Perceptual & Motor Skills: Exercise & Sport 2014, 119, 2, 363-376. © Perceptual & Motor Skills 2014
CONSTRAINTS OF RECREATIONAL SPORT PARTICIPATION: MEASUREMENT INVARIANCE AND LATENT MEAN DIFFERENCES ACROSS SEX AND PHYSICAL ACTIVITY STATUS1 JING DONG LIU AND PAK KWONG CHUNG Department of Physical Education, Hong Kong Baptist University WING PING CHEN Department of Health and Physical Education, Hong Kong Institute of Education Summary.—The purpose of the current study was to (a) examine the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status among the undergraduate students (N = 630) in Hong Kong and (b) compare the latent mean differences across groups. Measurement invariance of the Constraint Scale of Sport Participation across sex of and physical activity status of the participants was examined first. With receiving support on the measurement invariance across groups, latent mean differences of the scores across groups were examined. Multi-group confirmatory factor analysis revealed that the configural, metric, scalar, and structural invariance of the scale was supported across groups. The results of latent mean differences suggested that the women reported significantly higher constraints on time, partner, psychology, knowledge, and interest than the men. The physically inactive participants reported significantly higher scores on all constraints except for accessibility than the physically active participants.
The leisure constraints theory aims to look at the factors that thwart the pursuit of leisure activities (Crawford & Godbey, 1987). It introduced three general constructs of intrapersonal, interpersonal, and structural constraints, which correspond to individual, interpersonal, and contextual analytic levels, respectively. Since the theory was developed, it has received extensive attention (Godbey, Crawford, & Shen, 2010). Researchers from various fields, such as recreational sports (Alexandris & Carroll, 1997a, 1997b; Alexandris, Tsorbatzoudis, & Grouios, 2002; Alexandris, Zahariadis, Tsorbatzoudis, & Grouios, 2002; Alexandris, Barkoukis, Tsormpatzoudis, & Grouios, 2003; Alexandris, Kouthouris, & Girgolas, 2007; Alexandris, Funk, & Pritchard, 2011), travel and tourism (Funk, Alexandris, & Ping, 2009), outdoor recreation (Wright, Drogin Rodgers, & Backman, 2001), and urban and state park use (Scott & Mowen, 2010) have employed the theory as a theoretical framework in their studies. In the recreational sport field, Alexandris and Carroll (1997a, 1997b) developed the Constraint Scale of Sport Participation to measure seven constraints: psycholAddress correspondence to Jing Dong Liu, Department of Physical Education, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong or e-mail (jingdong.liu@hotmail. com). 1
DOI 10.2466/06.03.PMS.119c24z0
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ogy, knowledge, interest, partner, facility, accessibility, and time. Since the measure was developed, a series of studies investigated the constraints to recreational sport participation (Alexandris, et al., 2002, 2007, 2011). To better understand the measurement structure of the scale, researchers compared three competing measurement models (i.e., three-factor model, seven-factor model, and second-order factor model) (e.g., Casper, Bocarro, Kanters, & Floyd, 2011b; Chung, Liu, & Chen, 2013). It was consistently found that the seven-factor model performed better than others, although the items examined in these studies were not exactly the same. In addition, a previous study suggested that non-sport participants reported higher scores on all constraints than sport participants, providing supporting evidence for the nomologocial validity of the measure (Casper, et al., 2011b). These findings provided psychometric evidence for the sevenfactor structure of the Constraint Scale of Sport Participation and suggested that the measure could be used in future study and practice. Previous studies suggested inconsistent findings of the differences on the constraints to recreational sport participation (Jackson & Henderson, 1995; Alexandris & Carroll, 1997a; Rocklynn, 1998; Jackson, 2005; Son, Kerstetter, and Mowen, 2008; Casper, et al., 2011b). For example, Jackson and Henderson (1995) found that women were more constrained than men. Similarly, Casper, et al. (2011b) found that girls rated higher levels of constraints on accessibility (Cohen's d = 0.12), knowledge (Cohen's d = 0.14), partners (Cohen's d = 0.14), and psychology (Cohen's d = 0.14) than the boys. However, Alexandris, et al. (2003) did not find significant sex differences on the constraints of participation in a physical activity program among older adults. Son, et al. (2008) also failed to find sex differences on constraints of sport participation among adult participants. However, in Son, et al. (2008), the mean scores for general constraints (interpersonal, intrapersonal, and structural) were used rather than the specific constraints (e.g., partner, interest, facility). Many other studies have also investigated the relationship between constraints and sport participation (Kay & Jackson, 1991; Shaw, Bonen, & McCabe, 1991; Alexandris and Carroll, 1997a; Alexandris, et al., 2002; Masmanidis, Gargalianos, and Kosta, 2009; Casper, Bocarro, Kanters, & Floyd, 2011a). For example, Alexandris, et al. (2002) found that the perceived constraints may affect frequency of sport participation and sometimes may lead to complete non-participation. Shaw, et al. (1991) found that just two constraints (ill health and low energy) for women and three constraints (ill health, low energy, and lack of self-discipline) for men were found to correlate negatively with sport participation. Casper, et al. (2011a) conducted a study among adolescents and found non-sport participants reported significantly higher levels of constraints on psychology, time, knowledge, in-
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terest, accessibility, facility, and partner than the sport participants reported (Cohen's d between 0.23–1.20). Masmanidis, et al. (2009) found similar results among undergraduate students (Cohen's d between 0.21–0.71). Another study conducted among adults (Alexandris & Carroll, 1997a) found that non-sport participants reported significantly more constraints than the sport participants reported on interest (Cohen's d = 0.78), psychology (Cohen's d = 0.46), and knowledge (Cohen's d = 0.46). Thus, different constraints might play different roles among different populations. The benefits of a physically active lifestyle (Warburton, Nicol, & Bredin, 2006; Haskell, Lee, Pate, Powell, Blair, Franklin, et al., 2007) and the deleterious effect of physical inactivity (Bull & Bauman, 2011) on physiological and psychological health have been well documented. Bull, Armstrong, Dixon, Ham, Neiman, and Pratt (2004) defined physical inactivity as “doing no or very little physical activity at work, at home, for transport or in discretionary time” (p. 729). Based on this definition, both nonsport participants and those participants who do very little physical activity could be classified as a physically inactive population. Following this logic, Elavsky (2010) classified the individuals who exercised less than two times per week for 30 min. or more at moderate intensity as physically inactive or sedentary. Based on the literature review, it was found that previous studies that examined the differences on perceived constraints of sport participation were usually conducted using non-sport participants/ participants classification, with very few studies using physically inactive/active classification. In the previous literature that examined the between-group differences on constraints of sport participation, the commonly used analytic approaches were Student's t test, analysis of variance, or multivariate analysis of variance, which are usually used to compare the mean scores of observed variables (measured directly, e.g., weight, height). When these approaches were applied to compare the means of latent variables (not measured directly but represented using composite mean scores of several observed variables) across groups, the assumption of the measurement invariance of the instrument across groups must be satisfied. If no measurement invariance evidence exists, the differences or similarities regarding individuals and groups cannot be interpreted clearly (Horn & McArdle, 1992). Measurement invariance generally refers to the extent to which the content of each item is being perceived and interpreted in the same way across samples (Byrne & Watkins, 2003). In the case of constraints, if the measures of constraint operate differently across particular samples, and the variations are not taken into account in the measurement, it is inappropriate to compare the composite means (of latent variables) across groups using the traditional statistical techniques. Therefore, the researchers should not as-
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sume a particular instrument's measurement properties would be invariant across groups without going through stringent psychometric investigations (Vandenberg & Lance, 2000). The examination of the latent means derived from the multi-group comparisons performed via confirmatory factor analysis and measurement invariance techniques has been proposed as a powerful alternative to traditional statistical techniques when the variables of interest are latent constructs (Cheung & Rensvold, 2002). Research goal.—The purpose of the current study was to (a) examine the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status (physically active and physically inactive), and (b) compare the latent mean differences on constraints across groups. METHOD Participants and Procedure Participants were 630 undergraduate students (M age = 22.4 yr., SD = 1.6) from eight public universities in Hong Kong. Table 1 shows the characteristics of the participants. Ethical approval was obtained from the Human and Animal Research Ethic Committee of Hong Kong Baptist University. Convenience sampling method was used in this study. Teachers of the general education classes were contacted to obtain their permissions to approach the students in classes. The students were asked whether or not they had interest in taking part in a study examining constraints of sport TABLE 1 CHARACTERISTICS OF PARTICIPANTS Characteristic
n
%
Age ≤ 18
19
3.1
19
120
19.1
20
162
25.7
21
144
22.8
22
104
16.5
≥ 23
81
12.8
Sex Women
342
54.3%
Men
288
45.7%
Active
244 (61.9% men)
38.7%
Inactive
386 (35.5% men)
61.3%
630
100%
Physical activity status
Total
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participation. Once the students expressed interest, a brief introduction to the study and a set of questionnaires together with a copy of written informed consent form were provided to the participants. The participants completed the questionnaires in a quiet classroom and returned the questionnaires to the researchers immediately. Participants were informed prior to data collection that the anonymity and confidentiality of their answers would be preserved at all times. A total of 640 questionnaires were distributed, and 630 were completely returned with a 98.4% response rate. All participants participated in this study voluntarily. Measures A shortened version of the Constraint Scale of Sport Participation (Alexandris, et al., 1997a, 1997b) was used to measure the constraints of sport participation (Chung, et al., 2013). The shortened scale comprises 23 items and assesses seven factors, namely, accessibility, facility, interest, knowledge, psychology, partner, and time. Responses were provided on a sevenpoint Likert scale ranging from 1: Strongly disagree and 7: Strongly agree. Previous research suggested that the shortened scale has satisfactory factorial validity ( χ2 = 563.71, p < .001, χ2/df = 2.69, CFI = 0.903, RMSEA = 0.074, SRMR = 0.066) and internal consistency (αs = .70–.86) among Chinese undergraduate students (Chung, et al., 2013). A one-item self-report question was used to clarify participants' physical activity status (“How many times do you exercise per week for 30 min. or more at moderate or vigorous intensity?”). Participants who reported exercise less than two times per week for 30 min. or more at moderate intensity were categorized as physically inactive; participants who reported exercise two times or more per week for 30 min. or more at moderate intensity were categorized as physically active (Elavsky, 2010). Analyses A several hierarchically ordered steps strategy was used to test the measurement invariance of the Constraints Scale of Sport Participation across groups. First, a baseline model (M0) was established for each group (women vs men, physically active vs physically inactive) individually. Second, measurement invariance across groups was tested. A set of three nested models (Models 1 to 3) using multi-sample CFAs were tested for increasingly stringent levels of constrained equivalence across groups. Model 1 (M1: configural) required the same number of factors and pattern of factor loadings to be the same across groups. No invariance constraints were imposed. Thus, the same parameters that were estimated in the baseline model for each group individually were estimated again in the multiple-sample model. In Model 2 (M2: metric invariance), the factor loadings were constrained equal across groups. In Model 3 (M3: scalar invariance), in addition to the factor loadings, intercepts were constrained
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to be invariant across groups. Third, construct invariance was tested. In Model 4 (M4: factor loading and factor variance/covariance), in addition to factor loadings, factor variance and covariance were constrained invariant across groups. All analyses were conducted using MLR in the Mplus 6.12 program (Muthén & Muthén, 1998–2011). The overall model fit was evaluated using multiple goodness-of-fit indexes including the chi-squared value, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR). A CFI value approximating 1.0 indicates good fit, whereas a value above 0.90 represents acceptable model fit; an RMSEA value lower than 0.05 represents good fit, whereas between 0.05 and 0.08 indicates acceptable fit (Steiger, 1990); an SRMR value lower than 0.05 indicates good model fit, whereas between 0.05 and 0.08 indicates acceptable fit (Bentler, 1995). Traditionally, invariance testing has relied on the χ2 statistic, which has been criticized being influenced by sample size. Therefore, this study adopted Cheung and Rensvold (2002)'s recommendation, in which it used the absolute value of change in CFI of < 0.01 between increasingly more constrained models as the indicative of invariance. The test of latent mean differences requires that the hypothesized CFA model shows a good fit in both compared groups and that all factor loadings and intercepts (M3) be equivalent across groups. Building on the evidence of measurement invariance, one of the two compared groups was selected as the reference group through fixing its latent means to 0 (Mplus automatically fixes the factor means for the first group to zero by default). Latent means for the other group were freely estimated. Statistical significance associated with differences between the latent means for the groups was determined on the basis of the z statistic (Aiken, Stein, & Bentler, 1994). RESULTS Baseline Model The hypothesized model (M) of the Constraint Scale of Sport Participation, tested separately for each group, is presented in Fig. 1. In relation to sex of participants, as Table 2 shows, the hypothesized model (M) had acceptable model fit with the data for both women and men. However, a review of the modification index (MI) value revealed that MI for residual covariance between two items from facility factor (Item 14: the facilities are crowded; Item 15: the facilities are inadequate) displayed exceptionally large values for both women and men. In addition, for women, the MI for residual covariance between two items from knowledge factor (Item 10: I do not have anyone to teach me the activities I like; Item 12: I do not know where I can learn the activities) also displayed a very large value.
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369 Item 1
Time
Item 2 Item 3 Item 4
Partner
Item 5 Item 6 Item 7
Psychology
Item 8 Item 9 Item 10 Item 11
Knowledge Item 12 Item 13 Item 14 Item 15 Facility Item 16 Item 17 Item 18 Accessibility
Item 19 Item 20 Item 21
Interest
Item 22 Item 23
FIG. 1. Hypothesized measurement model of Constraint Scale of Sport Participation
These MI results suggested that, if the parameters were freely estimated, the model fit of the specified model (M0) would improve substantially. According to Byrne (2012), such covariances may result from overlapping item content, which appeared to be the case in this study. Thus, M0 was specified in which these parameters were freely estimated. Table 2 shows that when the parameters were freely estimated, the model (M0) fits were much improved for both women and men. Therefore, the group-specific baseline model for women and men were established (M0).
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Women Men Physically inactive Physically active
Model S-B χ2
df
CFI
M
490.92
209
0.909
RMSEA SRMR Comparison 0.063
0.061
M0
365.96
207
0.949
0.047
0.052
M
387.72
209
0.930
0.054
0.059
M0
346.67
208
0.946
0.048
0.058
M
487.58
209
0.910
0.059
0.059
M0
421.05
208
0.931
0.052
0.058
M
413.93
209
0.914
0.063
0.064
M0
333.93
207
0.947
0.050
0.056
ΔCFI
M0 vs M
0.04
M0 vs M
0.016
M0 vs M
0.021
M0 vs M
0.033
The same data analysis procedure was conducted between physically active and inactive groups. It was found that the hypothesized model (M) displayed acceptable model fit to the data of all individual groups. However, the MI revealed that the residual covariance between Items 14 and 15 also displayed exceptionally large values for all individual groups. In addition, for the physically active group, the MI for residual covariance between Items 10 and 12 displayed a very large value. The fit of the model (M0) with these parameters freely estimated to the data was improved substantially. Therefore, the group-specific baseline models for physically active and inactive groups were established (M0), respectively. Table 2 shows the goodness of fit of all the baseline models (M0). Measurement Invariance Multiple group CFAs were conducted to test the measurement invariance. As Table 3 shows, the acceptable model fit of M1 to the data suggested that the configural invariance was established across men and women as well as physically active and inactive participants. Based on the configural invariance, metric invariance was further examined by constraining the factor loadings equal across groups. Table 3 shows that the M2 displayed adequate model fit to the data and the additional constrains did not affect the model fit significantly (ΔCFI < 0.01; M1 vs M2), which suggested that the metric invariance was supported across women and men as well as physically active and inactive participants. Subsequently, the scalar invariance was tested by additionally constraining the item intercepts equal across groups. The results suggested that the M3 displayed adequate model fit to the data and the changes in CFI, compared with M2, were not significant (ΔCFI < 0.01), which indicated that the scalar invariance was supported across women and men as well as physically active and inactive participants.
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TABLE 3 MULTIPLE GROUP TESTS OF MEASUREMENT INVARIANCE ACROSS SEX AND PHYSICAL ACTIVITY STATUS S-B χ2
df
CFI
M1 (configural) 715.53
415
0.947
0.048
0.055
-
-
M2 (metric)
723.73
431
0.944
0.046
0.056
M2 vs M1
0.003
M3 (scalar)
812.67
454
0.938
0.050
0.064
M3 vs M2
0.006
M4 (variance/ covariance)
750.76
459
0.943
0.045
0.061
M4 vs M2
0.001
M1 (configural) 757.38
415
0.938
0.051
0.057
-
-
M2 (metric)
774.91
431
0.937
0.050
0.059
M2 vs M1
0.001
M3 (scalar)
844.58
454
0.929
0.055
0.067
M3 vs M2
0.008
M4 (variance/ covariance)
810.76
459
0.936
0.049
0.073
M4 vs M2
0.001
Model Sex
Physical activity status
RMSEA SRMR Comparison Δ CFI
Construct Invariance Once the measurement invariance was achieved, the construct invariance was further examined by constraining the factor variance and covariance equal in M2 across groups. Table 3 shows that M4 displayed adequate model fit to the data and the changes in CFI were smaller than 0.01, which suggested that the construct invariance was supported across men and women as well as physically active and inactive participants. Latent Mean Differences To examine the latent mean differences in constraints between groups, one of the groups was treated as a reference group (women and the physically inactive groups). The means of the constraints of the reference group were fixed to zero, and the means of the constraints of the other group were estimated freely. Table 4 shows the results of the latent mean differences analysis which suggested that women reported significantly higher conTABLE 4 LATENT MEAN DIFFERENCES FOR SCORES ON CONSTRAINT SCALE OF SPORT PARTICIPATION SUBSCALES Sex Subscale
Physical Activity Status
Estimate/ Reference Estimate SE
p
Reference Estimate
Estimate/ SE
p
Time
Women
−0.205
−2.21
.027
Inactive
−0.501
−5.425
< .001
Partner
Women
−0.452
−5.14
.000
Inactive
−0.489
−5.683
< .001
Psychology
Women
−0.301
−3.14
.002
Inactive
−0.667
−5.811
< .001
Knowledge
Women
−0.262
−2.99
.003
Inactive
−0.549
−5.452
< .001
Facility
Women
−0.098
−1.13
.256
Inactive
−0.341
−3.597
< .001
Accessibility Women
−0.093
−0.098
.325
Inactive
−0.167
−1.758
.08
Interest
−0.333
−3.55
.000
Inactive
−0.745
−6.804
< .001
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straints on time, partner, psychology, knowledge, and interest than men. No significant differences were found between women and men on scores of facility and accessibility constraints. Furthermore, physically inactive participants reported significantly higher levels of all constraints except for accessibility than physically active participants. DISCUSSION The current study examined the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status first, and further compared the latent mean differences on seven constraint factors across groups. Measurement invariance analysis suggested that different groups had the same structure of the Constraint Scale of Sport Participation and equal strengths of relations between the underlying constructs and specific scale items (measurement invariance), as well as same relationships among constraints (construct invariance). These findings provided further psychometric evidence for the Constraint Scale of Sport Participation. Latent mean difference analysis across sex revealed that women reported much higher levels of constraints than men did on time, partner, psychology, knowledge, and interest. The results were consistent with some findings of previous studies (Jackson & Henderson, 1995; Alexandris & Carroll, 1997a; Jackson, 2005; Casper, et al., 2011b), in which, e.g., women (e.g., adults or girls) reported higher levels of constraints on time, partner, knowledge, and psychology than did men. These results suggested that good time management skills, positive social support, and perception about sport and exercise, as well as scientific and reasonable exercise regiments and sport related knowledge, should be tailored to help women to overcome their sport participation constraints in future exercise promotion programs. Interestingly, in this study it was found that women reported higher level of constraint on interest than men, which complements the previous findings (Casper, et al., 2011b). This result suggested that lack of interest may be one of the main constraints for women to participate in recreational sport. Practitioners should allocate more attention on the issue (e.g., through enhancing the intrinsic motivation or providing the women with choices of physical activities) in their training or exercise promotion programs. Furthermore, although it was found in a previous study that girls reported significantly higher constraint on accessibility than boys did, no sex difference was found on the constraint in this study, which suggested that men and women in this study shared similar opportunities to access to sport participation. However, it should be noted that some of the findings of the current study were not consistent with previous studies (Alexandris, et al., 2003; Son, et al., 2008), in which no sex differences were reported in all constraints. It may be because that in Son, et al. (2008) the total scores of general constraints (intrapersonal, interpersonal, and contextual) were used, while in
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the current study the seven specific constraints were used. Another possible explanation might be the variations of the sample characteristics (i.e., age) in different studies. For example, the participants in Alexandris, et al.’s. (2003) study were older adults, while the participants in other studies (including the current study) were mainly young adults or adolescents. Finally, the inconsistency may also derive from the usage of the traditional statistical approaches in previous studies, which failed to capture the “true” differences. Owing to the lack of evidence suggesting that the measurement structures of the measures used in previous studies were invariant, it is difficult to judge whether the results were valid or not. The findings of the latent mean differences across physically inactive and active participants revealed that physically inactive participants reported higher scores than physically active participants did in all constraints except for accessibility, which are similar with previous findings among young people (e.g., Masmanidis, et al., 2009; Casper, et al., 2011a), but different from the findings among adults (e.g., Alexandris & Carroll, 1997b) between participants and non-participants. However, it is difficult to identify the potential reasons for these differences or similarities because previous studies failed to exclude the influences of the measurement issues on the results. Therefore, studies using latent mean difference approach are needed to further investigate the question. Although Kay and Jackson (1991) found that both frequent participants and nonparticipants reported high levels of constraints in their study and suggested that constraints may not always prevent leisure participation, results from the recreational sport field consistently suggested that non-participants reported perceived higher levels of constraints than the participants did (Masmanidis, et al., 2009; Casper, et al., 2011a). The findings of the current study provided evidence for the differences on constraints between physically active and inactive participants. These findings provided valuable information and practical instructions for practitioners and researchers in the sport and exercise field. For example, practitioners should try to motivate both sedentary individuals and individuals with low physical activity to become more physically active. Furthermore, Ebben and Brudzynski (2008) found that for non-exercisers the individuals who had more time, fewer demands, higher motivation, a workout partner or group, and a better facility location would be more likely to exercise. In addition, Chung and Liu (2013) found that intrinsic motivation and identified regulation were important motivational predictors of undergraduate students' frequency of participating in more vigorous exercise. Taking these findings collectively, future exercise and physical activity promotion programs should, on one hand, help individuals reduce interpersonal and intrapersonal constraints, and on the other hand help individuals develop and increase their autonomous motivations to exercise.
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In conclusion, although this is not the first study to examine the measurement invariance of constraints measure (recreational sport participation) across different populations, the findings of the current study extended the findings of previous studies in several aspects. First, the current study provided evidence of measurement invariance of Constraints Scale of Sport Participation among undergraduate students, which extends the previous findings among adolescents. Second, the results of the comparison between physically inactive and active participants would provide more straightforward information and instruction to practice and research. Finally, by using a multiple-group CFA approach instead of the traditional t test or analysis of variance to compare latent mean differences, the current study provided more robust statistical evidence regarding group differences. The results suggested that the significant differences found in this study were derived from the group differences instead of the measurement issues. However, there are some limitations which should be recognized. First, a convenient sampling method was used in this study, and only undergraduate students were investigated. A random sampling method should be encouraged in future studies, and other different populations, such as working people and elderly, should also be investigated. Second, the results of the current study were based on a cross-sectional design. Cross-sectional samples failed to capture within-person change and preclude assessment of measurement invariance over time (Tsaousis & Kazi, 2013). Third, a one-item self-report measure that focused on frequency of particular exercise participation was employed to classify the physical activity status. Although the authors thought that this kind of measure would be more cost-effective compared with other physical activity measures, there is no doubt that more objective measures of physical activity that can capture frequency and duration as well as energy expenditures will provide more accurate criteria for grouping. Therefore, more valid and reliable objective measures should be employed in future studies. REFERENCES
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ALEXANDRIS, K., FUNK, D. C., & PRITCHARD, M. (2011) The impact of constraints on motivation, activity attachment, and skier intentions to continue. Journal of Leisure Research, 43(1), 56-79. ALEXANDRIS, K., KOUTHOURIS, C., & GIRGOLAS, G. (2007) Investigating the relationships among motivation, negotiation, and alpine skiing participation. Journal of Leisure Research, 39(4), 648-667. ALEXANDRIS, K., TSORBATZOUDIS, C., & GROUIOS, G. (2002) Perceived constraints on recreational sport participation: investigating their relationship with intrinsic motivation, extrinsic motivation and amotivation. Journal of Leisure Research, 34(3), 233-252. ALEXANDRIS, K., ZAHARIADIS, P., TSORBATZOUDIS, C., & GROUIOS, G. (2002) Testing the sport commitment model in the context of exercise and fitness participation. Journal of Sport Behavior, 25(3), 217-230. BENTLER, P. M. (1995) EQS structural equations program manual. Encino, CA: Multivariate Software, Inc. BULL, F. C., ARMSTRONG, T. P., DIXON, T., HAM, S., NEIMAN, A., & PRATT, M. (2004) Physical inactivity. In M. Ezzati, A. D. Lopez, A. Rodgers, & C. J. L. Murray (Eds.), Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. Vol. 1. Geneva, Switzerland: World Health Organization. Pp. 731-883. BULL, F. C., & BAUMAN, A. E. (2011) Physical inactivity: the “Cinderella” risk factor for noncommunicable disease prevention. Journal of Health Communication, 16, 13-26. BYRNE, B. M., & WATKINS, D. (2003) The issue of measurement invariance revisited. Journal of Cross-Cultural Psychology, 34, 155-175. CASPER, J. M., BOCARRO, J. N., KANTERS, M. A., & FLOYD, M. F. (2011a) “Just let me play!”— understanding constraints that limit adolescent sport participation. Journal of Physical Activity and Health, 8(Suppl. 1), S32-S39. CASPER, J. M., BOCARRO, J. N., KANTERS, M. A., & FLOYD, M. F. (2011b) Measurement properties of constraints to sport participation: a psychometric examination with adolescents. Leisure Sciences, 33(2), 127-146. CHEUNG, G. W., & RENSVOLD, R. B. (2002) Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255. CHUNG, P. K., & LIU, J. D. (2013) Motivational regulations as predictors of exercise behavioral and affective consequences of Chinese university students. Journal of Sport Behavior, 36(3), 243-256. CHUNG, P. K., LIU, J. D., & CHEN, W. P. (2013) Perceived constraints on recreational sport participation: evidence from Chinese university students in Hong Kong. World Leisure Journal, 55(4), 347-359. CRAWFORD, D. W., & GODBEY, G. (1987) Reconceptualizing barriers to family leisure. Leisure Sciences, 9(2), 119-127. EBBEN, W. P., & BRUDZYNSKI, L. (2008) Motivation and barriers to exercise among college students. Journal of Exercise Physiology, 11(5), 1-11. ELAVSKY, S. (2010) Longitudinal examination of the exercise and self-esteem model in middle-aged women. Journal of Sport and Exercise Psychology, 32, 862-880. FUNK, D. C., ALEXANDRIS, K., & PING, Y. (2009) To go or stay home and watch: exploring the balance between motives and perceived constraints for major events: a case study of the 2008 Beijing Olympic Games. The International Journal of Tourism Research, 11(1), 41.
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GODBEY, G., CRAWFORD, D. W., & SHEN, X. (2010) Assessing hierarchical leisure constraints theory after two decades. Journal of Leisure Research, 42(1), 111-134. HASKELL, W. L., LEE, I. M., PATE, R. R., POWELL, K. E., BLAIR, S. N., FRANKLIN, B. A., HEATH, G. W., THOMPSON, P. D., & BAUMAN, A. (2007) Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation, 116, 1081-1093. HORN, J. L., & MCARDLE, J. J. (1992) A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18(3), 117-144. JACKSON, E. L. (2005) Constraints to leisure. State College, PA: Venture. JACKSON, E. L., & HENDERSON, K. A. (1995) Gender-based analysis of leisure constraints. Leisure Science, 17, 31-51. KAY, T., & JACKSON, G. A. M. (1991) Leisure despite constraint: the impact of leisure constraints on leisure participation. Journal of Leisure Research, 23(4), 301-313. MASMANIDIS, T., GARGALIANOS, D., & KOSTA, G. (2009) Perceived constraints of Greek university students' participation in campus recreational sport programs. Recreational Sports Journal, 33(2), 150-166. MUTHÉN, L. K., & MUTHÉN, B. O. (1998-2011) Mplus user's guide. (6th ed.) Los Angeles, CA: Muthén & Muthén. ROCKLYNN, C. H. (1998) Adolescent girls and outdoor recreation: a case study examining constraints and effective programming. Journal of Leisure Research, 30(3), 356-379. SCOTT, D., & MOWEN, A. J. (2010) Alleviating park visitation constraints through agency facilitation strategies. Journal of Leisure Research, 42(4), 535-550. SHAW, S. M., BONEN, A., & MCCABE, J. F. (1991) Do more constraints mean less leisure? Examining the relationships between constraints and participation. Journal of Leisure Research, 23(4), 286-300. STEIGER, J. H. (1990) Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research, 25, 173-180. SON, J. S., KERSTETTER, D. L., & MOWEN, A. J. (2008) Do age and gender matter in the constraint negotiation of physically active leisure? Journal of Leisure Research, 40(2), 267-289. TSAOUSIS, I., & KAZI, S. (2013) Factorial invariance and latent mean differences of scores on trait emotional intelligence across gender and age. Personality and Individual Differences, 53, 169-173. VANDENBERG, R. J., & LANCE, C. E. (2000) A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-70. WARBURTON, D. E., NICOL, C., & BREDIN, S. S. (2006) Health benefits of physical activity: the evidence. Canadian Medical Association Journal, 174, 801-809. WRIGHT, B. A., DROGIN RODGERS, E. B., & BACKMAN, K. F. (2001) Assessing the temporal stability of hunting participation and the structure and intensity of constraints: a panel study. Journal of Leisure Research, 33(4), 450-469. Accepted September 5, 2014.
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CONSTRAINTS OF RECREATIONAL SPORT PARTICIPATION: MEASUREMENT INVARIANCE AND LATENT MEAN DIFFERENCES ACROSS SEX AND PHYSICAL ACTIVITY STATUS1 JING DONG LIU AND PAK KWONG CHUNG Department of Physical Education, Hong Kong Baptist University WING PING CHEN Department of Health and Physical Education, Hong Kong Institute of Education Summary.—The purpose of the current study was to (a) examine the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status among the undergraduate students (N = 630) in Hong Kong and (b) compare the latent mean differences across groups. Measurement invariance of the Constraint Scale of Sport Participation across sex of and physical activity status of the participants was examined first. With receiving support on the measurement invariance across groups, latent mean differences of the scores across groups were examined. Multi-group confirmatory factor analysis revealed that the configural, metric, scalar, and structural invariance of the scale was supported across groups. The results of latent mean differences suggested that the women reported significantly higher constraints on time, partner, psychology, knowledge, and interest than the men. The physically inactive participants reported significantly higher scores on all constraints except for accessibility than the physically active participants.
The leisure constraints theory aims to look at the factors that thwart the pursuit of leisure activities (Crawford & Godbey, 1987). It introduced three general constructs of intrapersonal, interpersonal, and structural constraints, which correspond to individual, interpersonal, and contextual analytic levels, respectively. Since the theory was developed, it has received extensive attention (Godbey, Crawford, & Shen, 2010). Researchers from various fields, such as recreational sports (Alexandris & Carroll, 1997a, 1997b; Alexandris, Tsorbatzoudis, & Grouios, 2002; Alexandris, Zahariadis, Tsorbatzoudis, & Grouios, 2002; Alexandris, Barkoukis, Tsormpatzoudis, & Grouios, 2003; Alexandris, Kouthouris, & Girgolas, 2007; Alexandris, Funk, & Pritchard, 2011), travel and tourism (Funk, Alexandris, & Ping, 2009), outdoor recreation (Wright, Drogin Rodgers, & Backman, 2001), and urban and state park use (Scott & Mowen, 2010) have employed the theory as a theoretical framework in their studies. In the recreational sport field, Alexandris and Carroll (1997a, 1997b) developed the Constraint Scale of Sport Participation to measure seven constraints: psycholAddress correspondence to Jing Dong Liu, Department of Physical Education, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong or e-mail (jingdong.liu@hotmail. com). 1
DOI 10.2466/06.03.PMS.119c24z0
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ogy, knowledge, interest, partner, facility, accessibility, and time. Since the measure was developed, a series of studies investigated the constraints to recreational sport participation (Alexandris, et al., 2002, 2007, 2011). To better understand the measurement structure of the scale, researchers compared three competing measurement models (i.e., three-factor model, seven-factor model, and second-order factor model) (e.g., Casper, Bocarro, Kanters, & Floyd, 2011b; Chung, Liu, & Chen, 2013). It was consistently found that the seven-factor model performed better than others, although the items examined in these studies were not exactly the same. In addition, a previous study suggested that non-sport participants reported higher scores on all constraints than sport participants, providing supporting evidence for the nomologocial validity of the measure (Casper, et al., 2011b). These findings provided psychometric evidence for the sevenfactor structure of the Constraint Scale of Sport Participation and suggested that the measure could be used in future study and practice. Previous studies suggested inconsistent findings of the differences on the constraints to recreational sport participation (Jackson & Henderson, 1995; Alexandris & Carroll, 1997a; Rocklynn, 1998; Jackson, 2005; Son, Kerstetter, and Mowen, 2008; Casper, et al., 2011b). For example, Jackson and Henderson (1995) found that women were more constrained than men. Similarly, Casper, et al. (2011b) found that girls rated higher levels of constraints on accessibility (Cohen's d = 0.12), knowledge (Cohen's d = 0.14), partners (Cohen's d = 0.14), and psychology (Cohen's d = 0.14) than the boys. However, Alexandris, et al. (2003) did not find significant sex differences on the constraints of participation in a physical activity program among older adults. Son, et al. (2008) also failed to find sex differences on constraints of sport participation among adult participants. However, in Son, et al. (2008), the mean scores for general constraints (interpersonal, intrapersonal, and structural) were used rather than the specific constraints (e.g., partner, interest, facility). Many other studies have also investigated the relationship between constraints and sport participation (Kay & Jackson, 1991; Shaw, Bonen, & McCabe, 1991; Alexandris and Carroll, 1997a; Alexandris, et al., 2002; Masmanidis, Gargalianos, and Kosta, 2009; Casper, Bocarro, Kanters, & Floyd, 2011a). For example, Alexandris, et al. (2002) found that the perceived constraints may affect frequency of sport participation and sometimes may lead to complete non-participation. Shaw, et al. (1991) found that just two constraints (ill health and low energy) for women and three constraints (ill health, low energy, and lack of self-discipline) for men were found to correlate negatively with sport participation. Casper, et al. (2011a) conducted a study among adolescents and found non-sport participants reported significantly higher levels of constraints on psychology, time, knowledge, in-
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terest, accessibility, facility, and partner than the sport participants reported (Cohen's d between 0.23–1.20). Masmanidis, et al. (2009) found similar results among undergraduate students (Cohen's d between 0.21–0.71). Another study conducted among adults (Alexandris & Carroll, 1997a) found that non-sport participants reported significantly more constraints than the sport participants reported on interest (Cohen's d = 0.78), psychology (Cohen's d = 0.46), and knowledge (Cohen's d = 0.46). Thus, different constraints might play different roles among different populations. The benefits of a physically active lifestyle (Warburton, Nicol, & Bredin, 2006; Haskell, Lee, Pate, Powell, Blair, Franklin, et al., 2007) and the deleterious effect of physical inactivity (Bull & Bauman, 2011) on physiological and psychological health have been well documented. Bull, Armstrong, Dixon, Ham, Neiman, and Pratt (2004) defined physical inactivity as “doing no or very little physical activity at work, at home, for transport or in discretionary time” (p. 729). Based on this definition, both nonsport participants and those participants who do very little physical activity could be classified as a physically inactive population. Following this logic, Elavsky (2010) classified the individuals who exercised less than two times per week for 30 min. or more at moderate intensity as physically inactive or sedentary. Based on the literature review, it was found that previous studies that examined the differences on perceived constraints of sport participation were usually conducted using non-sport participants/ participants classification, with very few studies using physically inactive/active classification. In the previous literature that examined the between-group differences on constraints of sport participation, the commonly used analytic approaches were Student's t test, analysis of variance, or multivariate analysis of variance, which are usually used to compare the mean scores of observed variables (measured directly, e.g., weight, height). When these approaches were applied to compare the means of latent variables (not measured directly but represented using composite mean scores of several observed variables) across groups, the assumption of the measurement invariance of the instrument across groups must be satisfied. If no measurement invariance evidence exists, the differences or similarities regarding individuals and groups cannot be interpreted clearly (Horn & McArdle, 1992). Measurement invariance generally refers to the extent to which the content of each item is being perceived and interpreted in the same way across samples (Byrne & Watkins, 2003). In the case of constraints, if the measures of constraint operate differently across particular samples, and the variations are not taken into account in the measurement, it is inappropriate to compare the composite means (of latent variables) across groups using the traditional statistical techniques. Therefore, the researchers should not as-
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sume a particular instrument's measurement properties would be invariant across groups without going through stringent psychometric investigations (Vandenberg & Lance, 2000). The examination of the latent means derived from the multi-group comparisons performed via confirmatory factor analysis and measurement invariance techniques has been proposed as a powerful alternative to traditional statistical techniques when the variables of interest are latent constructs (Cheung & Rensvold, 2002). Research goal.—The purpose of the current study was to (a) examine the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status (physically active and physically inactive), and (b) compare the latent mean differences on constraints across groups. METHOD Participants and Procedure Participants were 630 undergraduate students (M age = 22.4 yr., SD = 1.6) from eight public universities in Hong Kong. Table 1 shows the characteristics of the participants. Ethical approval was obtained from the Human and Animal Research Ethic Committee of Hong Kong Baptist University. Convenience sampling method was used in this study. Teachers of the general education classes were contacted to obtain their permissions to approach the students in classes. The students were asked whether or not they had interest in taking part in a study examining constraints of sport TABLE 1 CHARACTERISTICS OF PARTICIPANTS Characteristic
n
%
Age ≤ 18
19
3.1
19
120
19.1
20
162
25.7
21
144
22.8
22
104
16.5
≥ 23
81
12.8
Sex Women
342
54.3%
Men
288
45.7%
Active
244 (61.9% men)
38.7%
Inactive
386 (35.5% men)
61.3%
630
100%
Physical activity status
Total
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participation. Once the students expressed interest, a brief introduction to the study and a set of questionnaires together with a copy of written informed consent form were provided to the participants. The participants completed the questionnaires in a quiet classroom and returned the questionnaires to the researchers immediately. Participants were informed prior to data collection that the anonymity and confidentiality of their answers would be preserved at all times. A total of 640 questionnaires were distributed, and 630 were completely returned with a 98.4% response rate. All participants participated in this study voluntarily. Measures A shortened version of the Constraint Scale of Sport Participation (Alexandris, et al., 1997a, 1997b) was used to measure the constraints of sport participation (Chung, et al., 2013). The shortened scale comprises 23 items and assesses seven factors, namely, accessibility, facility, interest, knowledge, psychology, partner, and time. Responses were provided on a sevenpoint Likert scale ranging from 1: Strongly disagree and 7: Strongly agree. Previous research suggested that the shortened scale has satisfactory factorial validity ( χ2 = 563.71, p < .001, χ2/df = 2.69, CFI = 0.903, RMSEA = 0.074, SRMR = 0.066) and internal consistency (αs = .70–.86) among Chinese undergraduate students (Chung, et al., 2013). A one-item self-report question was used to clarify participants' physical activity status (“How many times do you exercise per week for 30 min. or more at moderate or vigorous intensity?”). Participants who reported exercise less than two times per week for 30 min. or more at moderate intensity were categorized as physically inactive; participants who reported exercise two times or more per week for 30 min. or more at moderate intensity were categorized as physically active (Elavsky, 2010). Analyses A several hierarchically ordered steps strategy was used to test the measurement invariance of the Constraints Scale of Sport Participation across groups. First, a baseline model (M0) was established for each group (women vs men, physically active vs physically inactive) individually. Second, measurement invariance across groups was tested. A set of three nested models (Models 1 to 3) using multi-sample CFAs were tested for increasingly stringent levels of constrained equivalence across groups. Model 1 (M1: configural) required the same number of factors and pattern of factor loadings to be the same across groups. No invariance constraints were imposed. Thus, the same parameters that were estimated in the baseline model for each group individually were estimated again in the multiple-sample model. In Model 2 (M2: metric invariance), the factor loadings were constrained equal across groups. In Model 3 (M3: scalar invariance), in addition to the factor loadings, intercepts were constrained
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to be invariant across groups. Third, construct invariance was tested. In Model 4 (M4: factor loading and factor variance/covariance), in addition to factor loadings, factor variance and covariance were constrained invariant across groups. All analyses were conducted using MLR in the Mplus 6.12 program (Muthén & Muthén, 1998–2011). The overall model fit was evaluated using multiple goodness-of-fit indexes including the chi-squared value, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR). A CFI value approximating 1.0 indicates good fit, whereas a value above 0.90 represents acceptable model fit; an RMSEA value lower than 0.05 represents good fit, whereas between 0.05 and 0.08 indicates acceptable fit (Steiger, 1990); an SRMR value lower than 0.05 indicates good model fit, whereas between 0.05 and 0.08 indicates acceptable fit (Bentler, 1995). Traditionally, invariance testing has relied on the χ2 statistic, which has been criticized being influenced by sample size. Therefore, this study adopted Cheung and Rensvold (2002)'s recommendation, in which it used the absolute value of change in CFI of < 0.01 between increasingly more constrained models as the indicative of invariance. The test of latent mean differences requires that the hypothesized CFA model shows a good fit in both compared groups and that all factor loadings and intercepts (M3) be equivalent across groups. Building on the evidence of measurement invariance, one of the two compared groups was selected as the reference group through fixing its latent means to 0 (Mplus automatically fixes the factor means for the first group to zero by default). Latent means for the other group were freely estimated. Statistical significance associated with differences between the latent means for the groups was determined on the basis of the z statistic (Aiken, Stein, & Bentler, 1994). RESULTS Baseline Model The hypothesized model (M) of the Constraint Scale of Sport Participation, tested separately for each group, is presented in Fig. 1. In relation to sex of participants, as Table 2 shows, the hypothesized model (M) had acceptable model fit with the data for both women and men. However, a review of the modification index (MI) value revealed that MI for residual covariance between two items from facility factor (Item 14: the facilities are crowded; Item 15: the facilities are inadequate) displayed exceptionally large values for both women and men. In addition, for women, the MI for residual covariance between two items from knowledge factor (Item 10: I do not have anyone to teach me the activities I like; Item 12: I do not know where I can learn the activities) also displayed a very large value.
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369 Item 1
Time
Item 2 Item 3 Item 4
Partner
Item 5 Item 6 Item 7
Psychology
Item 8 Item 9 Item 10 Item 11
Knowledge Item 12 Item 13 Item 14 Item 15 Facility Item 16 Item 17 Item 18 Accessibility
Item 19 Item 20 Item 21
Interest
Item 22 Item 23
FIG. 1. Hypothesized measurement model of Constraint Scale of Sport Participation
These MI results suggested that, if the parameters were freely estimated, the model fit of the specified model (M0) would improve substantially. According to Byrne (2012), such covariances may result from overlapping item content, which appeared to be the case in this study. Thus, M0 was specified in which these parameters were freely estimated. Table 2 shows that when the parameters were freely estimated, the model (M0) fits were much improved for both women and men. Therefore, the group-specific baseline model for women and men were established (M0).
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J. D. LIU, ET AL. TABLE 2 BASELINE MODEL FOR SEX AND PHYSICAL ACTIVITY STATUS GROUPS Group
Women Men Physically inactive Physically active
Model S-B χ2
df
CFI
M
490.92
209
0.909
RMSEA SRMR Comparison 0.063
0.061
M0
365.96
207
0.949
0.047
0.052
M
387.72
209
0.930
0.054
0.059
M0
346.67
208
0.946
0.048
0.058
M
487.58
209
0.910
0.059
0.059
M0
421.05
208
0.931
0.052
0.058
M
413.93
209
0.914
0.063
0.064
M0
333.93
207
0.947
0.050
0.056
ΔCFI
M0 vs M
0.04
M0 vs M
0.016
M0 vs M
0.021
M0 vs M
0.033
The same data analysis procedure was conducted between physically active and inactive groups. It was found that the hypothesized model (M) displayed acceptable model fit to the data of all individual groups. However, the MI revealed that the residual covariance between Items 14 and 15 also displayed exceptionally large values for all individual groups. In addition, for the physically active group, the MI for residual covariance between Items 10 and 12 displayed a very large value. The fit of the model (M0) with these parameters freely estimated to the data was improved substantially. Therefore, the group-specific baseline models for physically active and inactive groups were established (M0), respectively. Table 2 shows the goodness of fit of all the baseline models (M0). Measurement Invariance Multiple group CFAs were conducted to test the measurement invariance. As Table 3 shows, the acceptable model fit of M1 to the data suggested that the configural invariance was established across men and women as well as physically active and inactive participants. Based on the configural invariance, metric invariance was further examined by constraining the factor loadings equal across groups. Table 3 shows that the M2 displayed adequate model fit to the data and the additional constrains did not affect the model fit significantly (ΔCFI < 0.01; M1 vs M2), which suggested that the metric invariance was supported across women and men as well as physically active and inactive participants. Subsequently, the scalar invariance was tested by additionally constraining the item intercepts equal across groups. The results suggested that the M3 displayed adequate model fit to the data and the changes in CFI, compared with M2, were not significant (ΔCFI < 0.01), which indicated that the scalar invariance was supported across women and men as well as physically active and inactive participants.
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TABLE 3 MULTIPLE GROUP TESTS OF MEASUREMENT INVARIANCE ACROSS SEX AND PHYSICAL ACTIVITY STATUS S-B χ2
df
CFI
M1 (configural) 715.53
415
0.947
0.048
0.055
-
-
M2 (metric)
723.73
431
0.944
0.046
0.056
M2 vs M1
0.003
M3 (scalar)
812.67
454
0.938
0.050
0.064
M3 vs M2
0.006
M4 (variance/ covariance)
750.76
459
0.943
0.045
0.061
M4 vs M2
0.001
M1 (configural) 757.38
415
0.938
0.051
0.057
-
-
M2 (metric)
774.91
431
0.937
0.050
0.059
M2 vs M1
0.001
M3 (scalar)
844.58
454
0.929
0.055
0.067
M3 vs M2
0.008
M4 (variance/ covariance)
810.76
459
0.936
0.049
0.073
M4 vs M2
0.001
Model Sex
Physical activity status
RMSEA SRMR Comparison Δ CFI
Construct Invariance Once the measurement invariance was achieved, the construct invariance was further examined by constraining the factor variance and covariance equal in M2 across groups. Table 3 shows that M4 displayed adequate model fit to the data and the changes in CFI were smaller than 0.01, which suggested that the construct invariance was supported across men and women as well as physically active and inactive participants. Latent Mean Differences To examine the latent mean differences in constraints between groups, one of the groups was treated as a reference group (women and the physically inactive groups). The means of the constraints of the reference group were fixed to zero, and the means of the constraints of the other group were estimated freely. Table 4 shows the results of the latent mean differences analysis which suggested that women reported significantly higher conTABLE 4 LATENT MEAN DIFFERENCES FOR SCORES ON CONSTRAINT SCALE OF SPORT PARTICIPATION SUBSCALES Sex Subscale
Physical Activity Status
Estimate/ Reference Estimate SE
p
Reference Estimate
Estimate/ SE
p
Time
Women
−0.205
−2.21
.027
Inactive
−0.501
−5.425
< .001
Partner
Women
−0.452
−5.14
.000
Inactive
−0.489
−5.683
< .001
Psychology
Women
−0.301
−3.14
.002
Inactive
−0.667
−5.811
< .001
Knowledge
Women
−0.262
−2.99
.003
Inactive
−0.549
−5.452
< .001
Facility
Women
−0.098
−1.13
.256
Inactive
−0.341
−3.597
< .001
Accessibility Women
−0.093
−0.098
.325
Inactive
−0.167
−1.758
.08
Interest
−0.333
−3.55
.000
Inactive
−0.745
−6.804
< .001
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straints on time, partner, psychology, knowledge, and interest than men. No significant differences were found between women and men on scores of facility and accessibility constraints. Furthermore, physically inactive participants reported significantly higher levels of all constraints except for accessibility than physically active participants. DISCUSSION The current study examined the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status first, and further compared the latent mean differences on seven constraint factors across groups. Measurement invariance analysis suggested that different groups had the same structure of the Constraint Scale of Sport Participation and equal strengths of relations between the underlying constructs and specific scale items (measurement invariance), as well as same relationships among constraints (construct invariance). These findings provided further psychometric evidence for the Constraint Scale of Sport Participation. Latent mean difference analysis across sex revealed that women reported much higher levels of constraints than men did on time, partner, psychology, knowledge, and interest. The results were consistent with some findings of previous studies (Jackson & Henderson, 1995; Alexandris & Carroll, 1997a; Jackson, 2005; Casper, et al., 2011b), in which, e.g., women (e.g., adults or girls) reported higher levels of constraints on time, partner, knowledge, and psychology than did men. These results suggested that good time management skills, positive social support, and perception about sport and exercise, as well as scientific and reasonable exercise regiments and sport related knowledge, should be tailored to help women to overcome their sport participation constraints in future exercise promotion programs. Interestingly, in this study it was found that women reported higher level of constraint on interest than men, which complements the previous findings (Casper, et al., 2011b). This result suggested that lack of interest may be one of the main constraints for women to participate in recreational sport. Practitioners should allocate more attention on the issue (e.g., through enhancing the intrinsic motivation or providing the women with choices of physical activities) in their training or exercise promotion programs. Furthermore, although it was found in a previous study that girls reported significantly higher constraint on accessibility than boys did, no sex difference was found on the constraint in this study, which suggested that men and women in this study shared similar opportunities to access to sport participation. However, it should be noted that some of the findings of the current study were not consistent with previous studies (Alexandris, et al., 2003; Son, et al., 2008), in which no sex differences were reported in all constraints. It may be because that in Son, et al. (2008) the total scores of general constraints (intrapersonal, interpersonal, and contextual) were used, while in
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the current study the seven specific constraints were used. Another possible explanation might be the variations of the sample characteristics (i.e., age) in different studies. For example, the participants in Alexandris, et al.’s. (2003) study were older adults, while the participants in other studies (including the current study) were mainly young adults or adolescents. Finally, the inconsistency may also derive from the usage of the traditional statistical approaches in previous studies, which failed to capture the “true” differences. Owing to the lack of evidence suggesting that the measurement structures of the measures used in previous studies were invariant, it is difficult to judge whether the results were valid or not. The findings of the latent mean differences across physically inactive and active participants revealed that physically inactive participants reported higher scores than physically active participants did in all constraints except for accessibility, which are similar with previous findings among young people (e.g., Masmanidis, et al., 2009; Casper, et al., 2011a), but different from the findings among adults (e.g., Alexandris & Carroll, 1997b) between participants and non-participants. However, it is difficult to identify the potential reasons for these differences or similarities because previous studies failed to exclude the influences of the measurement issues on the results. Therefore, studies using latent mean difference approach are needed to further investigate the question. Although Kay and Jackson (1991) found that both frequent participants and nonparticipants reported high levels of constraints in their study and suggested that constraints may not always prevent leisure participation, results from the recreational sport field consistently suggested that non-participants reported perceived higher levels of constraints than the participants did (Masmanidis, et al., 2009; Casper, et al., 2011a). The findings of the current study provided evidence for the differences on constraints between physically active and inactive participants. These findings provided valuable information and practical instructions for practitioners and researchers in the sport and exercise field. For example, practitioners should try to motivate both sedentary individuals and individuals with low physical activity to become more physically active. Furthermore, Ebben and Brudzynski (2008) found that for non-exercisers the individuals who had more time, fewer demands, higher motivation, a workout partner or group, and a better facility location would be more likely to exercise. In addition, Chung and Liu (2013) found that intrinsic motivation and identified regulation were important motivational predictors of undergraduate students' frequency of participating in more vigorous exercise. Taking these findings collectively, future exercise and physical activity promotion programs should, on one hand, help individuals reduce interpersonal and intrapersonal constraints, and on the other hand help individuals develop and increase their autonomous motivations to exercise.
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In conclusion, although this is not the first study to examine the measurement invariance of constraints measure (recreational sport participation) across different populations, the findings of the current study extended the findings of previous studies in several aspects. First, the current study provided evidence of measurement invariance of Constraints Scale of Sport Participation among undergraduate students, which extends the previous findings among adolescents. Second, the results of the comparison between physically inactive and active participants would provide more straightforward information and instruction to practice and research. Finally, by using a multiple-group CFA approach instead of the traditional t test or analysis of variance to compare latent mean differences, the current study provided more robust statistical evidence regarding group differences. The results suggested that the significant differences found in this study were derived from the group differences instead of the measurement issues. However, there are some limitations which should be recognized. First, a convenient sampling method was used in this study, and only undergraduate students were investigated. A random sampling method should be encouraged in future studies, and other different populations, such as working people and elderly, should also be investigated. Second, the results of the current study were based on a cross-sectional design. Cross-sectional samples failed to capture within-person change and preclude assessment of measurement invariance over time (Tsaousis & Kazi, 2013). Third, a one-item self-report measure that focused on frequency of particular exercise participation was employed to classify the physical activity status. Although the authors thought that this kind of measure would be more cost-effective compared with other physical activity measures, there is no doubt that more objective measures of physical activity that can capture frequency and duration as well as energy expenditures will provide more accurate criteria for grouping. Therefore, more valid and reliable objective measures should be employed in future studies. REFERENCES
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