{"id":4034,"date":"2020-07-08T07:34:57","date_gmt":"2020-07-08T07:34:57","guid":{"rendered":"http:\/\/revista-apunts.com\/?post_type=tablas&p=4034"},"modified":"2020-07-08T07:35:01","modified_gmt":"2020-07-08T07:35:01","slug":"table-4-141-02","status":"publish","type":"tablas","link":"https:\/\/revista-apunts.com\/en\/tablas\/table-4-141-02\/","title":{"rendered":"Table 4 – 141.02"},"content":{"rendered":"\n

Table 4<\/h2>\n\n\n\n

Model invariance in gender age, competitive level and type of sport<\/em><\/p>\n\n\n\n
<\/td><\/td>\u03c72<\/td>gl<\/td>p<\/td>NNFI<\/td>CFI<\/td>RMSEA<\/td>RMSEA 90% IC<\/td>\u0394\u03c72<\/td>\u0394gl<\/td>\u0394CFI<\/td><\/tr>
Gender<\/td>Modelo 0<\/td>904,68<\/td>320<\/td><0,01<\/td>0,979<\/td>0,982<\/td>0,068<\/td>0,063-0,073<\/td>—<\/td>—<\/td>—<\/td><\/tr>
<\/td>Modelo 1<\/td>927,87<\/td>331<\/td><0,01<\/td>0,979<\/td>0,982<\/td>0,067<\/td>0,062-0,072<\/td>23,19<\/td>11<\/td>*0,001<\/td><\/tr>
<\/td>Modelo 2<\/td>961,48<\/td>341<\/td><0,01<\/td>0,979<\/td>0,981<\/td>0,068<\/td>0,063-0,073<\/td>56,08<\/td>21<\/td>-0,001<\/td><\/tr>
Age<\/td>Modelo 0<\/td>916,66<\/td>320<\/td><0,01<\/td>0,978<\/td>0,981<\/td>0,069<\/td>0,063-0,074<\/td>—<\/td>—<\/td>—<\/td><\/tr>
<\/td>Modelo 1<\/td>966,46<\/td>331<\/td><0,01<\/td>0,977<\/td>0,980<\/td>0,070<\/td>
0,064-0,075<\/td>		49,80<\/td>11<\/td>-0,001<\/td><\/tr>
<\/td>Modelo 2<\/td>999,96<\/td>341<\/td><0,01<\/td>0,977<\/td>0,979<\/td>0,070<\/td>0,065-0,075<\/td>150,02<\/td>21<\/td>-0,002<\/td><\/tr>
Competitive level<\/td>Modelo 0<\/td>1437,64<\/td>320<\/td><0,01<\/td>0,996<\/td>0,997<\/td>0,094<\/td>0,089-0,099<\/td>—<\/td>—<\/td>—<\/td><\/tr>
<\/td>Modelo 1<\/td>1596,08<\/td>331<\/td><0,01<\/td>0,996<\/td>0,996<\/td>0,098<\/td>0,093-0,103<\/td>158,44<\/td>11<\/td>-0,001<\/td><\/tr>
<\/td>Modelo 2<\/td>1831,90<\/td>341<\/td><0,01<\/td>0,995<\/td>0,996<\/td>0,105<\/td>0,100-0,110<\/td>394,26<\/td>21<\/td>*0,001<\/td><\/tr>
Type of sport<\/td>Modelo 0<\/td>959,34<\/td>320<\/td><0,01<\/td>0,979<\/td>0,982<\/td>0,071<\/td>0,066-0,076<\/td>—<\/td>—<\/td>—<\/td><\/tr>
<\/td>Modelo 1<\/td>1013,36<\/td>331<\/td><0,01<\/td>0,979<\/td>0,981<\/td>0,072<\/td>0,067-0,077<\/td>54,02<\/td>11<\/td>-0,001<\/td><\/tr>
<\/td>Modelo 2<\/td>1085,40<\/td>341<\/td><0,01<\/td>0,977<\/td>0,980<\/td>0,074<\/td>0,069-0,079<\/td>126,06<\/td>21<\/td>-0,002<\/td><\/tr><\/tbody><\/table>\n\n\n\n

Note: \u03c72 = chi-squares; df = degrees of freedom; p= p value; NNFI = non-normed fit index; CFI = comparative fit index; RMSEA = root mean square error of approximation; IC = confidence interval; \u0394= difference between values; Model 0 = confidence model; Model 1 = invariant factorial loads; Model 2 = factorial loads and variances\/invariance factor covariances, *< 0.001<\/p>\n\n\n\n

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