CHAPTER 4: METHODOLOGY
4.9 DATA ANALYSIS
4.9.1 Data analysis of residents’ questionnaire
Demographics: The sample was described with respect to response rate, and demographics using descriptive statistics (frequencies for categorical variables and appropriate measures of central tendency i.e. mean (m) and standard deviation (sd) for numerical variables such as age and subscales total scores). The demographic variables (age, gender, race, home language, level of education, and length of stay in the residence) served the purpose of describing the sample. The number of living children, grand / great children and relatives were used to describe the network structure of the respondents. Information was calculated about the distribution of the scores.
For ease of reporting and based on the respondents’ responses data was recoded into different variables. The data for age was recoded into two groups (younger: old- 60-75 years; older old: 75+ years). The data for time staying in the residential facility was recoded into three categories, namely: “0-1 year”, “2-5 years” and “5+years”.
No married couples participated and no respondents cohabited, while only three respondents were separated from their spouses and recoded into data for the divorced persons. Hence marital status was recoded into the categories of “never married”, “divorced” and “widowed”. The response resulted in only the racial classification of “White” and “Indian” being reported. There were no isiZulu speaking respondents. Highest education level retained its four (4) categories.
Mental Wellbeing: Total scores were calculated for the dependent outcome variables for mental wellbeing. Cut off scores specific to the Kessler-6, WHO-5 and the 6-item Loneliness Scale were used based on the recommendations from the literature. A further variable for mental wellbeing was created where any negative response was recoded into a negative category. (Table 4: Psychometric properties of mental wellbeing and social connectedness scales). The 6-item Loneliness scale included two subscales, namely emotional and social loneliness. Social Loneliness scores were reverse scored for consistency. Bivariate correlations using Spearman’s Rho
and Cohen’s interpretation were also carried out to test the strength and direction of the relationship of the well validated outcome scales (Pallant, 2010).
Data was tested for normality and where relevant nonparametric tests of Mann- Whitney U test (U), Kruskal-Wallis Independent Samples test (K) and Chi-square or Fisher exact tests were used to test associations between the continuous and categorical variables (Pallant, 2010). Significance was set at p<.05.
Social capital: The framework identified individual and group determinants to be inclusive of demographics, trust, self-efficacy and social participation (Franke, 2006).
Trust and self-efficacy are discussed under network dynamics. Measures of central tendency were calculated for network size, volume, closeness, trust, self-efficacy and confidence in network.
Social networks were further described according to network structure, network density and network dynamics. Network structure included information on numbers of living children, great / grandchildren and living relatives and type of contact with these persons which allowed for a description of the sample. Information about the density of the respondents’ networks (frequency of contact) was recoded. The options “not applicable” and “never” remained, but “less than once a year, “once a year’ and “once a quarter” were recoded to “seldom”, “once a month” and “every two weeks” were recoded into “often” and “once a week” to “daily” was recoded to “very often”. Confidence indexes were calculated for the frequency of contact, closeness and likelihood to confide in the network.
Network dynamics was described using the information obtained about the closeness of the network members to the respondents, ability to confide in the network, trust and self-efficacy. Closeness was recoded, with the choice of “not very close” and “somewhat close” remaining, but “very close” and ‘extremely close were recoded into “close.” Confidence was identified from the respondents’ likelihood to confide in members of their network, where “not likely” remained as a category, but
“somewhat likely” and “very likely” were recoded into the category of “likely”. Trust was examined for the items relating to institutional and interpersonal trust and
recoded “great deal” and “quite a lot of confidence” into “high trust”, “moderate amount of confidence” into “moderate trust” and “not very much or no confidence”
into “low trust”. Trust total was calculated out of 25 with a score of five to the response representing highest trust and one to the lowest trust for each of the five items (ABS, 2004). Each of the items of self-efficacy was examined individually for association after they had been recoded from “strongly agree” and “agree” to
“agree”, “neither agree nor disagree” to neutral and “disagree and strongly disagree”
Social connectedness data was from questions about social participation. Questions that required information about the respondents’ involvement in activities in and outside the residential facility were scored as “yes”/”no” responses, while “contact with others” was on a likert scale of “0-4”, where “every day” scored the highest.
These items were recoded: “every day” and “few times a week” became very often,
““few times a month and once a month” became often and “not in the last month”
recoded into seldom (ABS, 2004). The Oslo-3 measured for social support as a sum of the three scores and used as a continuous variable and each of the individual items as categorical variables (Boen, 2012; Nosikov & Gudex, (Eds), 2003).
Table 7: Calculation Scale scores and subscale scores linked to social capital
Scale and related subscale scores aligned with
Canadian Policy Research Institute model as adapted for this study.
Q# Scoring Score interpretation Maximum Score
Social Connectedness (Support)
OSLO-3 Social Support Scale 3.2 Scale 1-4 and scale 1-5
3-8 = poor support 9-11 = moderate support;
2-14 = strong support 14
Social and Mental Health Outcomes
6-Item Loneliness Scale 4 Yes / No Yes =2; No =1
• Not Lonely =1-6
• Lonely = 7-11
• Intensely lonely = 12 12
Subscale Emotional Loneliness
4(a,e,f) Yes/No Yes =2; No =1
• Not Lonely =1-3
• Lonely = 4-5
• Intensely lonely = 6 6
Subscale Social Loneliness 4(b,c,d) Yes/No Yes =2; No =1
• Not Lonely =1-3
• Lonely = 4-5
6
Kessler 6 5(a,b,c,d,e,f) Scale 1-5 1= None of the time 5= All of the time
• Well = 6-11
• Mild to moderate psychological distress = 12-19
• Severe psychological distress = 20-30
30
WHO-5 Wellbeing Index 6(a,b,c,d,e) Scale 0-5 0=At no time 5=All of the time
• Raw score <13 is mentally not well
• or if answered 0/1 to any of 5 items = Poor sense of wellbeing
• Mental Wellbeing = 13-25
25
Technological Readiness: The Technological Acceptance Model identified external variables as demographic variables (Renaud & van Biljon, 2008) and two calculated key scores to assess readiness, namely a Perceived Ease of Use score (PEU) and a Perceived Usefulness Score (PU).
Current access to technologically assisted communication was described with frequencies and percentages of residents with landlines, cellphones and computers and each of the select activities. Descriptive statistics of duration of use which was recoded into that of “don’t use”, “less than six months”, “six months to a year” and
“greater than a year”. Frequency of use was recoded into “don’t use”, “seldom use”
(less than once a year to once a quarter, “often use “(once a month and once a fortnight), “very often use” (once a week to daily).
Technological readiness scores were calculated for each of the independent variables (PEU, PU, attitudes towards technology and behavioural intention to use technology). The derivation of these scores is presented below (Table 8: Calculation subscale scores for technological readiness). Positive and negative categories for each of the independent variables were calculated.
Table 8: Calculation subscale scores for technological readiness
Sub- scale
Subscale label aligned with Technology Acceptance Model
Q# Scoring Score interpretation Score 1 Perceived Ease of Use 7.2.1 Scale 0-4 0= Don’t use
4= Very easy to use
64 a Perceived Ease of Use cellphone 7.2.1 (a-j) Scale 0-4 0 = Don’t use
4= Very easy to use
40 1b Perceived Ease of Use computer 7.2.1 (k-p) Scale 0-4 0 = Don’t use
4= Very easy to use
24 2 Perceived Usefulness 7.2.3 Scale 0-4 0= Don’t use
4 = Very useful
64 2a Perceived Usefulness cellphone 7.2.3 (a-j) Scale 0-4 0= Don’t use
4 = Very useful
40 2b Perceived Usefulness computer 7.2.1(k-p) Scale 0-4 0= Don’t use
4 = Very useful
24
3 Attitude 7.3+7.5 Yes / No Yes = 1 ; No = 0 7
4 Behavioural intention 7.4 (b,c,d) Yes / No Yes =1; No =0 3
Appeal was analysed, using descriptive statistics as it related to greatest and least appeal of the provided options of software for technologically assisted communication.
Data was tested for normality and where relevant nonparametric tests of Mann- Whitney U test (U), Kruskal-Wallis Independent Samples test (K) and Chi-square or Fisher exact tests were used to test associations between the continuous and categorical variables, specifically age groups of younger old and older old (Pallant, 2010). Significance was set at p<.05.