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Confirmatory factor analysis of psychotic-like experiences in a general population sample
Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Contents lists available at ScienceDirect
Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
Short communication
Confirmatory factor analysis of psychotic-like experiences in a general population sample Sebastian Therman a,b,n, Tim B. Ziermans b,c a
Department of Health, National Institute of Health and Welfare, Helsinki, Finland Department of Clinical Child and Adolescent Studies, Leiden University, Leiden, The Netherlands c Leiden Institute for Brain and Cognition, Leiden, The Netherlands b
art ic l e i nf o
a b s t r a c t
Article history: Received 26 May 2015 Received in revised form 16 October 2015 Accepted 15 December 2015
Psychotic-like experiences (PLEs) are sub-psychotic expressions of the psychosis continuum. Several studies have suggested multifactorial models, including a bifactor model, of the putative PLEs assessed with the popular Community Assessment of Psychic Experiences (CAPE) questionnaire. Our confirmatory results in a gender-balanced population of adolescents and young adults support a three-factor Paranoia–Delusions–Hallucinations structure of PLEs, which excludes Grandiosity and Common Paranormal Beliefs. The best latent models achieved excellent fit when taking the categorical nature of the responses into consideration. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Latent structure Psychotic experiences Positive symptoms
1. Introduction Psychotic-like experiences (PLEs) are the subclinical variants of the psychosis continuum, which is also referred to as the “extended psychosis phenotype” (van Os and Linscott, 2012). As intermediate expressions of the continuum, PLEs offer complimentary ways to study psychosis risk as well as the etiology of psychotic disorders. In assessing PLEs, questionnaires are cost-effective in large-scale research, but require careful design and analysis to maximize their utility. For example, the relative severity of individual items and response alternatives needs to be assessed and taken into account by analyzing categorical data with suitable methods. This is especially important when the number of items is limited. Furthermore, PLEs do not constitute a single dimension of psychopathology, but appear to represent several correlated dimensions, as do the symptoms of manifest psychosis and trait schizotypy. Latent factor analysis has therefore been a preferred method for structural analysis of these phenomena – especially for the “positive” symptoms or experiences (Mason, 2014). In addition, factor analysis makes it possible to quantify the relevance of individual questionnaire items to their respective content domains, that is, the latent dimensions. Moreover, confirmatory factor analysis quantifies how well the data fit the theoretical constructs.
The Community Assessment of Psychic Experiences (CAPE-42: Stefanis et al., 2002; Hanssen et al., 2003) has seen widespread use in general populations, and there have been several attempts to identify the underlying latent dimensions of its 20 positive PLEs. After the initial validation study (Stefanis et al., 2002), which found that these items load reasonably well on a single factor, several publications on various clinical and general population populations have proposed 3- to 5-dimensional models of the positive items, based on exploratory (Yung et al., 2006, 2009; Armando et al., 2010, 2012; Barragan et al., 2011; Therman et al., 2014) and confirmatory (Brenner et al., 2007; Wigman et al., 2011, 2012; Capra et al., 2013) factor analysis. Furthermore, one study (Núñez et al., 2015) has proposed a bifactor extension of the Capra et al. (2013) model, which includes a general factor in addition to the specific symptom dimensions. Consequently, there are ample symptom structures available, providing scientists with the opportunity to move beyond exploratory research. In this study, we test and compare existing models of the positive symptoms of the CAPE in confirmatory analyses, to identify which one best fits the data from an independent population sample. Furthermore, we take the ordinal nature of the items into account, following recommendations of modern measurement theory. 2. Methods 2.1. Questionnaire
n
Correspondence to: P.O. Box 30, FI-00271 Helsinki, Finland. E-mail addresses: sebastian.therman@thl.fi (S. Therman), [email protected] (T.B. Ziermans).
The CAPE-42 (available online at http://cape42.homestead.com/) was designed to address, in the general population, experiences similar to those seen in psychotic
http://dx.doi.org/10.1016/j.psychres.2015.12.023 0165-1781/& 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i
S. Therman, T.B. Ziermans / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
disorders. The Positive subscale comprises 20 items. Responses to the CAPE are given on a four-category scale for each item, separately for frequency and distress. As previous studies have used only the frequency responses (Never – Sometimes – Often – Almost Always), we also ignored the secondary distress responses here. 2.2. Participants A gender-balanced and both geographically and occupationally diverse Swedish population sample (n¼ 1012) of ages 12–35 (mean 24), recruited from a voluntary panel of over 100,000 citizens, completed the CAPE online. The participant recruitment has been previously described in detail (Ziermans, 2013), and conformed to the Declaration of Helsinki. 2.3. Statistical analyses The previously published models were tested with confirmatory factor analysis for ordinal measures, using the weighted least squares means and variances adjusted (WLSMV) algorithm on the polychoric correlation matrix with default parameters in Mplus 7.2 (Muthén and Muthén, 2012). Response thresholds were estimated separately for each item with an inverse normal transformation of the cumulative response proportions. Item loadings on their assigned factors were also estimated from the data. The primary measure of interest was the comparative fit index (CFI), accompanied by the root mean square error of approximation (RMSEA) and weighted root mean square residual (WRMR). Not all models that were tested in this study included all 20 items (see Table 1); some had excluded items with a poor fit. In addition, one proposed model (Capra et al., 2013) had two variations, the first assigning the items to three dimensions, and the second specifying four additional cross-loadings.
3. Results The fit indices of the tested models are reported in Table 1. The single-dimensional model of the “positive” CAPE symptoms was borderline adequate, while all 4- and 5-dimensional models showed good fit, with RMSEA values under 0.05 and CFI values over 0.90. The best fit with a CFI of 0.98 was demonstrated by the second Capra et al. (2013) model, which specified three dimensions (Paranoia, Delusions, and Hallucinations), and four pairs of correlated item errors, but excluded five items. An excellent model fit was also achieved for all 20 items by the Wigman et al. (2011) and Therman et al. (2014) models (CFI 0.97 and 0.96, respectively). The Núñez et al. (2015) orthogonal bifactor model with a general factor and three subdimensions had an even better fit than the correlated-dimensions models (CFI 0.99). Nevertheless, in secondary full-information analyses using Maximum likelihood numerical integration, the bifactor model had a slightly poorer Bayesian Information Criterion (BIC) value than the corresponding Capra et al. (2013) model, indicating that the improved fit was at the expense of parsimony. In line with previous findings (Wigman et al., 2011; Núñez et al., 2015), the general latent factor and the Delusions subfactor decreased with age (r¼ 0.29 and 0.24, respectively), but not the other subfactors.
4. Discussion Our results show that current state-of-the-art models of positive PLEs fit the data very well in the general population. Furthermore, taking the categorical nature of responses into account led to considerably better fit, with approximately 0.08 higher CFIs in the same data (Ziermans, 2013). This methodological refinement is also essential for theoretical reasons related to the few response alternatives coupled with highly varying and skewed response frequencies. Note, however, that the use of categorical models (with separate parameters for each of the three response thresholds for each item) requires a larger data set than previous linear models, but the current sample size proved sufficient for stable results. Our results support the removal of five superfluous items from the CAPE as proposed by Capra et al. (2013), as there was a resulting advantage in model fit over the otherwise analogous Wigman et al. (2011) model. In addition, four of the five original items removed from the best-performing Capra et al. (2013) model were the ones loading on Common Supernatural Beliefs and Grandiosity in other studies (Wigman et al., 2011; Therman et al., 2014) – factors which have appeared to be unrelated to psychopathology in those studies. In addition to clinical correlates, the high endorsement rates on those items indicate that the reported experiences are benign rather than pathological. Consequently, the CAPE would need revised items to measure symptoms of mania and delusional magical thinking. The fifth removed item about personal messages from mass media was inconsistently loading on Delusions and Paranoia in the other two models, and was the poorest indicator of those factors. Our results verify that the positive PLEs of the CAPE have three primary subdimensions in a general population sample: Hallucinations, Paranoia, and Delusions. Note that some other authors (e.g. Armando et al., 2010; Capra et al., 2013) use the term Bizarre Experiences instead of Delusions, but we consider the more specific label sufficiently appropriate. The best-fitting models tested are highly convergent in their content; the differences are in assigning a few items, for instance, whether the rarely endorsed Capgras delusion item belongs to Delusions or Hallucinations, or whether the “messages from TV” item is an aspect of Paranoia or Delusions. The differing items seem to be of secondary importance, having lower explained variance overall. Some other items may also tap into non-psychotic experiences, especially the ones about “seeing things” and “electrical devices”. The latter is especially prone to misunderstandings due to a general increase in use of personal messaging and commenting in social media via electronic devices. A recent confirmatory study in a student sample also found good fit with the three-dimensional model using a modified
Table 1 Fit indices of confirmatory analyses of published factorial models. Model reference Núñez et al. (2015) Capra et al. (2013) v2 Capra et al. (2013) v1 Wigman et al. (2011) Armando et al. (2010) Therman et al. (2014) Barragan et al. (2011) Yung et al. (2009) Armando et al. (2012) Brenner et al. (2007) Yung et al. (2006) Stefanis et al. (2002) a b
Dimensions 1þ 3 3b 3 5 4 5 4 4 4 4 3 1
a
Variables
Explained variance (%)
RMSEA
CFI
WRMR
15 15 15 20 18 20 20 20 20 19 18 20
55 51 51 50 49 50 45 46 46 45 42 37
0.024 0.026 0.032 0.031 0.034 0.034 0.042 0.046 0.046 0.047 0.057 0.058
0.99 0.98 0.97 0.97 0.97 0.96 0.94 0.93 0.93 0.92 0.90 0.88
0.73 0.81 0.93 0.99 1.03 1.05 1.24 1.32 1.32 1.35 1.55 1.65
Bifactor model. Four Item pairs with correlated error terms.
Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i
S. Therman, T.B. Ziermans / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
recent-experiences-only version of the 15 relevant items (Capra et al., 2015). However, the three subdimensions are highly correlated with each other (r ¼0.65, 0.78, and 0.78 for the best-performing model in the present study), so treating them as a single dimension may be justifiable in clinical screening or research where a less detailed account of positive symptoms and experiences is required. Indeed, our results confirmed the finding of a strong general factor in a bifactor model by Núñez et al. (2015), which also makes the Delusions subdimension redundant. Although the bifactor model showed the best fit, its complexityadjusted fit was slightly poorer due to the greater number of parameters, and we therefore could not confirm its superiority over other models. In the future it is important to test the generalizability of the present findings in studies with broader content, in order to determine whether the three currently identified positive subdimensions are distinct from other aspects of positive PLEs. A shortcoming of the CAPE is that it currently does not appear to actually address mania and does not include items on some commonly recognized associated phenomena, notably disorganized cognition, which is a part of similar measures such as the SPEQ (Ronald et al., 2014). The bifactor model of a general factor – corresponding to theoretical conceptions of a psychosis continuum – may be a methodological improvement, and needs to be further tested and refined. In conclusion, we were able to confirm, in a representative population sample, the accuracy of models of positive PLEs with three correlated but distinguishable and clinically relevant subdimensions. Whether these three dimensions are better represented by a bifactor structure remained unclear. Furthermore, five positive items of the CAPE appear irrelevant and excluding those items results in better model fit. We also demonstrated that accounting for the categorical nature of responses is vital in the analysis of psychotic-like experiences.
Funding Funding Data collection was supported by a COFAS Marie Curie fellowship grant to TZ.
Acknowledgments The authors would like to thank Dr. Torkel Klingberg for his contribution.
3
References Armando, M., Nelson, B., Yung, A.R., Ross, M., Birchwood, M., Girardi, P., Fiori Nastro, P., 2010. Psychotic-like experiences and correlation with distress and depressive symptoms in a community sample of adolescents and young adults. Schizophr. Res. 119, 258–265, doi:10/b7zk55. Armando, M., Nelson, B., Yung, A.R., Saba, R., Monducci, E., Dario, C., Righetti, V., Birchwood, M., Fiori Nastro, P., Girardi, P., 2012. Psychotic experience subtypes, poor mental health status and help-seeking behaviour in a community sample of young adults. Early Interv. Psychiatry 6, 300–308, doi:10/d32c6t. Barragan, M., Laurens, K.R., Navarro, J.B., Obiols, J.E., 2011. Psychotic-like experiences and depressive symptoms in a community sample of adolescents. Eur. Psychiatry 26, 396–401, doi:10/bztn4n. Brenner, K., Schmitz, N., Pawliuk, N., Fathalli, F., Joober, R., Ciampi, A., King, S., 2007. Validation of the English and French versions of the Community Assessment of Psychic Experiences (CAPE) with a Montreal community sample. Schizophr. Res. 95, 86–95, doi:10/fvq9xc. Capra, C., Kavanagh, D.J., Hides, L., Scott, J., 2013. Brief screening for psychosis-like experiences. Schizophr. Res. 149, 104–107, doi:10/f2w243. Capra, C., Kavanagh, D.J., Hides, L., Scott, J.G., 2015. Current CAPE-15: a measure of recent psychotic-like experiences and associated distress. Early Interv. Psychiatry, doi:10/4rv. Hanssen, M., Peeters, F., Krabbendam, L., Radstake, S., Verdoux, H., van Os, J., 2003. How psychotic are individuals with non-psychotic disorders? Soc. Psychiatry Psychiatr. Epidemiol. 38, 149–154, doi:10/dr5gtn. Mason, O.J., 2014. The duality of schizotypy: is it both dimensional and categorical? Front. Psychiatry 5, 134, doi:10/4qb. Muthén, L.K., Muthén, B.O., 2012. Mplus User's Guide, 7th ed.. Núñez, D., Arias, V., Vogel, E., Gómez, L., 2015. Internal structure of the Community Assessment of Psychic Experiences—Positive (CAPE-P15) scale: evidence for a general factor. Schizophr. Res. 165, 236–242, doi:10/79c. Ronald, A., Sieradzka, D., Cardno, A.G., Haworth, C.M., McGuire, P., Freeman, D., 2014. Characterization of psychotic experiences in adolescence using the specific psychotic experiences questionnaire: findings from a study of 5000 16year-old twins. Schizophr. Bull. 40, 868–877, doi:10/vw2. Stefanis, N.C., Hanssen, M., Smirnis, N.K., Avramopoulos, D.A., Evdokimidis, I.K., Stefanis, C.N., Verdoux, H., Van Os, J., 2002. Evidence that three dimensions of psychosis have a distribution in the general population. Psychol. Med. 32, 347–358, doi:10/b297qg. Therman, S., Suvisaari, J., Hultman, C.M., 2014. Dimensions of psychotic experiences among women in the general population. Int. J. Methods Psychiatr. Res. 23, 62–68, doi:10/vrr. van Os, J., Linscott, R.J., 2012. Introduction: the extended psychosis phenotype— relationship with schizophrenia and with ultrahigh risk status for psychosis. Schizophr. Bull. 38, 227–230, doi:10/4rw. Wigman, J.T., Vollebergh, W.A., Raaijmakers, Q.A., Iedema, J., van Dorsselaer, S., Ormel, J., Verhulst, F.C., van Os, J., 2011. The structure of the extended psychosis phenotype in early adolescence—a cross-sample replication. Schizophr. Bull. 37, 850–860, doi:10/bkk4f8. Wigman, J.T., Vollebergh, W.A., Jacobs, N., Wichers, M., Derom, C., Thiery, E., van Os, J., 2012. Replication of the five-dimensional structure of positive psychotic experiences in young adulthood. Psychiatry Res. 197, 353–355. Yung, A.R., Buckby, J.A., Cotton, S.M., Cosgrave, E.M., Killackey, E.J., Stanford, C., Godfrey, K., McGorry, P.D., 2006. Psychotic-like experiences in nonpsychotic help-seekers: associations with distress, depression, and disability. Schizophr. Bull. 32, 352–359, doi:10/bpw7d8. Yung, A.R., Nelson, B., Baker, K., Buckby, J.A., Baksheev, G., Cosgrave, E.M., 2009. Psychotic-like experiences in a community sample of adolescents: implications for the continuum model of psychosis and prediction of schizophrenia. Aust. N. Z. J. Psychiatry 43, 118–128, doi:10/d43756. Ziermans, T.B., 2013. Working memory capacity and psychotic-like experiences in a general population sample of adolescents and young adults. Front. Psychiatry 4, 161, doi:10/vxs.
Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i
Contents lists available at ScienceDirect
Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
Short communication
Confirmatory factor analysis of psychotic-like experiences in a general population sample Sebastian Therman a,b,n, Tim B. Ziermans b,c a
Department of Health, National Institute of Health and Welfare, Helsinki, Finland Department of Clinical Child and Adolescent Studies, Leiden University, Leiden, The Netherlands c Leiden Institute for Brain and Cognition, Leiden, The Netherlands b
art ic l e i nf o
a b s t r a c t
Article history: Received 26 May 2015 Received in revised form 16 October 2015 Accepted 15 December 2015
Psychotic-like experiences (PLEs) are sub-psychotic expressions of the psychosis continuum. Several studies have suggested multifactorial models, including a bifactor model, of the putative PLEs assessed with the popular Community Assessment of Psychic Experiences (CAPE) questionnaire. Our confirmatory results in a gender-balanced population of adolescents and young adults support a three-factor Paranoia–Delusions–Hallucinations structure of PLEs, which excludes Grandiosity and Common Paranormal Beliefs. The best latent models achieved excellent fit when taking the categorical nature of the responses into consideration. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Latent structure Psychotic experiences Positive symptoms
1. Introduction Psychotic-like experiences (PLEs) are the subclinical variants of the psychosis continuum, which is also referred to as the “extended psychosis phenotype” (van Os and Linscott, 2012). As intermediate expressions of the continuum, PLEs offer complimentary ways to study psychosis risk as well as the etiology of psychotic disorders. In assessing PLEs, questionnaires are cost-effective in large-scale research, but require careful design and analysis to maximize their utility. For example, the relative severity of individual items and response alternatives needs to be assessed and taken into account by analyzing categorical data with suitable methods. This is especially important when the number of items is limited. Furthermore, PLEs do not constitute a single dimension of psychopathology, but appear to represent several correlated dimensions, as do the symptoms of manifest psychosis and trait schizotypy. Latent factor analysis has therefore been a preferred method for structural analysis of these phenomena – especially for the “positive” symptoms or experiences (Mason, 2014). In addition, factor analysis makes it possible to quantify the relevance of individual questionnaire items to their respective content domains, that is, the latent dimensions. Moreover, confirmatory factor analysis quantifies how well the data fit the theoretical constructs.
The Community Assessment of Psychic Experiences (CAPE-42: Stefanis et al., 2002; Hanssen et al., 2003) has seen widespread use in general populations, and there have been several attempts to identify the underlying latent dimensions of its 20 positive PLEs. After the initial validation study (Stefanis et al., 2002), which found that these items load reasonably well on a single factor, several publications on various clinical and general population populations have proposed 3- to 5-dimensional models of the positive items, based on exploratory (Yung et al., 2006, 2009; Armando et al., 2010, 2012; Barragan et al., 2011; Therman et al., 2014) and confirmatory (Brenner et al., 2007; Wigman et al., 2011, 2012; Capra et al., 2013) factor analysis. Furthermore, one study (Núñez et al., 2015) has proposed a bifactor extension of the Capra et al. (2013) model, which includes a general factor in addition to the specific symptom dimensions. Consequently, there are ample symptom structures available, providing scientists with the opportunity to move beyond exploratory research. In this study, we test and compare existing models of the positive symptoms of the CAPE in confirmatory analyses, to identify which one best fits the data from an independent population sample. Furthermore, we take the ordinal nature of the items into account, following recommendations of modern measurement theory. 2. Methods 2.1. Questionnaire
n
Correspondence to: P.O. Box 30, FI-00271 Helsinki, Finland. E-mail addresses: sebastian.therman@thl.fi (S. Therman), [email protected] (T.B. Ziermans).
The CAPE-42 (available online at http://cape42.homestead.com/) was designed to address, in the general population, experiences similar to those seen in psychotic
http://dx.doi.org/10.1016/j.psychres.2015.12.023 0165-1781/& 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i
S. Therman, T.B. Ziermans / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
disorders. The Positive subscale comprises 20 items. Responses to the CAPE are given on a four-category scale for each item, separately for frequency and distress. As previous studies have used only the frequency responses (Never – Sometimes – Often – Almost Always), we also ignored the secondary distress responses here. 2.2. Participants A gender-balanced and both geographically and occupationally diverse Swedish population sample (n¼ 1012) of ages 12–35 (mean 24), recruited from a voluntary panel of over 100,000 citizens, completed the CAPE online. The participant recruitment has been previously described in detail (Ziermans, 2013), and conformed to the Declaration of Helsinki. 2.3. Statistical analyses The previously published models were tested with confirmatory factor analysis for ordinal measures, using the weighted least squares means and variances adjusted (WLSMV) algorithm on the polychoric correlation matrix with default parameters in Mplus 7.2 (Muthén and Muthén, 2012). Response thresholds were estimated separately for each item with an inverse normal transformation of the cumulative response proportions. Item loadings on their assigned factors were also estimated from the data. The primary measure of interest was the comparative fit index (CFI), accompanied by the root mean square error of approximation (RMSEA) and weighted root mean square residual (WRMR). Not all models that were tested in this study included all 20 items (see Table 1); some had excluded items with a poor fit. In addition, one proposed model (Capra et al., 2013) had two variations, the first assigning the items to three dimensions, and the second specifying four additional cross-loadings.
3. Results The fit indices of the tested models are reported in Table 1. The single-dimensional model of the “positive” CAPE symptoms was borderline adequate, while all 4- and 5-dimensional models showed good fit, with RMSEA values under 0.05 and CFI values over 0.90. The best fit with a CFI of 0.98 was demonstrated by the second Capra et al. (2013) model, which specified three dimensions (Paranoia, Delusions, and Hallucinations), and four pairs of correlated item errors, but excluded five items. An excellent model fit was also achieved for all 20 items by the Wigman et al. (2011) and Therman et al. (2014) models (CFI 0.97 and 0.96, respectively). The Núñez et al. (2015) orthogonal bifactor model with a general factor and three subdimensions had an even better fit than the correlated-dimensions models (CFI 0.99). Nevertheless, in secondary full-information analyses using Maximum likelihood numerical integration, the bifactor model had a slightly poorer Bayesian Information Criterion (BIC) value than the corresponding Capra et al. (2013) model, indicating that the improved fit was at the expense of parsimony. In line with previous findings (Wigman et al., 2011; Núñez et al., 2015), the general latent factor and the Delusions subfactor decreased with age (r¼ 0.29 and 0.24, respectively), but not the other subfactors.
4. Discussion Our results show that current state-of-the-art models of positive PLEs fit the data very well in the general population. Furthermore, taking the categorical nature of responses into account led to considerably better fit, with approximately 0.08 higher CFIs in the same data (Ziermans, 2013). This methodological refinement is also essential for theoretical reasons related to the few response alternatives coupled with highly varying and skewed response frequencies. Note, however, that the use of categorical models (with separate parameters for each of the three response thresholds for each item) requires a larger data set than previous linear models, but the current sample size proved sufficient for stable results. Our results support the removal of five superfluous items from the CAPE as proposed by Capra et al. (2013), as there was a resulting advantage in model fit over the otherwise analogous Wigman et al. (2011) model. In addition, four of the five original items removed from the best-performing Capra et al. (2013) model were the ones loading on Common Supernatural Beliefs and Grandiosity in other studies (Wigman et al., 2011; Therman et al., 2014) – factors which have appeared to be unrelated to psychopathology in those studies. In addition to clinical correlates, the high endorsement rates on those items indicate that the reported experiences are benign rather than pathological. Consequently, the CAPE would need revised items to measure symptoms of mania and delusional magical thinking. The fifth removed item about personal messages from mass media was inconsistently loading on Delusions and Paranoia in the other two models, and was the poorest indicator of those factors. Our results verify that the positive PLEs of the CAPE have three primary subdimensions in a general population sample: Hallucinations, Paranoia, and Delusions. Note that some other authors (e.g. Armando et al., 2010; Capra et al., 2013) use the term Bizarre Experiences instead of Delusions, but we consider the more specific label sufficiently appropriate. The best-fitting models tested are highly convergent in their content; the differences are in assigning a few items, for instance, whether the rarely endorsed Capgras delusion item belongs to Delusions or Hallucinations, or whether the “messages from TV” item is an aspect of Paranoia or Delusions. The differing items seem to be of secondary importance, having lower explained variance overall. Some other items may also tap into non-psychotic experiences, especially the ones about “seeing things” and “electrical devices”. The latter is especially prone to misunderstandings due to a general increase in use of personal messaging and commenting in social media via electronic devices. A recent confirmatory study in a student sample also found good fit with the three-dimensional model using a modified
Table 1 Fit indices of confirmatory analyses of published factorial models. Model reference Núñez et al. (2015) Capra et al. (2013) v2 Capra et al. (2013) v1 Wigman et al. (2011) Armando et al. (2010) Therman et al. (2014) Barragan et al. (2011) Yung et al. (2009) Armando et al. (2012) Brenner et al. (2007) Yung et al. (2006) Stefanis et al. (2002) a b
Dimensions 1þ 3 3b 3 5 4 5 4 4 4 4 3 1
a
Variables
Explained variance (%)
RMSEA
CFI
WRMR
15 15 15 20 18 20 20 20 20 19 18 20
55 51 51 50 49 50 45 46 46 45 42 37
0.024 0.026 0.032 0.031 0.034 0.034 0.042 0.046 0.046 0.047 0.057 0.058
0.99 0.98 0.97 0.97 0.97 0.96 0.94 0.93 0.93 0.92 0.90 0.88
0.73 0.81 0.93 0.99 1.03 1.05 1.24 1.32 1.32 1.35 1.55 1.65
Bifactor model. Four Item pairs with correlated error terms.
Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i
S. Therman, T.B. Ziermans / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
recent-experiences-only version of the 15 relevant items (Capra et al., 2015). However, the three subdimensions are highly correlated with each other (r ¼0.65, 0.78, and 0.78 for the best-performing model in the present study), so treating them as a single dimension may be justifiable in clinical screening or research where a less detailed account of positive symptoms and experiences is required. Indeed, our results confirmed the finding of a strong general factor in a bifactor model by Núñez et al. (2015), which also makes the Delusions subdimension redundant. Although the bifactor model showed the best fit, its complexityadjusted fit was slightly poorer due to the greater number of parameters, and we therefore could not confirm its superiority over other models. In the future it is important to test the generalizability of the present findings in studies with broader content, in order to determine whether the three currently identified positive subdimensions are distinct from other aspects of positive PLEs. A shortcoming of the CAPE is that it currently does not appear to actually address mania and does not include items on some commonly recognized associated phenomena, notably disorganized cognition, which is a part of similar measures such as the SPEQ (Ronald et al., 2014). The bifactor model of a general factor – corresponding to theoretical conceptions of a psychosis continuum – may be a methodological improvement, and needs to be further tested and refined. In conclusion, we were able to confirm, in a representative population sample, the accuracy of models of positive PLEs with three correlated but distinguishable and clinically relevant subdimensions. Whether these three dimensions are better represented by a bifactor structure remained unclear. Furthermore, five positive items of the CAPE appear irrelevant and excluding those items results in better model fit. We also demonstrated that accounting for the categorical nature of responses is vital in the analysis of psychotic-like experiences.
Funding Funding Data collection was supported by a COFAS Marie Curie fellowship grant to TZ.
Acknowledgments The authors would like to thank Dr. Torkel Klingberg for his contribution.
3
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Please cite this article as: Therman, S., Ziermans, T.B., Confirmatory factor analysis of psychotic-like experiences in a general population sample. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.12.023i