Resilience Has Been and Will Always Be, but Rates Declared Are Inevitably Suspect

Our central argument is that it is wrong to make any definitive declarations about rates of resilience—not that these rates are high, low, or in between. We showed that prevalence rates of resilience using growth mixture models (GMM) are inevitably determined by a priori assumptions applied and our findings are cause to reconsider declared rates. We welcome the commentary by GalatzerLevy and Bonanno (2016, this issue) as a way to engage in an open discussion on approaches to studying resilience, and we respond to each of their criticisms in turn. The first criticism raised centered on concerns that we did not perform an exact reanalysis because we included more data points before and after the stressor than they had. Borrowing from principles of measurement, we would argue that just as the reliability of a scale generally increases with more items, additional time points should have led us to more “reliable” findings. And if indeed the addition of more data points engendered substantively different findings, then this would directly support our central contention that trajectories derived from GMM need to be reconsidered. Parenthetically, we note that in follow-up analyses, we reanalyzed our data to replicate prior analyses more exactly and found similar results; see Footnotes 1 and 2 in Infurna and Luthar (2016, this issue). 1 Galatzer-Levy and Bonanno then contended that our analytic choices were not optimal with regard to technicalities of model fit, slopes, and entropy, and we continue to maintain that they were. Assumptions we applied each had strong conceptual bases that were explained in nontechnical terms and with illustrative figures. A strong refutation of our central argument could perhaps have come from demonstration that (and of how) our analyses contained statistical errors, and this has not been done.2 Absent demonstrable errors, the disagreement boils down to which choices are best in setting up the GMM. Even assuming that both sets of assumptions (theirs and ours) might be judged as equally well-justified, this would yet again directly support our central argument: Our findings are cause to reconsider rates of resilience as determined by GMM. Next, we consider specific issues raised regarding model technicalities of the slope variances, model fit, and entropy. First, Galatzer-Levy and Bonanno mention that we freed all slope variances within and between classes in our reanalysis, but we emphasize that we additionally freely estimated the intercept variances within and between classes. This distinction has important implications for the number of trajectories found and proportions of people in each, but it is not acknowledged. Second, a critical preliminary step before utilizing GMM is to first find the best single-group representation of change (Ram & Grimm, 2009). The analyses we conducted across Parts A, B, and C illuminate which longitudinal model fit the data best by comparing the single-class models on key indicators of fit (e.g., BIC, CFI, and RMSEA; see Hu & Bentler, 1999). Taking spousal loss as an example, the model fit for Part C is markedly better based on the lower BIC (39,054 versus 39,517 and 39,292) and a higher CFI (0.923 versus 0.849 and 0.863) and lower RMSEA (0.068 versus 0.091 and 0.088; see note in Tables 1, 5, and 6 for CFI and RMSEA). These values show that constraining variance parameters severely impacts the overall model fit, just as they support decisions to relax a priori assumptions. Third, Galatzer-Levy and Bonanno heavily emphasize entropy, but there is consensus that multiple fit statistics (in addition to entropy) should be considered when determining the proper number of classes (see Muthén, 2004; Nylund, Asparouhov, & 621281 PPSXXX10.1177/1745691615621281Infurna, LutharResilience Has Been and Will Always Be research-article2016