Collective Efficacy and Team Performance: A Longitudinal Study of Collegiate Football Teams
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Group Dynamics: Theory, Research, and Practice Copyright 2004 by the Educational Publishing Foundation
2004, Vol. 8, No. 2, 126 –138 1089-2699/04/$12.00 DOI: 10.1037/1089-2699.8.2.126
Collective Efficacy and Team Performance: A Longitudinal Study
of Collegiate Football Teams
Nicholas D. Myers and Deborah L. Feltz Sandra E. Short
Michigan State University University of North Dakota
This study examined the reciprocal relationship between collective efficacy and team
performance over a season of competition in American football. Efficacy beliefs of
offensive football players from 10 teams were assessed prior to 8 consecutive games to
form 2 team-level measures of collective efficacy: aggregated self-efficacy and aggre-
gated collective efficacy. Game-level performance indexes produced a team-level
measure of offensive performance for each game. Within teams and across games,
aggregated collective efficacy prior to performance was a positive predictor of subse-
quent offensive performance; however, previous offensive performance was a negative
predictor of subsequent aggregated collective efficacy. Within weeks and across teams,
aggregated collective efficacy prior to performance also was a positive predictor of
subsequent offensive performance, and previous offensive performance was a positive,
rather than negative, predictor of subsequent aggregated collective efficacy.
Beliefs individuals hold regarding their own volve team competition, the relationships be-
ability to successfully execute given levels of tween collective efficacy and team performance
individual performance affect the choices they are also of interest. Bandura (1986) proposed
make, the amount of effort they expend, the the concept of collective efficacy as an exten-
degree to which they persevere in the face of sion of self-efficacy theory to explain group
adversity, and their thought patterns (Bandura, choices, effort, and persistence. He defined col-
1977). Self-efficacy, the belief in one’s capabil- lective efficacy as “a group’s shared belief in
ities to produce given levels of performance, their conjoint capabilities to organize and exe-
has been significantly correlated with perfor- cute the courses of action required to produce
mance across a number of sport tasks (Feltz & given levels of attainments” (Bandura, 1997, p.
Lirgg, 2001). Furthermore, experimental and 476).
path-analytic studies suggest that self-efficacy Although collective efficacy is a group’s
is a major determinant of individual athletic shared belief, it still reflects individuals’ percep-
performance (George, 1994; Haney & Long, tions of the team’s capabilities (Bandura, 1997).
1995; Kane, Marks, Zaccaro, & Blair, 1996; Bandura recommended two approaches for de-
Martin & Gill, 1995; McAuley, 1985). riving single estimates of a team’s collective
Research on the role of efficacy beliefs in efficacy from individual team members. The
sport has largely focused on the relationships first approach involves assessing each team
between self-efficacy and individual athletic per- member’s belief in his or her personal capabil-
formance. However, because many sports in- ities to perform within the group (i.e., self-
efficacy) and then aggregating these individual
self-efficacy measures to the team level. Ban-
dura argued that because individuals’ self-effi-
Nicholas D. Myers and Deborah L. Feltz, Department of cacy beliefs within a team context are not de-
Kinesiology, Michigan State University; Sandra E. Short,
Department of Physical Education and Exercise Science, tached from the interactive dynamics operating
University of North Dakota. within the group, individual self-efficacy mea-
We would like to acknowledge Jennifer Pressner for her sures can be aggregated to the team level to
work on data collection. provide a measure of a team’s collective effi-
Correspondence concerning this article should be ad-
dressed to Deborah L. Feltz, Department of Kinesiology,
cacy. We refer to this estimate of collective
Michigan State University, East Lansing, MI 48824. E- efficacy as aggregated self-efficacy. The second
mail: dfeltz@msu.edu approach involves assessing each team mem-
126COLLECTIVE EFFICACY 127 ber’s belief in his or her team’s capabilities as a competition. They surveyed six teams within 24 whole and then aggregating these individual hr prior to 32 competitions for 16 weekends. measures to the team level. We refer to this Teams played the same opponent within a estimate of collective efficacy as aggregated weekend. They reported that aggregated collec- collective efficacy. Bandura contended that ag- tive efficacy was a better predictor of team gregated collective efficacy will be more pre- performance than was aggregated self-efficacy dictive of team performance than will aggre- within teams and across games. Feltz and Lirgg gated self-efficacy when the group task is highly did not examine the dynamic week-by-week interdependent. influence of aggregated collective efficacy on Zaccaro, Blair, Peterson, and Zazanis (1995) team performance over the course of the com- were more explicit than Bandura was in regard petitive season within weeks and across teams. to the coordinative and integrative aspects of Collective efficacy is hypothesized to be in- collective efficacy in their definition of the con- fluenced by events and experiences similar to struct. They defined collective efficacy as “a those that influence self-efficacy (Bandura, sense of collective competence shared among 1997). As with self-efficacy, Bandura posited members when allocating, coordinating, and in- that mastery experiences of the group exert the tegrating their resources as a successful, con- most powerful influence on collective efficacy certed response to specific situational demands” beliefs. Feltz and Lirgg (1998) reported that (Zaccaro et al., 1995, p. 309). Various defini- previous game outcome affected subsequent ag- tions of collective efficacy have contributed to gregated collective efficacy but not subsequent multiple approaches to the measurement of the aggregated self-efficacy across teams and construct (Maddux, 1999). Measurement meth- games. They reasoned that because team ac- ods that are somewhat different from the two complishments were more apparent than an in- approaches advocated by Bandura have in- dividual’s accomplishments were in ice hockey, cluded aggregated collective efficacy based on team performance exerted a greater influence on Zaccaro et al.’s definition of the construct players’ efficacy judgments about their team (Paskevich, Brawley, Dorsch, & Widmeyer, than it did on players’ efficacy judgments about 1999) and single measures obtained from group themselves. Watson, Chemers, and Preiser discussion (Gibson, Randel, & Earley, 2000). (2001) found that team-level predictors of col- There is no evidence that a single measure of lective efficacy in collegiate basketball included collective efficacy derived from group discus- past performance, group size, and confident sion, or that an aggregated measure of collective leadership. Neither of these studies examined efficacy based on Zaccaro et al.’s definition, the influence of previous performance on sub- predicts team performance significantly better sequent collective efficacy within teams and than does an aggregated measure of collective across games or the week-by-week influence of efficacy based on either of the approaches ad- previous performance on subsequent collective vocated by Bandura (1997). For these reasons, efficacy within weeks and across teams. as well as impracticalities in implementing the Bandura (1997) contended that for group group discussion method with real sports teams tasks that are highly interdependent, aggregated in a longitudinal field study, we used the mea- collective efficacy would be a better predictor of surement methods suggested by Bandura and group performance than would aggregated self- previously used by Feltz and Lirgg (1998). efficacy. A meta-analysis of studies examining Collective efficacy beliefs are hypothesized the relationship between collective efficacy and to influence subsequent and proximal group per- team performance found that task interdepen- formances (Bandura, 1997). Hodges and Carron dence and level of analysis moderated this (1992) and Lichacz and Partington (1996) used relationship (Gully, Incalcaterra, Joshi, & lab tasks and found that teams with high collec- Beaubien, 2002). Specifically, effect sizes for tive efficacy outperformed and persisted longer the collective efficacy and group performance than did teams with low collective efficacy, and relationship were stronger at the team level than that failure resulted in lower collective efficacy at the individual level, and the relationship was on successive trials. Feltz and Lirgg (1998) ex- stronger when task interdependence was high. amined the influence of aggregated self-efficacy Thus, the relationship between collective effi- and aggregated collective efficacy on team per- cacy and team performance should be maxi- formance in men’s ice hockey over a season of mized when aggregated collective efficacy mea-
128 MYERS, FELTZ, AND SHORT
sures are used and the group task is highly gregated collective efficacy and offensive per-
interdependent. formance within weeks and across teams and
Interdependence has been conceptualized as over the course of a competitive season. Within
being defined by task, goal, and outcome inter- the second purpose of the study, a third and a
dependencies (Campion, Papper, & Medsker, fourth hypothesis were tested. Our third hypoth-
1996). Task interdependence refers to the de- esis was that aggregated collective efficacy
gree of task-driven interactions among team would be a positive predictor of subsequent
members (Shea & Guzzo, 1987). Goal interde- offensive performance within weeks and across
pendence refers to the interconnections among teams. Our fourth hypothesis was that previous
group members implied by the goals that direct offensive performance would be a positive pre-
collective performance and efforts (Saavedra, dictor of subsequent aggregated collective effi-
Earley, & Van Dyne, 1993). Outcome interde- cacy within weeks and across teams. Supporting
pendence refers to the existence of conse- either or both of these hypotheses would pro-
quences and outcomes that are shared by team vide evidence for the presumed reciprocal rela-
members (Shea & Guzzo, 1987). Ideally, offen-
tionships between collective efficacy and group
sive team members in football work on interde-
performance across time (Gully et al., 2002).
pendent tasks (e.g., execute a game plan), have
interdependent goals (e.g., score points), and
experience interdependent consequences for Method
their performance (e.g., receive praise from the
coaching staff). Thus, aggregated collective ef- Sample
ficacy should be strongly related to offensive
performance in football because the team’s Participants were 197 intercollegiate football
tasks, goals, and outcomes are highly players from 10 different universities compet-
interdependent. ing in two Midwestern Division III intercolle-
The first purpose of this study was to examine giate athletic conferences. Only athletes from
the relationship between collective efficacy the offensive teams were invited to participate
prior to performance and subsequent team per- in the study to minimize complications with
formance in a highly interdependent task within interdependency (to be discussed in the Analy-
teams and across games. Within the first pur- ses section). These participants were asked to
pose of the study, two hypotheses were tested. complete questionnaires prior to eight consecu-
Our first hypothesis was that aggregated collec- tive games. Each team played an opposing team
tive efficacy would be a positive and stronger only once during the season. The teams within
predictor of offensive performance than would each conference did not play teams from the
aggregated self-efficacy within teams and other conference.
across games. Supporting this hypothesis would Teams D, E, and J failed to submit data for
replicate the findings of Feltz and Lirgg (1998). one to three games, which totaled seven games.
Our second hypothesis was that previous offen-
Also, data from Team G were not retained, as
sive performance would be a positive and stron-
their data from Games 5 through 8 were highly
ger predictor of subsequent aggregated collec-
tive efficacy than would subsequent aggregated questionable. In these games, a number of team
self-efficacy within teams and across games. members created encompassing circles to an-
Supporting this hypothesis would extend the swer multiple items. The negative impact of
findings of Feltz and Lirgg, because they pro- nonattending responses on the internal consis-
vided evidence for a positive relationship only tency of self-administered surveys has been es-
across teams and games, which ignored the tablished (Barnette, 1999). Team G also did not
clustering of the data. Assuming that aggregated return data for Game 4. Thus, although seem-
self-efficacy would not be predictive of, or pre- ingly valid data for Team G were obtained for
dicted by, offensive performance, aggregated Games 1–3, regression analyses based on only
self-efficacy would be dropped as a measure of three observations would likely be unstable.
collective efficacy in addressing the second pur- Therefore, the total number of games for which
pose of this study. data were collected and retained was 65, and the
The second purpose of this study was to maximum number of participants for any week
examine the reciprocal relationship between ag- of data collection was 180.COLLECTIVE EFFICACY 129
Procedure football coach and a former professional foot-
ball player. Efficacy items were developed in
Permission was obtained from the institu- relation to the identified competency areas.
tional review board and the 10 head coaches Items were fit to scale structures similar to those
prior to data collection. An explanation of the used by Feltz and Lirgg (1998). Ratings were
study was presented to each team by the head made on an 11-point scale ranging from 0 (can-
coach. Informed consent was obtained from all not do at all) to 10 (certain can do). In addition
athletes. Athletes were guaranteed confidential- to the efficacy items, participants were asked
ity for their responses. Questionnaires were demographic questions such as whether they
completed within 24 hr before each game, usu-
had received playing time in the previous game
ally after practice on Friday. Games were held
and their injury status. The questionnaire was
on Saturday afternoons. One trainer on each
team administered the questionnaires to the pilot tested with former collegiate offensive
team for all games. Trainers who successfully football players to affirm that the directions
followed through over the entire season were were clear and that the items were relevant.
entered into a lottery for $100. All question- Self-efficacy. The self-efficacy scale con-
naires were returned through the mail to the tained four items that assessed the degree of
researchers after each weekend. This protocol confidence an athlete had in his ability to per-
was followed throughout data collection. form significant game competencies against the
upcoming opponent. Participants were asked to
rate their own confidence to (a) outperform their
Measures
opponent, (b) bounce back from performing
Offensive performance. Offensive perfor- poorly, (c) perform their job successfully in
mance indicators were obtained from confer- third- and fourth-down conversion situations,
ence headquarters after each game. Perfor- and (d) commit fewer penalties. Self-efficacy
mance indicators included time of possession, scores were established by averaging each ath-
sacks allowed, yards lost from penalties, fum- lete’s responses to the four items. An internal
bles lost, number of interceptions, number of consistency analysis on the self-efficacy items
punts, passing yardage, pass completion per- revealed a Cronbach’s alpha of .92.
centage, rushing yardage, total offensive yard- Collective efficacy. The collective efficacy
age, average yardage gained per play, game scale contained nine items that assessed the
score, and game outcome. Performance indica- degree of confidence an athlete had in his
tors were evaluated for their potential to con- team’s ability to perform significant game com-
tribute to a conceptually meaningful measure of petencies against the upcoming opponent. Par-
overall offensive performance. Indicators that ticipants were asked to rate their confidence in
were retained initially included (a) points their team in the following areas: (a) outplay (in
scored, (b) total yardage, (c) average yardage terms of yardage gained), (b) outhit, (c) quar-
gained per play, (d) number of turnovers com- terback can outperform opposing quarterback,
mitted, (e) number of punts, and (f) game out- (d) have fewer turnovers, (e) bounce back from
come. Although other indicators can also be performing poorly, (f) score in the red zone, (g)
indicative of offensive performance (e.g., sacks
commit fewer penalties, (h) make third- and
allowed, rushing performance, time of posses-
fourth-down conversions, and (i) win the game
sion), we decided that much of the information
contained in some of these indexes (e.g., sacks against the opposing team.1 Collective efficacy
allowed and rushing performance) was repre- scores were established by averaging each ath-
sented in more omnibus indicators of offensive lete’s responses to the nine items. An internal
performance (e.g., total yardage) or that the
indicator itself (e.g., time of possession) might 1
Performance accomplishments are not outcome expec-
be misleading for quick-strike offenses. tations. As Bandura (1997) clearly articulated, “a perfor-
Efficacy measures. Development of the ef- mance is an accomplishment; an outcome is something that
ficacy scales followed Bandura’s (1986) recom- flows from it. In short, an outcome is the consequence of a
performance, not the performance itself” (pp. 22–23). Per-
mendations. An analysis of the competence ar- formance accomplishments can take the form of letter
eas for offensive performance in collegiate foot- grades in academia or game outcome in sport (Feltz &
ball was performed in collaboration with a Lirgg, 2001).130 MYERS, FELTZ, AND SHORT
consistency analysis on the collective efficacy empirical relationships between turnovers and
items revealed a Cronbach’s alpha of .95. the other indicators could be explained concep-
tually (e.g., an offense can perform poorly and
Analyses commit no turnovers), the turnovers measure
was dropped and an EFA on the remaining
Offensive performance. Exploratory factor indicators was performed. The second EFA pro-
analyses (EFA) were performed to derive a par- duced one factor that was interpretable (i.e.,
simonious representation of overall offensive “offensive performance”), was above the lower
performance by modeling the correlations asymptote (i.e., eigenvalue ⫽ 3.70), was related
among the six identified indicators (Fabrigar, to all of the indicators (i.e., factor loadings
Wegener, MacCallum, & Stahan, 1999). How- ranged from 兩.65兩 to 兩.97兩), and accounted for
ever, we noted that the team performance indi- 74% of the shared variance among the five
cators were nested within games and that games indicators. The eigenvalue for the next unac-
were nested within teams. This dependency was cepted factor was 0.64. Factor scores were com-
deemed not to be meaningfully problematic in puted and were used as the offensive perfor-
relation to performing EFAs on the perfor- mance estimates in subsequent analyses (see
mance indicators, because the technique is de- Table 1).
scriptive rather than inferential (Elliot & Wex- Playing time. Self-efficacy and collective
ler, 1994; Kivlighan & Tarrant, 2001), because efficacy for athletes who received playing time
there was no compelling reason to believe that were compared with those who did not receive
the factor structure of team performance would playing time within a 2 ⫻ 2 multivariate anal-
be substantively variant across the season, and ysis of variance (MANOVA). Across teams and
because pooling the game-level indicators was games, those who played had significantly
desirable to maximize sample size (Tabachnick higher self-efficacy, F(1, 1120) ⫽ 48.06, p ⬍
& Fidell, 2001). .01, and collective efficacy, F(1, 1120) ⫽ 27.30,
Decisions regarding factor retention were p ⬍ .01, compared with those who did not play.
guided by a conceptual understanding of offen- However, because these data were not indepen-
sive performance, Kaiser’s criteria (Kaiser, dent (to be discussed in the Analyses section),
1960), the scree plot (Catell, 1966), and the the same analysis was performed within each
number and magnitude of factor loadings team to reduce the degree to which dependency
(Stevens, 1996). The initial EFA produced one was present (see Table 2). Although the signif-
factor that was above the lower asymptote (i.e., icance of the playing time effect varied within
eigenvalue ⫽ 3.77) and was reliable (i.e., four teams, we decided that data would be retained
loadings ⱖ 兩.60兩). Turnovers committed had a for only the athletes who played in an identified
low loading on the first factor (⫺.25) and a low game. Although this decision reduced the mean
initial (.18) communality. Because the weak number of athletes who influenced the aggre-
Table 1
Descriptive Statistics for Offensive Performance and Efficacies Within Teams and Across Games
Aggregated
Offensive Aggregated collective
performance self-efficacy efficacy Intercorrelations
between efficacies
Team n M SD M SD M SD (r)
All 65 0.00 1.00 9.39 0.33 9.24 0.48 .58
Team A 8 ⫺0.38 0.54 9.60 0.26 9.70 0.18 .63
Team B 8 ⫺0.62 0.58 9.09 0.28 8.95 0.35 .59
Team C 8 ⫺0.59 0.64 9.28 0.33 8.67 0.45 .91
Team D 7 0.48 0.57 9.75 0.14 9.58 0.13 ⫺.38
Team E 5 1.67 0.45 9.51 0.31 9.95 0.06 .79
Team F 8 ⫺0.71 0.68 9.47 0.13 8.90 0.31 .61
Team H 8 0.17 0.87 9.12 0.33 9.18 0.35 .94
Team I 8 0.56 1.00 9.23 0.24 9.38 0.23 .82
Team J 5 ⫺0.72 0.47 9.67 0.15 9.17 0.47 .77COLLECTIVE EFFICACY 131
Table 2 arguments of a similar magnitude have de-
Multivariate Analysis of Variance for Self- and fended the use of this statistic as a measure of
Collective Efficacies on Playing Time Within Teams interrater agreement (James, Demaree, & Wolf,
Team and 1993). We interpreted rwg estimates as indica-
efficacy df F p tors of interrater agreement.
A Estimates of rwg were computed assuming no
Self 1, 142 1.78 .18 response bias and continuous data (James, De-
Collective 1, 142 0.70 .41 maree, & Wolf, 1984). No response bias was
B assumed because the observed negative skew
Self 1, 121 4.16 .04 for the efficacy distributions matched the ex-
Collective 1, 121 0.02 .90
pected distributions (Feltz & Chase, 1998). The
C
Self 1, 69 0.75 .39 continuous assumption was employed because
Collective 1, 69 1.06 .31 the likelihood of respondents’ treating an 11-
D category structure as discrete is low (Zhu, Up-
Self 1, 87 7.54 .01 dyke, & Lewandowski, 1997). The continuous
Collective 1, 87 1.80 .18 assumption results in a more conservative com-
E putation of agreement estimates than does the
Self 1, 109 0.01 .91
Collective 1, 109 1.39 .24
discrete assumption (James, Demaree, & Wolf,
F 1984) A high degree of team consensus was
Self 1, 248 42.10 ⬍.0005 observed for self-efficacy (M ⫽ .91, SD ⫽ .10)
Collective 1, 248 16.36 ⬍.0005 and collective efficacy (M ⫽ .90, SD ⫽ .09)
H across all games. Thus, aggregating individual-
Self 1, 179 1.73 .19 level efficacies to the team level provided rea-
Collective 1, 179 0.28 .60
I
sonable estimates of collective efficacy prior to
Self 1, 113 0.88 .35 each game.
Collective 1, 113 0.45 .50 Transforming efficacies. Although the en-
J tire range of the scale was used on occasion for
Self 1, 36 8.84 .01 each item on the efficacy measures, most re-
Collective 1, 36 0.72 .40 sponses were on the upper end of the scale,
Note. Where significant differences were observed, the which was expected (Feltz & Chase, 1998).
mean for those who played was always greater than was the Means for aggregated self-efficacy ranged
mean for those who did not play. from 8.42 to 9.96, and those for aggregated
collective efficacy, from 7.74 to 9.99, across
games and teams (see Table 1). The present
gated efficacy measures within each game efficacy means showed a similar restriction in
from 17.37 to 13.31, it also provided a more range as was found by Feltz and Lirgg (1998).
precise test of the reciprocal relationship be- Therefore, as in the Feltz and Lirgg study, both
tween team performance and team-level effica- sets of efficacy scores were transformed with
cies. That is, including efficacies for those who negative base-10 logarithms. These transforma-
did not play was considered problematic from tions helped to normalize both efficacy distri-
both a conceptual (i.e., why would the efficacy butions in order to meet assumptions of general
of athletes who did not play in an identified linear modeling (Ferguson, 1976).
game influence team performance in that game) Assessing trends in the repeated measures.
and empirical (i.e., different means) standpoint. Because aggregated self-efficacy, aggregated
Consensus. Individual-level efficacies were collective efficacy, and team performance were
aggregated to the team level. However, as rec- repeatedly measured across time, a growth
ommended by Moritz and Watson (1998), de- model for each of the variables was explored to
gree of consensus was considered prior to ag- determine whether trends needed to be removed
gregation. Interrater agreement indices (rwg) es- prior to examining relationships among vari-
timated the degree of team consensus for both ables. Specifically, a linear growth model for
efficacies within each game. Although cogent each of these variables was explored in HLM5
arguments have been put forth to question the (Raudenbush, Bryk, Cheong, & Congdon,
validity of the rwg statistic as a measure of 2000). HLM5 was used instead of a more com-
interrater reliability (Schmidt & Hunter, 1989), mon statistical package because it can easily132 MYERS, FELTZ, AND SHORT
handle missing data. For each variable, a linear well suited to handle data that are dependent, it
growth model was imposed. More complex was not used in this study because we were
growth models (e.g., quadratic, cubic) were not focused on only game-level beliefs and perfor-
explored because the number of within-team mances (i.e., there were no Level 2 predictors).
observations (range ⫽ 5– 8 games) and the Still, the study design warranted an empirical
number of teams (9) were relatively sparse assessment of the degree to which the data were
(Raudenbush & Bryk, 2002). The model that is dependent because games were nested within
illustrated below was imposed on each of the teams.
variables. For simplicity, the model below is The degree of dependency was determined by
interpreted as it was for offensive performance. estimating how much of the variance in the
variables of interest was due to between- and
Level 1: Y ti ⫽ 0i ⫹ 1i a ti ⫹ e ti within-group differences (Raudenbush & Bryk,
2002). Intraclass correlation coefficients for ag-
Level 2: 0i ⫽  00 ⫹ r 0i gregated self-efficacy, aggregated collective ef-
ficacy, and offensive performance were .53, .74,
and .53, respectively. These coefficients sug-
1i ⫽ 10 ⫹ r 1i , gested that there was a substantial proportion of
variance due to both between-group (range ⫽
where Yti was the observed offensive perfor- 53% to 74%) and within-group differences
mance at observation t for team i; 0i was the (range ⫽ 26% to 47%) for all of the variables of
offensive performance score for team i at the interest. Thus, subsequent analyses needed to
first weekend of data collection; 1i was the occur within a framework that addressed the
expected growth rate in offensive performance dependency in the data.
from one weekend to the next over the data Addressing the dependency. To address
collection period for team i; eti was the residual similar dependency concerns, Feltz and Lirgg
for team i; 00 was the average offensive per- (1998) used a meta-analytic framework to dem-
formance score at the first weekend across onstrate homogeneity among teams by examin-
teams; 10 was the average growth rate in of- ing the betas from multiple regression analyses
fensive performance from one weekend to the with aggregated self-efficacy and aggregated
next over the data collection period across collective efficacy as predictors of performance
teams; r0i was the unique effect of team i on within each team. In this study, for the within-
the average offensive performance at the first team and across-games analyses, meta-analyses
weekend; and r1i was the unique effect of of simple regressions were selected because of a
team i on the average growth rate in offensive modest number of games per team and because
performance. of multicollinearity between aggregated self-
The average growth rate for all three vari- efficacy and aggregated collective efficacy mea-
ables from one weekend to the next over the sures for some teams (see Table 1). For the
period of data collection was not significantly within-week and across-teams analyses, meta-
different from zero (i.e., the p value for the 10 analyses of simple regressions were used be-
exceeded .05 in all three analyses). Empirically, cause only aggregated collective efficacy mea-
this implied that there were no time-series linear sures were retained. Utilizing meta-analyses al-
trends in the data that needed to be removed. lowed us to address dependencies in the data,
Conceptually, these results made sense because determine whether a relationship of interest was
all three variables were likely to be influenced homogeneous within teams or weeks, and col-
by the specific opponent each week, and team lapse information from all of the relevant ob-
schedules generally do not follow a linear pat- servations if a relationship of interest was ho-
tern across the season. mogeneous within teams or weeks.
Assessing dependency. Dependent data can Within-team and across-games analyses.
inflate test statistics and increase the probability Simple regressions modeled the influence of
of committing a Type I error if the groupings aggregated self- or aggregated collective effi-
are ignored (Barcikowski, 1981). In this study, cacy on subsequent offensive performance, and
the data were dependent because there were the influence of previous offensive performance
multiple observations for any given team. Al- on subsequent aggregated self- or aggregated
though hierarchical linear modeling (HLM) is collective efficacy within each team (range ⫽COLLECTIVE EFFICACY 133
4 – 8 observations per team). A three-step pro- for explicit details). First,  was determined by
cess followed computation of the team-level pooling the regression estimates across weeks.
regressions (Becker, 1992). First, mean beta ( ) Second, week-level betas (w) were compared
was determined by pooling the regression es- with  to determine whether the regressions
timates across teams. Specifically,  was were comparable across weeks via a chi-square
formed in four steps: (1a) The standard error for test. The critical value for this test was 2(7, N
each team-level beta (tl) was squared; (1b) the ⫽ 8) ⫽ 14.07, ns. A lack of significance indi-
inverse of the squared standard error for each tl cated that the w values could be considered
was computed; (1c) each tl was multiplied by homogeneous and that  was interpretable.
the inverse of its squared standard error; and Third, if the chi-square test was not significant,
(1d) the sum of the values from 1c was divided then  was subjected to a Z test to determine
by the sum of the values from 1b to determine whether it was significantly different from zero.
 . Thus, tl values were based on information Significance of  suggested that the specified
from all of the observations (N ⫽ 65 or N ⫽ 57, relationship was statistically significant when
respectively), and the influence of each tl on  w values were collapsed across weeks.
was weighted by the inverse of its squared stan-
dard error. Second, tl values were compared
with  values to determine whether the tl val- Results
ues were homogeneous. Specifically, the com- Influence of Aggregated Efficacies on
parability of the tl values was determined in
five steps: (2a)  was subtracted from each tl;
Offensive Performance Within Teams and
(2b) these values were squared; (2c) the squared Across Games
values were mltiplied by the inverse of the Table 3 summarizes the influence of aggre-
squared standard error for the matching tl; (2d) gated self-efficacy and aggregated collective ef-
these values were summed across teams; (2e) ficacy on subsequent offensive performance
this sum was compared with a chi-square dis- within each team and across games. For the
tribution with degrees of freedom equal to N team betas, 2(8, N ⫽ 9) ⫽ 2.89 and 2(8, N ⫽
– 1, where N ⫽ the number of teams, in this 9) ⫽ 1.06, ns, respectively. The mean beta
case, minus one. The critical value for this test representing the influence of aggregated self-
was 2(8, N ⫽ 9) ⫽ 15.51. A lack of signifi- efficacy on offensive performance ( ⫽
cance indicated that the tl values could be ⫺.06) was not significant (Z ⫽ .43), whereas
considered homogeneous and that  was inter- the mean beta representing influence of aggre-
pretable. Third, if the chi-square test was not gated collective efficacy on offensive perfor-
significant, then  was subjected to a Z test to mance ( ⫽ .29) was significant (Z ⫽ 2.89).
determine whether it was significantly different Thus, the first hypothesis was supported.
from zero. Specifically,  was divided by the
mean standard error (SE). SE was determined in
three steps: (3a) The inverse values of the Influence of Offensive Performance on
squared standard error for each tl were Aggregated Efficacies Within Teams and
summed; (3b) the square root of the sum was Across Games
determined; and (3c) the inverse of the value
from 3b determined SE. Significance of  sug- Table 4 summarizes the influence of previous
gested that the specified relationship was statis- offensive performance on subsequent aggre-
tically significant when tl values were col- gated self-efficacy and subsequent aggregated
lapsed across teams. collective efficacy within teams and across
Within-week and across-teams analyses. games. For the team betas, 2(7, N ⫽
Simple regressions modeled the influence of 8) ⫽ 28.33, p ⬍ .001, and 2(7, N ⫽
aggregated collective efficacy on subsequent of- 8) ⫽ 13.32, ns, respectively. Significance of the
fensive performance and the influence of previ- first chi-square test indicated heterogeneity of
ous offensive performance on subsequent ag- the regressions that examined the influence of
gregated collective efficacy within consecutive previous offensive performance on subsequent
weeks (range ⫽ 7–9 observations per week). A aggregated self-efficacy. Examination of the
three-step process followed computation of the team betas reinforced the variability of these
week-level regressions (see previous paragraph regressions (range ⫽ ⫺.91 to .56), and thus the134 MYERS, FELTZ, AND SHORT
Table 3
Influence of Aggregated Efficacies on Offensive Performance Within Teams and Across Games
Influence of aggregated self-efficacy on Influence of aggregated collective efficacy on
offensive performance offensive performance
Team n B SE B  Intercept B SE B  Intercept
A 8 0.09 0.36 .11 ⫺0.48 0.10 0.16 .24 ⫺0.54
B 8 ⫺0.91 0.67 ⫺.49 ⫺0.49 0.40 0.65 .24 ⫺0.62
C 8 ⫺0.24 0.59 ⫺.16 ⫺0.50 0.06 0.86 .03 ⫺0.58
D 7 ⫺0.01 0.24 ⫺.02 0.35 0.63 0.57 .44 ⫺0.23
E 5 ⫺0.07 0.29 ⫺.14 1.63 0.09 0.15 .35 1.22
F 8 2.27 0.75 .78 ⫺2.22 0.65 0.87 .29 ⫺0.68
H 8 ⫺0.54 1.07 ⫺.20 0.26 ⫺0.22 0.98 ⫺.09 0.23
I 8 1.78 1.14 .54 0.02 1.68 0.74 .68 ⫺0.37
J 5 ⫺0.42 0.50 ⫺.44 ⫺0.18 0.01 0.62 .01 ⫺0.69
relevant mean beta was deemed not interpret- sequent offensive performance and the influ-
able. The mean beta representing the influence ence of previous offensive performance on sub-
of previous offensive performance on subse- sequent aggregated collective efficacy within
quent aggregated collective efficacy ( ⫽ ⫺.25) weeks and across teams. For the week-level
was significant (Z ⫽ 3.58). However, because betas, 2(7, N ⫽ 8) ⫽ 3.74, ns, and 2(7, N ⫽
previous offensive performance exerted a neg- 9) ⫽ 8.95, ns, respectively. The mean beta for
ative influence on subsequent aggregated col- the influence of aggregated collective efficacy
lective efficacy, the second hypothesis was not on subsequent offensive performance ( ⫽ .61)
fully supported. was significant (Z ⫽ 6.83), and the mean beta
for the influence of previous offensive perfor-
Relationships Between Aggregated mance on subsequent aggregated collective ef-
Collective Efficacy and Offensive ficacy ( ⫽ .63) was also significant (Z ⫽ 5.66).
Performance Within Weeks and Across Thus, the third and fourth hypotheses were
Teams supported.
Because aggregated self-efficacy was not pre- Discussion
dictive of, or predicted by, offensive perfor-
mance in the previous set of analyses, it was not Our findings suggest that aggregated collec-
retained as a measure of collective efficacy in tive efficacy prior to performance positively
this set of analyses. Table 5 illustrates the in- influences subsequent offensive performance,
fluence of aggregated collective efficacy on sub- and that previous offensive performance nega-
Table 4
Influence of Offensive Performance on Aggregated Efficacies Within Teams and Across Games
Influence of offensive performance on Influence of offensive performance on
aggregated self-efficacy aggregated collective efficacy
Team n B SE B  Intercept B SE B  Intercept
A 7 0.45 0.30 .56 1.43 1.30 0.88 .55 2.23
B 7 0.19 0.21 .36 0.29 ⫺0.14 0.26 ⫺.23 ⫺0.11
C 7 0.18 0.33 .24 0.51 0.07 0.23 .14 ⫺0.24
D 6 0.10 0.60 .09 1.71 ⫺0.30 0.20 ⫺.60 1.06
E 5 ⫺1.43 0.37 ⫺.91 3.54 ⫺2.46 0.92 ⫺.84 8.21
F 7 0.04 0.12 .13 0.74 0.08 0.20 .18 0.02
H 7 ⫺0.09 0.10 ⫺.37 0.28 ⫺0.07 0.10 ⫺.31 0.38
I 7 ⫺0.10 0.16 ⫺.28 0.40 ⫺0.23 0.24 ⫺.39 0.67
J 4 ⫺1.02 0.85 ⫺.65 0.37 ⫺1.15 0.61 ⫺.80 ⫺0.79COLLECTIVE EFFICACY 135
Table 5
Relationships Between and Among Aggregated Collective Efficacy and Offensive Performance Within
Weeks and Across Teams
Week Meta-analyses
Path 1 2 3 4 5 6 7 8  2 Z
ACE3SOP .12 .55 .69 .31 .60 .28 .78 .42 .61* 3.74 6.83
POP3SACE .83 .52 .27 .46 ⫺.32 .56 .69 .63* 8.86 5.66
POP3SOP .76 .46 .82 .40 .64 .51 .44 .59* 2.15 5.54
PACE3SACE .75 .94 .58 .52 .29 .81 .77 .74* 3.19 6.89
Note. Arrows indicate “predicting.” ACE ⫽ aggregated collective efficacy; SOP ⫽ subsequent offensive performance;
POP ⫽ previous offensive performance; SACE ⫽ subsequent aggregated collective efficacy; PACE ⫽ previous aggregated
collective efficacy.
* p ⬍ .0005.
tively influences subsequent aggregated collec- and a group’s aggregated collective efficacy
tive efficacy within teams and across games. can be matched to a team’s performance
Aggregated self-efficacy prior to performance simultaneously.
did not influence subsequent offensive perfor- Previous offensive performance appears to
mance, and previous offensive performance did negatively influence subsequent aggregated col-
not influence subsequent aggregated self-effi- lective efficacy and bears no influence on sub-
cacy within teams and across games. Within sequent aggregated self-efficacy within teams
weeks and across teams, aggregated collective and across games. The negative influence of
efficacy prior to performance also was a posi- previous offensive performance on subsequent
tive predictor of subsequent offensive perfor- aggregated collective efficacy is in opposition
mance, and previous offensive performance was to findings by Feltz and Lirgg (1998). Feltz and
a positive, rather than negative, predictor of Lirgg reported that game outcome positively
subsequent aggregated collective efficacy. influenced subsequent collective efficacy across
Aggregated collective efficacy appears to teams and games (i.e., ignoring that games were
positively influence offensive performance, nested within teams). The findings in our study
whereas aggregated self-efficacy appears to are based on within-team and across-games
bear no influence on offensive performance analyses (i.e., addressing that games were
within teams and across games. This finding nested within teams). Thus, we conclude that
corroborates findings by Feltz and Lirgg (1998) our findings are stronger from a methodological
in men’s ice hockey. Although testing the influ- perspective, whereas the findings from Feltz
ence of aggregated self-efficacy on offensive and Lirgg are more consistent with theoretical
performance in both studies was important in expectations (i.e., previous performance should
terms of validating claims made within self- be a positive predictor of subsequent collective
efficacy theory, we note that there appears to be efficacy).
some discordance between collective efficacy The negative influence of offensive perfor-
as measured by aggregated self-efficacy and mance on aggregated collective efficacy within
team performance. Still, these findings provide teams and across games was not predicted, and
empirical evidence for the theoretical claim that speculation is needed to interpret this finding.
aggregated collective efficacy is more predic- First, we note that there was temporal disparity
tive of interdependent team performance than is between previous performance and subsequent
aggregated self-efficacy, and they reiterate the aggregated collective efficacy measures, that
need for coaches to concentrate on athletes’ task difficulty was not held constant, and that
confidences in collective capabilities when in- the effect was within teams. Temporal disparity
terested in affecting team performance. Future was a problem because 6 days transpired be-
researchers are encouraged to collect data on tween a previous performance and the measure-
both individual- and team-level performances ment of subsequent collective efficacy. Also
and then subject those data to multilevel mod- problematic was that, unlike with the hockey
eling where an individual’s self-efficacy can teams in Feltz and Lirgg’s (1998) study, teams’
be matched to an individual’s performance previous performance was against an opponent136 MYERS, FELTZ, AND SHORT
different from the opponent on whom the sub- the design limitations of the study. The design
sequent collective efficacy beliefs were based. limitations that were noted in the parallel with-
Poor previous performance against a top defen- in-team analysis were also present in the across-
sive team could have had little bearing on sub- teams analyses (e.g., temporal disparity and
sequent collective efficacy beliefs when the next variant task difficulties), and thus the negative
opponent had a weak defensive team. Last, after coefficient may be attributable to these limita-
successful performances, coaches may have tions. However, these same design limitations
spent much of the next week highlighting areas were present for Games 1–5 and Games 7 and 8,
of concern in the offense to decrease inflated where the coefficients were positive. Thus, the
collective efficacy beliefs in order to better fo- negative coefficient at Game 6 may have been
cus the team’s attention on preparing for the spurious.
upcoming opponent. However, decreasing col- Bandura (1997) contended that behavior does
lective efficacy within a team certainly does not not cause behavior. However, a relationship be-
imply that the resultant aggregated collective tween previous performance and subsequent per-
efficacy measure was low when compared with formance has been demonstrated in sport (Feltz,
other teams’ aggregated collective efficacy 1982). Although on theoretical grounds we
measure. agree with Bandura, we explored the possibility
Within weeks and across teams, aggregated of modeling the influence of aggregated collec-
collective efficacy prior to performance was a tive efficacy on offensive performance while
positive predictor of subsequent offensive per- holding previous performance constant. How-
formance, and previous offensive performance ever, within these multiple regressions both
was a positive, rather than negative, predictor of multicollinearity between predictors and insuf-
subsequent aggregated collective efficacy. ficient data (in some cases, a two-predictor re-
These results provide some empirical evidence gression was based on only five observations)
for the presumed reciprocal relationship be- caused us to question the validity of the result-
tween collective efficacy and group perfor- ant coefficients. Thus, we abandoned this model
mance across time. The magnitude of the mean and instead explored relationships between ad-
betas derived from the within-week and across- jacent performances across the season (see Ta-
teams analyses appeared to be much larger ble 5). The mean beta for sequential offensive
( s ⫽ .61 and .63) than were the parallel mean performances ( ⫽ .59) suggests that on aver-
betas derived from the within-team and across- age, the majority of the variance in offensive
games analyses ( s ⫽ .29 and ⫺.25). Empiri- performance was not accounted for by previous
cally, this may have been due to the fact that offensive performance within weeks and across
three quarters of the variance in aggregated teams and thus may have been accounted for by
collective efficacy was due to between-team other determinants (e.g., strength of opponent,
differences. Thus, on average, analyses per- injuries, luck, and collective efficacy).
formed within weeks and across teams had Watson et al. (2001) examined collective ef-
more variability in aggregated collective effi- ficacy within a multilevel model and reported
cacy measures than did analyses performed that individual-level collective efficacy was rea-
within teams and across games. Therefore, anal- sonably stable and that team-level collective
yses within teams and across games may have efficacy at Time 1 was strongly related to team-
produced attenuated coefficients due to a more level collective efficacy at Time 2. These find-
narrow range in aggregated collective efficacy ings were based on one measure prior to the first
measures within teams. game and another measure near the season’s
A few of the coefficients in Table 5 fail to end. Our team-level findings were similar but
demonstrate hypothesized relationships (i.e., provide information across most of the season
Game 1 and Game 6). That aggregated collec- (see Table 5). The mean beta for sequential
tive efficacy failed to predict offensive perfor- aggregated collective efficacies ( ⫽ .74) sug-
mance at Game 1 is defensible on theoretical gests that on average, approximately half of the
grounds. Prior to the first game, team members variance in aggregated collective efficacy was
may have lacked adequate information to make not accounted for by previous aggregated col-
accurate judgments regarding collective capa- lective efficacy within weeks and across teams
bilities in game situations. The negative coeffi- and thus may have been accounted for by other
cient at Game 6 was interpreted in reference to determinants (e.g., previous offensive perfor-COLLECTIVE EFFICACY 137
mance, coaching behavior, strength of Campion, M. A., Papper, E. M., & Medsker, G. J.
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