Skip to main content

Perceived Vulnerability to Disease Questionnaire: psychometric validation with a Portuguese sample

A Correction to this article was published on 15 June 2022

This article has been updated



Individual differences in one’s perceived vulnerability to infectious diseases are implicated in psychological distress, social and behavioral disease avoidance phenomena. The Perceived Vulnerability to Disease Questionnaire (PVD) is the most extensively used measure when it comes to assessing subjective vulnerability to infectious diseases. However, this measure is not yet accessible to the Portuguese population. The present study aimed to adapt and validate the PVD with 136 Portuguese participants.


Factorial, convergent and discriminant validity (of both the scale and between each factor), and reliability analysis were assessed.


A modified bifactorial model, comprised of Perceived Infectability and Germ Aversion factors, was obtained, with acceptable goodness-of-fit indices, adequate convergent and discriminant validity, and good internal consistencies.


Overall, the 10-items European-Portuguese PVD appears to be a reliable and valid measure of one’s perceived vulnerability to disease, with potential relevance for application in both research and clinical practice pertaining to disease-avoidance processes.

Peer Review reports


Humans, like other animals, possess specialized systems that protect them from pathogens, which pose high risks for the survival and reproductive fitness of the species [1, 2]. One of these systems is the biological immune system, responsible for detecting and destroying foreign elements once they enter the body. However, this complex set of responses is merely reactive, occurring only after an infection has settled, and entails some constraints for the individual, namely the high consumption of physiological energy and the loss of social opportunities (e.g., forming friendship/romantic relationships) [3, 4]. Thus, having a system capable of avoiding pathogens before they enter the body would reduce such costs.

The behavioral immune system (BIS) provides an early defensive response against pathogens. It is extremely sensitive to any cues (be it in objects or people) that suggest the presence of disease in the environment, even if they do not represent a real risk of contagion [5, 6]. Once detected, these cues trigger a disgust reaction, cognitive biases and behavioral avoidance responses with the ultimate goal of inhibiting contact with pathogens [4].

The intensity of these responses is influenced not only by the salience of disease cues, but also by individual differences. For instance, Mortensen et al. [7] showed that a high disease salience (disease versus neutral prime) can temporarily influence individual’s personality traits by making them perceive themselves as less extroverted and open to experience. These traits reflect less willingness to engage in social interactions, a response closely linked to the BIS. Moreover, these effects were more prominent in individuals who perceived themselves as more vulnerable to disease. This is in line with one assumption of the BIS, which states that individuals who are—or merely perceive themselves to be—more vulnerable to infection, display stronger aversive responses towards pathogen-connoting stimuli [5].

Different types of self-report instruments used to assess perceived vulnerability to disease exist, but only one reliably measures individual differences in perceived vulnerability to infectious diseases. For instance, measures of disgust sensitivity can be used to that end, but these tend to focus on a broad range of stimuli of which only a subset are directly relevant to disease transmission. Likewise, instruments designed to assess hypochondria and other health-relevant anxieties involve several potential health problems, not only infectious diseases [8]. Thus, while these measures can be used to infer perceived vulnerability to disease, they are not specific to infectious diseases, unlike the Perceived Vulnerability to Disease Questionnaire (PVD).

The PVD is widely used and was first introduced by Park et al. [9]. Throughout the years, it underwent multiple revisions (14 to 19 items) until Duncan and collaborators [8] developed and validated a 15-item version of the scale, which assesses one’s beliefs about personal susceptibility to and emotional discomfort associated with a potential contagion from infectious diseases. This version has shown good psychometric properties and can be used with the general population.

Studies using the PVD have been consistently showing that individuals chronically concerned about disease transmission seem to be more sensitive to cues heuristically associated with disease and, consequently, tend to adopt more overt discriminant behaviors towards others perceived as having a poor health status, like the obese, the elderly or people with physical disabilities [9,10,11]. They also tend to show more ethnocentric and xenophobic attitudes against strangers [12, 13]. Thus, despite the protective role of the BIS, it also seems to contribute to aversive responses towards people associated with a risk for contagion [2, 3]. Furthermore, while perceived vulnerability to disease leads people to engage more fully in proactive preventative behaviors, especially beneficial when the risk of contagion is high (e.g., Covid-19 pandemic), it has also been associated with considerable psychological distress, increasing both anxiety and depression levels [14, 15]. However, no instrument to measure the perceived vulnerability to infectious diseases exists for the Portuguese population.

Understanding how this variable contributes to the inhibition of social interactions can be helpful for the development of better social strategies aimed at dealing not only with negative behaviors (e.g., stigmatization or prejudice) against people who are (or appear to be) ill, but also with the increased psychological distress felt by those who regard themselves as more susceptible to diseases. Hence, this instrument has potential relevance for application in both research and clinical practice pertaining to disease-avoidance processes. With this in mind, the main goal of this study was to adapt and validate the PVD for the Portuguese population.



One-hundred ninety-five participants, aged between 18 and 65 years (155 women, M = 26.16, SD = 8.86) from three Portuguese Universities voluntarily filled the online protocol. Fifty-nine were excluded because they did not fully complete it. The final sample included 136 participants (109 women, M = 27.01, SD = 9.77).


The Perceived Vulnerability to Disease Questionnaire (PVD) [8], a 15-item seven-points scale (one = “Strongly Disagree” to seven = “Strongly Agree”) with two factors, “Perceived Infectability” (PI) and “Germ Aversion” (GA), was completed.

Participants also completed the Disgust Propensity and Sensitivity Scale-Revised (DPSS-R) [16], a 11-item five-points scale divided into two subscales: “Disgust Propensity” and “Disgust Sensitivity”; the Disgust Scale-Revised (DS-R) [17], a 27-item five-points scale measuring disgust in three dimensions: “Core Disgust”, “Animal-reminder Disgust” and “Contamination-based Disgust”; the Maudsley Obsessive Compulsive Inventory (MOCI) [18], with 30 true/false items measuring obsessive–compulsive symptoms in three subscales: “Doubting and Rumination”, “Checking” and “Cleaning”; the Spider Phobia Questionnaire-Revised (SPQ-R15) [19], with 15 true/false items measuring fear and avoidance of spiders; the Minnesota Multiphasic Personality Inventory–2 Hypochondria subscale (MMPI-2 Hs) [20], with true/false items assessing hypochondria symptoms and physical well-being; and the NEO-Five Factor Inventory (NEO-FFI) [21], a 60 yes/no measure assessing Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness.


The PVD was adapted to Portuguese using the translation/back-translation methodology [22]. First, it was translated into European-Portuguese by two bilingual individuals and reviewed by a highly proficient in English researcher. Afterwards, it was submitted to a think-aloud procedure, back-translated by a bilingual researcher and sent to the original authors for final approval of the Portuguese version.

The entire protocol was available online. The access link was sent to three Universities and divulged through their staff/student mailing lists. All participants were asked to read the instructions, provide informed consent and fill out the scales and sociodemographic information.

Statistical Analysis

All analysis were performed using R [23]. The following packages were used: highr [24], rio [25], psych [26], GPArotation [27], EFAtools [28], readxl [29] and lavaan [30]. See Additional file 1 for a more detailed analysis outline and additional results.

Mardia's Test was performed to assess multivariate normality of the sample [31] and the Jöreskog and Sörbom [32, p. 171] equation (see below) to verify the adequacy of the sample size for the factor analysis.

$$Number\,of\,Participants = \frac{{\left( {p + 1} \right)\left( {p + 2} \right)}}{2}$$

where p is the number of observed variables.

Construct validity was evaluated by calculating its three sub-components: Factorial, convergent and discriminant validity. Since versions from different countries show inconsistent factorial structures, an Exploratory Factor Analysis (EFA) followed by a Confirmatory Factor Analysis (CFA) using weighted least-square-mean and variance adjusted estimator (WLSMV) were conducted. Several CFAs were compared to verify the factor structure that best fits the data. The overall goodness-of-fit was assessed using the following indexes and cut-off points for “good adjustment”: Chi-square (χ2); Comparative Fit Index (CFI; 0.90 ≤ CFI ≤ 0.95); Tucker-Lewis Index (TLI; 0.90 ≤ TLI ≤ 0.95); Root Mean Square Error of Approximation (RMSEA; 0.05 ≤ RMSEA ≤ 0.70); P[rmsea ≤ 0.05]; and Standardized Root-Mean-Residual (SRMR; SRMR < 0.80) [33].

Convergent and discriminant validity between the two PVD factors, and of the scale, were assessed using the Fornell and Larcker [34] method and the correlational method, respectively. Thus, to show convergent validity of the scale, positive and significant Spearman correlations between PVD factors and both DPSS-R subscales, DS-R (total score, Core Disgust and Contamination-based Disgust subscales), MOCI, MMPI-2 Hs and NEO-FFI Neuroticism were expected. Particularly, a stronger correlation between GA and disgust propensity (i.e., DPSS-R Disgust Propensity, and DS-R subscales, especially Contamination-based Disgust), GA and MOCI, and PI and MMPI-Hs were expected. Conversely, discriminant validity of the scale was expected to result in low or non-significant correlations between PVD factors and DS-R Animal-reminder Disgust subscale, SPQ-R15 and NEO-FFI subscales (except Neuroticism).

Considering the ordinal nature of the data, a reliability analysis using Ordinal Cronbach's alpha, based on polychoric correlation, was performed to test the internal consistency of the factors.


Mardia’s test showed that data is not multivariate normal, g1p = 34.23, χSkew = 1243.78, p < 0.001; g2p = 281.09, ZKurtosis = 8.53, p < 0.001; χSMSkew = 1263.08, p < 0.001. Sample size was considered adequate for factorial analysis.

Factorial validity

Bartlett's test of sphericity was significant, χ2(105) = 599.93, p < 0.001, and the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO) analysis returned a value of 0.81 for the overall matrix, and values between 0.65 and 0.96 for all variables. Both indicators support factor analysis as a useful approach to the data.

Parallel analysis with unweighted least-squares estimator (ULS) indicated that three factors should be retained (see Fig. 1). However, only two principal factor eigenvalues reached values greater than 1 or 0.7 (old and new Keiser Criterion, respectively). Moreover, Hull method with CFI and RMSEA, and lower bound of RMSEA 90% CI, also support a two-factor retention. Given these results and the conceptual framework surrounding the bifactorial structure of the original scale, a two-factor solution was extracted.

Fig. 1
figure 1

Parallel analysis scree plot with ULS estimator

An EFA with Promax rotation using ULS estimator was performed. Factor loadings and respective R2, Uniqueness and Complexity by factor are shown in Table 1.

Table 1 Factor loadings and respective R2, uniqueness and complexity values

The two-factor solution accounted for 37% of the variance, with the PI factor explaining 18% and the GA factor 19% of the variance. The inter-factor correlation was 0.35. Interestingly, this factorial solution mimics the two-factor solution expected and postulated in the literature (e.g., [8]), with the highest loading of each item saturated in the theoretically correct factor.

Items Q2, Q6, Q9 and Q13 registered low loadings for the sample size (< 0.50) and some also showed cross-loadings with complexity and/or CL ratio values above those recommended [35, 36]. Despite an acceptable primary loading, item Q5 showed inadequate complexity and CL ratio, evidencing cross-loading. An average EFA with different oblique rotation models was performed to ensure elimination decision. Visual analysis of loadings distribution suggested an absence of major fluctuations (see Fig. 2). Although visually adequate, the ratio between loadings in item Q5 is still below the recommended (< 0.03) [36].

Fig. 2
figure 2

Average, maximum, and minimum loadings for each item per factor. Note: GA = Germ Aversion; PI = Perceived Infectability

Furthermore, two Polytomous Item Response Theory analysis using generalized partial credit model—one for each factor—were performed (see Table 2). Results showed that the five aforementioned items reached discrimination values below the acceptable (≥ 0.70) [37], suggesting that they are not good at discriminating the latent trait and, therefore, supporting their removal.

Table 2 Results of the PIRT analysis using Generalized Partial Credit Model per factor

A CFA with WLSMV was used to confirm the 10-items bifactorial structure obtained from the EFA. Results revealed an acceptable global adjustment, χ2(34) = 46.68; CFI = 0.93; TLI = 0.91; RMSEA = 0.05, RMSEA 90% CI [0.00, 0.09]; SRMR = 0.07. Moreover, all items reached high factor weights and appropriate individual reliabilities on latent variables (see Fig. 3).

Fig. 3
figure 3

Diagram of two-factor structure (10 items) obtained using CFA with WLSMV estimator. Note: PI = Perceived Infectability; GA = Germ Aversion

Several CFAs were also compared to verify the factor structure that best fits the data. Apart from the original factor structure, and considering the cultural proximity, structure models from two Spanish studies assessing the psychometric properties of the PVD were tested with our sample. All models are present in Table 3.

Table 3 Confirmatory Factor Analysis for all the models tested

Models 1, 2.1 and 3.1 obtained inadequate global and local adjustment values. Models 2.2 and 4.1, on the other hand, obtained acceptable values of global adjustment, but inappropriate local adjustment values. The remaining three models (i.e., 2.3, 3.2 and 4.2) reached acceptable values of global and local adjustments. Considering the conceptual framework and the original structure of the PVD, the bifactorial model obtained from the EFA (i.e., model 2.3) was adopted (see Additional file 2 for the original and final Portuguese version of the scale).

Convergent and discriminant validity of PVD factors

The Average Variance Extracted (AVE) and Composite Reliability (CR) values for both factors were as follows: AVEPI = 0.41, AVEGA = 0.33, CRPI > 0.79, CRGA > 0.74. While these AVE values are below those that are usually regarded as adequate [38], Fornell and Larcker [34] state that if the AVE values are less than 0.5, but the CR values are higher than 0.6, convergent validity of the construct is still considered adequate. Furthermore, both CR values are greater than 0.7, supporting the notion of an appropriate construct reliability. Thus, convergent validity of PVD factors was confirmed.

Furthermore, both AVE values were above the square of the correlation between the two factors (0.12), indicating only 12.3% of common information between them and confirming the discriminant validity of the two-factor model [34].

Convergent and discriminant validity of the scale

Regarding convergent validity, as shown in Table 4, both PVD subscales significantly correlated with DPSS-R subscales, MOCI, MMPI-Hs and NEO-FFI Neuroticism, while DS-R total score, Core Disgust and Contamination-based Disgust subscales only correlated with GA. Furthermore, DPSS-R Disgust Propensity subscale and MOCI correlated more strongly with GA, and MMPI-Hs with PI, as predicted.

Table 4 Polychoric Correlation Matrix among study variables

Evidence for discriminant validity was also found as DS-R Animal-reminder Disgust, SQ-R15, and NEO-FFI Extraversion and Openness subscales did not significantly correlate with PVD factors. Likewise, NEO-FFI Agreeableness and Conscientiousness subscales showed low correlations with PI and GA, respectively.


Both factors showed good levels of internal consistency [41], Ordinal Cronbach's αPI = 0.82, G6(smc)PI = 0.81, Median rPI = 0.54, Ordinal Cronbach's αGA = 0.82, G6(smc)GA = 0.80, Median rGA = 0.45.


The present study explored the psychometric properties of the PVD with a nonclinical sample of the Portuguese population. Results support the bifactorial structure proposed by several authors (e.g., [39, 42]), including Duncan and colleagues, which measures two conceptually distinct factors modestly intercorrelated: Perceived Infectability and Germ Aversion. Accordingly, PI seems to be more associated with rational appraisals, while GA reflects behavioral and emotional reactivity [8].

Although the original scale was comprised of 15 items, five problematic items were identified and, consequently, removed. Further analysis confirmed that these items did not reflect the latent factor they were supposed to be measuring. Regarding items Q5 and Q13, their reverse wording, which makes them harder to comprehend, might have been precluding participants from fully understanding their contents and causing the respective problematic loadings. In fact, both items have also been removed in other validation studies ([40], e.g., [43]). Furthermore, while most GA items mention behaviors or stimuli that, although from a broad spectrum, are commonly associated with disease (e.g., sharing a water bottle, washing/dirty hands), Q9’s content (i.e., wearing used clothes) appears to be the least directly disease-related item, which might explain the low loadings found. Likewise, despite sharing similar wording and content with the rest of the PI items, Q2 and Q6 still reached inadequate loadings. As the remaining items adequately measure the construct, both items were removed. Finally, another possible explanation is that the problematic loadings of all these items, from a conceptual point-of-view, may reflect cultural differences related to the prevalence of infectious diseases in different locations [8].

Thus, the aforementioned exclusion led to a stronger scale structure, when compared to various other models, with adequate goodness-of-fit indices, evidence for convergent and discriminant validity and good internal consistency levels for both factors. In particular, the GA factor obtained a reliability score higher than the one commonly found in the literature (e.g., [8, 43]).

Evidence for convergent validity of the scale was also found. First, both DPSS-R subscales positively correlated with PVD factors, with DPSS-R Disgust Sensitivity subscale correlating more strongly with PI, and DPSS-R Disgust Propensity subscale with GA. In fact, disgust propensity refers to how easily people respond with disgust and is more associated with avoidant action tendencies to repugnant materials, whereas disgust sensitivity is concerned with how unpleasant the experience of disgust really is and is linked with more general emotional sensitivity [44, 45]. Accordingly, if we look at the items from both PVD subscales, GA items involve actions (e.g., sharing a water bottle), while PI items include thoughts/beliefs about developing an infectious disease (e.g., higher susceptibility). Thus, these results add to the literature by suggesting that PI is more strongly related to disgust sensitivity and confirm the predicted link between GA and disgust propensity. Likewise, as a measure of disgust propensity, DS-R was expected to correlate with both PVD factors, albeit more strongly with GA. However, only a positive and significant correlation was found for the latter. This correlation was found for the total score, and both Core Disgust and Contamination-based Disgust subscales (both related to threat of infection) [46]. These results provide further evidence of the link between disgust propensity and GA and, more notably, highlight the conceptual distinction between both PVD factors.

A positive and significant correlation between both PVD factors and MMPI-Hs, MOCI and NEO-FFI Neuroticism was also found, as predicted, further corroborating the convergent validity of the scale. In particular, MMPI-Hs was found to correlate more strongly with PI, not surprisingly given that both constructs measure beliefs about disease. Conversely, GA correlated more strongly with both MOCI, as expected, and NEO-FFI Neuroticism. Interestingly, positive correlations between MOCI and both DS-R Core Disgust and Contamination-based Disgust subscales (i.e., disgust propensity measures correlated with GA) were also found. Taken together, these findings suggest that obsessive–compulsive symptoms may be tied to GA through disgust propensity, which is in line with a recent study that suggested that GA plays a mediator role between disgust propensity and contamination-based OCD [47]. Thus, our results corroborate the connection between these constructs even further. Finally, the positive correlation found between NEO-FFI Neuroticism and both PVD factors supports the findings of Duncan and collaborators [8], but contradicts those of Díaz et al. [40]. Since neuroticism is characterized by a tendency to experience negative emotions and psychological distress [21], the stronger correlation found for GA might be explained by this factor’s latent association with emotional reactivity.

Regarding the discriminant validity of the scale, DS-R Animal-reminder Disgust, SQ-R15, and NEO-FFI Extraversion and Openness subscales were not correlated with PVD factors, as predicted. Likewise, low correlations were found between PVD factors and NEO-FFI Agreeableness and Conscientiousness. Thus, these results provide further evidence of the PVD’s discriminant validity.

Despite the encouraging results, this study has some limitations, like the use of a nonclinical university sample and the high proportion of women, which limits the generalizability of the findings. Furthermore, cultural differences are influenced by the prevalence of infectious diseases and, thus, might be implicated in one’s perceived vulnerability to disease [8]. Consequently, our results might only be representative of regions with similar geographies or climates. Future research should use a more diverse and gender-balanced sample to further extend these results. Using a clinical sample would also be valuable to explore how one’s own health (physical or psychological) might influence perceived vulnerability to disease. Lastly, temporal stability analysis should also be explored.


Individual differences in perceived vulnerability to infectious diseases are involved in disease avoidance responses and have implications for various psychological outcomes. This research aimed to analyze the psychometric properties of the PVD for the Portuguese population. Overall, the 10-items bifactorial solution of the European-Portuguese PVD appears to be a reliable and valid measure of one’s perceived vulnerability to disease. Thus, this instrument may offer a significant contribute to study social cognition and behavioral processes related to disease avoidance.

Availability of data and materials

The dataset and additional materials supporting the conclusions of this article are available in the Open Science Framework repository, through a view-only link at

Change history

  • 10 June 2022

    The affiliations information of the authorship of this article has been corrected since publication; please see the linked correction for further details.

  • 15 June 2022

    A Correction to this paper has been published:



Average Variance Extracted


Behavioral Immune System


Confirmatory Factor Analysis


Comparative Fit Index




Composite Reliability


Disgust Propensity and Sensitivity Scale-Revised


Disgust Scale-Revised


Exploratory Factor Analysis


Germ Aversion


Kaiser–Meyer–Olkin Measure of Sampling Adequacy

MMPI-2 Hs:

Minnesota Multiphasic Personality Inventory–2 Hypochondria subscale


Maudsley Obsessive Compulsive Inventory


NEO-Five Factor Inventory


Perceived Infectability


Perceived Vulnerability to Disease Questionnaire


Root Mean Square Error of Approximation


Spider Phobia Questionnaire-Revised


Standardized Root-Mean-Residual


Tucker-Lewis Index


Unweighted Least-squares Estimator


Weighted Least-square-mean and Variance Adjusted Estimator

χ2 :



  1. Murray DR, Prokosch ML, Airington Z. PsychoBehavioroimmunology: connecting the behavioral immune system to its physiological foundations. Front Psychol. 2019;10:1–7.

    Google Scholar 

  2. Schaller M. The behavioural immune system and the psychology of human sociality. Philos Trans R Soc B Biol Sci. 2011;366(1583):3418–26.

    Article  Google Scholar 

  3. Ackerman JM, Hill SE, Murray DR. The behavioral immune system: current concerns and future directions. Soc Personal Psychol Compass. 2018;12(2):57–70.

    Article  Google Scholar 

  4. Murray DR, Schaller M. The behavioral immune system: Implications for social cognition, social interaction, and social influence. In: Olson JM, Zanna MP, editors. Advances in experimental social psychology. Academic Press; 2016. p. 75–129.

    Google Scholar 

  5. Schaller M, Park JH. The behavioral immune system (and why it matters). Curr Dir Psychol Sci. 2011;20(2):99–103.

    Article  Google Scholar 

  6. Miller SL, Maner JK. Overperceiving disease cues: The basic cognition of the behavioral immune system. J Pers Soc Psychol. 2012;102(6):1198–213.

    Article  Google Scholar 

  7. Mortensen CR, Becker DV, Ackerman JM, Neuberg SL, Kenrick DT. Infection breeds reticence: the effects of disease salience on self-perceptions of personality and behavioral avoidance tendencies. Psychol Sci. 2010;21(3):440–7.

    Article  Google Scholar 

  8. Duncan LA, Schaller M, Park JH. Perceived vulnerability to disease: development and validation of a 15-item self-report instrument. Pers Individ Differ. 2009;47(6):541–6.

    Article  Google Scholar 

  9. Park JH, Faulkner J, Schaller M. Evolved disease-avoidance processes and contemporary anti-social behavior: prejudicial attitudes and avoidance of people with physical disabilities. J Nonverbal Behav. 2003;27(2):65–87.

    Article  Google Scholar 

  10. Park JH, Schaller M, Crandall CS. Pathogen-avoidance mechanisms and the stigmatization of obese people. Evol Hum Behav. 2007;28(6):410–4.

    Article  Google Scholar 

  11. Duncan LA, Schaller M. Prejudicial attitudes toward older adults may be exaggerated when people feel vulnerable to infectious disease: evidence and implications. Anal Soc Issues Public Policy. 2009;9(1):97–115.

    Article  Google Scholar 

  12. Navarrete CD, Fessler DM, Eng SJ. Elevated ethnocentrism in the first trimester of pregnancy. Evol Hum Behav. 2007;28(1):60–5.

    Article  Google Scholar 

  13. Faulkner J, Schaller M, Park JH, Duncan LA. Evolved disease-avoidance mechanisms and contemporary xenophobic attitudes. Gr Process Intergr Relations. 2004;7(4):333–53.

    Article  Google Scholar 

  14. Makhanova A, Shepherd MA. Behavioral immune system linked to responses to the threat of COVID-19. Pers Individ Dif. 2020;167: 110221.

    Article  Google Scholar 

  15. Stangier U, Kananian S, Schüller J. Perceived vulnerability to disease, knowledge about COVID-19, and changes in preventive behavior during lockdown in a German convenience sample. Curr Psychol. 2021.

  16. Ferreira J, Bem-Haja P, Alho L, Soares SC. Subjective experience of disgust—Portuguese Version of Disgust Propensity and Sensitivity Scale—revised. Eur J Heal Psychol. 2021;28(1):13–21.

    Article  Google Scholar 

  17. Ferreira-Santos F, Martins E, Sousa P, Mauro C. Escala de Sensibilidade ao Nojo–Revista, versões Português de Portugal (DS-R-pt) e Português do Brasil (DS-R-br). 2011

  18. Nogueira V, Macedo A, Pereira AT, Marques M, Soares MJ, Valente J, et al. P-815 - Psychometric properties and factor structure of the portuguese version of the Maudsley Obsessional-Compulsive Inventory. Eur Psychiatry. 2012;27(1).

  19. Silva, Soares, Esteves. In preparation.

  20. Silva D, Novo R, Prazeres N, Pires R. Inventário Multifásico de Personalidade de Minnesota (Adultos): Versão experimental portuguesa do MMPI‐2 [Minnesota Multiphasic Personality Inventory (Adults): Portuguese experimental version of MMPI‐2]. Lisboa: Centro de Investigação em Psicologia e Faculdade de Psicologia da Universidade de Lisboa; 2006.

  21. Barros AM. Os valores e o modelo dos cinco fatores da personalidade: Aplicação de dois instrumentos de medida a uma amostra de adultos trabalhadores. Universidade de Lisboa; 1997.

  22. Gudmundsson E. Guidelines for translating and adapting psychological instruments. Nord Psychol. 2009;61(2):29–45.

    Article  Google Scholar 

  23. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.

  24. Xie Y, Qiu Y. highr: Syntax Highlighting for R Source Code. In: R package version 09. 2021.

  25. Chan C-H, Chan GCH, Leeper TJ, Becker J. rio: a Swiss-army knife for data file I/O. In: R package version 0527. 2021.

  26. Revelle W. psych: procedures for personality and psychological research. In: R package version 219. Evanston, Illinois, USA: Northwestern University; 2021.

  27. Bernaards CA, Jennrich RI. Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educ Psychol Meas. 2005;65:676–96.

    Article  Google Scholar 

  28. Steiner MD, Grieder SG. EFAtools: an R package with fast and flexible implementations of exploratory factor analysis tools. J Open Source Softw. 2020;5(53):2521.

    Article  Google Scholar 

  29. Wickham H, Bryan J. readxl: read excel files. In: R package version 131. 2019.

  30. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48(2):1–36.

    Article  Google Scholar 

  31. Mardia KV. Measures of multivariate skewness and kurtosis with applications. Biometrika. 1970;57:519.

    Article  Google Scholar 

  32. Jöreskog KG, Sörbom D. LISREL 8: user’s reference guide. Scientific Software International; 1996.

  33. Marôco J. Analysis of structural equations: Theoretical fundamentals, software & applications. 2nd ed. Pêro Pinheiro, Portugal: REPORTNUMBER; 2014. p. 1–400.

  34. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.

    Article  Google Scholar 

  35. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis: a global perspective. 7th ed. Upper Saddle: Prentice Hall; 2009.

    Google Scholar 

  36. Matsunaga M. How to factor-analysis your data: do’s, don’ts, and how-to’s. Int J Psychol Res. 2010;3(1):97–110.

    Article  Google Scholar 

  37. Embretson SE, Reise SP. Item response theory for psychologists. Matawah: Erlbaum Publishers; 2000.

    Google Scholar 

  38. Hair JF, Anderson R, Tatham R, Black W. Multivariate data analysis. Technometrics. 5th ed. Upper Saddle River: Prentice Hall; 1998.

    Google Scholar 

  39. Magallares A, Fuster-Ruiz MJ, Morales JF. Psychometric properties and criterion validity of the Perceived Vulnerability to Disease Scale (PVD) in the Spanish population. Int J Soc Psychol. 2017;32(1):164–95.

    Article  Google Scholar 

  40. Díaz A, Soriano JF, Beleña Á. Perceived Vulnerability to Disease Questionnaire: factor structure, psychometric properties and gender differences. Pers Individ Dif. 2016;101:42–9.

    Article  Google Scholar 

  41. Zumbo BD, Gadermann AM, Zeisser C. Ordinal versions of coefficients alpha and theta for likert rating scales. J Mod Appl Stat Methods. 2007;6(1):21–9.

    Article  Google Scholar 

  42. Murray DR, Jones DN, Schaller M. Perceived threat of infectious disease and its implications for sexual attitudes. Pers Individ Dif. 2013;54(1):103–8.

    Article  Google Scholar 

  43. Magallares A, Jauregui-Lobera I, Carbonero-Carreño R, Ruiz-Prieto I, Bolaños-Ríos P, Cano-Escoriaza A. Perceived vulnerability to disease and antifat attitudes in a sample of children and teenagers. Eat Weight Disord. 2015;20(4):483–9.

    Article  Google Scholar 

  44. van Overveld M, de Jong PJ, Peters ML. The Disgust Propensity and Sensitivity Scale—revised: its predictive value for avoidance behavior. Personal Individ Difer. 2010;49(7):706–11.

    Article  Google Scholar 

  45. Goetz AR, Lee HJ, Cougle JR, Turkel JE. Disgust propensity and sensitivity: differential relationships with obsessive-compulsive symptoms and behavioral approach task performance. J Obsessive Compuls Relat Disord. 2013;2(4):412–9.

    Article  Google Scholar 

  46. Olatunji BO, Williams NL, Tolin DF, Abramowitz JS, Sawchuk CN, Lohr JM, et al. The Disgust Scale: item analysis, factor structure, and suggestions for refinement. Psychol Assess. 2007;19(3):281–97.

    Article  Google Scholar 

  47. Brady RE, Badour CL, Arega EA, Levy JJ, Adams TG. Evaluating the mediating effects of perceived vulnerability to disease in the relation between disgust and contamination-based OCD. J Anxiety Disord. 2021;79: 102384.

    Article  Google Scholar 

Download references


The authors would like to thank the participants of the study.


This research was supported by the Portuguese Foundation for Science and Technology [Grant numbers SFRH/BD/85376/2012 and SFRH/BD/137993/2018]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations



JF: Conceptualization, Methodology, Software, Investigation, Writing—Review & Editing. ACM: Formal analysis, Writing—Original Draft, Writing—Review & Editing. PB-H: Software, Formal analysis, Writing—Review & Editing. LA: Conceptualization; Methodology. CFS: Supervision. SCS: Conceptualization, Writing—Review & Editing, Supervision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sandra C. Soares.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the University of Aveiro. Participation in the study was voluntary and anonymous. The informed consent was obtained from all subjects. All methods of the study were carried out in accordance with the Declaration of Helsinki and the standards of the American Psychological Association.

Consent for publication

Not applicable.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Full analysis outline and results.

Additional file 2

: Original and Portuguese PVD items.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ferreira, J., Magalhães, A.C., Bem-Haja, P. et al. Perceived Vulnerability to Disease Questionnaire: psychometric validation with a Portuguese sample. BMC Psychol 10, 130 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Perceived vulnerability to disease
  • Disease avoidance
  • Perceived infectability
  • Germ aversion
  • Individual differences