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Fear of Failure in Sport: A Portuguese Cross-cultural Adaptation

Abstract

The Performance Failure Appraisal Inventory (PFAI) is a multidimensional measure of threat appraisals associated with one's fear of failure. Whilst emerging research has supported the validity and reliability of the PFAI with North American and British sport participants, its psychometric proprieties remain untested within Portuguese samples. This study examined the psychometric proprieties of the PFAI with a sample of 556 Portuguese athletes. A confirmatory factor analysis was employed to test whether the proposed multi-factorial structure of the PFAI fits well the Portuguese data.All factors displayed good internal consistency, convergent validity, and discriminant validity. Multi-group analysis revealed cross-validity and the models' invariance. The correlations between fear of failure and sport anxiety measures revealed evidence of its concurrent validity. The PFAIappears to be a psychometrically sound measure anda valid and reliable tool for assessing fear of failure in Portuguese sport contexts.

Keywords:
fear of failure; cross-cultural validation; confirmatory factor analysis

Introduction

Sport represents an important achievement domain and the existenceof pressure to achieve top sporting performances can produce an increase in fear of failure among athletes(11 Hosek V, Man F. Training to reduce anxiety and fear in top athletes. In:Hackfort D, Spielberger CD, editors. Anxiety in sport: An international perspective.New York: Hemisphere. 1989.p. 247-259.).

In sport, limited research has shown fear of failure to be associated with cases of burnout(22 Rainey DW. Stress, burnout, and intention to terminate among umpires. Journal of Sport Behavior. 1995; 18(4): 312-323.), youth sport drop out, barriers to sport participation(33 Orlick TD. The athletic dropout: A high price of inefficiency Canadian Association for Health. Physical Education and Recreation Journal, 1974. Nov./Dec.: 21-27.),athletes'drug abuse(44 Anshel MH. Causes of drug abuse in sport: A survey of intercollegiate athletes. Journal of Sport Behavior 1991; 14: 283-307.), and athletic stress(55 Gould D, Horn T, Spreeman J. Sources of stress in junior elite wrestlers. Journal of Sport Psychology. 1983; 5: 159-171.).

Conroy, Willow, and Metzler(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.) proposed a multidimensional model of fear of failure (The Performance Failure Appraisal Inventory-PFAI), grounded in Lazarus's cognitive-motivational-relational theory of emotions and consistent with other multidimensional models of fear of failure(77 Birney RC, Burdick H, Teevan RC. Fear of failure. New York: Van Nostrand. 1969.).For the PFAI authors, one of the greatest advantages of this instrument over alternatives fear of failure measures(88 Alpert R, Haber RN. Anxiety in academic achievement situations. Journal of Abnormal and Social Psychology.1960; 10: 207-215.

9 Passer MW. Fear of failure, fear of evaluation, perceived competence, and self-esteem in competitive-trait-anxious children. Journal of Sport Psychology. 1983; 5: 172-188.

10 Sadd S, Lenauer M, Shaver PR, Dunivant N. Objective measurement of fear of success and fear of failure: A factor analytic approach. Journal of Consulting and Clinical Psychology. 1978; 46: 405-416.
-1111 Willis JD. Three scales to measure competition-related motives in sport. Journal of Sport Psychology. 1982; 4: 338-353.) may be its superior substantive foundation.

The Performance Failure Appraisal Inventory was developed by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.,1212 Conroy DE. Progress in the development of a multidimensional measure of fear of failure: The Performance Failure Appraisal Inventory (PFAI). Anxiety, Stress, and Coping2001; 14: 431-452.)to measure individuals' beliefs in specific aversive consequences of failing. This inventory comprises 25 items that measure five dimensions of threat appraisals associated with fear of failure: (1) fear of shame and embarrassment; (2) fear of devaluing one's self-estimate; (3) fear of having an uncertain future; (4) fear of important others losing interest; and (5) fear of upsetting important others.

The PFAI is the first fear of failure measure developed from the meta-theory of emotions and it examines fear of failure as a function of person-environment interaction (rather than a state or trait) and acknowledges the individual nature of perceptions of failure, rather than assuming it to be the same for all performers(1313 Sagar SS, Jowet S. Validation of a multidimensional measure of fear of failure in a British sample: The Performance Failure Appraisal Inventory (PFAI). International Journal of Coaching Science.2010; 4(1): 49-63.).

Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.) views fear of failure as the dispositional predisposition to appraise threat, to the achievement of personally meaningful goals, when one fails. Individuals with a higher fear of failure have learnt to associate failure with aversive consequences and typically perceive failure in evaluative situations as threatening. They also believe that aversive consequences will occur following a failure.

Confirmatory factor analysis conducted by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)revealed satisfactory goodness of fit indexes for the five dimensions when tested as a five correlated-factor model as well as a higher-order factor model.

Research using PFAI, showed good psychometric proprieties including internal consistency, factorial validity, and temporal stability(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.,1414 Conroy DE, Metzler JN. Patterns of self-talk associated with different forms of competitive anxiety. Journal of Sport & Exercise Psychology. 2004; 26: 69-89.

15 Conroy DE, Coatsworth JD, Kaye MP. Consistency of fear of failure score meanings among 8–18-year-old female athletes. Educational and Psychological Measurement. 2007; 67(2): 300-310.
-1616 Kaye MP, Conroy DE, Fifer AM. Individual differences in incompetence avoidance. Journal of Sport & Exercise Psychology. 2008;30: 110-132.). However, according to our knowledge, no author has verified the convergent and discriminant validity of each factor till date.

To our knowledge, the PFAI has been applied in sport and exercise contexts mainly with North American athletes(1414 Conroy DE, Metzler JN. Patterns of self-talk associated with different forms of competitive anxiety. Journal of Sport & Exercise Psychology. 2004; 26: 69-89.

15 Conroy DE, Coatsworth JD, Kaye MP. Consistency of fear of failure score meanings among 8–18-year-old female athletes. Educational and Psychological Measurement. 2007; 67(2): 300-310.

16 Kaye MP, Conroy DE, Fifer AM. Individual differences in incompetence avoidance. Journal of Sport & Exercise Psychology. 2008;30: 110-132.
-1717 Athanas EH. (2007). Fear of failure, division and experience as predictors of state anxiety in USFA epee fencers (Unpublished master's thesis). Georgia Southern University, Statesboro, GA.).There were, however, three other studies conducted outside the USA. Specifically, Sideridis and Kafetsios(1818 Sideridis G,Kafetsios K. Parental bonding, fear of failure and stress during class presentations. International Journal of Behavioural Development. 2008;32(2), 119-130.) examined fear of failure in high school and college students in an educational setting in Greece.While Sagar and Jowett(1313 Sagar SS, Jowet S. Validation of a multidimensional measure of fear of failure in a British sample: The Performance Failure Appraisal Inventory (PFAI). International Journal of Coaching Science.2010; 4(1): 49-63.)examined the psychometric proprieties of the PFAI with British sport participants, and two years later, the same authors(1919 Sagar S,Jowet S. The effects of age, gender, sport type and sport level on athletes’ fear of failure: Implications and recommendations for sport coaches. International Journal of Coaching Science. 2012;6(2): 61-82.) explored the effects of personal and contextual factors such as age, gender, sport type, and level of sport participation on athletes' fear of failure.

The aim of this study was to conduct a cross-cultural adaptation of the PFAI.More specifically, we intended to analyzethe factor structure as it was proposed by the PFAI's authors. Furthermore, into a more refined analysis, we tested the model to determine the following: its internal consistency, convergent,and discriminant validity; the invariance of the structure with a cross-validation strategy; and to explore its concurrent validity with Portuguese athletes.

Having a reliable measure that can provide individual beliefs of perceptions of failure in a sport context is essentialsince sport is a highly evaluative situation in one of the most popular achievement domains.

Method

Participants and data collection

A total of 556 athletes distributed in two convenience samples participated in the study. They competed in a variety of individual (e.g., athletics, climbing, surfing,tennis, orienteering, and swimming) and teamsports (e.g., soccer, volleyball, and basketball)at club and school level, from different competition levels. The mean age of the first sample (n=350)was 15.65 years old (SD=2.45) and approximately two-thirds were males (72%). Regarding the second sample (n=206), the mean age of the participants was 15.29 years old (SD=2.47) and the great majority were males (79.6%).

Prior to data collection, the study was reviewed by the University Ethics Board. Upon approval, participants were recruited. Clubs, sport associations and schools were contacted by e-mail or by telephone and were invited to participate.

Onceclub and school authorizations were provided, letters and parental consent forms were sent home to parents for participants under the age of 18 informing them of the nature of the study and requesting their permission for their child's participation.All participants, including minors, signed consent forms.

Measures

The Performance Failure Appraisal Inventory(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)is a multidimensional measure of threat appraisals associated with one's fear of failure. Participants were asked to rate how strongly they believed each of the 25 aversive consequences of failure were likely to occur to them following a failure. The measure assessed the strength of their beliefs about possible consequences of failure across five domains: experiencing shame and embarrassment, devaluing one's self-estimate, having an uncertain future, important others losing interest, and upsetting important others. Items were answered on a five-point Likertscale ranging from 1 (do not believe at all) to 5 (truly believe).

The Portuguese version of the Sport Anxiety Scale-SAS-2(2020 Smith RE., Smoll FL., Cumming SP, Grossbard JR. Measurement of multidimensional sport performance anxiety in children and adults: The Sport Anxiety Scale-2. Journal of Sport & Exercise Psychology. 2006; 28: 479-501.)translated and adapted by Cruz and Gomes(2121 Cruz JF., Gomes AR. Escala de Ansiedade no Desporto (EAD-2)–Versão para investigação [The Sport Anxiety Scale-2]. Braga (Portugal): Universidade do Minho; 2007.)has three subscales (somatic anxiety, worry, and concentration disruption) consisting of five items each. This scale was used for concurrent validity proposes. The 15 items of the SAS-2 were designed to reflect possible responses that young athletes may have before, or while, they compete in sports. For each item, athletes indicated how they typically felt, based upon a five-point Likert scale, ranging from1(not at all) to 5 (very much).

PFAI Translation Procedures

Once approval was obtained from the original scale author,the translation of the PFAI was performed using a five-stage process (e.g., translation, synthesis, back-translation, review of content validity, and pre-testing)(2222 Beaton DE, Bombardier C, Guillemin F, Ferraz MB. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine,25, 3186-3191.).

Data Analysis

Data were analyzed using AMOS 22.0 and a confirmatory factor analysis (CFA) was performed to assess the psychometric proprieties of the PFAI instrument. The maximum likelihood (ML) method was used since it is considerably more insensitive to variations in sample size and kurtosis and tends to be more stable, demonstrating higher accuracy in terms of empirical and theoretical fit compared to other estimators(2323 Olsson UH, Foss T, Troye SV, Howell RD. The performance of ML, GLS and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling. 2000;7: 557-595.).Standardized factor loadings, standard residuals, and modification indices were analyzed to screen for possible model misspecification.

In order to confirm the factorial structure of the PFAI model, an analysis was conducted using the Conroy, Willow, and Metzler(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)model. Given that this is a second-order model, we selected a two-step confirmatory strategy (as suggested by Byrne(2424 Byrne BM. Structural equation modeling with Amos: Basic concepts, applications, and programming. New York: Taylor and Francis Group; 2010.),Kline(2525 Kline RB. Principles and practices of structural equation modeling. 3rd ed. New York: The Guilford Press; 2011.), SchumakerandLomax26). Initially, a testwas conducted on the first-order model and after, in a second step, we tested the second-order model.

The appropriateness of the model was tested using a variety of goodness-of-fit indexes. Specifically, the measurement model was assessed with the chi-square (χ²) statistical test, the ratio of χ² to its degrees of freedom (χ²/df), comparative-of-fit-index (CFI), goodness-of-fit index (GFI), parsimony comparative-of-fit-index (PCFI), parsimony goodness-of-fit index (PGFI), and root mean square error of approximation (RMSEA). The χ² value has been identified as potentially problematic due to sample size sensitivity, but its value is reported since it represents the only true inferential statistic of model testing(2727 Markland D. The golden rule is that there are no golden rules: A commentary on Paul Barrett’s recommendations for reporting model fit in structural equation modeling. Personality and Individual Differences. 2007; 42:851-858.). Given its sensitivity to sample size, the χ2/df was also used as a measure of model fit(2828 Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington: American Psychological Association; 2004.). Research practices using these indices state that values for the χ 2/df should be less than 3, PCFI and PGFI above .60, while values above .90 for the CFI and GFI, and below .05 for the RMSEA represents a good fit(2929 Arbuckle J. AMOS18 reference guide(version 18). Chicago: Statistical Package for the Social Sciences; 2009.

30 Bentler PM, Bonett DG. (1980). Significance tests and goodness of fit in analysis of covariance structures. Psychological Bulletin, 88, 588-606.

31 Blunch NJ.Introduction to structural equation modeling using SPSS and AMOS. Thousand Oaks: SAGE; 2008.

32 Kline RB. Principles and practice of structural equation modeling. New York: Guilford. 1998
-3333 Marsh HW. Application of confirmatory factor analysis and structural equation modeling in sport/exercise psychology. In: Tenenbaum G, Eklund RC, editors. Handbook of sport psychology.New York: Wiley; 2007. p. 774-798.).

Internal consistency (reliability) of the constructs was assessed through composite reliability and we followed the recommendations of Fornell and Larcker(3434 Fornell C,Larcker D. Evaluation structural equations models with unobservable variable and measurement error. Journal of Marketing Research. 1981; 18(3): 39-50.) to calculate composite reliability (CR), in which it is recommended that values ≥ .7 indicates a proper value of CR.

Convergent validity was evaluated through the average variance extracted (AVE), whereby the values of AVE ≥ .5 are appropriate indicators of convergent validity(3535 Hair J, Anderson R, Tatham R, Black W. Multivariate data analysis. Upper Saddle River: Prentice Hall. 2009.).

Discriminant validity was established when AVE for each construct went beyond the squared correlations between that construct and any other(3535 Hair J, Anderson R, Tatham R, Black W. Multivariate data analysis. Upper Saddle River: Prentice Hall. 2009.).In order to verify PFAI's factorial invariance, cross-validation procedures were used with amulti-group analysis strategy(3636 Brown T. Confirmatory factor analysis for applied research. New York: The Guildford Press. 2006.

37 Davey A, Savla J. Statistical power analysis with missing data: A structural equation modeling approach. New York: Taylor and Francis Group. 2010.
-3838 Schumaker RE, Lomax RG. A beginner's guide to structural equation modeling 2nd ed. Mahwah: Psychology Press. 2004.).

A multi-group analysis was conducted to compare the first sample with the second sample in order to access cross-validity. The model's invariance was tested by comparing the unconstrained model with constrained models (factor loadings fixed and variances/co-variances fixed). Factorial invariance was accepted when the models did not differ significantly (p>0.05), according to the qui-square statistic(3939 Loehlin JC. Latent variable models: An introduction to factor, path, and structural equation analysis. Mahwah: Lawrence Erlbaum Associates. 2003.,4040 Marôco J. [Structural equation modeling: Theoretical foundations, software and aplications]. Lisboa, Portugal: Report Number; 2010. 374 p.Portuguese.).As the qui-square difference tests represent an excessively stringent test of invariance,we also considered Cheung and Rensvold's(4141 Cheung G, Rensvold R. (2002) Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233-255.) suggestion that a difference of CFI of less than or equal to .01 is an indication that the constrained parameters are invariant.

Concurrent validity was analyzed by Pearson's correlation coefficients between the PFAI's and the SAS's dimensions as well as their total score values(4242 Campbell D, Fiske D. (1959). Convergent and discriminant validation by the multitraitmulti method matrix. Psychological Bulletin, 56, 81-105.).

Results

Preliminary Analysis

Preliminary analyses were performed on the data in order to scan for evidence of non-normality, univariate and multivariate outliers, and patterns of missing data.No missing values were found within the data.

The assumption of normality for confirmatory factor analysis was examined using measures of skewness and kurtosis. Absolute values of skewness ranged from -0.132 to 2.211 (SD=0.56) and absolute values of kurtosis ranged from -1.324 to 4.842 (SD=1.42). According with these values, we decided that the data was approximately univariately normal, since items with absolute values of skewness lower than 3 and kurtosis lower than 7 did not deviate enough from the normal distribution(4343 Kline RB. Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association. 2004.).

Results revealed that data violated the assumption of a multivariate Gaussian distribution since Mardia's(1970) values have been noted as a sign of multivariate kurtosis(4444 Bentler PM, Wu EJC. (1993). EQS/Windows user's guide. Los Angeles, CA: BMDP Statistical Software.,4545 Walker DA. (2010). A confirmatory factor analysis of the Attitudes Toward Research scale. Multiple Linear Regression Viewpoints36, 17-26.).Based upon the Mahalanobis distance statistic, 14 multivariate outliers were identified from the sample and were consequently removed.Thus, it was decided to adjust the p value of the chi-square statistic with the bootstrapping procedure of Bolen and Stine(4646 Bollen KA, Stine RA. Bootstrapping goodness-of-fit measures in structural equation models. In: Bollen K, Long J, editors. Testing structural equation models. Newbury Park: Sage Focus Edition; 1993. p. 111-135.).

Evaluation of Model Fit

At first, not all estimated factor loadings exceeded the cut-off point of.50,(3939 Loehlin JC. Latent variable models: An introduction to factor, path, and structural equation analysis. Mahwah: Lawrence Erlbaum Associates. 2003.) ranging from .10 to .81. The goodness-of-fit indices produced for this first order measurement model also indicated poor fit ENT#091;χ 2=748.088, B-S p<0.01; χ 2/df=2.82, PCFI=.74, PGFI=.69, CFI=.84, GFI=.85, RMSEA=.07ENT#093; showing that the hypothesized measurement model is inconsistent with observed data, which is interpreted as evidence against the adequacy of measuring the model. This poor fit is specifically perceived in the values of the CFI and GFI that were below the cut-off point of.90.(3939 Loehlin JC. Latent variable models: An introduction to factor, path, and structural equation analysis. Mahwah: Lawrence Erlbaum Associates. 2003.).

Post hoc model adjustments were conducted in an effortto develop a better fitting model due to the lack of support from CFA.The results in the original model (first-order model) indicated that not all items loaded significantly on its construct. Since non significant parameters can be considered unimportant to the model, in the interest of scientific parsimony, all scale items that showed unacceptable factor loadings were removed(2424 Byrne BM. Structural equation modeling with Amos: Basic concepts, applications, and programming. New York: Taylor and Francis Group; 2010.).Furthermore, examination of the modification indices (MI) suggested that an improved model resulted in the elimination of specific items, following the intent of Chartrand, Robbins, Morril, and Boggs(4747 Chartrand JM, Robbins SB, Morril WH, Boggs K. (1990). Development and validation of the Career Factors Inventory. Journal of Counseling Psychology, 37, 491-501.) to create "pure measures of each factor" (p. 495) by allowing items to load on only one factor. According to Byrne(2424 Byrne BM. Structural equation modeling with Amos: Basic concepts, applications, and programming. New York: Taylor and Francis Group; 2010.), large MI argues the presence of factor cross-loadings (i.e., a loading on more than one factor) and error covariances, respectively.

The indices of fit indicated a noteworthy improvement ofthe hypothesized first-order model, as reported in table 1.

Table 1
PFAI's Re-specified 1st Order Model - Factor Loadings, Z-values, Composite Reliability (CR), and Average Variance Extracted (AVE)

Evidence of discriminant validity was accepted since none of the squared correlations exceeded the AVE values for each associated construct(3434 Fornell C,Larcker D. Evaluation structural equations models with unobservable variable and measurement error. Journal of Marketing Research. 1981; 18(3): 39-50.).

Afterthese procedures, the model adjusted to the data. The results demonstrated an acceptable fit ENT#091;χ²=146.63, B-S p<0.001; χ²/df=2.19, PCFI=0.70, PGFI=0.60, CFI=0.96, GFI=0.94, RMSEA=0.06ENT#093;.Composite reliability values ranged from .75(fear of devaluing one's self-estimate) to .79(fear of shame and embarrassment), indicating that the constructs were internally consistent(3535 Hair J, Anderson R, Tatham R, Black W. Multivariate data analysis. Upper Saddle River: Prentice Hall. 2009.). Evidence for convergent validity was obtained since AVE values ranged from .51 (fear of devaluing one's self-estimate)to .62 (fear of having an uncertain future), being greater than the recommended standard of .50.(3434 Fornell C,Larcker D. Evaluation structural equations models with unobservable variable and measurement error. Journal of Marketing Research. 1981; 18(3): 39-50.)

Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)hypothesized a higher-order factor model, whereby the five dimensions of fear of failure were incorporated under a general factor. The second-order measurement model showed an overall acceptable fit to the data ENT#091;χ²=176.32, B-Sp<0.001; χ²/df=2.42, PCFI=0.75, PGFI=0.64, CFI=0.94, GFI=0.93, RMSEA=0.07ENT#093;.

Cross-validity

Cross-validation procedures were used(3838 Schumaker RE, Lomax RG. A beginner's guide to structural equation modeling 2nd ed. Mahwah: Psychology Press. 2004.)in order to study the adequacy of model replication.More specifically, a cross validation technique using a multi-group analysis with two equivalent samplesin their characteristics (ntesting sample=350; nvalidation sample=206) and then a technique of parameter-invariance to verify the equivalence between the two groups(3636 Brown T. Confirmatory factor analysis for applied research. New York: The Guildford Press. 2006.).

As exposed in Table 2, the fit of the unconstrained model ENT#091;Model A: χ²(134)=210.77 (B-S p<.001), PCFI=0.71, PGFI=0.60, CFI=0.96, GFI=0.92, RMSEA=0.041ENT#093;was acceptable.The fit of this model provides the baseline value against which all subsequently specified invariance models are compared(2424 Byrne BM. Structural equation modeling with Amos: Basic concepts, applications, and programming. New York: Taylor and Francis Group; 2010.).The models with constrained factor loadings ENT#091;Model B: χ²(143)=216.05 (B-S p<.001), PCFI=0.75, PGFI=0.62, CFI=0.96, GFI=0.92, RMSEA=0.039ENT#093;, and with constrained variances/covariances ENT#091;Model C: χ²(158)=234.87 (B-S p<.001), PCFI=0.83, PGFI=0.69, CFI=0.96, GFI=0.91, RMSEA=0.04ENT#093;, showed a satisfactory fit.The qui-squarestatistic showed no significant differences between Model A and Model BENT#091;χ²dif (9)=5.28; p=.81ENT#093;, and also no significant differences between Model A and Model C ENT#091;χ²dif (24)=24.10; p=.46ENT#093;. There were no differences in the CFI values for all model comparisons. Thus, the results demonstrated the model's invariance in both samples, indicating that the factorial structure of the scale was stable in the two independent samples.

Table 2
Results of the Multi-Group Analysis across the Unconstrained Model and the Constrained Models of the PFAI (Testing Sample: n = 350; Validation Sample: n = 206)

Concurrent Validity

The results presented in table 3, related to the correlations used to examine the relationships among the PFAI and SAS-2,reveal that the PFAI and SAS-2 sub-scales were positively correlated. The fear of failure scores were also positively related to concentration disruption, somatic anxiety, worry, and total sport anxiety scores.

Table 3
Correlation Matrix between Performance Failure Appraisal Inventory (PFAI) and Sport Anxiety Scale-2 (SAS-2)

Discussion

This study aimed to investigate a cross-cultural adaptation of the PFAI originally developed by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)and totest its factorial validity in a Portuguese sport setting.

The construct of fear of failure has been mainly tested and used in North American and British populations, but it was yet to be explored within the Portuguese population.

The confirmatory factorial analysis performed on the PFAI with a sample of 556 athletes provided some support to the five-factor structure proposed by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.).Conversely, some items revealed unacceptable factor loadings in their different sub-scales and were eliminated,generating a more effective model. The items elimination procedure was done since it minimized content redundancy and shortened the questionnaire significantly, which is very convenient in competitive sport settings, without affecting its content broadness and relevance. As such, the scale modifications resulted in a shorter questionnaire containing 14 items, representing the original five factors originally developed by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.).

The confirmatory factorial analysis using the re-specified model showed an acceptable fit of the data. The first-order construct showed composite reliability, convergent validity, and discriminant validity for each factor. A second-order model was tested and the analysis revealed an adequate fit for this final model.

The model's invariance in two independent samples was supported, indicating cross validity.With this outcome, it is assumed that the instrument is operating exactly the same way and that the underlying construct being measured (i.e., fear of failure) has the same theoretical structure for both groups.

PFAI's concurrent validity has been ascertained with the Sport Anxiety Scale-2 (SAS-2), pointing to its high concurrent validity. PFAI scores have exhibited appropriate concurrent validity with measures of worry, sport anxiety, cognitive disruption, and somatic anxiety(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.,4848 Conroy DE, Metzler JN,Hofer SM. Factorial invariance and latent mean stability of performance failure appraisals. Factorial Equation Modeling. 2003;10(3): 401-422.).In this regard, it is important to acknowledge that fear of failure is a subclass of performance anxiety constructs in sport(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.), so it was not surprising that fear of failure was strongly related to sport anxiety.This result is similar to previous findings where fear of failure has been associated with dispositional performance anxiety (worry, concentration disruption, somatic anxiety)(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.,1515 Conroy DE, Coatsworth JD, Kaye MP. Consistency of fear of failure score meanings among 8–18-year-old female athletes. Educational and Psychological Measurement. 2007; 67(2): 300-310.,1616 Kaye MP, Conroy DE, Fifer AM. Individual differences in incompetence avoidance. Journal of Sport & Exercise Psychology. 2008;30: 110-132.).These results provide an additional outcome for the validity of the PFAI as an adequate tool for research.

All dimensions of the PFAI showed a statistically significant relationship with this second-order construct, with the strongest predictor being fear of important others losing interest (.82), followed immediately byfear of experiencing shame and embarrassment (.78) and fear of having an uncertain future (.78).

Finally, there are limitations that need to be acknowledged. First, there was a large age range in the present study, representing a wide variety of sport experiences. Therefore, future studies should collect stratified samples of sport populations and sport experiences to better understand the fear of failure among athletes. Second, the PFAI does not assess all possible fear of failure appraisals, such as the beliefs of failure associated with (a) wasting one's efforts, (b) losing a special opportunity, or (c) experiencing tangible losses(1212 Conroy DE. Progress in the development of a multidimensional measure of fear of failure: The Performance Failure Appraisal Inventory (PFAI). Anxiety, Stress, and Coping2001; 14: 431-452.).Furthermore, there may be other beliefs concerning aversive consequences of failure not identified by Conroy(1212 Conroy DE. Progress in the development of a multidimensional measure of fear of failure: The Performance Failure Appraisal Inventory (PFAI). Anxiety, Stress, and Coping2001; 14: 431-452.) or even existing cross-cultural differences that may provide different beliefs of aversive consequences of failure. On this behalf, it should be pertinent to include a qualitative inquiry in order to provide a holistic understanding of the athlete's fears of failure.Third, the present study is a cross-sectional survey and, as such, its findings do not inform us whether athletes' fear of failure appraisals levels oscillate over time in accordance with different events that take place in a sporting season or throughout a sport career. Future research will benefit from longitudinal designs over a sporting season and career, also essential for attaining predictive validity of the PFAI's Portuguese version. Fourth, the differences between our sample and the original sample used by Conroy(66 Conroy DE, Willow JP, Metzler JN. Multidimensional fear of failure measurement: The Performance Failure Appraisal Inventory. Journal of Applied Sport Psychology 2002; 14: 76-90.)could provide dissimilar interpretations. The sample from the original study was composed uniquely with college students, having a considerably higher mean age, comparatively with our sport leagues and federations sample. Therefore, it will be essential to examine in future studies the different characteristics such as athletes' age and as well as gender, sport type, and performance level associated with fear of failure.

Conclusions

The PFAI is a valuable instrument for researchers and practitioners (e.g., sport coaches, coaching scientists, and psychologists) who want to assess fear of failure in such diverse settings as sport, exercise, and education(1313 Sagar SS, Jowet S. Validation of a multidimensional measure of fear of failure in a British sample: The Performance Failure Appraisal Inventory (PFAI). International Journal of Coaching Science.2010; 4(1): 49-63.). The role of the PFAI in sport settings is vital, and this study provided a distinctive utility to scholars and coaches since from a practical point of view, it can be employed as a diagnostic tool in the assessment with implications for treatment and prevention of fear of failure, providing a precious help identifying individuals' maladaptive appraisals (or threat appraisals) associated with failure.

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Appendix

(Questionário Multidimensional do Medo de Falharno Desporto)

Medo de Sentir Vergonha e Embaraço (MSVE)

9. Quando estou a falhar, é embaraçoso quando estão outras pessoas a assistir.

13. Quando estou a falhar, preocupo-me com o que os outros pensam de mim.

14. Quando estou a falhar, preocupo-me que os outros pensem que não me estou a esforçar.

Medo de Desvalorizar a Autoestima (MDAE)

8. Quando não estou a ter sucesso, fico em baixo muito facilmente.

3. Quando estou a falhar, culpo a minha falta de talento/jeito.

4. Quando estou a falhar, tenho medo de não ter talento/jeito suficiente.

Medo de Ter um Futuro Incerto (MFI)

1. Quando estou a falhar, o meu futuro parece-me incerto.

5. Quando estou a falhar, perturba o meu "plano" para o futuro.

Medo que Outros Importantes Percam Interesse (MOPI)

6. Quando não estou a ter sucesso, as pessoas ficam menos interessadas por mim.

11. Quando não estou a ter sucesso, algumas pessoas perdem definitivamente o interesse por mim.

12. Quando não estou a ter sucesso, o meu valor diminui para algumas pessoas.

Medo de Preocupar Outros Importantes (MPOI)

2. Quando estou a falhar, preocupo as pessoas que são importantes para mim.

7. Quando estou a falhar, aqueles que são importantes para mim não ficam contentes.

10. Quando estou a falhar, aqueles que são importantes para mim ficam desapontados.

Note. Item numbers are provided to identify the sequence of administration.

Publication Dates

  • Publication in this collection
    Dec 2016

History

  • Received
    31 Mar 2016
  • Accepted
    23 May 2016
Universidade Estadual Paulista Universidade Estadual Paulista, Av. 24-A, 1515, 13506-900 Rio Claro, SP/Brasil, Tel.: (55 19) 3526-4330 - Rio Claro - SP - Brazil
E-mail: motriz.rc@unesp.br