LITERATURE REVIEW
3.4 Analytic Framework and Measures .1 Method of Analysis
Figure 3.1: CAPS Enumeration Areas
Source: Lam, Seekings & Sparks. (2006)
The sample was weighted based on the 1996 population census in order to be representative of the entire Cape Town Metropolitan area and to correct for an unequal probability of selection. However, due to the effect that weighting has on regression, all variables will appear to be significant when conducting logistic regression. Only unweighted proportions were used in the bivariate analysis and logistic regression.
3.4 Analytic Framework and Measures
Married young adults were not included in the study because motivations that inform the sexual behaviour of married young adults may be different from those that motivate unmarried young adults who are traditionally expected to abstain from sexual activities.
Table 3.2: Proportion of young adults married.
Current Marital Status Percent
Never Married 98.47
Married 1.33
Divorced 0.20
Total 100.00
About 1.33% (Table 3.2) of young adults who were married was dropped from the dataset.
Getting married and death are the only two ways (besides those who refused being re- interviewed) of dropping out ofthe longitudinal analysis.
Logistic regression
The logistic regression is a method of determining the association between a categorical outcome with a number of predictors (Lee, Forthofer & Lorimor, 1986). Logistic regression has the ability to incorporate a large number of predictor variables even if they are continuous variables (Lee, Forthofer & Lorimor, 1986). The general linear regression equation used is represented by the general formula used for a binary outcome variablep.
Logit(P) =
/30
+/31
Xli +/32
X2i
+ ... +/3
pX pi +CiWherep is the probability of the presence of the characteristics of interest - the dependent variable. In the studyp is represented by:
1. Ever had sex (Model 1).
11. Condom use at last sex (Model 2).
111. Two or more sexual partners in the last 12 months (Model 3).
/3j is the coefficients of the independent variables, Xj which in terms of this study is the indicator variables: SES index, gender, population group, age group, education level,
perceived HIV risk, time spent with mother and/or father and communication about personal matters with mother/father in the last 12 months.
Determination ofSES Index
The household survey was used to construct an SES Index that measures household wealth.
An SES index of a scale ranging from 0-5 was developed using household assets. The following assets were used to construct the SES index;
1. Internal piped water system
11. Flush toilet.
iii. Electricity
IV. Landline telephone v. Motor vehicle.
Each household was awarded a score of one for each asset owned. The equation used is:
SES Index = Wi + Tf+ E + Tp + V Where Wi = internal piped water
Tf= Flush Toilet E = Electricity
Tp = Landline Telephone V = Motor Vehicle
The scale was assigned categories: Low Socio-economic Status (Index 0-1), Middle Socio- economic Status (Index 2-3) and High Socio-economic Status (Index 4-5). For example a household which does not contain any of the items listed will have a score of zero indicating extreme poverty and a household which has all five items will have a score of 5, indicating a high SES (concept adopted from Thurman et al., 2006).
3.4.2 Variables
Sexual behaviour across all three Waves is measured using four proxies: ever had sex, age at first sex, condom use at first sex and number of sexual partners in the last 12 months. Sex in this study is defined as full penetrative vaginal sex. The predictor variables chosen for this study are divided into categories as directed by the theoretical framework. Personal factors were measured using the variable: 'assess your risk of HIV infection' with four possible options: 'no risk', 'low risk', 'medium risk' and 'high risk.' Education was measured using a young adult's level of education ranging from grade 0 to tertiary education. For further
analysis, dummy variables for primary, high school and tertiary education were created.
Family involvement was measured using time parents spend with their children and communication between parents and young adults. Time spent with parents was measured using the variables 'how often has mother spent time with just you?' and 'how often has father spent time with just you?', with both variables have four possible responses: never, rare, sometimes and often. Parent-child communication was measured using the variables 'discuss personal matters with mother?' and 'discuss personal matters with father?'
3.4.2.1 Variable selection summary
Trends ofSexual behaviour overtime
This section examines trends over time in sexual behaviour from Wave 1 to Wave 3.Inthose cases where results are missing, only Wave 1 and Wave 3 are used.
Variables
1. Age at first sex 2. Ever had sex
3. Having had two or more sexual partners in the last 12 months.
4. Condom use at last sex.
Determinants ofsexual behaviour
The dependent variable is sexual behaviour which IS determined usmg the following dependent variables.
Dependent variables
The following dependent variables were used;
1. Ever had sex (Model 1)
11. Condom use at last sex (Model 2)
111. Two or more sexual partners in the last 12 months (Model 3)
Predictor variables
The following predictor variables will be used. The predictors are divided into three classes:
personal factors, proximal and distal factors.
1. Individual factors
2. Demographic factors
1. Age
11. Sex
111. Population group
IV. Marital status
3. Family Involvement variables
1. Time spent with mother/father in the past twelve months
11. Communication about personal matters with mother/father
4. Sodo-economic variables
1. SES Index
3.5 Limitations of the research
Like in any longitudinal study, some subjects are lost to follow up and this may decrease the sample size as the analysis proceeds from Wave 1 (2002) to Wave 3 (2005). In total 1216 respondents where lost between Wave 1 and Wave 3. The time interval between the Waves is also short and may not have allowed for the observation of change. The multiple regression method used suffers from the inability to deal with confounding variables. Confounding occurs when one predictor variable has a correlation with another predictor variable. But since this research does not attempt to establish causality, the impact of confounding will be minimal. Data on sexual behaviour suffers from reporting biases. Sex is a private, personal and sensitive issue and is laden with hidden meaning. Thus, responses to questions of sex may not necessarily be accurate. In some instances, men may exaggerate the number of their sexual partners and women may underreport.
The Cape Town Metropolitan is home to a large pool of homeless young people. All the young adults in the study had some level of education and resided in a two parent household.
Thus the study fails to determine trends of sexual behaviour among orphans and homeless young adults residing in the Cape Town Metropolitan area.