Retrospective development of a novel resilience indicator using existing cohort data: The adolescent to adult health resilience instrument.

BACKGROUND: Cohort studies represent rich sources of data that can be used to link components of resilience to a variety of health-related outcomes. The Adolescent to Adult Health (Add Health) cohort study represents one of the largest data sets of the health and social context of adolescents transitioning into adulthood. It did not however use validated resilience scales in its data collection process. This study aimed to retrospectively create and validate a resilience indicator using existing data from the cohort to better understand the resilience of its participants.
METHODS: Questions asked of participants during one Add Health data collection time period (N = 15,701) were matched to items on a well-known and widely validated resilience scale called the Connor Davidson Resilience Scale. Factor analysis and psychometric analyses were used to refine and validate this novel Adolescent to Adult Health Resilience Instrument. Construct validity utilized participants' answers to the 10 item Center for Epidemiologic Studies Depression Scale, which has been used to validate other resilience scales.
RESULTS: Factor analysis yielded an instrument with 13 items that showed appropriate internal consistency statistics. Resilience scores in our study were normally distributed with no ceiling or floor effects. Our instrument had appropriate construct validity, negatively correlating to answers on the depression scale (r = -0.64, p<0.001). We also found demographic differences in mean resilience scores: lower resilience scores were seen among women and those who reported lower levels of education and household income.
CONCLUSIONS: It is possible to retrospectively construct a resilience indicator from existing cohort data and achieve good psychometric properties. The Adolescent to Adult Health Resilience Instrument can be used to better understand the relationship between resilience, social determinants of health and health outcomes among young adults using existing data, much of which is publicly available.

Introduction

Resilience is defined as an individual’s ability to positively adapt in the face of stress and/or adversity so as to maintain in relatively stable or even good psychological and physical health [1, 2]. Research on resilience and health has increased substantially in recent years due to a shift in focus from studying health predominantly from a pathogenic orientation (i.e. what causes disease) to exploring salutogenic forces (i.e. what promotes health) [3]. Individual psychological resilience appears to be protective against a variety of physical and mental health outcomes such as depression, anxiety and early mortality [4-6].

Resilience has been studied in a variety of ways: as a personality trait, a behavioral outcome and at times, as a dynamic process that can be modulated [5]. Although one body of literature has focused on resilience itself as the outcome [7-9], in other studies, investigators associated health-related behaviors, disease risk factors or health outcomes themselves with different levels of individual resilience [5, 6]. Still others have explored whether resilience moderates or mediates outcomes for other conditions [10]. In the mental health field, for instance, some researchers have sought to evaluate individual resilience as a mediator or moderator of treatment uptake or success [11].

These diverse conceptualizations have led to a variety of ways in which resilience has been operationalized and measured [12, 13]. Most studies looking at resilience and its relationship to health and disease have prospectively evaluated individual resilience through the use of resilience scales [4]. Although a wide range of scales exist, most of them are multi-dimensional, measuring several constructs or themes [4, 12]. Some of the more commonly recurrent constructs that are believed to represent high resilience and thus are often included in validated scales are a sense of personal agency, adaptive coping style, optimism and hopefulness and social support [4, 14, 15]. Existing scales usually yield an estimation of resilience as a discrete variable, with higher scores indicating higher resilience [12, 14, 16].

Given the costly and time-consuming nature of conducting prospective studies to assess resilience in a population using a validated scale, some groups have utilized retrospective strategies to assess individual resilience, by re-examining existing data from large cohorts. An example of one large nationally representative cohort whose data has been utilized in this way is the National Longitudinal Study of Adolescent to Adult Health (Add Health). The Add Health cohort study is a nationally-representative longitudinal cohort study that recruited students in the United States in grades 7–12 during the 1994–95 school year. After following adolescents into adulthood with in-home surveys collected over five waves of data collection spanning 24 years, it represents one of the broadest data sets on the social, economic, academic, psychological, and physical health status of adolescents transitioning into adulthood [17].

Add Health explores both risk and protective factors for young adult health and achievement. Many of the questions asked of participants over the years relate to themes of self-efficacy, optimism, persistence, social support, and faith/religiosity—themes that make up many validated resilience scales. However validated resilience scales were not used during any of the interviews. Over the years, researchers interested in the relationship between these themes and health have had to use alternate strategies to study resilience in this cohort [15, 18, 19]. Some research groups have written about resilience in the Add Health cohort as an adjective, exploring how or why individual or contextual factors may render some participants “resilient” to a pre-specified poor outcome, but without concretely measuring resilience itself [20-22]. Another angle has been the relationship between poor outcomes like depression, and individual concepts that are believed to represent one aspect of resiliency, such as personal agency [15], optimism [18] or social support [19]. In doing so, these researchers have utilized Add Health data to create a scale that operationalizes the specific resilience-related concept in question. For instance, Hitlin and Elder performed exploratory factor analysis using the first wave of data to construct a measurement model of agency [15] that has subsequently been used by others to measure the impact of agency on adolescent depression [18].

We built on this type of work by aiming to create an Add Health-based global resilience instrument that mirrored the structure of an existing widely used and well-validated resilience scale. The creation of such an instrument would allow the Add Health cohort study dataset to be used to more broadly examine resilience and its relationship to health outcomes, health behaviors, and social contexts.

Methods

Source of study sample

The present study was a cross-sectional analysis using 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. It represented 80.3% of the participants initially recruited in the first wave of the Add Health study and per the original investigators, there were minimal differences between wave 1 and wave 4 responders [23]. Our study population included all young adults surveyed in this wave (N = 15,701).

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 due to the publicly available de-identified nature of the Add Health data.

Existing resilience scales

Although a more extensive review of existing resilience scales is out of the scope of this paper, 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 [12].

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 [12]. Of these, the CD-RISC has been validated in the broadest range of subjects of varying ages, ethnicities and preferred languages [8, 24, 25]. The CD-RISC was initially developed as a 25 item scale [16]. More recently, Campbell-Sills and Stein developed a 10 item version of the CD-RISC [26], which has also been validated in a variety of cross-cultural populations [27, 28]. Given the widespread use of the CD-RISC scales and the breadth of the existing validation literature for the CD-RISC 25 in particular, we chose the CD-RISC 25 as the model for the Add Health Resilience Instrument.

Selection of candidate Add Health items

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 [16]. 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 Resilience Instrument (AHRI) (S1 Table). All of these items were scored on a Likert type scale.

Exploratory factor analysis

An exploratory factor analysis of candidate Add Health items was conducted using a principal component analysis and a direct oblimin oblique rotation to allow for inter-item correlation. Eigenvalues > 1 were retained.

Reliability

Internal consistency was evaluated by using Cronbach’s alpha, where the recommended value ranges from 0.7 to 0.95 [29]. Items were eliminated if they lowered the overall instrument’s alpha (i. the level of internal consistency between all the items) or if they did not load onto a factor well. 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 [30]. The item-total correlation shows how the item correlates with a scale computed from only the other items; ideal values are above 0.2 [31]. Inter-item correlations identify items too similar or not similar enough in a scale, with recommended values between 0.15 and 0.5 [32].

Instrument creation

After arriving at an instrument where 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 reverse coded into a Likert type scale, with the highest score indicating a participant “strongly agreed” with a positive statement or strongly disagreed with a negative 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 then summed to create an overall AHRI score. The AHRI was intended to be used similarly to existing resilience scales, where scores are treated as a continuous variable and higher scores indicate higher resilience at the time of sampling. 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 [29].

Construct validity

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 [26, 33]. High levels of resilience are known to be protective against adverse mental health outcomes like depression [34, 35] or post-traumatic stress disorder [36]. Thus, we evaluated for convergent 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 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 [37], the CESD-10 scale is widely used for the identification and evaluation of depression in the general and adolescent populations [38, 39]. Various shorter forms of the 20-item CESD have been evaluated over the years, including the CESD-10 developed by Andresen et al. [40]. 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 [41-43]. Resilience scores calculated using our AHRI were correlated with CESD-10 scores. Given the range and natural distribution of AHRI scores, we also created score tertiles to indicate low, medium or high resilience 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. Bonferroni correction was used to interpret results given the multiple tests done.

Statistical analyses

Add Health oversampled certain subgroups by design, thus all analyses of this dataset required survey weighting in order for results to remain nationally representative [17]. Descriptive statistics were used to characterize AHRI scores first in the overall sample, then by gender and age. STATA, version 14, was used for all statistical analyses.

Results

Study population

Table 1 depicts the demographics of our cohort and overall resilience score distribution. The average age was 28.4 years (SD 1.9) and a little over half of participants were female (57%). The racial/ethnic makeup of the cohort was similar to that reported in the 2008 American Community Survey Data [44], which helped confirm the national representativeness of our cohort and verified that survey results were weighted appropriately. The majority of participants in our study (60.8%) finished high school but had not completed college. In addition, 46% of the cohort had a household income of less than $50,000 a year, which was slightly lower than the median household income reported by the Census Bureau’s in 2008 [44].

Table 1

Demographic characteristics (N = 15,701).

Variable	Mean (SD) or % of Cohort
Age (yrs)	28.4 (1.9)
Gender (% Female)	57.0%
Race/Ethnicity
Non-Hispanic White	65.6%
Non-Hispanic Black	15.5%
Non-Hispanic American Indian	2.3%
Non-Hispanic Asian	3.4%
Other Non-Hispanics	1.2%
Hispanic	12.0%
Highest Level of Education Achieved
Less than High School	9.2%
Less than College	60.8%
College Degree	18.8%
More than College	11.2%
Household Income
<$20,000	12.7%
$20–49,999	33.0%
$50–99,999	39.4%
$100–149,999	10.2%
>$150,000	4.7%

Psychometric analyses

Principal component analysis was conducted on the 21 candidate Add Health items that aligned with specific CD-RISC items (S1 Table). Items which lowered the scale’s overall Cronbach’s alpha were eliminated until we arrived at a 13-item AHRI with 3 factors that had an overall alpha of 0.78. Table 2 depicts the internal consistency metrics of each included item and the overall AHRI, with the last column demonstrating what the overall AHRI alpha would decrease to if that particular item were eliminated. Items retained the original wording used by the Add Health study investigators to enable future investigators to more easily utilize the AHRI using the publicly accessible Add Health codebook. The data in S1 Table lists the original variable names for ease of reference in the publicly available original Add Health codebook. Items that were eliminated were related to being stressed easily, the strength of a partner’s commitment, the total number of close friends and religiosity (which made up part of the extended 25 item CD RISC but not the refined 10 item version). The average inter-item correlation was 0.18 (Table 2).

Table 2

Factor loadings and internal consistency of each item, factor and overall Add Health Resilience Instrument (AHRI).

Factor	Item on AHRI	Variable Name in Add Health Data	Factor Loading	Item-test correlationa	Item-total Correlationb	Average inter-item correlationc	Alphad
Factor 1
	1	H4pe39	.4379	.5966	.4771	.1733	.7560
	2	H4pe37	.7877	.5079	.4033	.1862	.7641
	3	H4pe15	.3522	.5485	.4328	.1800	.7608
	4	H4pe38	.7837	.5369	.4384	.1845	.7613
	5	H4pe41	.7658	.5427	.4595	.1872	.7613
	6	H4pe33	.5122	.4453	.3075	.1883	.7735
Factor 2
	7	H4mh4	.5335	.5532	.4335	.1787	.7606
	8	H4mh6	.7254	.6069	.4836	.1711	.7551
	9	H4mh2	.6990	.5070	.3846	.1835	.7656
	10	H4mh3	.8376	.6070	.4781	.1701	.7558
Factor 3
	11	H4pe14	.5885	.4176	.2732	.1907	.7773
	12	H4pe23	.6833	.4939	.3856	.1870	.7655
	13	H4pe7	.7878	.4556	.3334	.1888	.7702
Overall AHRI						.1823	.7779

a Ideally these values are similar for all items.

b Recommended values are above 0.2.

c Recommended values are between 0.15 and 0.5.

d Ideal values for individual items are between 0.7 and 0.95.

We labeled the 3 factors within the AHRI as personal competence, coping/isolation, and optimism. Each item loaded predominantly on only one factor (Table 2). The maximum possible score on our AHRI was 29. Resilience scores in our study population using this constructed scale appeared normally distributed (S1 Fig). The skewness (0.01) and kurtosis (2.8) of the resilience scores were also consistent with a normal distribution. There were no ceiling or floor effects in our cohort (Less than 1% of participants scored either the minimum or maximum) [29]. Mean resilience score for this population was 14.4 (Table 3). Low AHRI scores were defined as < 10, medium as 10–19 and high as 20–29. The majority of the cohort (68%) fell into the medium resilience category.

Table 3

Add Health Resilience Instrument.

Item	Max Score	Mean Score in Cohort	Mean Factor Score if Depressed
Factor 1. Personal Competence	12	5.2	3.6
1. Q.There are many things that interfere with what I want to do.*	2	0.6
2. There is little I can do to change the important things in my life.*	2	1.0
3. I hardly ever expect things to go my way.*	2	0.6
4. Other people determine most of what I can and cannot do.*	2	1.2
5. There is really no way I can solve the problems I have.*	2	1.1
6. I go out of my way to avoid having to deal with problems in my life.*	2	0.7
Factor 2: Coping & Isolation	11	6.8	3.7
7. In the last 30 days, I have often felt confident in my ability to handle my personal problems.	3	2.1
8. In the last 30 days, I have often felt that difficulties were piling up so high that I could not overcome them.*	3	1.8
9. I often feel isolated from others.*	2	1.1
10. In the last 30 days, I have often felt unable to control the important things in my life.*	3	1.8
Factor 3. Optimism	6	2.4	1.6
11. I am not easily bothered by things.	2	0.5
12. Overall, I expect more good things to happen to me than bad.	2	1.0
13. I'm always optimistic about my future.	2	0.9
Instrument Totals	29	14.4	9.0

* These items were reverse coded in order for higher scores to indicate higher resilience.

In looking at differences in resilience by basic demographics, we found that the mean AHRI score was significantly lower among women compared to men (14.1 vs 14.6, p<0.001) (Table 4). In addition, resilience scores increased with increasing education and household income.

Table 4

Differences in mean resilience scores by demographics.

Variable	Sample Size	Mean Resilience Scores (Standard deviation)	P-value
Gender			<0.001
Female	8352	14.1 (4.9)
Male	7349	14.6 (5.2)
Age			0.576
Age < 30	10,534	14.4 (5.0)
Age ≥ 30	5167	14.4 (5.4)
Race/Ethnicity			0.082
Non-Hispanic White	8266	14.5 (4.6)
Non-Hispanic Black	3341	14.2 (6.0)
Non-Hispanic American Indian	373	13.6 (5.5)
Non-Hispanic Asian	987	14.1 (7.0)
Other Non-Hispanic	190	14.6 (5.1)
Hispanic	2498	14.2 (5.7)
Highest Level of Education Achieved			<0.001
Less than High School	1252	11.9 (4.1)
Less than College	9492	14.0 (5.0)
College Degree	3044	15.8 (4.9)
More than College	1909	16.2 (5.2)
Household Income			<0.001
<$20,000	1756	11.9 (4.7)
$20–49,999	4801	13.9 (4.9)
$50–99,999	5786	15.3 (4.9)
$100–149,999	1563	16.2 (5.1)
>$150,000	756	16.5 (5.3)

For the evaluation of our scale’s convergent validity, we found a significant negative correlation between AHRI scores and CESD-10 scores for depression (r = -0.64, p<0.001). Tables 3 and 5 and S2 Fig all highlight that resilience scores increased as depression scores decreased. In addition, as seen in Table 5, the mean CESD-10 score for participants in the low Add Health resilience category was 11.6 and significantly different via pairwise comparisons to the mean CESD-10 scores in the medium and high Add Health resilience categories (mean CESD-10 scores 5.5 and 2.6, respectively, Bonferroni corrected p-values <0.001). Over half of the participants in the low resilience category qualified for a diagnosis of depression per the CESD-10. Finally, there were significantly more participants who reported ever receiving a diagnosis of depression, anxiety or post-traumatic stress in the low AHRI score category than in high resilience categories (Bonferroni corrected p<0.001 for all).

Table 5

Summary of construct validity findings.

Measure	Add Health Resilience Category			P-values
	Low (n = 2577)	Medium (n = 10,605)	High (n = 2519)
CESD-10 Score	11.6 (SD 4.9)	5.5 (3.8)	2.6 (2.6)	<0.001
Depressed per CESD-10	55.1%	43.5%	1.4%	<0.001
Ever received diagnosis of:
Depression	35.7%	57.1%	7.2%	<0.001
Anxiety	31.5%	60.4%	8.2%	<0.001
Post-traumatic stress disorder	38.8%	54.2%	7.0%	<0.001

Discussion

In this study, we showed that data from the National Longitudinal Study of Adolescent to Adult Health could be used to create a global resilience indicator that demonstrated appropriate psychometric properties among young adults. Using our indicator, we found that women had significantly lower resilience scores than men in this cohort. This has been documented by others noting gender differences in resilience using other scales [16, 28, 33]. We also found that resilience scores decreased as highest attained education level and household income decreased. This negative relationship between markers of socioeconomic status and resilience has been previously described [45-47], further validating our results.

Previous researchers had used Add Health data to explore the relationship between adolescent health and concepts related to resilience such as agency, self-efficacy or optimism [15, 18, 19]. Our work built on this existing literature in three important ways. We utilized data from when Add Health participants were firmly in their adult years (ages 24–32), creating the possibility of using the Add Health dataset to study resilience in adulthood in addition to adolescence. In addition, our study created an instrument that aims to capture a more global picture of resilience, rather than solely one aspect or factor. The three major factors we found in our constructed AHRI (personal competence, coping and isolation, and optimism) reflect major themes seen in prospectively validated resilience scales [14, 16], which speaks to the face validity of the AHRI and situates it within what has traditionally been believed to be important when assessing individual resilience. The main domain that exists on the CD RISC 25 which the AHRI does not capture well is the impact of spirituality on resilience. However, this domain was also dropped from the newer 10-item version of the CD RISC [26], with evidence of a more stable factor structure as a result. Thus, we did not feel the exclusion of spirituality to be a limitation of the AHRI per se. Finally, our construction of the AHRI shows that it is possible to take existing rich cohort data and re-purpose it for the identification of important constructs and outcomes beyond those conceived of during the design of the original study.

We do acknowledge some limitations. Given the existing nature of this data and the inability to re-contact participants, we were unable to assess the consistency of responses to our Add Health resilience scale over time, such as by looking at test-retest reliability. In addition, although Add Health has contacted the same participants over several waves, not every wave of interviews asks the same items and many of the resilience-related questions changed with each wave of interviews. Thus, participants’ responses to the items we used to construct our resilience scale could not be assessed at any other time point using existing data. Finally, at the time of the fourth wave of sampling, Add Health participants were between the ages of 24–32 and as such, may not have had time to complete college or establish themselves in their careers. While this may have impacted some of the resilience score comparisons we conducted by education and household income, our findings of increasing resilience as level of education and household income bracket increased were consistent with trends others have previously documented [45, 47].

Conclusions

The Add Health Resilience Instrument (AHRI) derived via interview data from a nationally representative longitudinal cohort study showed that a resilience indicator could be constructed retrospectively and evidence good psychometric properties. This instrument can be applied in future studies that utilize Add Health data to further explore the relationship between resilience, health, health behaviors and community context in young adults.

Characteristics of candidate Add Health items considered for inclusion in new instrument.

(DOCX)

Click here for additional data file.

Distribution of Add Health Resilience Instrument scores.

(TIF)

Click here for additional data file.

Evidence of the AHRI’s construct validity using CESD-10 scores.

(TIF)

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4 Sep 2020

PONE-D-20-14860

Retrospective development of a novel resilience indicator using existing cohort data: the adolescent to adult health resilience instrument

PLOS ONE

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Reviewer #1: Introduction

Please critically review previous instruments used to assess resilience.

Please add 21 candidate Add Health items that were evaluated for inclusion in AHRI as a supplement file.

Which method of factor rotation was used, why did you use this method, please add explanation into method section? Direct Oblimin Method? Promax Rotation?

The Methods section should be written as concisely as possible but should contain all elements necessary to allow interpretation and replication of the results. Such a sub titles something like these are recommended

The items & instruments

Study Samples (brief explanation about sampling in the fourth wave of original research)

Statistical analysis, Exploratory factor analysis, Known group comparison (construct validity), Reliability, Ethics

Please explain, is the scale is a screening one or a detective? Is there a cut-off point score for the scale?

Table 2, the last column is somewhat confusing. Did you indicate the alpha values for the scale if item deleted?

The alpha level of factor 3 is not satisfactory. What is the researcher explanation for such an alpha level?

Aren’t the data from 2008 too old to be presented as an original research?

Reviewer #2: 1. Is the manuscript technically sound, and do the data support the conclusions?

The proposed AHRI can be a useful tool for researchers working with the Add Health study. The manuscript is reasonably sound, and the evidence seems to support the conclusions the authors present. However, some technical matters require more attention and discussion.

I base the following six observations and recommendations on my comparison of the manuscript content with desiderata for factor analysis listed in page 94 of Bandalos’s and Finney’s 2010 “Factor Analysis: Exploratory and Confirmatory,” in Hancock’s and Mueller’s (editors) The Reviewer’s Guide to Quantitative Methods in the Social Sciences, published by Routledge.

1. The authors introduce the concept of resilience only very briefly. Although they mention typically used components of the latent construct, a fuller discussion of the theory behind or conceptual understanding of this construct would be helpful. For example, in the Discussion section, the authors mention that there is an ongoing debate about the best way to understand resilience: as a presumably fixed personality trait, as a behavioral outcome, or as a dynamic process. It stands to reason that the use of the concept may vary depending on different research questions, so the authors should make clear those research questions and the conceptual understanding or theory that links the concept of resilience to upstream predecessors or downstream outcomes. Making the conceptual understanding of resilience explicit will also help evaluate how well the candidate and selected Add Health items operationalize the concept.

2. Authors should indicate whether their factor analysis is exploratory or confirmatory (EFA, or CFA) and justify their decision, as each type of analysis serves a different purpose. The general guidance is to use the first type for newly developed constructs or when the theoretical basis is weak and to use CFA when the structure of the variable is well-established, as when using an independent sample to test a well-studied structure.

3. The authors should also indicate whether any of the variables selected as candidates for the factor analysis are not continuous. If so, they should note whether the variables are dichotomous or scales with less than five points. Such items may result in biased solutions and require a different type of analysis. Authors should identify all items, and including the variables’ codes will be helpful to researchers who wish to adopt the new instrument presented in this research paper. Also, the authors could have discussed whether the candidate items provide good-enough coverage of the dimensions of the latent concept. A table of the CD-RISC’s items, organized by domain, with the corresponding Add Health items lined up would be beneficial and illustrate whether any domains from the CD-RISC cannot be operationalized.

4. A discussion of the size of the analytical sample is missing: over 35 percent of the Wave IV sample was lost. Did one or a few items result in this loss of observations? If so, how does this affect the operationalization of the latent concept? Were non-responses concentrated among certain age, gender, racial or ethnic groups? If so, what impact does that have on the applicability of the proposed AHRI?

5. Difficulty factors that lead to misleading factor solutions may arise when variables with similar skewness and kurtosis can result in artefactual factors. To rule this out, or identify such factors, the authors should provide a summary of the candidate item’s descriptive statistics (mean, standard deviation, and values of skewness and kurtosis). Perhaps the table I proposed above can include CD-RISC items first, the corresponding candidate ASRI items next, then the percent missing responses for each item, and the descriptive statistics last.

6. I expected some discussion of the dimensionality of the chosen solution. The authors indicated that the CD-RISC operationalizes five domains, but the AHRI only has three. For example, the authors excluded the religiosity items from the selected solution, so the proposed AHRI does not operationalize the domain of spiritual influences. Is this not an important limitation of the proposed AHRI? Can the religiosity items from Add Health be retained to provide coverage for that domain, balancing a fuller operationalization with a weaker mathematical solution? What is the other domain not covered in the chosen solution, and can alternative solutions solve this?

The following seven observations concern my reading of the Methodology and Results sections:

7. I am not familiar with the evaluation of convergent validity. Based on its description (line 154 on), it seems to consist in evaluating the proposed AHRI through its correlation with a validated scale for depression, the CESD-10, which is empirically associated with extant resilience scales. I would appreciate a brief description of justification for this type of validation. Am I correct in thinking it consists of logically inverting the independent variable (resilience, which is theoretically upstream from the outcome of depression) with the dependent variable?

8. The second factor in the chosen factor solution, “Social Support / Feeling Overwhelmed,” is problematic. Having good social support and feeling overwhelmed strike me as related by not necessarily covarying concepts. A person who perceives the support of friends and family may feel overwhelmed by particularly stressful situations or traumatic events, while a person without support may feel overwhelmed more frequently.

9. I appreciated the analysis of average resilience scores for different demographic groups on page 11. However, given that some of the participants in the Add Health study may not have completed college, become financially independent, or had time to establish themselves in their careers *yet*, the comparisons by educational attainment and household income may not be the most meaningful.

10. Finally, I may be misinterpreting information on Tables 4 and 5 (on pages 11 and 12), but it seems that the confidence intervals for the scores of some of the comparison groups overlap. For example, at the top of Table 4, the mean AHRI score for Female is 13.8 with SD of 5.0, so the 95% confidence interval would run from around 3.8 to 23.8, roughly speaking, and would overlap the mean for Male, which is 14.4. Therefore, the means for Female and Male are not significantly different at the 95% confidence level, let alone at the 99.9% level.

2. Has the statistical analysis been performed appropriately and rigorously?

Yes, it has. However, as indicated above, some of the decisions the authors made require more discussion.

3. Have the authors made all data underlying the findings in their manuscript fully available?

No, the authors are contractually barred from sharing the restricted-use data from the Add Health study. However, Add Health makes a probability sample from the same wave the authors used available for public use. Confirming the results of the author’s analysis should be possible with that data.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Yes, but the authors should proofread for punctuation errors. They can also tighten the structure of the narrative to better guide the reader through their work.

Specific suggestions:

1. Some necessary commas are missing. For example, as written in line 116, the last domain of the CD-RISC seems to be, “control and spiritual influences,” when these are two distinct domains. I recommend the use of the Oxford comma, which adds clarity in lists. The authors may consider revising their manuscript to avoid using the passive voice, although this may be a field-specific preference.

2. I recommend the authors use the full name of the Add Health study in the first mention: National Longitudinal Study of Adolescent to Adult Health. The authors may be more familiar with cohort studies and retrospective analyses, but I consider it is more accurate to describe Add Health as a longitudinal study and to describe the author’s work as a cross-sectional analysis. It seems to be that a retrospective analysis would require predicting outcomes in Wave 4 (the wave from which the authors drew data) using independent variables from one of the earlier waves, but that is not the case. It would also be preferable if the authors distinguished the terms study and dataset (Add Health is a study, and several datasets are available for the various questionnaires that make up the study.) It is important to note that Add Health participants should not be described as patients, as being a patient was not a selection for inclusion.

3. Wave IV, conducted in 2008, is not the Add Health study’s original wave of data collection (line 155).

4. The description of the resilience scale developed by Hitlin and Elder previously belongs in the Introduction, together with other extant evidence is presented, rather than appearing towards the end of the paper.

**********

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Reviewer #1: Yes: Marzieh Araban

Reviewer #2: No

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Submitted filename: PONE-D-20-14860_comments to the author.pdf

Click here for additional data file.

5 Nov 2020

A Microsoft word formatted version of these responses have been attached.

Reviewer 1:

1. Please critically review previous instruments used to assess resilience.

Thank you for this comment. More information has been added to both the introduction and the methods. Although we utilized the Adolescent to Adult Health dataset, we have not focused on resilience scales specific to adolescents as suggested by the editor because our study sample was when participants in this cohort were firmly adults (ages 24-32). Thus the interpretation of our work and any potential future use of the instrument should be restricted to use within Add Health participants after they became adults.

a. Inclusion of the following information in the introduction: “Resilience has been studied in a variety of ways: as a personality trait, a behavioral outcome and at times, as a dynamic process that can be modulated1 Most studies looking at resilience and its relationship to health and disease have prospectively evaluated individual resilience through the use of resilience scales.2 Although one body of literature has focused on resilience itself as the outcome,3–5, in other studies, investigators associated health-related behaviors, disease risk factors or health outcomes themselves with different levels of individual resilience.1,6 Still others have explored whether resilience moderates or mediates outcomes for other conditions.7 In the mental health field, for instance, some researchers have sought to evaluate individual resilience as a mediator or moderator of treatment uptake or success.8”

Location: Page 3, paragraph 2

b. “Existing scales yield an estimation of resilience as a continuous outcome, with higher scores indicating higher resilience.”

Location: Page 4, paragraph 1

c. The following paragraph was moved to the introduction: “Over the years, researchers interested in the relationship between these themes and health have had to use alternate strategies to study resilience in this cohort.[15,18,19] Some research groups have written about resilience in the Add Health cohort as an adjective, exploring how or why individual or contextual factors may render some participants “resilient” to a pre-specified poor outcome, but without concretely measuring resilience itself.[20-22] Another angle has been the relationship between poor outcomes like depression, and individual concepts that are believed to represent one aspect of resiliency, such as personal agency[15], optimism[18] or social support.[19] In doing so, these researchers have utilized Add Health data to create a scale that operationalizes the specific resilience-related concept in question. For instance, Hitlin and Elder performed exploratory factor analysis using the first wave of data to construct a measurement model of agency[15] that has subsequently been used by others to measure the impact of agency on adolescent depression.[18]”

Location: page 5, paragraph 1

d. We also discuss existing resilience instruments in one of the subsections of the Methods. We have relabeled this section “Existing Resilience Scales” for clarity and for ease of reference to readers.

Location: page 6, paragraph 3

Given the existing strong review of resilience scale which we heavily relied on (Windle et al., 2011), we have added the following wording to the relabeled “Existing Resilience Scales” subsection of the Methods: “Although a more extensive review of existing resilience scales is out of the scope of this paper, a recent systematic review….”

Location: page 6, paragraph 3

Finally, we believe it is important to justify why we picked the CD RISC as the model scale for our novel AHRI and thus revised the last paragraph of the “Existing Resilience Scales” subsection as follows: “Of these, the CD-RISC has been validated in the broadest range of subjects of varying ages, ethnicities and preferred languages.4,9,10”

Location: page 7, paragraph 1

2. Please add 21 candidate Add Health items that were evaluated for inclusion in AHRI as a supplement file.

A new S1 Table has been created that lists all 21 candidate items along with other information requested by Reviewer 2 (Variable name in publicly available Add Health codebook, corresponding CD RISC factor, % missing as well as skewness and kurtosis of items included in factor analysis per Reviewer #2’s request).

Location: Page 7, paragraph 1

3. Which method of factor rotation was used, why did you use this method, please add explanation into method section? Direct Oblimin Method? Promax Rotation?

We employed direct oblimin rotation. This has been added to the “Exploratory Factor Analysis subsection of the Methods.

Location: Page 8, Paragraph 1

4. The Methods section should be written as concisely as possible but should contain all elements necessary to allow interpretation and replication of the results. Such a sub titles something like these are recommended

a. The items & instruments, Study Samples (brief explanation about sampling in the fourth wave of original research), Statistical analysis, Exploratory factor analysis, Known group comparison (construct validity), Reliability, Ethics

We have revised the existing subtitles as follows on pages 4-8.

- Source of Study Sample

- Existing Resilience Scales

- Selection of Candidate Add Health Items

- Exploratory Factor Analysis

- Reliability

- Instrument Creation

- Construct Validity

- Statistical Analyses

Location: Pages 5-10

5. Please explain, is the scale a screening one or a detective? Is there a cut-off point score for the scale?

Thank you for this opportunity to clarify. Resilience scales typically indicate level of resilience on a continuum. The creators of existing scales have at times defined high, medium or low levels of resilience, such as the creators of Brief Resilience Scale,11 but for the most part scores are generated and analyzed as discrete variables in association studies. With regards to the CD-RISC scale, which was we used to create the AHRI, the creators did not specify a cut-off score; higher scores were meant to indicate higher resilience, though in initial validation studies, the creators did defined median scores and score quartiles from their samples.12 Subsequent literature has attempted to make associations between other conditions, variables or characteristics and different levels of resilience. For instance, patients with depression or anxiety have been shown to have lower CD RISC scores by some investigators.13 In short, resilience scales are not really conceptualized as screening or detective but rather indicative of the amount of resilience an individual might possess or be capable of at that moment. We conceptualized and developed the Add Health Resilience Instrument in the same way, whereby higher scores indicate higher levels of resilience at the time of the sampling. We did develop tertiles based on the distribution in our sample in order to allow for associations to be drawn between AHRI scores and CESD-10 depression scores provided by the original Add Health investigators for our construct validity analyses and to provide some information about AHRI scores by demographic characteristics as others have done. However, we did not conceptualize a specific cut-off score for the AHRI and envision future researchers using AHRI scores as a discrete count variable for future association studies.

We have added the following language to the “Instrument Creation” section of the Methods to indicate this intent: “The AHRI was intended to be used similarly to existing resilience scales, where scores are treated as discrete variables and higher scores indicate higher resilience at the time of sampling.”

Location: page 9, paragraph 1

We have also changed the language in the new “Construct validity” section of the Methods to better explain our approach in creating score tertiles. “Given the range and natural distribution of AHRI scores, we also created score tertiles to indicate low, medium or high resilience and compared CESD-10 scores in each of these three resilience categories via ANOVA analyses.”

Location: page 10, paragraph 1

6. Table 2, the last column is somewhat confusing. Did you indicate the alpha values for the scale if the item was deleted?

Thank you for this request to clarify. Yes, these alpha values depict how the overall alpha for the instrument would change if that particular item was dropped. In other words, would the overall cronbach’s alpha increase or decrease if we dropped that item? Items were excluded if eliminating them would increase the overall internal consistency of the AHRI and were retained if eliminating them would decrease the overall instrument’s alpha. We have added the following text to the “Psychometric analyses” subsection of the Results:

“Table 2 depicts the internal consistency and reliability metrics of each included item and the overall AHRI, with the last column demonstrating what the overall AHRI alpha would decrease to if that particular item were eliminated.

Location: page 12, paragraph 1

7. The alpha level of factor 3 is not satisfactory. What is the researcher's explanation for such an alpha level?

While the alpha level for the combination of items that make up factor 3 is slightly lower than ideal values, all the items contained within Factor 3 contributed to a higher overall Cronbach’s alpha for the overall instrument and met a high threshold for alpha strength individually. In addition, there are references that suggest an alpha above 0.5-0.6 is acceptable in qualitative research.14 This comment, though, led us to consider whether it is necessary to include the alpha value for each factor, given that it may lead to confusion about the interpretation of alpha values. Thus we have eliminated the alphas for each sub-factor from Table 2 and just reported the alpha for each item and the overall AHRI alpha as that is traditionally what is reported in scale validation studies. If the reviewer and editors would like us to replace factor-level alphas, we are more than happy to put them back into Table 2.

8. Aren’t the data from 2008 too old to be presented as an original research?

The originality of our research was in creating a novel instrument through the use of existing data so that this existing data can be explored by future researchers in novel ways.

Reviewer 2:

1. The authors introduce the concept of resilience only very briefly. Although they mention typically used components of the latent construct, a fuller discussion of the theory behind or conceptual understanding of this construct would be helpful. For example, in the Discussion section, the authors mention that there is an ongoing debate about the best way to understand resilience: as a presumably fixed personality trait, as a behavioral outcome, or as a dynamic process. It stands to reason that the use of the concept may vary depending on different research questions, so the authors should make clear those research questions and the conceptual understanding or theory that links the concept of resilience to upstream predecessors or downstream outcomes. Making the conceptual understanding of resilience explicit will also help evaluate how well the candidate and selected Add Health items operationalize the concept.

Thank you for this suggestion. We have revised and expanded the introduction significantly to include a more in-depth conceptualization of resilience and how it has been previously studied. Specific attention was paid to how resilience is at times the outcome, sometimes the exposure and at other times the mediator or moderator in studies associating resilience with health behaviors, disease risk factors or outcomes.

Location: page 3, paragraph 2 and 3

2. Authors should indicate whether their factor analysis is exploratory or confirmatory (EFA, or CFA) and justify their decision, as each type of analysis serves a different purpose. The general guidance is to use the first type for newly developed constructs or when the theoretical basis is weak and to use CFA when the structure of the variable is well-established, as when using an independent sample to test a well-studied structure.

Thank you for this comment. This was an exploratory factor analysis as a resilience instrument utilizing Add Health items does not currently exist and we were developing a new instrument using existing data. We have clarified this in the Methods section by relabeling a subsection “Exploratory Factor Analysis.” It is conceivable that if more waves of data collection are conducted by Add Health researchers, they could utilize the work described in this paper to conduct an confirmatory factor analysis in a prospective fashion.

Changes to the manuscript: The words “An exploratory factor analysis of candidate Add Health items…” was added to the new “Exploratory Factor Analysis” subsection of the Methods.

Location: page 7, last line and page 8, paragraph 1

3. The authors should also indicate whether any of the variables selected as candidates for the factor analysis are not continuous. If so, they should note whether the variables are dichotomous or scales with less than five points. Such items may result in biased solutions and require a different type of analysis. Authors should identify all items, and including the variables’ codes will be helpful to researchers who wish to adopt the new instrument presented in this research paper. Also, the authors could have discussed whether the candidate items provide good-enough coverage of the dimensions of the latent concept. A table of the CD-RISC’s items, organized by domain, with the corresponding Add Health items lined up would be beneficial and illustrate whether any domains from the CD-RISC cannot be operationalized.

All items that were included in the AHRI had likert type scales for the answers that had at least five points. This detail has been added to the Selection of Candidate Add Health Items subsection of the Methods:

Changes to the manuscript: “All of these items were scored on a likert type scale.”

Location: page 7, paragraph 2

We also made a new S1 Table with the 21 candidate Add Health items that were initially pulled from the fourth wave of questions utilized by the original Add Health investigators and indicated which 13 of these 21 items made the final cut into the AHRI. We have also added a column for the variable name within the Add Health Codebook that is publicly available online both in this supplementary table as well as in Table 3 in the main document for ease of reference. This new table also indicates which CD RISC factors we felt the candidate Add Health items mapped to. The domain within the original CD-RISC 25 that could not be operationalized within the AHRI was the one related to spiritual influences. We include a reference to this in our discussion, and also point out that the CD RISC 10 which is believed to be a stronger instrument with a more stable factor structure also dropped the spirituality domain of the original CD RISC 25.

Changes to manuscript:“The main domain which the AHRI does not capture well is the impact of spirituality on resilience. However, this domain was also dropped from the newer 10-item version of the CD RISC,15 with evidence of a more stable factor structure as a result. Thus, we did not feel the exclusion of spirituality to be a limitation of the AHRI perse.”

Location: page 17, paragraph 1.

4. A discussion of the size of the analytical sample is missing: over 35 percent of the Wave IV sample was lost. Did one or a few items result in this loss of observations? If so, how does this affect the operationalization of the latent concept? Were non-responses concentrated among certain age, gender, racial or ethnic groups? If so, what impact does that have on the applicability of the proposed AHRI?

Thank you for this astute observation. Our initial analyses were run on a sample which had been restricted to those in wave 4 who were already parents based on future planned analyses. That is how 35% of the sample was lost. However, given this reviewer’s comment, we went back and re-did all of our analyses using the full cohort of 15,701 participants, as being a parent is not an absolute requirement for this scale. As the new S1 Table shows, data for each of the AHRI items was missing less than 1% of the time. Overall, there were minimal changes to the remainder of the results. Changes are summarized here:

- Differences between resilience scores in those below and over age 30 became non-significant (Table 1). The associated language describing that difference as significant has been removed from the results and the discussion.

- Small differences to the racial/ethnic, education and household income breakdowns reported in Table 1 such as the mean age of the cohort changing from 28.8 to 28.4 years for instance, the Hispanic percentage changing from 13% to 12% or the percentage of participants having earned a college degree changing from 11.6 to 18.8%

- No change to the items retained within the AHRI but one change to the factor structure. The item “I hardly ever expect things to go my way” moved to Factor 1.

- Minimal, non-significant change to the hundredth or thousandth decimal place for the numbers in Table 2.

5. Difficulty factors that lead to misleading factor solutions may arise when variables with similar skewness and kurtosis can result in artifactual factors. To rule this out, or identify such factors, the authors should provide a summary of the candidate item’s descriptive statistics (mean, standard deviation, and values of skewness and kurtosis). Perhaps the table I proposed above can include CD-RISC items first, the corresponding candidate ASRI items next, then the percent missing responses for each item, and the descriptive statistics last.

In the new S1 Table, we list the 21 candidate Add Health items, associated CD RISC 25 Factors and % missing responses. This new table also reports skewness and kurtosis data for the items that underwent factor analysis. None of those items had problematic skewness or kurtosis, using existing parameters in the literature.16 Mean scores for each AHRI item were already included in Table 3 where we believe they fit better, especially given the size of this new S1 Table.

6. I expected some discussion of the dimensionality of the chosen solution. The authors indicated that the CD-RISC operationalizes five domains, but the AHRI only has three. For example, the authors excluded the religiosity items from the selected solution, so the proposed AHRI does not operationalize the domain of spiritual influences. Is this not an important limitation of the proposed AHRI? Can the religiosity items from Add Health be retained to provide coverage for that domain, balancing a fuller operationalization with a weaker mathematical solution? What is the other domain not covered in the chosen solution, and can alternative solutions solve this?

Please see discussion above about the exclusion of the religiosity items in the AHRI and in more recent, perhaps more internally consistent and stable versions of the CD-RISC. We chose to model the AHRI off the CD RISC 25 vs. the CD RISC 10 in the hopes we would begin the exploratory factor analysis with the most inclusive sample of items possible. However, we feel that the resultant factor structure of the AHRI not only models the best internal consistency, but it also reflects the improved versions of the CD RISC and shows strong construct validity.

Changes to the manuscript: The following language was added to the discussion.

“The main domain that exists on the CD RISC 25 which the AHRI does not capture well is the impact of spirituality on resilience. However, this domain was also dropped from the newer 10-item version of the CD RISC,15 with evidence of a more stable factor structure as a result. Thus, we did not feel the exclusion of spirituality to be a limitation of the AHRI per se.”

Location: page 17, paragraph 1

7. I am not familiar with the evaluation of convergent validity. Based on its description (line 154 on), it seems to consist in evaluating the proposed AHRI through its correlation with a validated scale for depression, the CESD-10, which is empirically associated with extant resilience scales. I would appreciate a brief description of justification for this type of validation. Am I correct in thinking it consists of logically inverting the independent variable (resilience, which is theoretically upstream from the outcome of depression) with the dependent variable?

That is correct. As there is no gold standard way to evaluate resilience, construct validity for many resilience scales relies on existing evidence of higher resilience negatively correlating with adverse mental health outcomes such as depression, anxiety or posttraumatic stress disorder. This is especially true given that it is unknown whether resilience is up or downstream of outcomes such as depression (or both). For instance, someone with lower resilience might be more likely to become depressed. However, given the documented modifiable nature of resilience, someone who develops depression may experience a decreased level of resilience than they might have evidenced previously. Given these ongoing conversations, we utilized the same convergent validity approach employed by other resilience researchers in previous scale validation work, looking for negative associations between resilience and adverse psychological or psychiatric outcomes like post-traumatic stress disorder9 and depression.15,17

Location: This justification is described on page 9, paragraph 2, under the section labeled “Construct Validity.”

8. The second factor in the chosen factor solution, “Social Support / Feeling Overwhelmed,” is problematic. Having good social support and feeling overwhelmed strike me as related by not necessarily co-varying concepts. A person who perceives the support of friends and family may feel overwhelmed by particularly stressful situations or traumatic events, while a person without support may feel overwhelmed more frequently.

On looking at the items in Factor 2 again, we realize the phrase “social support” was not representative of the isolation item contained within factor 2 and thus have renamed Factor 2 “Coping/Isolation.” These two related topics are similar to Factor 3 in the original CD RISC 25 that was described as “related to positive acceptance of change and secure relationships.”12

9. I appreciated the analysis of average resilience scores for different demographic groups on page 11. However, given that some of the participants in the Add Health study may not have completed college, become financially independent, or had time to establish themselves in their careers *yet*, the comparisons by educational attainment and household income may not be the most meaningful.

This is a fair point and we have made a note in the limitations about this. We also included previous literature which found similar trends of increased resilience as education and income increased, which we believe lends some validity to our findings.

Changes to the manuscript:

“Finally, at the time of the fourth wave of sampling, Add Health participants were between the ages of 24-32 and as such, may not have had time to complete college or establish themselves in their careers. While this have impacted some of the resilience score comparisons we conducted by education and household income, our findings of increasing resilience as level of education and household income bracket increased were consistent with trends others have previously documented.18,19

Location: Page 18, paragraph 1

10. Finally, I may be misinterpreting information on Tables 4 and 5 (on pages 11 and 12), but it seems that the confidence intervals for the scores of some of the comparison groups overlap. For example, at the top of Table 4, the mean AHRI score for Female is 13.8 with SD of 5.0, so the 95% confidence interval would run from around 3.8 to 23.8, roughly speaking, and would overlap the mean for Male, which is 14.4. Therefore, the means for Female and Male are not significantly different at the 95% confidence level, let alone at the 99.9% level.

This is a common misconception. The standard deviations presented (and the confidence intervals that can be constructed from them) are for estimations of each individual parameter (i.e. female score, male score, score for those < 30, etc.). They are not confidence intervals for the estimation of the difference between the groups. The only statistic we included that relates to the difference between groups is the p-value, which, in the gender example provided in the reviewer comment, is statistically significant to a p-value < 0.001.

Specific suggestions:

1. Some necessary commas are missing. For example, as written in line 116, the last domain of the CD-RISC seems to be, “control and spiritual influences,” when these are two distinct domains. I recommend the use of the Oxford comma, which adds clarity in lists. The authors may consider revising their manuscript to avoid using the passive voice, although this may be a field-specific preference.

Oxford commas have been added throughout the manuscript. We will defer to the editors regarding the use of active vs. passive voice.

2. I recommend the authors use the full name of the Add Health study in the first mention: National Longitudinal Study of Adolescent to Adult Health. The authors may be more familiar with cohort studies and retrospective analyses, but I consider it is more accurate to describe Add Health as a longitudinal study and to describe the author’s work as a cross-sectional analysis. It seems to be that a retrospective analysis would require predicting outcomes in Wave 4 (the wave from which the authors drew data) using independent variables from one of the earlier waves, but that is not the case. It would also be preferable if the authors distinguished the terms study and dataset (Add Health is a study, and several datasets are available for the various questionnaires that make up the study.) It is important to note that Add Health participants should not be described as patients, as being a patient was not a selection for inclusion.

a. We agree with this reviewer’s conceptualization of our study as a cross-sectional analysis. However, we used the word retrospective in the title and manuscript to highlight the novel approach of creating a new instrument using data that has already been collected in a retrospective fashion. We worry that replacing the word retrospective, as we have employed it, with cross-sectional will mislead readers into wondering whether we collected data from Add Health participants ourselves. We wanted instead to emphasize that this was a retrospective approach in that we looked back and used existing data. We did double check to ensure that we did not ever describe our study as a retrospective study. Instead, each of the 5 instances in which that word was used was as an adjective or adverb, qualifying the construction of the instrument itself as retrospective. To ensure readers would not think our study design itself was a retrospective study as this reviewer describes, we have added this language to the first sentence of the Methods section:

“The present study was a cross-sectional analysis using….”

Location: page 6, paragraph 1

b. Thank you for your recommendation to include the full name of the Add Health study. The full name of the study was added to the introduction.

Location page 4, paragraph 2.

c. Thank you for catching our use of the word “patients” instead of “participants.” We have corrected this. Location, page 15, paragraph 1

3. Wave IV, conducted in 2008, is not the Add Health study’s original wave of data collection (line 155).

The word “original” in this sentence has been deleted.

Location, page 9, paragraph 2

4. The description of the resilience scale developed by Hitlin and Elder previously belongs in the Introduction, together with other extant evidence is presented, rather than appearing towards the end of the paper.

The agency scale created by Hitlin and Elder as well as the previous Add Health resilience-related research had been cited in the introduction in this sentence, “Researchers interested in the relationship between these themes and health have had to use alternate strategies to study resilience in this [Add Health] cohort, often focusing on single discrete concepts such as agency, self-efficacy, or optimism.20–22” However, given this reviewer’s excellent suggestion, we have re-organized the writing and expanded the description of previous Add Health-based resilience-related research in the introduction.

Location: page 5, paragraph 1

References

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2. MacLeod S, Musich S, Hawkins K, Alsgaard K, Wicker ER. The impact of resilience among older adults. Geriatr Nurs. 2016;37(4):266-272.

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24 Nov 2020

Retrospective development of a novel resilience indicator using existing cohort data: the Adolescent to Adult Health Resilience Instrument

PONE-D-20-14860R1

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2 Dec 2020

PONE-D-20-14860R1

Retrospective Development of a Novel Resilience Indicator using Existing Cohort Data: The Adolescent to Adult Health Resilience Instrument

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