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Validation of the child and youth resilience measure among South African adolescents

  • Kaymarlin Govender,

    Roles Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliations Discipline of Psychology, School of Applied Human Sciences, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa, Health Economics and HIV and AIDS Research Division, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa

  • Richard G. Cowden ,

    Roles Data curation, Formal analysis, Project administration, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Institute of Psychology and Wellbeing, School of Psychosocial Behavioral Sciences, North-West University, Potchefstroom, North West, South Africa

  • Kwaku Oppong Asante,

    Roles Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing

    Affiliation Discipline of Psychology, School of Applied Human Sciences, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa

  • Gavin George,

    Roles Conceptualization, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Health Economics and HIV and AIDS Research Division, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa

  • Candice Reardon

    Roles Conceptualization, Investigation, Methodology, Project administration, Writing – review & editing

    Affiliation Health Economics and HIV and AIDS Research Division, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa


Resilience is a dynamic, interactive process between resources that contribute to safeguarding a person and the adversities they experience. Within this promotional framework of resilience, this study sought to validate the Child and Youth Resilience Measure (CYRM-28) among a sample of South African adolescents (N = 1854). Confirmatory factor analysis supported a superior level of fit for a 24-item, three-factor model (i.e., individual/social, familial, and community/spiritual). Internal consistency and test-retest reliability estimates at a 12-month interval (N = 648) supported the reliability of the scales. Higher scores on the scales were associated with feeling more connected at school, greater parental monitoring perceptions, and lower sexual risk, confirming the convergent and criterion validity of the instrument. Partial discriminative power was evidenced based on selected scale distinctions according to age and sex groupings. Collectively, the findings suggest the 24-item CYRM is a valid and reliable self-report measure to assess the availability of resources associated with resilience in South African youth.


Despite continuing debate on how best to define resilience and the variety of approaches that are used to study the construct, resilience is typically viewed as the capacity to successfully adapt to disturbances (e.g., adversity, stressors, or demands) that have the potential to disrupt a person’s functioning. As a multi-dimensional process that mediates the effects of stressors on the achievement of positive outcomes, resilience is associated with individual capacities (e.g., self-regulation, cognitive skills, and personality temperament), relationships (e.g., family, friends, peers, and community members), and the availability of community resources and opportunities (e.g., educational, health, recreational and social services) [12]. The recent view of resilience as a series of interactions (or transactions) between an individual’s environment and an individual’s assets is a clear departure from previous definitions of resilience, which referred to inherent and static traits within the individual [34]. Many scholars have investigated and discussed the interactive, dynamic nature of resilience among young people–including how it is impacted by personal, relational, and contextual factors–a key population that is often seen as particularly vulnerable to engaging in health risk behaviors [5]. As a promoter of positive short- and long-term development, psychological functioning, and physical well-being [67], resilience has been connoted as a construct that may protect adolescents from participating in maladaptive, health risk behaviors [8].

Alongside the identification of factors that characterize resilience, researchers have developed instruments to measure the construct. However, previous research has tended to focus on outcomes that (a) emphasize mainstream populations from Westernized, individualistic cultural contexts upon which healthy functioning is defined, and (b) lack sensitivity to community and cultural factors that contextualise how resilience is defined by different populations and manifested in everyday settings [9]. As a result, there has been little cross-cultural validation of resilience within non-western cultures and settings.

Although several instruments have been validated for use with children and youth, the Child and Youth Resilience Measure (CYRM-28 [10]) represents one of the few that has been developed using items generated from a multi-cultural framework and investigator panel and validated using a multi-country sample. Based on validation efforts, a multidimensional, 28-item measure emerged that accounted for the resilience factors–individual, relational, and contextual–that are common across a diverse range of cultural groups [4].

In developing the instrument, however, Ungar and Liebenberg [10] noted several methodological limitations, such as the sole reliance on exploratory factor analysis. The authors advocate the employment of confirmatory factor analysis to continue the construct validation process of the instrument. There were also five items that were retained on the CYRM-28, despite statistical evidence warranting their removal (i.e., failure to load significantly on the first factor in one or more sub-sample). With findings of unique factors, such as the concept of Batho (i.e., community interdependence, connectedness, and respect for community values), underlying the resilience process in South African youth [11], it is necessary to determine the relevance of the CYRM-28 structure and item-factor loadings in the South African adolescent context. In fact, recent single-country studies have revealed selected items may be inapplicable in certain contexts [12]. Even though Ungar and Liebenberg’s [10] initial validation included a small group of adolescents from South Africa (N = 60), it is unknown whether the sample is representative of, and the results generalizable to, broader South African youth.

The data for this paper was drawn from a longitudinal study (i.e., Youth HIV/AIDS Prevention Project [Y-HAPP]) on factors associated with sexual risk among school going youth in the province of KwaZulu-Natal (KZN), South Africa, which has the highest HIV prevalence rate in South Africa [13]. Often a target of programmatic HIV-focused interventions, resilience is particularly relevant in this context for preventing sexually risky behaviors among adolescents given the high rate of heterosexual transmission of HIV [14]. Although prior research has identified various factors (e.g., school belonging, parental monitoring) act as protectors that reduce the likelihood of sexually risky behaviors among adolescents in this region [15], studies have typically examined protective factors in isolation and have relied on measures that have been validated among Westernized populations [16].

Building on Ungar and Liebenberg’s [10] initial work, this study sought to examine the psychometric properties of the CRYM-28 using a larger sample of South African adolescents. Specifically, we sought to confirm whether the original three-factor, 28-item measure represents the most suitable factor structure, and, using the most parsimonious version of the CYRM in this study, assessed the convergent validity, discriminative ability, and reliability of the instrument.



Time 1.

Initial data collection was conducted at seven rural (N = 768) and five urban (N = 1086) secondary schools within the Bergville Education and Central Durban Education districts, respectively, and each were partner schools on the Y-HAPP project. The Bergville district is characterised by rural, lower income, Black African families (poverty quintiles 1 to 3), whereas the Central Durban district has greater racial diversity and higher socioeconomic status (poverty quintiles 4 and 5). A combination of coeducational and single-sex education schools were included, with the number of purposively sampled participants from each school ranging from 32 to 320. Across the schools, 1854 (Mage = 14.88 yrs, SD = 1.68) adolescent girls (N = 900) and boys (N = 954) were taken from Grades 8 (N = 865) and 10 (N = 989). The sample was primarily comprised of Black adolescents (N = 1543), followed by Indian (N = 139), White, (N = 92), Coloured (N = 56), and ‘Other’ (N = 14) race group participants.

Time 2.

After a 12-month interval, a total of 648 male (N = 324) and female (N = 324) participants (Mage = 15.93 yrs, SD = 1.50) from the same seven rural (N = 219) and five urban (N = 429) schools completed the questionnaire for a second time. The participants were in Grades 9 (N = 278) and 11 (N = 370), one grade level higher than at time 1. The majority of the time 2 participants were Black (N = 520), with Indian (N = 58), White, (N = 43), Coloured (N = 17), and ‘Other’ (N = 10) race groups comprising the remainder of the sample.



The Child and Youth Resilience Measure (CYRM-28 [10]) is an indirect measure of resilience; it measures the availability of resources that increase the likelihood of demonstrating resilience when adversity or risk (whatever type or degree) is experienced.

The multidimensional instrument contains 28 Likert-type items (1 = not at all, 5 = a lot) across three dimensions of resilience: individual factors (11 items–Do you strive to finish what you start?), relational factors (seven items–Do you think your family will always stand by you during difficult times?), and contextual factors (10 items–Do you participate in organized religious activities?).

The three-dimensional structure has been corroborated on several occasions [4, 17]. Researchers have found construct and concurrent validity support for the instrument [12, 18]. Adequate internal consistency has been reported across a number of studies [4, 19] and test-retest reliability estimates have revealed total and subscale score stability over short (i.e., two to five weeks) and long intervals (i.e., three months [4, 12]).

School connectedness.

Perceptions of school connectedness was measured using the Psychological Sense of School Membership scale (PSSM [20]). The 18-item instrument assesses adolescent school issues related to inclusion, acceptance, respect, encouragement, and sense of belonging (e.g., I feel like a part of my school). The PSSM responses are in the form of a five-point Likert scale ranging from 1 (not at all true) to 5 (completely true).

Positive relationships with measures of success in school (e.g., grade point average) and hopeful life outcome expectations [2122], and associations with lower anxiety and depression levels [23] support the concurrent and predictive validity of the instrument. Internal consistency estimates have been acceptable to good (α = 0.76 to .84) across a number of studies [2426]. In this study, internal consistency was α = 0.72.

Parental monitoring.

Six items were adapted from the Alabama Parenting Questionnaire-Child Form (APQ [27]) to measure adolescents’ parental monitoring perceptions. The items (e.g., I tell my parents/caregiver who I am going to be with before I go out) are rated on a five-point Likert-type scale anchored at 1 (never) and 5 (very often). Research has supported the convergent validity of the APQ through associations with antisocial behavioral tendencies and conduct problems [2829] and the APQ have been found to discriminate children who have conduct problems from those that do not [30]. Subscale internal consistency estimates (alpha) have range from .54 to .83 across a number of studies [28, 31]. Cronbach’s alpha for the six-item scale in this study was .76.

Sexual risk.

Seven items were used to ascertain the participants’ past and present sexual experiences and combined for a composite sexual risk index. The items measured sexual activity safety (i.e., The last time you had sexual intercourse, did you or your partner use a condom?), number of sexual partners (i.e., In total, how many sexual partners have you had?), age of sexual debut (i.e., How old were you the first time you had sex?), pregnancy incidence (i.e., Have you ever been pregnant or made someone pregnant?), relationships with older partners (i.e., Have you ever had a girlfriend or boyfriend who was more than 5 years older than you?), sexual regret (i.e., Have you ever done something sexual with someone else that you wish you had not done?), and sexual exchanges for gifts (i.e., Have you ever given or received a gift in exchange for sex?). Internal consistency (alpha) for the composite index was .97.


Representatives from a number of schools were first approached in order to explain the purpose of the study and the pupils’ proposed involvement in it. Subsequent to schools’ permission to access students and ethical clearance to conduct the study, written informed consent was obtained from child’s parents/legal guardians. Informed consent was also discussed with the adolescents and written consent obtained prior to them completing the questionnaire. In line with Parry’s [32] recommendations, a specific process of translation, back-translation, and adjustment to assure linguistic equivalence was followed. Two members of the research team, one of whom has a Master’s degree in social science and the other a research officer, translated the questionnaires into IsiZulu. Both were native to the research area and fluent in IsiZulu. Each item was discussed in detail to determine an appropriate translation. A third member of the team who was also fluent in IsiZulu back-translated the items. The adolescents from the Bergville Education district completed questionnaires that were in IsiZulu. The students from the Central Durban Education district requested to complete the English version of the questionnaire. All questionnaires were self-completed under standardised administration conditions. Researchers were available to provide guidance and support to students, when required. Ethical approval for this recruitment and data collection process was granted from the University of KwaZulu-Natal’s Human and Social Science Research Ethics Committee and the Provincial Department of Basic Education, KwaZulu-Natal. On average, data collection lasted two days at each of the schools, and the questionnaire required approximately 30 minutes to complete.

Data analyses

Measurement model analysis was conducted using AMOS 23, whereas the remaining analyses were computed using SPSS 23. The number of missing values for each CYRM-28 item was less than 2% (i.e., .3% to 1.5%); missing values were subsequently replaced using expectation maximisation [33]. Although maximum likelihood estimation with large sample sizes (> 500 [34]) is quite robust to normality assumption violations [35], hypothesis testing assumptions were examined before conducting confirmatory factor analysis using the approach. General recommendations are that, in addition to chi-square (χ2), a number of incremental and absolute fit indices should be used to assess model fit [36]. These indices are particularly important to consider, as χ2 p-values will often achieve significance when models are estimated using large sample sizes [37]. In this study, incremental fit indices [Tucker-Lewis index (TLI) and comparative fit index (CFI)], absolute fit indices [standardized root mean square residual (SRMR) and root mean square error of approximation (RMSEA)], and a comparative fit index [Akaike Information Criterion (AIC)], were examined. Incremental fit indices compare a baseline model (i.e., all variables are uncorrelated) to a specified model, absolute fit indices provide an indication of how adequately the specified model fits the data, and comparative fit indices enable comparisons to be made between tested models [38].

Although normative criteria for incremental and absolute fit indices have been proposed, researchers caution against stringent and absolutist application of fit index guidelines [39]. The fit indices should also be considered in conjunction with one another and model fit decisions based on any single index should be avoided [40]. Among the most commonly used fit index recommendations are > .90 for CFI and TLI, < .08 for SRMR, and < .06 for RMSEA [39, 4142]. AIC values are compared across models, with lower values indicating a superior fitting model. There are several important caveats to applying fit index rules of thumb. Rigdon [43] suggests RMSEA is better suited for confirmatory analyses in which the purpose is to determine the adequacy of an established model, and the CFI is most appropriate for exploratory purposes in which the goal is to determine the ‘best’ fitting model. When RMSEA values approach .05 and RMSEA null values are less than .158, CFI and TLI estimates are uninformative and unlikely to achieve values above .90 [4445]. There is also evidence that the CFI index tends to reject correctly specified models when factor loadings are low-to-medium, which is often the case with psychological research [46].

The relationships between the latent factors were also explored, and Cronbach’s was alpha computed for each of the scales at time 1. Alpha criteria of excellent (> .90) good (> .80), acceptable (> .70), questionable (> .60), poor (> .50), and unacceptable (< .50) were used to assess internal consistency [47]. Subscale test-retest reliability was performed using intraclass correlation coefficients (ICC), which are more appropriate than Pearson’s correlations when group means are comparable and there is individual score variability across data collection points [48]. Mean subscale score differences were tested using paired-samples t-tests.

The convergent validity of the CYRM scales was tested using adolescents’ self-reported parental monitoring and school connectedness perceptions, whereas criterion validity was examined using a measure of sexual risk. Sex and age group comparisons were performed to determine the discriminative ability of the scales. With under 15 years of age an important benchmark for identifying adolescents at risk (e.g., early sexual debut [49]), age was dichotomised into younger than 15 years and 15 years and older groups. To preserve familywise alpha, Bonferroni adjustments were applied to follow-up univariate analyses.


Confirmatory factor analysis

The factor-level analysis results for the three models are presented in Table 1. The χ2 exact fit index was statistically significant for each model (p < .001), but the RMSEA and SRMR values indicated a good fit for each model. The null RMSEA value for each model was below the threshold of .158, and, when RMSEA values approach .05, it is questionable whether CFI and TLI could produce values above .90 [45]. Therefore, the interpretability of these fit indices are limited in this study. Also, with CFI estimates tending to indicate model rejection with low-to-medium item-factor loadings [46], the loadings in this study (see Table 2) may account for the substandard CFI index for each model. Collectively, the results suggest a reasonable level of fit for each model, but the best fit was found for Model 2. Comparing the fit indices and the standardized item factor loadings for each model, the three-factor, 24-item structure (hereafter referred to as CYRM-24) offers the most parsimonious measurement model.

Table 2. Standardized CYRM item-factor loadings for all tested models.


Cronbach’s alpha coefficients revealed internal consistency was good for the CYRM-24 individual/social scale (α = .82) and acceptable for the familial (α = .71) and community/spiritual (α = .70) scales. The ICC computations were above .60 for each scale (see Table 3), supporting the temporal stability of the scores from time 1 to time 2 [50]. The paired-samples t-tests revealed a significant time 1 to time 2 difference on the individual/social scale (p = .015) but not on the familial (p = .443) and community/spiritual (p = .196) scales. However, applying Cohen’s [51] effect size standards, each of the effect sizes was small (d = .03 to .10). On the whole, the results support the reliability of the three scales.

Table 3. Baseline to 12-month intraclass correlation coefficients and paired-samples t-tests.

Construct validity

Supporting the convergent validity of the CYRM-24 scales, the individual/social, familial, and community/spiritual scales correlated positively with school connectedness and parental monitoring, all of which were medium in effect size (see Table 4). The descriptive statistics for the MANOVA analyses are presented in Table 5. The distribution for the sexual risk index was bimodal, resulting in the dichotomization of the variable into low and high risk. The MANOVA for sexual risk was significant, F (3, 1738) = 8.98, p < .001, Wilk's Λ = .985, = .015, with differences found on the individual/social [F (1, 1740) = 6.30, p = .036, = .004], familial [F (1, 1740) = 25.12, p < .001, = .014], and community/spiritual [F (1, 1740) = 9.21, p = .006, = .005] between those categorised into low and high sexual risk.

Table 4. Pearson correlations between the CYRM, school connectedness, and parental monitoring scales.

Table 5. Descriptive statistics for CYRM-24 subscales by sexual risk, sex, and age groupings.

The MANOVA for sex indicated significant male and female differences, F (3, 1850) = 7.45, p < .001, Wilk's Λ = .988, = .012, although this pertained strictly the community/spiritual scale [F (1, 1852) = 6.57, p = .030, = .004] and not the individual/social [F (1, 1852) = 1.25, p = .792, = .001] or familial [F (1, 852) = 6.30, p = .036, = .004] scales. Although the MANOVA for age group was significant, F (3, 1849) = 2.69, p = .045, Wilk's Λ = .996, = .004, univariate analyses indicated differences on the familial [F (1, 1851) = 7.27, p = .021, = .004] scale, but not the individual/social [F (1, 1851) = 5.18, p = .069, = .003] or community/spiritual [F (1, 1851) = 5.45, p = .060, = .003] scales. Collectively, the selected sexual risk, age, and sex group differences were generally small to medium in effect size [52]. When significant, subscale scores were higher for the low sexual risk, female, and youth younger than 15 years of age (see Table 5).


The purpose of this study was to examine the psychometric properties of the CYRM-28 in a sample of South African adolescents in a high HIV prevalence setting. This study, therefore, builds on both the initial development and subsequent validation of the instrument in more developed countries [4, 10, 53]. The results showed that the original CYRM-28 revealed a reasonable level of fit, however, a superior level of fit was found for three-factor–individual/social, familial, community/spiritual– 24-item model (items 1, 3, 5, and 28 removed) reported by Daigneault et al. [12]. This may be because selected items, such as item 5 (i.e., Do you feel that your parent(s) watch you closely?), were among the exceptional items retained on the CYRM-28. Also, item 28 (i.e., Are you proud to be South African?) assesses national pride, which is much broader in scope than the other items included on Liebenberg et al.’s [4] contextual subscale. The finding of an improved factor structure with these items removed is not isolated to this study, as previous researchers have also revealed problematic factor loadings in previous validation studies using the original English version of the scale, particularly for items 1, 5 and 28 [4, 54]. Sexual risk prevention research relies substantially on determining the availability of protective factors in this region, which can be leveraged to target those at risk for sexually transmitted diseases [55]. Given high HIV prevalence rates among adolescents and young women in the region [56], the findings in this study may coalesce the diversity in resilience measurement in South Africa [9] and facilitate the targeting of resilience resources that are relevant and available to these key populations.

The results in this study further showed that the CYRM-24 and its subscales are internally consistent. The Cronbach’s alpha values for each of the CYRM-24 scales were similar to those reported by previous validation studies [4, 12], and the test-retest reliability estimates support the consistency of the scales over an interval of 12 months. The findings also indicated positive correlations between all the CYRM-24 subscales, parental monitoring, and sense of school connectedness. This indicates that adolescents with greater features of resilience also reported greater perceived parental monitoring and a stronger sense of school connectedness. These findings corroborate the results of other studies that have linked resilience to good parenting practices and school belongingness [5759].

In support of the discriminative power of the revised 24-item version, a difference between age group was observed on the familial subscale, with younger adolescents having greater familial resilience than older adolescents. This finding may reflect the developmental phase of adolescents, as younger adolescents are more likely to be comfortable and safer when they are with their families. Older adolescents, on the other hand (particularly those over 15 years), are more likely to associate with their peers [60]. Females were found to score higher on the community/spirituality subscale. With the items on this subscale measuring aspects related to emotional and social strength, attributes that favour adolescent girls [61], this may account for the difference found between males and females. All three subscales of the CYRM-24 were associated with low sexual risk behaviors, with participants reporting higher levels of resilience scoring lower on the sexual risk index. This is consistent with suggestions that resilience among young adolescents may serve as a protective factor against health risk behaviors [8]. The finding provides support for the protective model of resilience, which posits that protective factors assist in neutralizing the effect of risk, thereby reducing the likelihood of maladaptive consequences [58].

Strengths and limitations

This is one of the first studies to provide preliminary evidence on the suitability of the CYRM-28 in the South African context, a setting in which resilience has historically been measured using instruments that have been validated in Western populations [9]. Based on the psychometric support received for the CYRM-24 in this study, the revised version represents a culturally and contextually-specific measure of resilience among South African adolescents. This is likely to benefit the application and measurement of resilience in programmatic interventions that target sexual risk within high HIV prevalence regions such as KwaZulu-Natal. Despite these strengths, key limitations should be noted. Although the study includes a cross-ethnic analysis, the sample includes South African adolescents from schools located within two educations districts in a single province. As such, replication research appears necessary with samples of youth, both nationally internationally, in order to ascertain and maintain the instrument’s distinction as a cross-culturally relevant measure of resilience.

Given the different theoretical perspectives on resilience evident in the literature [62], this study adopted the view that resilience in all people is an interactive process involving varying degrees of risk and the availability of resources that can be accessed to serve as protective mechanisms for the individual. This view informed the decision not to exclusively focus on so-called ‘high-risk’ individuals (i.e., those reporting high-risk sexual behaviors) in the analysis. Further, the heterogeneous nature of our sample (i.e., adolescents with different levels of sexual risk behavior) was beneficial for assessing the broad applicability of the instrument for South African adolescents. Another strength of the study was the inclusion of a large sample. However, the participants were conveniently sampled, which has generalisability implications. It should also be noted that, given the content of some questionnaire items (e.g., sexual risk), there may have been a tendency for the participants to respond in a socially desirable manner. Therefore, caution should be applied when interpreting the results that are based on indexes containing such items.


This study examined the psychometric properties of the CYRM-28 in a sample of South African adolescents. The findings supported a superior, revised, three-factor structure comprising 24 items (CYRM-24), with each factor representing the individual/social, familial, and community/spiritual dimensions. Convergent validity support was evidenced in the positive associations between the CYRM-24 scales, school connectedness, and parental monitoring. An appropriate level of reliability was found for each scale, and selected scales were able to discriminate between sex, age, and sexual risk groupings. Collectively, the results suggest the CYRM-24 is a valid and reliable measure of adolescent and youth resilience, though additional research may be required to validate the instrument for use with multiple South African sub-groups and to determine whether further refinements to the CYRM-24 are required.

Supporting information


This publication was made possible through the permission provided by the South African Department of Education and the adolescent participants’ who freely gave their time to participate in the study.


  1. 1. Masten A. Global perspectives on resilience in children and youth. Child Dev. 2014; 85: 6–20. pmid:24341286
  2. 2. Ungar M. Resilience across cultures. Br J Soc Work. 2008; 38: 218–235.
  3. 3. Lerner R, Almerigi J Theokas C, Lerner J. Positive youth development: A view of the issues. J Early Adolesc. 2015; 25: 10–16.
  4. 4. Liebenberg L, Ungar M, van de Vijver F. Validation of the Child and Youth Resilience Measure-28 (CYRM-28) among Canadian youth. Res Soc Work Pract. 2012; 22: 219–226.
  5. 5. Capuzzi D, Gross D. Youth at risk: A prevention resource for counselors, teachers, and parents. Alexandria: American Counseling Association; 2014.
  6. 6. Rutten B, Hammels C, Geschwind N, Menne-Lothmann C, Pishva E, Schruers K. et al. Resilience in mental health: Linking psychological and neurobiological perspectives. Acta Psychiatr Scand. 2013; 128: 3–20. pmid:23488807
  7. 7. Ungar M. Resilience, trauma, context, and culture. Trauma Violence Abuse. 2013; 14: 255–266. pmid:23645297
  8. 8. Mistry R, McCarthy W, Yancey A, Lu Y, Patel M. Resilience and patterns of health risk behaviors in California adolescents. Prev Med. 2009; 48: 291–297. pmid:19159644
  9. 9. Theron L, Theron A. A critical review of studies of South African youth resilience, 1990–2008. S Afr J Sci. 2010; 106(7/8), Art. #252.
  10. 10. Ungar M, Liebenberg L. Assessing resilience across cultures using mixed methods: Construction of the Child and Youth Resilience Measure. J Mix Methods Res. 2011; 5: 126–149.
  11. 11. Theron L, Theron A, Malindi M. Toward an African definition of resilience: A rural South African community’s view of resilient Basotho youth. J Black Psychol. 2012; 39: 63–87.
  12. 12. Daigneault I, Dion J, Hébert M, McDuff P, Collin-Vézina D. Psychometric properties of the Child and Youth Resilience Measure (CYRM-28) among samples of French Canadian youth. Child Abuse Negl. 2013; 37: 160–171. pmid:23260113
  13. 13. Kharsany A, Cawood C, Khanyile D, Grobler A, Mckinnon L, Samsunder N, et al. Strengthening HIV surveillance in the antiretroviral therapy era: Rationale and design of a longitudinal study to monitor HIV prevalence and incidence in the uMgungundlovu District, KwaZulu-Natal, South Africa. BMC Public Health. 2015; 15(1149): 1–11.
  14. 14. Mueller J, Alie C, Jonas B, Brown E, Sherr L. A quasi-experimental evaluation of a community-based art therapy intervention exploring the psychosocial health of children affected by HIV in South Africa. Trop Med Int Health. 2011; 16: 57–66. pmid:21073640
  15. 15. Govender K, Naiker S, Meyer-Weitz A, Fanner J, Naidoo A, Penfold W. Associations between perceptions of school connectedness and adolescent health risk behaviors in South African high school learners. J School Health. 2013; 83: 614–622. pmid:23879780
  16. 16. Cowden R, Govender K, Oppong Asante K, Reardon C, George G. Validation of the Perceived Sense of School Membership Scale: A South African version. J Psychoeduc Assess. 2016; Advance Online Publication.
  17. 17. Resilience Research Centre. The Child and Youth Resilience Measure (CYRM) youth version. Halifax, Canada: Resilience Research Centre, Dalhousie University; 2013.
  18. 18. Ungar M, Liebenberg L. Ethnocultural factors, resilience, and school engagement. Sch Psychol Int. 2013; 34: 514–526.
  19. 19. Ungar M, Liebenberg L, Dudding P, Armstrong M, van de Vijver F. Patterns of service use, individual and contextual risk factors, and resilience among adolescents using multiple psychosocial services. Child Abuse Negl. 2013; 37: 150–159. pmid:23260119
  20. 20. Goodenow C. The psychological sense of school membership among adolescents: Scale development and educational correlates. Psychol Sch. 1993; 30: 79–90.
  21. 21. Booker K. Likeness, comfort, and tolerance: Examining African American adolescents’ sense of school belonging. Urban Rev. 2007; 39: 301–317.
  22. 22. Kia-Keating M, Ellis B. Belonging and connection to school in resettlement: Young refugees, school belonging, and psychosocial adjustment. Clin Child Psychol Psychiatry. 2007; 12: 29–43. pmid:17375808
  23. 23. Shochet I, Dadds M, Ham D, Montague R. School connectedness is an underemphasized parameter in adolescent mental health: Results of a community prediction study. J Clin Child Adolesc Psychol. 2006; 35: 170–179. pmid:16597213
  24. 24. Basterfied C, Reardon C, Govender K. Relationship between constructions of masculinity, health risk behaviors and mental health among adolescent high school boys in Durban, South Africa. Int J Mens Health. 2014; 13: 101–120.
  25. 25. Govender K, Naiker S, Meyer-Weitz A, Fanner J, Naidoo A, Penfold W. Associations between perceptions of school connectedness and adolescent health risk behaviors in South African high school learners. J School Health. 2013; 83: 614–622. pmid:23879780
  26. 26. Sánchez B, Colon Y, Esparza P. The role of sense of school belonging and gender in the academic adjustment of Latino adolescents. J Youth Adolesc. 2005; 34: 619–628.
  27. 27. Frick P. The Alabama Parenting Questionnaire [unpublished rating scale]. Tuscaloosa: University of Alabama. 1991.
  28. 28. Essau C, Sasagawa S, Frick P. Psychometric properties of the Alabama Parenting Questionnaire. J Child Fam Stud. 2006; 15: 597–616.
  29. 29. Hawes D, Dadds M. Assessing parenting practices through parent-report and direct observation during parent-training. J Child Fam Stud. 2006; 15: 554–567.
  30. 30. Shaffer A, Lindhiem O, Kolko D, Trentacosta C. Bidirectional effects between parenting practices and child externalizing behavior: A cross-lagged panel analysis in the context of a psychosocial treatment and 3-year follow-up. J Abnorm Child Psychol. 2013; 41: 199–210. pmid:22821450
  31. 31. Shelton K, Frick P, Wootton J. The assessment of parenting practices in families of elementary school-aged children. J Clin Child Psychol. 1996; 25: 317–327.
  32. 32. Parry C. A review of psychiatric epidemiology in Africa: Strategies for increasing validity when using instruments transculturally. Transcult Psychiatry. 1996; 33: 173–188.
  33. 33. Rubin L, Witkiewitz K, St. Andre J, Reilly S. Methods for handling missing data in the behavioral neurosciences: Don’t throw the baby rat out with the bath water. J Undergrad Neurosci Educ. 2007; 5: A71–A77. pmid:23493038
  34. 34. Munro B. Statistical method for health care research. 5th ed. Philadelphia: Lippincott Williams & Wilkins; 2005.
  35. 35. Chou C, Bentler P. Estimates and tests in structural equation modeling. In: Hoyle R, editor. Structural equation modelling: Concepts, issues, and applications. Thousand Oaks: Sage; 1995. pp. 37–55.
  36. 36. Kline R. Principles and practice of structural equation modelling. 3rd ed. New York: Guilford Press; 2011.
  37. 37. Stevens J. Applied multivariate statistics for the social sciences. 5th ed. New York: Routledge; 2009.
  38. 38. McDonald R, Ho M. Principles and practice in reporting structural equation analyses. Psychol Methods. 2002; 7: 64–82. pmid:11928891
  39. 39. Marsh H, Hau K, Wen Z. In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers of overgeneralizing Hu and Bentler’s (1999) findings. Struct Equ Model. 2004; 11: 320–341.
  40. 40. Lei P, Wu Q. Introduction to structural equation modeling: Issues and practical considerations. Educ Meas: Issues and Pract. 2007; 26: 33–43.
  41. 41. Hoe S. Issues and procedures in adopting structural equation modeling technique. J Appl Quant Methods. 2008; 3: 76–83.
  42. 42. Hu L, Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999; 6: 1–55.
  43. 43. Rigdon E. CFI versus RMSEA: A comparison of two fit indexes for structural equation modelling. Struct Equ Model. 1996; 3: 369–379.
  44. 44. Aschan L, Goodwin L, Cross S, Moran P, Hotopf M, Hatch S. Suicidal behaviours in South East London: Prevalence, risk factors and the role of socio-economic status. J Affect Disord. 2013; 150: 441–449. pmid:23726782
  45. 45. Kenny D. Measuring model fit. 2015.
  46. 46. Themessl-Huber M. Evaluation of the χ2-statistic and different fit-indices under misspecified number of factors in confirmatory factor analysis. Psychol Test and Assess Model. 2014; 3: 219–236.
  47. 47. George D, Mallery P. SPSS for Windows step by step: A simple guide and reference. 11.0 update. 4th ed. Boston: Allyn & Bacon; 2003.
  48. 48. Rodgers J, Nicewander W. Thirteen ways to look at the correlation coefficient. Am Stat. 1998; 42: 59–66.
  49. 49. Baumgartner J, Geary C, Tucker H, Wedderburn M. The influence of early sexual debut and sexual violence on adolescent pregnancy: A matched case-control study in Jamaica. Int Perspect Sex Reprod Health. 2009; 35: 21–28. pmid:19465345
  50. 50. Chinn S. Repeatability and method comparison. Thorax. 1991; 46: 454–456. pmid:1858087
  51. 51. Cohen J. A power primer. Psychol Bull. 1992; 112: 155–159. pmid:19565683.
  52. 52. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New Jersey: Lawrence Erlbaum; 1988.
  53. 53. Sanders J, Mumford R, Thimasarn-Anwar T, Liebenberg L. Validation of the Child and Youth Resilience Measure (CYRM-28) on a sample of at-risk New Zealand youth. Res Soc Work Pract. 2015; Advance online publication.
  54. 54. Resilience Research Centre. The Child and Youth Resilience Measure-28: User manual. Halifax: Resilience Research Centre, Dalhousie University; 2009.
  55. 55. Makiwane M, Mokomane Z. South Africa youths’ higher-risk sexual behaviour: An eco-developmental analysis. Afr J AIDS Res. 2010; 9: 17–24. pmid:25860409
  56. 56. de Oliveira T, Kharsany A, Gräf T, Cawood C, Khanyile D, Grobler A, et al. Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: A community-wide phylogenetic study. The Lancet HIV. 2017; 4(1): e41–e50. pmid:27914874
  57. 57. Benard B, Slade S. Listening to students: Moving from resilience research to youth development practice and school connectedness. In: Gilma R, Huevner E, Furlong M, editors. Handbook of positive psychology in schools. New York: Routledge/Taylor & Francis; 2009. pp. 353–369.
  58. 58. Fergus S, Zimmerman M. Adolescent resilience: A framework for understanding healthy development in the face of risk. Annu Rev Public Health. 2005; 26: 399–419. pmid:15760295
  59. 59. Lewis R. Resilience: Individual, family, school, and community perspectives. In: Capuzzi D, Gross D, editors. Youth at risk: A prevention resource for counsellors, teachers, and parents. 4th ed. Alexandria: American Counselling Association; 2006. pp. 35–65.
  60. 60. Sumter S, Bokhorst C, Steinberg L, Westenberg P. The developmental pattern of resistance to peer influence in adolescence: Will the teenager ever be able to resist? J Adolesc. 2009; 32: 1009–1021. pmid:18992936
  61. 61. Merrell K, Felver-Gant J, Tom K. Development and validation of a parent report measure for assessing social-emotional competencies of children and adolescents. J Child Fam Stud. 2011; 20: 529–540.
  62. 62. Davydov D, Stewart R, Ritchie K, Chaudieu I. Resilience and mental health. Clin Psychol Rev. 2010; 30: 479–495. pmid:20395025