key: cord-1004129-qjiw1z6e
authors: Olino, Thomas M.; Case, Julia A. C.; Hawes, Mariah T.; Szenczy, Aline; Nelson, Brady; Klein, Daniel N.
title: Testing Invariance of Measures of Internalizing Symptoms Before and After a Major Life Stressor: The Impact of COVID-19 in an Adolescent and Young Adult Sample
date: 2021-05-20
journal: Assessment
DOI: 10.1177/10731911211015315
sha: 68f3c13a361328ad06fa096d68aeda466b749da5
doc_id: 1004129
cord_uid: qjiw1z6e

There are reports of increases in levels of internalizing psychopathology during the COVID-19 pandemic. However, these studies presume that measurement properties of these constructs remained unchanged from before the pandemic. In this study, we examined longitudinal measurement invariance of assessments of depression, anxiety, and intolerance of uncertainty (IU) in adolescents and young adults from ongoing longitudinal studies. We found consistent support for configural and metric invariance across all constructs, but scalar invariance was unsupported for depression and IU. Thus, it is necessary to interpret pandemic-associated mean-level changes in depression and IU cautiously. In contrast, mean-level comparisons of panic, generalized, and social anxiety symptoms were not compromised. These findings are limited to the specific measures examined and the developmental period of the sample. We acknowledge that there is tremendous distress accompanying disruptions due to the COVID-19 outbreak. However, for some instruments, comparisons of symptom levels before and during the pandemic may be limited.

Longitudinal studies of MI have primarily used naturalistic observational designs in youth and early adulthood. For example, Mathyssek et al. (2013) examined MI of youth self-reports of depression and anxiety in a large (N = 2,230) epidemiologic study spanning aged 11 to 16 years using the Revised Child Anxiety and Depression Scale (Chorpita et al., 2000) . The authors found strong support (i.e., trivial reductions in model fit) for metric invariance, but modest support (i.e., some aspects of fit showed substantive reduction in fit) for scalar invariance. However, models continued to be an excellent fit to the data. Based on this evidence the authors concluded that the comparisons of constructs are valid across this developmental span. Olino et al. (2018) examined longitudinal metric and scalar MI of anxiety in a moderately sized (n = 487) sample of youth from age 9 to 12. For symptoms of generalized anxiety disorder (GAD), panic disorder, and social anxiety disorder using the Screen for Child Anxiety Related Emotional Disorders (SCARED; Birmaher et al., 1997) , there was sufficient metric and scalar MI to support comparisons of levels of these symptom dimensions over time. Wu (2017) examined MI of the Beck Depression Inventory in a study of adolescents (n = 730) spanning ages 13 to 16 years. The author found support for configural, metric, and scalar invariance of a three-dimensional model (negative attitudes, performance difficulties, and somatic complaints). Similarly, Leadbeater et al. (2012) found support for configural and metric, but not scalar invariance of depressive and anxiety symptoms across four waves of data collection (n = 662) spanning ages 12 to 26 years using the Brief Child and Family Phone Interview (Cunningham et al., 2009 ). Finally, Tyrell et al. (2019) found configural, metric, and scalar invariance of two measures of depressive symptomatology, an abbreviated version of the Children's Depression Inventory (CDI) and the Anxious/Depressed subscale of the Adult Self-Report (ASR) from ages 12 to 21 years (n = 392). Thus, these studies show general support, with some exceptions, for MI over the course of adolescence and early adulthood.

There have been fewer examinations of MI when participants experience contextual changes. Fried et al. (2016) examined longitudinal MI in adults (n = 649) as they completed interventions for depression. The authors found that the structure of the depression measures-the Hamilton Rating Scale for Depression, the clinician-rated Inventory of Depressive Symptoms, the clinician-rated Quick Inventory of Depressive Symptoms, and the selfrated Inventory of depressive symptoms-changed in such a fashion, including the number of factors needed to explain the observed data, that comparisons across time were not valid. There also have been several studies (Goodman-Williams & Ullman, 2020; Lommen et al., 2014; Platt et al., 2016; Wang et al., 2012) that have examined MI across multiple assessments following natural disasters or unpredictable stressors. However, these studies are not able to speak to changes in psychometric functioning from before to after these exposures. Other studies have examined MI across predictable, but stressful traumatic events, such as before and after deployment into military conflict contexts (Contractor et al., 2017) . Contractor et al. (2017) failed to find support for MI (i.e., factor loadings and/or item thresholds were not equivalent) for the measurement of symptoms of posttraumatic stress disorder before and after deployment (n = 867). In a different study, Miller et al. (2018) examined MI in a measure of sleep problems in spouses of military members (n = 686) before and during periods of deployment. The authors found support for invariance, permitting rankorder and mean-level comparisons across assessments. Thus, exposure to some contexts appears to influence the measurement of some constructs in some individuals, but effects may vary depending on the event, measure, and sample examined. However, studies examining MI before and after significant life events have not yet been conducted spanning adolescence and early adulthood.

Beyond examination of symptoms, there are multiple correlates of internalizing problems that may also be of particular interest. One such construct is IUS (Carleton, 2016a (Carleton, , 2016b Freeston et al., 1994) . We included this as a construct of interest in the current study, as diffuse fears of infection and safety were heightened during the initial outbreak (Center for Disease Control and Prevention, 2020). We are aware of only one study examining MI in the context of Intolerance of Uncertainty Scale (IUS). This study found evidence for partial invariance across sex in adolescents (Dekkers et al., 2017) .

The previous work examining longitudinal changes in the context of major life stressors relied on events with temporally well-defined onsets and offsets. In contrast, during the COVID-19 pandemic, potential exposure to the virus is ongoing and uncertain. Moreover, public health interventions, particularly stay at home orders and quarantines, may have impacts of their own. Thus, the impact of the pandemic may have stronger or more persistent impacts on functioning than events with demarcated onsets and offsets. Here, we examine MI from the assessment of symptoms of depression and anxiety and the construct of IU (Buhr & Dugas, 2002) in a sample composed of participants (n = 505) from two ongoing studies of youth on Long Island, NY, one of the initial epicenters of the pandemic (Hawes et al., 2021) . Initial measures were completed during planned study assessments and follow-up assessments were completed between March 27th and May 15th 2020, spanning roughly the 1.5 months following the New York state shut-down order (Jacobs et al., 2020) . Although there is mixed evidence for specific events (e.g., military deployment; psychological interventions) affecting measurement properties of symptom scales, we hypothesized that the experience of the COVID-19 pandemic would be associated with altered measurement properties of depression, anxiety, and IU. Stony Brook Temperament Study (SBTS) . The SBTS is an ongoing longitudinal study designed to explore early antecedents and pathways to depressive and anxiety disorders in a community-based sample of youth from Long Island, NY (Klein & Finsaas, 2017) . Families with a 3-year-old child were contacted through commercial mailing lists and were eligible to participate if the primary caretaker spoke English and was the child's biological parent, and if the child did not have a significant medical disorder or developmental disability. One child per family was enrolled. Children and their families were invited to participate in follow-up assessments every 3 years. Pre-COVID measures were taken from the 5th wave of data collection. This was the nearest assessment to the start of the pandemic and the wave with the availability of the same measures administered during the pandemic. SBTS participants were between the ages of 14 and 17 years (M = 15.01, SD = 0.37, n = 308) at the pre-COVID wave and 15 and 19 years (M = 17.09, SD = 0.86, N = 335) during COVID survey. The SBTS sample reported on here is predominantly female (52.8%), White/non-Hispanic (91.6%), with college-educated mothers (57.4%). Between 9 months and 4 years elapsed between participants' pre-COVID and during COVID assessments (M = 2.05 years, SD = 0.86). Selfreports were administered in-person for the pre-COVID and remotely for the during COVID assessments.

Adolescence (iPANDA) Project. The iPANDA project is an ongoing, multimethod, longitudinal study aimed at investigating within-subject trajectories of reward sensitivity and depressive symptoms in a large community sample of adolescent girls from Long Island, NY (Burani et al., 2019) . Participants were recruited using a commercial mailing list of families in the Stony Brook area with daughters between the ages of 8 and 14 years, as well as through flyers, online postings, and references from participating families. Families who met eligibility criteria, which included having a daughter in the targeted age range with no known medical or developmental disability, a biological parent willing to participate, and ability to read and write English, were invited to participate in the study. Following the first wave of data collection, the girls were reassessed at 2-year time intervals through adolescence. Pre-COVID measures were taken from the 2nd wave of data collection. This was the nearest assessment to the start of the pandemic and the wave with the availability of the same measures administered during the pandemic. Participants from the iPANDA project were between the ages of 10 and 17 years (M = 14.13, SD = 1.72, n = 160) at the pre-COVID wave and 12 and 22 years (M = 18.38, SD = 1.82, n = 170) at the during COVID survey. The sample is predominantly White/non-Hispanic (82.9%), with a college-educated parent (72.4%). Between 2 and 6 years elapsed between participants' pre-COVID and during COVID assessments (M = 4.19 years, SD = 0.86). Self-reports were administered in-person for the pre-COVID and remotely for the during COVID assessments.

In both samples and at both pre-COVID and during COVID assessments, consent was obtained by parents of participants younger than 18 years prior to contacting the minor directly. After obtaining written informed consent from individuals 18-years-old or older, or assent from individuals younger than 18 years, participants completed a battery of questionnaires, including measures of depression, anxiety, and IU. Study procedures were approved by the Stony Brook University Institutional Review Board.

Children's Depression Inventory. The CDI is a 27-item self-report questionnaire designed to assess symptoms of depression occurring in the past 2 weeks in youth aged 7 to 17 years (Kovacs, 1984) . Items are rated on a 3-point scale, ranging from symptom absent (0) to symptom present (2), and summed to create a total severity of depressive symptoms score. Adequate reliability and validity of the CDI in assessing depression in youth has been demonstrated . In our sample, the CDI possessed excellent internal consistency (α = .90).

The SCARED is a 41-item inventory intended to measure anxiety disorder symptoms over the past month in youth aged 8 to 18 years (Birmaher et al., 1997) . The SCARED is composed of 5 subscales capturing different clusters of anxiety symptoms (somatic/panic, general anxiety, separation anxiety, social phobia, and school phobia) that can be summed to create a total anxiety score. Participants rate the frequency of symptoms from almost never (0) to often (2). The school phobia subscale was not administered at the during COVID assessment, so the current analyses are based on the remaining 37 items. The SCARED has demonstrated good psychometric properties in youth samples (Rappaport et al., 2017) . Internal consistency was excellent in our sample (α = .94).

Intolerance of Uncertainty Scale. The IUS-12 is a short form of the original 27-item IUS. It contains 12 items that are designed to assess emotional, cognitive, and behavioral responses to uncertainty as well as implications of being uncertain and attempts to control the future (Carleton et al., 2007) . Participants rate items on a 5-point Likert-type scale, ranging from not at all characteristic of me (1) to entirely characteristic of me (5). Items can be summed to create a total score. The IUS-12 has been shown to be a reliable and valid measure of IU in clinical and nonclinical samples (Khawaja & Yu, 2010) . In the current sample, internal consistency for the IUS total score was excellent (α = .92). The IUS-12 has been used extensively as a single factor measure, but has also been examined having a two-factor structure with prospective and inhibitory factors.

In line with a model building approach and to identify whether one-factor models were appropriate for testing, we estimated a series of initial single-factor CFAs separately for youth self-reports of depression, anxiety, and IUS at the longitudinal assessment preceding and shortly after the peak of the COVID-19 pandemic in New York. These models are foundational before testing MI. We examined unidimensional models for each measure. If measures did not show at least adequate fit, we examined multidimensional structures. If multidimensional models did not yield adequate fit, we examined model residuals to identify areas of model strain. When indicated, model modifications were made to enhance model fit. Next, models were fit sequentially to evaluate MI. We followed the progression of testing MI used in examinations of longitudinal invariance (Widaman et al., 2010) . In all longitudinal models, we included residual covariances between the same items across time. We tested first for configural invariance, or whether the pattern of significant (i.e., non-zero) factor loadings was similar across assessment waves while permitting the factors to be correlated. These models were specified freely estimating all factor loadings and thresholds and fixing the latent variable variance at 1 for purposes of model identification. Next, we tested for metric invariance, or whether factor loadings for each item were equal across assessments. In these models, we freely estimated the variance of the latent factor at the COVID-19 assessment as fixing factor loadings to be equal across time permits this constraint to be relaxed for one assessment wave. Finally, we tested for scalar invariance, or whether the probability of item endorsement was similar across time, by constraining the thresholds across waves to be equal. In these models, we freely estimated the mean of the latent factor at the COVID-19 assessment, as fixing thresholds to be equal across assessments permits this constraint to be relaxed for one occasion. If all three types of invariance hold, this indicates that the scales measure the same constructs across time on the same scale. Thus, differences in mean trait levels can be interpreted as true score differences, as opposed to differences in measurement.

For models that did not achieve full MI, we tested partial MI, to identify whether some, but not all, items were invariant across informants and/or time. We examined differences in factor loadings using the MODEL CONSTRAINT command in Mplus to assess differences in configural invariance. When factor loadings were identified that did not significantly differ at p < .05, a partial metric invariant model was estimated that included equality constraints on those factor loadings, while allowing other factor loadings to be freely estimated. In this partial metric invariance model, we also used the MODEL CONSTRAINT command to test whether the between threshold parameters significantly differed, to examine the presence of comparable item thresholds. When item thresholds were identified that did not significantly differ at p < .05, a partial scalar invariant model was estimated that included equality constraints on those item thresholds, while allowing other thresholds to be freely estimated. The supplementary materials (https://osf.io/hpxa5/) provide complete information for these model tests.

All models were estimated in Mplus version 8 (Muthén & Muthén, 1998) using the weighted least squares estimator (Flora & Curran, 2004) , which is a robust estimator suited for modeling ordinal data. We evaluated models on two goodness of fit indices. Specifically, we used the comparative fit index (CFI; Bentler, 1990 ) and root mean square error of approximation (RMSEA; Steiger, 1990) . Although cut-offs are somewhat arbitrary (Marsh et al., 2004) , current conventions suggest that excellent model fit is indicated by CFI values ≥.95 (Hu & Bentler, 1999) and RMSEA values ≤.05 (MacCallum et al., 2006) ; and adequate fit is indicated by CFI greater than .90 and a RMSEA between .05 and .08. Model fit comparisons were evaluated by investigating change in both CFI and RMSEA using Chen's (2007) guidelines across levels of invariance (i.e., configural vs. metric; metric vs. scalar). Chen (2007) recommended interpreting reductions in CFI of .01 and RMSEA of .015 as indicating noninvariance (i.e., failure to demonstrate MI). Implementation of the models was supported by the MplusAutomation package (Hallquist & Wiley, 2018) in R (R Core Team, 2018).

We first fit unifactorial models to the items comprising each of the measures (Table 1) . For the CDI and IUS pre-and during COVID-19 assessments, there was at least adequate fit for the individual models, although the pre-COVID-19 IUS model had a RMSEA value higher than would be preferred. There was also equivocal fit for a two-factor model of the IUS with correlated prospective and inhibitory IU dimensions. Inclusion of up to two post hoc residual covariance paths did not substantially improve fit. Thus, we proceeded with the unidimensional model for the IUS. Substantively similar conclusions were reached when analyses were repeated on the two-factor IUS model.

For the SCARED, we first tested a unidimensional model. This was a poor fit to the data for both the pre-and during COVID-19 assessments. We then tested a correlated four-factor model. Fit for the pre-COVID-19 assessment model was good, but the model was a poor fit to the data for the during COVID-19 assessment. Thus, we proceeded to test the individual anxiety subscales of the SCARED separately. The fit for the pre-and during COVID-19 assessments of panic symptoms was very good. For the GAD, separation anxiety, and social anxiety scales, the initial unidimensional models did not consistently fit the data well.

We added one post hoc residual covariance path in the GAD (Items 23 and 28) and social anxiety (Items 3 and 10) models and two post hoc residual covariance paths in the separation anxiety models (Items 16 and 20 and Items 8 and 29). After including these residual covariance paths, the models fit the data at least adequately.

Results of tests of longitudinal MI are shown in Table 2 . For the CDI, the unidimensional model of longitudinal configural invariance was a good fit to the data. Introduction of equality constraints to the factor loadings to like items across assessments did not show a decrement in model fit. However, when applying constraints on the thresholds for testing scalar invariance, the model showed a substantial reduction of the CFI and RMSEA. Direct tests of equivalence of like thresholds showed that 48 of 54 thresholds significantly differed across time. Only 4 of 27 items had Note. CDI = Child Depression Inventory; IUS = Intolerance of Uncertainty Scale; SCARED = Screen for Childhood Anxiety and Related Disorders; GAD = generalized anxiety disorder symptoms; Panic = panic disorder symptoms; and Social Anxiety = social anxiety symptoms; df = degrees of freedom; CFI = comparative fit index; RMSEA = root mean square error of approximation. noninvariant thresholds across time. Estimating a partial scalar invariant model for which item thresholds for those four items were constrained to equality across time showed a good fit to the data. When there were significant differences in item thresholds across time, comparisons showed that the thresholds for pre-COVID assessments were higher than those during the COVID pandemic. For the Panic/Somatic and GAD SCARED subscales, longitudinal configural invariance models were both a good fit to the data. Introduction of equality constraints to the factor loadings to like items across assessments did not show a decrement in model fit. Similarly, when applying equality constraints on the item thresholds, there was not a significant decrement in model fit for either dimension. Thus, there was support for full scalar invariance for these subscales.

For the separation anxiety SCARED subscale, the configural invariance model was a good fit to the data. Introduction of equality constraints to the factor loadings to like items across assessments did not show a decrement in model fit. However, when applying constraints on the thresholds for testing scalar invariance, the model showed a substantial reduction of the CFI and RMSEA. Direct tests of equivalence of like thresholds showed that 6 of 16 thresholds significantly differed across time. Only three of eight items had noninvariant thresholds across time. Estimating a partial scalar invariant model for which item thresholds for those three items were constrained to equality across time also showed a good fit to the data.

For the Social Anxiety SCARED subscale, the configural invariance model was a good fit to the data. Introduction of equality constraints to the factor loadings to like items across assessments led to a decrement in model fit according to the RMSEA. Direct tests of equivalence of like factor loadings showed that two of seven loadings significantly differed across time. The partial metric invariance model was a good fit to the data. Introduction of equality constraints to the thresholds of like items across assessments did not show a Note. Changes in CFI and RMSEA are calculated as differences between the noted models in the comparison column. CDI = Child Depression Inventory; IUS = Intolerance of Uncertainty Scale; SCARED = Screen for Childhood Anxiety and related Disorders; GAD = generalized anxiety disorder symptoms; Panic = panic disorder symptoms; and Social Anxiety = Social Anxiety Symptoms; df = degrees of freedom; CFI = comparative fit index; RMSEA = root mean square error of approximation. decrement in model fit. Because of noninvariance of twofactor loadings, thresholds for those items were not constrained to be equal in this model. Thus, six of eight total items had factor loadings and thresholds constrained to be equal for this subscale and partial scalar invariance was supported for Social Anxiety. For the IUS, the unidimensional model of longitudinal configural invariance was a good fit to the data. Introduction of equality constraints to the factor loadings to like items across assessments did not show a decrement in model fit. However, when applying constraints on the thresholds for testing scalar invariance, the model showed a substantial reduction of the CFI and RMSEA. Direct tests of equivalence of like thresholds showed that 31 of 48 thresholds significantly differed across time. Only 2 of 12 items had noninvariant thresholds across time. When there were significant differences in item thresholds across time, comparisons showed that 19 of the 31 thresholds for pre-COVID assessments were lower than those during the COVID pandemic. Estimating a partial scalar invariant model for which item thresholds for those two items were constrained to equality across time also showed a good fit to the data. Analyses were estimated for a correlated prospective and inhibitory two-factor model. Overall, the same pattern of findings was obtained, with supportive evidence for configural and metric invariance, but a lack of support for scalar invariance.

There have been extensive concerns and multiple reports of increased internalizing symptomatology during the COVID-19 global pandemic (Loades et al., 2020; Vindegaard & Benros, 2020) . In evaluating the magnitude of changes in levels of depression, anxiety, and other relevant and closely related constructs, such as IU, it is critical to identify whether the meaning of the constructs is the same during the pandemic as it was before this period. This requires evaluation of MI (Millsap, 2011; Widaman et al., 2010) . Previous psychometric studies examining changes in the psychometric functioning of measures as a function of exposure to natural disasters have been limited as assessments were only available after the event (Goodman-Williams & Ullman, 2020; Lommen et al., 2014; Platt et al., 2016; Wang et al., 2012) . Some previous research, such as studies of military deployment (Contractor et al., 2017; Miller et al., 2018) , have had both pre-and post-event data. However, the results of those studies have been mixed. The pandemic is a somewhat unique stressor, as its time course is less clearly delimited. Nonetheless, a critical challenge in evaluating the effects of the pandemic on mental health is the availability of psychometrically equivalent assessments both before and during the pandemic. In this study, we capitalized on data from two ongoing longitudinal studies of adolescents and young adults who completed measures of depression, anxiety, and IU several years prior to the pandemic, and around the time of its peak in the New York area. There was support for configural and metric invariance that suggests that the results of research examining the relative stability of symptoms (e.g., multiple linear regression analyses) can be interpreted with confidence from a measurement perspective. However, we found mixed support for scalar invariance. This suggests caution in interpreting mean-level increases in symptoms from before to during the COVID-19 pandemic.

For the SCARED Panic/Somatic and GAD subscales, we found support for scalar invariance. Our results provide strong support for the psychometric validity of tests of mean-level change from pre-to during the COVID-19 pandemic. This indicates that changes in construct scores are not contaminated by differences in measurement properties of the items.

The models for the Social Anxiety subscale also showed support for consistent measurement across time. For this model, however, there were two items ("I feel shy with people I don't know well." and "I am shy") that showed different relationships with the underlying latent factor. The changes in loadings for these items were not systematic, with one item having an increase and the other a decrease in magnitude across time. However, aside from these items, further constraints did not substantially worsen model fit. Thus, there was good evidence for comparability of social anxiety across assessments.

For the separation anxiety subscale, we observed only partial scalar invariance. The partial scalar invariance model had three items for which all item thresholds did not significantly differ across time. Previous work (Hambleton et al., 1991; Kolen & Brennan, 2014; Pokropek et al., 2019) has suggested that factors with only a few items showing invariance of thresholds are robust enough to anchor mean-level comparisons. However, mean-level comparisons may be more tenuous, as few items showed equivalence across assessments.

For the assessment of depression, the loadings of the items were consistent over time. However, the model substantially deteriorated when applying constraints on the item thresholds, indicating that there were differences in these loadings across time. The thresholds for endorsing higher levels of depression were typically lower during, relative to before, the COVID-19 pandemic. This indicates that, given a similar level of depression, participants were more likely to endorse more severe response options during the pandemic. We speculate that media attention to the impact of social distancing on experience of isolation and depressive symptoms may have led reduced stigma surrounding reporting of symptoms. This may have manifest in the lowered thresholds of endorsement of those items, in particular. Thus, the evidence suggests that there are important changes in the measurement properties of depression assessments, complicating the interpretation of meanlevel changes in the CDI as a function of the pandemic.

The conclusions regarding IU are mixed. Similar to depressive symptoms, we failed to find support for scalar invariance, as only two items had thresholds that did not differ across time. Thus, there is not adequate information with which to calibrate scaling across time. In contrast to the results for depression, however, the pattern of changes in item thresholds was not consistent. Over half of the intercepts were lower pre-than during the COVID pandemic. This suggests that, given the same levels of latent IUS, participants were less likely to endorse higher severity responses for some items, but more likely to endorse higher severity options for other items, during the COVID pandemic.

In sum, there is mixed evidence for the equivalence of some psychometric characteristics of measures of depression, anxiety, and IU as adolescents and young adults live through a pandemic. This basic measurement work suggests that it can be challenging to evaluate some of the results from the burgeoning research on mental health outcomes of COVID-19, and potentially for studies of the effects of disasters, traumas, and life stressors on psychological functioning. Our results suggest that although relative levels of symptoms appear to be rated consistently from before to during the pandemic (i.e., as metric invariance was found for nearly all measures/scales), ratings of the absolute levels of some symptoms may not be comparable. Thus, interpreting analyses examining absolute change (i.e., changes in levels) of symptoms requires careful attention as these patterns appear to vary as a function of constructs or measures, and perhaps due to the nature of the event and the sample.

This work benefits from two ongoing longitudinal studies for which assessments using the same instruments were obtained from the same participants before and during the pandemic. Thus, our analyses are sensitive in that we have a fully within-person design. A key strength to emphasize is the locale and timing of the study. Long Island was at one time the world's epicenter of the pandemic, and we were able to capture data in this location and period in this study (Jacobs et al., 2020) .

There also are important limitations to consider. Psychometric functioning for these scales may have differed because of developmental processes, rather than because of changes due to stressful life experiences during the COVID-19 pandemic. However, the large range of ages of respondents and overlap of age ranges in the before to during COVID assessments somewhat mitigates this concern. For anxiety and depression, previous work has supported longitudinal MI (Mathyssek et al., 2013; Olino et al., 2018; Stumper et al., 2019) . Stumper et al. (2019) used the CDI and Olino et al. (2018) used the SCARED. Thus, there is some support that these measures show longitudinal invariance. However, these data come from naturalistic studies of repeated measures without the experience of a shared stressor, whereas the present study focused on change as a function of a pandemic. Unfortunately, there are no published studies of longitudinal MI for the IUS. Thus, we cannot leverage previous studies to discern whether our IUS results may be confounded by development. The pre-and during COVID assessments also differed in administration method. Thus, we cannot rule out that this assessment procedural difference may have affected results. However, a systematic review of studies examining comparability of internet and in-person administration (Alfonsson et al., 2014) concluded minimal impact of the effects of these changes. The results of this work are also limited to the specific measures examined and the models tested. Alternative models, such as network models, may show different patterns of changes across waves (e.g., Curtiss et al., 2018) .

Based on these results, we find that comparisons of depression, IUS, and some, but not all, dimensions of anxiety are not entirely psychometrically equivalent from before to during the COVID-19 pandemic. Thus, caution is required in interpreting comparisons of levels of internalizing problems as a function of COVID-19. While mean-level change of some measures may be difficult to interpret, other indices, such as relative change (i.e., rank-order), should be largely unbiased. These measurement concerns, however, do not take away from individuals' experience of distress during this time. It will be critical to evaluate whether the psychometric functioning of these measures changes further as the pandemic wanes.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Institute of Mental Health Grants R01 MH069942 (Dr. Klein), R01 MH097767 (Dr. Nelson), and R01 MH107495 (Dr. Olino).

Thomas M. Olino https://orcid.org/0000-0001-5139-8571

Aline Szenczy https://orcid.org/0000-0002-2576-7879

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