Confirmatory Factor Analysis of the Group Environment Questionnaire With an Intercollegiate Sample

JOURNAL OF SPORT & EXERCISE PSYCHOLOGY, 19%. 18,49-63 O 1996 Human Kinetics Publishers, Inc. Confirmatory Factor Analysis of the Group Environment Questionnaire With an Intercollegiate Sample Fuzhong Li Oregon Social Learning Center Peter Harmer Willamette University This study was designed to assess the factorial construct validity of the Group Environment Questionnaire (GEQ; Carron, Widmeyer, & Brawley, 1985) within a hypothesis-testing framework. Data were collected from 173 male and 148 female intercollegiate athletes. Based on Carron et al.'s (1985) conceptual model of group cohesion, the study examined (a) the extent to which the first-order four-factor model could be confirmed with an intercollegiate athlete sample and (b) the degree to which higher order factors could account for the covariation among the four first-order factors. The a priori models of GEQ, including both the first- and second-order factor models, were tested through confirmatory factor analysis (CFA). CFA results showed that the theoretically specified first- and second-order factor models fit significantly better than all alternative models. These results demonstrated that the GEQ possesses adequate factorial validity and reliability as a measure of the sport group cohesion construct for an intercollegiate athlete sample. Key Words: group cohesion, factorial validity Within the theoretical framework of group dynamics, Carron, Widmeyer, and Brawley (1985) developed a conceptual model of group cohesion involving two broad categories of group cohesiveness (represented by group members' perceptions about what personally attracts them to the group and how the group functions as a total unit): Individual Attractions to the Group (IAG) and Group Integration (GI) (Brawley, Carron, & Widmeyer, 1987). Both perceptions are claimed to help bind the group. It has also been proposed that perceptions of IAG and GI could be composed of task (i.e., group goals, objectives) and social aspects (i.e., social relationships) of group orientation. Accordingly, four distinct but interrelated constructs have Fuzhong Li is with the Oregon Social Learning Center, 207 East 5th Avenue, Suite 202, Eugene, OR 97401. Peter Harmer is with the Department of Exercise Science at Willamette University, Salem, OR 97301.

50 / Li and Harmer been postulated: Individual Attractions to Group-Task (IAGT), Individual Attraction to Group-Social (IAGS), Group Integration-Task (GIT), and Group Integration-Social (GIs). The conceptual framework of group cohesion resulted in the development of the 18-item Group Environment Questionnaire (GEQ, Carron et al., 1985; Widmeyer, Brawley, & Carron, 1985). The GEQ measures four dimensions of team cohesion as outlined by the conceptual model: IAGT, IAGS, GIT, and GIs. Psychometric evidence of the measure has been well documented (e.g., Brawley et al., 1987; Carron et al., 1985; Widmeyer et al., 1985). Empirical research on group cohesion has been facilitated by the GEQ (see Widmeyer, Brawley, & Carron, 1992, for a review). However, initial GEQ validation studies have primarily relied on exploratory factor analyses. Given the existence of a theoretical model, as well as the increasing use of the GEQ, it is important that the factorial validity of the GEQ be tested and confirmed within a hypotheses-testing framework. Confirmatory factor analysis (CFA) is often used to examine the factorial validity of multidimensional scales such as GEQ. Confirmatory approaches are appropriate in this context because the focus is on validating the GEQ model for item factors that have been specified a priori. To date, only one study (Schutz, Eom, Smoll, & Smith, 1994) has adopted this approach with the GEQ. Using a sample of high school varsity athletes, Schutz et al. (1994) tested a series of models within the configuration of Carron et al.'s (1985) conceptual model, including a four-factor first-order structure (i.e., IAGT, IAGS, GIT, and GIs) and a two-factor second-order structure (representing task and social orientation). Results showed surprisingly little support for the hypothesized factor structures of the GEQ, raising questions about the utility of the GEQ. However, because the GEQ was originally constructed from data based on samples of both college- and adult-age athletes, it is possible Schutz et al.'s (1994) findings are sample specific. Therefore, it seems prudent to conduct a CFA investigation of the factorial validity of the measure using comparable samples from which the measure was derived. Using the CFA approach in this fashion could provide valuable insights for group cohesion research should the validity of the GEQ be confirmed based on the conceptual model of group cohesion (Carron et al., 1985). Moreover, given the fact that the validity of the GEQ has been repeatedly demonstrated in numerous studies (see Widmeyer et al., 1992), conclusions and implications regarding the validity of the GEQ drawn from Schutz et al.'s (1994) study cannot be considered definitive; rather, the implications warrant further empirical investigation and replication. It should be emphasized that the claim for construct validity is an inference made by researchers based upon the empirical "test." However, no single empirical result, no matter how "significant," is sufficient to confer the status of construct validity on a concept. Instead, judgments of construct validity are based on a pattern of significant empirical findings that are consistent with theoretical expectations, indicating that the construct "acts" as hypothesized. To further clarify the nature of the GEQ, the present study tested the

Confirmatory Factor Analysis of GEQ / 51 measurement models of the GEQ with an intercollegiate athlete sample to establish its factorial validity within a hypothesis-testing framework by (a) attempting to confirm the hypothesized factor structures, and (b) examining the existence of higher order factor structures that incorporate distinct first-order factors. Participants Method Participants consisted of 173 male and 148 female intercollegiate baseball and softball players, ages 18 to 24 years (M = 20.28) for males and 18 to 25 years (M = 19.86) for females, from competitive Division I (n = 4) and Division I1 (n = 5) universities. Participants averaged 13 years of playing experience. Measure The Group Environment Questionnaire (Carron et al., 1985) contains 18 items: 5 for measuring IAGS, 4 for IAGT, 4 for GIs, and 5 for GIT. Items are measured on a Likert-type scale ranging from 1 (strongly disagree) to 9 (strongly agree). Procedure Initial contact was made with the coach of each team to obtain permission for the study. Informed consent was obtained from the players before data collection. The GEQ was administered after a scheduled practice session during the last 3 weeks of the players' competitive seasons to avoid situation-specific response bias that might occur if the GEQ was administered immediately prior to or after team competition (Brawley et al., 1987). The time period between the beginning of the season and collection of data was considered sufficient for the players to formulate cognitions regarding group cohesiveness. The a Priori Model Specifications In this study, model specifications were based on the hypothesized configuration of the group cohesion model proposed by Carron et al. (1985). In this respect, four first-order CFA models were first formulated. The first first-order factor model (FM,) hypothesized a single factor structure model representing a general group cohesion construct. The second one (FM3 included two global dimensions of IAG and GI, reflecting individuals' perceptions of group cohesion as discussed by Carron et al. The third first-order factor model (FM,) included two global dimensions of "social" and "task," representing individuals' perception of group orientation (social and task cohesion). The final first-order factor model (FM,) represented a substantive model that was based on the conceptualization of the group cohesion model incorporating the four dimensions of group cohesion: IAGT, IAGS, GIT, and GIs. Because the four dimensions of group cohesiveness can be represented by some broader concepts (i.e., task vs. social, individual vs. group), higher order factor structures that subsume the four first-order dimensions of group cohesion

52 / Li and Harmer were specified next. Such model specification allows one to address the question of how well a higher order (second order) factor structure accounts for the firstorder factors. Specifically, three second-order factor models were hypothesized and tested. The first second-order factor model (SM,) posited one general factorgroup cohesion. This is a parsimonious model, but perhaps not a very plausible one theoretically. Two less parsimonious but more plausible models postulated the existence of two intercorrelated second-order factors corresponding to (a) the two types of group orientation: social, defined by IAGS and by GIs, and task, defined by IAGT and by GIT (SM,); and (b) the two types of group cohesion: IAG defined by IAGS and by IAGT, and GI defined by GIs and by GIT (SM,). The latter represents the theoretical model of interest. Data Analyses Because the models discussed above are based on an a priori hypothesis, CFAs were performed using the LISREL program (Joreskog & Sorbom, 1993a). Since the observed variables were all ordinal, PRELIS (Joreskog & Sorbom, 1993b), a preprocessor of LISREL, was used to generate the polychoric correlation and its corresponding asymptotic covariance matrix. Both matrices were used as input for the LISREL program and analyzed by the weighted least squares method (WLS). WLS does not assume multivariate normality. However, it does require analysis of an asymptotic covariance matrix of the elements in the variancecovariance matrix, and the asymptotic covariance matrix requires a large sample to get stable estimates. Joreskog and Sorbom (1993b) define a large sample as k (k - 1)/2 cases where k equals the number of observed variables. It should be noted that although Joreskog and Sorbom's criteria of sample size was met in this study-that is, 18 (18-1)/2 = 153, k = 18-an even larger sample size would be desirable to achieve more reliable estimations (e.g., standard errors of parameter estimates and chi-square goodness-of-fit measures) (Chou & Bentler, 1995; Tanaka, 1987; West, Finch, & Curran, 1995). At the suggestion of an anonymous reviewer, we also analyzed the data with normal-theory maximum likelihood (ML) methods. It should be noted that in the presence of nonnormal data, the standard errors, z tests, and chi-square tests are not correct for ML (Bollen, 1989; Joreskog & Sorbom, 1989). The ML results showed that the chi-square estimate was higher, x2 (129, N = 321) = 364.205, compared to WLS, x2 (129, N = 321) = 275.283. All major parameter estimates (i.e., factor loadings and correlations between latent constructs) were smaller than those estimated from WLS. In addition, the standard errors from ML were found to be larger than those from WLS both in the factor loadings (ML, M =.065; WLS, M =.031, respectively) and correlations between latent constructs (ML, M =.056; WLS, M =.026, respectively). This indicated that a ML-fitting function resulted in inflated asymptotic standard errors when the Pearson's correlation matrix was analyzed, whereas, the WLS-fitting function introduced less bias in the estimated standard errors when the polychoric correlation matrix with the weight matrix as produced by PRELIS program was analyzed (see also Bollen, 1989; Rigdon & Ferguson, 1991). As a result, final results were reported and interpreted based on parameter estimates produced by the WLS estimator.

Confirmatory Factor Analysis of GEQ / 53 The overall goodness of fit of the models was evaluated with the chisquare/degrees of freedom ratio (x2/dfl, Bentler's (1990) Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), and the root mean square of error approximation (RMSEA; Steiger, 1990). In addition, Marsh's (1987) target coefficient (TC) was used to gauge the goodness of fit of the higher order models. The selection of these indexes was based on the suggestions made by Bollen and Long (1993) and on those evaluated most positively in reviews by Bentler (1990), Browne and Cudeck (1993), McDonald and Marsh (1990), and Marsh, Balla, and McDonald (1988). Chi-square difference tests were performed for model comparisons that were nested (Bentler & Bonett, 1980; Long, 1983). The significance of model components was estimated using critical ratios (i.e., t statistics). Upon establishing the model fit to the data, convergent validity and discriminant validity of the GEQ were then examined. Descriptive Statistics Results Table 1 contains the means, standard deviations, skewness, and kurtosis of the 18-items of GEQ, as well as means and standard deviations of GEQ subscales. In general, all observed means were relatively high (i.e., above 6.0 on a 9-point scale, with 9 indicating positive feelings of group cohesion). Furthermore, the means of the subscales were higher than those reported in the literature (e.g., Schutz et al., 1994; Widmeyer et al., 1985). Skewness index ranged from -.400 to -1.295 (M = -.968). Kurtosis index ranged from 1.298 to -1.94 (M =.664). Although in most cases values of the univariates of skewness and kurtosis are minimal, tests of zero multivariate skewness and zero multivariate kurtosis from PRELIS indicated significance: skewness = 43.094, (p <.001), kurtosis = 15.438, (p <.001). The results suggested a nonnormal multivariate distribution, thus justifying the use of asymptotically distribution free (ADF) analysis (Browne, 1984) with LISREL WLS method (Joreskog & Sorbom, 1993a). First-Order Factor Analyses Overall Model Fit. Model fitting for all first-order factor models discussed previously is presented in Table 2. Although the chi-square measure is complicated by sample size, other goodness-of-fit indices (i.e., x2/df, CFI, TLI, and RMSEA) indicate a reasonable fit of FM,, the model of theoretical interest, to the data. Next, comparisons of the FM, with a series of models that postulate different factor configurations (i.e., FM,, FM,, and FM,) were made to examine the GEQ model's structure. Because the examination involved a set of "nested" models,' the chi-square differences test was used. The tests showed that in general, the FM, provided a better fit than the three alternative models. Specifically, chisquare difference values between FM, and FM,, x2 (6, N = 321) = 106.536, p <.001; between FM2 and FM,, x2 (5, N = 321) = 84.606, p <.001; and between FM, and FM,, x2 (5, N = 321) = 62.592, p <.001, were all significant, indicating a significant loss of fit moving from the hypothesized four-factor model to the

54 / Li and Harmer Table 1 Descriptive Statistics for GEQ Subscales Skewness Kurtosis Variable IAGS l IAGS2 IAGS3 IAGS4. IAGSS IAGTl IAGT2 IAGT3 IAGT4 GIs l GIs2 GIs3 GIs4 GIT l GIT2 GIT3 GIT4 GIT5 Subscale I AGS IAGT GIs GIT Note. N = 321. IAGS = Individual Attractions to the Group-Social; IAGT = Individual Attractions to the Group-Task; GIs = Group Integration-Social; GIT = Group Integration-Task. one- and two-factor models. Results from these analyses suggest that the fourfactor model (FM,) is discriminant from one- and two-factor models and therefore represents the most appropriate factor configuration for the observed GEQ data in this study. Convergent Validity. Convergent validity is reflected by the degree to which several presumed measures (i.e., observed variables) of the same construct empirically "converge" as indicators of that construct. This was evaluated by examining (a) whether each of the items had a statistically significant loading of substantial size on the hypothesized factor, and (b) whether each of the hypothesized latent constructs was able to account for a large proportion of the variance in its measured indicators. Factor loadings and uniqueness terms for the model (based on LISREL completely standardized solution) are shown in Figure 1. Inspection of the factor loadings indicated that all factor loadings were

Confirmatory Factor Analysis of GEQ / 55 Table 2 Fit Statistics for Hypothesized and Alternative Models Model x2 df x2/df CFl TLI RMSEA TCa First order FMI 381.919 135 2.829 347 325.077 - FM2 359.989 134 2.686,860 340.073 - FM3 337.975 134 2.522 273.855.069 - FM4 275.383 129 2.135.910 392,059 - Second order Note. CFI = Comparative Fit Index. TLI = Tucker-Lewis Index. RMSEA = root mean square of error approximation. TC = target coefficient. FM, = one-factor model: group cohesion. FM, = two-factor model: Individual Attractions to the Group and Group Integration. FM3 = two-factor model: social and task. FM4 = four-factor model: Individual Attraction to the Group-Social, Individual Attraction to the Group-Task, Group Integration-Social, and Group Integration-Task. SM, = one-factor second-order model: group cohesion. SM, = two-factor second-order model: social and task. SM, = two-factor second-order model: Individual Attraction to the Group and Group Integration. The TC was computed as (FU - 7')/(FU - F), where T and F were the X' values for the second-order model and first-order model, respectively (in which all factor covariances were estimated), and FU was the X' of the first-order model in which all factor covariances were constrained to be zero. TC varies between 0 and I. statistically significant (p <.001) and moderate in size (the average standardized loading was.657). Additional evidence of convergent validity was obtained in the variance extracted estimate (Fornell & Larcker, 1981). This value represents the average proportion of variance in the items accounted for by their underlying factors (a level of.50 or above has been advocated as satisfactory; Fornell & Larcker, 1981). For the two GI subscales, this level was achieved (i.e., GIs =.523, GIT =.520). Values for the two IAG subscales approached the standard (i.e., IAGS =.479, IAGT =.482). Taken together, the GEQ achieved a reasonable level of convergent validity with the four hypothesized constructs. Discriminant Validity. Discriminant validity refers to the extent to which measures of constructs exhibit uniqueness. Although the parameter estimates and fit statistics suggest that the GEQ scale has adequate convergent validity, this does not address the issue of whether the dimensions are distinct. In addition, the individual correlations among the GEQ subscales were generally high (M =.819), indicating questionable discriminant validity. To address this issue, we conducted discriminant validity tests among the four subscales by (a) testing models by constraining to unity the estimated correlation parameter, phi (Q), between a pair of construct and then performing

.753 \. Social, D L y ii; r y 7,., ';3 ~;3 r41 Figure 1 -Group cohesion: First-order factor model. Estimates are based on the completely standardized solution. All path coefficients between first-order factors are significant (p c.001). All item loadings are significant @ <.001).

Confirmatory Factor Analysis of GEQ / 57 a chi-square difference test on the values obtained for the constrained and unconstrained model (cf. Anderson & Gerbing, 1988), and (b) assessing the confidence interval for 9s to determine whether the correlation between the two constructs was significantly less than unity (Anderson & Gerbing, 1988). In the first test of discriminant validity, we constrained the phi value between combinations of two subscales to unity and then estimated the resulting measurement model. The fit of the restricted model was compared with that of the original model. Results indicated that the relevant chi-square difference tests were all significant. Because the model in which the factor correlation was not constrained provided a significantly better fit than the constrained model, discriminant validity was indicated (Anderson & Gerbing, 1988; Bagozzi, Yi, & Phillips, 1991). The second test of discriminant validity was to establish confidence intervals (cf. Anderson & Gerbing, 1988). If factors are unique, then intercorrelations (9s) must be significantly less than unity. It was important to verify this, even though it is not a very stringent requirement, because a correlation less than unity is a necessary (but not sufficient) condition for discriminant validity. The respective 95% confidence intervals demonstrated that all factors were indeed distinct and thus supported discriminant validity of the four constructs. Table 3 presents the intercorrelations (9s) between the factors and their corresponding confidence intervals. It should be noted that the parameter estimates for the @s have been corrected for attenuation due to measurement error. Table 3 Intercorrelations and Confidence Interval (95%) Among the First-Order Factors Intercorrelations Factor IAG-Social IAG-Task GI-Social GI-Task Confidence interval (95%) Note. IAG = Individual Attraction to the Group. GI = Group Integration. Values in parentheses are standard error of estimates. The symbol @ represents factor correlation.

58 / Li and Harmer Reliability. The final step in construct validation involved computing coefficient reliability for each GEQ subscale. The GEQ was examined for internal consistency on two criteria: (a) the coefficient alpha (Cronbach, 1951) and (b) composite reliability estimates (Fomell & Larcker, 1981). The alphas averaged.75, ranging from a low of.632 (IAGS) to a high of 318 (GIT). Only one (IAGS) was below Nunnally's (1978) recommended level of.70. The composite reliabilities had average estimates of.770, ranging from a low value of.741 (IAGS) to a high value of 342 (GIT). These estimates suggest that all GEQ subscales have modest measurement properties. Second-Order Factor Analyses Since the first-order factors demonstrated satisfactory fit to the data, the analysis on the second-order factor models was justified (Marsh, 1987). The estimated substantive model (SM,) is shown in Figure 2. The fit indices for all three second-order factor models are shown in the bottom portion of Table 2. Although fit indices were acceptable for all three models tested, the SM, (the model of theoretical interest), provided a significantly better fit than the SM, and SM2, with the smallest chi-square value, RMSEA, and highest fit indices. Table 2 shows that for SM,, CFI and TLI values were acceptable (i.e., approximately.90), x2/df ratio was small, and RMSEA was low, indicating a reasonable fit of the second-order model to the data. It can be seen from Table 2 that the fit of SM, was almost identical with FM4 (the first-order model of theoretical interest). In a standard second-order model analysis, when the fit of a higher order model approaches that of the corresponding first-order model (i.e., target model), support for the higher order model is demonstrated (Marsh, 1985). Although Marsh and Hocevar (1985) indicated that the goodness-of-fit indices can never be better than the fit of the corresponding correlated first-order model (because the higher order model represents an attempt to explain all the covariation among the first-order factors with fewer parameters), the chi-square difference test (which tests the loss of fit caused by adding the second-order factor to the model) between SM3 and FM4 was not significant, x2(1, N = 321) =.494, p =.249, suggesting that the hypothesized higher order model (SM,) did not deviate from the target model (FM4). In this respect, support for the SM, was documented. We next compared the fit of SM, with the SM,. The comparison showed that the SM3 fit significantly better than the SM,, with the chi-square difference test significant, x2(1, N = 321) = 11.748, p <.001; the fit indices (Table 2) were also better for SM3 than for SMl, indicating the hypothesized two-factor higher order model represents a significant increase in the explanation of the first-order construct covariations than the hypothesized one-factor higher order model. For SM,, the fit was almost identical with SM, across all fit indices. There was no significant difference between the two nested models (SMl < SM,), x2 (1, N = 321) =.018, p =.354. However, there was significant loss in fit moving from the FM, to SM2 model configuration, x2(1, N = 321) = 12.062, p <.001. Thushhere was no empirical support for the SM2 model of a higher-order factor structure composed of task and social constructs. Although the fit of SM3 and SM, cannot be compared directly using the

i ~"lndividual Attractions) i ' Group Integration ') Figure 2 -Group cohesion: Higher order factor model. Estimates are based on the completely standardized solution. All path coefficients between first- and second-order factors are significant (p <.001). All item loadings are significant (p <.001). "Residual variance. \ VI w

60 / Li and Harmer chi-square difference test (because the two models were not nested), goodnessof-fit indices as indicated by both the chi-square and practical fit indices showed that in general, SM3 represented better fitting than did SM2. Figure 2 shows the structures of the SM, higher order factorial model and its parameter estimates (based on the LISREL completely standardized solution) for the second-order factor loadings and residual variances. All first-order factor loadings (not shown) were high2 and statistically significant (p =.001). The second-order factor loadings were all substantial, ranging from.843 to.986 and statistically different from zero (p <.001 for all loadings). The IAG accounted for 71% of the variance in IAGS, and 82% in IAGT. The GI accounted for 81% of variance in GIs, and 97% in GIT. The TC index, which assesses a higher order model's ability to explain the covariation among first-order factors, indicates that a substantial amount of covariation (99.8%) among the four GEQ factors can be accounted for by the two second-order factors. Thus, although the goodness-offit indices support modeling the constructs as four first-order factors, a case can be made for conceptualizing the four dimensions of the group cohesion construct as indictors of two higher order factors. Discussion The GEQ, as a measure of team group cohesion, has been widely used in the sport psychology literature. Its factorial validity, however, has recently been questioned because the hypothesized factor structure was found not tenable in a sample of high school athletes (Schutz et al., 1994). To further investigate this issue, the present study examined the factorial validity of the GEQ with an intercollegiate sample. Using confirmatory methods, which are inherently theory driven and theory oriented, we obtained results that supported predictions about the ability of the GEQ items to measure various facets of sport team cohesion. The hypothesized four f~st-order factor model (FM,) based on theoretical considerations was found to provide the best fit for the sample. This indicates that at item level, measures of various dimensions of the group cohesion construct were internally consistent and reliable and converged on their respective constructs. All constructs of group cohesion were shown to be significantly related as proposed by the theory, yet their measures demonstrated a reasonable level of discriminant validity: indicating that they were unique constructs representing various facets of the group cohesion construct. On the basis of these findings, we believe that the GEQ (with respect to the present 18 items) is appropriately construed and measures the hypothesized first-order psychosocial factors of sport team group cohesion. The relatively high intercorrelations between the four group cohesion factors suggested that a second-order factor model was plausible. The construction of hierarchical factor structures of group cohesion was based on the logic of the conceptual model (Carron et al., 1985). Although all three models tested appeared to provide fit to the data, the SM3 was retained based upon both statistical criteria and theoretical considerations. The results indicate that the two second-order factors from SM3, IAG and GI, contain a substantial portion of the four firstorder factors. It appears that although the group cohesion construct is better conceptualized at the level of the first-order factors in this analysis, the higher order analysis of the structure of the GEQ is very promising as a parsimonious

Confirmatory Factor Analysis of GEQ / 61 way of accounting for covariance structures or as a means of obtaining purer measures of a central group cohesion construct. Taken together, in Contrast to the results of Schutz et al. (1994), findings obtained from this intercollegiate sample offer evidence of the factorial validity of team group cohesion construct positing the existence of both IAG and GI factors dominating the factor space in the presence of four first-order factors. In evaluating the data, a number of possible causes (either individually or in combination) for the differences between this study and Schutz et al. (1994) present themselves: Because the GEQ was originally developed with college and adult athletes, it seems reasonable to expect the model to fit better with individuals from these populations rather than younger individuals. Developmental differences between the two groups (e.g., social, linguistic, affective) may underlie the poor fit of the GEQ measurement model presented by Schutz et al. (1994) and indicate that additional work is needed before the GEQ can be appropriately applied to young athletes. In addition, procedural differences between the two studies, such as the timing of the GEQ administration, may have influenced the outcomes. Data in the present study were collected approximately 2 weeks prior to the end of the season, whereas Schutz et al. (1994) waited until 1 week after the end of the season. From a theoretical perspective, the present findings provide a comprehensive basis for construct validity of the GEQ and reinforce the theoretical model of the multi-dimensional group cohesion construct proposed by Carron et al. (1985). Furthermore, the model of higher order perceptions of group cohesion (i.e., IAG and GI) is a very useful approximation of the relationships among the four separate dimensions of group cohesion constructs. From a measurement perspective, the advantages of CFA were demonstrated by (a) specifying various hypothesized configurations of GEQ item factor structures, (b) testing empirically among competing models that varied in factor structures, (c) comparing the alternative factor structures, and (d) selecting final models that were most consistent with theoretical implications. As a result, we found support for the hypothesized GEQ factor structures. This support should assist in making meaningful comparisons of factor scores and provide justification for the use of such factor scores in models of group cohesion research when dealing with intercollegiate athlete samples. Although the present study produced important findings (e.g., factorial validity of the GEQ), the results should be further replicated because the only two CFA studies conducted to date have produced conflicting results. Continued confirmatory-factor-analytic research on the measurement of group cohesion is advocated. In particular, research concerning the measurement equivalence of GEQ across multiple subsamples and populations would be informative. Because individual members' perceptions about group cohesiveness may vary as a function of various factors (e.g., social influence, team structures, personal attributes), measuring the perception of team group cohesion that is activated in various situations may facilitate the establishment of construct validity, as well as generalizability. In addition, research concerning the structural relationships of situational and personal factors to group cohesion would be enlightening. References Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103,411-423.

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