ± 18.42 t(136) = 3.40, p = 0.001**
Psychological 61.20 ± 14.78 54.49 ± 14.36 57.97 ± 14.91 t(135) = 2.69, p = 0.008**
Social relations 67.38 ± 20.30 51.77 ± 22.00 59.80 ± 22.47 t(134) = 4.30, p < 0.001**
Environment 60.13 ± 15.83 50.12 ± 13.78 55.31 ± 15.65 t(137) = 3.96, p < 0.001**
*p < 0.05, **p < 0.0125, X2
= Chi-Square test, t = Student’s t-test, SD= standard deviation, SPA= self-
perception of aging, QOL= quality of life
3.2 Hypotheses
Below are the outcomes of the regression analysis to test the stipulated hypotheses as
described in “1.4.2 Hypotheses”. Additionally, from “Appendix 10 Study summary:
statistical diagrams” a graphical summary of the findings can be obtained.
3.2.1 Hypothesis one: living arrangement and quality of life
Multiple regression analysis was used to test H1 that older people in old age homes have
a lower quality of life compared to older people living in the community controlling for
sex, age and health. Living arrangement was the predictor. Each quality of life domain
was tested separately. The results are summarized in Table 5.
Living arrangement significantly predicted physical health, psychological health, social
relationships and the environment domain, independent of the covariates sex, age and
health. Living in an old age home was associated with a decrease on average in physical
health of 10.07 points, in psychological health of 7.19 points, in social relationships of
16.02 and in the environment domain score of 10.05. The model explained a significant
proportion of variance of 39.62% in physical health, 25.91% in psychological health,
17.95% in social relationships and 20.06% in the environment domain score.

23
Based on the regression results and t-test mentioned in Table 4 above, H1 for the quality
of life domains physical health, psychological health, social relationships and environment
was accepted.
Table 5: Regression results for living arrangement on quality of life controlling for
covariates
Unstandardized
coefficients
Predictor β SE t p Model summary
Dependent variable: quality of life physical healtha
Constant 58.82 11.65 5.05 < 0.001**
R2
= 0.396
F(4, 133) = 21.82, p < 0.001**
LA -10.07 2.55 -3.96 < 0.001**
Sex 2.99 2.68 1.11 0.267
Age -0.20 0.15 -1.40 0.163
Health 9.78 1.24 7.86 < 0.001**
Dependent variable: quality of life psychological healtha
Constant 49.65 10.41 4.77 < 0.001**
R2
= 0.259
F(4, 132) = 11.54, p < 0.001**
LA -7.19 2.29 -3.14 0.002**
Sex -4.37 2.41 -1.81 0.072
Age -0.14 0.13 -1.09 0.279
Health 6.50 1.11 5.83 < 0.001**
Dependent variable: quality of life social relationshipsa
Constant 76.63 16.75 4.57 < 0.001**
R2
= 0.179
F(4, 131) = 7.17, p < 0.001**
LA -16.02 3.66 -4.38 < 0.001**
Sex -6.85 3.84 -1.78 0.077
Age -0.26 0.21 -1.25 0.213
Health 4.14 1.80 2.30 0.023
Dependent variable: quality of life environment domaina
Constant 64.39 11.34 5.68 < 0.001**
R2
= 0.201
F(4, 134) = 8.41, p < 0.001**
LA -10.05 2.48 -4.06 < 0.001**
Sex -2.82 2.60 -1.08 0.282
Age -0.20 0.14 -1.39 0.165
Health 4.46 1.21 3.67 < 0.001**
**p < 0.0125, a
= higher values indicate higher quality of life domain scores, SE= standard error, t = Student’s
t-test, F= F-test, OAH= old age home, LA= living arrangement [1= community, 2= OAH], sex [0= female, 1=
male]

24
3.2.2 Hypothesis two: living arrangement and self-perception of aging
Multiple regression analysis was used to test H2 that older people in old age homes have
a lower SPA compared to older people living in the community adjusting for sex, age and
health. Living arrangement was the predictor. The results are reported in Table 6.
Living arrangement did not significantly predict the SPA score. Living in an old age home
was associated with a non-significant decrease in SPA of 0.39 points. The model
explained a significant proportion of variance of 18.56% in the SPA score.
Based on the regression analysis and t-test results (see Table 4) H2 is rejected.
Table 6: Regression results for living arrangement on self-perception of aging controlling
for covariates
Unstandardized
coefficients
Constant 1.78 1.19 1.50 0.137
R2
= 0.186
F(4, 134) = 7.63, p < 0.001*
LA -0.39 0.26 -1.49 0.139
Sex 0.15 0.27 0.55 0.581
Age 0.00 0.01 -0.07 0.943
Health 0.66 0.13 5.20 < 0.001*
Dependent variable: self-perception of aging (SPA), *p < 0.05, higher values indicate a higher SPA score, SE=
standard error, t = Student’s t-test, F= F-test, OAH= old age home, LA= living arrangement [1= community,
2= OAH], sex [0= female, 1= male]
3.2.3 Hypothesis three: self-perception of aging and quality of life
Multiple regression analysis was also used to test H3 that older people with a more
negative perception of aging report lower quality of life compared to older people with a
more positive perception of aging controlling for sex, age and health. SPA was the
predictor. The results of the regression analysis are reported in Table 7.
After controlling for the covariates, SPA predicted physical health and psychological
health significantly. SPA did not significantly predict the environment and social
relationships domain.
One unit increase in the SPA score was associated with a significant increase on average
in physical health of 3.04 points and in psychological health of 3.14 points. The increase

25
in the environment domain score of 2.10 points and in social relationships of 0.79 points
was not significant.
The model of SPA as a predictor and the covariates explained a significant proportion of
38.64% in physical health, 29.98% in psychological health and 14.17% in the
environment domain score. The model explained a non-significant proportion of 6.19% in
the social relationships domain.
Based on the regression analysis, H3 seems to be true for the physical health domain
and the psychological health domain. H3 for the environment and social relationships
domain seems to be not true.
Table 7: Regression results for self-perception of aging on quality of life controlling for
covariates
Unstandardized
coefficients
Constant 50.52 11.75 4.30 < 0.001**
R2
= 0.386
F(4, 133) = 20.94, p < 0.001**
SPA 3.04 0.85 3.55 0.001**
Sex 3.43 2.68 1.28 0.203
Age -0.31 0.14 -2.11 0.029
Health 7.48 1.37 5.47 < 0.001**
Constant 41.42 10.20 4.06 < 0.001**
R2
= 0.300
F(4, 132) = 14.13, p < 0.001**
SPA 3.14 0.73 4.27 0.001**
Sex -4.12 2.33 -1.77 0.079
Age -0.20 0.12 -1.63 0.105
Health 4.27 1.19 3.60 < 0.001**
Constant 68.74 17.87 3.85 < 0.001**
R2
= 0.062
F(4, 131) = 2.16, p = 0.077
SPA 0.79 1.30 0.62 0.538
Sex -4.68 4.08 -1.15 0.253
Age -0.42 0.22 -1.94 0.054
Health 3.07 2.08 1.48 0.142
Constant 56.46 11.77 4.80 < 0.001**
R2
= 0.142
F(4, 134) = 5.53, p < 0.001**
SPA 2.10 0.85 2.48 0.014
Sex -1.99 2.69 -0.74 0.459
Age -0.29 0.14 -2.02 0.045
Health 2.78 1.37 2.03 0.045
**p < 0.0125, a
t-test, F= F-test, SPA= Self-perception of aging, sex [0= female, 1= male]

26
3.2.4 Hypothesis four: mediating role of self-perception of aging
According to Baron and Kenny (1986), H4 that the association between living
arrangement and quality of life is partly explained by SPA cannot be true because one
path of the model (path a, see subsection “3.2.2 Hypothesis two: living arrangement and
self-perception of aging”) was not significant. Furthermore, the significant relationship
between living arrangement and the quality of life domains still persist after entering SPA
into the model (see Table 8). Following Baron and Kenny (1986), if the relationship
between living arrangement and quality of life would have been (partly) explained by
SPA, then the β coefficients of living arrangement should not be significant anymore.
Living arrangement still significantly predicted the quality of life domains. However, in
addition to the approach suggested by Baron and Kenny (1986), a more recent approach
was adopted in the present study (Hayes, 2009, 2013; Preacher & Hayes, 2004; Rucker
et al., 2011; Shrout & Bolger, 2002): bootstrapping and the Sobel test were used to
estimate the indirect effect (i.e. H4). The results are reported below.8
The β coefficient of
living arrangement from the final model (see Table 8) provides an estimation of the
direct effect on quality of life and is reported here as well (although this is not crucial to
answer the hypothesis but it will provide the reader with further information).
Quality of life domain: physical health
The direct effect of living arrangement on the quality of life domain physical health was
significant. A bootstrapped confidence interval (CI) for the indirect effect of living
arrangement on physical health through SPA was significant at the 95% CI, β = -1.14,
95% bias-corrected and accelerated bootstrap (BCa) CI [-3.21, -0.05], because the
range does not include zero. To adjust for multiple comparisons, however, the 99% CI
was calculated as well. The effect vanished at the 99% CI, β = -1.10, 99% BCa CI [-
3.81, 0.38]; the CI now includes zero thus indicating a non-significant result. The Sobel
test supports the non-significance of the indirect effect (z = -1.44, p = 0.150).
Based on the bootstrapped indirect effect estimation, the Sobel test and the Baron and
Kenny (1986) procedure, H4 for the physical health domain seems to be not true.
8
Note: bootstrapping and the Sobel test were calculated with PROCESS (v. 2.11) for SPSS (more information
on PROCESS in Hayes, 2013).

27
Quality of life domain: psychological health
This direct effect of living arrangement on the quality of life domain psychological health
was significant. A bootstrapped CI for the indirect effect of living arrangement on
psychological health through SPA was not significant, β = -1.09, 95% BCa CI [-2.99,
0.25], because the range does include zero. The Sobel test supports this finding (z = -
1.33, p = 0.183).
Kenny (1986) procedure, H4 for the psychological domain is rejected.
Quality of life domain: social relationships
The direct effect of living arrangement on the quality of life domain social relationships
was significant. A bootstrapped CI for the indirect effect of living arrangement on social
relationships through SPA was not significant, β = 0.00, 95% BCa CI [-1.27, 1.27]. The
Sobel test indicates a similar result (z = 0.00, p = 0.999).
Kenny (1986) procedure, H4 for the social relationships domain is rejected.
Quality of life domain: environment
The direct effect of living arrangement on the quality of life domain environment was
significant. A bootstrapped CI for the indirect effect of living arrangement on the
environment score through SPA was not significant, β = -0.66, 95% BCa CI [-2.40,
0.08]. The Sobel test supports this finding (z = -1.13, p = 0.258).
Kenny (1986) procedure, H4 for the environment domain is rejected.

28
Table 8: Regression results for living arrangement on quality of life controlling for self-
perception of aging and covariates
Unstandardized
coefficients
Constant 55.17 11.33 4.87 < 0.001**
R2
= 0.441
F(5, 132) = 20.85, p < 0.001**
LA -8.91 2.47 -3.60 < 0.001**
SPA 2.67 0.82 3.27 0.001**
Sex 2.40 2.58 0.93 0.354
Age -0.25 0.14 -1.62 0.107
Health 8.01 1.32 6.07 < 0.001**
Constant 44.64 10.01 4.46 < 0.001**
R2
= 0.340
F(5, 131) = 13.49, p < 0.001**
LA -6.16 2.18 -2.82 0.006**
SPA 2.88 0.72 4.00 0.001**
Sex -4.83 2.28 -2.12 0.036
Age -0.14 0.12 -1.14 0.257
Health 4.64 1.16 3.98 < 0.001**
Constant 76.98 16.90 4.55 < 0.001**
R2
= 0.178
F(5, 130) = 5.62, p < 0.001**
LA -15.79 3.69 -4.28 < 0.001**
SPA 0.13 1.22 0.11 0.916
Sex -6.49 3.85 -1.68 0.095
Age -0.26 0.21 -1.26 0.209
Health 4.00 1.97 2.03 0.044
Constant 61.36 11.29 5.43 < 0.001**
R2
= 0.226
F(5, 133) = 7.77, p < 0.001**
LA -9.39 2.46 -3.81 < 0.001**
SPA 1.71 0.81 2.10 0.038
Sex -3.07 2.57 -1.19 0.235
Age -0.19 0.14 -1.40 0.164
Health 3.33 1.31 2.53 0.013
**p < 0.0125, a
t-test, F= F-test, SPA= Self-perception of aging, OAH= old age home, LA= living arrangement [1= community,
2= OAH], sex [0= female, 1= male]

Maastricht University | FHML | Tom Kafczyk | Master’s thesis | Chapter: 4. Discussion and conclusion
29
4. Discussion and conclusion
This last chapter discusses first the results within the context of the hypotheses that
were formulated. Next, some study limitations that threaten the internal and external
validity are presented. Based on the study findings and limitations, several
recommendations for future research and policies are elaborated. Finally, a conclusion is
formulated.
4.1 Discussion
Quality of life and SPA in older persons is barely studied in the Indian context in different
living arrangements. The present study shed further light on these important concepts
and their interplay. It might have been the first study that used a quantitative approach
to examine the role of SPA in differences in quality of life among non-institutionalized and
institutionalized Indian elderly.
Hypothesis one: living arrangement and quality of life
As hypothesized, institutionalized elderly had a significantly lower quality of life in the
domains physical health, psychological health, social relationships and environment. The
mean difference between the living arrangements in the domain scores was 10.26 for
physical health, 6.71 for psychological health, 15.61 for social relationships and 10.01 for
the environment domain (see Table 4).
Particularly, differences in social relationships have been found. This means that older
persons in old age homes are on average less satisfied with, for example, their personal
relationships and social support. This is what has been expected. Dongre and Deshmukh
(2012) stated that social activities and the relationship to the family are factors that
determine quality of life. Such factors can be expected to be less or lower respectively in
old age homes in India, although empirical data is missing. Furthermore, the social
capital could be lower because residents might not be part of a wider social network and
have less social commitments. Social capital has been defined by Putnam (1995, p. 77)
as the “[...] features of social organization such as networks, norms, and social trust that
facilitate coordination and cooperation for mutual benefit.” The social capital was found to
be an associated factor with well-being and health of older adults in the community
outside of India (e.g. Cramm, van Dijk, & Nieboer, 2013; Momtaz, Haron, Ibrahim, &

30
Hamid, 2014; Norstrand & Chan, 2014). However, what role social capital plays in India
specifically in old age homes needs to be investigated empirically.
That the physical and psychological health domain scores are lower is not surprising as
well. This means, for example, that older persons in old age homes report to have less
mobility and have more negative feelings. Mahapatra (2010) mentioned physical and
mental decline as well as the feeling of loneliness as reasons to move to an institution.
Following this reasoning, participants had already a lower quality of life when they shifted
to the institution. Alternatively, it can also be argued that the sense of purpose in the old
age home is lower and therefore led to differences observed in psychological health.
Participants in the community (M = 3.43, SD = 0.90) stated that their life is to a greater
extend meaningful compared to those in old age homes (M = 3.11, SD = 0.87), t(135) =
2.13, p = 0.035.9
However, due to the cross-sectional design of the present study causal
inferences can not be made.
The environment domain as an aspect of quality of life was lower as well in
institutionalized elderly. This means that elderly people living in old age homes are on
average less satisfied with, for example, their financial resources, their freedom, the
accessibility and quality of health and social care, the old age home environment, the
opportunity to acquire new skills or the opportunity for leisure activities compared to
elderly in the community. For instance, elderly people in the community (M = 2.58, SD =
1.18) experienced on average more to have enough money to meet their needs
compared to those in old age homes (M = 1.96, SD = 1.16), t(137) = 3.16, p = 0.002.10
This was expected; poverty was mentioned as one of the main reasons to shift to an old
age home (Devi & Roopa, 2013). The pension coverage could contribute to the
difference. However, the coverage in old age homes is not established (Pension watch,
2014). It seems that old age homes cannot provide the same quality of life in the aspect
environment that is given in the community. One reason might be that old age homes
have not the resources (financial-wise, space-wise etc.). For instance, all old age homes
in this study had shared bedrooms with up to 25 older people sleeping in one room (see
Table 2). Residents in shared bedrooms might feel a lack of freedom/privacy what
negatively affects quality of life.
9
The results are from the question from the psychological health domain of the WHOQOL-BREF: “To what
extent do you feel your life to be meaningful?” (see “Appendix 4 WHOQOL-BREF and domain facets”). The
question was rated on a scale from 1 (= not at all) to 5 (= an extreme amount).
10
The results are from the question from the environment domain of the WHOQOL-BREF: “Have you enough
money to meet your needs?” (see “Appendix 4 WHOQOL-BREF and domain facets”). The question was rated on
a scale from 1 (= not at all) to 5 (= completely).

31
The results of H1 support researchers from India stating that the family is the better
environment for elderly people (Dongre & Deshmukh, 2012) and that non-
institutionalized older people are better adjusted because of the tradition in the Indian
society that it is the duty of the family to care for the elderly (Raju, 2011).
The difference in quality of life is in accordance with an outcome of a study by Mathew et
al. (2009). In contrast, results from Devi and Roopa (2013) indicated that older people in
the community have a lower quality of life compared to older people living in old age
homes. However, our study and studies of Mathew et al. (2009) and Devi and Roopa
(2013) are only partially comparable. The studies had different inclusion criteria and were
conducted in different settings. The results, nevertheless, are highly relevant given the
rapid increase of the older population in India (Global AgeWatch, 2013; United Nations,
2001) and the accompanying rise of the number of old age homes (Liebig, 2003;
Mahapatra, 2010). From a human rights perspective (United Nations, 2014), the findings
highlight the need to ensure a standard of living in which elderly people can grow older
with more quality in their lives.
Hypothesis two: living arrangement and self-perception of aging
Contrary to what has been hypothesized, living in an old age home compared to living in
the community was not associated with significant differences in self-perception of aging
(SPA). This means that the living arrangement is not associated with the SPA. The results
contradict findings of a study by Dubey et al. (2011). An explanation would be that SPA
stronger depends on common stereotypes of aging present in society than on differences
between living arrangements (Kotter-Gruhn & Hess, 2012; Levy, Slade, Kunkel, et al.,
2002). Possible is also that moving to an old age home initially affects the SPA but that
due to adaptation processes an adjustment in a positive direction occurs. This is similar
to what Gilbert, Pinel, Wilson, Blumberg, and Wheatley (1998) described as the
psychological immune system. Likely is furthermore that moving to an old age home
could have been an improvement for some elderly that were “lonely and forsaken” before
(Devi & Roopa, 2013, p. 7). Individuals in old age homes could contrast their perception
of aging to other more negative (life) events in the past compared to community elderly
who had more positive experiences. This is known as a contrast effect (Amundson,
2010). Mahapatra (2010, p. 120) described this point as follows: “For many older people
who have no one to support them, old age homes serve as a safe heaven.” Another
possible explanation is that participants in institutions compared their aging to that of
other residents and not to something as the “general population” leading to a response
shift (Amundson, 2010).

32
Hypothesis three: self-perception of aging and quality of life
In accordance with what has been hypothesized, SPA did significantly predict differences
in quality of life in the domains physical health and psychological health. It did not
predict the environment and social relationships domain. This means that a low SPA
score predicts a low quality of life score (see Figure 5). This is in line with a previous
study of Low et al. (2013).
Hypothesis four: mediating role of self-perception of aging
One might argue that the above discussed differences in quality of life are partly
explained by SPA, but there was no evidence that SPA functions as an intermediate
variable. An explanation could be that SPA is not influenced by living arrangement (as
discussed above) what affects the likelihood of a significant indirect effect through the
variables X  M  Y. Alternatively, SPA could change the relationship between living
arrangement and quality of life. This latter assumption was tested with a regression
analysis (see “Appendix 11 Regression with interaction term”). An interaction term of
living arrangement by SPA was added to the model reported in Table 8. The results
indicate that SPA was a moderator in the relationship between living arrangement and
physical health, β = -5.58, t(131) = -3.63, p < 0.001, social relationships, β = -1.88,
t(129) = -1.37, p = 0.002, and environment, β = -4.04, t(132) = -2.91, p = 0.004. SPA
did not function as a significant moderator between living arrangement and the quality of
life domain psychological health, β = -1.88, t(130) = -1.37, p = 0.173. The results
suggest that with an increasing SPA score elderly in the community report significantly
higher quality of life scores (see Figure 5). When the SPA score is low, no significant
relationship between living arrangement and quality of life can be observed (see also
“Appendix 11 Regression with interaction term”).

33
Figure 5: Quality of life domains as a function of self-perception of aging per living
arrangement
(a) (b)
(c) (d)
SPA= self-perception of aging, (a) WHOQOL-BREF domain physical health, (b) psychological health, (c) social
relationships and (d) environment
Furthermore, the SPA score and quality of life domain scores are correlated stronger for
older people living in the community. For these elderly, there was a significant positive
correlation between physical health and SPA, r(71) = 0.69, p < 0.001, psychological
health and SPA, r(71) = 0.55, p < 0.001, social relationships and SPA, r(70) = 0.36, p =
0.002, and the environment domain and SPA, r(72) = 0.46, p < 0.001. In elderly in
institutions, the relationship between living arrangement and quality of life was weaker.
There was a non-significant correlation between physical health and SPA, r(67) = 0.17, p
= 0.180, a significant positive but weak correlation between psychological health and
SPA, r(66) = 0.33, p = 0.007, a non-significant correlation between social relationships
and SPA, r(66) = -0.19, p = 0.118, and a non-significant correlation between the
environment domain and SPA, r(67) = 0.06, p = 0.625. In non-institutionalized elderly
SPA correlates therefore positive with the quality of life domains while in institutionalized
elderly SPA only correlates positive with psychological health.

34
The results indicate that quality of life and the perception of aging have different
relationships depending on the living situation. This has practical implications. While in
community elderly strategies to enhance the SPA are more indicated, in elderly in
institutions strategies targeting directly the quality of life aspects might be more
promising. The following quote derived from Jang et al. (2004, p. 486) who refer to a
paper of Levy, Slade, Kunkel, et al. (2002) may be partially relevant for community-
living elderly people in India: “Enhancement of positive self-perception has important
practical implications because it is strongly connected with better adjustment to changes
in old age and it leads to a higher quality of life regardless of objective life circumstances
[...].”
Covariates
The covariates sex and health did not vary significantly between the groups. The
covariate age differed significantly between the living arrangements. Older persons in
this study living in old age homes were on average older (approximately 3.60 years).
This difference is not surprising since people are more likely to move to an old age home
with progressing age. Age, however, did not influence the study outcome: after the effect
of age on quality of life was removed from the effect of living arrangement on quality of
life, the main effects for all quality of life domains were still highly significant.
Research question
To answer the research question if living arrangement (community versus old age home)
determines differences in quality of life and if this difference is partly explained by the
perception of aging among older adults over 55, the answer is two-fold. The living
arrangement determines differences in quality of life but SPA did not partly explain this
relationship. Instead, SPA seems to be a moderator indicating that the association
between living arrangement and quality of life depends on the perception of aging. If the
SPA is low, no significant differences between the living arrangements in quality of life
can be observed. On the other hand, if the SPA is high, significant differences were
observed.
4.2 Study limitations
Some study limitations are important to mention here. First, some of the effects
observed in this study could be attributable to unmodeled effects within the old age

35
homes or within the different areas and neighbourhoods. Bhushanam, Sreedevi, and
Kameshwaran (2013), for instance, suggested differences in the nutritional status of
residents in paid and unpaid old age homes. To test these “clusters” or organizational
units need to be part of a follow-up study. Multilevel modelling could be used assuming
an appropriate sample size (Hayes, 2013).
Second, due to practical constraints only three covariates and in total six variables were
examined in this study. This was necessary to not jeopardize the statistical power;
however, six variables can barely reflect the complex reality. Other not assessed
variables could have influenced or explained some of the observed variation in the
dependent variable. On an individual level, these are variables such as marital status,
feeling of personal control, social network, objective health status, income, education or
the participation in daily life (Barua et al., 2005; Cantor & Sanderson, 1999; Peterson,
1999; Pinquart & Sorensen, 2000; Thiyagarajan J, Prince, & Webber, 2014). On the level
of the old age home and household respectively, these are variables such as living in a
shared bedroom (more relevant in old age homes) or hygienic conditions. On the level of
the town, these are variables such as age-friendly transportation, availability of geriatric
health facilities or the availability and accessibility of recreational facilities. The covariate
“health” was measured here with only one question. There are problems with one-
question variables that methodologically need to be clarified in the future (Schwarz &
Strack, 1999). Participants might have had a different interpretation of the concept
“health” and thus a different interpretation of “How satisfied are you with your health?”.
One can also argue that objectively participants in old age homes have had a lower
health status but subjectively the participants reported the same health satisfaction
(Frederick & Loewenstein, 1999).
Third, another weakness of the study is that with the cross-sectional data collected here,
it is hard to make any causal claims. Longitudinal data has certainly advantages to
disentangle causal associations. Similarly, the direction of the effects is unclear. While
the relationship X  M  Y was modelled, it could as well have been X  Y  M (Hayes,
2013).
Lastly, another limitation is that different data collection methods (interview-form and
questionnaire-form). The majority of the data was collected in interview-form. While this
was necessary because most people did not want to or could not self-administer the
questionnaires, it is unclear whether the collection method biased the results. It is known
that differences in the mode of questionnaire administration can have effects on the
quality and accuracy of the data (Bowling, 2005). Both methods applied here are

36
associated with different biases such as recall bias, social desirability bias or interviewer
bias (Bowling, 2005).11
Likewise, the different languages (Kannada and English) might
have biased the outcome. The meanings of the questionnaires in both languages might
have been the same, but the interpretation could have been different. In “Appendix 7
Translation of the self-perception of aging questionnaire” a discussion of the limitations of
the translation of the SPA scale is presented. In addition, the settings and places where
data was collected (e.g. a kitchen or common room) varied resulting in differences in the
amount of privacy. The interaction of mode of administration, language and data
collection setting on the data quality is unclear.
Inferences based on the present results should therefore be done with caution. The data
could behave differently if the data would have been analyzed in its respective
organizational units or clusters. Furthermore, the time, location, principal investigator
and translator are further restrictions to the generalizability of the results. Likewise, the
selected participants itself could be a limitation to the generalizability. Older persons
unwilling to participate are not reflected in the data. As well, other groups of elderly,
such as homeless elderly, have not been studied. It is moreover worth pointing out, that
inferences are only possible within the boundaries of the operational definitions and
methods chosen for the present study.
4.3 Future directions
In this section, future directions for research and implications for policies based on the
study results are discussed.
4.3.1 Research
Further research is warranted in order to build on the present findings and shed more
light on the important topic examined here - a follow-up study is recommended. Future
research should try to minimize the study limitations mentioned above.
Multilevel-analysis and multiple comparisons need to be employed to test for differences
within the organizational units and clusters; these methods could yield to new insights
11
The recall bias means that different cognitive processes are necessary to recall information in an interview-
style (oral information) compared to a self-administrated form (visual information) what could bias the results
(Bowling, 2005). The social desirability bias is the tendency of participants to present themselves in the best
possible way (Bowling, 2005). The interviewer bias was introduced above (see footnote 4).

37
into factors at the different levels responsible for the observed variation in quality of life.
Mixed methods including qualitative research could yield to more information as well.
Qualitative research can provide in-depth information about “the how and why”. For
example, ethnography can be employed to answer the question: “Which specific aspects
in an old age home characterize the life of an older person and how do these aspects
relate to the interpretation of quality of life and SPA?” Another question answerable with
qualitative methods that derived from the present study is: “Why does quality of life
depends less on SPA in institutionalized elderly compared to non-institutionalized?”
Future research should also place more emphasis on the role of SPA as a moderator;
more complex models (e.g. mediated moderation, multiple moderators) could be
considered (Jang et al., 2004). Moreover, because differences in age- and sex-sub-
groups in quality of life have been reported in India (Barua et al., 2005; Devi & Roopa,
2013), sub-group analysis by sex and age could be considered. Other variables not
considered here such as education need to be collected and assessed (see section “4.2
Study limitations”). Health as a covariate could be measured more rigorous and
methodologically sound. Longitudinal data would have an advantage in terms of causal
attributions of study effects; for example, the direction of the effect could be tested in
this way (e.g. X  Y  M versus X  M  Y). Furthermore, the effect of the interaction
of mode of questionnaire administration, language and data collection setting on the
quality of the data needs to be tested.
The discussion above brought up some topics that could be investigated in future
research in order to increase the understanding of the findings presented here. For
example, the role of stereotypes of aging, adaptation processes or contrast effects on
SPA (see section “4.1 Discussion”). Moreover, the role of social capital and pension
coverage in India specifically in institutions could be an interesting field of study in the
context of quality of life.
4.3.2 Policy implications
The present study provides evidence for the need to improve the quality of life
particularly in institutionalized elderly in the geographical study area. Especially the
improvement of social relationships could increase quality of life.
On the local level, within old age homes, an environment has to be created that is
adequate for the health and well-being of the residents. This includes meaningful
activities. To offer more (social) leisure activities might be an option for old age homes to

38
improve the quality of life domain environment and social relationships (Ra, An, & Rhee,
2013). To improve physical and psychological health, regular services are an option, such
as psychological counselling (Hepple, 2004), physiotherapy (Harada, Chiu, Fowler, Lee, &
Reuben, 1995) or occupational therapy (Steultjens et al., 2004). However, there is no
clear-cut evidence for a single measure. Instead, a holistic approach including multiple
measures on various levels should be considered.
The present work suggests the development of community strategies to enhance the
perception of aging in order to improve quality of life. An example would be programmes
for older adults that build on the principles of lifelong learning and active aging. Such
programmes have shown to be effective in enhancing the SPA in older adults in Spain,
Mexico, Chile and Cuba (Fernández-Ballesteros et al., 2013).
To indirectly benefit the local situation, on the national level, an increase of the share of
the gross domestic product that is devoted to health is an option. In this way, universal
health care for the aged population is more likely. As well, efforts to increase the pension
scheme coverage could be considered (Pension watch, 2014); this might lead to an
enhancement of financial security and consequently the domain environment of quality of
life. Another option is the development of a national monitoring and evaluation system of
the living situation of elderly in institutions. With such a system, the quality of care and
quality of life of residents in old age homes nationwide could be assessed and evaluated
regularly. This could lead to informed decision- and policy making.
On the global level, high income countries that have gone through the demographic
transition earlier and are experienced in institutionalized care for older people might play
an advisory role for India and be involved in other forms of development assistance to
improve institutionalized care.
The recommendations, however, are only partially valid. First, further research is
required obtaining high quality data. In addition, the process of policy-making should
involve older persons as well (participatory policy making). Second, implications might
vary depending on the old age home and geographical area. And lastly, financial aspects
and financial responsibilities have to be clarified. Who will pay for better facilities and
more services? Policies in India only encourage the construction and maintenance of old
age homes (Dongre & Deshmukh, 2012).

Quality of Life and Perception of Aging in Institutionalized and Non-Institutionalized Elderly in the South Canara Region in India

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