Sociodemographic Characteristics
Table 1 presents the sociodemographic characteristics of the study sample. Fifty-six percent (56 %) of the sample are girls. The mean age is 12.7 years. Boys are more likely to be older (mean age 12.9 years) than girls (mean age 12.4 years) (t = 8.77, p ≤ .001). The majority of the participants are single orphans (78.9 %). Girls are more likely to be in the single orphan category than boys (χ
2 = 6.38, p ≤ .01). Thirty-nine percent (39 %) of the participants report a biological parent (father or mother) as their primary caregiver. The average household size is 6.4 people, with 3.2 children living in the household.
Table 1 Sociodemographic characteristics of the sample (n = 1410)
Educational, Psychosocial, and Economic Preferences of Study Participants
Results from the bivariate analyses for children’s educational, psychosocial, and economic preferences are presented in Table 2. The majority of the participants (95 %) had plans of continuing with secondary school education after completing primary schooling. Boys were more likely to report plans of continuing with secondary school education than girls (χ
2 = 10.2, p ≤ .001). Although more boys (38 %) than girls (25 %) reported aspirations of graduating from college, more girls (50 %) aspired to go on to graduate school to get a second degree than boys (25 %). The difference between the two groups (boys and girls) with regard to educational aspirations was statistically significant (χ
2 = 119.9, p ≤ .001). Girls were also more likely to talk to their current guardian about schoolwork than boys (t = 11.6, p ≤ .001). In terms of psychosocial functioning, boys were more likely to report higher levels of self-concept than girls (t = 4.08, p ≤ .001). No other significant differences were observed among psychosocial variables.
Table 2 Educational, psychosocial, and economic preferences of study participants (n = 1410)
Our analyses also locate statistically significant differences within economic preferences. Specifically, boys were more likely to report owning savings (t = 50.8, p ≤ .001), value saving for a specific goal as important (t = 8.06, p ≤ .001), have a high confidence level in saving for a specific goal (t = 7.18, p ≤ .001), and were more likely to report availability of household assets than girls (t = 3.19, p ≤ .001). Girls, on the other hand, were more likely to report that their caregiver was saving money for them (t = 15.9, p ≤ .001).
In the following section, we report variations in educational, psychosocial, and economic preferences based on orphanhood status.
Educational, Psychosocial, and Economic Preferences by Orphanhood Status
As indicated in Table 2, both groups of children (double and single orphans) report high levels of educational plans to continue with secondary schooling after completing primary schooling (96 vs. 94 %, respectively). On the other hand, however, more single orphans (40 %) reported educational aspirations of going to graduate school than double orphans (34 %). The difference between the two groups on this measure (educational aspirations) is statistically significant (χ
2 = 14.4, p ≤ .05). In addition, single orphans were more likely to report talking to their current guardians about schoolwork than double orphans (t = 8.99, p ≤ .01).
With regard to psychosocial functioning, double orphans were more likely to report a higher level of likeness for their future self than single orphans (t = 2.95, p ≤ .01). No other significant predictors were observed among psychosocial variables.
Regarding economic preferences, double orphans were more likely to view saving for a specific goal as important (t = 1.97, p ≤ .05) and to have a high level of confidence in saving for that particular goal (t = 2.03, p ≤ .05). Single orphans were more likely to have knowledge that their caregiver is saving money for them than double orphans (t = 12.7, p ≤ .01).
The bivariate results presented above indicate several statistical differences—across gender and orphanhood status—on the following study outcomes: children’s educational, psychosocial, and economic preferences. Below, we present results from hierarchical regression analyses predicting the major factors that influence these outcomes.
Predictors of Educational Aspirations
Table 3 presents results from the hierarchical regression analysis predicting children’s educational aspirations using three blocks of predictors: children’s and family sociodemographic characteristics, children’s psychosocial and family processes, and economic preferences/predictors. Controlling for children’s sociodemographic characteristics (model 1), we are able to explain 6.9 % of the variance in children’s education aspirations. When we add children’s psychosocial predictors and family processes (model 2), we observe a 6.2 percentage change from model 1, resulting into a 13.1 % variance explained in children’s educational aspirations. This change is statistically significant (p≤.001). Adding economic predictors/preferences (model 3)—the final predictors to be entered—we are able to explain 13.9 % of the variance in children’s educational aspirations (R
2=.139, Adjusted R
2 = .128). The 0.8 percentage change between model 2 and model 3 is statistically significant (p≤.01).
Table 3 Hierarchical regression results: influence of sociodemographic characteristics, psychosocial and family processes, and economic preferences on children’s educational preferences (n = 1410)
Within each individual model on educational aspirations (Table 4), we observe the following: age (b =−.18, p≤.001) and gender (b =−.42, p≤.001) are associated with reduced educational aspirations. Among psychosocial predictors, care for future self (b=.20, p≤.01), likeness for future self (b =−.24, p≤.01), high levels of family cohesion (b =−.018, p≤.05), high levels of school satisfaction (b=.03, p≤.01) and talking to the participants’ current caregiver about schoolwork (b=.49, p≤.001) were all associated with increased educational aspirations. High levels of hopelessness were associated with reduced educational aspirations (b =−.05, p≤.001). Interestingly, availability of family assets (b =−.02, p≤.05) was associated with a reduction in educational aspirations. We are not sure why this trend—specifically, why availability of family assets would be associated with reduced educational aspirations. We can only speculate that it is probable that children in households with no resources (specifically, assets) count on education (if given a chance) as their only option out of poverty and eventual success in life.
Table 4 Regression results: sociodemographic characteristics, psychosocial and family processes, economic preferences and children’s educational aspirations (n = 1410)
In the following section, we present results from the hierarchical regression analyses predicting the factors that influence the following children’s psychosocial outcomes: depressive symptoms, hopelessness, and self-concept. In Table 5, we present the influence of each block of predictors on children’s psychosocial outcomes. The contribution of the individual variables in each model is presented in Table 6.
Table 5 Hierarchical regression results: influence of sociodemographic characteristics, psychosocial and family processes, and economic preferences on children’s depression, hopelessness and self-concept (n = 1410)
Table 6 Regression results: sociodemographic characteristics, psychosocial and family processes, economic preferences and children’s depression, hopelessness, and self-concept (n = 1410)
Predictors of Depression
As presented in Table 5 above, the block containing children’s sociodemographic characteristics accounts for less than 1 % (R
2=.007) of the variance explained in depressive symptoms. When we include psychosocial and family processes into the model, we are able to explain 28.2 % of the variance in depressive symptoms. The 27.5 percentage change between model 1 and model 2 is statistically significant (p≤.001). This means that psychosocial and family processes play an important role in explaining AIDS-orphaned children’s reported depressive symptoms. For this sample, children’s demographic characteristics—represented in model 1 are less predictive. When we add economic predictors/preferences (model 3) into the regression, we are able to capture an additional 1.2 % (from 28.2 to 29.4 %) in the variance explained in children’s depressive symptoms (R
2 = .294, Adjusted R
2 = .286). The additional percentage change is statistically significant (p≤.01).
Within each individual model predicting children’s depressive symptoms (Table 6), we find that gender (b=.71, p≤.01) and the number of children living in a household (b=.16, p≤.01) were associated with increased depressive symptoms. Specifically, being a male child was associated with a .71-point increase in depressive symptoms and each additional child living in the household was associated with a .16-point increase in depressive symptoms of the participant. Among psychosocial variables, high levels of self-concept were associated with a reduction in depressive symptoms (b =−.18, p≤.001). In addition, high levels of hopelessness were associated with an increase in children’s depressive symptoms (b=.43, p≤.001). Among economic variables, family assets were the only significant predictor associated with a reduction in children’s depressive symptoms (b =−.15, p≤.001).
Predictors of Hopelessness
The block containing sociodemographic characteristics of the sample accounts for less than 1 % (R
2 = .003) of the variance explained in children’s hopelessness (Table 5). When we include psychosocial and family processes into the model, we are able to explain 22 % of the variance in hopelessness. The 21.8 percentage change between model 1 and model 2 is statistically significant (p≤.001). An addition of economic predictors/preferences allows us to explain 22.5 % of the variance in hopelessness (R
2 = .225, Adjusted R
2 = .216).
An analysis of the individual models predicting hopelessness (Table 6) indicates the following: High levels of self-concept were associated with a reduction in hopelessness (b = − 07, p≤.001). Depressive symptoms, on the other hand, were associated with a .14 increase in hopelessness (b=.14, p≤.001). Among economic predictors, availability of basic needs was associated with a reduction in hopelessness (b =−.14, p≤.001). No significant predictors were observed among children’s sociodemographic characteristics.
Predictors of Self-concept
In Table 5, the block containing sociodemographic characteristics of the sample accounts for 1.9 % of the variance explained in self-concept (R
2 = .019). When we include psychosocial and family processes into the model, we are able to explain 34.5 % of the variance in self-concept. The 32.6 percentage change between model 1 and model 2 is statistically significant (p≤.001). An addition of economic predictors/preferences allows us to explain 35.2 % of the variance in self-concept (R
2 = .352, Adjusted R
2 = .345). Although the additional change between model 2 and model 3 is less than a percentage point, it is statistically significant (p≤.001).
Within each individual model predicting self-concept (Table 6), we observe that age (b =−.42, p≤.05) and gender (b = 2.1, p≤.001) were associated with self-concept. Specifically, being older was associated with a .42-point reduction in self-concept and being a male child was associated with a 2.1-point increase in self-concept. Among psychosocial variables, both care about future self (b = 1.1, p≤.05) and family cohesion (b=.37, p≤.001) were associated with an increase in self-concept. High levels of hopelessness (b =−.72, p≤.001) and depressive symptoms (b =−.60, p≤.001) were associated with a reduction in self-concept. Among economic variables, knowledge of caregiver’s savings (b=.002, p≤.05) and high confidence level to save for a specific goal (b=.18, p≤.01) were associated with an increase in self-concept.