Borrador del análisis
Infante, L; De La Morena, L.
21 marzo, 2019
Autoaceptación: 1,7,17,24 Relaciones positivas: 2,8,12,22,25 Autonomía: 3,4,9,13,18,23 Dominio del entorno: 5,10,14,19,29 Crecimiento personal: 21,26,27,28 Propósito en la vida: 6,11,15,16,20
1 Muestra
Cargamos los datos iniciales y explorando las primeras líneas:
df=read_spss("IES TOTAL BE CL ES y FA.sav")
describe(df) %>% knitr::kable()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| investigadora | 1 | 160 | 758.00 | 2336.59 | 11.0 | 9.16 | 0.00 | 6 | 8000 | 7994 | 2.76 | 5.66 | 184.72 |
| numerosujeto | 2 | 578 | 1595.32 | 2472.26 | 500.5 | 964.04 | 220.91 | 200 | 10011 | 9811 | 2.10 | 3.21 | 102.83 |
| IES | 3 | 543 | 1.85 | 1.12 | 1.0 | 1.69 | 0.00 | 1 | 4 | 3 | 0.92 | -0.69 | 0.05 |
| curso | 4 | 578 | 2.75 | 1.58 | 2.0 | 2.60 | 1.48 | 1 | 6 | 5 | 0.46 | -0.94 | 0.07 |
| edad | 5 | 577 | 14.15 | 1.72 | 14.0 | 14.04 | 1.48 | 12 | 19 | 7 | 0.32 | -1.01 | 0.07 |
| sexo | 6 | 573 | 1.58 | 0.49 | 2.0 | 1.60 | 0.00 | 1 | 2 | 1 | -0.33 | -1.90 | 0.02 |
| separados | 7 | 571 | 0.25 | 0.43 | 0.0 | 0.18 | 0.00 | 0 | 1 | 1 | 1.18 | -0.61 | 0.02 |
| trabajamama | 8 | 569 | 0.73 | 0.44 | 1.0 | 0.79 | 0.00 | 0 | 1 | 1 | -1.03 | -0.94 | 0.02 |
| enquetrabajmma | 9 | 287 | 2.33 | 0.64 | 2.0 | 2.40 | 1.48 | 1 | 3 | 2 | -0.42 | -0.71 | 0.04 |
| trabajapapa | 10 | 553 | 0.92 | 0.27 | 1.0 | 1.00 | 0.00 | 0 | 2 | 2 | -2.92 | 7.82 | 0.01 |
| enquetrabajapp | 11 | 369 | 2.17 | 0.64 | 2.0 | 2.22 | 0.00 | 1 | 3 | 2 | -0.18 | -0.67 | 0.03 |
| numpersonascasa | 12 | 532 | 4.04 | 1.20 | 4.0 | 3.90 | 0.00 | 2 | 13 | 11 | 1.97 | 8.41 | 0.05 |
| numpersonpp | 13 | 80 | 2.95 | 1.99 | 3.0 | 2.91 | 1.48 | 0 | 7 | 7 | -0.09 | -0.89 | 0.22 |
| numpersonmm | 14 | 97 | 2.85 | 1.73 | 3.0 | 2.86 | 1.48 | 0 | 6 | 6 | -0.24 | -0.75 | 0.18 |
| calif.lugar.vive | 15 | 571 | 4.46 | 0.67 | 5.0 | 4.55 | 0.00 | 1 | 5 | 4 | -1.35 | 3.07 | 0.03 |
| calif.rel.mm | 16 | 575 | 4.57 | 0.73 | 5.0 | 4.72 | 0.00 | 1 | 5 | 4 | -2.17 | 5.99 | 0.03 |
| calif.rel.pp | 17 | 568 | 4.41 | 0.92 | 5.0 | 4.60 | 0.00 | 1 | 5 | 4 | -1.88 | 3.62 | 0.04 |
| calif.rel.hnos | 18 | 518 | 4.23 | 0.95 | 4.0 | 4.38 | 1.48 | 1 | 5 | 4 | -1.39 | 1.94 | 0.04 |
| gusta.IES | 19 | 573 | 3.29 | 1.08 | 3.0 | 3.33 | 1.48 | 1 | 5 | 4 | -0.43 | -0.37 | 0.04 |
| gusta.clase | 20 | 572 | 3.81 | 0.99 | 4.0 | 3.92 | 1.48 | 1 | 5 | 4 | -0.78 | 0.40 | 0.04 |
| llevas.compIES | 21 | 573 | 4.36 | 0.71 | 4.0 | 4.44 | 1.48 | 1 | 5 | 4 | -1.43 | 3.85 | 0.03 |
| llevas.com.clase | 22 | 572 | 4.39 | 0.74 | 5.0 | 4.51 | 0.00 | 1 | 5 | 4 | -1.43 | 3.09 | 0.03 |
| relc.profe | 23 | 574 | 4.00 | 0.77 | 4.0 | 4.05 | 0.00 | 1 | 6 | 5 | -0.89 | 2.07 | 0.03 |
| buenosamigosIES | 24 | 561 | 1.00 | 0.47 | 1.0 | 1.00 | 0.00 | 0 | 10 | 10 | 13.56 | 244.56 | 0.02 |
| cuantos.amigos | 25 | 490 | 8.24 | 6.21 | 8.0 | 7.47 | 2.97 | 0 | 80 | 80 | 4.29 | 38.45 | 0.28 |
| calific.estudiante | 26 | 574 | 3.71 | 0.86 | 4.0 | 3.72 | 0.00 | 1 | 10 | 9 | 0.12 | 5.71 | 0.04 |
| nota.media | 27 | 539 | 6.68 | 1.58 | 7.0 | 6.72 | 1.48 | 0 | 10 | 10 | -0.53 | 0.85 | 0.07 |
| gusta.estudiar | 28 | 503 | 0.28 | 0.67 | 0.0 | 0.19 | 0.00 | 0 | 9 | 9 | 6.87 | 76.35 | 0.03 |
| tiempo.estudio.dia | 29 | 542 | 1.62 | 1.17 | 1.5 | 1.53 | 0.74 | 0 | 12 | 12 | 1.91 | 11.17 | 0.05 |
| lugar.estudio | 30 | 416 | 1.23 | 0.79 | 1.0 | 1.03 | 0.00 | 0 | 5 | 5 | 3.23 | 10.58 | 0.04 |
| espacio.propio | 31 | 565 | 0.90 | 0.30 | 1.0 | 1.00 | 0.00 | 0 | 2 | 2 | -2.55 | 5.34 | 0.01 |
| ejercicio.fisico | 32 | 575 | 0.69 | 0.54 | 1.0 | 0.73 | 0.00 | 0 | 7 | 7 | 2.29 | 31.48 | 0.02 |
| cual.ej.físico | 33 | 419 | 2.86 | 3.11 | 1.0 | 2.46 | 1.48 | 0 | 11 | 11 | 0.80 | -0.66 | 0.15 |
| activ.extraescol | 34 | 575 | 0.42 | 0.51 | 0.0 | 0.39 | 0.00 | 0 | 4 | 4 | 0.86 | 2.01 | 0.02 |
| cual.activid.extra | 35 | 421 | 1.56 | 2.02 | 0.0 | 1.36 | 0.00 | 0 | 8 | 8 | 0.68 | -1.22 | 0.10 |
| fruta | 36 | 555 | 4.27 | 2.44 | 5.0 | 4.44 | 2.97 | 0 | 9 | 9 | -0.28 | -1.28 | 0.10 |
| verdura | 37 | 550 | 3.20 | 2.41 | 3.0 | 3.12 | 2.97 | 0 | 7 | 7 | 0.24 | -1.19 | 0.10 |
| ensalada | 38 | 551 | 2.66 | 2.40 | 2.0 | 2.44 | 2.97 | 0 | 7 | 7 | 0.50 | -1.06 | 0.10 |
| legumbres | 39 | 549 | 1.91 | 1.61 | 2.0 | 1.72 | 1.48 | 0 | 7 | 7 | 0.95 | 0.73 | 0.07 |
| pasta | 40 | 552 | 2.54 | 1.60 | 2.0 | 2.35 | 1.48 | 0 | 8 | 8 | 0.96 | 0.43 | 0.07 |
| carne | 41 | 550 | 3.41 | 1.63 | 3.0 | 3.36 | 1.48 | 0 | 8 | 8 | 0.24 | -0.47 | 0.07 |
| pescado | 42 | 547 | 1.94 | 1.41 | 2.0 | 1.82 | 1.48 | 0 | 7 | 7 | 0.90 | 0.72 | 0.06 |
| dulces | 43 | 547 | 3.01 | 2.41 | 2.0 | 2.89 | 2.97 | 0 | 9 | 9 | 0.52 | -1.05 | 0.10 |
| zumos | 44 | 549 | 4.00 | 2.70 | 5.0 | 4.10 | 2.97 | 0 | 10 | 10 | -0.24 | -1.40 | 0.12 |
| refrescoscongas | 45 | 547 | 2.44 | 2.48 | 2.0 | 2.18 | 2.97 | 0 | 7 | 7 | 0.72 | -0.87 | 0.11 |
| pan | 46 | 548 | 5.14 | 2.25 | 6.0 | 5.49 | 1.48 | 0 | 9 | 9 | -0.93 | -0.36 | 0.10 |
| fritos | 47 | 540 | 2.67 | 1.85 | 2.0 | 2.56 | 1.48 | 0 | 7 | 7 | 0.53 | -0.38 | 0.08 |
| alaplancha | 48 | 543 | 2.92 | 1.93 | 3.0 | 2.83 | 1.48 | 0 | 7 | 7 | 0.39 | -0.62 | 0.08 |
| codidos.asados | 49 | 542 | 2.54 | 1.74 | 2.0 | 2.41 | 1.48 | 0 | 9 | 9 | 0.76 | 0.24 | 0.07 |
| horassueño | 50 | 563 | 7.98 | 1.77 | 8.0 | 7.92 | 1.48 | 1 | 35 | 34 | 6.49 | 95.62 | 0.07 |
| horasmovil.tabl | 51 | 545 | 3.67 | 3.01 | 3.0 | 3.22 | 1.48 | 0 | 24 | 24 | 3.19 | 16.17 | 0.13 |
| VAR00023 | 52 | 0 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | NA | NA | NA |
| BPS01 | 53 | 571 | 4.12 | 1.42 | 4.0 | 4.21 | 1.48 | 1 | 6 | 5 | -0.40 | -0.70 | 0.06 |
| BPS02 | 54 | 571 | 5.12 | 1.50 | 6.0 | 5.47 | 0.00 | 1 | 6 | 5 | -1.65 | 1.47 | 0.06 |
| BPS03 | 55 | 567 | 4.25 | 1.70 | 5.0 | 4.43 | 1.48 | 1 | 6 | 5 | -0.58 | -0.94 | 0.07 |
| BPS04 | 56 | 560 | 4.39 | 1.66 | 5.0 | 4.60 | 1.48 | 1 | 6 | 5 | -0.69 | -0.75 | 0.07 |
| BPS05 | 57 | 558 | 4.39 | 1.66 | 5.0 | 4.59 | 1.48 | 1 | 6 | 5 | -0.68 | -0.77 | 0.07 |
| BPS06 | 58 | 565 | 4.44 | 1.57 | 5.0 | 4.63 | 1.48 | 1 | 6 | 5 | -0.68 | -0.68 | 0.07 |
| BPS07 | 59 | 564 | 4.56 | 1.55 | 5.0 | 4.77 | 1.48 | 1 | 6 | 5 | -0.87 | -0.35 | 0.07 |
| BPS08 | 60 | 566 | 4.83 | 1.61 | 6.0 | 5.12 | 0.00 | 1 | 6 | 5 | -1.17 | 0.04 | 0.07 |
| BPS09 | 61 | 566 | 4.35 | 1.73 | 5.0 | 4.56 | 1.48 | 1 | 6 | 5 | -0.66 | -0.94 | 0.07 |
| BPS10 | 62 | 548 | 3.76 | 1.65 | 4.0 | 3.83 | 1.48 | 1 | 6 | 5 | -0.21 | -1.12 | 0.07 |
| BPS11 | 63 | 562 | 4.38 | 1.44 | 5.0 | 4.55 | 1.48 | 1 | 6 | 5 | -0.64 | -0.39 | 0.06 |
| BPS12 | 64 | 569 | 4.87 | 1.33 | 5.0 | 5.09 | 1.48 | 1 | 6 | 5 | -1.18 | 0.68 | 0.06 |
| BPS13 | 65 | 534 | 4.34 | 1.60 | 5.0 | 4.51 | 1.48 | 1 | 6 | 5 | -0.59 | -0.80 | 0.07 |
| BPS14 | 66 | 559 | 4.41 | 1.44 | 5.0 | 4.59 | 1.48 | 1 | 6 | 5 | -0.76 | -0.20 | 0.06 |
| BPS15 | 67 | 565 | 4.36 | 1.57 | 5.0 | 4.55 | 1.48 | 1 | 6 | 5 | -0.69 | -0.55 | 0.07 |
| BPS16 | 68 | 559 | 4.24 | 1.54 | 4.0 | 4.38 | 1.48 | 1 | 6 | 5 | -0.52 | -0.76 | 0.07 |
| BPS17 | 69 | 570 | 4.68 | 1.39 | 5.0 | 4.89 | 1.48 | 1 | 6 | 5 | -0.99 | 0.20 | 0.06 |
| BPS18 | 70 | 565 | 4.55 | 1.44 | 5.0 | 4.75 | 1.48 | 1 | 6 | 5 | -0.85 | -0.11 | 0.06 |
| BPS19 | 71 | 548 | 4.03 | 1.63 | 4.0 | 4.15 | 1.48 | 1 | 6 | 5 | -0.37 | -1.00 | 0.07 |
| BPS20 | 72 | 560 | 4.14 | 1.65 | 4.0 | 4.28 | 1.48 | 1 | 6 | 5 | -0.47 | -1.00 | 0.07 |
| BPS21 | 73 | 565 | 4.72 | 1.37 | 5.0 | 4.91 | 1.48 | 1 | 6 | 5 | -0.94 | 0.07 | 0.06 |
| BPS22 | 74 | 555 | 4.41 | 1.67 | 5.0 | 4.59 | 1.48 | 1 | 6 | 5 | -0.62 | -0.95 | 0.07 |
| BPS23 | 75 | 552 | 4.23 | 1.69 | 5.0 | 4.41 | 1.48 | 1 | 6 | 5 | -0.52 | -0.98 | 0.07 |
| BPS24 | 76 | 563 | 4.74 | 1.45 | 5.0 | 4.98 | 1.48 | 1 | 6 | 5 | -1.07 | 0.17 | 0.06 |
| BPS25 | 77 | 563 | 4.93 | 1.41 | 5.0 | 5.19 | 1.48 | 1 | 6 | 5 | -1.31 | 0.78 | 0.06 |
| BPS26 | 78 | 567 | 4.45 | 1.71 | 5.0 | 4.68 | 1.48 | 1 | 6 | 5 | -0.77 | -0.76 | 0.07 |
| BPS27 | 79 | 565 | 4.69 | 1.29 | 5.0 | 4.86 | 1.48 | 1 | 6 | 5 | -0.88 | 0.20 | 0.05 |
| BPS28 | 80 | 567 | 4.52 | 1.36 | 5.0 | 4.69 | 1.48 | 1 | 6 | 5 | -0.76 | -0.05 | 0.06 |
| BPS29 | 81 | 566 | 4.67 | 1.51 | 5.0 | 4.91 | 1.48 | 1 | 6 | 5 | -0.96 | -0.12 | 0.06 |
| VAR00006 | 82 | 0 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | NA | NA | NA |
| CLIMA01 | 83 | 569 | 4.18 | 1.14 | 5.0 | 4.41 | 0.00 | 1 | 6 | 5 | -1.35 | 0.95 | 0.05 |
| CLIMA02 | 84 | 567 | 3.47 | 2.15 | 4.0 | 3.45 | 1.48 | 1 | 47 | 46 | 14.53 | 292.99 | 0.09 |
| CLIMA3 | 85 | 555 | 3.94 | 1.15 | 4.0 | 4.11 | 1.48 | 1 | 5 | 4 | -1.00 | 0.21 | 0.05 |
| CLIMA04 | 86 | 551 | 3.33 | 1.40 | 4.0 | 3.41 | 1.48 | 1 | 6 | 5 | -0.36 | -1.12 | 0.06 |
| CLIMA05 | 87 | 567 | 2.69 | 1.26 | 3.0 | 2.63 | 1.48 | 1 | 5 | 4 | 0.21 | -0.99 | 0.05 |
| CLIMA06 | 88 | 566 | 2.51 | 1.46 | 2.0 | 2.38 | 1.48 | 1 | 22 | 21 | 4.43 | 54.24 | 0.06 |
| CLIMA07 | 89 | 564 | 2.63 | 1.15 | 3.0 | 2.58 | 1.48 | 1 | 5 | 4 | 0.30 | -0.67 | 0.05 |
| CLIMA08 | 90 | 568 | 3.59 | 1.31 | 4.0 | 3.74 | 1.48 | 1 | 5 | 4 | -0.61 | -0.77 | 0.05 |
| CLIMA09 | 91 | 566 | 3.64 | 1.34 | 4.0 | 3.80 | 1.48 | 1 | 5 | 4 | -0.66 | -0.79 | 0.06 |
| CLIMA10 | 92 | 567 | 3.39 | 1.31 | 4.0 | 3.49 | 1.48 | 1 | 5 | 4 | -0.36 | -0.97 | 0.05 |
| CLIMA11 | 93 | 559 | 2.64 | 1.22 | 3.0 | 2.56 | 1.48 | 1 | 5 | 4 | 0.31 | -0.81 | 0.05 |
| CLIMA12 | 94 | 560 | 3.08 | 1.32 | 3.0 | 3.10 | 1.48 | 1 | 5 | 4 | -0.12 | -1.09 | 0.06 |
| CLIMA13 | 95 | 562 | 3.22 | 1.34 | 3.0 | 3.27 | 1.48 | 1 | 5 | 4 | -0.22 | -1.08 | 0.06 |
| CLIMA14 | 96 | 565 | 2.81 | 1.51 | 3.0 | 2.76 | 1.48 | 1 | 5 | 4 | 0.20 | -1.40 | 0.06 |
| VAR00007 | 97 | 0 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | NA | NA | NA |
| FAMILIA01 | 98 | 559 | 0.93 | 0.25 | 1.0 | 1.00 | 0.00 | 0 | 1 | 1 | -3.48 | 10.13 | 0.01 |
| FAMILIA02 | 99 | 553 | 0.56 | 0.50 | 1.0 | 0.57 | 0.00 | 0 | 1 | 1 | -0.23 | -1.95 | 0.02 |
| FAMILIA03 | 100 | 548 | 0.37 | 0.48 | 0.0 | 0.34 | 0.00 | 0 | 1 | 1 | 0.52 | -1.73 | 0.02 |
| FAMILIA07 | 101 | 557 | 0.49 | 0.50 | 0.0 | 0.49 | 0.00 | 0 | 1 | 1 | 0.05 | -2.00 | 0.02 |
| FAMILIA10 | 102 | 552 | 0.52 | 0.50 | 1.0 | 0.52 | 0.00 | 0 | 1 | 1 | -0.07 | -2.00 | 0.02 |
| FAMILIA11 | 103 | 538 | 0.63 | 0.48 | 1.0 | 0.66 | 0.00 | 0 | 1 | 1 | -0.55 | -1.71 | 0.02 |
| FAMILIA12 | 104 | 556 | 0.78 | 0.41 | 1.0 | 0.85 | 0.00 | 0 | 1 | 1 | -1.36 | -0.14 | 0.02 |
| FAMILIA13 | 105 | 550 | 0.71 | 0.45 | 1.0 | 0.77 | 0.00 | 0 | 1 | 1 | -0.94 | -1.12 | 0.02 |
| FAMILIA17 | 106 | 551 | 0.54 | 0.50 | 1.0 | 0.54 | 0.00 | 0 | 1 | 1 | -0.14 | -1.98 | 0.02 |
| FAMILIA20 | 107 | 553 | 0.53 | 0.50 | 1.0 | 0.53 | 0.00 | 0 | 1 | 1 | -0.10 | -1.99 | 0.02 |
| FAMILIA21 | 108 | 552 | 0.74 | 0.44 | 1.0 | 0.80 | 0.00 | 0 | 1 | 1 | -1.11 | -0.77 | 0.02 |
| FAMILIA22 | 109 | 546 | 0.61 | 0.49 | 1.0 | 0.64 | 0.00 | 0 | 1 | 1 | -0.46 | -1.79 | 0.02 |
| FAMILIA23 | 110 | 555 | 0.21 | 0.41 | 0.0 | 0.14 | 0.00 | 0 | 1 | 1 | 1.41 | 0.00 | 0.02 |
| FAMILIA27 | 111 | 555 | 0.27 | 0.44 | 0.0 | 0.21 | 0.00 | 0 | 1 | 1 | 1.03 | -0.94 | 0.02 |
| FAMILIA30 | 112 | 556 | 0.44 | 1.04 | 0.0 | 0.37 | 0.00 | 0 | 22 | 22 | 16.12 | 331.34 | 0.04 |
| FAMILIA31 | 113 | 543 | 0.75 | 0.44 | 1.0 | 0.81 | 0.00 | 0 | 1 | 1 | -1.13 | -0.73 | 0.02 |
| FAMILIA32 | 114 | 549 | 0.65 | 0.48 | 1.0 | 0.68 | 0.00 | 0 | 1 | 1 | -0.62 | -1.62 | 0.02 |
| FAMILIA33 | 115 | 547 | 0.72 | 0.45 | 1.0 | 0.77 | 0.00 | 0 | 1 | 1 | -0.96 | -1.08 | 0.02 |
| FAMILIA37 | 116 | 551 | 0.56 | 0.50 | 1.0 | 0.57 | 0.00 | 0 | 1 | 1 | -0.24 | -1.95 | 0.02 |
| FAMILIA40 | 117 | 535 | 0.66 | 0.63 | 1.0 | 0.68 | 0.00 | 0 | 10 | 10 | 5.87 | 89.83 | 0.03 |
| FAMILIA41 | 118 | 550 | 0.55 | 0.50 | 1.0 | 0.56 | 0.00 | 0 | 1 | 1 | -0.18 | -1.97 | 0.02 |
| FAMILIA42 | 119 | 546 | 0.50 | 0.50 | 0.0 | 0.50 | 0.00 | 0 | 1 | 1 | 0.01 | -2.00 | 0.02 |
| FAMILIA43 | 120 | 550 | 0.27 | 0.45 | 0.0 | 0.22 | 0.00 | 0 | 1 | 1 | 1.02 | -0.97 | 0.02 |
| FAMILIA47 | 121 | 549 | 0.80 | 0.40 | 1.0 | 0.88 | 0.00 | 0 | 1 | 1 | -1.52 | 0.32 | 0.02 |
| FAMILIA50 | 122 | 547 | 0.62 | 0.49 | 1.0 | 0.65 | 0.00 | 0 | 1 | 1 | -0.48 | -1.77 | 0.02 |
| FAMILIA51 | 123 | 550 | 0.81 | 0.39 | 1.0 | 0.89 | 0.00 | 0 | 1 | 1 | -1.57 | 0.46 | 0.02 |
| FAMILIA52 | 124 | 540 | 0.52 | 0.50 | 1.0 | 0.52 | 0.00 | 0 | 1 | 1 | -0.07 | -2.00 | 0.02 |
| FAMILIA53 | 125 | 548 | 0.19 | 0.39 | 0.0 | 0.11 | 0.00 | 0 | 1 | 1 | 1.58 | 0.49 | 0.02 |
| FAMILIA57 | 126 | 545 | 0.68 | 0.47 | 1.0 | 0.72 | 0.00 | 0 | 1 | 1 | -0.76 | -1.43 | 0.02 |
| FAMILIA60 | 127 | 539 | 0.36 | 0.48 | 0.0 | 0.32 | 0.00 | 0 | 1 | 1 | 0.60 | -1.64 | 0.02 |
| FAMILIA61 | 128 | 548 | 0.71 | 0.46 | 1.0 | 0.76 | 0.00 | 0 | 1 | 1 | -0.91 | -1.17 | 0.02 |
| FAMILIA62 | 129 | 539 | 0.56 | 0.50 | 1.0 | 0.57 | 0.00 | 0 | 1 | 1 | -0.23 | -1.95 | 0.02 |
| FAMILIA63 | 130 | 547 | 0.26 | 0.44 | 0.0 | 0.20 | 0.00 | 0 | 1 | 1 | 1.12 | -0.76 | 0.02 |
| FAMILIA67 | 131 | 534 | 0.41 | 0.49 | 0.0 | 0.39 | 0.00 | 0 | 1 | 1 | 0.36 | -1.88 | 0.02 |
| FAMILIA70 | 132 | 545 | 0.62 | 0.49 | 1.0 | 0.65 | 0.00 | 0 | 1 | 1 | -0.48 | -1.77 | 0.02 |
| FAMILIA71 | 133 | 548 | 0.85 | 0.36 | 1.0 | 0.93 | 0.00 | 0 | 1 | 1 | -1.94 | 1.76 | 0.02 |
| FAMILIA72 | 134 | 547 | 0.31 | 0.46 | 0.0 | 0.26 | 0.00 | 0 | 1 | 1 | 0.83 | -1.31 | 0.02 |
| FAMILIA73 | 135 | 546 | 0.21 | 0.41 | 0.0 | 0.14 | 0.00 | 0 | 1 | 1 | 1.43 | 0.04 | 0.02 |
| FAMILIA77 | 136 | 540 | 0.64 | 0.49 | 1.0 | 0.67 | 0.00 | 0 | 3 | 3 | -0.36 | -0.78 | 0.02 |
| FAMILIA80 | 137 | 524 | 0.34 | 0.49 | 0.0 | 0.30 | 0.00 | 0 | 3 | 3 | 0.91 | -0.01 | 0.02 |
| FAMILIA81 | 138 | 543 | 0.68 | 0.48 | 1.0 | 0.72 | 0.00 | 0 | 3 | 3 | -0.52 | -0.53 | 0.02 |
| FAMILIA82 | 139 | 542 | 0.75 | 0.45 | 1.0 | 0.80 | 0.00 | 0 | 3 | 3 | -0.82 | 0.22 | 0.02 |
| FAMILIA83 | 140 | 544 | 0.38 | 0.50 | 0.0 | 0.35 | 0.00 | 0 | 3 | 3 | 0.71 | -0.48 | 0.02 |
| FAMILIA87 | 141 | 548 | 0.67 | 0.48 | 1.0 | 0.70 | 0.00 | 0 | 3 | 3 | -0.46 | -0.64 | 0.02 |
| FAMILIA90 | 142 | 532 | 0.45 | 0.50 | 0.0 | 0.44 | 0.00 | 0 | 1 | 1 | 0.19 | -1.97 | 0.02 |
| VAR00001 | 143 | 0 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | NA | NA | NA |
| F1CLIescolar | 144 | 540 | 24.79 | 6.27 | 25.0 | 24.69 | 5.93 | 8 | 40 | 32 | 0.08 | -0.11 | 0.27 |
| F2CLIescolar | 145 | 526 | 20.38 | 5.21 | 20.0 | 20.38 | 5.93 | 6 | 63 | 57 | 0.90 | 7.29 | 0.23 |
| AUTOACEPTACION | 146 | 546 | 18.10 | 4.51 | 19.0 | 18.59 | 4.45 | 4 | 24 | 20 | -0.90 | 0.41 | 0.19 |
| RELApositiva | 147 | 531 | 24.21 | 4.87 | 25.0 | 24.76 | 4.45 | 6 | 30 | 24 | -0.94 | 0.43 | 0.21 |
| AUTONOMIA | 148 | 494 | 26.21 | 5.72 | 26.0 | 26.43 | 5.93 | 8 | 36 | 28 | -0.38 | -0.10 | 0.26 |
| DOMINIO | 149 | 502 | 21.18 | 4.63 | 21.0 | 21.28 | 4.45 | 8 | 45 | 37 | 0.02 | 0.74 | 0.21 |
| CRECIMIENTO | 150 | 551 | 18.43 | 3.84 | 19.0 | 18.68 | 4.45 | 6 | 24 | 18 | -0.57 | -0.13 | 0.16 |
| PROPOSITO | 151 | 522 | 21.60 | 5.34 | 22.0 | 21.91 | 5.93 | 5 | 30 | 25 | -0.49 | -0.22 | 0.23 |
| TOTALbienestar | 152 | 408 | 129.36 | 20.41 | 131.0 | 130.33 | 20.76 | 58 | 174 | 116 | -0.44 | -0.02 | 1.01 |
| fAco | 153 | 490 | 6.61 | 2.23 | 7.0 | 6.90 | 2.97 | 0 | 9 | 9 | -0.95 | 0.28 | 0.10 |
| FAex | 154 | 491 | 5.24 | 1.80 | 5.0 | 5.29 | 1.48 | 0 | 9 | 9 | -0.33 | -0.36 | 0.08 |
| FAct | 155 | 493 | 3.30 | 1.96 | 3.0 | 3.16 | 1.48 | 0 | 9 | 9 | 0.67 | 0.07 | 0.09 |
| FAsr | 156 | 491 | 5.02 | 1.82 | 5.0 | 5.06 | 1.48 | 0 | 10 | 10 | -0.16 | -0.65 | 0.08 |
| FAcn | 157 | 464 | 4.50 | 2.11 | 4.0 | 4.44 | 1.48 | 0 | 27 | 27 | 2.96 | 28.67 | 0.10 |
| TOTALfamiliar | 158 | 382 | 24.48 | 4.13 | 25.0 | 24.57 | 4.45 | 12 | 42 | 30 | -0.11 | 0.61 | 0.21 |
Las Variables se dividen en varios grupos:
varBPS = df %>% select(starts_with("BPS")) %>% names()
varCLIMA= df %>% select(starts_with("CLIMA")) %>% names()
varFAM= df %>% select(starts_with("FAMILIA")) %>% names()- BPS: BPS01, BPS02, BPS03, BPS04, BPS05, BPS06, BPS07, BPS08, BPS09, BPS10, BPS11, BPS12, BPS13, BPS14, BPS15, BPS16, BPS17, BPS18, BPS19, BPS20, BPS21, BPS22, BPS23, BPS24, BPS25, BPS26, BPS27, BPS28, BPS29
- CLIMA: CLIMA01, CLIMA02, CLIMA3, CLIMA04, CLIMA05, CLIMA06, CLIMA07, CLIMA08, CLIMA09, CLIMA10, CLIMA11, CLIMA12, CLIMA13, CLIMA14
- FAM: FAMILIA01, FAMILIA02, FAMILIA03, FAMILIA07, FAMILIA10, FAMILIA11, FAMILIA12, FAMILIA13, FAMILIA17, FAMILIA20, FAMILIA21, FAMILIA22, FAMILIA23, FAMILIA27, FAMILIA30, FAMILIA31, FAMILIA32, FAMILIA33, FAMILIA37, FAMILIA40, FAMILIA41, FAMILIA42, FAMILIA43, FAMILIA47, FAMILIA50, FAMILIA51, FAMILIA52, FAMILIA53, FAMILIA57, FAMILIA60, FAMILIA61, FAMILIA62, FAMILIA63, FAMILIA67, FAMILIA70, FAMILIA71, FAMILIA72, FAMILIA73, FAMILIA77, FAMILIA80, FAMILIA81, FAMILIA82, FAMILIA83, FAMILIA87, FAMILIA90
Dentro de las BPS, la división es: - Autoaceptación: 1,7,17,24 - Relaciones positivas: 2,8,12,22,25 - Autonomía: 3,4,9,13,18,23 - Dominio del entorno: 5,10,14,19,29 - Crecimiento personal: 21,26,27,28 - Propósito en la vida: 6,11,15,16,20
FacBPS=list("Autoaceptación"=c(1,7,17,24),
"Rel.positivas"=c(2,8,12,22,25),
"Autonomía"=c(3,4,9,13,18,23),
"Dominio"=c(5,10,14,19,29),
"Crecimiento"=c(21,26,27,28),
"Propósito"=c(6,11,15,16,20)) %>%
map(~ sprintf("BPS%02d",.))
FacBPS## $Autoaceptación
## [1] "BPS01" "BPS07" "BPS17" "BPS24"
##
## $Rel.positivas
## [1] "BPS02" "BPS08" "BPS12" "BPS22" "BPS25"
##
## $Autonomía
## [1] "BPS03" "BPS04" "BPS09" "BPS13" "BPS18" "BPS23"
##
## $Dominio
## [1] "BPS05" "BPS10" "BPS14" "BPS19" "BPS29"
##
## $Crecimiento
## [1] "BPS21" "BPS26" "BPS27" "BPS28"
##
## $Propósito
## [1] "BPS06" "BPS11" "BPS15" "BPS16" "BPS20"2 Exploración de los items por familia de variables
Cabría esperar que en cada familia de variables existiese una cierta asociación (directa o inversa) entre ellas. O que al menos existiese cierta estructura factorial. Exploremos todas las variables conjuntamente para pasar después a estudiarlas por familias.
dfTrabajo=df %>% select(varBPS,varCLIMA,varFAM) %>% filter(complete.cases(.))
corr <- round( cor( dfTrabajo), 1)
p.mat <- cor_pmat(dfTrabajo)
ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = FALSE,tl.cex=7,tl.srt = 90)
2.1 Exploración de BPS
2.1.1 Descriptiva
Exploremosla descriptiva de cada item:
varTrabajo=FacBPS %>% unlist()
dfTrabajo=df %>% select(varTrabajo) %>% filter(complete.cases(.))
dfLong=dfTrabajo %>% gather(Item,Puntos)
table(dfLong$Puntos)##
## 1 2 3 4 5 6
## 734 859 1544 2015 2465 4215Se ve algún error de codificación. Lo dejamos fuera en las siguiente gráfica:
dfLong=dfLong %>% filter(Puntos<=6)
ggplot(dfLong,aes(x=Puntos)) +
geom_bar()+
theme_classic()+
facet_wrap(~Item)
Alguno de los items, como el BPS02 no parecen tener mucha capacidad de discriminar
2.1.2 Adecuación de la muestra (Índice KMO)
Lo usamos como una indicación de si los items tienen una estructura factorizable. Si la relación entre cada par de variables fuese explicable en buena medida por el resto de variables (hay estructura factorial), veríamos un KMO alto. Arriba más o menos se aprecian algunos factores, pero vamos a confirmarlo:
corr <- cor( dfTrabajo)
indicesKMO=KMO(corr)
dfKMO=data.frame(Variable=c("Global",names(indicesKMO$MSAi)),KMO=c(indicesKMO$MSA,indicesKMO$MSAi)) %>% as_tibble() %>%
mutate(Interpretacion=cut(KMO,breaks = c(0,.5,.6,.7,.8,.9,1),labels = c("Unacceptable","Miserable","Mediocre","Middling","Meritorious","Marvelous")))
dfKMO %>% knitr::kable()| Variable | KMO | Interpretacion |
|---|---|---|
| Global | 0.88 | Meritorious |
| Autoaceptación1 | 0.93 | Marvelous |
| Autoaceptación2 | 0.90 | Meritorious |
| Autoaceptación3 | 0.90 | Meritorious |
| Autoaceptación4 | 0.91 | Marvelous |
| Rel.positivas1 | 0.86 | Meritorious |
| Rel.positivas2 | 0.86 | Meritorious |
| Rel.positivas3 | 0.82 | Meritorious |
| Rel.positivas4 | 0.84 | Meritorious |
| Rel.positivas5 | 0.77 | Middling |
| Autonomía1 | 0.86 | Meritorious |
| Autonomía2 | 0.76 | Middling |
| Autonomía3 | 0.78 | Middling |
| Autonomía4 | 0.77 | Middling |
| Autonomía5 | 0.89 | Meritorious |
| Autonomía6 | 0.88 | Meritorious |
| Dominio1 | 0.91 | Marvelous |
| Dominio2 | 0.90 | Meritorious |
| Dominio3 | 0.87 | Meritorious |
| Dominio4 | 0.86 | Meritorious |
| Dominio5 | 0.91 | Marvelous |
| Crecimiento1 | 0.91 | Marvelous |
| Crecimiento2 | 0.73 | Middling |
| Crecimiento3 | 0.91 | Marvelous |
| Crecimiento4 | 0.87 | Meritorious |
| Propósito1 | 0.91 | Marvelous |
| Propósito2 | 0.91 | Marvelous |
| Propósito3 | 0.92 | Marvelous |
| Propósito4 | 0.91 | Marvelous |
| Propósito5 | 0.91 | Marvelous |
Las etiquetas que se han usado viene de aquí:
In his delightfully flamboyant style, Kaiser (1975) suggested that KMO > .9 were marvelous, in the .80s, mertitourious, in the .70s, middling, in the .60s, medicore, in the 50s, miserable, and less than .5, unacceptable.
2.1.3 correlaciones
Las correlaciones existentes entre los items de esta parte del cuestionario está reflejada en la siguiente matriz de correlaciones:
dfTrabajo=df %>% select(varTrabajo) %>% filter(complete.cases(.))
corr <- round( cor( dfTrabajo), 1)
ggcorrplot(corr,lab = TRUE,lab_size = 3)
Dejemos de lado las correlaciones que no sean significativas:
p.mat <- cor_pmat(dfTrabajo)
ggcorrplot(corr, p.mat = p.mat, insig = "blank")
2.2 Análisis Factorial
Vamos a ver que tal es eso de los 6 factores comparado con otras posibilidades:
There are multiple ways to determine the appropriate number of factors in exploratory factor analysis. Routines for the Very Simple Structure (VSS) criterion allow one to compare solutions of varying complexity and for different number of factors. Graphic output indicates the “optimal” number of factors for different levels of complexity. The Velicer MAP criterion is another good choice. nfactors finds and plots several of these alternative estimates.
Así que probemos con algunas posibilidades:
fa.parallel(dfTrabajo)
## Parallel analysis suggests that the number of factors = 5 and the number of components = 4analisisvss=vss(dfTrabajo)
print(summary(analisisvss))##
## Very Simple Structure
## VSS complexity 1 achieves a maximimum of 0.64 with 1 factors
## VSS complexity 2 achieves a maximimum of 0.76 with 3 factors
##
## The Velicer MAP criterion achieves a minimum of 0.02 with 2 factors
## NULLplot(analisisvss)
Vamos a elegir 6 factores. Aunque sabemos cuáles son de antemano, vemos si coincide con el análisis exploratorio:
analisisfa=fa(dfTrabajo,nfactors = 6)
print(summary(analisisfa))##
## Factor analysis with Call: fa(r = dfTrabajo, nfactors = 6)
##
## Test of the hypothesis that 6 factors are sufficient.
## The degrees of freedom for the model is 247 and the objective function was 0.88
## The number of observations was 408 with Chi Square = 345 with prob < 3.6e-05
##
## The root mean square of the residuals (RMSA) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## Tucker Lewis Index of factoring reliability = 0.94
## RMSEA index = 0.033 and the 10 % confidence intervals are 0.023 0.039
## BIC = -1140
## With factor correlations of
## MR1 MR5 MR2 MR3 MR6 MR4
## MR1 1.00 0.50 -0.01 0.32 0.17 0.30
## MR5 0.50 1.00 0.20 0.37 0.23 0.26
## MR2 -0.01 0.20 1.00 0.20 0.39 0.12
## MR3 0.32 0.37 0.20 1.00 0.23 0.21
## MR6 0.17 0.23 0.39 0.23 1.00 0.10
## MR4 0.30 0.26 0.12 0.21 0.10 1.00
## MR1 MR5 MR2 MR3 MR6 MR4
## MR1 1.00 0.50 -0.01 0.32 0.17 0.30
## MR5 0.50 1.00 0.20 0.37 0.23 0.26
## MR2 -0.01 0.20 1.00 0.20 0.39 0.12
## MR3 0.32 0.37 0.20 1.00 0.23 0.21
## MR6 0.17 0.23 0.39 0.23 1.00 0.10
## MR4 0.30 0.26 0.12 0.21 0.10 1.00RMSEA no tiene mal aspecto con 6 factores (RMSEA<0.05, IC 95% incluido)
plot(analisisfa)
Vamos a visualizar la estructura factorial que se nos sugiere con el número de factores elegidos.
fa.diagram(analisisfa)
Los factores automáticos por el análisis exploratorios no han coincidido con los del instrumento de medida. Pero hay otras formas de agruparlos:
iclust(dfTrabajo)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = dfTrabajo)
##
## Purified Alpha:
## C27 C25
## 0.87 0.75
##
## G6* reliability:
## C27 C25
## 1 1
##
## Original Beta:
## C27 C25
## 0.67 0.47
##
## Cluster size:
## C27 C25
## 19 10
##
## Item by Cluster Structure matrix:
## O P C27 C25
## Autoaceptación1 C27 C27 0.53 0.27
## Autoaceptación2 C27 C27 0.62 0.37
## Autoaceptación3 C27 C27 0.69 0.33
## Autoaceptación4 C27 C27 0.71 0.37
## Rel.positivas1 C25 C25 0.30 0.51
## Rel.positivas2 C25 C25 0.36 0.60
## Rel.positivas3 C27 C27 0.47 0.27
## Rel.positivas4 C25 C25 0.19 0.46
## Rel.positivas5 C27 C27 0.42 0.35
## Autonomía1 C27 C27 0.35 0.30
## Autonomía2 C25 C25 0.11 0.53
## Autonomía3 C25 C25 0.13 0.58
## Autonomía4 C25 C25 0.07 0.39
## Autonomía5 C27 C27 0.48 0.24
## Autonomía6 C25 C25 0.22 0.51
## Dominio1 C25 C25 0.36 0.50
## Dominio2 C27 C27 0.55 0.19
## Dominio3 C27 C27 0.28 -0.03
## Dominio4 C25 C25 0.19 0.52
## Dominio5 C27 C27 0.49 0.24
## Crecimiento1 C27 C27 0.48 0.07
## Crecimiento2 C25 C25 0.20 0.31
## Crecimiento3 C27 C27 0.63 0.32
## Crecimiento4 C27 C27 0.41 0.14
## Propósito1 C27 C27 0.49 0.09
## Propósito2 C27 C27 0.55 0.15
## Propósito3 C27 C27 0.63 0.26
## Propósito4 C27 C27 0.58 0.16
## Propósito5 C27 C27 0.49 0.16
##
## With eigenvalues of:
## C27 C25
## 5.3 2.8
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## C27 C25
## C27 0.87 0.44
## C25 0.36 0.75
##
## Cluster fit = 0.71 Pattern fit = 0.96 RMSR = 0.06omega(dfTrabajo,nfactors = 6)
## Omega
## Call: omega(m = dfTrabajo, nfactors = 6)
## Alpha: 0.87
## G.6: 0.9
## Omega Hierarchical: 0.62
## Omega H asymptotic: 0.68
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## Autoaceptación1 0.42 0.37 0.34 0.66 0.53
## Autoaceptación2 0.53 0.31 0.20 0.24 0.48 0.52 0.59
## Autoaceptación3 0.60 0.44 0.59 0.41 0.62
## Autoaceptación4 0.58 0.29 0.20 0.22 0.53 0.47 0.63
## Rel.positivas1 0.36 0.41 0.24 0.42 0.58 0.32
## Rel.positivas2 0.41 0.46 0.20 0.49 0.51 0.34
## Rel.positivas3 0.41 0.58 0.52 0.48 0.32
## Rel.positivas4 0.27 -0.22 0.25 0.21 0.27 0.73 0.28
## Rel.positivas5 0.42 0.61 0.56 0.44 0.31
## Autonomía1 0.37 0.33 0.32 0.68 0.44
## Autonomía2 0.22 0.63 0.46 0.54 0.11
## Autonomía3 0.25 0.69 0.54 0.46 0.12
## Autonomía4 0.40 0.26 0.27 0.73 0.07
## Autonomía5 0.45 0.47 0.43 0.57 0.47
## Autonomía6 0.30 0.25 0.29 0.71 0.32
## Dominio1 0.35 0.21 0.33 0.34 0.66 0.37
## Dominio2 0.39 0.46 0.37 0.63 0.42
## Dominio3 0.20 0.11 0.89 0.26
## Dominio4 0.25 0.48 0.20 0.37 0.63 0.18
## Dominio5 0.38 0.20 0.26 0.74 0.56
## Crecimiento1 0.34 0.25 0.32 0.68 0.37
## Crecimiento2 0.47 0.29 0.71 0.12
## Crecimiento3 0.51 0.47 0.56 0.44 0.47
## Crecimiento4 0.30 0.30 0.23 0.77 0.39
## Propósito1 0.34 0.31 0.28 0.72 0.41
## Propósito2 0.38 0.43 0.40 0.60 0.35
## Propósito3 0.47 0.46 0.45 0.55 0.49
## Propósito4 0.43 0.43 0.45 0.55 0.42
## Propósito5 0.34 0.41 0.29 0.71 0.39
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 4.25 1.68 1.05 1.28 0.77 0.95 0.98
##
## general/max 2.5 max/min = 2.2
## mean percent general = 0.37 with sd = 0.15 and cv of 0.41
## Explained Common Variance of the general factor = 0.39
##
## The degrees of freedom are 247 and the fit is 0.88
## The number of observations was 408 with Chi Square = 345 with prob < 3.6e-05
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.033 and the 10 % confidence intervals are 0.023 0.039
## BIC = -1140
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 377 and the fit is 3.6
## The number of observations was 408 with Chi Square = 1425 with prob < 1.8e-121
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.084 and the 10 % confidence intervals are 0.078 0.087
## BIC = -841
##
## Measures of factor score adequacy
## g F1* F2* F3* F4*
## Correlation of scores with factors 0.80 0.77 0.77 0.83 0.64
## Multiple R square of scores with factors 0.65 0.60 0.59 0.69 0.42
## Minimum correlation of factor score estimates 0.29 0.19 0.18 0.38 -0.17
## F5* F6*
## Correlation of scores with factors 0.76 0.75
## Multiple R square of scores with factors 0.57 0.56
## Minimum correlation of factor score estimates 0.14 0.13
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4*
## Omega total for total scores and subscales 0.90 0.77 0.59 0.61 0.67
## Omega general for total scores and subscales 0.62 0.44 0.25 0.11 0.38
## Omega group for total scores and subscales 0.15 0.33 0.34 0.50 0.29
## F5* F6*
## Omega total for total scores and subscales 0.69 0.54
## Omega general for total scores and subscales 0.22 0.24
## Omega group for total scores and subscales 0.47 0.30analisisalpha=alpha(dfTrabajo)
analisisalpha$total %>% knitr::kable()| raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r | |
|---|---|---|---|---|---|---|---|---|---|
| 0.87 | 0.87 | 0.9 | 0.19 | 6.8 | 0.01 | 4.5 | 0.7 | 0.19 |
analisisalpha$item.stats## n raw.r std.r r.cor r.drop mean sd
## Autoaceptación1 408 0.53 0.53 0.51 0.48 4.1 1.4
## Autoaceptación2 408 0.62 0.63 0.62 0.57 4.5 1.6
## Autoaceptación3 408 0.65 0.66 0.66 0.60 4.6 1.4
## Autoaceptación4 408 0.68 0.69 0.69 0.64 4.8 1.4
## Rel.positivas1 408 0.46 0.45 0.43 0.40 5.0 1.6
## Rel.positivas2 408 0.53 0.52 0.50 0.47 4.8 1.6
## Rel.positivas3 408 0.47 0.48 0.46 0.42 4.9 1.3
## Rel.positivas4 408 0.37 0.36 0.32 0.30 4.5 1.7
## Rel.positivas5 408 0.47 0.48 0.46 0.41 4.9 1.4
## Autonomía1 408 0.42 0.42 0.39 0.35 4.4 1.6
## Autonomía2 408 0.34 0.32 0.29 0.27 4.4 1.6
## Autonomía3 408 0.38 0.35 0.33 0.30 4.4 1.7
## Autonomía4 408 0.26 0.25 0.20 0.19 4.4 1.5
## Autonomía5 408 0.48 0.49 0.46 0.42 4.5 1.4
## Autonomía6 408 0.42 0.40 0.36 0.34 4.3 1.7
## Dominio1 408 0.51 0.49 0.47 0.44 4.4 1.7
## Dominio2 408 0.51 0.52 0.49 0.45 3.7 1.6
## Dominio3 408 0.25 0.26 0.21 0.18 4.4 1.4
## Dominio4 408 0.40 0.38 0.35 0.33 4.0 1.6
## Dominio5 408 0.49 0.50 0.47 0.43 4.6 1.5
## Crecimiento1 408 0.42 0.43 0.40 0.36 4.7 1.4
## Crecimiento2 408 0.33 0.32 0.28 0.25 4.4 1.7
## Crecimiento3 408 0.60 0.61 0.61 0.56 4.7 1.3
## Crecimiento4 408 0.39 0.41 0.37 0.33 4.5 1.3
## Propósito1 408 0.44 0.45 0.42 0.37 4.4 1.5
## Propósito2 408 0.49 0.51 0.48 0.44 4.3 1.4
## Propósito3 408 0.59 0.60 0.59 0.54 4.3 1.6
## Propósito4 408 0.52 0.53 0.51 0.46 4.2 1.5
## Propósito5 408 0.47 0.47 0.44 0.40 4.2 1.63 Análisis confirmatorio
Vemo si las respuestas a los cuestionarios se adecuan a el modelo propuesto de 6 factores.
dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.))
Modelo <-
'Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24
RelPositivas =~ BPS02+BPS08+BPS12+BPS22+BPS25
Autonomia =~ BPS03+BPS04+BPS09+BPS13+BPS18+BPS23
Dominio =~ BPS05+BPS10+BPS14+BPS19+BPS29
Crecimiento =~ BPS21+BPS26+BPS27+BPS28
Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20'
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-3 ended normally after 64 iterations
##
## Optimization method NLMINB
## Number of free parameters 73
##
## Number of observations 408
##
## Estimator ML
## Model Fit Test Statistic 926.335
## Degrees of freedom 362
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3359.927
## Degrees of freedom 406
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.809
## Tucker-Lewis Index (TLI) 0.786
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -20459.466
## Loglikelihood unrestricted model (H1) -19996.299
##
## Number of free parameters 73
## Akaike (AIC) 41064.932
## Bayesian (BIC) 41357.754
## Sample-size adjusted Bayesian (BIC) 41126.113
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062
## 90 Percent Confidence Interval 0.057 0.067
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion =~
## BPS01 1.000 0.794 0.561
## BPS07 1.386 0.135 10.293 0.000 1.100 0.704
## BPS17 1.224 0.118 10.391 0.000 0.972 0.716
## BPS24 1.337 0.124 10.771 0.000 1.061 0.764
## RelPositivas =~
## BPS02 1.000 0.954 0.615
## BPS08 1.087 0.115 9.459 0.000 1.038 0.658
## BPS12 0.715 0.087 8.264 0.000 0.683 0.537
## BPS22 0.688 0.106 6.477 0.000 0.657 0.396
## BPS25 0.826 0.095 8.694 0.000 0.788 0.576
## Autonomia =~
## BPS03 1.000 0.687 0.427
## BPS04 1.449 0.218 6.652 0.000 0.996 0.609
## BPS09 1.623 0.239 6.782 0.000 1.115 0.646
## BPS13 0.833 0.160 5.205 0.000 0.573 0.376
## BPS18 0.703 0.145 4.847 0.000 0.484 0.337
## BPS23 1.252 0.201 6.215 0.000 0.860 0.516
## Dominio =~
## BPS05 1.000 0.742 0.443
## BPS10 1.182 0.159 7.418 0.000 0.878 0.535
## BPS14 0.463 0.110 4.196 0.000 0.344 0.238
## BPS19 0.643 0.130 4.946 0.000 0.478 0.291
## BPS29 0.968 0.138 7.025 0.000 0.718 0.484
## Crecimiento =~
## BPS21 1.000 0.716 0.524
## BPS26 0.820 0.152 5.405 0.000 0.587 0.338
## BPS27 1.392 0.159 8.766 0.000 0.997 0.779
## BPS28 0.967 0.129 7.515 0.000 0.693 0.531
## Proposito =~
## BPS06 1.000 0.789 0.510
## BPS11 1.012 0.125 8.104 0.000 0.798 0.559
## BPS15 1.350 0.149 9.069 0.000 1.064 0.685
## BPS16 1.259 0.143 8.804 0.000 0.993 0.646
## BPS20 1.088 0.139 7.836 0.000 0.858 0.530
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion ~~
## RelPositivas 0.432 0.070 6.207 0.000 0.570 0.570
## Autonomia 0.240 0.051 4.680 0.000 0.440 0.440
## Dominio 0.539 0.083 6.473 0.000 0.915 0.915
## Crecimiento 0.400 0.063 6.298 0.000 0.703 0.703
## Proposito 0.524 0.077 6.806 0.000 0.836 0.836
## RelPositivas ~~
## Autonomia 0.404 0.075 5.375 0.000 0.615 0.615
## Dominio 0.459 0.081 5.642 0.000 0.648 0.648
## Crecimiento 0.294 0.060 4.904 0.000 0.430 0.430
## Proposito 0.350 0.067 5.262 0.000 0.466 0.466
## Autonomia ~~
## Dominio 0.256 0.059 4.364 0.000 0.503 0.503
## Crecimiento 0.138 0.041 3.356 0.001 0.281 0.281
## Proposito 0.098 0.041 2.404 0.016 0.181 0.181
## Dominio ~~
## Crecimiento 0.389 0.069 5.602 0.000 0.731 0.731
## Proposito 0.558 0.089 6.236 0.000 0.953 0.953
## Crecimiento ~~
## Proposito 0.386 0.065 5.937 0.000 0.683 0.683
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BPS01 1.371 0.104 13.194 0.000 1.371 0.685
## .BPS07 1.229 0.103 11.901 0.000 1.229 0.504
## .BPS17 0.898 0.077 11.734 0.000 0.898 0.487
## .BPS24 0.804 0.074 10.838 0.000 0.804 0.416
## .BPS02 1.495 0.130 11.499 0.000 1.495 0.622
## .BPS08 1.412 0.131 10.785 0.000 1.412 0.567
## .BPS12 1.153 0.093 12.450 0.000 1.153 0.712
## .BPS22 2.316 0.172 13.445 0.000 2.316 0.843
## .BPS25 1.253 0.104 12.026 0.000 1.253 0.669
## .BPS03 2.118 0.162 13.104 0.000 2.118 0.818
## .BPS04 1.687 0.152 11.122 0.000 1.687 0.630
## .BPS09 1.734 0.166 10.428 0.000 1.734 0.582
## .BPS13 1.996 0.149 13.417 0.000 1.996 0.859
## .BPS18 1.820 0.134 13.607 0.000 1.820 0.886
## .BPS23 2.036 0.165 12.348 0.000 2.036 0.733
## .BPS05 2.251 0.166 13.538 0.000 2.251 0.803
## .BPS10 1.924 0.151 12.751 0.000 1.924 0.714
## .BPS14 1.959 0.138 14.161 0.000 1.959 0.943
## .BPS19 2.461 0.175 14.079 0.000 2.461 0.915
## .BPS29 1.683 0.127 13.251 0.000 1.683 0.765
## .BPS21 1.359 0.108 12.585 0.000 1.359 0.726
## .BPS26 2.677 0.195 13.722 0.000 2.677 0.886
## .BPS27 0.642 0.089 7.180 0.000 0.642 0.392
## .BPS28 1.224 0.098 12.516 0.000 1.224 0.718
## .BPS06 1.771 0.133 13.280 0.000 1.771 0.740
## .BPS11 1.403 0.108 12.975 0.000 1.403 0.688
## .BPS15 1.285 0.110 11.648 0.000 1.285 0.531
## .BPS16 1.377 0.113 12.175 0.000 1.377 0.583
## .BPS20 1.887 0.143 13.166 0.000 1.887 0.719
## Autoaceptacion 0.630 0.110 5.753 0.000 1.000 1.000
## RelPositivas 0.911 0.153 5.957 0.000 1.000 1.000
## Autonomia 0.473 0.123 3.853 0.000 1.000 1.000
## Dominio 0.551 0.128 4.295 0.000 1.000 1.000
## Crecimiento 0.513 0.104 4.953 0.000 1.000 1.000
## Proposito 0.622 0.123 5.061 0.000 1.000 1.000How Large a Sample Size Do I Need? Rules of Thumb Ratio of Sample Size to the Number of Free Parameters Tanaka (1987): 20 to 1 (Most analysts now think that is unrealistically high.) Goal: Bentler & Chou (1987): 5 to 1 Several published studies do not meet this goal.
En este caso el número d parámetros libres es 73. Tomando una ratio de 5 a 1, con muestra de 73*5=225 estáis bien, y ya tenéis más de 400 en la base actual. No necesitáis más.
Para estudiar la capacidad del modelo prouesto de reproducir los datos, se pueden estudiar varias medidas. Una de ellas es RMSEA, que veis con el intervalo de confianza incluido. en principio sería un modelo adecuado:
Root Mean Square Error of Approximation (RMSEA) This absolute measure of fit is based on the non-centrality parameter.The RMSEA is currently the most popular measure of model fit and it now reported in virtually all papers that use CFA or SEM and some refer to the measure as the “Ramsey.”
MacCallum, Browne and Sugawara (1996) have used 0.01, 0.05, and 0.08 to indicate excellent, good, and mediocre fit, respectively. However, others have suggested 0.10 as the cutoff for poor fitting models. These are definitions for the population. That is, a given model may have a population value of 0.05 (which would not be known), but in the sample it might be greater than 0.10. Use of confidence intervals and tests of PCLOSE can help understand the sampling error in the RMSEA. There is greater sampling error for small df and low N models, especially for the former. Thus, models with small df and low N can have artificially large values of the RMSEA. For instance, a chi square of 2.098 (a value not statistically significant), with a df of 1 and N of 70 yields an RMSEA of 0.126. For this reason, Kenny, Kaniskan, and McCoach (2014) argue to not even compute the RMSEA for low df models.
A confidence interval can be computed for the RMSEA. Ideally the lower value of the 90% confidence interval includes or is very near zero (or no worse than 0.05) and the upper value is not very large, i.e., less than .08. The width of the confidence interval is very informative about the precision in the estimate of the RMSEA.
Otro indicador es TLI que también aparece ahí:
Tucker Lewis Index or Non-normed Fit Index (NNFI) If the index is greater than one, it is set at one. A value between .90 and .95 is now considered marginal, above .95 is good, and below .90 is considered to be a poor fitting model.
Ha salido Tucker-Lewis Index (TLI) 0.787
Así que hay que intentar mejorarlo.
Note that the TLI (and the CFI which follows) depends on the average size of the correlations in the data. If the average correlation between variables is not high, then the TLI will not be very high.
Esto es algo que ocurre si miramos la matriz de correlaciones. Lo mismo habría que revisar si ciertos items deberían estar.
dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.))
Modelo <-
'Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24
RelPositivas =~ BPS02+BPS08+BPS12+BPS25
Autonomia =~ BPS04+BPS09+BPS23
Crecimiento =~ BPS21+BPS27+BPS28
Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20'
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-3 ended normally after 70 iterations
##
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 408
##
## Estimator ML
## Model Fit Test Statistic 344.505
## Degrees of freedom 142
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2233.883
## Degrees of freedom 171
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.902
## Tucker-Lewis Index (TLI) 0.882
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13073.502
## Loglikelihood unrestricted model (H1) -12901.249
##
## Number of free parameters 48
## Akaike (AIC) 26243.004
## Bayesian (BIC) 26435.544
## Sample-size adjusted Bayesian (BIC) 26283.233
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent Confidence Interval 0.051 0.067
## P-value RMSEA <= 0.05 0.030
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion =~
## BPS01 1.000 0.793 0.560
## BPS07 1.401 0.137 10.248 0.000 1.110 0.711
## BPS17 1.233 0.119 10.326 0.000 0.977 0.720
## BPS24 1.326 0.125 10.605 0.000 1.051 0.756
## RelPositivas =~
## BPS02 1.000 0.933 0.602
## BPS08 1.059 0.120 8.831 0.000 0.988 0.627
## BPS12 0.781 0.093 8.364 0.000 0.729 0.573
## BPS25 0.897 0.103 8.707 0.000 0.837 0.611
## Autonomia =~
## BPS04 1.000 1.113 0.680
## BPS09 1.117 0.136 8.183 0.000 1.243 0.720
## BPS23 0.667 0.097 6.843 0.000 0.742 0.445
## Crecimiento =~
## BPS21 1.000 0.736 0.538
## BPS27 1.303 0.150 8.710 0.000 0.959 0.750
## BPS28 0.944 0.125 7.535 0.000 0.695 0.532
## Proposito =~
## BPS06 1.000 0.797 0.515
## BPS11 1.003 0.125 8.038 0.000 0.799 0.559
## BPS15 1.348 0.149 9.027 0.000 1.074 0.691
## BPS16 1.236 0.142 8.690 0.000 0.985 0.640
## BPS20 1.061 0.138 7.696 0.000 0.845 0.522
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion ~~
## RelPositivas 0.425 0.069 6.123 0.000 0.574 0.574
## Autonomia 0.261 0.066 3.951 0.000 0.296 0.296
## Crecimiento 0.424 0.066 6.391 0.000 0.727 0.727
## Proposito 0.528 0.078 6.787 0.000 0.836 0.836
## RelPositivas ~~
## Autonomia 0.513 0.094 5.483 0.000 0.494 0.494
## Crecimiento 0.306 0.062 4.914 0.000 0.445 0.445
## Proposito 0.362 0.068 5.332 0.000 0.487 0.487
## Autonomia ~~
## Crecimiento 0.099 0.061 1.624 0.104 0.120 0.120
## Proposito 0.059 0.062 0.951 0.341 0.067 0.067
## Crecimiento ~~
## Proposito 0.421 0.069 6.065 0.000 0.718 0.718
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BPS01 1.373 0.105 13.127 0.000 1.373 0.686
## .BPS07 1.208 0.104 11.654 0.000 1.208 0.495
## .BPS17 0.887 0.077 11.502 0.000 0.887 0.481
## .BPS24 0.826 0.076 10.804 0.000 0.826 0.428
## .BPS02 1.535 0.134 11.444 0.000 1.535 0.638
## .BPS08 1.511 0.137 11.042 0.000 1.511 0.607
## .BPS12 1.087 0.092 11.845 0.000 1.087 0.672
## .BPS25 1.174 0.104 11.296 0.000 1.174 0.626
## .BPS04 1.440 0.171 8.421 0.000 1.440 0.538
## .BPS09 1.433 0.198 7.238 0.000 1.433 0.481
## .BPS23 2.225 0.174 12.792 0.000 2.225 0.802
## .BPS21 1.331 0.108 12.305 0.000 1.331 0.711
## .BPS27 0.717 0.092 7.762 0.000 0.717 0.438
## .BPS28 1.221 0.099 12.363 0.000 1.221 0.717
## .BPS06 1.758 0.134 13.115 0.000 1.758 0.735
## .BPS11 1.403 0.110 12.805 0.000 1.403 0.687
## .BPS15 1.264 0.112 11.241 0.000 1.264 0.523
## .BPS16 1.394 0.116 11.992 0.000 1.394 0.590
## .BPS20 1.908 0.146 13.072 0.000 1.908 0.728
## Autoaceptacion 0.628 0.110 5.712 0.000 1.000 1.000
## RelPositivas 0.871 0.153 5.712 0.000 1.000 1.000
## Autonomia 1.239 0.210 5.900 0.000 1.000 1.000
## Crecimiento 0.542 0.107 5.046 0.000 1.000 1.000
## Proposito 0.635 0.125 5.061 0.000 1.000 1.000dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.))
Modelo <-
'Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24
RelPositivas =~ BPS02+BPS08+BPS12+BPS25
Autonomia =~ BPS04+BPS09+BPS23
Crecimiento =~ BPS21+BPS27+BPS28
Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20'
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-3 ended normally after 70 iterations
##
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 408
##
## Estimator ML
## Model Fit Test Statistic 344.505
## Degrees of freedom 142
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2233.883
## Degrees of freedom 171
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.902
## Tucker-Lewis Index (TLI) 0.882
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13073.502
## Loglikelihood unrestricted model (H1) -12901.249
##
## Number of free parameters 48
## Akaike (AIC) 26243.004
## Bayesian (BIC) 26435.544
## Sample-size adjusted Bayesian (BIC) 26283.233
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent Confidence Interval 0.051 0.067
## P-value RMSEA <= 0.05 0.030
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion =~
## BPS01 1.000 0.793 0.560
## BPS07 1.401 0.137 10.248 0.000 1.110 0.711
## BPS17 1.233 0.119 10.326 0.000 0.977 0.720
## BPS24 1.326 0.125 10.605 0.000 1.051 0.756
## RelPositivas =~
## BPS02 1.000 0.933 0.602
## BPS08 1.059 0.120 8.831 0.000 0.988 0.627
## BPS12 0.781 0.093 8.364 0.000 0.729 0.573
## BPS25 0.897 0.103 8.707 0.000 0.837 0.611
## Autonomia =~
## BPS04 1.000 1.113 0.680
## BPS09 1.117 0.136 8.183 0.000 1.243 0.720
## BPS23 0.667 0.097 6.843 0.000 0.742 0.445
## Crecimiento =~
## BPS21 1.000 0.736 0.538
## BPS27 1.303 0.150 8.710 0.000 0.959 0.750
## BPS28 0.944 0.125 7.535 0.000 0.695 0.532
## Proposito =~
## BPS06 1.000 0.797 0.515
## BPS11 1.003 0.125 8.038 0.000 0.799 0.559
## BPS15 1.348 0.149 9.027 0.000 1.074 0.691
## BPS16 1.236 0.142 8.690 0.000 0.985 0.640
## BPS20 1.061 0.138 7.696 0.000 0.845 0.522
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion ~~
## RelPositivas 0.425 0.069 6.123 0.000 0.574 0.574
## Autonomia 0.261 0.066 3.951 0.000 0.296 0.296
## Crecimiento 0.424 0.066 6.391 0.000 0.727 0.727
## Proposito 0.528 0.078 6.787 0.000 0.836 0.836
## RelPositivas ~~
## Autonomia 0.513 0.094 5.483 0.000 0.494 0.494
## Crecimiento 0.306 0.062 4.914 0.000 0.445 0.445
## Proposito 0.362 0.068 5.332 0.000 0.487 0.487
## Autonomia ~~
## Crecimiento 0.099 0.061 1.624 0.104 0.120 0.120
## Proposito 0.059 0.062 0.951 0.341 0.067 0.067
## Crecimiento ~~
## Proposito 0.421 0.069 6.065 0.000 0.718 0.718
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BPS01 1.373 0.105 13.127 0.000 1.373 0.686
## .BPS07 1.208 0.104 11.654 0.000 1.208 0.495
## .BPS17 0.887 0.077 11.502 0.000 0.887 0.481
## .BPS24 0.826 0.076 10.804 0.000 0.826 0.428
## .BPS02 1.535 0.134 11.444 0.000 1.535 0.638
## .BPS08 1.511 0.137 11.042 0.000 1.511 0.607
## .BPS12 1.087 0.092 11.845 0.000 1.087 0.672
## .BPS25 1.174 0.104 11.296 0.000 1.174 0.626
## .BPS04 1.440 0.171 8.421 0.000 1.440 0.538
## .BPS09 1.433 0.198 7.238 0.000 1.433 0.481
## .BPS23 2.225 0.174 12.792 0.000 2.225 0.802
## .BPS21 1.331 0.108 12.305 0.000 1.331 0.711
## .BPS27 0.717 0.092 7.762 0.000 0.717 0.438
## .BPS28 1.221 0.099 12.363 0.000 1.221 0.717
## .BPS06 1.758 0.134 13.115 0.000 1.758 0.735
## .BPS11 1.403 0.110 12.805 0.000 1.403 0.687
## .BPS15 1.264 0.112 11.241 0.000 1.264 0.523
## .BPS16 1.394 0.116 11.992 0.000 1.394 0.590
## .BPS20 1.908 0.146 13.072 0.000 1.908 0.728
## Autoaceptacion 0.628 0.110 5.712 0.000 1.000 1.000
## RelPositivas 0.871 0.153 5.712 0.000 1.000 1.000
## Autonomia 1.239 0.210 5.900 0.000 1.000 1.000
## Crecimiento 0.542 0.107 5.046 0.000 1.000 1.000
## Proposito 0.635 0.125 5.061 0.000 1.000 1.000Comparative Fit Index (CFI) If the index is greater than one, it is set at one and if less than zero, it is set to zero. It is interpreted as the previous incremental indexes. If the CFI is less than one, then the CFI is always greater than the TLI. CFI pays a penalty of one for every parameter estimated. Because the TLI and CFI are highly correlated only one of the two should be reported. The CFI is reported more often than the TLI, but I think the CFI’s penalty for complexity of just 1 is too low and so I prefer the TLI even though the CFI is reported much more frequently than the TLI.
Standardized Root Mean Square Residual (SRMR) The SRMR is an absolute measure of fit and is defined as the standardized difference between the observed correlation and the predicted correlation. It is a positively biased measure and that bias is greater for small```{r}
dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.)) Modelo <- ‘Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24 RelPositivas =~ BPS02+BPS08+BPS12+BPS25 Autonomia =~ BPS04+BPS09+BPS23 Crecimiento =~ BPS21+BPS27+BPS28 Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20’
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE) ``` N and for low df studies. Because the SRMR is an absolute measure of fit, a value of zero indicates perfect fit. The SRMR has no penalty for model complexity. A value less than .08 is generally considered a good fit (Hu & Bentler, 1999).
En este caso tenemos un buen indicador de ajuste: Standardized Root Mean Square Residual:
SRMR 0.075
En conclusión, vais bien de muestra, tenéis casi el doble de lo necesario.
En lo siguiente os muestro qué pasaría si eliminamos alguna diumensión que no parece tener buenos coeficientes y eliminamos algunos items que numéricamente invitan a ello. Se tiene un modelo (que no se si tiene sentido), pero que está muy cerca de presentar indicadores que se suelen considerar buenos:
dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.))
Modelo <-
'Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24
RelPositivas =~ BPS02+BPS08+BPS12+BPS25
Autonomia =~ BPS04+BPS09+BPS23
Crecimiento =~ BPS21+BPS27+BPS28
Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20'
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-3 ended normally after 70 iterations
##
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 408
##
## Estimator ML
## Model Fit Test Statistic 344.505
## Degrees of freedom 142
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2233.883
## Degrees of freedom 171
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.902
## Tucker-Lewis Index (TLI) 0.882
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13073.502
## Loglikelihood unrestricted model (H1) -12901.249
##
## Number of free parameters 48
## Akaike (AIC) 26243.004
## Bayesian (BIC) 26435.544
## Sample-size adjusted Bayesian (BIC) 26283.233
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent Confidence Interval 0.051 0.067
## P-value RMSEA <= 0.05 0.030
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion =~
## BPS01 1.000 0.793 0.560
## BPS07 1.401 0.137 10.248 0.000 1.110 0.711
## BPS17 1.233 0.119 10.326 0.000 0.977 0.720
## BPS24 1.326 0.125 10.605 0.000 1.051 0.756
## RelPositivas =~
## BPS02 1.000 0.933 0.602
## BPS08 1.059 0.120 8.831 0.000 0.988 0.627
## BPS12 0.781 0.093 8.364 0.000 0.729 0.573
## BPS25 0.897 0.103 8.707 0.000 0.837 0.611
## Autonomia =~
## BPS04 1.000 1.113 0.680
## BPS09 1.117 0.136 8.183 0.000 1.243 0.720
## BPS23 0.667 0.097 6.843 0.000 0.742 0.445
## Crecimiento =~
## BPS21 1.000 0.736 0.538
## BPS27 1.303 0.150 8.710 0.000 0.959 0.750
## BPS28 0.944 0.125 7.535 0.000 0.695 0.532
## Proposito =~
## BPS06 1.000 0.797 0.515
## BPS11 1.003 0.125 8.038 0.000 0.799 0.559
## BPS15 1.348 0.149 9.027 0.000 1.074 0.691
## BPS16 1.236 0.142 8.690 0.000 0.985 0.640
## BPS20 1.061 0.138 7.696 0.000 0.845 0.522
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion ~~
## RelPositivas 0.425 0.069 6.123 0.000 0.574 0.574
## Autonomia 0.261 0.066 3.951 0.000 0.296 0.296
## Crecimiento 0.424 0.066 6.391 0.000 0.727 0.727
## Proposito 0.528 0.078 6.787 0.000 0.836 0.836
## RelPositivas ~~
## Autonomia 0.513 0.094 5.483 0.000 0.494 0.494
## Crecimiento 0.306 0.062 4.914 0.000 0.445 0.445
## Proposito 0.362 0.068 5.332 0.000 0.487 0.487
## Autonomia ~~
## Crecimiento 0.099 0.061 1.624 0.104 0.120 0.120
## Proposito 0.059 0.062 0.951 0.341 0.067 0.067
## Crecimiento ~~
## Proposito 0.421 0.069 6.065 0.000 0.718 0.718
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BPS01 1.373 0.105 13.127 0.000 1.373 0.686
## .BPS07 1.208 0.104 11.654 0.000 1.208 0.495
## .BPS17 0.887 0.077 11.502 0.000 0.887 0.481
## .BPS24 0.826 0.076 10.804 0.000 0.826 0.428
## .BPS02 1.535 0.134 11.444 0.000 1.535 0.638
## .BPS08 1.511 0.137 11.042 0.000 1.511 0.607
## .BPS12 1.087 0.092 11.845 0.000 1.087 0.672
## .BPS25 1.174 0.104 11.296 0.000 1.174 0.626
## .BPS04 1.440 0.171 8.421 0.000 1.440 0.538
## .BPS09 1.433 0.198 7.238 0.000 1.433 0.481
## .BPS23 2.225 0.174 12.792 0.000 2.225 0.802
## .BPS21 1.331 0.108 12.305 0.000 1.331 0.711
## .BPS27 0.717 0.092 7.762 0.000 0.717 0.438
## .BPS28 1.221 0.099 12.363 0.000 1.221 0.717
## .BPS06 1.758 0.134 13.115 0.000 1.758 0.735
## .BPS11 1.403 0.110 12.805 0.000 1.403 0.687
## .BPS15 1.264 0.112 11.241 0.000 1.264 0.523
## .BPS16 1.394 0.116 11.992 0.000 1.394 0.590
## .BPS20 1.908 0.146 13.072 0.000 1.908 0.728
## Autoaceptacion 0.628 0.110 5.712 0.000 1.000 1.000
## RelPositivas 0.871 0.153 5.712 0.000 1.000 1.000
## Autonomia 1.239 0.210 5.900 0.000 1.000 1.000
## Crecimiento 0.542 0.107 5.046 0.000 1.000 1.000
## Proposito 0.635 0.125 5.061 0.000 1.000 1.000dfTrabajo=df %>% select(as.character(varTrabajo)) %>% filter(complete.cases(.))
Modelo <-
'Autoaceptacion =~ BPS01+BPS07+BPS17+BPS24
RelPositivas =~ BPS02+BPS08+BPS12+BPS25
Autonomia =~ BPS04+BPS09+BPS23
Crecimiento =~ BPS21+BPS27+BPS28
Proposito =~ BPS06+BPS11+BPS15+BPS16+BPS20'
Modelo.fit <- cfa(model = Modelo, data = dfTrabajo )
summary(Modelo.fit,standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-3 ended normally after 70 iterations
##
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 408
##
## Estimator ML
## Model Fit Test Statistic 344.505
## Degrees of freedom 142
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2233.883
## Degrees of freedom 171
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.902
## Tucker-Lewis Index (TLI) 0.882
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13073.502
## Loglikelihood unrestricted model (H1) -12901.249
##
## Number of free parameters 48
## Akaike (AIC) 26243.004
## Bayesian (BIC) 26435.544
## Sample-size adjusted Bayesian (BIC) 26283.233
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent Confidence Interval 0.051 0.067
## P-value RMSEA <= 0.05 0.030
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion =~
## BPS01 1.000 0.793 0.560
## BPS07 1.401 0.137 10.248 0.000 1.110 0.711
## BPS17 1.233 0.119 10.326 0.000 0.977 0.720
## BPS24 1.326 0.125 10.605 0.000 1.051 0.756
## RelPositivas =~
## BPS02 1.000 0.933 0.602
## BPS08 1.059 0.120 8.831 0.000 0.988 0.627
## BPS12 0.781 0.093 8.364 0.000 0.729 0.573
## BPS25 0.897 0.103 8.707 0.000 0.837 0.611
## Autonomia =~
## BPS04 1.000 1.113 0.680
## BPS09 1.117 0.136 8.183 0.000 1.243 0.720
## BPS23 0.667 0.097 6.843 0.000 0.742 0.445
## Crecimiento =~
## BPS21 1.000 0.736 0.538
## BPS27 1.303 0.150 8.710 0.000 0.959 0.750
## BPS28 0.944 0.125 7.535 0.000 0.695 0.532
## Proposito =~
## BPS06 1.000 0.797 0.515
## BPS11 1.003 0.125 8.038 0.000 0.799 0.559
## BPS15 1.348 0.149 9.027 0.000 1.074 0.691
## BPS16 1.236 0.142 8.690 0.000 0.985 0.640
## BPS20 1.061 0.138 7.696 0.000 0.845 0.522
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Autoaceptacion ~~
## RelPositivas 0.425 0.069 6.123 0.000 0.574 0.574
## Autonomia 0.261 0.066 3.951 0.000 0.296 0.296
## Crecimiento 0.424 0.066 6.391 0.000 0.727 0.727
## Proposito 0.528 0.078 6.787 0.000 0.836 0.836
## RelPositivas ~~
## Autonomia 0.513 0.094 5.483 0.000 0.494 0.494
## Crecimiento 0.306 0.062 4.914 0.000 0.445 0.445
## Proposito 0.362 0.068 5.332 0.000 0.487 0.487
## Autonomia ~~
## Crecimiento 0.099 0.061 1.624 0.104 0.120 0.120
## Proposito 0.059 0.062 0.951 0.341 0.067 0.067
## Crecimiento ~~
## Proposito 0.421 0.069 6.065 0.000 0.718 0.718
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BPS01 1.373 0.105 13.127 0.000 1.373 0.686
## .BPS07 1.208 0.104 11.654 0.000 1.208 0.495
## .BPS17 0.887 0.077 11.502 0.000 0.887 0.481
## .BPS24 0.826 0.076 10.804 0.000 0.826 0.428
## .BPS02 1.535 0.134 11.444 0.000 1.535 0.638
## .BPS08 1.511 0.137 11.042 0.000 1.511 0.607
## .BPS12 1.087 0.092 11.845 0.000 1.087 0.672
## .BPS25 1.174 0.104 11.296 0.000 1.174 0.626
## .BPS04 1.440 0.171 8.421 0.000 1.440 0.538
## .BPS09 1.433 0.198 7.238 0.000 1.433 0.481
## .BPS23 2.225 0.174 12.792 0.000 2.225 0.802
## .BPS21 1.331 0.108 12.305 0.000 1.331 0.711
## .BPS27 0.717 0.092 7.762 0.000 0.717 0.438
## .BPS28 1.221 0.099 12.363 0.000 1.221 0.717
## .BPS06 1.758 0.134 13.115 0.000 1.758 0.735
## .BPS11 1.403 0.110 12.805 0.000 1.403 0.687
## .BPS15 1.264 0.112 11.241 0.000 1.264 0.523
## .BPS16 1.394 0.116 11.992 0.000 1.394 0.590
## .BPS20 1.908 0.146 13.072 0.000 1.908 0.728
## Autoaceptacion 0.628 0.110 5.712 0.000 1.000 1.000
## RelPositivas 0.871 0.153 5.712 0.000 1.000 1.000
## Autonomia 1.239 0.210 5.900 0.000 1.000 1.000
## Crecimiento 0.542 0.107 5.046 0.000 1.000 1.000
## Proposito 0.635 0.125 5.061 0.000 1.000 1.000