development and preliminary validation of the collective efficacy questionnaire for sports

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Development and PreliminaryValidation of the CollectiveEfficacy Questionnaire forSportsSandra E. Short , Philip Sullivan & Deborah L. FeltzPublished online: 18 Nov 2009.

To cite this article: Sandra E. Short , Philip Sullivan & Deborah L. Feltz (2005)Development and Preliminary Validation of the Collective Efficacy Questionnaire forSports, Measurement in Physical Education and Exercise Science, 9:3, 181-202, DOI:10.1207/s15327841mpee0903_3

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Development and Preliminary Validationof the Collective Efficacy Questionnaire

for Sports

Sandra E. ShortDepartment of Physical Education and Exercise Science

University of North Dakota

Philip SullivanDepartment of Physical Education and Kinesiology

Brock University

Deborah L. FeltzDepartment of KinesiologyMichigan State University

This study presents the development and preliminary validation of the Collective Ef-ficacy Questionnaire for Sports (CEQS). The study was conducted in 3 phases. InPhase 1, a 42-item questionnaire was developed and tested with 271 college-agedstudent-athletes. An exploratory factor analysis revealed 5 collective efficacy factorswith 27 items retained. In Phase 2, again using college-aged student-athletes (N =286), confirmatory factor analysis (CFA) supported a 5-factor, 20-item measure.These factors were named Ability, Effort, Preparation, Persistence, and Unity. InPhase 3, preliminary support for the construct validity (i.e., convergent, predictive,and discriminant validity) of the CEQS was obtained by examining correlationsamong the CEQS subscales and a measure of team cohesion (Group EnvironmentQuestionnaire; Widmeyer, Brawley, & Carron, 1985). A second CFA was conductedon the CEQS to cross-validate the measure. Combined results establish preliminarysupport for the CEQS.

Key words: collective efficacy, team confidence, measurement, scale development

MEASUREMENT IN PHYSICAL EDUCATION AND EXERCISE SCIENCE, 9(3), 181–202Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Requests for reprints should be sent to Sandra E. Short, Department of Physical Education and Ex-ercise Science, University of North Dakota, Box 8235, Grand Forks, ND 58202. E-mail: [email protected]

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Collective efficacy refers to a “group’s shared belief in its conjoint capability to or-ganize and execute the courses of action required to produce given levels of attain-ment” (Bandura, 1997, p. 477). In sport, it has also been referred to as team effi-cacy or team confidence. Collective efficacy beliefs are important because,theoretically, they influence what people choose to do as team members, howmuch effort they put into their team endeavors, and their persistence when collec-tive efforts fail to produce quick results or encounter forcible opposition (Bandura,1986, 1997). According to Bandura (2001), the higher the perceived collective ef-ficacy, the higher the teams’ motivational investment in their undertakings, thestronger their staying power in the face of impediments and setbacks, and thegreater their performance accomplishments.

Collective efficacy is not the same as self-efficacy. Self-efficacy refers to an in-dividual’s judgment or perception of one’s own capabilities and efforts (Bandura,1977). According to Bandura (1997), self-efficacy and collective efficacy differ inthe unit of agency; self-efficacy is an individual level phenomenon, whereas col-lective efficacy exists on the team level. For example, an individual may be effica-cious in one’s ability to make the correct play during a competition (self-efficacy),but this player may have lower efficacy in the team because this individual be-lieves that the team lacks the ability to be successful (collective efficacy).

Self-efficacy and collective efficacy are related in that collective efficacy isrooted in self-efficacy (Bandura, 1986, 1997). They can both be considered as cog-nitive mediators of performance in that they both serve similar functions and oper-ate through similar processes (Bandura, 2001). Although collective efficacy is hy-pothesized to be influenced by events and experiences similar to those thatinfluence self-efficacy (Bandura, 1997), collective efficacy beliefs depict theteams’ shared confidence in the team’s ability to generate collective action andsuccessfully complete a sport task relative to a specific goal or criteria. Magyar,Feltz, and Simpson (2004) stated that although Bandura (1997) proposed that indi-vidual perceptions of collective efficacy represent an emergent effect that ema-nates from the team rather than being the exclusive sum of the individual teammembers’ self-efficacy beliefs, he also acknowledged that these collective percep-tions of confidence are rooted within individual perceptions of self-efficacy. Spe-cifically, self-efficacy and collective efficacy may be considered similar yet dis-tinct from one another. Researchers in sport have shown a moderate relationbetween self-efficacy and collective efficacy (e.g., Feltz & Lirgg, 1998; Moritz,1998; Watson, Chemers, & Preiser, 2001).

Conceptually, collective efficacy is considered a team-level attribute. Its mea-surement almost always occurs on an individual level (except in those rare caseswhen team members make collective efficacy judgments together in a forced con-sensus format; see Moritz, 1998; Prussia & Kinicki, 1996). At the individual levelof assessment, people make judgments about their team’s capabilities to accom-

182 SHORT, SULLIVAN, FELTZ

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plish certain tasks; teams do not. Issues pertaining to levels of analysis concernhow the data are used in research. Analyses at the individual level would use indi-vidual perceptions of collective efficacy and would be appropriate for investigat-ing the relation between perceptions of collective efficacy and player satisfaction,for example. At the team level, the individual perceptions of collective efficacycould be aggregated (to reflect the “shared beliefs” component of the team levelconceptualization) so that each team has one collective efficacy score (i.e., the av-erage value of the individual level perceptions of collective efficacy for the teammembers), and these means would be used for subsequent analysis. This type ofanalysis would be suited for research questions pertaining to the relation betweencollective efficacy and team performance, for example. Asserting that one of theselevels (i.e., the individual vs. team level) is more important than the other may bemisguided (Moritz & Watson, 1998). Individual perceptions of collective efficacyare just as important as the collective efficacy that is a shared team attribute. Theresearch question should guide the level of analysis.1

According to Bandura (1997), progress in collective efficacy research requiresthe development of suitable measurement tools and the lack of sound measuresplaces a methodological damper on research. To date, the theorizing and researchconducted on efficacy beliefs has almost exclusively centered on personal influ-ence exercised individually (Bandura, 2001). People do not live their lives autono-mously; many of the outcomes they seek are achievable only through interdepen-dent efforts. Therefore, a measure of collective efficacy is needed that can achievevarying levels of results (Bandura, 1997). Our review shows that there are severalsport-specific measures of collective efficacy; for example, baseball (Sturm &Short, 2004), basketball (Bray & Widmeyer, 2000), bowling (Moritz, 1998), foot-ball (Myers, Feltz, & Short, 2004), hockey (Feltz & Lirgg, 1998), rowing (Magyaret al., 2004), rugby (Greenlees, Nunn, Graydon, & Maynard, 1999; Kozub &McDonnell, 2000), and volleyball (Paskevich, Brawley, Dorsch, & Widmeyer,1999). The characteristics of these sport-specific measures are similar. For exam-ple, Feltz and Lirgg’s (1998) measure for hockey assessed confidence in the areasof out-skating, out-checking, forcing more turnovers, bouncing back from per-forming poorly, scoring on powerplays, killing penalties, and having an effectivegoalkeeper who could stop a high percentage of shots. Kozub and McDonnell fol-lowed Feltz and Lirgg’s strategy to come up with a specific list of tasks relevant to

COLLECTIVE EFFICACY QUESTIONNAIRE 183

1The process of aggregating individual level data to the team level is more complex than what ispresented here. Controversy exists regarding whether consensus should be established prior to aggre-gation and what to do with nonconsensual team level data. There are a number of statistical tests thatcan be used to demonstrate the presence of “shared beliefs,” as well as techniques to analyze team leveland multilevel data. If interested, readers are encouraged to read Moritz and Watson (1998) for a dis-cussion of these issues.

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the sport of rugby (e.g., passing, tackling, retaining the ball in a ruck, retaining theball in a maul, providing support, retaining possession in a line out, and scrum-maging). Paskevich et al. measured the team confidence in volleyball players byassessing ability to perform offensive, defensive, and transitional physical taskskills; to communicate; to remain motivated; to overcome obstacles associatedwith the loss of a key player and associated with teammates; as well as generalitems related to the pursuit of normal team functions.

Although all of these sport-specific measures were appropriate for their re-search purpose, what the literature still lacks is a sport domain measure of collec-tive efficacy that is tailored to team functioning across different sports. Consider,as examples, the Sport Confidence Inventory (Vealey, 1986; Vealey & Knight,2004) or the self-confidence subscales of the Competitive State Anxiety Inven-tory–2 (Martens, Vealey, & Burton, 1990) and the Athletic Coping Skills Inven-tory (Smith, Schutz, Smoll, & Ptacek, 1995). These measures are all well-used insport psychology research. Their utility may be tied to the fact that they are spe-cific to sport but not limited to just one sport. That is, they allow for a comparisonof confidence levels within and across sports. When assessing team confidence, orcollective efficacy, no such measure is available. This study, however, addressesthat void by presenting the development and preliminary validation of the Collec-tive Efficacy Questionnaire for Sports (CEQS).

METHOD

The development of the CEQS is consistent with Bandura’s (2001) guidelines forconstructing efficacy scales. Our approach to the scale development process wasboth theory-driven and data-driven. We initiated the process with a wide variety ofbehaviors or cognitions that reflect the collective efficacy construct from a theoret-ical and practical standpoint, and then relied on factor analytic techniques (explor-atory and confirmatory) to determine the structure of the questionnaire. Focusgroups with collegiate coaches, student-athletes, and sport psychology profession-als were used several times during the development process. Phase 1 includes themethods used for the preliminary scale development and concludes with the pre-sentation of the results of an exploratory factor analysis. The related statistics (i.e.,descriptives, correlations, reliabilities) for the initial measure are also reported.Phase 2 includes the techniques used to modify and to improve the CEQS. Resultsfrom a confirmatory factor analysis are presented, along with other indicators ofthe psychometric properties of the revised version. In Phase 3, we documented ini-tial evidence for the construct validity of the CEQS (i.e., convergent, divergent,and predictive validity). In addition, because the sample size was adequate, we rananother confirmatory factor analysis (CFA) to cross-validate the CEQS.


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Phase 1: Preliminary Scale Development and InternalFactor Structure

The conceptualization of collective efficacy as a multidimensional measure wasgrounded in efficacy theory. According to Bandura (2001), treating efficacy be-liefs as an unitary trait sacrifices validity for internal consistency because behavioris better predicted by people’s beliefs in their capabilities to do whatever is neededto be successful than by their beliefs in only one aspect of efficacy related to thedomain. Furthermore, “efficacy beliefs involve different types of capabilities,such as management of thought, affect, action, and motivation” (Bandura, 1997, p.45). For these reasons, a multidimensional measure was considered more appro-priate for collective efficacy, similar to the multidimensional Coaching EfficacyScale (Feltz, Chase, Moritz, & Sullivan, 1999). Multidimensional efficacy scalesnot only have predictive power but also provide insights into the dynamics of be-havior (Bandura, 2001). In addition, we knew that the measure would be designedto tap not only behavior but also other key determinants such as effort and persis-tence as specified by Bandura (2001).

The initial pool of items was developed after taking into account severalsources. First, we considered the sources and outcomes of collective efficacy asdescribed by Bandura (1986, 1997, 2001) and Zaccaro, Blair, Peterson, andZazanis (1995). Second, we looked at the items from various sport-specific collec-tive efficacy questionnaires (Bray & Widmeyer, 2000; Feltz & Lirgg, 1998;Greenlees et al., 1999; Kozub & McDonnell, 2000; Moritz, 1998; Myers et al.,2004; Paskevich et al., 1999). We also examined the items from a measure ofgroup potency (Guzzo, Yost, Campbell, & Shea, 1993), which is somewhat similarto the collective efficacy construct. After we had a list of potential items, we metwith our first focus group comprised of coaches and student-athletes. We relied onthis group to provide us with an informative conceptual analysis (Bandura, 2001)of the thoughts and behaviors that they thought were essential to “team confi-dence” and that we may have overlooked. In addition, they commented on item ap-plicability, item clarity, and face validity. Overall, we developed an initial list of42 items.

From a design standpoint, we selected the stem “Rate your team’s confidence,in terms of the upcoming competition, that your team has the ability to …”. Thereappears to be some controversy in the field of team dynamics related to whether anindividual can rate an overall team’s confidence or if collective efficacy is betterassessed by an average of each individual team member’s confidences. More sim-ply stated, should collective efficacy items ask a participant to “rate your team’sconfidence” or “rate your confidence that your team …”. In the first case, teammembers are considered to be acting as “informants” in assessing their team’s col-lective efficacy (Lindsley, Brass, & Thomas, 1995). Some sport-specific examplesof items using this approach include the following: “Rate your team’s confidence


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that your team can average 100 points in this game” (Moritz, 1998, p. 112), and“Our team’s confidence that we can spike from the left side of the court is …”(Paskevich et al. 1999, p. 215). The rationale is that this stem directs participants tofocus on the team’s belief, rather than an individual’s belief in the team. Thismethod is assumed to measure the emergent properties of the collective mani-fested in the social cognitions of the team shared by all the members but not neces-sarily espoused on an individual basis (Lindsley et al., 1995).

The use of the second stem “rate your confidence that your team …” is designedto obtain individuals’ perceptions of their confidence in their team. Examples ofthis type of assessment include the following: “Rate your confidence in yourteam’s ability to bounce back from performing poorly” (Feltz & Lirgg, 1998, p.559); “Do you think that your group will achieve a better pull than the normativepull?” (Lichasz & Partington, 1996, p. 150); “How confident are you that yourteam will retain possession of the ball in a line out?” (Kozub & McDonnell, 2000,p. 124); “How confident are you that your team will attain <a particular placing>?”(Spink, 1990, p. 305). Some have argued that these types of measures may not re-flect the “collective” mind of the team (Lindsley et al., 1995).

According to Lindsley et al. (1995), the empirical difference between these twostems may be minor when respondents share beliefs about the team’s capabilities;however, the rationale for using team members as informants to estimate theteam’s collective efficacy is based on the notion that there are certain cognitionsthat team members have that are different and distinguishable from the beliefs theyexperience as individuals or in other contexts outside the team. These cognitionsare collective, team-based beliefs arising from an individual’s ability tocognitively consider social entities larger than her or himself.

Concurrent with this study, Short et al. (2002) conducted a study with footballplayers to determine if collective efficacy ratings varied according to whether thestem read “rate your confidence that your team …” or “rate your team’s confi-dence …”. Data were collected over three time periods. Both versions of the col-lective efficacy measure for all time periods were reliable. The correlations be-tween the versions for the different time periods ranged from .65 to .90. Repeatedmeasures multivariate analyses of variance showed that there were no differencesbetween these two assessment methods on collective efficacy ratings. The authorsconcluded that either stem was adequate.

Another factor that influenced our stem decision had to do with they way othermeasures used in team research were structured. The Group Environment Ques-tionnaire (GEQ; Widmeyer et al., 1985), a popular measure of team cohesion, forexample, makes two distinctions: individual versus group concerns and task ver-sus social concerns. For the individual concerns (for both task and social), the fo-cus of the questions is on the self. Participants are asked about their feelings abouttheir own personal involvement (i.e., “I like the style of play on this team”). Thegroup subscales (for both task and social) assess participants’ perceptions of their


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team as a whole. These items are phrased as “Our team is united in trying to reachits performance goals.” Thus, there is a shift from I to our when switching betweenindividual versus team components. Similarly, items on the group potency ques-tionnaire are group-focused. For example, one item reads “No task is too tough forour group.” Taken together, there seemed to be theoretical and empirical supportfor phrasing the stem this way.

The other components in the stem warrant comment as well. The phrase “in termsof the upcoming competition” was selected because Bandura (2001) recommendedassessing efficacy beliefs as a state (not a trait). When designing efficacy measures,he stated that people should be asked to judge their capabilities as of now, not theirpotential capabilities or their expected future capabilities. The end of the stem “thatyour team has the ability to” was selected because, according to Bandura, efficacyitems should be phrased in terms of “can do” rather than “will do.” All items werescored on a 10-point Likert-type scale where 0 (not at all confident) to 9 (extremelyconfident) to gauge efficacy strength. This format is consistent with Bandura’s(2001) guidelines and with the format of other efficacy measures (e.g., the CoachingEfficacy Scale; Feltz et al., 1999). The Likert-scale format also allows for the com-putation of efficacy strength scores, which are generally more sensitive and more in-formative than efficacy level scores (Bandura, 2001).

Sample and procedure. Permission to conduct this study was obtained bythe appropriate Institutional Review Boards. Informed consent was obtained fromall of the participants. This sample consisted of 271 college-age student-athletes(146 women, 125 men) from 11 sports. The sports were soccer (n = 54), basketball (n= 51), volleyball (n = 32), water polo (n = 30), swimming (n = 27), softball (n = 25),dance line (n = 18), hockey (n = 12), football (n = 12), tennis (n = 6), and curling (n =4). The participants were from approximately 40 different teams; intact teams werenot used so that student-athletes were not nested within teams. We deliberately sam-pled a number of sports that varied in task type to make this sample as heterogeneousas possible. Also, both men and women were included (e.g., the basketball group in-cludes 34 men and 17 women) although some sports (e.g., dance line and football)were gender-specific. The average age of the participants was 20.74 years (SD =4.24). The sample varied according to level of sport played (i.e., recreational/intra-mural, club, varsity, and professional) but the majority were college varsity stu-dent-athletes. The average time spent playing with their team was 1.87 years (SD =1.72). Consistent with the administration of other state measures, all student-ath-letes completed the questionnaire no more than 1 hr prior to competition; thus, allwere participating in their sport at the time of testing (i.e., “in-season”). Coacheswere not present at the time the CEQS was being completed.

Exploratory factor analysis (EFA). An EFA was conducted through SPSSusing principal axis extraction with an oblique rotation. An oblique rotation was


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selected based on previous efficacy scale development research (Feltz et al.,1999), which showed that subscales in a multidimensional measure were corre-lated. Furthermore, Bandura (2001) stated that efficacy items tapping the same do-main of efficacy should be correlated with each other and with the total score. Thesolution revealed five factors with eigenvalues over 1. The factor loadings fromthe pattern matrix for all 42 items are in Table 1. Our approach to item reductionwas statistical and conceptual. Statistically, we required an item to have a factorloading of at least .35 on one factor and to load no more than .25 on another factorto be representative of a certain factor. Conceptually, we held a second focus groupwith coaches, sport psychology graduate students, and student-athletes andshowed them the SPSS output, including items and loadings. In several cases, anitem was statistically sound, but the focus group indicated that it was a poor itembecause it was conceptually vague, cliché, redundant with other items, poorlyworded, and so on. Occasionally, the reverse occurred in which the focus groupadvocated for keeping items that could have been considered statistically bor-der-line. For example, consider the items and loadings for Factor 1 in Table 1. Tenitems had loadings close to .35, satisfying our factor loading criteria. Statisticallywe would have eliminated Items 2, 3, 5, 6, and 10 because of their cross-loadings.The focus group, however, advocated for the retention of Items 2 and 3 becausethey felt that they were important components of this factor. They recommendedeliminating items 5 (too cliché), 6 and 10 (redundant to other items). They alsosuggested eliminating item 9, despite its strong loadings because they felt that thecontent of the item was more of the coaches’ responsibility compared to the stu-dent-athletes’.

Overall, 15 items were eliminated from the initial 42-item questionnaire. Twoof the 15 items were cut on the basis of the focus group’s recommendations eventhough they satisfied our statistical criteria, and 8 items were retained because the


TABLE 1Phase 1: Exploratory Factor Analysis Item Loadings

Factor Loadings

Item Ability EffortKeepingControl Persistence

Preparation

Show more ability than the otherteam*

.70 –.02 –.03 .14 –.14

Outplay the opposing team* .69 –.14 .06 .26 –.10Win this event* .68 –.15 .05 .27 –.07Be successful* .57 .25 –.14 .02 .00Be more mentally tough than the

opposing team.47 .20 .09 .28 –.03

Be competitive .47 .23 –.06 –.16 –.29

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(continued)TABLE 1 (continued)

Factor Loadings

Item Ability EffortKeepingControl Persistence

Preparation

Demonstrate physical ability* .44 .15 –.14 –.04 –.23Display sound fundamentals* .41 .20 –.08 .12 –.18Modify its strategy or game plan

when needed.36 .06 –.23 .24 –.03

Achieve its goals .35 .21 –.16 .12 –.26Work hard as a team* .02 .82 .04 .10 .01Give maximum effort* –.02 .74 .05 .01 –.13Demonstrate a strong work ethic* –.03 .72 –.05 –.06 –.24Play to your capabilities* –.08 .65 –.01 .22 –.15Stay motivated* .09 .54 –.18 .12 –.13Work together as a team .28 .49 –.28 .03 .17Maintain its focus* .14 .46 –.18 .12 –.18Play as well as possible .07 .45 –.27 .08 –.06Show more desire than the other

team*.26 .38 –.01 .20 –.14

Play unselfishly .00 .39 –.36 .11 .05Be more determined than the

opposing team.28 .35 –.07 .17 –.10

Resolve conflicts* –.10 –.06 –.88 .08 –.07Solve problems in a collaborative

manner–.07 –.13 –.83 .06 –.27

Coordinate tasks .04 –.06 –.65 .07 –.36Be united* .09 .33 –.60 –.03 .07Keep a positive attitude* .17 .24 –.58 –.03 .15Keep cool under pressure* .07 .03 –.50 .26 –.03Work together .13 .47 –.48 –.10 .07Keep a winning attitude .37 .19 –.42 .05 .13Maintain control throughout the

game or event*.29 .19 –.35 .06 –.04

Communicate effectively whileplaying

.26 .20 –.33 .13 .10

Come from behind to be successful* –.06 .13 .03 .88 –.01Bounce back from behind if

performing poorly*.11 –.04 –.10 .75 .03

Perform under pressure* .18 .03 –.05 .55 –.13Adapt to different situations* .19 .04 –.24 .39 –.15Physically prepare for this

competition*.09 .06 –.02 .08 –.75

Adequately prepare* .04 .16 –.01 .13 –.72Mentally prepare for this

competition*–.06 .22 –.10 .10 –.65

Recognize your opponentsweaknesses*

.30 –.09 –.11 .03 –.48

Set goals* .10 .26 –.11 .00 –.42Maintain commitment to goals .33 .18 –.22 –.09 –.37

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focus group advocated for them despite their statistical weaknesses (the statisticalweaknesses were not severe, they were cross-loadings higher than .25; range =.26–.33). We tended to side with the focus group because of our interest in having ameasure that had strong face validity (Bandura, 2001) and our interest in forward-ing the items perceived by them to best represent the construct (Raedeke & Smith,2001).

The result of Phase 1 was a revised 27-item questionnaire. The five-factorstructure accounted for 67.33% of the variance. The names of the five factors andthe amount of variance each accounted for is as follows: Factor 1, Effort (50.22%);Factor 2, Ability (6.41%); Factor 3, Preparation (4.34%); Factor 4, Keeping Con-trol (3.65%); and Factor 5, Persistence (2.72%). These names were selected be-cause they described the items in a more general sense (i.e., similarity or homoge-neity in item content; Bandura, 2001) and because they were consistent withselected components of Bandura’s (1997) efficacy theory. Factor 4, Keeping Con-trol, was interesting to us; the items reflected themes of concentration/focus andcohesion/unity. We selected this name but felt as though this factor, compared toall others, was conceptually the weakest at this point.

Table 2 contains the means, standard deviations, and ranges for the total CEQSand the five subscales. The means are fairly high, which is typical of collective ef-ficacy scores in sport (e.g., Feltz & Lirgg, 1998). Also in Table 2 are the estimatesof reliability using Cronbach’s (1951) alpha, as recommended by Bandura (2001).These reliability coefficients range from .87 to .96, all being excellent (Nunnally,1978). The correlations between each of the factors are in Table 3, and as expected,the subscales are moderately to fairly highly related to each other, and all arehighly correlated with the total CEQS score (Bandura, 2001).


TABLE 2Phase 1: Descriptive Statistics and Reliability Estimates for the Collective

Efficacy Questionnaire for Sports (CEQS)

Descriptive Statistics

Subscale M SD Range Alpha

Ability 7.43 1.07 1.80–9.00 .90Effort 7.57 1.06 2.71–9.00 .92Keeping control 7.35 1.07 2.60–9.00 .87Persistence 7.02 1.26 1.75–9.00 .87Preparation 7.33 1.22 1.80–9.00 .89Total CEQS 7.37 0.98 3.93–9.00 .96

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Phase 2: Scale Modification and Internal Factor Structure

Phase 2 was designed to improve the CEQS by modifying items and to examinethe psychometric properties of the revised version. Our goal was to develop a mea-sure containing a core of 4 items for each of the five factors of the CEQS. We con-sidered 20 items to be the optimal number for the questionnaire so that completingit would not require a great deal of time given that its administration would be asclose to competition/game time as possible.

Although we had 27 items that were retained from the EFA, new items wereadded to the CEQS. At this stage in the development process, we emphasized atheoretical-driven approach rather than data driven. We were working with fivenamed factors that, for the most part, made sense theoretically, conceptually, prac-tically, and statistically. We believed that we could improve the CEQS by chang-ing certain items to make them clearer and more reflective of their names. Anotherfocus group was used to help with this process.

Changes made to some of the items are detailed below. In some cases, scenarioswere added to items so that participants would perceive that there were challenges,impediments, or obstacles to overcome (Bandura, 2001). To the ability factor, weadded the items “play more skillfully than the opponent” and “perform better thanthe opposing team.” For persistence, we added scenarios to the items: “persistwhen obstacles are present,” “persist in the face of failure,” “stay in the game whenit seems like your team isn’t getting any breaks,” “work through difficult situa-tions,” and “play well without your best player.” For preparation we added sevennew items, which included “devise a successful strategy.” This item was the mostcontentious among members of our focus group because some of them believedthat all strategy-related “things” were the coaches’ responsibility, but others be-lieved that student-athletes also have to make critical decisions regarding strate-gies at certain times. We were most satisfied with the effort subscale, but we added


TABLE 3Phase 1: Correlations among the Collective Efficacy Questionnaire

for Sports (CEQS) Subscales

Subscale 1 2 3 4 5 6

Ability 1.00Effort .66 1.00Keeping Control .65 .72 1.00Persistence .72 .58 .59 1.00Preparation .68 .69 .58 .60 1.00Total CEQS .88 .87 .83 .81 .84 1.00

Note. All correlations are significant at p = .05.

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four new items including “show enthusiasm.” The focus group suggested explor-ing the “focusing” component of this factor and suggested the items “overcomedistractions” and “concentrate.” Six items were added to the factor named “keep-ing control” to explore its dimensions more thoroughly. Examples of items in-cluded “appear confident in front of others” and “maintain composure when callsgo against you.” Also, the coaches and student-athletes expressed some concernover the item “maintain control throughout the game or event” from this subscaleand wanted to clarify its meaning so two new items were developed: “maintaincontrol” and “remain in control in challenging situations.” All of these new itemswere included to ensure that the best items would be included on the final measurein case any of the core items performed poorly as indicators of their respective col-lective efficacy factor (Raedeke & Smith, 2001).

The subsequent 49-item CEQS was reviewed by a fourth focus group. Similarto Phase 1, the purpose of this focus group was to go over item applicability, clar-ity, and to test the measure for face validity. The stem on the questionnaire re-mained the same as Phase 1: “Rate your team’s confidence, in terms of the upcom-ing competition, that your team has the ability to …”. All items were scored on ascale of 0 (not at all confident) to 9 (extremely confident).

Sample and procedure. For Phase 2, 286 college-age student-athletes(167 women, 117 men, 2 participants did not indicate gender) completed thequestionnaire. Specific sports represented included basketball (n = 55), track (n= 55), hockey (n = 51), volleyball (n = 38), soccer (n = 22), softball (n = 13),and dance line (n = 11). Similar to Phase 1, intact teams were not used, and wetried to include a number of sports that differed in task type and gender to makea heterogeneous sample. The average age of the participants was 20.3 (SD =1.79) years. The average time spent playing with their team was 1.75 years (SD= 1.13). The sample varied according to level of sport played (i.e., recre-ational/intramural, club, varsity, and professional) but the majority were collegevarsity student-athletes. The CEQS was completed during the hour prior to com-petition. Coaches were not present at the time the CEQS was being completed.

CFA. A CFA was conducted on the 49-item CEQS questionnaire. The sam-ple size of 286 participants can be considered an adequate size (Tabachnick &Fidell, 2001). All variables appeared to be normally distributed or only mildlyskewed (the largest value for skewness was 1.48, and for kurtosis was 3.48).

CFA assessed the fit between the data in this sample and the model proposedby the EFA. Data were analyzed through the maximum likelihood method usingEQS. Typically, the χ2 of a model is presented as an indicator of model fit, al-though it is almost always statistically significant due to sensitivity to sample size.Global indexes of fit presently used to test the model were the Comparative Fit In-dex (CFI), Non-Normal Fit Index (NNFI), Root Mean Square Error of Approxi-


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mation (RMSEA), and Standardized Root Mean Residual (SRMR). CFI and NNFIvalues greater than .90 are indicative of an acceptable fit to the data (Browne &Cudeck, 1993; Jöreskog & Söbom, 1996). RMSEA should be less than .06,whereas the SRMR should be less than .08 (Hu & Bentler, 1999).

The hypothesized factor structure included all 49 items on five factors. Becausethese factors were theoretically related, and the EFA was based on an oblique rota-tion, the factor structure tested included paths between all factors. This first factoranalysis showed a poor fit of the data to the model, χ2(1070) = 4143.89, p <.001,NNFI = .78, CFI = .79; RMSEA = .10, SRMR = .06. The 90% confidence for theRMSEA was bounded by .097 and .103. Given this poor fit, the large number ofitems included in this model, and the practical need for a parsimonious measure,the model was gradually reduced to produce a second model. Criteria for model re-duction were the desire to reduce the largest standardized residuals and to elimi-nate any linear dependency. This procedure was consistent with previous factoranalyses of efficacy measures in sport (Feltz et al., 1999). Alterations to the modelwere made one item at a time, and each reduction resulted in a significant ∆χ2, in-dicative of a significant improvement of the fit of the model to the data (Tabach-nick & Fidell, 2001).

The second model included 25 items and still reflected the five-factor modelsuggested by the EFA. The factors of Ability, Effort, Persistence, and KeepingControl contained 4 items, and Preparation consisted of 9 items. The fit indexes forthis model all approached acceptability, χ2(264) = 930.83, p < .001, NNFI = .88,CFI = .90, SRMR = .05, RMSEA = .09 (90% CI = .087–.101). A final criterion foritem reduction was the desire to have an equal number of items for each of the fivefactors. Preparation was reduced to 4 items. The items retained were the ones withthe highest standardized loadings. Each item was eliminated one at a time, andeach modification resulted in a significantly improved model fit, as seen by a sig-nificant ∆χ2 for each model. Finally, each item that was eliminated was agreed bythe authors as not contributing any unique face or construct validity to the model,and thus was conceptually, as well as statistically, redundant.

The third model consisted of the five factors with four items per factor. Themodel fit was good, χ2(160) = 574.29, p < .001, NNFI = .90, CFI = .92, SRMR =.04, RMSEA = .09 (90% CI = .87–.104). Although the value of the RMSEA wasslightly higher than expected, the other indices of fit were consistently strong. Ta-ble 4 shows the standardized factor loadings and error variances for the items inthis final model. Table 5 summarizes the fit statistics of the various models.

The final solution consisted of the four hypothesized factors and a modifiedfifth factor. Four of the five were more parsimonious representations of the EFAfactors. One factor appeared to reflect a different component of collective efficacythan hypothesized. Originally conceptualized as Keeping Control, this factoremerged with four items that displayed a strong conceptual fit, but did not appearto reflect this theme. The items (the ability to resolve conflicts, be united, keep a


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positive attitude, and maintain effective communication) instead are a reflection ofthe overall well-being of a team, and this factor was renamed Unity. Table 6 givesthe descriptive statistics of the final model, and a correlation matrix of the factorsis given in Table 7. The complete questionnaire can be found in the Appendix.


TABLE 4Phase 2: Standardized Factor Loadings and Error Variances

for Final Model

Factor Item Loading Error Variance

Ability 2 .80 .6014 .80 .6039 .93 .3840 .92 .38

Unity 5 .67 .7415 .78 .6337 .81 .5947 .79 .61

Persistence 7 .72 .6923 .83 .5627 .74 .6835 .67 .74

Preparation 11 .64 .7736 .82 .5745 .84 .5546 .85 .53

Effort 26 .82 .5633 .80 .6041 .78 .6243 .84 .55

TABLE 5Phase 2: Summary of Fit for all Attempted Models

Model df 2 NNFI CFI SRMR RMSEA

1 1,070 4,143.89 .78 .79 .06 .102 264 930.83 .88 .90 .05 .093 160 574.29 .90 .92 .04 .09

Note. NNFI = Non-Normal Fit Index; CFI = Comparative Fit Index; SRMR = Standarized RootMean Residual. Model 1 = five factors with all 49 items; Model 2 = five factors with 25 items; Model 3= five factors with 4 items per factor.

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Phase 3: Construct Validity of the CEQS

Phase 3 was designed to study the construct validity of the CEQS by examiningconvergent, divergent, and predictive validity; also provided was more evidencefor the reliability of the subscales. Using the most researched psychological con-struct in group dynamics history to date, team cohesion, we hypothesized thatthere would be a positive relation between collective efficacy and team cohesion(Paskevich et al., 1999; Zaccaro et al., 1995). To assess team cohesion, the GEQ(Widmeyer et al., 1985) was used. This multidimensional measure has foursubscales: Individual Attractions to Group–Task (IAG–T), Individual Attractionsto Group–Social (IAG–S), Group Integration–Task (GI–T) and Group Integra-tion–Social (GI–S). The GEQ makes two distinctions: individual versus groupconcerns and task versus social concerns. For the IAG–T and IAG–S subscales thefocus of the questions is on the self; student-athletes are asked about their feelingsconcerning their own personal involvement with their team in terms of the team’stask and social aspects. The GI–T and GI–S subscales assess an athlete’s percep-tions of his or her team as a whole with respect to task and social activities. Scoreson all subscales are computed to result in a value ranging from 1 to 9 where higher


TABLE 6Phase 2: Descriptive Statistics and Reliability Estimates

for the Collective Efficacy Questionnaire for Sports (CEQS)



Ability 7.34 1.36 1.00–9.00 .92Effort 7.44 1.18 2.25–9.00 .88Unity 7.27 1.20 2.25–9.00 .85Persistence 7.26 1.16 2.75–9.00 .85Preparation 7.33 1.25 2.25–9.00 .89Total CEQS 7.33 1.12 2.45–9.00 .97

TABLE 7Phase 2: Correlations Among the Collective Efficacy Questionnaire

for Sports (CEQS) Subscales

Subscale 1 2 3 4 5 6

Ability 1.00Unity .62 1.00Persistence .74 .81 1.00Preparation .78 .78 .85 1.00Effort .69 .86 .88 .86 1.00Total CEQS .85 .89 .94 .94 .94 1.00

Note. All correlations are significant at p = .05.

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scores indicate perceptions of higher cohesiveness. The development and psy-chometric properties of the GEQ are described in detail elsewhere (Carron,Brawley, & Widmeyer, 1998; Widmeyer et al., 1985).

The nomological network for this portion of the study was that the correlationsamong the CEQS and GEQ would be statistically significant because they are bothteam characteristics. Showing this relation would be evidence of predictive valid-ity. For convergent and discriminant validity, the correlations among the CEQSsubscales should be higher than the correlations between the CEQS and GEQ(Marsh, 1998). Given that we were dealing with two multidimensional measures,additional hypotheses were tested. First, of all the GEQ subscales, collective effi-cacy would have the highest correlations with the GI–T subscale of the GEQ be-cause this subscale considers the group and the task. There is no evidence to sug-gest that collective efficacy should be related to social cohesion. Second, that theUnity subscale of the CEQS would have the highest correlation with all of theGEQ subscales because Unity efficacy and cohesion seemed to be the most con-ceptually similar.

Sample and procedure. For Phase 3, 171 college-age student-athletes (103women, 68 men) from eight sports participated. Specific sports represented in-cluded basketball (n = 62), volleyball (n = 36), hockey (n = 23), soccer (n = 22),softball (n = 13), football (n = 9), broomball (n = 4), and rugby (n = 2). Similar tothe other phases, intact teams were not used, and we tried to include a number ofsports that differed in gender composition to make a more heterogeneous sample;however, we only sampled interacting sports. The GEQ is a widely used scale insport psychology but recently its use in sport research with coacting teams wasquestioned by Sullivan, Short, and Cramer (2002). Its validity for interactingteams appears adequate (Li & Harmer, 1996). For this reason, we used only stu-dent-athletes from interactive sports in this phase. The average age of the partici-pants was 20.38 (SD = 1.98) years. The average time spent playing with their teamwas 1.69 years (SD = 1.29). The sample varied according to level of sport played(i.e., recreational/intramural, club, varsity, and professional) but the majority werevarsity student-athletes. All student-athletes completed the questionnaires nomore than 1 hr prior to playing. Coaches were not present at the time the CEQSwas being completed.

Validity. Descriptive statistics for the CEQS and the GEQ can be found in Ta-ble 8. Also in Table 8 are the estimates of reliability using Cronbach’s alpha. Thesereliability coefficients range from .81 to .96 for the CEQS, and between .63 and .82for the GEQ subscales. The correlations among the CEQS and GEQ are in Table 9.All correlations were statistically significant (p < .01). The correlation matrixshows that the range of correlations among the CEQS subscales was from .59 to.95. The range of correlations between the CEQS and GEQ subscales was lower


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197

TABLE 8Phase 3: Descriptive Statistics and Reliability Estimates

for the Collective Efficacy Questionnaire for Sports (CEQS)and Group Environment Questinnaire (GEQ)



CEQSAbility 7.22 1.48 1.00–9.00 .91Effort 7.21 1.32 2.25–9.00 .87Unity 7.00 1.41 2.00–9.00 .85Persistence 7.04 1.21 2.75–9.00 .81Preparation 7.09 1.40 2.25–9.00 .87Total CEQS 7.13 1.22 2.45–9.00 .96

GEQIAG-T 7.39 1.42 1.25–9.00 .63IAG-S 7.16 1.53 1.20–9.00 .68GI-T 6.47 1.35 1.40–9.00 .68GI-S 6.51 1.73 1.50–9.00 .82

Note. IAG–T = Individual Attractions to Group–Task; IAG–S = Individual Attractions to Group–Social; GI–T = Group Integration–Task; GI–S = Group Integration–Social.

TABLE 9Phase 3: Correlations Among the Collective Efficacy Questionnaire for

Sports (CEQS) and Group Environment Questinnaire (GEQ)

Subscale 1 2 3 4 5 6 7 8 9 10

CEQSAbility 1.00Effort .70 1.00Persistence .69 .86 1.00Preparation .76 .87 .80 1.00Unity .59 .86 .78 .76 1.00Total

CEQS.84 .95 .90 .93 .88 1.00

GEQIAG-T .20 .36 .34 .29 .42 .32 1.00IAG-S .32 .47 .47 .41 .44 .45 .32 1.00GI-T .37 .56 .50 .51 .57 .54 .62 .53 1.00GI-S .29 .41 .40 .43 .43 .42 .23 .64 .53 1.00

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(.20–.57). Taken together, these two findings support the convergent and dis-criminant validity of the CEQS. The highest correlations were between the CEQSsubscales and the GI–T subscale of the GEQ (range = .37–57), and of these corre-lations, the one with unity efficacy was highest (.57). The results support our hy-potheses as specified in the nomological network.

A CFA of the model established in Phase 2 with these data yielded results simi-lar to those in the earlier phases. The fit statistics were as follows: CFI = .92, NNFI= .90, SRMR = .06, and RMSEA = .10, with a 90% confidence interval rangingfrom .09 to .12.

DISCUSSION

This research program outlines the development and preliminary validation for amultidimensional measure of collective efficacy that can be used in sport research.The scale is composed of five interrelated factors: Ability, Effort, Preparation, Per-sistence and Unity. All of the subscales are correlated with each other and with thetotal score; Bandura (2001) suggested then that the total score or the five subscalescores could be used to measure collective efficacy.

The main difference between the CEQS and other collective efficacy measuresis that the CEQS is tailored to team sport functioning in general, and therefore canbe used across sports. In addition, the majority of items used by previous research-ers dealt specifically with the team’s ability, whereas the development of theCEQS, which included much work to support the validity of the scale, suggestedthat other aspects of team functioning were salient in determining collective effi-cacy. Specifically, factors such as Effort, Persistence, Preparation, and Unity wereclarified and endorsed through focus groups with coaches, graduate students, andprofessionals in sport psychology, and student-athletes. Of the measures outlinedin the introduction, Paskevich et al. (1999) came closest to ascertaining these no-tions, however, their scale is still largely skewed toward performance and abilityissues. The CEQS offers equal and parsimonious measurement of its factors, aswell as a multidimensional framework to understand the nature of relationshipsbetween these factors. Further, in line with Bandura’s (1997, 2001) conceptualiza-tion, the CEQS is framed as a state measurement, with the instructions specificallyreferring to these perceptions relative to the upcoming competition. Thus, the scaleis well designed for repeated measures and case study designs, and this flexibilitywill promote future research within teams as well as between different sports. Thisis a valuable and promising feature of a scale in a field ripe for much empiricalwork.

With respect to how the CEQS will compare to these sport specific measures ofcollective efficacy, we are aware of only one unpublished study that used theCEQS and a sport specific collective efficacy measure in baseball. Sturm and


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Short (2004) found the CEQS was highly correlated with a sport-specific measureof collective efficacy when data were analyzed at the individual level (r = .85, n =126) and aggregated to the team level (r = .98, n = 9). They also found that teamshigher in collective efficacy (as assessed by the CEQS) performed better thanlower efficacy teams, which further supports the construct validity of the CEQS.

The GEQ was selected for the construct validity portion of this research pro-gram given its importance and its popularity in group dynamics research. Previousresearch showing a relation between cohesion and collective efficacy (Paskevichet al., 1999), coupled with theoretical underpinnings to support a relation betweenthese two constructs (i.e., Zaccaro et al.. 1995) made the decision to use cohesionquite easy. The measure of cohesion, in form of the GEQ, has been heavily criti-cized. Researchers have had problems with its factor structure (Schutz, Eom,Smoll, & Smith, 1994) especially for coacting teams (Sullivan et al., 2002). In ad-dition, as this study shows, internal consistency values for certain subscales of theGEQ are often less than desirable (Nunnally, 1978), which may affect interpreta-tion of results. This study is understood to be preliminary research. Future re-searchers should focus on the relations between team confidence and other con-structs such as communication and player satisfaction to validate the CEQS.Additional validation studies of the CEQS are also warranted. Samples comprisedof younger, high school level athletes should be used and invariance tests for gen-der, level of competition, type of sport, and level of sport should be conducted.

Of course, there are more unanswered than answered questions regarding thesources and outcomes of collective efficacy in sport. Most researchers in this areahave used self-efficacy theory to guide their research questions. This is acceptableif collective efficacy is conceptualized as an extension of self-efficacy theory tothe team level. Recently, Chen et al. (2002) found both similarities and dissimilari-ties between individual and team level sources and outcomes of efficacy beliefs,suggesting that the assumption of homology in models of efficacy should be revis-ited. This finding sets the stage for researchers to examine the relation betweencollective efficacy and every variable ever associated with self-efficacy to deter-mine if the theory holds up across levels. Additional uses of the CEQS are morepractically related. Coaches and sport psychology consultants could use the mea-sure to gauge the strength of a team’s efficacy beliefs. Interventions could then betargeted to specific individuals, or they could be developed to increase an overallteam’s confidence in key areas such as their ability to prepare, persist, or be united.Badding (2004) found that there was a relation between team size and collectiveefficacy (using the CEQS) where larger sized teams were more confident thansmaller- sized teams on all factors except for Unity efficacy. This result shows thatthe CEQS can be used to identify potential trouble areas in team functioning.Hopefully, this measure allows researchers to continue their quest in understand-ing collective efficacy, and its sources and outcomes, with the ultimate goal of en-hancing team functioning in sport.


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APPENDIXCollective Efficacy Questionnaire

Rate your team’s confidence, in terms of the upcoming game or competition, that your team has theability to …

Not at All Confident Extremely Confident1. Outplay the opposing team 0 1 2 3 4 5 6 7 8 9 102. Resolve conflicts 0 1 2 3 4 5 6 7 8 9 103. Perform under pressure 0 1 2 3 4 5 6 7 8 9 104. Be ready 05. Show more ability than the

other team0 1 2 3 4 5 6 7 8 9 10

6. Be united 0 1 2 3 4 5 6 7 8 9 107. Persist when obstacles are

present0 1 2 3 4 5 6 7 8 9 10

8. Demonstrate a strong workethic

0 1 2 3 4 5 6 7 8 9 10

9. Stay in the game when itseems like your teamisn’t getting any breaks

0 1 2 3 4 5 6 7 8 9 10

10. Play to its capabilities 0 1 2 3 4 5 6 7 8 9 1011. Play well without your best

player0 1 2 3 4 5 6 7 8 9 10

12. Mentally prepare for thiscompetition

0 1 2 3 4 5 6 7 8 9 10

13. Keep a positive attitude 0 1 2 3 4 5 6 7 8 9 1014. Play more skillfully than the

opponent0 1 2 3 4 5 6 7 8 9 10

15. Perform better than theopposing team(s)

0 1 2 3 4 5 6 7 8 9 10

16. Show enthusiasm 0 1 2 3 4 5 6 7 8 9 1017. Overcome distractions 0 1 2 3 4 5 6 7 8 9 1018. Physically prepare for this

competition0 1 2 3 4 5 6 7 8 9 10

19. Devise a successful strategy 0 1 2 3 4 5 6 7 8 9 1020. Maintain effective

communication0 1 2 3 4 5 6 7 8 9 10

Factors: Ability: Items 1, 5, 14, 15. Effort: Items 8, 10, 16, 17. Persistence: Items 3, 7, 9, 11. Prepa-ration: Items 4, 12, 18, 19. Unity: Items 2, 6, 13, 20.D

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development and preliminary validation of the collective efficacy questionnaire for sports

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