Measuring Positive Emotion With the Mood and Anxiety Symptom Questionnaire: Psychometric Properties of the Anhedonic Depression Scale

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1 569528ASMXXX / AssessmentKendall et al. research-article2015 Article Measuring Positive Emotion With the Mood and Anxiety Symptom Questionnaire: Psychometric Properties of the Anhedonic Depression Scale Assessment 2016, Vol. 23(1) The Author(s) 2015 Reprints and permissions: sagepub.com/journalspermissions.nav DOI: / asm.sagepub.com Ashley D. Kendall 1, Richard E. Zinbarg 1, Lyuba Bobova 1, Susan Mineka 1, William Revelle 1, Jason M. Prenoveau 2, and Michelle G. Craske 3 Abstract Low positive emotion distinguishes depression from most types of anxiety. Formative work in this area employed the Anhedonic Depression scale from the Mood and Anxiety Symptom Questionnaire (MASQ-AD), and the MASQ-AD has since become a popular measure of positive emotion, often used independently of the full MASQ. However, two key assumptions about the MASQ-AD that it should be represented by a total scale score, and that it measures time-variant experiences have not been adequately tested. The present study factor analyzed MASQ-AD data collected annually over 3 years (n = 618, mean age = 17 years at baseline), and then decomposed its stable and unstable components. The results suggested the data were best represented by a hierarchical structure, and that less than one quarter of the variance in the general factor fluctuated over time. The implications for interpreting past findings from the MASQ-AD, and for conducting future research with the scale, are discussed. Keywords positive emotion, anhedonia, Mood and Anxiety Symptom Questionnaire Anhedonic Depression (MASQ-AD), structure, stability There is currently a great deal of interest in the role of positive emotion (PE) in depression and anxiety. This interest is rooted in research from the 1970s and 1980s showing strong links between the two conditions on both the symptom and diagnostic levels (e.g., Clark & Watson, 1991; Mineka, Watson, & Clark, 1998). Recognition of this substantial overlap naturally gave rise to the question: How can depression and anxiety best be distinguished from each other? Since the 1980s, several theoretical models have been proposed to at least partially address this question. For example, Clark and Watson s (1991) highly influential tripartite model, later reformulated as the integrative hierarchical model (Mineka et al., 1998), situated the symptoms of depressive and anxiety disorders in a three-factor space. High levels of negative emotion (NE) were shown to be common to both types of disorders, largely accounting for their overlap. For instance, irritability is associated with NE and is a symptom of both depression and anxiety (American Psychiatric Association, 2013). In contrast, low levels of PE were shown to be relatively specific to depression. Lassitude, for example, is associated with low PE and is recognized as a symptom of depression but not anxiety (American Psychiatric Association, 2013). Finally, high levels of anxious arousal were shown to be relatively specific to anxiety. Anxious arousal refers to somatic manifestations of tension, such as muscle tension. There is now substantial cross-sectional evidence that low PE is indeed associated with depressive symptoms and disorders, but not with most types of anxiety symptoms or disorders (see Watson & Naragon-Gainey, 2010). An exception is that low PE has also been consistently linked to social anxiety on the symptom and diagnostic levels, although less strongly than with depressive symptoms or disorders (e.g., Brown, Chorpita, & Barlow, 1998). The Mood and Anxiety Symptom Questionnaire (MASQ) was developed by Watson and Clark (1991) to test the three symptom groups proposed in the tripartite model, and has since become a widely used measure of depressive 1 Northwestern University, Evanston, IL, USA 2 Loyola University Maryland, Baltimore, MD, USA 3 University of California at Los Angeles, Los Angeles, CA, USA Corresponding Author: Ashley D. Kendall, Department of Psychology, Northwestern University, 2029 Sheridan Road, Swift 102, Evanston, IL 60208, USA. kendall@u.northwestern.edu

2 Kendall et al. 87 and anxiety symptoms. The Anhedonic Depression scale (MASQ-AD), designed to measure the symptoms of low PE and anhedonia believed to be specific to depression, has played an important role in informing views on the relations between depression and anxiety. For example, a formative study that used the full MASQ to test the tripartite model in five samples found that the MASQ-AD differentiated depression from anxiety (Watson, Weber, Assenheimer, & Clark, 1995). As interest in the study of PE continues to grow, we expect that the MASQ-AD which has enjoyed widespread inclusion as part of the MASQ even in studies not originally designed with a focus on PE (e.g., Zinbarg et al., 2010) will increasingly be turned to as a readily available measure of (low) PE and anhedonia. Given the formative role of the MASQ-AD in illuminating the structure of depression and anxiety, as well as its continued use in research on PE, it is important to thoroughly study its psychometric properties. Although the full MASQ has received some factor analytic attention, focus on the MASQ-AD scale in particular is lacking. To our knowledge, the MASQ-AD items have never been factor analyzed in isolation, leaving their structure independent of the full MASQ unclear. Moreover, the assumption that the MASQ-AD is a symptom measure of time-variant/unstable emotional states as opposed to primarily tapping a timeinvariant/stable dimension of trait positive emotionality has to our knowledge not been tested. The present study thus sought to address two aims. The first was to determine the factor structure and general factor saturation of the MASQ-AD. The second aim was to determine, if there was a general factor, the proportions of variance in the general factor measured over a 3-year period that were unstable versus stable. Time-variance/ instability refers to the variance in the MASQ-AD data that was attributable to changes in rank ordering (rather than absolute change) over time. Time-invariance/stability reflects the variance due to people retaining their positions on the MASQ-AD relative to their peers (i.e., the mean) over time. The period of late adolescence/young adulthood, when the present data were collected, provides a useful context for addressing these aims. From a psychopathology perspective, the period is of particular relevance because it is when many of the symptoms of depression and anxiety increase in frequency (e.g., Burke, Burke, Regier, & Rae, 1990; Hankin et al., 1998). Furthermore, because late adolescence/young adulthood is characterized by a high number of substantial life changes (e.g., beginning college, first cohabitation with a romantic partner; see Arnett, 2000), instability in a construct should be observable. Given that such changes have been related to subsequent increases in depression (see Hammen, 2005), an important feature of which is low PE, it seems likely that PE in particular will fluctuate during this period. Regarding the structure of the MASQ-AD, the scale was originally conceived of as tapping a single underlying (low) PE factor (Watson, Weber, et al., 1995). It consists of a majority of reverse-keyed items designed to directly assess the high end of PE (e.g., Felt really cheerful ), and a minority intended to tap low PE (e.g., Felt really bored ). Studies employing the MASQ-AD have typically used a total scale score (e.g., Light, Heller, Johnstone, & Kolden, 2011; Wacker, Dillon, & Pizzagalli, 2009), suggesting that in a large portion of the literature, a unidimensional model of the scale has been at least implicitly adopted. However, factor analyses conducted on the full MASQ suggested that a unidimensional model was not likely to adequately represent the MASQ-AD data. Watson, Clark, Weber, and Assenheimer (1995) identified three factors consistent with the tripartite model in the full MASQ: a factor representing high NE common to depression and anxiety, a factor representing low PE relatively specific to depression, and a factor representing high anxious arousal relatively specific to anxiety. Although all but one of the reverse-keyed MASQ-AD items loaded on the low PE factor, the majority of the remaining MASQ-AD items loaded on the high NE factor. Factor analyses of the full MASQ in a large undergraduate sample provided similar results (Keogh & Reidy, 2000). The existing literature is thus mixed in its treatment of the structure of the MASQ-AD. Furthermore, to our knowledge it remains unknown whether or not a general factor underlies the MASQ-AD. It is therefore possible that the data are best represented by a multidimensional model, but include a general factor, justifying the use of a total scale score. We tested and compared three competing models of the structure of the MASQ-AD. The first model was that the data would have a unidimensional structure (consistent with the common practice of deriving a total scale score). The second model was that the MASQ-AD would have a multidimensional structure, but would include a general factor. The third model was that the MASQ-AD data would have a multidimensional structure but would have no general factor, indicating that the use of a total scale score was not appropriate. We did not have a priori expectations about the number of group factors that would be included in either multidimensional structure, but rather planned to use exploratory factor analyses to determine the number of group factors. Regarding the stability of the PE construct measured by the MASQ-AD, the scale, like the full MASQ, was designed to assess time-variant symptom experiences (Watson, Clark, et al., 1995; Watson, Weber, et al., 1995). Importantly, however, advances in longitudinal analysis have revealed that all measures of psychological constructs tap, to varying degrees, both unstable and stable variance (see Cole, Martin, & Steiger, 2005; Roberts & DelVecchio, 2000). It follows that the MASQ-AD taps some amount of time-invariant positive

3 88 Assessment 23(1) Figure 1. Visual depiction of a trait state occasion (TSO) model for a construct measured over four time points. Note. T = trait factor; O = occasion factor; S = state factor for four waves indicated by manifest variable Y. Subscripts indicate time points. Source. Adapted from Cole, Martin, and Steiger (2005). emotionality. The present study examined whether the majority of the variance in PE measured longitudinally with the MASQ-AD was time-variant, supporting the use of the scale as a symptom measure. Identifying the proportion of unstable variance in PE could help clarify interpretations of past findings from the MASQ-AD, as well as indicate appropriate future uses for the scale. For example, low PE as measured by the MASQ-AD has been shown to be associated with symptoms of depression, social anxiety, and generalized anxiety (e.g., Prenoveau et al., 2010). Findings such as these are generally considered to be evidence that low PE is itself a symptom of each condition. It is possible, however, that it is largely the stable variance in positive emotionality driving these associations. Without disentangling the stable and fluctuating parts of the construct, it is impossible to conclude whether the low PE experienced by, for example, depressed people reflects a dispositional trait that is characteristic of this group and may be a risk factor, and/or a state departure from normal emotional experience resulting from the depression. Even if the MASQ-AD does primarily tap symptomatic experiences, as intended, the inclusion of an unknown amount of stable variance may compromise the accuracy of the estimates between low PE and mental health outcomes, artificially attenuating or inflating the relations. The present study used the trait state occasion (TSO) latent variable model (Cole et al., 2005) to test the hypothesis that the MASQ-AD taps primarily fluctuating variance. The TSO model is a state-of-the-art method of decomposing the time-variant and time-invariant components of psychological constructs. It is built from indicators (there must be at least two) of a latent construct measured over time (at least three waves; see Figure 1). Participants standing on the latent construct at each time point is represented by the state factor in the TSO model. The variance in each state factor is decomposed entirely into three parts. First, that which is stable across time is represented by a trait factor onto which each state loads at unity. Second, the fluctuating variance is represented by occasion factors, onto which each corresponding state factor loads at unity. Third, an autoregressive structure is imposed on the occasion factors such that each one can be partially predicted by the previous factor through an autoregressive pathway. The autoregressive pathways allow a simplex structure to be modeled, in which measurements taken closer together are more highly correlated than those taken farther apart. Method Participants Participants came from the Northwestern UCLA Youth Emotion Project, a two-site longitudinal study of the risk factors for depression and anxiety. High school juniors were recruited over three academic years from two public high schools, one in suburban Chicago and one in suburban Los Angeles (Zinbarg et al., 2010). Those with high scores on a 22-item version of a screening questionnaire for neuroticism (Eysenck & Eysenck, 1975) were oversampled to overcome statistical problems that can arise

4 Kendall et al. 89 when predicting rare outcomes (e.g., Hauner, Zinbarg, & Revelle, 2014). Neuroticism was selected because it appears to be a risk factor for depression and anxiety (e.g., Clark, Watson, & Mineka, 1994). The sample at baseline consisted of 618 participants (mean age = 16.9 years, SD = 0.43), the majority of whom were medium (23%) or high (59%) scorers on the neuroticism screener and female (69%). This gender imbalance reflected the higher levels of neuroticism that occur among females versus males (Costa, Terracciano, & McCrae, 2001), as well as the greater willingness on the part of females to participate in the Youth Emotion Project (see Zinbarg et al., 2010). Close to half of the sample (48%) identified as Caucasian, with the remaining participants identifying as Hispanic/Latin American (15%), African American (13%), Asian (5%), Pacific Islander (1%), belonging to multiple ethnicities (13%), or other (5%). Procedures and Measures Data for the present study were collected over four waves, spanning a 3-year period. The waves were identified as T1 (baseline) through T4 (the third year). At each wave after baseline, participants were eligible to provide data again starting 10 months after the completion of their previous time point; after 18 months, they could not contribute data until the next wave. The MASQ-AD was administered at each wave as part of the full MASQ along with other questionnaires not used in the present study (see Zinbarg et al., 2010). The MASQ-AD consists of 22 items, each rated on a 5-point scale to indicate the strength of emotional experiences over the past week. Fourteen of these items are reverse-keyed to directly assess high PE (e.g., Felt really happy ). These items were included on the basis of previous research suggesting that items tapping high PE are stronger and purer markers of the underlying factor (see Watson, Clark, & Carey, 1988). The remaining eight items were designed to assess the opposite end of the PE pole (e.g., Felt like nothing was very enjoyable ). The MASQ has demonstrated good psychometric properties in college students (Watson, Weber, et al., 1995). In our sample, coefficient alpha for the MASQ-AD at T1 was.90, and coefficient omega hierarchical (ω h ; Revelle & Zinbarg, 2009; Zinbarg, Revelle, Yovel, & Li, 2005) was.69 (based on the two-factor hierarchical model reported in the Results section). Data Analyses Data analyses were performed in Mplus Version 6.11 (Muthén & Muthén, ) and in R version (R Core Team, 2014) using the psych package (Revelle, 2014) within R. Missing data were accommodated using full information maximum likelihood. Model goodness of fit was evaluated using the following fit indices: root mean square error of approximation (RMSEA; Steiger, 1989), standardized root mean square residual (SRMR), and comparative fit index (CFI; Bentler, 2004). We considered the following cutoffs to conclude good fit between the observed data and the hypothesized model fit: RMSEA 0.06, SRMR 0.08, and CFI 0.95 (see Hu & Bentler, 1998; Yu, 2002). However, we used these cutoffs somewhat flexibly based on the advice of Marsh, Hau, and Wen (2004). Furthermore, the original recommendations for the cutoffs from Hu and Bentler applied to interpreting pairs of fit indices (e.g., RMSEA and CFI), rather than triplets of indices, which were used in the present study. Results Factor Structure of the MASQ-AD To determine the structure of the PE data, we first tested one- through four-factor solutions in the 22 MASQ-AD items from T1 using exploratory factor analyses. The decision to test up to four factors was guided by the eigenvalues of factors derived from these data (see Figure 2). We used parallel analysis (Horn, 1965) comparing the eigenvalues of the observed data to random resamples of the data. It should be noted that the parallel analysis based on the principal components suggested retaining two components. Similarly, results from Velicer s minimum average partial test supported the retention of two factors. All items were treated as continuous using the minimum residuals method, based on our findings that treating the items as categorical using item response theory did not substantially improve model fit (i.e., none of the fit indices changed by more than a hundredth of a point), and doing so is less parsimonious. An oblimin rotation was used to allow for an oblique structure to possibly emerge from the data. In interpreting the factor loadings, we assigned an item to a group factor if its largest loading was on that factor and if the item loading exceeded an absolute value of In the two-factor solution, the 14 reverse-keyed high-pe items loaded on the first factor, which we labeled High PE, and the eight low-pe items loaded on the second factor, referred to as Low PE (see Table 1). In the three-factor solution, the majority of the reverse-keyed items loaded on the first factor. The eight low-pe items all loaded on the second factor. The remaining reverse-keyed items (Item 49 Was proud of myself, Item 35 Felt like I had accomplished a lot, Item 86 Felt really good about myself, and Item 78 Felt hopeful about the future ) were assigned to the third factor. The items in the four-factor solution followed the structure of the three-factor solution, except that two of the items from the first factor (Item 23 Felt like I was having a lot of fun, and Item 30 Looked forward to things with enjoyment ) broke out onto the fourth factor.

5 90 Assessment 23(1) Figure 2. Principal components (PCs) and factors (FAs) derived from the Anhedonic Depression scale data from the Mood and Anxiety Symptom Questionnaire (MASQ-AD; Watson, Weber, Assenheimer, & Clark, 1995) at baseline. We then used confirmatory factor analyses to test the one- through four-factor models in the same data in which the exploratory analyses had been conducted. Capitalization on sampling error could have resulted from conducting the exploratory factor analyses, which determined the group factor structure, in our sample. Our tests of temporal invariance, described below, provided a partial check against capitalizing on sampling error by testing the fit in a wave different from the one from which the exploratory factor analyses were derived. In each confirmatory factor analysis model, items were not allowed to load on more than one of the group factors. However, in each of the two- through four-factor confirmatory models, we included a general factor consisting of all the items. The group factors in each hierarchical (also known as bifactor) model (but see, Revelle and Wilt, 2009, for an alternative discussion of this terminology) were constrained to be orthogonal to the general factor and to each other. The fit indices for the one- through three-factor confirmatory models were as follows: one-factor model RMSEA = with 90% confidence interval (CI) = [0.11, 0.12], SRMR = 0.12, CFI = 0.71; two-factor model RMSEA = with 90% CI = [0.06, 0.07], SRMR = 0.04, CFI = 0.91; three-factor model RMSEA = with 90% CI = [0.05, 0.06], SRMR = 0.04, CFI = The four-factor solution did not converge either with or without the general factor, even when allowed 20,000 iterations. To evaluate the inclusion of the general factor in the twoand three-factor solutions, chi-square difference tests were conducted comparing each model with a similar one that did not include a general factor. The tests revealed that modeling the general factor significantly improved model fit in the case of the two-factor solution, χ 2 (22) = , p <.001, as well as in the case of the three-factor solution, χ 2 (22) = , p <.001. Once it was established that the general factor should be included in each of the group-factor models, chi-square difference tests were conducted to determine which of the onethrough three-factor confirmatory models provided the best fit. Results from the difference tests indicated that compared with the one-factor model, both the two-factor model with a general factor, χ 2 (22) = , p <.001, and the three-factor model with a general factor, χ 2 (22) = , p <.001, were a better representation of the data. The two- and threefactor models could not be directly compared because they did not stand in a nested relation to each other. We therefore removed the general factor and allowed the group factors to be oblique, which set the two- and three-factor models in a nested relation. A chi-square comparison indicated that the three-factor model provided significantly better fit than did the two-factor model, χ 2 (2) = , p <.001. Ultimately, we selected the two-factor hierarchical model to represent the data. This decision was guided in part by the fact that including two versus three group factors

6 Kendall et al. 91 Table 1. Factor Pattern of the Anhedonic Depression Scale Items From the Mood and Anxiety Symptom Questionnaire (MASQ-AD; Watson, Weber, Assenheimer, & Clark, 1995) After Oblimin Rotation for a Two-Factor Exploratory Extraction at Baseline. Item High positive emotion Low positive emotion 58. Felt really up or lively Felt like I was having a lot of fun Felt really good about myself Felt really happy Was proud of myself Felt like I had a lot to look forward to Felt like I had a lot of energy Felt like I had accomplished a lot Felt like I had a lot of interesting things to do Looked forward to things with enjoyment Seemed to move quickly and easily Felt optimistic Felt cheerful Felt hopeful about the future Felt like there wasn t anything interesting or fun to do Felt like nothing was very enjoyable Felt withdrawn from other people Felt unattractive Felt really slowed down Felt really bored Felt like it took extra effort to get started Thought about death or suicide Note. Loadings in boldface indicate the factor to which an item was assigned. The correlation between the high positive emotion and low positive emotion factors was r = was more parsimonious. Furthermore, the retention of two factors was supported by the parallel analysis based on principal components, as well as by the results of the minimum average partial test. At the same time, a reasonable case could be made for selecting a three-factor hierarchical model. If a model with more factors fits reliably (i.e., significantly) better than one with fewer factors, the resulting improvement in explanatory power could be deemed worth the reduction in parsimony. Although our focus in this article is on the two-factor hierarchical model, we therefore also report findings for the three-factor hierarchical model in the Notes section. An ω h value of 0.69 provided support for the presence of a moderate to strong general factor in the two-factor hierarchical model, indicating that 69% of the variance in the total score was attributable to the general factor. We examined the metric and configural invariance of the general factor at the first and last time points, expecting that any change in the structure should be the greatest over the longest period of time. Imposing across-time equality constraints on the factor loadings in the model of the two-factor hierarchical structure using subscales corresponding to the two group factors as indicators did not result in a significant decrement in model fit, χ 2 (2) = 0.07, p >.05. The fit indices for the metric invariant model were RMSEA = with 90% CI = [0.00, 0.00], SRMR = 0.00, and CFI = Those for the configural invariant model were RMSEA = with 90% CI = [0.00, 0.00], SRMR = 0.00, and CFI = We could thus conclude that any changes in the general PE factor over time were due to changes in the level of the latent construct, rather than changes in the structure, and hence the meaning, of that measure. 1 Stability and Change in PE as Measured by the MASQ-AD Having determined the structure of the MASQ-AD data, we next sought to decompose the stability and change in these data using the TSO model. In order to avoid fitting a TSO model in which a very large number of parameters relative to the number of participants would have to be estimated, we obtained subscale scores for each of the two group factors at each wave. Additionally, each wave included a single factor corresponding to the general factor, on to which each of the two subscales loaded. Across-time equality constraints were imposed on the autoregressive pathways between occasion factors, on the pathways from occasions to states, and on pathways from each state to each subscale (e.g., the pathways from each state to each High PE subscale were set equal to each other over time). The variance

7 92 Assessment 23(1) Figure 3. Trait-only version of the trait state occasion (TSO) model for four waves of Anhedonic Depression scale data from the Mood and Anxiety Symptom Questionnaire (MASQ-AD; Watson, Weber, Assenheimer, & Clark, 1995) with standardized path estimates. Note. All paths were estimated and all were significant. T = trait positive emotion factor; O = occasion positive emotion factor; S = state positive emotion factor; LO = low positive emotion factor; HI = high positive emotion factor. Subscripts indicate time points. for the trait factor was set to 1, the variance for the first occasion factor was set to 1 with the remaining occasion factor variances set equal to each other, and the residual variance for each state factor was set to 0. Setting the state factor residual variances to 0 forced the residual at each time point into the corresponding occasion, as the occasion factors were the residuals of the state factors after accounting for the time-invariant higher order factor. The fit indices for the full TSO model were RMSEA = with 90% CI = [0.08, 0.12], SRMR = 0.08, CFI = A follow-up chi-square difference test was performed to compare the full TSO model with a trait-only model in which the autoregressive pathways were removed, RMSEA = with 90% CI = [0.08, 0.12], SRMR = 0.09, CFI = Comparisons could not be made to an autoregressive occasion-only model in which the trait component was removed, as this model did not converge. The full TSO model did not fit significantly better than did the trait-only model, χ 2 (1) = 0.51, p >.05, and was less parsimonious, leading us to select the trait-only model to represent the data (see Figure 3). The model comparisons thus indicated that there was a significant trait component in PE as measured by the MASQ-AD in our sample over 3 years, and that there was not a significant simplex structure to these data. Finally, we calculated the proportions of variance accounted for by the components of the trait-only model. Less than a quarter of the variance (23%), on average, fluctuated over the study period, as represented by the proportion of the occasion components of the model that was independent of the prior occasions. More than three quarters of the variance (77%), on average, was stable, being attributable to the trait component. 2 Discussion Our analyses of MASQ-AD data collected over a 3-year period of late adolescence/young adulthood revealed that the data were best represented by a hierarchical structure that included two group factors and a general factor. Furthermore, we showed that less than a quarter of the variance in the general factor fluctuated over time. What is the Structure of the MASQ-AD Data? The presence of a moderate to strong general factor in our analyses of the MASQ-AD data suggested that there was a unitary PE construct underlying the scale, as opposed to the scale measuring multiple orthogonal constructs in the absence of a construct common to all its items. This finding, together with the ω h estimate of.69, justifies the use of a total scale score. However, the results were inconsistent with a unidimensional conceptualization of the MASQ-AD, as they showed that two group factors labeled High PE and Low PE should be included along with the general PE factor. We thus suggest that future studies using the MASQ-AD consider structuring the data using this hierarchical factor model.

8 Kendall et al. 93 Do the Low PE Items Merit Special Consideration? In studies that choose to emphasize the use of a total MASQ-AD score, we advise special consideration of the items that loaded on the Low PE factor in the present study. The presence of a moderate to strong general PE factor in our work justified the inclusion of both group factors. However, the subscales that served as indicators of the Low PE factors had lower loadings on the general PE factor (standardized b = ) than did subscales corresponding to the High PE factors (b = ; see Figure 3). It is possible that this discrepancy reflects the inadvertent infusion of NE into the items designed to tap low PE (e.g., Thought of death or suicide ). High NE is known to relate broadly to all types of depression and anxiety (e.g., Clark & Watson, 1991). Thus, when interpreting past work relating low PE to these conditions, the possibility that high NE might be contributing to the associations should be considered. Going forward, we advise running models relating the MASQ-AD to depression and anxiety outcomes with and without the low-pe items in order to help rule out this possibility. Alternatively, in future studies in which there are a sufficient number of participants per parameter to use a hierarchical representation of the data at each wave, the hierarchical factor model could be used to address this concern (Uliaszek et al., 2009). Is the MASQ-AD a Symptom Measure? Although the MASQ-AD is designed to measure symptomatic experiences of low PE, our longitudinal analyses suggested that less than a quarter of the variance was attributable to the fluctuating components of our model. This raises the possibility that the associations between low state PE as measured by the MASQ-AD and mental health outcomes demonstrated by past studies (e.g., Prenoveau et al., 2010; Watson, Clark, et al., 1995) may have been attributable to a substantial degree to low levels of enduring trait positive emotionality. Whenever possible, it would be prudent for future studies examining symptomatic experiences of low PE to (a) collect multiple measures of the MASQ-AD over time, (b) use a latent variable modeling technique such as the TSO model to decompose the stable and fluctuating sources of variance, and (c) relate only the time-variant components of the model to the desired outcome measures. At a minimum, the implications of the substantial trait variance likely included in the MASQ-AD to interpreting the findings from future studies should be discussed when relevant. Considering these findings in a broader assessment context, it seems likely that a strong trait component would be found in any symptom measure. A study by Prenoveau et al. (2011), conducted in the same sample and over the same time period as the present analyses, supported this idea. Prenoveau et al. applied the TSO model to symptom measures of depression, social anxiety, specific phobias, neuroticism, and extraversion. They found that the percentages of variance attributable to the trait component for the anxiety and personality constructs were roughly equal to each other (73% to 84%), and even greater than the variance explained by the trait component for depression (46%), an important feature of which is anhedonia. Extracting Trait Variance From the MASQ-AD An additional benefit of decomposing the fluctuating and stable components of a scale such as the MASQ-AD is that doing so produces a pure measure of trait variance from a symptom scale. This may be especially valuable when a trait scale was not originally included in a longitudinal study, but later came to be desired. And far from representing a compromised measure, extracting pure trait variance in such a study enhances the likelihood of identifying associations between a trait factor and an outcome, as compared with relying on a trait scale administered only once that is contaminated by the time-variant dimension of a construct (Cole et al., 2005; Roberts & DelVecchio, 2000). There is evidence, for example, that isolating the trait variance in measures of extraversion/positive emotionality and neuroticism/negative emotionality using the TSO model enhances the effects of these constructs in predicting changes in depression and social anxiety symptoms (Naragon-Gainey, Gallagher, & Brown, 2013). Limitations and Conclusions Five main limitations to the present study should be noted. First, the use of a primarily female sample of older adolescents and young adults oversampled for high neuroticism limits the generalizability of the findings. Simulation studies have demonstrated, however, that such oversampling does not bias effect sizes to a substantial degree and, under some conditions, can prevent seriously biased effect sizes that might result without oversampling (e.g., Hauner et al., 2014). Second, the fit indices for the TSO model did not all meet the conventional cut-points for adequate fit. 3 Third, the data collection method in any study specifically, the frequency of administration of a scale and total length of the follow-up period is likely to influence the estimates of fluctuation and stability in a construct represented using the TSO model. Had we analyzed data collected over a longer period, our calculations would likely have revealed a smaller trait estimate, owing to the fact that the correlation between the first and last waves would have been lower. Had we sampled levels of PE more frequently than once per year, we would likely have shown a larger autoregressive component in our solution because the correlations between

9 94 Assessment 23(1) time points closer together would have been larger. Fourth, our analyses pertained to changes in PE based on rank ordering, but not on absolute changes in PE. Evaluating rank ordering was necessary to address the aims of the present study. It will be useful for future research on normative developmental trends to also examine absolute changes in PE. Finally, although we examined a developmental period characterized by substantial life changes, and it seems plausible that these changes would differentially affect PE in different individuals, we do not know if this was the case. The present study nonetheless aids in the interpretation of past work with the MASQ-AD, and lays the groundwork for productive future applications of the scale to the study of PE. We hope that our analyses will encourage hierarchical factor modeling of MASQ-AD data, special consideration for the low-pe items, and decomposition of the fluctuating and stable sources of variance in PE measured by the MASQ-AD over time. Properly modeling the data in these ways will not only help to clarify past work on the relations between symptoms of depression and anxiety but also enable new insights into the role of state and trait PE in mental health outcomes. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by National Institute of Mental Health Grants R01 MH65651 and R01 MH65652 to Michelle G. Craske, Susan Mineka, and Richard E. Zinbarg. Notes 1. A similar conclusion could be drawn from tests of metric and configural invariance of the general factor in the three-factor hierarchical model. When across-time equality constraints were imposed on the factor loadings, with subscales corresponding to the three group factors as indicators, there was no significant decrement in model fit, χ 2 (3) = 1.78, p >.05. In the metric invariant model, the fit indices were RMSEA = with 90% CI = [0.00, 0.03], SRMR = 0.04, and CFI = The fit indices for the configural invariant model were RMSEA = with 90% CI = [0.00, 0.04], SRMR = 0.02, and CFI = A TSO model was similarly applied to the data based on the three-factor hierarchical structure, with subscale scores corresponding to each of the three group factors. 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