The sport environment is a context of competence and achievement where motivational factors generated by significant others play an important role in the effects of sport participation on children and youths’ psychosocial development (Smith, Smoll, & Cumming, Reference Smith, Smoll and Cumming2009). During childhood, parents’ influence seem to be a crucial factor in the athletes’ sport experience. However, during adolescence, influences from coaches and peers, especially on young people’s perceptions of competence, become more significant, while the parents’ impact decreases (Boixadós, Valiente, Mimbrero, Torregrosa, & Cruz, Reference Boixadós, Valiente, Mimbrero, Torregrosa and Cruz1998; Chan, Lonsdale, & Fung, Reference Chan, Lonsdale and Fung2012). In this line, previous literature has taken interest in analyzing the influence of the team environment created by coaches and team-mates on youth athletes (e.g., Keegan, Spray, Harwood, & Lavallee, Reference Keegan, Spray, Harwood and Lavallee2010; Reinboth & Duda, Reference Reinboth and Duda2006). Two major motivational theories, Achievement Goal Theory (AGT; e.g., Duda & Hall, Reference Duda, Hall, Singer, Hausenblas and Janelle2001) and Self-Determination Theory (SDT; e.g., Deci & Ryan, Reference Deci and Ryan2000), have focused on this issue.

Within the study of team environment, AGT is aimed particularly at motivational climate (e.g., Vazou, Ntoumanis, & Duda, Reference Vazou, Ntoumanis and Duda2006) and SDT at autonomy support (e.g., Adie, Duda, & Ntoumanis, Reference Adie, Duda and Ntoumanis2008). Motivational climate refers to a person’s perceptions of the environment motivational indicators and expectancies (Ames, Reference Ames, Meece and Schunk1992). According to this author, motivational climates are defined in terms of mastery and performance. Later studies used the terms task-involving and ego-involving to describe mastery and performance climates respectively (e.g., Newton, Duda, & Yin, Reference Newton, Duda and Yin2000). In a mastery (or task-involving) climate, success is defined as individual effort and improvement. In contrast, in a performance (or ego-involving) climate, the focus of learning is on interpersonal comparison and evaluation is based on normative standards (for a review, see Ntoumanis & Biddle, Reference Ntoumanis and Biddle1999). Vazou et al. (Reference Vazou, Ntoumanis and Duda2006) studied the relation between ego and task climates promoted by both coaches and peers. Their results showed that ego and task coach-created climates were positively related to ego and task peer-created climates.

Within the AGT, some studies have selected goal orientation as a personal variable in which team environment has an influence (e.g., Balaguer, Castillo, Duda, & García-Merita, Reference Balaguer, Castillo, Duda and García-Merita2011). Goal orientation is defined as the predominant dispositional goal in achievement situations, depending on how people evaluate their success and interpret their ability (Nicholls, Reference Nicholls1989). Two major goal dispositions have been proposed: task orientation and ego orientation. When task-oriented, individuals perceive their ability as self-referenced and focus on personal improvement, task mastery and exerted effort. When ego-oriented, perceptions of ability are other-referenced and individuals need to show superiority to feel competent (for a review, see Duda & Ntoumanis, Reference Duda and Ntoumanis2003). Nicholls (Reference Nicholls1989) viewed task and ego orientations as orthogonal and later studies confirmed this aspect (e.g., Balaguer, Castillo, & Tomás, Reference Balaguer, Castillo and Tomás1996). Moreover, repeated exposure to certain climates can lead to subsequent modifications of task and ego orientations. In fact, previous research has shown how athletes’ task and ego orientation were respectively influenced by coaches’ (e.g., Smith et al., Reference Smith, Smoll and Cumming2009) and peers’ (e.g., Vazou, Reference Vazou2010) task-involving and ego-involving climates. Also, earlier studies related these constructs to the regulations defined in the SDT (e.g., Deci & Ryan, Reference Deci and Ryan2000) and found that task orientation predicted intrinsic motivation and identified regulation, and ego orientation predicted introjected and external regulations (e.g., Ntoumanis, Reference Ntoumanis2001).

In the framework of SDT, autonomy support defines how the others allow and encourage initiative and freedom of decision, and share the players’ vision in solving problems (Gagné, Ryan, & Bargmann, Reference Gagné, Ryan and Bargmann2003). Usually, research has focused on coaches’ autonomy support and has found that it positively predicts athletes’ self-determined motivation (e.g., Álvarez, Balaguer, Castillo, & Duda, Reference Álvarez, Balaguer, Castillo and Duda2009). However, to the best of the authors’ knowledge previous works have not studied peers’ autonomy support (for an exception, see Ramis, Torregrosa, Viladrich, & Cruz, Reference Ramis, Torregrosa, Viladrich and Cruz2013). In Ramis et al. study (2013), peers’ autonomy support only had a slight effect on self-determined motivation. Moreover, results showed that coaches’ and athletes’ autonomy support were moderately correlated.

In this context, a number of questionnaires have been developed to assess the influence of team environment on athletes’ performance, affect or cognition. However, studies have increasingly needed more items as theories and analyses have become more complex (see results of our review below). This requirement lengthens the time participants spend on completing the questionnaires, and it can lead to fatigue, boredom and apathy, especially in children and youths. For this reason, the time used filling in the instruments might affect the quality of data obtained (e.g., the number of missing values). This issue has appeared not only in the study of the team environment but also in the whole arena of sport psychology research, as we will refer to later.

Given this scenario, it is clear that there is a need to improve the methodology for obtaining quantitative data in sport psychology research. To this end, short-forms of questionnaires appear to be useful in this field, where shortening has not been a common practice (for an exception, see Terry, Lane, Lane, & Keohane, Reference Terry, Lane, Lane and Keohane1999). In addition, it is worthwhile noting that shortening an existing questionnaire has some advantages over creating a new shorter instrument (Coste, Guillemin, Pouchot, & Fermanian, Reference Coste, Guillemin, Pouchot and Fermanian1997) because: (a) it enables phases of instrument development (e.g., item pool composition) to be bypassed; and (b) the instrument will appear familiar to users of the original form.

Although not common in sport psychology, within the field of clinical and health psychology shortening has been a widespread practice (e.g., Cox et al., Reference Cox, Oliver, Rial-González, Tomás, Griffiths and Thompson2006; Mühlan, Bullinger, Power, & Schmidt, Reference Mülhan, Bullinger, Power and Schmidt2008) and has helped to obtain a more economical and efficient diagnosis. The economy of diagnosis refers to the length reduced and time saved with the short-form compared with its original. Thus, short-forms make the response process more amenable, decreasing the burden on participants and lessening the resources spent on the study. From our point of view, research in sport psychology should learn from this perspective. An analysis of the quantitative papers about Sport Psychology published in 2010 in the four Sport Psychology journals with the highest impact factor (i.e., Journal of Sport & Exercise Psychology, Psychology of Sport and Exercise, The Sport Psychologist and Journal of Applied Sport Psychology) and in the four Spanish journals with the highest impact factor (i.e., The Spanish Journal of Psychology, Psicothema, Anales de Psicología and Revista de Psicología del Deporte) showed that researchers usually ask young athletes to respond to big amounts of items (e.g., athletes under 18 years old answered an average of 64 items in the international journals; detailed information about this review is available from the first author).

Efficiency balances economic effort with the loss of information or validity (Mülhan et al., Reference Mülhan, Bullinger, Power and Schmidt2008). When using instruments assessing health-related quality of life, efficiency is particularly important to ease clinical trials and clinical practice (Moran, Guyatt, & Norman, Reference Moran, Guyatt and Norman2001). However, shortening per se could entail losses in questionnaires’ psychometric properties, especially in terms of reliability and content validity (for a review on the sins of short-forms development, see Smith, McCarthy, & Anderson, Reference Smith, McCarthy and Anderson2000). Thus, the shortening process should include a careful selection of items in order to minimize these potential losses. As recommended by Coste et al. (Reference Coste, Guillemin, Pouchot and Fermanian1997), this selection should mainly consider content-driven criteria, and data-driven criteria should only be regarded when theory arguments are not enough to make a choice. Watson and Clark (Reference Watson and Clark1997) addressed the issue of how much a questionnaire might be shortened by and reported that less than four items per factor would yield an insufficient internal reliability (see also rationale by Jokovic, Locker, & Guyatt, Reference Jokovic, Locker and Guyatt2006). However, our review of the papers published in 2010 in the Sport Psychology journals and in the Spanish research journals with highest impact factor revealed that participants had to respond to many more items than just four per factor.

Developing economical and efficient questionnaires (i.e., short-forms) is important not only because it reduces the resources participants and researchers spend in the study, but also because shortening questionnaires might help to improve the quality of quantitative data, in terms of: (a) decreasing the number of missing values; (b) diminish the number of aberrant response patterns (i.e., “persons with item score patterns that are unexpected”; Meijer & Sijtsma, Reference Meijer and Sijtsma1995, p. 262), and (c) improving response rate (Edwards, Roberts, Sandercock, & Frost, Reference Edwards, Roberts, Sandercock and Frost2004).

Considering the benefits that shortening can bring to sport psychology research and the fact that no previous works in sport psychology have aimed to study how short-forms could help to improve data quality, our purpose was to develop short-forms to assess motivational climate, autonomy support and goal orientation. We wanted these short-forms to retain the core of the main dimensions of the AGT constructs (i.e., task and ego). According to Vazou et al. (Reference Vazou, Ntoumanis and Duda2006), focusing on the main points of the task (e.g., effort) and ego (e.g., social comparison) dimensions, instead of assessing the specific aspects of each climate, would enable the comparison between goal orientation and motivational climates, which could not be done with the original instruments (see also Whitehead, Andrée, & Lee, Reference Whitehead, Andrée and Lee2004).

In summary, the main goal of this work was to develop short-forms of four instruments assessing motivational factors from the AGT (i.e., peer-created motivational climate and goal orientation) as well as the SDT (i.e., coach and peer autonomy support) and provide evidence of their psychometric properties. Specifically, our study focused on the Peer Motivational Climate in Youth Sport Questionnaire (PeerMCYSQ; Ntoumanis & Vazou, Reference Ntoumanis and Vazou2005), the Sport Climate Questionnaire (SCQ; Deci, Reference Deci2001) and the Task and Ego Orientation in Sport Questionnaire (TEOSQ; Duda, Reference Duda1989). We also wanted to find out if these short-forms could improve data quality (i.e., less missing values and aberrant response patterns), as hypothesized.

We carried out three different studies. Study 1 describes the development of the short-forms and includes the selection of items (version 1 of the short-forms), judgmental validation and adjustment (i.e., expert meetings and focus groups; version 2). Study 2 focuses on the validation of these instruments and their iterative improvement (versions 3 and 4). Study 3 validates version 4 in a new sample and also includes structural equation modeling (SEM) in order to assess criterion validity. Figure 1 describes the entire process.

Figure 1.

Flowchart describing the process followed in the studies 1, 2, and 3.

Results

Data quality comparison

The data quality was assessed in terms of the number of missing values and aberrant response patterns (e.g., alignment errors). Table 1 shows a comparison between the quality of the data obtained with the original versions of the PeerMCYSQ, the SCQPeer, the SCQCoach, and the TEOSQ, and with their short-forms (Sample 2). Although athletes in Study 1 had responded to the complete versions, those 114 participants were not enough to do this comparison. Consequently, data were obtained from previous studies of our research group that had used the same administration protocol with samples that were considered equivalent to those of this work. A total of 648 cases had responded to the complete questionnaires. Analyses showed that short-forms had less missing values, with some of the constructs having all values completed. Results of a t test revealed that using the short-forms we obtained significantly less missing responses per item: t(655) = −4.01, p < .001, d = −0.13, 95% CI [−0.19, –0.01]. The analyses of the aberrant response patterns did not show relevant results and consequently are not presented. Moreover, Table 1 shows the time needed to respond to the original instruments and to the short-forms. On average, participants completed the short-forms in a third of the time spent on the complete versions.

Table 1.

Response Time and Data Quality Comparison (Study 2)

Note:

Average time indicates how many minutes a participant usually needs to complete each questionnaire. It was calculated as the completion time needed by 95% of participants averaged across the administration sessions.

Descriptive statistics and reliability

Table 2 presents the descriptive statistics and Cronbach’s Alpha coefficients for the scales in Study 2 (Sample 2). Also, we offer reliability coefficients from the Study 1 sample (α _study1 ) to facilitate comparison. The mean value of the variables displays a desirable pattern of a positive sports experience: high peers’ task climate, moderate-to-high coach and peer autonomy support, and high task goal orientation. All of these means were well above the midpoint of the scale range. The scales did not present any relevant skewness or kurtosis, indicating relative normality. The only exception was task goal orientation, which showed slight negative skewness and small positive kurtosis. All the internal reliability coefficients from Sample 2 ranged from .70 to .85, thus achieving Nunnally’s (Reference Nunnally1978) above .70 criterion for psychological scales. Additionally, nearly all the items contributed to the Alpha coefficient for their dimensions. Only deletion of Item 4 from the PeerMCYSQ (i.e., “Criticize their team-mates when they make mistakes”) made the Alpha coefficient increase.

Table 2.

Descriptives (Sample 2) and Internal Consistency for each Measure

Note:

Range for all variables is 1-7. MC = motivational climate. α _study2 = α of data from Study 2; α _study1 = α of data from Study 1.

For the imputation of missing values, we replaced them with the participant’s mean on the factor that included each particular missing value. According to Graham’s criterion (2009), this approach should not have consequences on the data analyses because missing values were less than 5% of all data points.

Confirmatory Factor Analyses (CFA)

Confirmatory Factor Analyses (CFA) were conducted with MPlus 5.2 to assess the adequacy of the data to the a priori models, using the Weighted Least Squares Means and Variance Adjusted (WLSMV), which utilizes a diagonal weight matrix with robust standard errors and a χ²-test adjusted by mean and variance (Muthén & Muthén, Reference Muthén and Muthén2008). We present the following fit indices: the χ², the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI), and the Tucker-Lewis Index (TLI). According to Jackson, Gillaspy and Purc-Stephenson’s criterion (2009), a good fit to the model might be considered when CFI and TLI values are close to .90 and the RMSEA value is close to .08. In the best scenario, the χ ² test is also expected not to be statistically significant. CFA using data from Sample 2 were conducted. The results in Sample 2 were not satisfactory and leaded us to work in the improvement of the short-forms. The two-factor model did not fit to the PeerMCYSQ data well: χ²(19) = 146.18, p < .001, CFI = .87, TLI = .88, RMSEA = .20. The CFA for the SCQCoach also revealed inadequate factor structure: χ²(9) = 88.17, p < .001, CFI = .94, TLI = .89, RMSEA = .17. The SCQPeer was found to have an acceptable factor structure: χ²(9) = 35.43, p < .001, CFI = .98, TLI = .97, RMSEA = .08. The two-factor model showed a correct fit to the TEOSQ data, except for RMSEA: χ²(19) = 74.28, p < .001, CFI = .93, TLI = .94, RMSEA = .13. All the factor loadings were statistically significant and above .40. Modification indices revealed two issues: (1) Item 4 from PeerMCYSQ (i.e. “Criticize their team-mates when they make mistakes”), which was predicted to be an item of the ego factor, was inversely related to the task factor (i.e., had a negative factor loading on task dimension); and (2) the TEOSQ items from the ego factor grouped in pairs, differentiating those referring to myself from those referring to the peers. Exploratory Factor Analyses (EFA) results led to the same conclusions. These results are not reported here but are available from the corresponding author. Due to these results and the qualitative data obtained from expert meetings and focus groups, we chose a different item from the original instrument and we included it in Version 4 of the PeerMCYSQ (i.e., “Complaint when the team doesn’t win”) in replacement of Item 4.

Correlations between variables

First, analysis of the bivariate correlations between short-forms dimensions showed that: (a) Task and ego peer-created motivational climates were moderately correlated (r = .33, p < .01) and (b) task and ego goal orientations were almost not correlated, although the coefficient was significant (r = .11, p < .05), supporting the TEOSQ orthogonal structure.

Table 3 contains the bivariate correlations between factors of the short-forms and variables that were hypothesized to be related to. All the expected correlations were positive, statistically significant (p < .01) and ranged from .19 to .58, with most of the values being above .30. It may be considered that all these correlations were low (.20 < r < .40) or moderate (.40 < r < .60). Specifically, task goal orientation had a low correlation to perceived coach’s and peers’ task-involving climates and to perceived competence, and had a moderate correlation with intrinsic motivation. Ego goal orientation, in turn, had a low correlation with perceived coach-created ego climate, introjected regulation and competence, and a moderate correlation with peer-created ego-involving climate. Also, low correlations appeared between coach’s task-involving climate and his/her autonomy support, and between peers’ task-involving climate and their autonomy support. Finally, perceived autonomy correlated slightly to coach and peer autonomy support.

Table 3.

Bivariate Correlations between Study 2 Variables

Note:

Correlations with p > .01 are not shown. Correlations showing evidence of concurrent validity of the short-forms are presented in bold. GO = goal orientation; MC = motivational climate. Line separates between short-forms and other measures.

**p < .01.

Discussion

In Study 2 we began the process of validating the short-forms developed in Study 1. Evidence concerning their reliability, concurrent validity and improvement of data quality was obtained. However, the results of their internal structure were not entirely satisfactory. Thus, experts meetings and focus groups were used with the aim of improving the short-forms. A future study was then necessary to test the last version of the short-forms (Version 4).

Results

Confirmatory Factor Analyses (CFA)

Table 4 presents the fit indices for all the short-forms. All of them showed a better fit to the models compared to the previous CFA in Study 2 and were found to have an acceptable factor structure. Only the CFA for the PeerMCYSQ did not meet the RMSEA criteria proposed by Jackson et al. (Reference Jackson, Gillaspy and Purc-Stephenson2009). All the items had statistically significant factor loadings above .50 and can be found in Table 5.

Table 4.

Fit Indices for the CFA (Sample 3)

Note:

df = degrees of freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Robust Comparative Fit Index; TLI = Tucker-Lewis Index.

**p < .01.

Table 5.

Items and Factor Loadings that Comprise the Short-forms (Sample 3, Version 4)

Note:

Contains Spanish and English wording of the items included in Version 4 of the short-forms. The Spanish wording is not a direct translation, as both experts and focus groups participants proposed changes to make the items more understandable.

Criterion validity

Analyses of Structural Equation Modeling (SEM) were performed in order to explore the criterion validity of our short-forms. Specifically, we examined two different models, one for the Achievement Goal Theory and other for the Self-Determination Theory. The first one studied the effect of the peer-created motivational climate on goal orientation, which in turn had an effect on motivational regulations (Figure 2). According to the previous literature, we expected task and ego peer-created motivational climates to correlate and to have an influence on both task and ego orientations. The task orientation was hypothesized to positively predict self-determined types of motivation (i.e., intrinsic motivation, integrated regulation and identified regulation). The ego orientation was expected to have a positive effect on controlled motivation (i.e., introjected and external regulations) and amotivation. The second model examined the influence of peers’ and coaches’ autonomy support on motivational regulations (Figure 3). In this model, we expected perceptions of peers’ and coaches’ autonomy support to correlate. We hypothesized that the positive effect of coaches’ autonomy support on self-determined types of motivation and negative effect on controlled regulations and amotivation would be bigger than peers’. In both models self-determined types of motivation were expected to correlate highly. Also, controlled regulations and amotivation were hypothesized to have high correlations.

Both models displayed a good fit to the data: AGT model, χ²(708) = 1003.01, p < .001, CFI = .93, TLI = .93, RMSEA = .05; SDT model, χ²(566) = 756.20, p < .001, CFI = .95, TLI = .95, RMSEA = .04. As can be seen in the Figures 2 and 3, the models showed most of the hypothesized paths. However, there were some exceptions. In the AGT model, peer-created task climate moderately predicted adopting an ego orientation. Also, ego orientation positively predicted identified regulation, a type of self-determined motivation. In the SDT model, perceptions of coach’ autonomy support did not have a negative effect on controlled regulations or amotivation. Moreover, peers’ autonomy support did not predict self-determined types of motivation.

Figure 2.

Structural Equation Model Based on the Achievement Goal Theory

Note. For presentation simplicity purposes only significant paths and correlations are showed, and item indicators are not presented. Discontinuous lines show negative paths. *p < .05; **p < .01; ***p < .001.

Figure 3.

Structural Equation Model Based on the Self-Determination Theory

Note. For presentation simplicity purposes only significant paths and correlations are showed, and item indicators are not presented. Discontinuous lines show negative paths. *p < .05; **p < .01; ***p < .001.