Participants & Data Source
The present study uses data specifically from the fourth wave of data collection from Add Health participants. Conducted in 2008, this wave consisted of in-home interviews of 15,701 young adults ages 24-32. We excluded participants who had not answered the questions that became candidate items for our Add Health Resilience Instrument, which left 9852 participants in this validation study population.
Although some Add Health data is publicly available via the Add Health website (https://www.cpc.unc.edu/projects/addhealth/documentation/publicdata), this study utilized the extensive restricted-use data available by contractual agreement. This study was deemed exempt by our local institutional review board to the publicly available de-identified nature of the Add Health data.
Model Resilience Scale
A recent systematic review of resilience measurement scales by Windle et al. found that there is no current gold standard amongst the scales that have published psychometric validation data, in part because there is no gold standard for criterion validity for resilience.6
Windle’s group did conclude that the Connor-Davidson Resilience (CD-RISC) Scale, the Resilience Scale for Adults and the Brief Resilience Scale had the best psychometric ratings.6 The CD-RISC was initially developed as a 25 item scale,12 and has been validated in a wide range of subjects of varying ages.13–15 More recently, Campbell-Sills and Stein developed a 10 item version of the CD-RISC,16 which has also been validated in a variety of cross-cultural populations.17,18 Given the widespread use of the CD-RISC scales and the breadth of their existing validation literature, we used the CD-RISC as the model for the Add Health Resilience Instrument.
Items from Add Health
The original CD-RISC is composed of 5 major domains: personal competence or tenacity, strengthening effects of stress, positive acceptance of change, control and spiritual influences.12 In order to capture any Add Health data that might indicate resilience, all Add Health questions that reflected the items on the original expanded CD-RISC were pulled from the Add Health Wave 4 interview dataset. This led to 21 candidate Add Health items that were evaluated for inclusion in our Add Health Health Resilience Instrument (AHRI).
Principal component analyses of the 21 Add Health items were conducted on all available responses using oblique rotation to allow for inter-item correlation. Eigenvalues > 1 were retained. Internal consistency was evaluated by using Cronbach’s alpha, where the recommended value ranges from 0.7 to 0.95.19 We also assessed for internal consistency using item-test, item-rest and inter-item correlations. Item-test correlations determine how well each item correlates with the overall scale and should be roughly similar for all items.20 The item-total correlation shows how the item correlates with a scale computed from only the other items; ideal values are above 0.2.21 Inter-item correlations identify items too similar or not similar enough in a scale, with recommended values between 0.2 and 0.5.22
After arriving at a scale where included items showed the most optimal internal consistency, items were reverse coded if negatively worded in order for higher scores to indicate higher resilience. Responses were coded into a likert type scale, with the highest score indicating a participant “strongly agreed” with a positive statement. There was no differential weighting for items; questions which asked participants to pick the frequency of feeling certain positive attributes over the past month had a maximum score of 3 while all other items had a maximum score of 2. This was done to better reflect the original potential answer choices as defined by Add Health investigators. Scores were them summed to create an overall AHRI score. Descriptive statistics were used to characterize the AHRI scores first in the overall population, then by gender and age. Ceiling and floor effects were analyzed by calculating the frequency of participants showing the minimum and maximum possible scores. Floor and ceiling effects of greater than 15% indicates limited content validity.(Terwee et al. 2007)
As there are no gold standard criterion validity measures for resilience, we evaluated the AHRI through construct validity as others have done for the validation of other resilience scales.16,23 High levels of resilience are known to be protective against adverse mental health outcomes like depression24,25 or post-traumatic stress disorder.26 Thus, we evaluated for discriminant validity by calculating the correlation between AHRI scores and participants’ scores on a depression scale that had been originally embedded in the fourth wave of Add Health in home interviews. During the original 2008 wave of data collection, participants completed the short form of the Centers for Epidemiologic Studies Depression Scale (CESD-10). Initially developed with 20 items,27 the CESD-10 scale is widely used for the identification and evaluation of depression in the general and adolescent populations.28,29 Various shorter forms of the 20-item CESD have been evaluated over the years, including the CESD-10 developed by Andresen et al.30 Using the existing CESD-10 data present in the original 2008 Add Health dataset, we were able to define a depression score for participants in our cohort. Possible responses for CESD-10 related items ranged from never/rarely (0) to most/all the time (3) resulting in a possible scale of 0–30. We categorized a score > 10 as indicative of adult depressive symptoms as has been recommended and done by others using this data for a similar purpose.31–33 Resilience scores calculated using our AHRI were correlated with CESD-10 scores. In addition, we created three levels of Add Health resilience (low, medium or high) and compared CESD-10 scores in each of these three resilience categories via ANOVA analyses. Finally, we assessed for differences in AHRI scores between participants who had ever received a diagnosis of anxiety, depression or post-traumatic stress disorder in their lives and those who had not using Chi Square tests.
Add Health oversampled certain subgroups by design, thus all analyses of this dataset required survey weighting in order for results to remain nationally representative.9 The software STATA, version 14, was used for all statistical analyses.