Impact of Employment and Age on Quality of Life of Individuals With Disabilities

Abstract

The purposes of this study are (a) to explore how people with disabilities (PWD) experience differences in their quality of life (QOL) over the course of their lives, (b) to examine the effect of employment on QOL, and (c) to investigate the impact of age on overall QOL for PWD both inter- and intra-individually. To address these aims, this study used the data set for the Panel Survey of Employment for the Disabled (PSED) collected by the Korean Employment Agency for the Disabled (KEAD). The target populations of the study were 5,092 registered persons with disability. No longitudinal approach to examine the impact of employment and age on QOL for individuals with disabilities has been considered in the previous literature. Thus, multilevel modeling was used to examine the relationship between employment, age, and QOL for PWD. Results of this study indicated that employment status and age were significant predictors of QOL among PWD. These findings call attention for the need to consider the impact of employment and age on QOL of PWD both inter- and intra-individually.

Keywords

quality of life disability employment aging

The World Health Organization (WHO) describes quality of life (QOL) as “individuals’ perceptions of their position in life in the context of culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns” (WHOQOL, 1998, p. 2). Many researchers use this notion to reflect subjective QOL and to equate it with life satisfaction or subjective well-being (SWB; Roessler, 1990). QOL has functioned as an important concept and an appropriate framework for better understanding the process of psychosocial adaptation to disabilities (Bishop, 2005). Rehabilitation counseling professionals (Bishop, Chapin, & Miller, 2008) also have defined QOL as “a multidimensional construct, involving assessment of psychological, social, economic, physical, and other domains that may be targeted in rehabilitation counseling” (p. 51). This concept of QOL thus acts as an outcome criterion for individuals with disabilities (Aigner et al., 2006; Bishop et al., 2008) and remains the underlying goal of rehabilitation counseling (Crewe, 1980).

Bishop (2005) reported that disability is an important life event as well as a form of physical, psychological, environmental, and social change. As such, disability significantly affects QOL and overall life satisfaction, a relationship that many researchers have previously studied (e.g., Livneh, 2001; Livneh, Martz, & Wilson, 2001; Wright, 1983). For instance, Piitulainen, Ylinen, Kautiainen, and Häkkinen (2012) demonstrated the significant relationship between higher functional disabilities and lower qualities of life. Manca, Eldabe, Buchser, Kumar, and Taylor (2010) echoed such findings, indicating that disability is significantly associated with health-related QOL. According to Bishop (2005), however, only a few researchers have described the relationship between disability and QOL in a clinically meaningful way. In addition, as of yet no studies have examined longitudinally the differences in QOL between and within individuals with disabilities. Researching the QOL of individuals with disabilities at both the inter- and intra-individual levels would help rehabilitation counselors to apply more effectively their knowledge and skills in assisting their clients. Therefore, the first purpose of current study is to undertake just this task, to explore how people with disabilities (PWD) experience differences in their QOL over the course of their lives both inter- and intra-individually.

Over time, researchers have investigated, confirmed, and organized components of QOL into what are known as life domains. Hayward and Schmidt-Davis (2003) reported that certain factors (e.g., self-confidence, social comfort and aptitude, the ability to develop and maintain friendships) relate to QOL because they deal with the psychological and social components of life that make up QOL. Bishop (2005) argued that in many cases, PWD tend to spend more time in a specific domain if they actively engage within that domain. In other words, an individual with a disability may consider one domain more important than another. Thus, it is crucial that researchers evaluate QOL in the more highly valued domains before the lesser ones (Frisch, 1999; Pavot & Diener, 1993).

Among the various life domains that closely intersect with QOL (Bishop, 2005; WHOQOL, 1998), researchers consistently identify employment or productive activities as most determinate of the overall QOL among PWD (Bishop & Allen, 2003; Jalowiec, 1990; Padilla, Grant, & Ferrell, 1992). The challenges that PWD face in maintaining their work, socio-economic independence, and functionality (Smart & Smart, 2006) suggest a critical relationship between employment and QOL. Chan, Wang, Muller, and Fitzgerald (2011) gestured toward such a connection: “Without a doubt, lack of employment opportunities and work incentives exclude people with disabilities from full community participation, significantly affecting the quality of their lives” (p. 3). Chapin and Holbert (2010) also found a relationship between employment and QOL among people with spinal cord injuries (SCIs) by comparing those who received rehabilitation services for employment with those who did not. In the study, people with SCIs who held a job reported higher levels of QOL than unemployed individuals, a finding also observed in other studies (Chapin & Holbert, 2009; Leduc & Lepage, 2002). In addition, in a study of employment and subject well-being (SWB), da Silva Cardoso, Blalock, Allen, Chan, and Rubin (2004) indicated that functional skills significantly correlated with SWB among people with disability. As these studies have shown, it is unsurprising that employment or increased independence positively affects QOL (Fleming, Fairweather, & Leahy, 2013). Therefore, the second purpose of this study is to examine simultaneously the effect of employment on QOL at the intra-individual level and the inter-individual level.

Despite the importance of employment, researchers such as Clayton and Chubon (1994) have indicated that employed individuals with disabilities did not have significantly higher levels of QOL than the unemployed. Although other researchers have noted several pieces of empirical evidence supporting the interrelationship between employment and QOL, it is important to note that additional factors may play a significant role in the relationship. One such factor is aging. According to Chappell and Cooke (2010), researchers often define disability as the restricted capacity to function independently in basic life activities or an inability to perform daily tasks. For individuals with disabilities, increasing age brings with it an increasing likelihood of disability (Kemp, 1999). When people live longer without encountering fatal diseases, their illnesses become chronic rather than acute (Kemp, 1999). Thus, the conjunction of older age and increased disability can produce a negative view of aging as well as effect a reduction in QOL. Examining the relationship between aging and QOL of PWD, Kemp (1999) has shown that as these individuals age, many experience changes in their health and functionality that challenge their life satisfaction. In addition, he also noted that individuals’ current age and the age at the onset of their disabilities had a significant effect on QOL.

In line with Kemp’s (1999) findings, other studies (e.g., Bauman & Waters, 2004; Bruyère, Erickson, Wilson, & Sommerville, 2004) also support that aging correlates with the employment of individuals with disabilities. These researchers reported that as individuals with disabilities enter their middle or later years, their typical aging experiences—increases in pain, fatigue, weakness, and secondary health conditions—likely affect the ability to maintain employment that can consequently lead to early retirement or reduced work hours. Although the possibility of leaving work increases for all people with age, it can be a more significant issue for individuals with disabilities. Mitchell, Adkins, and Kemp (2006) conducted a study on the employment patterns and changes in people with physical disabilities and then compared the findings with those for a non-disabled group. They discovered that individuals with disability experience a sharper decrease in employment in their later years than those without disability. For employees with disability, the decline typically begins around their 40s, but for non-disabled workers this same decline begins in the 50s or even 60s. Furthermore, for the 20s and 30s age groups, employment rates were lower for individuals with disabilities. The findings of this study strongly suggest that for individuals with disabilities, aging directly relates to employment loss, especially in the later decades. Because of the dearth of research focusing both on lifetime QOL differences for individuals with disabilities and on how disability influences their QOL, the final aim of the current study is to investigate the impact of age on the relationship between employment status (ES) and life satisfaction for PWD.

Research Questions

The present study was conducted to explore the impact of disability, employment, and age on QOL among individuals with disabilities, specifically addressing three research questions:

Research Question 1: Do PWD experience differences in QOL both inter-individually (between individuals) and intra-individually (within individuals)?

Research Question 2: How is ES associated with overall QOL both between and within PWD?

Research Question 3: How is age associated with overall QOL of PWD?

Method

Research Design and Sample

The research design of this study was a type of longitudinal cohort study through nationwide panel data where multiple samples, called cohorts, were followed. The study measured QOL of PWD in each cohort and the relationships with specific predictors such as ES and age. This study used a data set to explore the associations between QOL, ES, and age among individuals with disabilities. The Korean Employment Agency for the Disabled (KEAD) collected this data set with the aim of addressing the economic activities of disabled individuals. The study surveyed a sample of individuals with disabilities annually by asking them questions. In our own study, we analyzed 4 years of the data set: 2009, 2010, 2011, and 2012. The target populations of the survey were registered persons with disability selected in late 2007 for the Panel Survey of Employment for the Disabled (PSED). The study analyzed employment characteristics and economic activity of individuals with disabilities from the ages of 15 to 75. The main data consist of three primary types of variables: demographic variables (age, gender, education, disability type, disability status, disability grade); economic participation variables that use the questions and standards of the Korea National Statistical Office; and variables for employed, unemployed, non-economically active population. In addition, the survey also contains the individual as well as environmental factors that affect economic activities, and 5,092 PWD completed the survey. The measure of QOL was part of the PSED data set and included self-reported responses. For this study, data cleaning was performed, and statistical estimates of missing values were computed.

Data Analyses

This study used a multilevel approach to examine the impact of disability, ES, and age on QOL among individuals with disabilities. This allowed us to partition the variance in the outcome variable into within-group and between-group components that produced more accurate parameter estimates (Hox, 2010; Singer & Willet, 2003).

Specifically, by accounting for two levels of analysis, we could better estimate factors influencing the QOL of PWD. The use of the multilevel method in this study was supported by our initial finding from the unconditional model that showed overall QOL varying significantly both across and within individuals over a 4-year span.

Variables

The dependent variable measured the overall QOL of individuals with disabilities. This item asks, “How much are you satisfied with your everyday life?” and uses a 5-point Likert-type scale: 1 signifying very dissatisfying to 5 as very satisfying in their life. Table 1 provides the descriptive statistics for the outcome variables for five age cohorts.

Table 1.

Descriptive Statistics.

	Total sample profile		Descriptive statistics of quality of life
Age (years)	N	%	n	M	SD	Missing
15–30	271	5.32	241	3.122	0.719	30
30–40	506	9.94	455	3.076	0.765	51
40–50	1,248	24.51	1,154	3.027	0.721	94
50–60	2,025	39.77	1,869	2.973	0.722	156
60–75	1,042	20.46	965	2.927	0.718	77

Level 1 variables

The Level 1 variables related to time varying (TV) predictors, which refer to the effect within individuals with disabilities. We specified years and ES as TV predictors.

Years

Singer and Willet (2003) demonstrated the interpretive benefits for re-centering the predictor used to represent time. Following their results, we did not specify 4 years (2009–2012). Instead, we used initial status re-centering because if the constant chosen represents a study’s first wave of data collection, then we can simplify interpretation further by referring to the intercept as “individual was true ‘initial status.’” Otherwise, we subtracted 1 from the YEARS values. So, 2009 to 2012 generally centered around 0, 1, 2, and 3.

ES

Four dichotomous dummy variables were set up, corresponding to each of the 4 years for ES (0 = unemployed, 1 = employed). For instance, if an individual had a job at least one time in each of the 4 years, we coded him or her as 1, 1, 1, and 1. If, however, an individual was unemployed each of the 4 years, we would code him or her as 0, 0, 0, and 0.

Level 2 variables

EMP_GRAND

To test the between-person effect of ES, we created a new variable from the original TV dummy indicator (0 = unemployed, 1 = employed). Singer and Willet (2003) illustrated how re-centering time-invariant (TI) predictors could alter its parameter’s meaning. The easiest strategy for re-centering a TI predictor is to subtract its sample mean from each observed value (Singer & Willet, 2003). When we centered a predictor on its sample mean, the Level 2 fitted intercepts represented the average fitted values of the initial status (or rate of change).

Specifically, each individual had an average rate of ES. For instance, if the individual had been employed for 4 years, then he or she might have 100% of employment rate indicated by an average of 1.00 (sum = 4/4 years = 1.00). If, however, a different person had a job for only 1 year, then he or she might have an average rate of 25% of employment (sum = 1/4 years = 0.25). First, we made an average of each individual’s employment rate score, and then we subtracted the overall sample mean, 0.397 (grand mean centering), which then resulted in a new TI variable. We called the new TI predictor EMP_GRAND.

AGE_GRAND

The range for age was 15 to 75. We centered each individual’s mean age during the 4 years using the grand age mean of 51, putting the result in a new variable called AGE_GRAND. As a result, we created a new variable called AGE_GRAND in which an individual might have a constant value across 4 years.

Different age centering variables

Rather than entering time as a predictor in its raw form, Singer and Willet (2003) suggested that age variables could be subtracted by a constant from each observed value, thus creating variables like AGE-20, AGE-30, or AGE-40. The primary rationale for temporal re-centering is that it simplifies interpretation. If we subtract a constant from the temporal predictor, the intercept in the sub-model will refer to the true value of a dependent variable at that particular age (20, 30, or 40; Singer & Willet, 2003).

In Research Question 3, we compared each age group such as 20, 30, 40, 50, 60, or 70. There, the age variable centered on each age level. The process resulted in the creation of a number of new variables, each with different intercepts and meanings.

Results

Research Question 1

To test the first research question, we specified Models A and B (see Table 2). In Model A, the intercept represented the overall average QOL across all PWD for a span of 4 years. Thus, we concluded that PWD had the overall average (2.992, p < .001). Nonetheless, the random component associated with the intercept was significant (0.264, p < .001) and indicated that PWD differ from one another in average QOL values. The within-person random component was significant (0.267, p < .001), demonstrating that PWD’s QOL values vary over time.

Table 2.

Model Comparison by Adding Different Predictors.

Predictors	Model A	Model B	Model C	Model D	Model E	Model F	Model G
Fixed effects
Initial status
Intercept	2.992** (0.008)	2.999** (0.010)	2.866** (0.012)	2.861** (0.011)	2.940** (0.013)	2.940** (0.013)	2.939** (0.013)
ES			0.352** (0.017)	0.365*** (0.013)	0.155** (0.022)	0.155*** (0.022)	0.156* (0.022)
EMP_GRAND					0.318** (0.030)	0.319** (0.027)	0.310** (0.027)
AGE_GRAND							−.005** (0.001)
Rate of change
Intercept		−0.005 (0.004)	−0.010^† (0.004)	−0.007 (0.004)	−0.006 (0.004)	−0.006 (0.004)	−0.006 (0.004)
ES			0.008 (0.007)
EMP_GRAND					0.001 (0.008)
AGE_GRAND							0.001^† (0.0003)
Random Effects
VC: Level 1
Within-person	0.267** (0.003)	0.253** (0.004)	0.249** (0.004)	0.249** (0.004)	0.250** (0.004)	0.250** (0.004)	0.250** (0.004)
VC: Level 2
In initial status	0.264** (0.007)	0.317** (0.010)	0.319** (0.012)	0.319** (0.012)	0.309** (0.012)	0.309** (0.012)	0.308** (0.012)
In the rate of year change		0.008** (0.001)	0.008** (0.001)	0.008** (0.001)	0.008** (0.001)	0.008** (0.001)	0.008** (0.001)
In the rate of ES change			0.113** (0.020)	0.113** (0.020)	0.074** (0.018)	0.074** (0.018)	0.074** (0.018)
Deviance	36,096.5	36,030.3	35,295.3	35,296.5	35,164.1	35,164.1	35,130.5
AIC	36,102.5	36,042.3	35,317.3	35,316.5	35,188.1	35,186.1	35,156.5
BIC	36,122.1	36,081.5	35,389.2	35,381.9	35,266.6	35,258.0	35,241.4

Note. ES = employment status; VC = variance components; AIC = Akaike information criterion; BIC = Bayesian information criterion.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Next, to examine variability of QOL over 4 years, we specified an unconditional growth model as Model B. In other words, we specified years as the TV predictor. The intercept represented the average QOL at the first year. We concluded that the first year average PWD’s QOL was significant (2.999, p < .001). The rate of change represented the average “true” linear rate of change in QOL across the 4 years. We cannot reject the null hypothesis that this value was 0 and therefore concluded that, on average, PWD’s QOL values were not significant (−0.005, p < .1752) and did not change each year. It was, however, interesting that a significant random variation existed in the slope parameter (0.008, p < .0001), implying different values of QOL across the sample. We needed to test with extra predictors to explain the reason for such variation across the PWD. Also, the within-PWD variation significantly differed from 0 (0.253, p < .0001). Thus, we concluded that only 4.2% of the within-person variation in QOL is systematically associated with linear time.

Research Question 2

First, we treated ES as TV predictor as a Level 1 variable. In other words, we tested whether QOL changed within an individual’s ES. Intercept indicated that average initial QOL for the average unemployed PWD was equal to 2.87 (p < .0001). The rate of change indicated that average change in QOL across 4 years for unemployed PWD equaled −0.01 units (p < .05). If a person with disability was to lose a job at a specific year, then the level of QOL would be 2.86. And, if the individual still would not have a job after 2 years, then the person would have a 2.85 QOL.

On the other hand, the effect of ES showed that the average initial QOL was significantly higher for an individual when employed than when unemployed (difference = 0.33 QOL units, p < .0001). So, at the first year of employment, an individual would have a 3.20 QOL. The effect of ES on the rate of change indicated that the employed PWD’s average QOL did not increase over 4 years (p = .26). In other words, an individual would have the same effect of ES regardless of when he or she had a job.

In Model C, we added ES as a within-individual predictor. However, the random effect of Level 1 was still significant (estimation 0.249; p < .0001). At Level 2, the random effect, both ES and YEARS, differed significantly from 0. As a result, across sample, ES had a differential effect on QOL.

In addition, the interaction effect with ES and YEARS was not significantly different than 0. We compared the model by removing the interaction effect with the original model. Between two models, the deviance statistics were 35296.5 − 35295.3 = 1.2. The 1.2 was not a significantly different model in the 1 df level (p < .05). As a result, we should choose a more simple and parsimonious model such as Model D for the next step.

To test the between-person effect of employment, we added the variable of EMP_GRAND as a TI predictor. As with Table 2, in Model E the interaction effect of EMP_GRAND and YEARS was not significantly different than 0 (0.001, p = .923). So, we might remove the interaction effect unless the deviance statistics was a significantly different model. The two models had the same deviance statistics level of 35164.1. Ultimately, in terms of parsimony, Model F was better than Model E. In Model F, the initial average QOL was 2.940 (p < .0001), controlling for both effects ES and EMP_GRAND. Also, for average QOL, both ES and EMP_GRAND had a significant effect of 0.155 and 0.318 (p < .0001). Therefore, an employed individual would have 3.095 QOL compared with when he or she may not have a job, controlling for EMP_GRAND. In addition, when comparing a person with a higher employment with a person with a lower employment rate, the individual with an employment rate one unit higher had 3.258 QOL initial average status. On the other hand, EMP_GRAND and ES did have a stable effect on the rate of change in QOL over 4 years, employment rate, and ES.

Research Question 3

Finally, we used the AGE_GRAND variable as a new Level 2 predictor in the model. The intercept indicated an average initial QOL for grand mean age (51 years old) of 2.94 (p < .0001), controlling for between-person effect of employment rate and within-person effect of ES. The effect of ES suggested that the average initial QOL for an employed individual was significantly higher than that for the same person when he or she did not have a job (difference = 0.156 QOL units, p < .0001), controlling for the effects of AGE_GRAND and EMP_GRAND. Also, the effect of EMP_GRAND indicated that across the sample, higher employment rate individuals might have 0.31 higher QOL for their initial average. In the case of AGE_GRAND, overall 1 year older might have −0.005 (p < .0001) less QOL; although, this difference by AGE_GRAND weakened over 1 year, 0.001 (p < .05). The result showed that the negative effect of age for life satisfaction was lesser over 4 years in the sample.

In addition, we tested this model for each age cohort in terms of exploratory perspectives such as ages 20, 30, 40, 50, 60, and 70 with different age centering. Table 3 showed the result of each age’s centering examples. Interestingly, for each age level’s initial average of QOL, older individuals had a lower QOL initial average, controlling for the effect of ES, EMP_GRAND, and AGE level. The results thus demonstrated that the older population in this sample had a less than average life satisfaction level. Also, it is not surprising that several effects of ES, EMP_GRAND, and AGE levels were equivalent across the sample regardless of mean age. Controlling for other effects, the rate of intercept changes were, interestingly enough, flatter for the older population (above 50) and not significant.

Table 3.

Adding Different Age Centering Variables.

Fixed effects	Age 20 centering	Age 30 centering	Age 40 centering	Age 50 centering	Age 60 centering	Age 70 centering
Initial status
Intercept	3.086*** (0.029)	3.039*** (0.013)	2.993*** (0.016)	2.946*** (0.013)	2.899*** (0.015)	2.852*** (0.020)
ES	0.156*** (0.022)	0.156*** (0.022)	0.156*** (0.022)	0.156*** (0.022)	0.156*** (0.022)	0.156*** (0.022)
EMP_GRAND	0.310*** (0.027)	0.310*** (0.027)	0.310*** (0.027)	0.310*** (0.027)	0.310*** (0.027)	0.310*** (0.027)
Age	−0.005*** (0.001)	−0.005*** (0.001)	−0.005*** (0.001)	−0.005*** (0.001)	−0.005*** (0.001)	−0.005*** (0.001)
Rate of change
Intercept	−0.026* (0.011)	−0.019* (0.008)	−013* (0.005)	−0.007 (0.004)	−0.0001 (0.004)	0.006 (0.007)
Age	0.001* (0.0003)	0.001* (0.0003)	0.001* (0.0003)	0.001* (0.0003)	0.001* (0.0003)	0.001* (0.0003)

p < .05. ***p < .001.

Discussion

For Research Question 1, which investigated differences in QOLs of PWD both inter-individually and intra-individually, the results indicated that PWD differ from one another in average QOL values. This finding is consistent with previous research, which noted that disability significantly affects QOL (Manca et al., 2010; Piitulainen et al., 2012) and that individuals with disabilities perceive different levels of QOL (Kemp, 1999; Schwartz, Andresen, Nosek, & Krahn, 2007). According to Schwartz et al. (2007), this could be possible because of differences among individuals or changes within individuals with disabilities about values, internal standards, or the conceptualization of QOL, which could encompass aspects of adaptability, role performance, physical status, and existential experience. However, we found no within-person differences for QOL systematically associated with linear time across 4 years in the current study. This finding supports the fact that QOL is a stable construct in situational change because it is adaptive to the occurrence of the change (Allison, Locker, & Feine, 1997; Chamberlain & Zika, 1992). Although we found no significant difference in QOL at the intra-individual level, this result confirmed that disability still plays an important role in the formation of QOL.

For Research Question 2, which explored the impact of ES on overall QOL both between and within PWD, the results revealed that PWD would differ in the effect of employment on QOL. When comparing a person with a higher employment rate with a person with a lower employment rate, the individual with higher employment rate had a higher QOL status. For the 4-year span, the initial score of QOL was higher for employed individuals than those unemployed. This result is congruent with the findings of others (Bishop & Allen, 2003; Chan et al., 2011), which show the positive relationship between employment and the QOL of PWD. However, at the intra-individual level, the result showed that QOL remains stable for an individual with disability. This also could imply that employment cannot be the only factor that affects QOL, a finding that aligns with other research (e.g., Clayton & Chubon, 1994).

For Research Question 3, which examined the impact of age on overall QOL for PWD, the results showed that older age would be associated with lower levels of QOL. For each age level’s initial average of QOL, older individuals had a lower QOL initial average than younger individuals. This result is consistent with other studies (Kemp, 1999; Krause & Crewe, 1991) that indicated the significant relationship between age and life satisfaction of individuals with a disability. Our finding also relates with the results of many national surveys (Leitman, Cooner, & Risher, 1994) where people without a disability reported increased QOL over 10 years whereas individuals with a disability did not. According to Albrecht and Devlieger (1999), this disparity arises because disabled individuals do not obtain the same relative increase in benefits or income as the general population. Similarly, individuals with disabilities are more likely to have severe disabilities as they age and eventually become much more limited in their daily activities (Kemp, 1999); thus, older individuals with disabilities would have a lower QOL initial average than younger ones.

At the intra-individual level, people in their 20s, 30s, and 40s showed decreased QOL within 4 years. However, individuals in their 50s, 60s, and 70s did not show the difference of QOL, indicating a stable life satisfaction across 4 years. It could mean that ES might not have significant impact on the QOL for individuals with disabilities at some point: in this study, for PWD in their 50s, 60s, and 70s. In addition, the different QOLs of different cohorts may reflect the time at which their expectations were considered rather than changed over the course of an employment. This result parallels other research that found that as people grow with disability, resiliency and inner strength come from having a balanced perspective and clear values on life (Albrecht & Devlieger, 1999). Kemp (1999) also stated that the longer people live with disabilities, the more stable their lives become.

QOL is consistently the underlying goal of rehabilitation counseling (Crewe, 1980). Although there has been much discussion about the needs of QOL in terms of measuring the life span perspective for PWD (Bishop et al., 2008; Chapin, Miller, Ferrin, Chan, & Rubin, 2004), the outcome measurements of vocational rehabilitation heavily focus on ES and, to a lesser extent, QOL. Researchers and policy makers tend to simplify QOL as merely an important and useful way to measure the impact of rehabilitation services (Chan, Rubin, Kubota, Chronister, & Lee, 2003; Frisch, 2004). We might, however, broaden our understanding of QOL and consider it beyond rehabilitation outcomes or competitive employment (Chan et al., 2003).

In addition, much previous research (e.g., Bishop, 2005; Chan et al., 2003) has argued that more longitudinal research is required to obtain a better understanding about the QOL of individuals with disability. Rubin, Chan, Bishop, and Miller (2003) also supported longitudinal approaches by noting that QOL may better assess long-term perspectives because employment and life functioning become more internalized for individuals with time. The current study, therefore, demonstrates the effectiveness of the longitudinal research approach in assessing topics associated with QOL. We believe that the study makes several contributions to the literature on the impact that ES and age have on the QOL of PWD.

Limitations and Recommendations for Future Research

The limitations of this study lie mainly in the fact that although maintaining employment and age are crucial to QOL, it is likely that QOL is a much more complex, individually based perception than can be interpreted by the single outcome measurement (Cummins, 2003). The present study assumes that ES and age are both critical in maintaining QOL for PWD. Some literature, however, shows that family support and alternative rehabilitation services can serve as buffers in stabilizing the QOL of individuals with disabilities (e.g., Bertelli, Bianco, Rossi, Scuticchio, & Brown, 2011; Davis, 2010). Although prior studies (e.g., Bauman & Waters, 2004; Chapin & Holbert, 2010) have successfully used the variables of employment and age, it is possible that the selection of the variables is not entirely crucial. Thus, further study should consider life events such as income, marital status, and physical health (Chapin, & Holbert, 2010), because QOL may be a multidimensional concept or an individualized construct (Cummins, 2003; Deiner, 1984).

This study was a type of longitudinal cohort study, thus the use of different cohorts with different QOL expectations depending on their age can be another limitation. Also, the current study only divided six categories of age for individuals with disabilities (i.e., 20s, 30s, 40s, 50s, 60s, and 70s). Therefore, the future research that considers more specific age categories might be useful to better understand QOL for individuals with disabilities.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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