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. Author manuscript; available in PMC: 2023 Feb 15.
Published in final edited form as: J Affect Disord. 2021 Dec 2;299:215–222. doi: 10.1016/j.jad.2021.11.069

Evaluating the Item-Level Factor Structure of Anhedonia

Julia A C Case 1, Holly Sullivan-Toole 1, Matthew Mattoni 1, Ross Jacobucci 2, Erika E Forbes 3, Thomas M Olino 4
PMCID: PMC8766928  NIHMSID: NIHMS1763470  PMID: 34864118

Abstract

Background:

Anhedonia has long been theorized to be a multidimensional construct, focusing on domains of reward stimuli and temporal relationship to reward. However, little empirical work has directly examined whether there is support for this assertion.

Methods:

The study used data from young adults from four independent samples (n = 2098). Participants completed multiple measures of anhedonia.

Results:

We used rigorous conducted exploratory and confirmatory factor analyses on items from six commonly used anhedonia measures to examine dimensions underlying anhedonia. Results suggested a four-factor solution with factors reflecting social reward, social disinterest, status/achievement, and physical/natural reward. The identified factors reflected broad content domains of pleasure, but not specific reward processes. The four factors were modestly associated with one another, suggesting a weak common underlying anhedonia trait that manifests across multiple dimensions. Factor scores were associated with personality measures, reward-related indices, and depression symptoms, supporting the validity of the factors.

Limitations:

Participants were all young adults and we assessed anhedonia only at the level of self-report.

Conclusion:

Anhedonia is a multidimensional construct. However, the dimensions of anhedonia only distinguish domains of, but not temporal processes of anhedonia. Future work should continue to refine the structures underlying the construct of anhedonia through iterative theory- and data-driven research and examine associations between anhedonia and clinical outcomes.

Keywords: anhedonia, social, physical, reward, factor analysis

Anhedonia is broadly defined as the diminished capacity to experience pleasure (Klein, 1984). Anhedonia is a transdiagnostic symptom. It is a common feature of schizophrenia and depression (American Psychiatric Association, 2013) and is present in substance use disorder (Garfield et al., 2014) and post-traumatic stress disorder (Kashdan et al., 2006). Anhedonia is associated with poor outcomes across disorders and is one of the most difficult symptoms to treat using traditional/existing means (Craske et al., 2014; McMakin et al., 2012; Sarkar et al., 2015). Thus, understanding the nature of anhedonia is critical.

Anhedonia has historically been described as a multidimensional construct, with early writings discriminating between physical and social dimensions (Chapman et al., 1976), and later work discriminating between temporal phases of reward processing (Gard et al., 2006; Klein, 1984). Yet, measures of anhedonia typically emphasize unidimensional scores that presume homogeneous item content. Moreover, factor analyses of individual measures have identified heterogenous content within single and across multiple instruments (Cicero et al., 2016; Gooding & Pflum, 2014; Reise et al., 2011). Thus, these measures may actually include multiple discrete facets of anhedonia. The current study examined the factor structure of anhedonia focusing on items from several widely used scales, with the goal of identifying a parsimonious factor structure of this multidimensional construct.

The Chapman Scales for Physical and Social Anhedonia (CPAS and CSAS; Chapman et al., 1976) were developed to assess domains of the construct (later revised; RPAS and RSAS; Chapman et al., 1995; Eckblad et al., 1982). In their initial study of anhedonia in schizophrenia, Chapman, Chapman, & Raulin (1976) identified three pleasure domains: physical pleasures (i.e., pleasures of eating, touching, feeling, sex, temperature, movement, smell, and sound), interpersonal pleasure (i.e., non-physical pleasures of being with people, talking, exchanging expressions of feelings, doing things with others, competing, loving, and interacting in multiple other ways); and other pleasures that are neither physical nor interpersonal (i.e., intellectual pleasure; the pleasure of achievement). However, despite identifying these three pleasure domains, the published measures included only scales assessing physical and social anhedonia, asking participants to respond true or false to self-statements regarding feeling pleasure from normally pleasurable stimuli and activities within these two domains.

Whereas the Chapman RPAS and RSAS scales were developed to assess anhedonia within schizophrenia, the Fawcett-Clark Pleasure Scale (FCPS; Fawcett et al., 1983) was developed to assess anhedonia in patients with depression. Patients were asked to imagine their degree of pleasure in response to a variety of presumably enjoyable situations. The scale expanded upon the prior binary-response scales by Chapman, Chapman, & Raulin (1976) by relying on a five-point Likert scale for item responses, permitting greater variability in normative ranges of hedonic capacity. The FCPS included item content for physical pleasures, social pleasures, and other pleasures. Several items included content across different domains of pleasure (e.g., “You sit with good friends, huddled close to a warm bonfire and roasting marshmallows on a chilly night,” reflects social, physical, and food-related pleasure). Despite disparate item content, this measure is used as a single sum score measure.

The Snaith-Hamilton Pleasure Scale (SHAPS; Snaith et al., 1995) was developed to reduce demographic-based biases, particularly concerns that assessments of anhedonia may be affected by social class, sex, age, dietary habits, and nationality of respondents. The measure has been referred to as the “gold standard” for measuring anhedonia in depression (Rizvi et al., 2016) and is also used frequently with non-clinical samples (Franken et al., 2007; Langvik 2018; Leventhal 2015; Nakonezny 2010; Nakonezny 2015). The SHAPS assesses pleasure capacity by asking participants to agree or disagree with statements about hedonic response to a wide range of pleasurable experiences, including interests and pastimes, social interactions, sensory experiences, and food and drink. Participants respond based on their experience of pleasure “in the last few days,” using a four-point Likert scale. However, scoring recommendations are to collapse across different magnitude responses, thereby truncating scores to reflect either agreeing or disagreeing with statements. Although items include content related to both physical and social anhedonia, the SHAPS yields a single total score. Additionally, although the instructions for the scale describe it as measuring “ability to experience pleasure in the last few days,” each item starts with, “I would enjoy…” suggesting a future-orientation, which may lead to inconsistencies in how items are interpreted or may bias participants’ interpretations toward anticipatory pleasure.

Although these three scales are commonly used in research on anhedonia, they fail to consider temporal facets of anhedonia. Klein (1984) highlighted the distinction between anticipatory and consummatory pleasure, arguing that anticipatory pleasure is more closely linked to motivation and goal-directed behavior, whereas consummatory pleasure is more closely linked to satiation, or a resolution of desire. Animal studies examining the neurobiology of reward have identified similar distinctions between “wanting” and “liking” reward (Berridge & Robinson, 2003) and human neuroimaging studies have identified somewhat dissociable neural circuitry associated with processing the anticipation and consummation of reward (Oldham et al., 2018). These temporal distinctions are acknowledged in the anhedonia criterion for major depressive episodes, which differentiates loss of interest from loss of pleasure. Building upon these distinctions, Gard and colleagues (2006) developed the Temporal Experience of Pleasure Scale (TEPS) that assesses both anticipatory and consummatory pleasure on a six-point Likert scale. Similar to the FCPS and the SHAPS, items span multiple pleasure domains. Although the anticipatory and consummatory subscales of the TEPS are moderately, positively correlated, it has been argued that these subscales measure separable constructs (Gard et al., 2006).

Lastly, Gooding and Pflum (2014) developed the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS) to assess the temporal aspects of social anhedonia. The ACIPS was intended to be an update of the RSAS that reflected more contemporary values, behaviors, and linguistic style (Gooding & Pflum, 2014). The ACIPS is scored on a six-point Likert scale. The ACIPS items were designed to reflect both anticipatory and consummatory pleasure within the social domain by assessing an individual’s ability to look forward to interactions with other people (i.e., anticipatory social pleasure), as well as their ability to experience pleasure in social/interpersonal interactions when they occur (i.e., consummatory social pleasure). Despite theoretical distinctions between temporal aspects of interpersonal pleasure, initial factor analyses of the ACIPS did not distinguish between anticipatory and consummatory social pleasure. The authors hypothesized that temporal distinctions may not be saliently discriminated for self-report assessments of social and interpersonal pleasure.

Based on the operationalization of measures of anhedonia, there is an assumption that there is a common underlying trait influencing manifestations across multiple presentations (Ritsner, 2014). In contrast to this assumption, there are conceptual arguments that anhedonia is a multidimensional construct comprised of different domains of pleasurable content and different temporal phases of reward processing. Despite strong theoretical suppositions, few extensive tests have been conducted to identify the structure of anhedonia using a data-driven approach. Thus, the current study examined the factor structure of hedonic capacity across multiple commonly used anhedonia measures. Specifically, we completed exploratory and confirmatory factor analyses on items from the RPAS, the RSAS, the FCPS, the SHAPS, the TEPS, and the ACIPS. Based on theoretical conjecture and the constructs that motivated the development of these measures, we anticipated our analyses would find factors reflecting social and physical domains, as well as factors distinguishing temporal phases of reward processing.

Methods

Samples

This study used a combined dataset comprised of four samples. Aggregated demographics are reported here. All participants were undergraduates at large universities, compensated for their participation with course credit. The total number of participants in the aggregated sample was 2098. The sample was comprised almost entirely of young adults (mean age 20.15 ± 2.99), was majority female (66.4% female; 32.8% male) and represented a diversity of ethnic/racial groups (64.8% White; 13.2% Black; 13.1% Asian; 4.1% Multi-racial; 4.2% Other). Further details of each sub-sample are reported below, along with the self-report measures each sub-sample completed.

Sample 1

Data were taken from a psychometric study assessing approach motivation system measures (Olino et al., 2018). This sub-sample was comprised of 698 undergraduates (mean age 18.76 ± 1.21; 63.6% female; 20.6% racial minority) from a large northeastern university.

Sample 2

Data were taken from self-report data collected as part of a more extensive study investigating behavioral performance and self-report measures of approach motivation (Olino et al., 2021). This sub-sample was comprised of 400 undergraduates (mean age 20.49 ± 2.10; 75.5% female; 41% ethnic minority) from a large northeastern university.

Sample 3

Data were taken from self-report data collected as part of a more extensive study investigating approach and avoidance in reward learning (Case & Olino, 2020). This sub-sample was comprised of 199 undergraduates (mean age 21.36 ± 3.88; 69.8% female; 48.7% ethnic minority) from the same large northeastern university as Sample 2.

Sample 4

Data were comprised of self-report measures provided by 801 individuals from a larger subject pool of undergraduates (mean age 20.92 ± 3.59; 63.4% female; 41.7% ethnic minority) from the same large northeastern university as Samples 2 and 3.

Self-Report Measures: Anhedonia

Revised Scale for Physical Anhedonia (RPAS) and Revised Scale for Social Anhedonia (RSAS)

The RPAS consists of 61 items that assess anhedonia in terms of the pleasure derived from typically pleasurable stimuli like food, sex, and pleasant surroundings, via statements that participants rate as true or false (Chapman et al., 1976). The RPAS was administered to Samples 1, 2, and 3.

The RSAS consists of 40 items that assess social anhedonia, or the pleasure derived from social and interpersonal experiences, via statements that participants rate as true or false (Chapman et al., 1976). The RSAS was administered to Samples 1, 2, 3, and 4.

Temporal Experience of Pleasure Scale (TEPS)

The TEPS consists of 18 items that assess anticipatory and consummatory experiences of pleasure across two sub-scales (Gard et al., 2006). Participants rate items on a six-point Likert scale ranging from “very false for me” to “very true for me.” The TEPS was administered to Samples 1, 2, 3, and 4.

Fawcett-Clark Pleasure Scale (FCPS)

The FCPS consists of 36 items that assess pleasure sensitivity by asking participants to rate imagined hedonic responses to hypothetical pleasurable situations (Fawcett et al., 1983). Participants rate items on a five-point Likert scale ranging from “no pleasure at all” to “extreme and lasting pleasure.” The FCPS was administered to Samples 1, 2, and 3.

Snaith-Hamilton Pleasure Scale (SHAPS)

The SHAPS consists of 14 items that assess hedonic capacity (Snaith et al., 1995), via participants rating items on a four-point Likert scale, ranging from “strongly disagree” to “strongly agree”. Following the recommended scoring, both disagree response options were coded as a 1 and both agree response options were coded as a 0 and these re-coded values were used in the factor analyses. The SHAPS was administered to Samples 1, 2, and 3.

Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS)

The ACIPS consists of 17 items assessing the respondents’ ability to experience pleasure in the interpersonal domain (Gooding & Pflum, 2014). Participants rate items on a six-point Likert scale, ranging from “very false for me” to “very true for me.” The ACIPS was administered to Samples 3 and 4.

Self-Reported Criterion Constructs

To assess convergent and discriminant validity of uncovered latent factors, we examined associations with self-report constructs of personality, reward-related indices, and psychopathology.

Multidimensional Personality Questionnaire – Brief Form (MPQ-BF).

Normal personality was assessed using the MPQ-BF (Patrick et al., 2002; Tellegen & Waller, 2008), a 155 item self-report measure that uses true-false response items. The MPQ-BF assesses broad domains of personality as well as specific narrow band traits. In the present paper, we examined scales from the broad dimensions of Positive Emotionality (PE; with narrow dimensions of Well-Being [14-items], Social Potency [14-items], Achievement [12-items], Social Closeness [12-items]), Negative Emotionality (NE; with narrow dimensions of Stress Reaction [15-items], Alienation [12-items], Aggression [12-items]), Constraint (CON; with narrow dimensions of Harm Avoidance [12-items], Traditionalism [12-items], Control [12-items]), and Absorption (ABS; 12-items). These broad dimensions and primary trait scales on the MPQ-BF are highly correlated with the full form version of the MPQ (Patrick et al., 2002). The MPQ-BF was administered to Samples 1, 2, and 3. Internal consistency for these scales in the aggregated sample were all at least good (αs > 0.80; mean: .88; range: .81-.93).

Big Five Inventory (BFI).

Big five personality traits were measured using the BFI (John et al., 1991), a 44-item self-report measure that uses a 5-point Likert-type scale, ranging “disagree strongly” to “agree strongly.” The BFI assesses the big five traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Higher scores indicate greater levels of the assessed trait. The BFI was administered to Samples 1, 2, and 4. Internal consistency for the scales in the aggregated sample were all at least good (αs > 0.80; mean: .86; range: .82-.89).

Behavioral Inhibition System/Behavioral Activation System Scales (BIS/BAS).

Reward and punishment sensitivity was assessed using the BIS/BAS (Carver & White, 1994), a 24-item self-report measure that uses a 4-point Likert-type scale, ranging from “very true for me” to “very false for me.” The BIS/BAS includes scales to represent unidimensional behavioral inhibition (BI) and behavioral activation, as well as three behavioral activation subscales: Reward Responsiveness, Drive, and Fun Seeking. The current factor structure has displayed satisfactory psychometric properties across previous administrations (Leone et al., 2001), although this is not uniformly found in the literature (e.g., Pagliaccio & Barch, 2016). Higher scores indicate greater levels of the assessed trait. The BIS/BAS was administered to Samples 1 and 2. Internal consistency for the scales in the aggregated sample were all at least adequate (αs > 0.75; mean: .81; range: .76-.84).

Patient Reported Outcomes Measurement Information System – Depression (PROMIS-Dep).

Current depressive symptomatology was also assessed using the PROMIS-Dep (Pilkonis et al., 2011), a 28 item self-report measure that uses a 5-point Likert-type scale, ranging from “never” to “always.” Higher scores indicate a greater severity of depressive symptoms over the previous week. The PROMIS-Dep was administered to Samples 1, 2, 3, and 4. Internal consistency for this scale in the current aggregated sample was excellent (α = 0.96).

State Anxiety.

Participants’ state anxiety was assessed using the state items from the State Trait Anxiety Inventory (STAI; Spielberger et al., 1983). The state anxiety subscale of the STAI is a 20-item self-report assessment of anxiety at the current moment (e.g., statements like: “I feel calm”). Responses are rated on a 4-point scale from “not at all” to “very much so.” In previous studies, the STAI has shown internal consistency (all αs > 0.90; (Kabacoff et al., 1997; Spielberger et al., 1983). The STAI was administered to Samples 1, 2, and 3. Internal consistency for this scale in the current aggregated sample was excellent (α = 0.95).

Data analysis

Before conducting analyses, we examined all 186 items contributed by the included anhedonia measures. Items were screened a priori for redundant content across scales. Authors (J.C., H.S.T., M.M., and T.M.) agreed on 18 instances where two or three items had redundant content. For these identified redundant items, we retained a single item from the redundant set using the following guidelines: negatively worded or reverse coded items were discarded; items with fewer response options were discarded (e.g., SHAPS, RPAS, or RSAS items); items were discarded if they appeared to be more cognitively demanding compared to the other items in the set; and/or items were discarded if they appeared to potentially contain multiple constructs within a single item. In addition to the items sets that were identified as redundant a priori, two item pairs were also identified as redundant due to having polychoric correlation values > |.70|. From each pair, the item with the lowest standard deviation was discarded. A total of 31 items were discarded across all instruments for a set of 155 items retained for further analyses.

All analyses were conducted using Mplus 8.4 (Muthén & Muthén, 1998) and were facilitated using the MplusAutomation package (Hallquist & Wiley, 2018) in R (R Core Team, 2018). We also estimated parallel analyses on polychoric correlation matrices using the fa.parallel function in the psych package (Revelle, 2018). To focus analyses on items that informed the factors, we estimated exploratory factor analysis (EFA) models iteratively. We estimated parallel analyses to identify the maximum number of factors to retain. We inspected factor loading solutions up to the maximum number of factors and identified items that failed to show substantial loadings on any factors. Our original preregistration relied on a threshold of loadings > |.35|; however, factor solutions were not interpretable. Thus, analyses were repeated with the more stringent threshold of loadings > |.50|. After removing those items, we repeated the process until all included items had substantial loadings on at least one factor. All EFAs were estimated using the oblique geomin rotation. Models were estimated with 50 random start values to identify a global minimum fit function value.

After reducing the item pool, the factor solutions from the EFA were inspected to identify the preferred solution. We considered overall model fit, including information from the parallel analysis, indices of model fit, including the comparative fit index, the root mean square error of approximation, and conceptual evaluation of the solutions. The parallel analysis is a simulation, using the same qualities of the observed data, to identify eigenvalues of a comparably sized, but uncorrelated, dataset (Horn, 1965). Eigenvalues from simulated and real data are compared to provide information about the upper limit number of factors to be retained. 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., 1996) and adequate fit is indicated by CFI greater than .90 and a RMSEA between .05 and .08. After examination in an EFA framework, we estimated a CFA model removing cross-loadings that did not exceed |.50|. Finally, associations between the latent factors and individual difference characteristics were estimated within CFA models. These models estimated correlations between the anhedonia latent constructs and observed self-report variables. Given the large number of correlations tested, we applied the Benjamini-Hochberg correction to adjust associations for multiple tests (Benjamini & Hochberg, 1995).

Results

Item pool selection

The initial EFA was estimated for 155 items. After inspecting the loadings, we found that five items failed to load substantively on any factor. Thus, the EFA was estimated again with these items removed. Ultimately, we iteratively conducted four EFAs in order to identify items that loaded on at least one factor substantively. Details on the items removed from the analysis at each stage of iterations is available in the supplementary material. Through these iterations, a total of 52 items were trimmed from the list of items, leading to final analysis of 103 items.

Model Selection

For the final pool of items, the parallel analysis showed that the observed eigenvalues exceeded those of the simulated eigenvalues for the first 4 factors. The EFA failed to return admissible solutions for the 8 and 9 factor solutions. Those solutions were not considered further. Full information about factor loadings for the 3–6 factor solutions are available in the Supplementary Material.

Table 1 shows model fit information for these solutions. The model fit for the EFA solutions with one and two factors were a poor fit to the data according to the CFI, but not the RMSEA. The three-factor solution was a marginal fit to the data. Solutions with four and greater factors were good fits to the data (CFI > .93, RMSEA < .023). The four-factor solution identified factors reflecting the following: Factor 1 reflected interpersonal/social reward and extraversion (20 items with loadings > .50); we refer to this as the social reward factor. Factor 2 reflected status, achievement, and social praise (22 items with loadings > .50); we refer to this as the status/achievement factor. Factor 3 reflected social isolation and disinterest (20 items with loadings > .50); we refer to this as the social disinterest factor. Factor 4 reflected content related to pleasant sensory and nature experiences (14 items with loadings > .50); we refer to this as the physical/natural reward factor. In this solution, 27 items failed to show a loading greater than |.50| on any of the four factors, and no items had cross-loadings greater than |.50| onto multiple factors. Factor correlations were modest (ranging from |.09| to |-.41|, with an average absolute correlation of .24). Given the good fit of the four-factor model and the result of the parallel analysis, the four-factor model was retained for further analysis.

Table 1:

Model fit information for exploratory factor analyses.

Factors Parameters χ2 df CFI RMSEA (90% CI) SRMR
1 103 25371.565 5150 0.735 0.043 (0.043–0.044) 0.142
2 205 17015.006 5048 0.843 0.034 (0.033–0.034) 0.105
3 306 11406.497 4947 0.915 0.025 (0.024–0.026) 0.091
4 406 10189.21 4847 0.93 0.023 (0.022–0.024) 0.072
5 505 9094.176 4748 0.943 0.021 (0.020–0.022) 0.068
6 603 8434.261 4650 0.95 0.020 (0.019–0.020) 0.064
7 700 7814.357 4553 0.957 0.018 (0.018–0.019) 0.062
10 985 6200.551 4268 0.975 0.015 (0.014–0.015) 0.053
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Solutions for the 8 and 9 factors solutions were inadmissible

Model Evaluation

We estimated a post hoc confirmatory factor analysis including only the items that showed substantive (> |.50|) loadings on their respective factors. Items that did not load on any of the factors were not included. The CFA model was an adequate fit to the data (χ2(2768) = 7659.11, p < .0001; CFI = .913; RMSEA = .029 [.028-.030]). All items loaded significantly and strongly on their respective factors. Associations between latent factors are shown in Table 2.

Table 2:

Correlations between latent factors.

Social Reward Status/Achievement Social Disinterest Physical/Nature
Social Reward
Status/Achievement .31*
Social Disinterest −.41* −.16*
Physical/Nature .09 .25* −.21*
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*

p < .05

Associations between anhedonia latent factors and self-report measures are shown in full in Table 3. Here, we highlight associations between latent constructs and self-report measures exceeding |.30| for convergent validity and smaller than |.10| for divergent validity. The social reward factor showed moderate, positive, and significant correlations with Positive Emotionality, Well-being, Social Closeness, BAS total score, and Fun Seeking. The social reward factor showed very weak correlations with Negative Emotionality, Aggression, Alienation, Stress Reaction, Control, Traditionalism, Neuroticism, and STAI. The status/achievement factor showed moderate, positive, and significant correlations with the BAS total score, Reward Responsiveness, Fun Seeking, and Positive Emotionality. The status/achievement factor had only a small association with the MPQ Achievement scale. The status/achievement factor showed very weak correlations with theoretically unrelated constructs including Negative Emotionality, Aggression, Alienation, Stress Reaction, Control, Harm Avoidance, BIS Total score, and STAI. The social disinterest factor showed moderate to strong correlations with positively and negatively valenced constructs in expected directions. Specifically, the social disinterest factor was positively associated with Negative Emotionality, Alienation, and Stress Reaction, and was negatively associated with Social Closeness, Positive Emotionality, Well-being, Extraversion, and Agreeableness. In line with divergent validity, the social disinterest factor showed very weak correlations with theoretically unrelated constructs MPQ–Achievement, Control, Traditionalism, Openness, BIS Total score, and STAI. The physical/natural reward factor showed moderate, positive correlations with Absorption and Openness. Supporting divergent validity, this factor showed very weak correlations with Social Closeness, Constraint, Stress Reaction, Control, Traditionalism, BIS–Total, BFI–Neuroticism, PROMIS-D, and STAI.

Table 3:

Associations between factors and self-report constructs.

Social Reward Status/Achievement Social Disinterest Physical/Nature
MPQ–Positive Emotionality 0.404 ** 0.319 ** −0.500** 0.246**
MPQ–Wellbeing 0.360 ** 0.284** −0.381** 0.258**
MPQ–Achievement 0.176** 0.157** −0.031 0.181**
MPQ–Social Closeness 0.407 ** 0.198** −0.711** 0.082*
MPQ–Social Potency 0.202** 0.208** −0.199** 0.129**
MPQ–Negative Emotionality −0.059 −0.058* 0.546 ** −0.165**
MPQ–Aggression −0.084* −0.016 0.291** −0.228**
MPQ–Alienation −0.084* −0.043 0.484 ** −0.132**
MPQ–Constraint 0.129* 0.125** −0.114** −0.062*
MPQ–Stress Reaction 0.009 −0.061* 0.450 ** −0.057
MPQ–Control 0.063 0.047 −0.075* 0.025
MPQ–Harm Avoidance 0.109* 0.053 −0.123** −0.119**
MPQ–Absorption 0.137* 0.166** 0.238** 0.373 **
MPQ–Traditionalism 0.093* 0.167** −0.026 −0.036
BAS–Total 0.311 ** 0.412 ** −0.222** 0.230**
BAS–Drive 0.194** 0.274** −0.095* 0.117**
BAS–Reward Responsiveness 0.263** 0.405 ** −0.292** 0.177**
BAS–Fun Seeking 0.354 ** 0.309 ** −0.138** 0.268**
BIS–Total 0.117* 0.093* −0.085* 0.027
BFI–Extraversion 0.241** 0.269** −0.332** 0.139**
BFI–Agreeableness 0.232** 0.277** −0.368** 0.223**
BFI–Conscientiousness 0.157** 0.233** −0.219** 0.155**
BFI–Neuroticism −0.013 −0.121** 0.253** −0.056*
BFI–Openness 0.195** 0.161** −0.078* 0.362 **
PROMIS−D −0.115** −0.123** 0.265** −0.046
STAI −0.039 0.081* 0.091* −0.042
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Note:

*

p < .05;

**

p < .001.

p-values were Benjamini-Hochberg corrected for multiple comparisons. Bolded correlations exceed |0.30|. MPQ = Multidimensional Personality Questionnaire – Brief Form. BAS = Behavioral Activation. BIS = Behavioral Inhibition. BFI = Big Five Inventory. PROMIS-D = Patient Reported Outcomes Measurement Information System – Depression. STAI = State Trait Anxiety Inventory.

Discussion

This study examined the factor structure of anhedonia from commonly used anhedonia measures, including the RPAS, RSAS, FCPS, SHAPS, TEPS, and ACIPS. Exploratory factor analysis identified a four-factor solution as a good fit, and a reduced, parsimonious post-hoc CFA model was also a good fit to the data. Identified factors reflected social reward, status/achievement, social disinterest, and physical/natural reward. Individual factor scores showed convergent and divergent validity with a range of measures of personality, reward-related indices, and psychopathology symptom measures. Specifically, the social reward factor was associated with positive emotionality, social closeness, and behavioral activation but only weakly related to negative emotion and neuroticism constructs. The social disinterest factor was positively associated with negative emotion, stress reaction, and alienation, and negatively associated with social closeness, extraversion, and positive emotionality, among others. The status/achievement factor was associated with behavioral activation and positive emotionality but only weakly associated with negative emotion-related constructs. Finally, the physical/natural reward factor was positively associated with absorption and openness.

We expected that we would find factors consistent with different stimulus domains, including social and physical anhedonia, and consistent with different temporal phases of reward processing, including anticipation and receipt. Factors reflecting those patterns were partially found. Consistent with early distinctions of social and physical anhedonia (Chapman et al., 1976), we found distinctions between modalities of reward. Two of the identified factors centered on social components of reward, and the other factors focused on the reward domains of achievement, and enjoyment of physical sensations and nature. Thus, consistent with the earlier anhedonia measures, these results suggest that individuals may be particularly sensitive to discriminating between social and physical domains of pleasure.

Human and animal neuroscience research has identified distinctions between anticipation and receipt of rewards (e.g., Berridge & Robinson, 2003; Oldham et al., 2018). Recent work has also examined changes in state anhedonia, rather than focusing solely on trait anhedonia (e.g., Winer et al., 2014). Although we expected to see factors distinguishing temporal phases of reward processing, we did not find such factors. Previous studies of self-report measures have had mixed success in finding anticipation and consummatory factors. While the TEPS (Gard et al., 2006) found temporal distinctions within their item pools, the ACIPS (Gooding & Pflum, 2014), which focused on the social domain, did not. It is possible that individuals may be better able to discriminate between temporal processes for physical, relative to social stimuli. However, in the context of multiple additional items, our solutions did not find factors reflecting distinct temporal processes.

Recent efforts to assess dimensions of anhedonia support similar conclusions. Rizvi and colleagues (2015) developed and validated the Dimensional Anhedonia Rating Scale (DARS) that measures desire, motivation, effort, and consummatory pleasure across hedonic domains, including hobbies, food/drink, social activities, and sensory experiences. Khazanov and colleagues (2019) developed the Positive Valence Systems Scale (PVSS) to assess reward valuation, reward expectancy, effort valuation, reward anticipation, action selection, initial responsiveness, and reward satiation with items spanning multiple kinds of rewards, including food, physical touch, outdoors, positive feedback, social interactions, hobbies, and goals. Although these more recently developed measures were designed to target focal anhedonic processes (e.g., motivation vs. reward consummation), both measures identified factors that reflected different domains of reward stimuli, rather than distinct reward processes. Thus, despite using a different item set, our findings are consistent with results from these more recently developed measures.

We found modest associations between each of our factors. The social reward, social disinterest, and status/achievement factors all had weak correlations with each other, with the largest association between social reward and social disinterest (r = −.41). The physical/nature factor was not significantly correlated with the social reward factor but had weak associations with the status/achievement (r = .25) and social disinterest (r = −.21) factors. An assumption of much of the work on anhedonia is that there is a common underlying trait influencing manifestations across multiple dimensions (Ritsner, 2014). This could be reflected by a single factor explaining all item responses, or more strongly associated inter-factor correlations. However, the poor fit of the single factor model and modest inter-factor associations lead to questions about the coherence of multiple domains of anhedonia using self-report.

Overall, we found convergence in associations between dimensions of reward (e.g., positive emotionality, extraversion, and behavioral activation) and our latent factors of anhedonia. There were modest associations between positive emotionality and extraversion with the social reward and social disinterest factors. The positive emotionality and extraversion indices include social closeness and potency, reflecting similar content. A previous study, involving a subset of these participants, found that social closeness and social anhedonia could be modeled on the same latent dimension (Olino et al., 2016). This same pattern may be reflected in the current study by the strong negative correlation (−.71) found between the social disinterest factor and MPQ social closeness. Together, these results suggest that social disinterest/social anhedonia and social closeness may lie on a single trait-like continuum.

We also found moderate associations between behavioral activation, particularly reward responsiveness, and the status/achievement factor. Interestingly, we did not find a strong association between the MPQ Achievement scale and the status/achievement factor. This is likely explained by items within the MPQ Achievement scale focusing more on enjoying the process of work or drive towards success as opposed to the items within our status/achievement factor, which focused on social status and enjoying the outcomes of labor or luck. Some dimensions of negative emotionality were associated with the social disinterest factor, which is partially consistent with anhedonia being included on the loss domain of the RDoC Negative Valence Systems (Insel et al., 2010). Lastly, the only strong associations to emerge with the physical/nature anhedonia factor were with MPQ Absorption and BFI Openness to Experience. Absorption and Openness to Experience are associated with psychosis and psychosis-proneness. Thus, it is possible that the physical/nature factor may reflect additional shared vulnerability with psychosis (Grazioplene et al., 2016; Wilson & Sponheim, 2014).

The current work benefits from a fairly large sample that included participants from different universities, and the assessment battery included many of the anhedonia measures frequently used in the literature. We relied on rigorous modeling methods to comprehensively evaluate model solutions. We also examined associations between identified factors and multiple additional correlates. However, the work should also be considered in light of its limitations. First, although the sample was fairly large, it was also relatively homogenous. Participants were all undergraduate students, with generally lower levels of psychopathological symptoms. Thus, there are outstanding questions about the generalizability of the findings to other developmental and samples with more clinically significant levels of psychopathology. Second, we assessed anhedonia exclusively using self-report measures; future work should incorporate additional methods, such as experience sampling, behavioral, physiological, and/or neurobiological measures of hedonic responses to test the cross- and multi-level structure of the construct. Third, our presented models were fully exploratory. Thus, the results were fully data driven and could be specific to the age of participants. However, we also estimated some hybrid models (i.e., exploratory structural equation models) with a priori factors for anticipatory and consummatory anhedonia items and exploratory factors to model other variation. These models did not improve fit of the model and yielded factors with many weak loadings on the a priori factors. Fourth, it is possible that some factors may reflect methods, rather than substantive factors. Several of the identified factors were dominated by items from a single scale. The social reward factor had 15 of its 22 items from the ACIPS, the status/achievement factor was entirely comprised of FCPS items, and the social disinterest factor had 17 of its 20 items from the RSAS. The physical/natural reward factor was more heterogenous in terms of scales, with 5 TEPS items, 7 RPAS items, and 2 FCPS items. This method effect possibly results from scales being developed with the purpose of measuring a particular aspect of anhedonia, such as social or physical anhedonia.

This work contributes to the long-term goal of understanding the structure of anhedonia at the level of self-reports. In doing so, this work will also enhance other work seeking to examine associations with assessments of anhedonia at other units of analysis (e.g., brain structure or function, task-based behavior; Patrick & Hajcak, 2016). With greater precision in the focal constructs at each unit of analysis, stronger mappings of associations across units of analysis are more likely to be found.

Overall, we examined dimensions of anhedonia though exploratory and confirmatory factor analysis of individual items across multiple commonly used measures of anhedonia. We identified a four-factor solution with factors representing social reward, social disinterest, status/achievement, and physical/natural reward. These factors reflected broad domains of content but did not show distinctions at the processes-level. Factors showed only modest associations with each other, calling into question the widespread assumption of a common underlying anhedonia trait reflecting multiple dimensions of anhedonia. The identified factors showed patterns of convergent and divergent association with personality and other reward-related indices, supporting the validity of the latent factors. Future work should continue to refine the latent structures comprising the umbrella term ‘anhedonia’ through iterative theory-driven and data-driven research. As anhedonia self-report measures generally appear to capture reward stimulus domains, but not lower-level processes, future work could advance the goal of integrating across units of analysis by developing new self-report measures alongside relevant measures of behavior and/or brain function to assess anhedonia constructs at the processes-level.

Supplementary Material

1

Highlights:

  • Examined the factorial structure of anhedonia using items from multiple instruments.

  • Anhedonia was represented by four factors that were modestly correlated.

  • Factors showed distinct patterns of associations with other self-report measures.

  • Dimensions of anhedonia reflect domains, not temporal processes.

Acknowledgements

Funding:

This work was supported by National Institute of Mental Health Grants R01 MH107495 (Dr. Olino) and T32 MH018951 (Dr. David A. Brent)

Role of the funding source

The funding sources did not have a role in the study design, collection, analysis, and interpretation of data; writing of the report; or in the decision to submit the article for publication.

Footnotes

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Contributor Information

Ross Jacobucci, University of Notre Dame

Erika E. Forbes, University of Pittsburgh

Thomas M. Olino, Temple University

References

  1. Benjamini Y, & Hochberg Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. [Google Scholar]
  2. Berridge KC, & Robinson TE (2003). Parsing reward. Trends in Neurosciences, 26(9), 507–513. 10.1016/S0166-2236(03)00233-9 [DOI] [PubMed] [Google Scholar]
  3. Carver CS, & White TL (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social Psychology, 67(2), 319–333. 10.1037/0022-3514.67.2.319 [DOI] [Google Scholar]
  4. Case JAC, & Olino TM (2020). Approach and avoidance patterns in reward learning across domains: An initial examination of the Social Iowa Gambling Task. Behaviour Research and Therapy, 125. 10.1016/j.brat.2019.103547 [DOI] [PubMed] [Google Scholar]
  5. Chapman JP, Chapman LJ, & Kwapil TR (1995). Scales for the measurement of schizotypy. In Schizotypal personality (pp. 79–106). Cambridge University Press. 10.1017/CBO9780511759031.006 [DOI] [Google Scholar]
  6. Chapman LJ, Chapman JP, & Raulin ML (1976). Scales for physical and social anhedonia. Journal of Abnormal Psychology, 85(4), 374–382. 10.1037/0021-843X.85.4.374 [DOI] [PubMed] [Google Scholar]
  7. Cicero DC, Krieg A, Becker TM, & Kerns JG (2016). Evidence for the Discriminant Validity of the Revised Social Anhedonia Scale From Social Anxiety. Assessment, 23(5), 544–556. 10.1177/1073191115590851 [DOI] [PubMed] [Google Scholar]
  8. Craske MG, Niles AN, Burklund LJ, Wolitzky-Taylor KB, Vilardaga JCP, Arch JJ, Saxbe DE, & Lieberman MD (2014). Randomized controlled trial of cognitive behavioral therapy and acceptance and commitment therapy for social phobia: Outcomes and moderators. Journal of Consulting and Clinical Psychology, 82(6), 1034–1048. 10.1037/a0037212 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Eckblad ML, Chapman LJ, Chapman JP, & Mishlove M (1982). The revised social anhedonia scales.
  10. Fawcett J, Clark DC, Scheftner WA, & Gibbons RD (1983). Assessing anhedonia in psychiatric patients: The Pleasure Scale. Archives of General Psychiatry, 40(1), 79. [DOI] [PubMed] [Google Scholar]
  11. Franken IHA, Rassin E, & Muris P (2007). The assessment of anhedonia in clinical and non-clinical populations: Further validation of the Snaith-Hamilton Pleasure Scale (SHAPS). Journal of Affective Disorders, 99(1–3), 83–89. 10.1016/j.jad.2006.08.020 [DOI] [PubMed] [Google Scholar]
  12. Gard DE, Gard MG, Kring AM, & John OP (2006). Anticipatory and consummatory components of the experience of pleasure: A scale development study. Journal of Research in Personality, 40(6), 1086–1102. 10.1016/j.jrp.2005.11.001 [DOI] [Google Scholar]
  13. Garfield JBB, Lubman DI, & Yücel M (2014). Anhedonia in substance use disorders: A systematic review of its nature, course and clinical correlates. The Australian and New Zealand Journal of Psychiatry, 48(1), 36–51. 10.1177/0004867413508455 [DOI] [PubMed] [Google Scholar]
  14. Gooding DC, & Pflum MJ (2014). The assessment of interpersonal pleasure: Introduction of the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS) and preliminary findings. Psychiatry Research, 215(1), 237–243. 10.1016/j.psychres.2013.10.012 [DOI] [PubMed] [Google Scholar]
  15. Grazioplene RG, Chavez RS, Rustichini A, & DeYoung CG (2016). White matter correlates of psychosis-linked traits support continuity between personality and psychopathology. Journal of Abnormal Psychology, 125(8), 1135–1145. 10.1037/abn0000176 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hallquist MN, & Wiley JF (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 621–638. 10.1080/10705511.2017.1402334 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Horn JL (1965). A RATIONALE AND TEST FOR THE NUMBER OF FACTORS IN FACTOR ANALYSIS. Psychometrika, 30, 179–185. 10.1007/BF02289447 [DOI] [PubMed] [Google Scholar]
  18. Hu L, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  19. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, & Wang P (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7), 748–751. 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]
  20. John OP, Donahue EM, & Kentle RL (1991). The big five inventory—Versions 4a and 54. Berkeley: University of California, Berkeley, Institute of Personality and Social Research. [Google Scholar]
  21. Kabacoff RI, Segal DL, Hersen M, & Van Hasselt VB (1997). Psychometric properties and diagnostic utility of the Beck Anxiety Inventory and the State-Trait Anxiety Inventory with older adult psychiatric outpatients. Journal of Anxiety Disorders, 11(1), 33–47. 10.1016/s0887-6185(96)00033-3 [DOI] [PubMed] [Google Scholar]
  22. Kashdan TB, Elhai JD, & Frueh BC (2006). Anhedonia and emotional numbing in combat veterans with PTSD. Behaviour Research and Therapy, 44(3), 457–467. 10.1016/j.brat.2005.03.001 [DOI] [PubMed] [Google Scholar]
  23. Khazanov GK, Ruscio AM, & Forbes CN (2019). The Positive Valence Systems Scale: Development and Validation: Assessment. 10.1177/1073191119869836 [DOI] [PubMed] [Google Scholar]
  24. Klein D (1984). Depression and anhedonia. In Clark DC& Fawcett J (Eds.), Anhedonia and affect deficit states (pp. 1–14). PMA Publishing. [Google Scholar]
  25. Leone L, Perugini M, Bagozzi RP, Pierro A, & Mannetti L (2001). Construct validity and generalizability of the Carver-White Behavioural Inhibition System/Behavioural Activation System Scales. European Journal of Personality, 15(5), 373–390. [Google Scholar]
  26. Leventhal AM, Chasson GS, Tapia E, Miller EK, & Pettit JW (2006). Measuring hedonic capacity in depression: A psychometric analysis of three anhedonia scales. Journal of Clinical Psychology, 62(12), 1545–1558. 10.1002/jclp.20327 [DOI] [PubMed] [Google Scholar]
  27. MacCallum RC, Browne MW, & Sugawara HM (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. 10.1037/1082-989X.1.2.130 [DOI] [Google Scholar]
  28. Marsh HW, Hau K-T, & Wen Z (2004). In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler’s (1999) Findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. 10.1207/s15328007sem1103_2 [DOI] [Google Scholar]
  29. McMakin DL, Olino TM, Porta G, Dietz LJ, Emslie G, Clarke G, Wagner KD, Asarnow JR, Ryan ND, Birmaher B, Shamseddeen W, Mayes T, Kennard B, Spirito A, Keller M, Lynch FL, Dickerson JF, & Brent DA (2012). Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment-resistant depression. Journal of the American Academy of Child and Adolescent Psychiatry, 51(4), 404–411. 10.1016/j.jaac.2012.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Muthén LK, & Muthén BO (1998). Mplus User’s Guide. Eighth Edition. Muthén & Muthén. [Google Scholar]
  31. Oldham S, Murawski C, Fornito A, Youssef G, Yücel M, & Lorenzetti V (2018). The anticipation and outcome phases of reward and loss processing: A neuroimaging meta-analysis of the monetary incentive delay task. Human Brain Mapping, 39(8), 3398–3418. 10.1002/hbm.24184 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Olino TM, Case JAC, Versella MV, Cerra CE, & Genaro BG (2021). Associations between individual differences in approach motivation and effort-based task performance. Personality and Individual Differences, 169, 109903. 10.1016/j.paid.2020.109903 [DOI] [Google Scholar]
  33. Olino TM, Horton LE, & Versella MV (2016). A comparison of psychometric and convergent validity for social anhedonia and social closeness. Psychological Assessment, 28(11), 1465–1474. 10.1037/pas0000291 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Olino TM, McMakin DL, & Forbes EE (2018). Toward an Empirical Multidimensional Structure of Anhedonia, Reward Sensitivity, and Positive Emotionality: An Exploratory Factor Analytic Study. Assessment, 25(6), 679–690. 10.1177/1073191116680291 [DOI] [PubMed] [Google Scholar]
  35. Pagliaccio D, & Barch DM (2016). Chapter 2 - Early Life Adversity and Risk for Depression: Alterations in Cortisol and Brain Structure and Function as Mediating Mechanisms. In Frodl T (Ed.), Systems Neuroscience in Depression (pp. 29–77). Academic Press. 10.1016/B978-0-12-802456-0.00002-9 [DOI] [Google Scholar]
  36. Patrick CJ, Curtin JJ, & Tellegen A (2002). Development and validation of a brief form of the Multidimensional Personality Questionnaire. Psychological Assessment, 14(2), 150–163. [DOI] [PubMed] [Google Scholar]
  37. Patrick CJ, & Hajcak G (2016). RDoC: Translating promise into progress. Psychophysiology, 53(3), 415–424. 10.1111/psyp.12612 [DOI] [PubMed] [Google Scholar]
  38. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D, & PROMIS Cooperative Group. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283. 10.1177/1073191111411667 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing,. URL https://www.R-project.org/. [Google Scholar]
  40. Reise SP, Horan WP, & Blanchard JJ (2011). The challenges of fitting an item response theory model to the Social Anhedonia Scale. Journal of Personality Assessment, 93(3), 213–224. 10.1080/00223891.2011.558868 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Revelle W (2018). psych: Procedures for Personality and Psychological Research (1.8.12) [Computer software]. https://CRAN.R-project.org/package=pscyh
  42. Ritsner M (Ed.). (2014). Anhedonia: A Comprehensive Handbook Volume I: Conceptual Issues And Neurobiological Advances. Springer; Netherlands. 10.1007/978-94-017-8591-4 [DOI] [Google Scholar]
  43. Rizvi SJ, Pizzagalli DA, Sproule BA, & Kennedy SH (2016). Assessing anhedonia in depression: Potentials and pitfalls. Neuroscience and Biobehavioral Reviews, 65, 21–35. 10.1016/j.neubiorev.2016.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Rizvi SJ, Quilty LC, Sproule BA, Cyriac A, Michael Bagby R, & Kennedy SH (2015). Development and validation of the Dimensional Anhedonia Rating Scale (DARS) in a community sample and individuals with major depression. Psychiatry Research, 229(1), 109–119. 10.1016/j.psychres.2015.07.062 [DOI] [PubMed] [Google Scholar]
  45. Sarkar S, Hillner K, & Velligan DI (2015). Conceptualization and treatment of negative symptoms in schizophrenia. World Journal of Psychiatry, 5(4), 352–361. 10.5498/wjp.v5.i4.352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Snaith RP, Hamilton M, Morley S, Humayan A, Hargreaves D, & Trigwell P (1995). A scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale. The British Journal of Psychiatry: The Journal of Mental Science, 167(1), 99–103. [DOI] [PubMed] [Google Scholar]
  47. Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, & Jacobs GA (1983). Manual for the State-Trait Anxiety Inventory. Consulting Psychologists Press. [Google Scholar]
  48. Tellegen A, & Waller NG (2008). Exploring Personality Through Test Construction: Development of the Multidimensional Personality Questionnaire. In The SAGE Handbook of Personality Theory and Assessment: Volume 2—Personality Measurement and Testing (pp. 261–292). SAGE Publications Ltd. 10.4135/9781849200479 [DOI] [Google Scholar]
  49. Wilson S, & Sponheim SR (2014). Dimensions underlying psychotic and manic symptomatology: Extending normal-range personality traits to schizophrenia and bipolar spectra. Comprehensive Psychiatry, 55(8), 1809–1819. 10.1016/j.comppsych.2014.07.008 [DOI] [PubMed] [Google Scholar]
  50. Winer ES, Veilleux JC, & Ginger EJ (2014). Development and validation of the Specific Loss of Interest and Pleasure Scale (SLIPS). Journal of Affective Disorders, 152–154, 193–201. 10.1016/j.jad.2013.09.010 [DOI] [PubMed] [Google Scholar]

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