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:                                       approach involves assessing each team mem-
COLLECTIVE 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
                                                    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
                          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               .77
COLLECTIVE 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
    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
                                                            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 easily
132                                     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 the
134                                      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         ⫺0.79
COLLECTIVE 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 opponent
136                                  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.
opponent).                                               (1996). Relations between work group character-
   The primary limitations of this study were the        istics and effectiveness: A replication and exten-
possibility for model misspecification, variant          sion. Personnel Psychology, 49, 429 – 452.
                                                       Catell, R. B. (1966). The scree test for the number of
task difficulties, and the temporal disparity be-
                                                         factors. Multivariate Behavioral Research, 1, 245–
tween previous performance and subsequent ef-            276.
ficacy measures. Model misspecification is a           Elliot, R., & Wexler, M. M. (1994). Measuring the
possibility in that the effect of previous perfor-       impact of sessions in process-experiential therapy
mance on subsequent performance was not held             of depression: The session impact scale. Journal of
constant when examining the influence of col-            Counseling Psychology, 41, 166 –174.
lective efficacy on subsequent performance. Be-        Fabrigar, L. R., Wegener, D. T., MacCallum, R. C.,
cause previous performance was likely related            & Stahan, E. J. (1999). Evaluating the use of
to both subsequent collective efficacy and sub-          exploratory factor analysis in psychological re-
sequent performance, not including it when               search. Psychological Methods, 4, 272–299.
modeling the influence of collective efficacy on       Feltz, D. L. (1982). Path analysis of the causal ele-
                                                         ments in Bandura’s theory of self-efficacy and an
subsequent performance may have led to in-
                                                         anxiety-based model of avoidance behavior. Jour-
flated estimates of the effect of collective effi-       nal of Personality and Social Psychology, 42,
cacy on subsequent performance. Future studies           764 –781.
with a greater number of game-level observa-           Feltz, D. L., & Chase, M. A. (1998). The measure-
tions may allow for controlling for previous             ment of self-efficacy and confidence in sport. In
performance (i.e., a more complex model) and             J. L. Duda (Ed.), Advancements in sport and ex-
could provide a more conservative test of the            ercise psychology measurement (pp. 65– 80). Mor-
proposed relationship. The inability to control          gantown, WV: Fitness Information Technology.
for task difficulty and the time lag between           Feltz, D. L., & Lirgg, C. D. (1998). Perceived team
some measures in this study limit confidence in          and player efficacy in hockey. Journal of Applied
the observed relationships between previous of-          Psychology, 83, 557–564.
                                                       Feltz, D. L., & Lirgg, C. D. (2001). Self-efficacy
fensive performance and subsequent collective
                                                         beliefs of athletes, teams, and coaches. In R. N.
efficacy. Future studies in which task difficulty        Singer, H. A. Hausenblas, & C. Janelle (Eds.),
is constant and the time lag between measure-            Handbook of sport psychology (2nd ed., pp. 340 –
ment of performance and subsequent collective            361). New York: Wiley.
efficacy is reduced could provide stronger tests       Ferguson, G. A. (1976). Statistical analysis in psy-
of the influence of previous performance on              chology and education (4th ed). New York:
subsequent collective efficacy.                          McGraw-Hill.
                                                       George, T. R. (1994). Self-confidence and baseball
                                                         performance: A causal examination. Journal of
                   References                            Sport and Exercise Psychology, 16, 381–399.
                                                       Gibson, C. B., Randel, A., & Earley, P. C. (2000).
Bandura, A. (1977). Self-efficacy: Toward a unifying     Understanding group efficacy: An empirical test of
  theory of behavioral change. Psychological Re-         multiple assessment methods. Group & Organiza-
  view, 84, 191–215.                                     tion Management, 25, 67–97.
Bandura, A. (1986). Social foundations of thought      Gully, S. M., Incalcaterra, K. A., Joshi, A., &
  and action: A social cognitive theory. Englewood       Beaubien, J. M. (2002). A meta-analysis of team-
  Cliffs, NJ: Prentice-Hall.                             efficacy, potency, and performance: Interdepen-
Bandura, A. (1997). Self-efficacy: The exercise of       dence and level of analysis as mediators of ob-
  control. New York: Freeman.                            served relationships. Journal of Applied Psychol-
Barcikowski, R. S. (1981). Statistical power with        ogy, 87, 819 – 832.
  group mean as the unit of analysis. Journal of       Haney, C. J., & Long, B. C. (1995). Coping effec-
  Educational Statistics, 6, 267–285.                    tiveness: A path analysis of self-efficacy, control,
Barnette, J. J. (1999). Nonattending respondent ef-      coping, and performance in sport competitions.
  fects on internal consistency of self-administered     Journal of Applied Social Psychology, 25, 1726 –
  surveys: A Monte Carlo simulation study. Educa-        1746.
  tional and Psychological Measurement, 59, 38 –       Hodges, L., & Carron, A. (1992). Collective efficacy
  46.                                                    and group performance. International Journal of
Becker, B. J. (1992). Using results from replicated      Sport Psychology, 23, 48 –59.
  studies to estimate linear models. Journal of Edu-   James, L. R., Demaree, R. G., & Wolf, G. (1984).
  cational Statistics, 17, 341–362.                      Estimating within-group rater reliability with and
138                                     MYERS, FELTZ, AND SHORT

  without response bias. Journal of Applied Psychol-     Raudenbusch, S. W., Bryk, A. S. (2002). Hierarchi-
  ogy, 69, 85–98.                                          cal linear models: Applications and data analysis
James, L. R., Demaree, R. G., & Wolf, G. (1993).           methods (2nd ed.). Thousand Oaks, CA: Sage.
  rwg: An assessment of within-group rater agree-        Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., &
  ment. Journal of Applied Psychology, 78, 306 –           Congdon, R. (2000). HLM5: Hierarchical and lin-
  309.                                                     ear modeling. Homewood, IL: Scientific Software
Kaiser, H. F. (1960). The application of electronic        International.
  computers to factor analysis. Educational and Psy-     Saavedra, R., Earley, P., & Van Dyne, L. (1993).
  chological Measurement, 20, 141–151.                     Complex interdependence in task-performing
Kane, T. D., Marks, M. A., Zaccaro, S. J., & Blair, V.     groups. Journal of Applied Psychology, 78, 61–72.
  (1996). Self-efficacy, personal goals, and wres-       Schmidt, F. L., & Hunter, J. E. (1989). Interrater
                                                           reliability coefficients cannot be computed when
  tlers’ self-regulation. Journal of Sport & Exercise
                                                           only one stimulus is rated. Journal of Applied
  Psychology, 18, 36 – 48.
                                                           Psychology, 74, 368 –370.
Kivlighan, D. M., & Tarrant, J. M. (2001). Does
                                                         Shea, G. P., & Guzzo, R. A. (1987). Groups as
  group climate mediate the group leadership-group         human resources. In K. M. Rowland & G. R. Ferris
  member outcome relationship? A test of Yalom’s           (Eds.), Research in personnel and human re-
  hypotheses about leadership priorities. Group Dy-        sources (Vol. 5, pp. 323–356). Greenwich, CT:
  namics: Theory, Research, and Practice, 5, 220 –         JAI Press.
  234.                                                   Stevens, J. P. (1996). Exploratory and confirmatory
Lichacz, F. M., & Partington, J. T. (1996). Collective     factor analysis. In J. P. Stevens (Ed.), Applied
  efficacy and true performance. International Jour-       multivariate statistics for the social sciences (3rd
  nal of Sport Psychology, 27, 146 –158.                   ed., pp. 362–392). Mahwah, NJ: Erlbaum.
Maddux, J. E. (1999). The collective construction of     Tabachnick, B. G., & Fidell, L. S. (2001). Principal
  collective efficacy: Comment on Paskevich, Braw-         components and factor analysis. In Using multi-
  ley, Dorsch, and Widmeyer. Group Dynamics:               variate statistics (4th ed., pp. 582– 652). Boston:
  Theory, Research, and Practice, 3, 223–226.              Allyn & Bacon.
Martin, J. J., & Gill, D. L. (1995). The relationships   Watson, C. B., Chemers, M. M., & Preiser, N.
  of competitive orientations and self-efficacy to         (2001). Collective efficacy: A multilevel analysis.
  goal importance, thoughts, and performance in            Personality and Social Psychology Bulletin, 27,
  high school distance runners. Journal of Applied         1056 –1068.
  Sport Psychology, 7, 50 – 62.                          Zaccaro, S. J., Blair, V., Peterson, C., & Zazanis, M.
McAuley, E. (1985). Modeling and self-efficacy.            (1995). Collective efficacy. In J. E. Maddux (Ed.),
  Journal of Sport Psychology, 7, 283–295.                 Self-efficacy, adaptation and adjustment: Theory,
Moritz, S. E., & Watson, C. B. (1998). Levels of           research and application (pp. 308 –330). New
  analysis issues in group psychology: Using effi-         York: Plenum.
                                                         Zhu, W., Updyke, W. F., & Lewandowski, C. (1997).
  cacy as an example of a multilevel model. Group
                                                           Post-hoc Rasch analysis of optimal categorization
  Dynamics, 2, 285–298.
                                                           of an ordered-response scale. Journal of Outcome
Paskevich, D. M., Brawley, L. R., Dorsch, K. D., &         Measurement, 1, 286 –304.
  Widmeyer, W. N. (1999). Relationship between
  collective efficacy and team cohesion: Conceptual                             Received October 1, 2003
  and measurement issues. Group Dynamics: The-                          Revision received March 15, 2004
  ory, Research and Practice, 3, 210 –222.                                      Accepted March 15, 2004 䡲
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