Factors Affecting Quality Of Life In Older Adults

Your task is to write a brief research report evaluating the criterion validity of the Feelings scale. The report should include; an abstract, introduction, method, results, discussion and reference section. Total length is about 3000 words. The Method section could contain a summary of an exploratory factor analysis as evidence of the instruments construct validity.

The Subjective Nature of Quality of Life

According to Fayers & Machin (2013) there has been a lot of focus on the definition of quality of life in the contemporary years in terms of social policies which in turn have directed providing care and service provision which impacts positively on the quality of life of an individual.

According to Buffel, Philipson & Scharf (2012), quality of life is very subjective to the individual since it is not scientifically measureable. Thus, it depends on an individual’s ageing experience. Consequently, the term life satisfaction and well-being are used in many occasions to mean attaining an understanding into the degree in which a person views how they get to experience quality of life. Hence, various authors give diverse spheres as the significance though the significance are subjective to whet the older individual accesses as the most vital feature to them (Nay & Garratt, 2009). Nay & Garratt (2009) argue that the quality of life typically measures general health, cognitive, emotional, sexual and physical functioning while also measuring the happiness of the person. However, it is subjective to the individual since what one person views as vital may vary from another individual. Thus, Zekovic & Renwick (2003) add that factors such as activities and social networks, employment, self-identity, cognitive and physical function, and social security should be considered to impact on life quality.

Standard of living, social networks, and health are vital factors within an individual’s life though the prominence of the factors differ as an individual continues along their course of life (Pekmezovic et al., 2011). There are complexities in defining the main factors within the quality of life as shown by various research sources. The preliminary reading by the researchers underlined the numerous elements which are grouped loosely within three sub-themes in relation to the quality of life. The three sub-themes include physical factors, economic factors and social structure factors.

Stuart-Hamilton (2012) claimed that focusing on a narrow domain can lead to some domains appearing more vital within the research than they possibly are. Domains such as income, environment and health have an impact on the life satisfaction of a person in general and are all inter-related. However, a person’s personality   also impact on how they measure their life satisfaction.

According to Van der Maesen & Walker (2005), quality of life does not have distinct significant factors thereby making most researchers to focus on environment, health, relationships and employment. The environment can either reduce or enhance the quality of life since it embroils social, physical, cultural, and economic elements. Health entails the general health, mental, emotional and physical health of an individual. Employment entails income and is related to an individual’s wealth. According to Sosnowski et al., (2017), the World Health Organisation considers life to compromise of three components. They include functionality (physical and mental wellbeing), environment and affiliations (social functioning) and development (performance of roles in life and general wellbeing). Basically, the quality of life is the range in which an individual can make use of the opportunities brought by life. Thus the most important areas in human life are belonging, being and becoming. Being is comprised of three main domains; psychological being, physical being, and spiritual being.

The Impact of Physical, Economic, and Social Structure Factors on Quality of Life

It should be noted that the quality of life does not have any distinct key factors. Most researchers however choose to focus on health, employment, environment, and relationships. The environment has physical, cultural, economic and social elements which can enhance or diminish the quality of life. The following study aims at looking at how the quality of life is impacted by cultural (age, gender), social environment (marital status) and social economic factors (education and occupation).

Thus, the following hypotheses were developed.

H₁: There is an association between the quality of life and cultural factors

H₂: There is an association between the quality of life and social environment factors

H₃: There is an association between the quality of life and social economic status 

The capacity to respond to a research question is as good as the instrument developed (Cooper & Schindler, 2006). A survey instrument that is well-developed should provide a researcher with data of high quality in order to answer or solve a problem. The study aims to test validity aspects of the instrument and reliability. Validity entails examining how truthful the instruments are. It involves how the instrument measure what is claims to measure. Generally, researches evaluate validity through asking of a number of questions and then searching for answers in other researches. Validity of the instruments will be done through content validity, construct validity and criterion validity.

Research design is the whole approach selected to integrate diverse factors of the research in a logical and coherent way thus warranting that it will address efficiently the research problem (Lewis, 2015). The research adopted a mixed-method design. The method is best stied since it is used in representing more of an approach to examine a problem than a methodology. The method I characterised through focusing on research problems that require an examination of real-life contextual understanding, intentional perspectives and influence of culture and an objective drawing on the strengths of qualitative and quantitative techniques of techniques of data gathering in formulating a holistic interpretive framework for creating conceivable solutions or new problems’ understanding (Tashakkori & Cresswell, 2017).

The dependent variables to be used will be the aggregate scores of the various components which are used to describe the quality of life. Thus, variables LS1 to LS32 will be aggregated.

The independent variables include gender, occupation, age, education and marital status. The age variable is continuous while gender, occupation, educational and marital status are categorical variables. Gender entails male and female as the two categories in which male is coded as 0 and female as 1. Occupation entails four categories which are student, blue collar, grey collar and white collar. The four categories are coded from 1 to 4 respectively as they represent the various ranks of the occupation (Burton & Turrel, 2000). Education also entailed four categories which include primary, secondary, senior secondary and tertiary coded from 1 to 4 respectively. The order of these categories are in line with the order in which individuals acquire education in Australia.  The final independent variable was marital status. The variable was categories into 4 categories of single, in a relationship, married/de facto, and separated/divorced/widowed/others from 1 to 4 respectively. The coding is justified as it can be seen to be the levels of companionship in the society. 

Testing Validity and Reliability of Research Instrument

Construct is the preliminary notion, concept, hypothesis or question which evaluates which data is to be collected and how it is to be collected (Hagströmer, Oja, & Sjöström). Construct validity is the degree in which an instrument evaluates the theoretical construct or trait which is intended to be measured. In this study, construct validity will be demonstrated by a 3 factor structure which shows items grouped into these categories in accordance with the arguments by Sosnowski et al., (2017). The three factor structure are satisfaction with functionality, environment and affiliations, and development.

Construct validity was conducted through a factor analysis. Precisely, a Principal Component Analysis (PCA) was the method of used for the variable reduction (Jolliffe, 2011). The assumptions made during the test was that there are multiple variables, there is a linear relationship between all variables, there is sampling adequacy, the data is suitable for reduction and that there are no significant outliers. Subsets that loaded strongly on the various components will considered while those that did not would have to be dropped.

Content validity entails the measure in which the instrument measures or assesses the interested construct fully (Heale & Twycross, 2015). According to Wynd, Schmidt & Schaefer (2003), the development of a content valid instrument entails achieving it through an instrument rational analysis by rates (ideally 3 to 5) conversant with the interested construct. To determine content validity, the following research used content validity index (CVI).  Content validity index is the most widely used when conducting a quantitative evaluation. CVI entails two types, I-CVI and S-CVI. Computing a modified kappa statistics can be used in adjusting I–CV for chance agreement. S-CVI/Ave and S-CVI-UA are both levels of scales for CVI with different formulas. It is recommended by researchers that a scale with excellent content validity needs to be composed of I-CVIs of 0.78 or higher and S-CVI/Ave and S-CVI/UA of 0.9 and 0.8 or higher respectively.

Criterion validity is evaluated when there is an interest in establishing the association of scores on a test to a precise criterion. The method of the most powerful tool that is used in establishing pre-employments validity of tests. A test can be considered to have criterion validity if it is useful in predicting the behaviour or performance in another situation. The first measure in a criterion validity test is usually considered as the predictor variable while the second measure is usually considered as the criterion variable.

The Role of Cultural, Social, and Economic Factors in Quality of Life

A regression model is used in predicting the value of a variable based on two or more other variables values (Craig et al., 2003). The variable to be forecasted is usually referred to as the dependent variable while the variable used in forecasting the value of the dependent variables are called the independent variables. In this study the tool used in criterion validity was a Hierarchical multiple regression. A Hierarchical multiple regression was the most suitable since we are predicting a dependent variable that entails of count data while there are one or more independent variables. In this regression, the total quality of life was regressed against age, gender, occupation, marital status and education.

Reliability is the measure in which the results are consistent over a period of time (Joppe, 2000). Thus, a representation that is accurate of the population under study is stated to be having the component of reliability. A research instrument is measured as reliable when the study results can be produced with a similar methodology. A questionnaire, observation, test or any measurement procedure is considered reliable if it produces the same results when the trials are repeated (Golafshani, 2003). Consequently, the degree to which the responses of individual in a survey stay the same over time is a measure of reliability.

To measure reliability, the Cronbach’s alpha will be used. The Cronbach’s alpha is commonly used for surveys or questionnaires which have multiple Likert questions which form a scale. The Cronbach’s alpha reliability coefficient usually ranges from 0 to 1. When the coefficient is closer to 1, it can be deduced that internal consistency of the items or variables is great. The Cronbach’s alpha usually increases as the number of items increases. Consequently, it also increases as the average inter-item correlations increase, that is, when holding the number of items is constant. According to (Tavakol & Dennick, 2011), a Cronbach’s alpha that is greater than 0.7 is considered acceptable in most social science research situations. In the study, the various variables were loaded into their respective factors. Then the average scores of the four factors were obtained. From the results, the average score were subjected to a reliability test by obtaining the Cronbach’s alpha.

The number of participants in this survey was 361. The descriptive statistics of the participants’ age is as shown below:

Table 1: Age descriptive statistics

Minimum	Maximum	Mean	Std. Deviation
16	83	32.9	15.047

The mean age of the participants was 32.9 with a standard deviation of 15. The figure below shows the distribution of the participants by gender.

It is evident that most of the participants were female with a representation of 67%. On the other hand, the male participants had a representation of 33%.

Majority of the participants whether undertaking or had completed education at the tertiary level (68%). Those who had reached senior secondary were second with 27% while those with secondary education had a representation of 5%. The primary level of education was represented by 0%.

From figure 4 above, it can be seen that most of the participants were undertaking grey collar jobs (31%) closely by those with white collar jobs (36%). Students had a representation of 24% while the blue collar jobs were the least presented with 9%.

As seen above, majority of the participants were single (47%) while those married or in de facto relationships were represented by 37% of the participants. The least presented people were those in a relationship (9%) and those who were separated, divorced, widowed or others (7%). 

the actual factors that were extracted. The rotation sums of squared loadings shows only those factors that met the cut-off criterion (extraction method).  It is seen that there were three factors with eigenvalues that were greater than 1. The percentage of variance shows how much of the total variability can be accounted for by each of these factors. That is factor 1 accounts for 21.7% of the variability, factor 2 accounts for 12.59% of the variability while factor 3 accounts for 9.48% of the variability.

Content Validity Index

Table 5: I-CVI

S-CVI/Ave	0.82
Total Agreement	0
S-CVI/UA	#DIV/0!

From table 5 above, it is evident that the variables do not meet the recommendation of most researchers of having an S-CVI/Ave of 0.82. However, the S-CVI/AU was not applicable since there were no total agreement among the raters. However, decision should not be based on S-CVI/AU intuitively since the value of S–CVI/Ave can be considered to be high at 0.82.

From the model summary in table 7 above, it is seen that the Durbin-Watson test was 2.225. The test result is between the values of 1.5<d<2.5. Thus, it is safe to say that there was no linear auto-correlation of the first order in our hierarchical multiple regression data.

Consequently, the results shows that the accounted variance (R²) with the first two indicators (age and gender) was equal to 0.0007 (adjusted R2 = 0.0001). However, it was observed that this was not significantly different from 0 (F_(2,293) = 1.025, p>0.05. Socio-economic factors (education and occupation) was factored into the regression equation. The change in accounted variance (DR²) was equal to 0.012. Likewise, this was not a statistically significant increase in the accounted variance from the step one model (DF_(2,291) = 1.77, p > 0.05. In step three, social environment factors (marital status) were factored into the regression model. The change in accounted variance (DR2) was equal to 0.55. However, this was not a statistically significant increase in accounted variance by the previous predictor variables entered in the second step (DF _(1,290) = 0.55, p>0.05.

it can be seen that at the 5% confidence interval no variable was statistically significant. All the other factors of social economic factors (age, gender), social environment factors (marital status) and cultural factors (age and gender) were not statistically significant.

The survey instrument used in gathering data proved to be indeed valid and reliable. Indeed, the survey instrument passed the construct validity test, criterion validity test, content validity test and the reliability test. The World Health Organisation classifies quality of life into three components. Through construct validity, the variables were all found to load heavily on their various components as seen in table 3. From the hierarchical multiple regression it was observed that none of the predictors was statistically significant. Thus, there are no coefficients which are interpretable. If there were statistical significance in any of the betas, then it would be possible to use the weights of the betas and multiply them with every individual person’s score on the independent variable in order to get what individuals predicted score are on the dependent variable.

Future researchers should consider more underlying factors in carrying out a similar study. As a result, they will be able to determine the exact association between the variables. The number of participants should also be increased in order to avoid bias in future.

References

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