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A confirmatory factor analysis of the Pain Catastrophizing Scale: invariant factor structure across clinical and non-clinical populations
Pain 96 (2002) 319–324 www.elsevier.com/locate/pain
A confirmatory factor analysis of the Pain Catastrophizing Scale: invariant factor structure across clinical and non-clinical populations Stefaan Van Damme a,*, Geert Crombez a, Patricia Bijttebier b,1, Liesbet Goubert a,2, Boudewijn Van Houdenhove c a
Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium b Department of Psychology, University of Leuven, Leuven, Belgium c Department of Medicine, University of Leuven, Leuven, Belgium Received 12 July 2001; received in revised form 12 October 2001; accepted 2 November 2001
Abstract This study examined the factor structure of the Pain Catastrophizing Scale in three different Dutch-speaking samples: 550 pain-free students, 162 chronic low back pain patients, and 100 fibromyalgia patients. Confirmatory factor analyses were used to compare three different models of pain catastrophizing (one factor, two oblique factors, three oblique factors), and to investigate the invariance of the factor structure across the three different samples. The results indicated that a three-factor oblique model with a four-item rumination factor, a threeitem magnification factor, and a six-item helplessness factor provided the best fit to the data in the three samples. Furthermore, it was found that this model could be considered as invariant across three samples (pain-free students, chronic low back pain patients, and fibromyalgia patients) and across gender, indicating that the same processes are measured in different subgroups. q 2002 International Association for the Study of Pain. Published by Elsevier Science B.V. All rights reserved. Keywords: Chronic pain; Back pain; Fibromyalgia; Catastrophizing; Factor structure
1. Introduction The role of catastrophic cognitions in the development and maintenance of anxiety disorders and hypochondriasis has been widely recognized (Salkovskis and Clark, 1993). In these settings, catastrophizing has been conceived as a cognitive style that involves the tendency to misinterpret and exaggerate the threat value of situations (e.g. bodily sensations). Although the criteria for catastrophizing about pain have never been explicitly stated, it has been broadly defined as an exaggerated negative orientation towards actual or anticipated pain experiences (Sullivan et al., 1995). Catastrophizing about pain has been identified as one of the most important psychological variables in explaining responses to pain in clinical and non-clinical situations (Sullivan et al., 1995). Considerable research has shown that catastrophizing contributes to more intense pain (Sulli-
* Corresponding author. Tel.: 132-9-2649105; fax: 132-9-2649149. E-mail address: [email protected] (S. Van Damme). 1 Postdoctoral fellow of the Fund for Scientific Research – Flanders (Belgium) (F.W.O.). 2 Research assistant of the Fund for Scientific Research – Flanders (Belgium) (F.W.O.).
van et al., 1995, 1997; Sullivan and Neish, 1999), disability (Sullivan et al., 1998), and increased emotional distress in response to pain (Heyneman et al., 1990; Sullivan et al., 2001a). Furthermore, catastrophizing has been associated with overprediction of pain, increased pain behavior, increased use of health care services, longer hospital stays, and increased use of medication (Goubert et al., 2001; Sullivan et al., 2001b). In a prospective study, Linton et al. (1998) found that catastrophic thinking about pain is associated with an increased risk for pain complaints and disability 1 year later. Another prospective study, investigating predictors of back pain chronicity 1 year after the acute onset, revealed catastrophizing as the most powerful predictor (Burton et al., 1995). Catastrophic thinking about pain has been conceptualized in different ways. First, based upon interview responses from dental patients, Chaves and Browne (1987) described catastrophizers as individuals who have a tendency to magnify or exaggerate the threat value or seriousness of pain sensations. Second, analyzing the thoughts of participants undergoing an experimental pain procedure, Spanos et al. (1979) classified individuals as catastrophizers when their thought reflected worry, fear, and the inability to divert
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attention away from pain. Finally, Rosenstiel and Keefe (1983) related catastrophizing in patients with chronic low back pain to helplessness and pessimism concerning one’s ability to deal with the pain experience. All three studies have in common the idea that pain catastrophizing is a form of negative pain-related cognition, but they differ in the specific content of this cognition. Osman et al. (1997) noted that the growing amount of research sharply contrasts with the few attempts to develop and validate self-report measures for assessing catastrophizing about pain. An important contribution in this area came from Sullivan et al. (1995), who developed the Pain Catastrophizing Scale (PCS). This is a 13-item self-report measure for use in clinical and non-clinical populations, in patients with pain and pain-free individuals, and in people with acute and chronic pain. The items were selected from the above described conceptualizations of catastrophizing (Spanos et al., 1979; Rosenstiel and Keefe, 1983; Chaves and Browne, 1987). In the PCS participants reflect on past painful experiences and indicate the degree to which they experienced a number of thoughts or feelings when experiencing pain. Sullivan et al. (1995) investigated the factor structure of the PCS in a sample of 439 students. An exploratory principal components analysis with oblique rotation of the items revealed three factors: rumination (four items), magnification (three items), and helplessness (six items). Osman et al. (1997) tried to replicate this factor structure in a sample of 288 students, using the same procedure as Sullivan et al. (1995). Their exploratory principal components analysis with oblique rotation revealed a twofactor solution: the first factor consisted of seven items (relating to magnification and helplessness), while the second factor consisted of six items (relating to rumination). In a subsequent study in the same article, Osman et al. (1997) used a confirmatory factor analysis (CFA) to evaluate the adequacy of fit of the found two-factor solution, compared to the original three-factor oblique model of Sullivan et al. (1995). The three-factor model provided the best fit to the observed data, replicating the original factor structure of Sullivan et al. (1995). This structure was also replicated in a community sample by Osman et al. (2000). Evidence for the three-factor model was also found by Sullivan et al. (2000a), performing a CFA on a sample of sport participants. Furthermore, they investigated whether the PCS measured the same content across two samples: a sample of sporting persons and a sample of sedentary persons. They found that the factor structure was the same in both samples. The current study is an attempt to further validate the PCS by investigating its internal structure. Although the PCS is used extensively in clinical research, minimal information about the psychometric properties of the PCS is available for clinical populations. Almost all psychometric research has been conducted on pain-free participants. Therefore, the first aim of the study was to replicate the factor structure of the PCS in three Dutch-speaking samples, i.e. chronic low
back pain patients, fibromyalgia patients, and pain-free students, by means of a CFA. In addition, it is not known to what extent the PCS measures the same processes in different subgroups. It is possible that the factor structure of a pain-related construct such as catastrophizing varies as a function of differences in the experience and the localization of pain and as a function of gender (Sullivan et al., 2000b). Therefore, the second and most important aim of the study was to investigate the stability of the factor structure in different subgroups. This study is the first to investigate the invariance of the factor structure of the PCS across different pain patient groups and persons without pain complaints and across gender by means of a multi-sample analysis.
2. Methods 2.1. Participants The PCS was completed by 550 pain-free Dutch-speaking Belgian first-year psychology students (147 males and 403 females, mean age ¼ 18.72 years, SD ¼ 1.42) and 262 pain patients (83 males and 179 females, mean age ¼ 42.70 years, SD ¼ 10.77) from Belgian and Dutch pain clinics. The sample of pain patients consisted of 162 chronic low back pain patients (63 males and 99 females, mean age ¼ 41.50 years, SD ¼ 11.55) and 100 fibromyalgia patients (20 males and 80 females, mean age ¼ 44.64 years, SD ¼ 9.11), fulfilling the American College of Rheumatology (ACR) criteria (Wolfe et al., 1990). The data were obtained in different studies. Medians, means, and standard deviations of the PCS-scores in each sample are presented in Table 1. 2.2. PCS The Dutch version of the PCS (PCS-DV; Crombez et al., 1999) was used. The psychometric characteristics of the PCS-DV were reviewed by Van Damme et al. (2000). The questionnaire showed to be valid and highly reliable. A good internal consistency of the PCS-DV (Cronbach’s alpha between 0.85 and 0.91) was found (Crombez et al., 1998, 1999). Furthermore, evidence was found for the construct validity (Crombez et al., 1999) and the concurrent validity (Crombez et al., 1998). Table 1 Median, means, and standard deviations of PCS-scores Samples
Median
Mean
SD
Students ðn ¼ 550Þ Chronic low back pain patients ðn ¼ 162Þ Fibromyalgia patients ðn ¼ 100Þ
16.00 22.00
16.56 21.99
7.78 9.31
25.50
24.81
12.24
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2.3. CFA 2.4. Assessing three models The factor structure of the PCS-DV was investigated using the CFA module of the INTERACTIVE LISREL 8.30 framework (Du Toit et al., 1999). In line with the recommendations of Bollen and Long (1993), several fit indices were used to assess model fit. In the present study, model fit is assessed using the following goodness-of-fit indices: x 2, Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI). (a) x 2 is the most frequently used goodness-of-fit index. A significant chi-square suggests that a significant amount of observed covariance between items remains unexplained by the model, while non-significance implies a good fit to the data (Cole, 1987). A disadvantage of this index is its sensitivity to sample size. In a small sample size, a poor fit may nevertheless result in a non-significant x 2. It is also possible that in a large sample size a good fit results in a statistically significant x 2 (Marsh et al., 1988). In the present study, the x 2 is the normal theory weighted least squares x 2, as the other fit indices are based upon this index in the LISREL 8.30 program. (b) The RMSEA (Steiger, 1990) is a fit measure based on the population error of approximation. The idea behind it is that it is unreasonable to assume that the model holds exactly in the population. The RMSEA takes account of the error of approximation in the population. According to Browne and Cudeck (1993), a RMSEA value of 0.05 indicates a close fit and values up to 0.08 represent reasonable errors of approximation in the population. (c) The CFI (Bentler, 1990) is an incremental fit index. It represents the proportionate improvement in model fit by comparing the target model with a baseline model (usually a null model in which all the observed variables are uncorrelated). CFI values greater than 0.90 indicate an adequate fit.
In the present study, CFA is used to compare models. This requires the specification of nested models. Nested models contain the same parameters, and the set of free parameters in one model is a subset of the set of free parameters in another model. The fit of nested models can be compared by an inspection of the x 2 test, since in these models the distribution of x 2 change equals the distribution of x 2 (e.g. when the difference in degrees of freedom between two models is 1, a difference in x 2 of 3.84 is significant at an alpha level of 0.05). 3. Results 3.1. Comparison of three models of pain catastrophizing To assess the factor structure of the PCS, three models were compared. Model 1 is a one-factor model, in which the 13 items are assumed to be indicators of a single latent factor (pain catastrophizing). Model 2 is the oblique twofactor model found by Osman et al. (1997), in which the seven magnification–helplessness items and the six rumination items are assumed to measure two correlated dimensions. Model 3 is the oblique three-factor model proposed by Sullivan et al. (1995), in which the four rumination items, three magnification items, and six helplessness items are assumed to measure three correlated dimensions. Furthermore, the stability of the solution was tested in three samples: pain-free students, chronic low back pain patients, and fibromyalgia patients. Based on the goodness-of-fit summary shown in Table 2, it can be concluded that the three-factor model provides the best fit for the data in all samples, whereas the one-factor model provides the poorest fit. In the student sample, the three-factor model shows acceptable fit to the data
Table 2 Goodness-of-fit summary for the different models tested a
x 2 (d.f.)
RMSEA
CFI
Student sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
542.22**** (65) 457.20**** (64) 291.84**** (62)
0.12 0.11 0.08
0.82 0.84 0.91
Chronic low back pain sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
210.24**** (65) 191.73**** (64) 158.71**** (62)
0.12 0.11 0.10
0.83 0.84 0.88
Fibromyalgia sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
126.05*** (65) 125.04*** (64) 107.74*** (62)
0.10 0.10 0.09
0.92 0.92 0.93
a
***P , 0:001; ****P , 0:0001.
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Table 3 Standardized factor loadings and internal consistency of the oblique three-factor solution Students 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:81Þ – – – – – – – 0.61 0.80 0.82 0.68 – –
Magnification ða ¼ 0:59Þ – – – – – 0.75 0.42 – – – – – 0.54
Helplessness ða ¼ 0:76Þ 0.48 0.66 0.55 0.77 0.76 – – – – – – 0.36 –
CLBP patients 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:81Þ – – – – – – – 0.61 0.74 0.77 0.73 – –
Magnification ða ¼ 0:70Þ – – – – – 0.87 0.48 – – – – – 0.54
Helplessness ða ¼ 0:79Þ 0.71 0.60 0.72 0.75 0.62 – – – – – – 0.33 –
FM patients 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:84Þ – – – – – – – 0.74 0.65 0.83 0.77 – –
Magnification ða ¼ 0:72Þ – – – – – 0.89 0.61 – – – – – 0.53
Helplessness ða ¼ 0:89Þ 0.82 0.80 0.87 0.87 0.81 – – – – – – 0.41 –
(RMSEA , 0.08, CFI . 0.90), whereas the two-factor and the one-factor model explain the data poorly (RMSEA . 0.08, CFI , 0.90). In the fibromyalgia sample, all three models show more or less acceptable fit to the data. In the chronic low back pain sample, only the three-factor model approaches an acceptable fit to the data. Because the models are nested, we can statistically compare their adequacy using x 2 difference tests. The comparison of the one-factor model with the three-factor model reveals that the latter model explains the data significantly better than the former in the student sample (Dx2 ð3Þ ¼ 250:38, P , 0:0005), in the chronic low back pain sample (Dx2 ð3Þ ¼ 51:53, P , 0:0005), and in the fibromyalgia sample (Dx2 ð3Þ ¼ 18:31, P , 0:001). Furthermore, the three-factor model explains the data significantly better than the two-factor model in the student sample
(Dx2 ð2Þ ¼ 165:36, P , 0:0005), in the chronic low back pain sample (Dx2 ð2Þ ¼ 33:02, P , 0:0005), and in the fibromyalgia sample (Dx2 ð2Þ ¼ 17:30, P , 0:0005). These results indicate that the PCS-items can be considered as representing three correlated latent dimensions, namely rumination, magnification, and helplessness. It should be noted that the three latent factors were highly correlated in the chronic low back pain sample and the fibromyalgia sample, and moderately correlated in the student sample. The standardized factor loadings and internal consistency of the subscales in each sample are presented in Table 3. 3.2. Invariance of the oblique three-factor model across subgroups To examine whether the three-factor oblique model is
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invariant across subgroups, multi-sample analyses were conducted. In the first multi-sample analysis, the model was fit separately for pain-free students, chronic low back pain patients, and fibromyalgia patients, and a x 2 statistic for the overall fit was computed to assess overall parameter invariance. A very restrictive model was tested by equating the number of factors, the factor loadings, and the correlations between the factors. The results of this multi-sample analysis show that the specified model can be considered as adequately fitting the data (CFI ¼ 0.91), which means that the model is invariant across the three samples. The 13 PCS items represent three underlying dimensions and contribute equally to their respective factors in pain-free students, chronic low back pain patients, and fibromyalgia patients (invariance of factor loadings). Furthermore the intercorrelations between the latent rumination, magnification, and helplessness dimensions do not differ across samples (invariance of factor intercorrelations). A similar multi-sample analysis was performed to investigate the invariance of the factor structure across gender. The model was fit separately for all males ðn ¼ 228Þ and females ðn ¼ 580Þ, and a x 2 statistic for the overall fit was computed to assess overall parameter invariance. The results showed that the specified model could be considered as adequately fitting the data (CFI ¼ 0.90), which means that the model is invariant across gender.
4. Discussion The present study investigated the factor structure of the PCS-DV using CFA in three different samples: chronic low back pain patients, fibromyalgia patients, and pain-free students. Furthermore, the invariance of the factor structure across these samples and across gender was examined by means of a multi-sample analysis. With respect to the factor structure of the PCS-DV, we found that an oblique three-factor solution provided the best fit to the data in all three samples. Furthermore, this threefactor model adequately fitted the data for the pain-free students and the data for the fibromyalgia patients, and almost adequately fitted the data for the chronic low back patients. Taking into account that the factor structure was found to be invariant across these three samples, it is reasonable to conclude that the three-factor model is robust in different clinical and non-clinical samples. This finding, which is consistent with previous research (Osman et al., 1997, 2000; Sullivan et al., 2000a), supports the theoretical distinction between rumination, magnification, and helplessness as independent but strongly related dimensions of pain catastrophizing (Sullivan et al., 1995). Although the above evidence suggests that the PCS assesses the same processes in different populations, similar models do not guarantee the equivalence of item measurement or theoretical factor structure (Byrne et al., 1989). Factorial stability can only be tested using a multi-sample
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analysis. An example of this strategy is the study of Lousberg et al. (1999), who found invariance of the factor structure of the Multidimensional Pain Inventory across fibromyalgia and back pain patients. A similar analysis was performed in the present study. The oblique three-factor model yields a structure that is invariant across three samples (pain-free students, chronic low back pain patients, and fibromyalgia patients) and across gender. One of the most restrictive models was tested, with the invariance pertaining to the number of factors, the factor loadings, and the factor intercorrelations. This is an important finding, as researchers assume too easily that questionnaire scores have the same meaning for all groups in all settings. In spite of the availability of appropriate statistical procedures, many validation studies insufficiently investigate factorial invariance across different groups. The current study is the first to support the invariance of the factor structure of the PCS across different pain patient samples and a non-clinical sample, and across gender. The results from this study have a number of implications. First, they support the idea that the PCS is an instrument that can be used for several pain problems and in different settings. Although the persons in this study show large differences in the experience and localization of pain, the PCS seems to assess the same processes of catastrophizing. Second, they indicate that results obtained in one population or situation are likely to generalize as differences in catastrophizing are quantitative and not qualitative in nature. Promising is the idea that studies using experimental pain in healthy volunteers can capture the same catastrophic processes as in pain patients. This facilitates the generalization of both clinical and non-clinical research using the PCS. Third, our results indicate that gender differences in pain catastrophizing are also quantitative and not qualitative in nature. Therefore the PCS is a valuable instrument to investigate the explanatory role of pain catastrophizing in gender differences in pain perception and experience (Sullivan et al., 2000b). Fourth, our results suggest a crosscultural invariance of the factor structure of the PCS, as we were able to replicate the same factor structure using a translated version of the PCS and using non-American participants. Further research is needed on the relation between the PCS-factors to develop comprehensive theories addressing the interplay between psychological processes (such as catastrophizing) and the experience of pain (Sullivan et al., 2001b). An example of such theory came from Sullivan et al. (2001b), who recently proposed a hierarchical model in which appraisal processes (Lazarus and Folkman, 1984) play a role in the link between catastrophizing and pain experience. Magnification and rumination are considered as primary appraisal processes in which individuals exaggerate and focus on the threat value of pain sensations (Eccleston and Crombez, 1999), whereas helplessness may be related to secondary appraisal processes in which individuals negatively evaluate their ability to cope effectively
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with their pain (Sullivan et al., 2001b). Furthermore, research is needed to confirm the factor structure of the PCS in other pain-free samples and other pain problems. A limitation of the current study is that only a sample of students is used to represent pain-free subjects. In summary, the present study supports the original factor structure of the PCS in Dutch-speaking samples. The factor structure is invariant across pain-free students, chronic low back pain patients, and fibromyalgia patients, and across gender. Across a strong variety of pain characteristics, catastrophizing consists of three related dimensions reflecting the tendency to focus excessively on pain sensations, to magnify the threat value of pain sensations, and to perceive oneself as unable to control the intensity of pain (Sullivan et al., 2000a). Acknowledgements This study was supported by research grants G.0107.00 and G.0107.98 of the Fund for Scientific Research, Flanders, Belgium. The authors like to thank Johan Vlaeyen for providing part of the questionnaire data. References Bentler PM. Comparative fit indices in structural models. Psychol Bull 1990;217:238–246. Bollen KA, Long JS. Introduction. In: Bollen KA, Long JS, editors. Testing structural equation models, Newbury Park: Sage Publications, 1993. pp. 1–9. Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing structural equation models, Newbury Park: Sage Publications, 1993. pp. 136–162. Burton AK, Tillotson KM, Main CJ, Hollis S. Psychosocial predictors of outcome in acute and subchronic low back trouble. Spine 1995;20:722– 728. Byrne BM, Shavelson RJ, Muthen B. Testing for the equivalence of factor covariance and mean structures: the issues of partial measurement invariance. Psychol Bull 1989;105:456–466. Chaves JF, Browne JM. Spontaneous cognitive strategies for the control of clinical pain and stress. J Behav Med 1987;10:263–276. Cole DA. Utility of confirmatory factor analysis in test validation research. J Consult Clin Psychol 1987;55:584–594. Crombez G, Eccleston C, Baeyens F, Eelen P. When somatic information threatens, pain catastrophizing enhances attentional interference. Pain 1998;75:187–198. Crombez G, Vlaeyen JWS, Heuts PHTG, Lysens R. Pain-related fear is more disabling than pain itself: evidence on the role of pain-related fear in chronic low back pain disability. Pain 1999;80:329–339. Du Toit S, Du Toit M, Jo¨ reskog KG, So¨ rbom D. Interactive LISREL: user’s guide. Chicago, IL: Scientific Software International Inc, 1999. Eccleston C, Crombez G. Pain demands attention: a cognitive-affective model of the interruptive function of pain. Psychol Bull 1999;125:356– 366. Goubert L, Francken G, Crombez G, Vansteenwegen D, Lysens R. Exposure to physical movement in chronic back pain patients: no evidence for generalization across different movements. Behav Res Ther 2001 (in press).
Heyneman NE, Fremouw WJ, Gano D, Kirkland F, Heiden L. Individual differences and the effectiveness of different coping strategies for pain. Cognit Ther Res 1990;14:63–77. Lazarus RS, Folkman S. Stress, appraisal, and coping. New York, NY: Springer, 1984. Linton SJ, Buer N, Vlaeyen J, Hellsing A. Are fear-avoidance beliefs related to the inception of an episode of back pain? A prospective study. Psychol Health 1998;14:1051–1059. Lousberg R, Van Breukelen GJP, Groenman NH, Schmidt AJM, Arntz A, Winter FAM. Psychometric properties of the Multidimensional Pain Inventory, Dutch language version (MPI-DLV). Behav Res Ther 1999;37:167–182. Marsh HW, Balla JR, McDonald RP. Goodness-of-fit indices in confirmatory factor analysis: the effect of sample size. Psychol Bull 1988;103:391–410. Osman A, Barrios FX, Kopper BA, Hauptmann W, Jones J, O’Neill E. Factor structure, reliability, and validity of the Pain Catastrophizing Scale. J Behav Med 1997;20:589–605. Osman A, Barrios FX, Gutierrez PM, Kopper BA, Merrifield T, Grittman L. The Pain Catastrophizing Scale: further psychometric evaluation with adult samples. J Behav Med 2000;23:351–365. Rosenstiel AK, Keefe FJ. The use of cognitive strategies in chronic low back pain patients: relationship to patient characteristics and current adjustment. Pain 1983;17:33–44. Salkovskis PM, Clark DM. Panic disorder and hypochondriasis. Adv Behav Res Ther 1993;15:23–48. Spanos NP, Radtke-Bodorik HL, Ferguson JD, Jones B. The effects of hypnotic susceptibility, suggestions for analgesia, and utilization of cognitive strategies on the reduction of pain. J Abnorm Psychol 1979;88:282–292. Steiger JH. Structural model evaluation and modification: an interval estimation approach. Multivar Behav Res 1990;25:173–180. Sullivan MJL, Neish N. The effects of disclosure on pain during dental hygiene treatment: the moderating role of catastrophizing. Pain 1999;79:155–163. Sullivan MJL, Bishop SR, Pivik J. The Pain Catastrophizing Scale: development and validation. Psychol Assess 1995;7:524–532. Sullivan MJL, Rouse D, Bishop S, Johnston S. Thought suppression, catastrophizing, and pain. Cognit Ther Res 1997;21:555–568. Sullivan MJL, Stanish W, Waite H, Sullivan ME, Tripp D. Catastrophizing, pain, and disability following soft tissue injuries. Pain 1998;77:253– 260. Sullivan MJL, Tripp DA, Rodgers WM, Stanish W. Catastrophizing and pain perception in sport participants. J Appl Sport Psychol 2000a;12:151–167. Sullivan MJL, Tripp DA, Santor D. Gender differences in pain and pain behavior: the role of catastrophizing. Cognit Ther Res 2000b;24:121– 134. Sullivan MJL, Rodgers WM, Kirsch I. Catastrophizing, depression and expectancies for pain and emotional distress. Pain 2001a;91:147–154. Sullivan MJL, Thorn B, Haythornthwaite J, Keefe F, Martin M, Bradley L, Lefebvre JC. Theoretical perspectives on the relation between catastrophizing and pain. Clin J Pain 2001b;17:52–64. Van Damme S, Crombez G, Vlaeyen JWS, Goubert L, Van den Broeck A, Van Houdenhove B. De Pain Catastrophizing Scale: psychometrische karakteristieken en normering. Gedragstherapie 2000;3:211–222. Wolfe F, Smyte HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, Tugwell P, Campbell SM, Abeles M, Clark P, Fam AG, Farber SJ, Fiechtner JJ, Franklin CM, Gatter RA, Hamaty D, Lessard J, Lichtbroun AS, Masi AT, McCain GA, Reynolds WJ, Romano TJ, Russell IJ, Sheon RP. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia: report of the Multicenter Criteria Committee. Arthritis Rheum 1990;33:160–172.
A confirmatory factor analysis of the Pain Catastrophizing Scale: invariant factor structure across clinical and non-clinical populations Stefaan Van Damme a,*, Geert Crombez a, Patricia Bijttebier b,1, Liesbet Goubert a,2, Boudewijn Van Houdenhove c a
Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium b Department of Psychology, University of Leuven, Leuven, Belgium c Department of Medicine, University of Leuven, Leuven, Belgium Received 12 July 2001; received in revised form 12 October 2001; accepted 2 November 2001
Abstract This study examined the factor structure of the Pain Catastrophizing Scale in three different Dutch-speaking samples: 550 pain-free students, 162 chronic low back pain patients, and 100 fibromyalgia patients. Confirmatory factor analyses were used to compare three different models of pain catastrophizing (one factor, two oblique factors, three oblique factors), and to investigate the invariance of the factor structure across the three different samples. The results indicated that a three-factor oblique model with a four-item rumination factor, a threeitem magnification factor, and a six-item helplessness factor provided the best fit to the data in the three samples. Furthermore, it was found that this model could be considered as invariant across three samples (pain-free students, chronic low back pain patients, and fibromyalgia patients) and across gender, indicating that the same processes are measured in different subgroups. q 2002 International Association for the Study of Pain. Published by Elsevier Science B.V. All rights reserved. Keywords: Chronic pain; Back pain; Fibromyalgia; Catastrophizing; Factor structure
1. Introduction The role of catastrophic cognitions in the development and maintenance of anxiety disorders and hypochondriasis has been widely recognized (Salkovskis and Clark, 1993). In these settings, catastrophizing has been conceived as a cognitive style that involves the tendency to misinterpret and exaggerate the threat value of situations (e.g. bodily sensations). Although the criteria for catastrophizing about pain have never been explicitly stated, it has been broadly defined as an exaggerated negative orientation towards actual or anticipated pain experiences (Sullivan et al., 1995). Catastrophizing about pain has been identified as one of the most important psychological variables in explaining responses to pain in clinical and non-clinical situations (Sullivan et al., 1995). Considerable research has shown that catastrophizing contributes to more intense pain (Sulli-
* Corresponding author. Tel.: 132-9-2649105; fax: 132-9-2649149. E-mail address: [email protected] (S. Van Damme). 1 Postdoctoral fellow of the Fund for Scientific Research – Flanders (Belgium) (F.W.O.). 2 Research assistant of the Fund for Scientific Research – Flanders (Belgium) (F.W.O.).
van et al., 1995, 1997; Sullivan and Neish, 1999), disability (Sullivan et al., 1998), and increased emotional distress in response to pain (Heyneman et al., 1990; Sullivan et al., 2001a). Furthermore, catastrophizing has been associated with overprediction of pain, increased pain behavior, increased use of health care services, longer hospital stays, and increased use of medication (Goubert et al., 2001; Sullivan et al., 2001b). In a prospective study, Linton et al. (1998) found that catastrophic thinking about pain is associated with an increased risk for pain complaints and disability 1 year later. Another prospective study, investigating predictors of back pain chronicity 1 year after the acute onset, revealed catastrophizing as the most powerful predictor (Burton et al., 1995). Catastrophic thinking about pain has been conceptualized in different ways. First, based upon interview responses from dental patients, Chaves and Browne (1987) described catastrophizers as individuals who have a tendency to magnify or exaggerate the threat value or seriousness of pain sensations. Second, analyzing the thoughts of participants undergoing an experimental pain procedure, Spanos et al. (1979) classified individuals as catastrophizers when their thought reflected worry, fear, and the inability to divert
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attention away from pain. Finally, Rosenstiel and Keefe (1983) related catastrophizing in patients with chronic low back pain to helplessness and pessimism concerning one’s ability to deal with the pain experience. All three studies have in common the idea that pain catastrophizing is a form of negative pain-related cognition, but they differ in the specific content of this cognition. Osman et al. (1997) noted that the growing amount of research sharply contrasts with the few attempts to develop and validate self-report measures for assessing catastrophizing about pain. An important contribution in this area came from Sullivan et al. (1995), who developed the Pain Catastrophizing Scale (PCS). This is a 13-item self-report measure for use in clinical and non-clinical populations, in patients with pain and pain-free individuals, and in people with acute and chronic pain. The items were selected from the above described conceptualizations of catastrophizing (Spanos et al., 1979; Rosenstiel and Keefe, 1983; Chaves and Browne, 1987). In the PCS participants reflect on past painful experiences and indicate the degree to which they experienced a number of thoughts or feelings when experiencing pain. Sullivan et al. (1995) investigated the factor structure of the PCS in a sample of 439 students. An exploratory principal components analysis with oblique rotation of the items revealed three factors: rumination (four items), magnification (three items), and helplessness (six items). Osman et al. (1997) tried to replicate this factor structure in a sample of 288 students, using the same procedure as Sullivan et al. (1995). Their exploratory principal components analysis with oblique rotation revealed a twofactor solution: the first factor consisted of seven items (relating to magnification and helplessness), while the second factor consisted of six items (relating to rumination). In a subsequent study in the same article, Osman et al. (1997) used a confirmatory factor analysis (CFA) to evaluate the adequacy of fit of the found two-factor solution, compared to the original three-factor oblique model of Sullivan et al. (1995). The three-factor model provided the best fit to the observed data, replicating the original factor structure of Sullivan et al. (1995). This structure was also replicated in a community sample by Osman et al. (2000). Evidence for the three-factor model was also found by Sullivan et al. (2000a), performing a CFA on a sample of sport participants. Furthermore, they investigated whether the PCS measured the same content across two samples: a sample of sporting persons and a sample of sedentary persons. They found that the factor structure was the same in both samples. The current study is an attempt to further validate the PCS by investigating its internal structure. Although the PCS is used extensively in clinical research, minimal information about the psychometric properties of the PCS is available for clinical populations. Almost all psychometric research has been conducted on pain-free participants. Therefore, the first aim of the study was to replicate the factor structure of the PCS in three Dutch-speaking samples, i.e. chronic low
back pain patients, fibromyalgia patients, and pain-free students, by means of a CFA. In addition, it is not known to what extent the PCS measures the same processes in different subgroups. It is possible that the factor structure of a pain-related construct such as catastrophizing varies as a function of differences in the experience and the localization of pain and as a function of gender (Sullivan et al., 2000b). Therefore, the second and most important aim of the study was to investigate the stability of the factor structure in different subgroups. This study is the first to investigate the invariance of the factor structure of the PCS across different pain patient groups and persons without pain complaints and across gender by means of a multi-sample analysis.
2. Methods 2.1. Participants The PCS was completed by 550 pain-free Dutch-speaking Belgian first-year psychology students (147 males and 403 females, mean age ¼ 18.72 years, SD ¼ 1.42) and 262 pain patients (83 males and 179 females, mean age ¼ 42.70 years, SD ¼ 10.77) from Belgian and Dutch pain clinics. The sample of pain patients consisted of 162 chronic low back pain patients (63 males and 99 females, mean age ¼ 41.50 years, SD ¼ 11.55) and 100 fibromyalgia patients (20 males and 80 females, mean age ¼ 44.64 years, SD ¼ 9.11), fulfilling the American College of Rheumatology (ACR) criteria (Wolfe et al., 1990). The data were obtained in different studies. Medians, means, and standard deviations of the PCS-scores in each sample are presented in Table 1. 2.2. PCS The Dutch version of the PCS (PCS-DV; Crombez et al., 1999) was used. The psychometric characteristics of the PCS-DV were reviewed by Van Damme et al. (2000). The questionnaire showed to be valid and highly reliable. A good internal consistency of the PCS-DV (Cronbach’s alpha between 0.85 and 0.91) was found (Crombez et al., 1998, 1999). Furthermore, evidence was found for the construct validity (Crombez et al., 1999) and the concurrent validity (Crombez et al., 1998). Table 1 Median, means, and standard deviations of PCS-scores Samples
Median
Mean
SD
Students ðn ¼ 550Þ Chronic low back pain patients ðn ¼ 162Þ Fibromyalgia patients ðn ¼ 100Þ
16.00 22.00
16.56 21.99
7.78 9.31
25.50
24.81
12.24
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2.3. CFA 2.4. Assessing three models The factor structure of the PCS-DV was investigated using the CFA module of the INTERACTIVE LISREL 8.30 framework (Du Toit et al., 1999). In line with the recommendations of Bollen and Long (1993), several fit indices were used to assess model fit. In the present study, model fit is assessed using the following goodness-of-fit indices: x 2, Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI). (a) x 2 is the most frequently used goodness-of-fit index. A significant chi-square suggests that a significant amount of observed covariance between items remains unexplained by the model, while non-significance implies a good fit to the data (Cole, 1987). A disadvantage of this index is its sensitivity to sample size. In a small sample size, a poor fit may nevertheless result in a non-significant x 2. It is also possible that in a large sample size a good fit results in a statistically significant x 2 (Marsh et al., 1988). In the present study, the x 2 is the normal theory weighted least squares x 2, as the other fit indices are based upon this index in the LISREL 8.30 program. (b) The RMSEA (Steiger, 1990) is a fit measure based on the population error of approximation. The idea behind it is that it is unreasonable to assume that the model holds exactly in the population. The RMSEA takes account of the error of approximation in the population. According to Browne and Cudeck (1993), a RMSEA value of 0.05 indicates a close fit and values up to 0.08 represent reasonable errors of approximation in the population. (c) The CFI (Bentler, 1990) is an incremental fit index. It represents the proportionate improvement in model fit by comparing the target model with a baseline model (usually a null model in which all the observed variables are uncorrelated). CFI values greater than 0.90 indicate an adequate fit.
In the present study, CFA is used to compare models. This requires the specification of nested models. Nested models contain the same parameters, and the set of free parameters in one model is a subset of the set of free parameters in another model. The fit of nested models can be compared by an inspection of the x 2 test, since in these models the distribution of x 2 change equals the distribution of x 2 (e.g. when the difference in degrees of freedom between two models is 1, a difference in x 2 of 3.84 is significant at an alpha level of 0.05). 3. Results 3.1. Comparison of three models of pain catastrophizing To assess the factor structure of the PCS, three models were compared. Model 1 is a one-factor model, in which the 13 items are assumed to be indicators of a single latent factor (pain catastrophizing). Model 2 is the oblique twofactor model found by Osman et al. (1997), in which the seven magnification–helplessness items and the six rumination items are assumed to measure two correlated dimensions. Model 3 is the oblique three-factor model proposed by Sullivan et al. (1995), in which the four rumination items, three magnification items, and six helplessness items are assumed to measure three correlated dimensions. Furthermore, the stability of the solution was tested in three samples: pain-free students, chronic low back pain patients, and fibromyalgia patients. Based on the goodness-of-fit summary shown in Table 2, it can be concluded that the three-factor model provides the best fit for the data in all samples, whereas the one-factor model provides the poorest fit. In the student sample, the three-factor model shows acceptable fit to the data
Table 2 Goodness-of-fit summary for the different models tested a
x 2 (d.f.)
RMSEA
CFI
Student sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
542.22**** (65) 457.20**** (64) 291.84**** (62)
0.12 0.11 0.08
0.82 0.84 0.91
Chronic low back pain sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
210.24**** (65) 191.73**** (64) 158.71**** (62)
0.12 0.11 0.10
0.83 0.84 0.88
Fibromyalgia sample Model 1: one factor (13 items) Model 2: two oblique factors (7 1 6 items) Model 3: three oblique factors (3 1 4 1 6 items)
126.05*** (65) 125.04*** (64) 107.74*** (62)
0.10 0.10 0.09
0.92 0.92 0.93
a
***P , 0:001; ****P , 0:0001.
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Table 3 Standardized factor loadings and internal consistency of the oblique three-factor solution Students 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:81Þ – – – – – – – 0.61 0.80 0.82 0.68 – –
Magnification ða ¼ 0:59Þ – – – – – 0.75 0.42 – – – – – 0.54
Helplessness ða ¼ 0:76Þ 0.48 0.66 0.55 0.77 0.76 – – – – – – 0.36 –
CLBP patients 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:81Þ – – – – – – – 0.61 0.74 0.77 0.73 – –
Magnification ða ¼ 0:70Þ – – – – – 0.87 0.48 – – – – – 0.54
Helplessness ða ¼ 0:79Þ 0.71 0.60 0.72 0.75 0.62 – – – – – – 0.33 –
FM patients 1 2 3 4 5 6 7 8 9 10 11 12 13
Rumination ða ¼ 0:84Þ – – – – – – – 0.74 0.65 0.83 0.77 – –
Magnification ða ¼ 0:72Þ – – – – – 0.89 0.61 – – – – – 0.53
Helplessness ða ¼ 0:89Þ 0.82 0.80 0.87 0.87 0.81 – – – – – – 0.41 –
(RMSEA , 0.08, CFI . 0.90), whereas the two-factor and the one-factor model explain the data poorly (RMSEA . 0.08, CFI , 0.90). In the fibromyalgia sample, all three models show more or less acceptable fit to the data. In the chronic low back pain sample, only the three-factor model approaches an acceptable fit to the data. Because the models are nested, we can statistically compare their adequacy using x 2 difference tests. The comparison of the one-factor model with the three-factor model reveals that the latter model explains the data significantly better than the former in the student sample (Dx2 ð3Þ ¼ 250:38, P , 0:0005), in the chronic low back pain sample (Dx2 ð3Þ ¼ 51:53, P , 0:0005), and in the fibromyalgia sample (Dx2 ð3Þ ¼ 18:31, P , 0:001). Furthermore, the three-factor model explains the data significantly better than the two-factor model in the student sample
(Dx2 ð2Þ ¼ 165:36, P , 0:0005), in the chronic low back pain sample (Dx2 ð2Þ ¼ 33:02, P , 0:0005), and in the fibromyalgia sample (Dx2 ð2Þ ¼ 17:30, P , 0:0005). These results indicate that the PCS-items can be considered as representing three correlated latent dimensions, namely rumination, magnification, and helplessness. It should be noted that the three latent factors were highly correlated in the chronic low back pain sample and the fibromyalgia sample, and moderately correlated in the student sample. The standardized factor loadings and internal consistency of the subscales in each sample are presented in Table 3. 3.2. Invariance of the oblique three-factor model across subgroups To examine whether the three-factor oblique model is
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invariant across subgroups, multi-sample analyses were conducted. In the first multi-sample analysis, the model was fit separately for pain-free students, chronic low back pain patients, and fibromyalgia patients, and a x 2 statistic for the overall fit was computed to assess overall parameter invariance. A very restrictive model was tested by equating the number of factors, the factor loadings, and the correlations between the factors. The results of this multi-sample analysis show that the specified model can be considered as adequately fitting the data (CFI ¼ 0.91), which means that the model is invariant across the three samples. The 13 PCS items represent three underlying dimensions and contribute equally to their respective factors in pain-free students, chronic low back pain patients, and fibromyalgia patients (invariance of factor loadings). Furthermore the intercorrelations between the latent rumination, magnification, and helplessness dimensions do not differ across samples (invariance of factor intercorrelations). A similar multi-sample analysis was performed to investigate the invariance of the factor structure across gender. The model was fit separately for all males ðn ¼ 228Þ and females ðn ¼ 580Þ, and a x 2 statistic for the overall fit was computed to assess overall parameter invariance. The results showed that the specified model could be considered as adequately fitting the data (CFI ¼ 0.90), which means that the model is invariant across gender.
4. Discussion The present study investigated the factor structure of the PCS-DV using CFA in three different samples: chronic low back pain patients, fibromyalgia patients, and pain-free students. Furthermore, the invariance of the factor structure across these samples and across gender was examined by means of a multi-sample analysis. With respect to the factor structure of the PCS-DV, we found that an oblique three-factor solution provided the best fit to the data in all three samples. Furthermore, this threefactor model adequately fitted the data for the pain-free students and the data for the fibromyalgia patients, and almost adequately fitted the data for the chronic low back patients. Taking into account that the factor structure was found to be invariant across these three samples, it is reasonable to conclude that the three-factor model is robust in different clinical and non-clinical samples. This finding, which is consistent with previous research (Osman et al., 1997, 2000; Sullivan et al., 2000a), supports the theoretical distinction between rumination, magnification, and helplessness as independent but strongly related dimensions of pain catastrophizing (Sullivan et al., 1995). Although the above evidence suggests that the PCS assesses the same processes in different populations, similar models do not guarantee the equivalence of item measurement or theoretical factor structure (Byrne et al., 1989). Factorial stability can only be tested using a multi-sample
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analysis. An example of this strategy is the study of Lousberg et al. (1999), who found invariance of the factor structure of the Multidimensional Pain Inventory across fibromyalgia and back pain patients. A similar analysis was performed in the present study. The oblique three-factor model yields a structure that is invariant across three samples (pain-free students, chronic low back pain patients, and fibromyalgia patients) and across gender. One of the most restrictive models was tested, with the invariance pertaining to the number of factors, the factor loadings, and the factor intercorrelations. This is an important finding, as researchers assume too easily that questionnaire scores have the same meaning for all groups in all settings. In spite of the availability of appropriate statistical procedures, many validation studies insufficiently investigate factorial invariance across different groups. The current study is the first to support the invariance of the factor structure of the PCS across different pain patient samples and a non-clinical sample, and across gender. The results from this study have a number of implications. First, they support the idea that the PCS is an instrument that can be used for several pain problems and in different settings. Although the persons in this study show large differences in the experience and localization of pain, the PCS seems to assess the same processes of catastrophizing. Second, they indicate that results obtained in one population or situation are likely to generalize as differences in catastrophizing are quantitative and not qualitative in nature. Promising is the idea that studies using experimental pain in healthy volunteers can capture the same catastrophic processes as in pain patients. This facilitates the generalization of both clinical and non-clinical research using the PCS. Third, our results indicate that gender differences in pain catastrophizing are also quantitative and not qualitative in nature. Therefore the PCS is a valuable instrument to investigate the explanatory role of pain catastrophizing in gender differences in pain perception and experience (Sullivan et al., 2000b). Fourth, our results suggest a crosscultural invariance of the factor structure of the PCS, as we were able to replicate the same factor structure using a translated version of the PCS and using non-American participants. Further research is needed on the relation between the PCS-factors to develop comprehensive theories addressing the interplay between psychological processes (such as catastrophizing) and the experience of pain (Sullivan et al., 2001b). An example of such theory came from Sullivan et al. (2001b), who recently proposed a hierarchical model in which appraisal processes (Lazarus and Folkman, 1984) play a role in the link between catastrophizing and pain experience. Magnification and rumination are considered as primary appraisal processes in which individuals exaggerate and focus on the threat value of pain sensations (Eccleston and Crombez, 1999), whereas helplessness may be related to secondary appraisal processes in which individuals negatively evaluate their ability to cope effectively
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with their pain (Sullivan et al., 2001b). Furthermore, research is needed to confirm the factor structure of the PCS in other pain-free samples and other pain problems. A limitation of the current study is that only a sample of students is used to represent pain-free subjects. In summary, the present study supports the original factor structure of the PCS in Dutch-speaking samples. The factor structure is invariant across pain-free students, chronic low back pain patients, and fibromyalgia patients, and across gender. Across a strong variety of pain characteristics, catastrophizing consists of three related dimensions reflecting the tendency to focus excessively on pain sensations, to magnify the threat value of pain sensations, and to perceive oneself as unable to control the intensity of pain (Sullivan et al., 2000a). Acknowledgements This study was supported by research grants G.0107.00 and G.0107.98 of the Fund for Scientific Research, Flanders, Belgium. The authors like to thank Johan Vlaeyen for providing part of the questionnaire data. References Bentler PM. Comparative fit indices in structural models. Psychol Bull 1990;217:238–246. Bollen KA, Long JS. Introduction. In: Bollen KA, Long JS, editors. Testing structural equation models, Newbury Park: Sage Publications, 1993. pp. 1–9. Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing structural equation models, Newbury Park: Sage Publications, 1993. pp. 136–162. Burton AK, Tillotson KM, Main CJ, Hollis S. Psychosocial predictors of outcome in acute and subchronic low back trouble. Spine 1995;20:722– 728. Byrne BM, Shavelson RJ, Muthen B. Testing for the equivalence of factor covariance and mean structures: the issues of partial measurement invariance. Psychol Bull 1989;105:456–466. Chaves JF, Browne JM. Spontaneous cognitive strategies for the control of clinical pain and stress. J Behav Med 1987;10:263–276. Cole DA. Utility of confirmatory factor analysis in test validation research. J Consult Clin Psychol 1987;55:584–594. Crombez G, Eccleston C, Baeyens F, Eelen P. When somatic information threatens, pain catastrophizing enhances attentional interference. Pain 1998;75:187–198. Crombez G, Vlaeyen JWS, Heuts PHTG, Lysens R. Pain-related fear is more disabling than pain itself: evidence on the role of pain-related fear in chronic low back pain disability. Pain 1999;80:329–339. Du Toit S, Du Toit M, Jo¨ reskog KG, So¨ rbom D. Interactive LISREL: user’s guide. Chicago, IL: Scientific Software International Inc, 1999. Eccleston C, Crombez G. Pain demands attention: a cognitive-affective model of the interruptive function of pain. Psychol Bull 1999;125:356– 366. Goubert L, Francken G, Crombez G, Vansteenwegen D, Lysens R. Exposure to physical movement in chronic back pain patients: no evidence for generalization across different movements. Behav Res Ther 2001 (in press).
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