Source: CLIQ participants et al. (2011:13). Layout: Jessica Nicholson Original Photos: Heidi Attwood; Sifiso Biyela; Elise Bjastad; Jessie Knott
“When sick and you call ambulance, there is no road for the ambulance to get to you” (SliF30).
7.1 Local Indicators of Quality-of-Life
PB_
Thirdly, diagramming and discussion about perceived changes in QoL (also during the final IIDI) allowed participants to reflect on how and why their well-being had or had not changed. These reasons for well-being change provided insight into what participants retrospectively regarded as impacting on their QoL. A comparison between S’boF52’s lifeline (Figure 7-2) showing only material indicators and Khumbizile’s lifeline (Figure 7-3) showing only non-material indicators, illustrates some of the variance between participants’
conceptions of QoL.
Figure 7-3: KhumbizileF24’s QoL-line (eNtshonalanga, 2010) Figure 7-2: S’boF52’s QoL-line (eNingizimu, 2010)
Table 7-1: Local indicators of QoL
Common indicators Less common indicators 1. Housing 7. Friends and networks 2. Study/ Education
(for self) 8. Attitude, behaviour &
state-of-being
3. Jobs 9. Community / voluntary activity & participation 4. Small Business 10. Basic services 5. Money (access &
affordability)
11. Family
12. Education (for family) 6. Car/ Travel 13. Other assets
(e.g.laptop)
The way in which participants expressed some indicators was similar across all three exercises (such as jobs) while for other indicators it differed. The detail of the different ways that participants expressed some indicators provides a much richer insight into meaning than any coded terms (e.g. more friends, socialising and networks) that were generated post-field, and participants’ words illustrate linkages across indicators.
An example of the enrichment provided by using three perspectives on indicators of well-being is provided by the young man whose own business concerned the provision of illegal electricity connections for others in the area. One of his goals was to have a legal electricity connection …. Therefore when considering access to basic services as an indicator, the nature of access is important. While attitudes, behaviour, and states-of-being were not in the group of the most common indicators, they were mentioned across all three methods. Expressions of this indicator as a goal included ‘work hard’ and ‘become an independent woman'; as a descriptor of low QoL included ‘can’t even think’ and ‘selfish’ for high QoL; and as a reason for change included various expressions of feelings of empowerment, self-confidence, hope and direction. (Attwood, 2013:11-12)
The various indicators arising from these three exercises were compared alongside each other post-field, creating a common list of indicators. A copy of the comparative table of descriptors, goals and reasons as presented in Attwood (2013:10) is included in Appendix K (p334). Table 7-1 lists the six most common, and the next seven less common indicators from a total of 21. Ideally, such a
universal list of local indicators of well- being would have been generated through iterative debate among
participants from all the areas, however project resources did not allow for this.78 Nevertheless, post-field compilation of common indicators remains useful because it provides a broad view of local understandings of well-being which all participants subscribed to, at least in
78 This is an example of a trade-off discussed under par principles in section 4.3 and fieldwork challenges in subsection 6.2.1.
part. It also serves as a useful background to the impact that CLIQ had on participants’ lives, as presented in this chapter.
Differences found in references to money, quality of food or clothing and livelihood status across the three sources of indicators of well-being (see Attwood, 2013:11&13), suggest that goals targeting a particular livelihood status (like getting a job) or financial resources were often regarded as a means to an end. The primary ends were lifestyle goals related to basic needs, like type of housing. However from the many different sequences of activities that participants undertook, there were examples where an achievement like increased social networks was a means for further achievements (e.g. getting a job); and where the same achievement of increased networks was the result of a previous
achievement (e.g. getting a job). Thus no clear distinction between specific goals (e.g.
getting a job) as means or ends emerged from the data. Rather, over time, goals reached became the means with which to achieve new goals. This is an example of circular and multi-directional causality – often complicating efforts to understand the impact of an intervention.
The importance of local engagement with the concept of well-being was the potential that the process of thinking and reflecting on QoL would assist participants in their effort to improve their lives - this is taken forward in subsection 7.3.6 on inner empowerment. Having computer skills and increased world knowledge both emerged mainly as reasons for QoL change in 2010, but not as goals or descriptors in 2008. This illustrates they were not initially regarded as indicators of QoL, suggesting that engagement with ICTs together with repeated discussion on the nature of ‘a good life’, changed participants’ conceptions of well- being and ill-being.
Figure 7-4: Changes in participants’ QoL Causality is difficult to establish given the variety of influences on human being and human doing and the complexity of QoL (see subsections 2.1.2, p21 and 3.3.1, p50).
Nevertheless, the challenge was to distinguish between changes that would probably have occurred in participants’ lives regardless of CLIQ and those that were influenced by their project engagement. Through a series of questions applied post-field to data from IIDIs (see Attwood et al., 2014:191), participants were grouped for the purposes of analysis into three groups:
a] CLIQ affect on QoL (those who linked CLIQ impact to at least one of their reasons for QoL change);
b] CLIQ impact (those who noted an impact of CLIQ on their life but did not link the impact to any of their reasons for QoL change); and
c] No or unclear CLIQ impact (those who did not indicate any impact from CLIQ on their lives or for whom the nature of CLIQ impact was unclear).
Just over a third of participants (36%) noted an impact from CLIQ that directly contributed to one of their reasons for QoL change. Another two fifths (41%) mentioned CLIQ impact on their life, although they did not associate the impact directly with any of their reasons for QoL change. Five participants indicated that CLIQ did not impact on their QoL at all over the two years and for the remaining 21 participants (19%), CLIQ impact was unclear.
Table 7-2 cross tabulates participants’ QoL-change group and their CLIQ-impact group, showing that 85% of participants who noted a CLIQ affect directly on their QoL, experienced an improvement in their QoL and 70% of those reporting some impact from CLIQ, improved their QoL, while only 27% of those with no or unclear impact, experienced an improvement in their QoL. Combining those with declined or unchanged QoL, almost three quarters of those noting no or unclear impact (73%), experienced a decline or no change in their QoL.
This figure drops to 30% and 15% respectively for those in the CLIQ impact and CLIQ affect
on QoL groups. Appendix J (p330) includes four area tables that indicate for each
participant, the nature of change in their QoL and the nature of CLIQ impact (according to the categories used in Table 7-2).
Table 7-2: Change in QoL according to CLIQ impact
Changes in QoL
Impact Sample
Core Sample
Total CLIQ
Affect on QoL CLIQ
Impact No or Unclear Impact
Improved QoL 74 65% 35 85% 32 70% 7 27% 70 76%
Unchanged QoL 25 22% 2 5% 6 13% 17 65% 8 9%
Declined QoL 14 12% 4 10% 8 17% 2 8% 14 15%
No. of Participants
(Impact Sample %) 113
(100%) 41
(36%) 46
(41%) 26
(23%) 92
(81%)
Using the core sample as a base (which excludes those with whom we lost contact - see Table 6-4, p158), three quarters of participants improved their QoL and 15% experienced a decline in QoL (see Table 7-2). In most cases (92%) where CLIQ impacted on the lives of participants, the impact was positive, although 17% of the core sample mentioned negative impacts (see Appendix I-Table 8, p329).79 Considering six specific cases where CLIQ directly affected a reason for QoL change and the person’s QoL remained unchanged or declined (see Table 7-2), the impact from CLIQ was positive and other factors were responsible for a decline in QoL. Multiple counteractive reasons for changing QoL were found in these cases, illustrating the complexity of life and therefore well-being. Table 7-3 provides examples of cases that fit within each of the various combinations of QoL change outcomes and CLIQ impact.
Analysis variables based on level of individual participation and level of area
implementation presented in subsection 6.4.1( p152), established an intensity ranking of the four research areas. Using this ranking, the relationships between participation,
implementation, QoL change and CLIQ impact were explored in order to gain a quantitative indication of the extent to which CLIQ did make a difference. Table 7-4 shows a clear
79 These are discussed in subsection 7.3.5.
correlation between the intensity rank and the proportion of participants with improved QoL. eMpumalanga and eNingizimu recorded higher proportions of participants with improved QoL (73% and 70% respectively), when compared to eNyakatho and
eNtshonalanga (65% and 57% respectively), where the intervention intensity rank was lower.
Table 7-3: Participants’ stories illustrating QoL change and CLIQ impact Impact from
CLIQ
Change in QoL
CLIQ Impact Group a] CLIQ Affect on QoL (41 participants)
CLIQ impact linked to at least one reason for person’s QoL change.
b] CLIQ Impact (46 participants)
CLIQ impact not linked by participant to reason for QoL change.
c] No / Unclear CLIQ Impact (26 participants) QoL change is unrelated to CLIQ. Insufficient data on impact: 21 p’pnts.
No impact: 5 p’pnts.
QoL Change Group
i. Improved QoL (74 p’pnts)
35 participants, e.g.:
MabasoM23 said his life improved because he got a job using a CV that he typed on computer, using his free hours, after learning about making CV’s with CLIQ.
32 participants, e.g.:
MilliF19 improved her life, mainly through having a baby and moving in with her boyfriend. Although CLIQ did have a positive impact on her this was not linked to her reasons for improved life.
7 participants, e.g.:
JojoM31 improved his life by getting a job through a contact unrelated to the CLIQ process. He did not return for the final-QLA, so CLIQ impact (if any) is unclear.
ii.
Unchanged QoL
(25 p’pnts)
2 participants, e.g.:
SafarF35 made progress with business plans for her co-operative business using new knowledge from CLIQ, but the project stalled because they were waiting for government for approval. Her QoL did not change because her co-op was not progressing.
6 participants, e.g.:
DinahF21’s QoL did not change. She mentioned positive impacts from CLIQ as empowerment from learning to use computers, socialising, and internet use to help her studies. She felt good from goal-setting activities.
17 participants, e.g.:
BongiM20 learnt computer skills but his QoL did not change and he felt that CLIQ had no impact on his life.
iii.
Lower QoL (14 p’pnts)
4 participants, e.g.:
SallyF24 felt that her life declined because her father died, even though she got job at a local car wash as a supervisor because she had computer skills.
8 participants, e.g.:
KwaziM20’s QoL declined because both his air-time business and his father’s taxi business were making less money than usual.
Kwazi benefitted from CLIQ in terms of
knowledge and typing and printing his CV.
2 participants, e.g.:
SaneF50 could not attend CLIQ activities because she had the option of temporary work each time there was a CLIQ activity.
Her well-being went down because in 2010 there was less temporary work available.
Table 7-4: Change in QoL by area
All Areas eMpuma-
langa eNingi-
zimu eNya-
katho eNtshona- langa
CLIQ Intensity Ranking 1st 2nd 3rd 4th
Improved QoL 74 65% 24 73% 14 70% 15 65% 21 57%
Unchanged / Declined QoL 39 35% 9 27% 6 30% 8 35% 16 43%
Impact Sample
(Sample %) 113
(100%) 33
(29%) 20
(18%) 23
(20%) 37
(33%)
Further correlations between level of participation, level of implementation, QoL change and CLIQ impact were tested through a series of cross tabulations using alternate combinations of these four variables. This led to a number of findings.80
a] As shown in Table 7-4, areas with better implementation (eMpumalanga and eNingizimu) recorded higher proportions of participants with improved QoL, than areas with poorer implementation (eNyakatho and eNtshonalanga).
b] A direct CLIQ impact on participants’ lives was more common among participants from eMpumalanga and eNingizimu; than participants from eNyakatho and eNtshonalanga, where implementation was less successful (see Appendix I-Table 5, p328).
c] Those with improved QoL, were more likely to have participated well in CLIQ; than those with unchanged or declined QoL (see Appendix I-Table 6, p329).
d] Virtually all participants with good individual participation noted an impact from CLIQ on their lives (whether or not they attributed any change in QoL to this impact), while the bulk of those who participated poorly, did not indicate any impact (see Appendix I-Table 7, p329).
e] Lastly, better individual participation overall was found in areas where implementation was better (see Table 6-2, p153).
These findings are visually combined in the form of a causal flow diagram (see Figure 7-5). 81
80 Attwood et al. (2014: 192-195) presents an analysis of the tables referred to.
81 This diagram first appeared in Attwood et al. (2011:16).
A greater proportion of participants from eMpumalanga and eNingizimu with increased QoL, when compared to participants from eNyakatho and eNtshonalanga, suggests there is a point on the continuum of intervention intensity beyond which
participants’ engagement in the process is associated with a greater positive effect. This is described in relation to complex interventions as a tipping point (Rogers, 2008). With the intensity of the intervention higher in eNingizimu than eNyakatho, the tipping point lies between what was achieved in these two areas, as illustrated in Figure 7-6.
The causality suggested through the flow diagram in Figure 7-5 is indicated when considering the correlations found in the quantitative data as discussed above. However,
Figure 7-6: Location of tipping point on CLIQ intensity continuum
Figure 7-5: CLIQ intervention intensity, impact and changes in QoL
Reasons for QoL change or CLIQ impact were not pre-determined; rather they
emerged from data analysis through a coding process guided by principles of GT (see Strauss and Corbin, 1998). This method of data analysis is consistent with the concept of emergence within complex programme theory (Rogers, 2008), as well as the principles of participatory research (see section 4.3, p80); and results more accurately reflect participants’ assessment of their changing situation. The different factors listed in Table 7-5 each encompass a few reasons for QoL change or project impacts. For example, getting a job, getting a promotion, losing a job and resigning are all included in the job status factor (as well as community activist work that pays a stipend).
Table 7-5: Factors affecting participants’ lives Discussed in detail in subsections 7.3.2
through to 7.3.6 Overview provided in subsection 7.3.1 1: Friends and social networks 6: Job status
2: Information, knowledge and further study 7: State of own business
3: ICT skills and use 8: Family member’s access to money
4: Community activity and participation 9: Getting or losing government grants or Identity Documents (IDs)
5: Inner empowerment 10: Housing and household composition
Note: The list differs from the list of ten main reasons for changes in QoL presented in the CLIQ community report (2011:29) due to further analysis of data with a focus on impact, causality and empowerment.
Both positive and negative outcomes have been grouped under a single factor, because the nature of impact depended on specific individual circumstances and
experiences. For example, SamkeF53 quit her job to spend more time on her co-operative business, which for her was a good thing, while for NdodaM20, the loss of his job meant a decline in QoL. Multiple reasons affecting QoL would either have a compounding or
Box 7-1: KhaboF25 (eNtshonalanga) Khabo’s well-being declined. In 2008, she lost two of her brothers, one in April and one in August. One was working away from home as a security guard and the other had a learnership to study at a technikon. Both were sending money to the household. However, in 2009, she was able to secure a part-time job at the LDC and was able to provide for her family. By 2010, things got worse because her father was ill and could not work. He had applied for a pension but was still waiting for approval. Khabo lost her job and the only money coming into the household was from her child’s grant.
counteractive affect on QoL. KhaboF25’s story illustrates that when a participant experienced a number of negative factors,
the combined impact was severe (see Box 7-1). There were also examples of positive compounding effects. For example,
BhalisileF19 and her sister each got a job and another sister got a promotion, which allowed the sisters to buy a four-roomed house together.
The seven factors encompassing the most common reasons for QoL change are shown in Table 7-6. The top three factors
(changes in job status, small business or family members’ income) relate to access to money, while the last three relate to ICT skills, education, information and knowledge; and housing and household composition. When comparing reasons given by those whose QoL improved to those whose QoL stayed the same or declined, differences are expected. The top three reasons for improved QoL, related to increased access to money by getting a job, starting or improving a small business or increased income of family members.
Table 7-6: Common factors underlying QoL change No. of
p’pnts Top seven factors affecting QoL
(mentioned by more than 10% of impact sample) % of
p’pnts 63 Getting, improving, loosing , getting and losing, or resigning from a job 56%
35 Starting, improving, declining or ending a small business 31%
24 Changes in level of family members’ income from jobs or small business 21%
18 No change in opportunities 16%
16 Acquired computer skills, computer use or did other training 14%
16 Increased knowledge and information or started to study or advanced with
drivers/ learners’ license 14%
12 Changes in housing, including improvements to current home, building new
home or moving home 11%
Impact
Sample Participants mentioned two reasons on average for QoL change 113
The most common reason for unchanged or declined QoL was ‘no change in
opportunities’, while the next three related to job loss, deterioration or cessation of own business, or declined income from family members. This places access to money as a key determinant of well-being, which was also found by Tiwari (2009:134) in her work comparing local and academic perspectives on poverty: “Consistently the respondents demonstrated a greater concern with livelihood security and basic needs”. This does not equate to
experiences of low well-being as mainly economic, but rather that financial resources are critical in order to access essential items to sustain human life (e.g. water, food, shelter).
The nature of CLIQ impact was analysed on the sample base of those from whom we collected sufficient impact data - the core sample. Table 7-7 presents the six most
frequently mentioned CLIQ impacts, disregarding whether or not the impact was also a reason for QoL change. Some form of inner empowerment was mentioned by three quarters of participants. The second and third most common impacts were more friends and networks and the acquisition of computer skills. These top three impacts were the same regardless of direction of QoL change. Empowerment, friends and computer skills each form part of personally held resources, namely psychological, social and education resources.
Table 7-7: Common CLIQ impacts No. of
p’pants Top six CLIQ impacts
(mentioned by more than 25% of core sample) % of
p’pnts 71 Felt empowered or increased self-esteem, hope, direction, motivation,
happiness and/or confidence 77%
51 More friends, networks and social interaction 55%
49 Acquired computer skills or attended computer training 53%
41 Computer use or free computer use 45%
39 Greater world knowledge, increased access to information, or an open mind 42%
24 Increased cell-phone use or expanded ways of using cell-phones 26%
Core
Sample Participants mentioned between five and six impacts on average 92
The greatest difference in impact between those with improved QoL and those with unchanged or declined QoL, was that negative feelings from CLIQ engagement were more common for the latter group. However, more friends, social interaction and networks (17%
more) and improved cell-phone use (14% more), were also more common among those with