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 Table of Contents  
Year : 2019  |  Volume : 13  |  Issue : 1  |  Page : 30-37

Construct validity and factor analysis of the Gujarati version of the fear-avoidance beliefs questionnaire

Department of Musculoskeletal Physiotherapy, Sarvajanik College of Physiotherapy, Rampura, Surat, Gujarat, India

Date of Submission06-Apr-2018
Date of Acceptance01-Nov-2018
Date of Web Publication29-Jun-2019

Correspondence Address:
Dr. Dibyendunarayan Dhrubaprasad Bid
Department of Musculoskeletal Physiotherapy, Sarvajanik College of Physiotherapy, Rampura, Surat - 395 003, Gujarat
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/PJIAP.PJIAP_11_18

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BACKGROUND: Although commonly used, no reports exist on the testing of the construct validity and factor analysis of the Gujarati version of the Fear-Avoidance Beliefs Questionnaire (FABQ-G).
STUDY DESIGN: This is an observational prospective study.
OBJECTIVE: The objective of the study is to assess the construct validity and factor analysis of the Gujarati version of the FABQ.
MATERIALS AND METHODS: Item analysis, factor analysis, and construct validity were done with 128 chronic low back pain (CLBP) patients. Convergent and divergent validity (Pearson's correlation) was assessed by comparing FABQ-G to Numerical Pain Rating Scale, pressure pain threshold, Central Sensitization Inventory-Gujarati (CSI-G), Roland–Morris Disability Questionnaire-Gujarati (RMDQ-G), trunk flexors endurance, and trunk extensors endurance in CLBP patients. Potential ceiling and floor effects and prediction power were measured along with internal and external responsiveness of FABQ.
Results: This study shows a three-factor model for FABQ-G. The convergent validity of the FABQ-G was supported by the pattern of correlations with the RMDQ-G (r = 0.514 and P < 0.000) and CSI-G (r = 0.455 and P < 0.000) in our study. The divergent validity was seen by negative correlation or no correlation with trunk flexors and extensors endurance (r = −0.266 and P < 0.002). No ceiling and floor effects were detected in the questionnaire. The FABQ showed good prediction power and responsiveness in both internal and external responsiveness analyses.
Conclusion: The reasonable validity of the three-factor FABQ-G shown in this study makes it appropriate for the clinical use with Gujarati CLBP patients.

Keywords: Chronic low back pain, construct validity, factor analysis, Fear-Avoidance Beliefs Questionnaire-Gujarati version

How to cite this article:
Bid DD, Alagappan TR. Construct validity and factor analysis of the Gujarati version of the fear-avoidance beliefs questionnaire. Physiother - J Indian Assoc Physiother 2019;13:30-7

How to cite this URL:
Bid DD, Alagappan TR. Construct validity and factor analysis of the Gujarati version of the fear-avoidance beliefs questionnaire. Physiother - J Indian Assoc Physiother [serial online] 2019 [cited 2022 Aug 15];13:30-7. Available from: https://www.pjiap.org/text.asp?2019/13/1/30/261813

  Introduction Top

This study is the extension of our previous article.[1] Chronic low back pain (CLBP) is defined as a condition, in which the back pain may last for >12-week duration.[2] Among the psychosocial factors, the fear-avoidance beliefs (FABs) as described in the FABs model developed by Lethem et al.[3] have been hypothesized as one of the most important, specific, and powerful cognitive variables that may have an impact on disability and treatment outcomes in patients with LBP.[3],[4],[5],[6] The main basis of this model is the perception that pain is not only influenced by organic pathology but also by induced pain-related fears. Cognitive and affective variables are pertinent determining factors of pain experience and disability.[7] A prior and major notion of patients with LBP is that activity will aggravate pain and lead to avoidance of activities, which consequently turns out to be major contributors to the maintenance of LBP. Several authors described the relationship between fear of pain and avoidance. According to cognitive-behavioral theory, avoidance leads to a vicious circle indicated by decreased self-efficacy, fear, further avoidance, and disability and is maintained by the reduction of anxiety, which is attained through avoidance of feared activities.[3],[8],[9],[10],[11] Therefore, appropriate belief assessment is essential for research studies and clinical practice;[12] hence, self-reported outcome measures are best applicable.[13]

From our previous study,[1] we have already established that the content validity and face validity, which were found to be excellent. Gujarati version of the FAB Questionnaire (FABQ-G) exhibited excellent internal consistency shown by a Cronbach's α value of 0.843, and subscales FABQ-G-W and FABQ-G-PA also showed good internal consistencies (α = 0.652 and 0.654, respectively). The test–retest reliability was excellent in CLBP patients (intraclass correlation [ICC] = 0.915) and ICC = 0.864 and 0.818 for the FABQ-W and FABQ-PA, respectively. The pain intensity score had a high correlation with FABQ-W (r = 0.819 and P < 0.01) and with the FABQ-PA (r = 0.852 and P < 0.01) for participants with CLBP showing good convergent validity with FABQ-G.[1]

The objectives of this study were to test the construct validity and factor analysis of FABQ-G in Gujarati-speaking CLBP patients.

  Materials and Methods Top


Native Gujarati-speaking 128 patients with CLBP were recruited for the study from five physiotherapy outpatient departments in Surat. Patients were excluded if they had back pain related to vertebral fracture, myelopathy, back surgery, brain surgery, clinically recognizable cognitive impairment, cardiovascular or respiratory problems, neurological deficits, cancer, or other systemic diseases with a possible effect on the musculoskeletal system. The ethical approval of the study was obtained from the Institutional Ethics Committee of Nirmal Hospital, Surat. Subsequently, the protocol was registered in the Clinical Trial Registry of India bearing registration number CTRI/2017/007683. Written informed consent was taken from each patient before participation.

Questionnaires/outcome measures

Fear-Avoidance Beliefs Questionnaire-Gujarati

FABQ was developed by Waddell et al.[11] to measure FABs in LBP patients. It is a 16-item, self-reporting questionnaire, in which each item is graded on a seven-point Likert scale strongly disagree to strongly agree, and factor analysis showed two subscales, one subscale focused on patients beliefs about how physical activities affect their pain (FABQ-PA) and the other focused on patient's beliefs about how work affects their pain (FABQ-W).[11],[14],[15] Here, FABQ Gujarati version [1] was used in this study. The FABQ has been demonstrated to be valid and reliable in a chronic LBP population.[11]

Central Sensitization Inventory-Gujarati

The Central Sensitization Inventory-Gujarati (CSI-G) contains Part-A of 25 statements related to current health symptoms. Each of these items is measured on a five-point temporal Likert scale, with the following numeric rating scale: never (0), rarely (1), sometimes (2), often (3), and always (4). A cumulative score ranges from 0 to 100. In addition, information is collected in Part-B on previously diagnosed CS and related conditions.

Roland–Morris Disability Questionnaire

Internal consistency of the Roland–Morris Disability Questionnaire (RMDQ) is found to be adequate (>0.65) at both times, with high ICCs also at both time points. Internal construct validity of the scale is good, indicating a single underlying construct.[16] Gujarati version of RMDQ is reliable and valid.[16]

Pressure pain threshold: Infraspinatus and tibialis anterior

Pressure stimuli were measured using a handheld digital algometer (Orchid Scientific, Nashik). A pain detection threshold was measured on the infraspinatus muscle and tibialis anterior muscle. The pressure was increased at a rate of approximately 1 Kg/s. Participants had to report when the feeling of pressure alone changed into a feeling of pressure and pain (pain detection threshold). The mean of two measurements, taken 30 s apart from each other, was used.

Numerical pain rating scale

A two-point change on the numerical pain rating scale (NPRS) represents the clinically meaningful change that exceeds the confines of measurement error.[17]

Trunk flexors and extensors endurance

These tests were performed as described by Ito et al.,[18] and these tests are having excellent reliability and internal consistency.

Global rating of change scale

The 15-point global rating of change (GROC) has been used as it has been well documented and extensively used in research as an outcome measure to review physiotherapy outcome.[19]


Translation and cross-cultural adaptation of FABQ in Gujarati were done in our previous study.[1] Furthermore, content validity, test–retest reliability, minimum detectable change, and agreement for FABQ-G were evaluated in our previous study.[1] The Gujarati translation of FABQ is given in [Appendix 1].

Psychometric testing

Construct validity of Gujarati version of the Fear-Avoidance Beliefs Questionnaire

Construct validity is frequently measured as convergent and divergent validity and factor analysis. In this study (n = 128), convergent validity was evaluated by two parallel questionnaires CSI-G and RMDQ-G. Besides these, pressure pain threshold (PPT) and NPRS measurement also support the convergent validity of FABQ-G. Divergent validity was tested by Pearson's correlation coefficients by showing that the FABQ-G measurement concept is different from the measurement concept of trunk flexors and extensors endurance. In addition, factor analysis was done for determining construct validity.

Construct validity was assessed by calculating Pearson's correlation coefficients (r),[13] comparing the extent to which the expected relationships between the various constructs were fulfilled using the FABQ-G. All the expected relationships were based on the literature. The r values yield the degree of correlation between two measures where 0 = no correlation between the two scores and 1 or −1 = the absolute correlation between the two scores. Pearson's correlation coefficients are interpreted as follows: 0.00–0.19 = very weak correlation; 0.20–0.39 = weak correlation; 0.40–0.69 = moderate correlation; 0.70–0.89 = strong correlation; and 0.90–1 = very strong correlation.[20],[21]

Based on previous studies with similar objectives and our clinical experience, we hypothesized the following relationships between the various constructs a priori.

  1. FABQ-G and CSI-G would have a high correlation
  2. FABQ-G and PPT-infraspinatus (PPT-IS) and PPT-tibialis anterior (PPT-TA) would have moderate-to-high correlation
  3. NPRS would have moderate-to-high correlations with FABQ-G
  4. FABQ-G and RMDQ-G would have moderate-to-high correlations
  5. FABQ-G and trunk flexors and extensors endurance would have low to negative correlation.

Statistical analyses

Descriptive statistics (percentages, means, and standard deviations) were used to describe demographic characteristics within the study. Item analysis was used to find corrected item correlation as an index of validity of the items. The higher the value of corrected item correlation, the greater the tendency that the item to vary as the total score of the questionnaire varies. Exploratory factor analysis was performed to determine the dimensionality of the items of the questionnaires. Factor structure was analyzed using principal component analysis (PCA) with varimax rotation method. Eigenvalues ≥1 were retained, and items with loadings ≥0.4 were considered satisfactory.[22] A PCA [23],[24] was run to establish construct validity of the items in the scale. The acceptable level of communalities and factor loadings for items would be 0.5 and eigenvalue more than one would be considered for component factors. An item analysis was done to check the reliability of the scale components and its Cronbach's alpha. For the data reduction, the following norms were considered: PCA, varimax rotation, communalities >0.5, factor loading >0.5 (as the study sample size is more than 120), sample size 128, KMO/MSA >0.45, anti-image correlation matrix >0.45, correlation matrix >30%, and eigenvalue >1. The factor structure from exploratory factor analysis (EFA) was verified by confirmatory factor analysis (CFA) using structural equation modeling (SEM) to validate stability. AMOS software, version 20 of SPSS 20, IBM, Armonk, NY, USA, was used to test the construct. Potential ceiling and floor effects were measured by calculating the percentage of patients indicating the minimum and maximum possible scores in both questionnaires. Ceiling and floor effects are considered to be present if more than 15% of respondents achieved the lowest or highest possible total score and was explored by calculating the skewness of the distribution.[13] Responsiveness (internal sensitivity to change) was assessed by a standardized effect size (Partial eta squared). Statistical significance of differences in the change scores was tested by analysis of variance. An effect size of more than 0.80 was considered a good effect size, 0.40–<0.80 a moderate effect size, and <0.40 a small effect size.[25],[26] External responsiveness was assessed by constructing ROC curves using dichotomized GROC rates to categorize participants as “improved” and “not improved.” The analysis is based on the area under the curve (AUC), and values of at least 0.70 are considered responsive.[25] A hierarchical regression model was used (both cross-sectional and longitudinal) to find the prediction power of the FABQ-G. All analyses of the study were conducted using SPSS 20, IBM, Armonk, NY, USA, with a 95% confidence interval limits.

  Results Top

Psychometric properties of Gujarati version of the Fear-Avoidance Beliefs Questionnaire

A total of 128 CLBP patients were recruited and answered all the questionnaires. [Table 1] shows the characteristics of the study participants. Overall, patients were able to complete the FABQ-G, CSI-G, RMDQ-G, and GROC questionnaires without help. There were very few missing values.
Table 1: Demographic characteristics of subjects

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Item analysis

Item analysis revealed that the corrected item-total correlation validity index was >0.2, and there was no negative and noncorrelation, indicating a satisfactory degree of discrimination among the items of the questionnaire contributing to the total score.

Construct validity for Gujarati version of the Fear-Avoidance Beliefs Questionnaire

The pain intensity score had a high correlation with FABQ-W (r = 0.819; P < 0.01) and with the FABQ-PA (r = 0.852 and P < 0.01) for participants with CLBP, showing good convergent validity with FABQ-G. The FABQ-G total score was significantly positively correlated with CSI-G, PPT-IS, PPT-TA, and RMDQ-G, but there were no correlations obtained for trunk flexors and extensors endurance with FABQ-G [Table 2].
Table 2: Correlation between various constructs (n=128) with FABQ-G

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Factorial validity of Gujarati version of the Fear-Avoidance Beliefs Questionnaire

The results of factor analyses of the 15 items of the FABQ-G are presented in [Table 3]. Item Q-8 of the original questionnaire was omitted as it is not applicable in our country. Three factors were extracted for the FABQ-G, which accounted for 74.56% of the total variance in the PCA with varimax rotation. The use of SEM in CFA analysis showed that a three-factor model had some weak model fit indices and a model deleting two items from factor 1 and 2 showed a near satisfactory model fit [Table 4] and [Table 5], [Figure 1].
Table 3: Varimax Rotated Factor Loading Matrix of the FABQ-G

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Table 4: Confirmatory factor analysis to test the stability of three factor measurement model of FABQ-G

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Table 5: Standardized Regression Weights for FABQ-G

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Figure 1: Confirmatory factor analysis to test the stability of the three-factor measurement model of Gujarati version of the Fear-Avoidance Beliefs Questionnaire

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Floor and ceiling effect

There were no ceiling or floor effects for the FABQ total score; none of the participants achieved the highest possible score or lowest score. The skewness of data distribution was 0.023 (0.214), i.e., between −1 and +1.[13]

Internal responsiveness

Analysis revealed a significant reduction in the score at follow-ups with that at baseline (F = 511.293 and P < 0.001). The effect size (partial eta squared), indicating responsiveness of the FABQ-G to intervention, was 0.80 [Table 6].
Table 6: Internal responsiveness

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External responsiveness

The AUC value for the FABQ was 0.837 (0.769–0.906) which was significantly high, indicating a good external responsiveness of FABQ [Figure 2].
Figure 2: Receiver operating characteristic curve for Gujarati version of the Fear-Avoidance Beliefs Questionnaire

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Prediction power

In cross-sectional and longitudinal hierarchical regression analysis of low back disability, the FABQ-G proved to be a significant independent predictor of disability, explained quite a large proportion of the variance [Table 7].
Table 7: Summary of cross sectional and longitudinal hierarchical regression analysis of low back disability

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  Discussion Top

The FABQ-G was highly acceptable and easily understood and was found suitable for self-administration. It required approximately 5–6 min filling up. Hence, it seems to be appropriate in routine clinical practice. Avoidance behavior led by FABs in patients with CLBP leads to the development of chronic disability. In reality, fear-avoidance behavior was shown to be a significant risk factor for chronicity. Hence, encouraging patients to change their beliefs and behaviors has become more crucial in managing CLBP, especially in the early stage. It is important to focus on educating patients regarding pain along with gradual exposure to activities to help reduce pain-related fear, rather than allowing patients believing the imaging reports leading to the development of fear-avoidance behavior. The FABQ helps clinicians identify patient's FABs and helps create an effective management strategy to prevent CLBP.

The item analysis showed a satisfactory internal discrimination of items of FABQ-G. The EFA was used to examine the structure of the FABQ-G instead of a confirmatory factor analysis model since the number of possible factors expected was not predetermined from the literature and either a two- or a three-factor model was anticipated.[11],[27] PCA modeling identified three distinct factors with salient loadings of the items. Initially, the factorability of the 15 items excluding item number 8 of the FABQ-G scale was examined. Several well-recognized criteria for the factorability of a correlation were used. First, all the 15 items correlated at least 0.3 with at least one other item and determinant was 0.001. Moreover, inspection of the correlation matrix revealed that more than 30% of correlations are significant at the 0.01 level, suggesting reasonable factorability. Second, the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.888, above the recommended value of 0.6, and Bartlett's test of sphericity was statistically significant (Chi-square (105) = 1584.791 and P < 0.05). The diagonals of the anti-image correlation matrix were all over 0.5, supporting the inclusion of each item in the factor analysis. Finally, the communalities were all above 4, further confirming that each item shared some common variance with other items. Given these overall indicators, factor analysis was conducted with all 15 items.

Principal component analysis was used because the primary purpose was to identify and compute composite coping scores for the factors underlying the scale. The initial analysis considering factors more than one eigenvalue produced a three-factor solution with a 74.56% total variance. The initial eigenvalues showed that the first factor explained 32.45% of the variance, the second factor 32.44% of the variance, and a third factor 9.67% of the variance. A two-factor solution was examined, using a varimax rotation of the factor loading matrix. It explained only 67.77% of the variance. A three-factor solution was preferred because of the “leveling off” of eigenvalues on the scree-plot after three factors. There was little difference between the varimax and oblimin solutions, and thus, both solutions were examined in the subsequent analyses before deciding on varimax rotation for the final solution.

A serious concern in factorial models is the adequacy of sampling, resulting in desired samples consisting of 300 and more participants.[24] Although, in this study, only 128 participants participated, the factors identified which had more than 0.5 loadings above eigenvalues >1 [Table 3], confirming a reliable model regardless of sample size.[28] Therefore, it can be argued that the three-factor model, as established in this study, is statistically sound and acceptable for use. The result of the three-factor CFA by SEM indicated that there was a little stability issue of the construct which was settled by removing two items from the model. This must have been due to low sample size used for the analysis. The internal consistency (Cronbach's alpha) of the subscales of the scale version was FABQ-PA-G = 0.881; FABQW-G = 0.868 showed excellent consistency. The convergent validity of the FABQ was supported by the pattern of correlations with the RMDQ-G (r = 0.514 and P < 0.000) and CSI-G (r = 0.455 and P < 0.000) in our study. The divergent validity is seen by negative correlation with trunk flexors endurance (r = −0.266 and P < 0.002).

No ceiling and floor effects were detected in the questionnaire. The FABQ showed good responsiveness in both internal and external responsiveness analyses after receiving physiotherapy intervention. However, the present study results were contrary to the results of a Norwegian and Brazilian Portuguese version of FABQ which reported low responsiveness. The reason could be responsiveness was low as it was checked in acute low back pain cases.[29],[30] The prediction analyses confirm that FABs are important factors and influence the pain-related disability.[11],[31]


This study has a few limitations that should be pointed out. First, it is possible that pain-related fear leads to increased activity avoidance and disability, but the reverse also may be possible. Second, the associations between self-reported beliefs and physical tests were not taken into consideration. In future studies, this may be explored. Third, our study was limited to only CLBP, and it is doubtful whether our result can be generalized to acute or subacute LBP and other complaints of the musculoskeletal system. Hence, this may well be further investigated in future studies.

  Conclusion Top

The psychometric testing indicated that the FABQ-G shows psychometric properties similar to the English version. This study reveals that the FABQ-G is a reliable and valid measure to assess “fear-avoidance beliefs” in Gujarati-speaking CLBP patients, and results of FABQ-G can be compared to international studies using other translated versions. The reasonable validity and reliability of the FABQ-G shown in this study make it appropriate for clinical use with Gujarati CLBP patients.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

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  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]


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