Análisis DSE: multinivel

Univariados

Univariados generales (incluyendo solo el experimento 4).

Data Frame Summary

datos_viñetas

Dimensions: 2696 x 35
Duplicates: 0

nota [numeric]

¿Cuál fue tu promedio final de notas el año pasado? Si no lo recuerdas exactamente, indica una nota aproximada. (entre 1.0 y 7.0)

Mean (sd) : 6 (0.6)
min ≤ med ≤ max:
3 ≤ 6 ≤ 7
IQR (CV) : 0.7 (0.1)

28 distinct values

2696 (100.0%)

0 (0.0%)

merecimiento_nota [numeric]

¿Te parece que este promedio fue más o menos de lo que merecías?

Mean (sd) : 2 (0.5)
min ≤ med ≤ max:
1 ≤ 2 ≤ 3
IQR (CV) : 0 (0.2)

1	:	356	(	13.2%	)
2	:	2104	(	78.2%	)
3	:	232	(	8.6%	)

2692 (99.9%)

4 (0.1%)

nota_justa [numeric]

En caso que tu promedio no es lo que merecías ¿Qué promedio piensas que merecías? (entre 1.0 y 7.0)

Mean (sd) : 6 (0.9)
min ≤ med ≤ max:
1 ≤ 6 ≤ 7
IQR (CV) : 0.8 (0.1)

23 distinct values

588 (21.8%)

2108 (78.2%)

d2_o2 [numeric]

Establecimiento eduacional

Mean (sd) : 5.2 (2.6)
min ≤ med ≤ max:
1 ≤ 6 ≤ 9
IQR (CV) : 4 (0.5)

1	:	360	(	13.4%	)
2	:	160	(	5.9%	)
3	:	388	(	14.4%	)
4	:	128	(	4.7%	)
5	:	228	(	8.5%	)
6	:	592	(	22.0%	)
7	:	272	(	10.1%	)
8	:	156	(	5.8%	)
9	:	412	(	15.3%	)

2696 (100.0%)

0 (0.0%)

p17_o2 [numeric]

¿Cuál es el último curso o nivel de estudios que completó tu madre?

Mean (sd) : 3.7 (1.1)
min ≤ med ≤ max:
1 ≤ 4 ≤ 6
IQR (CV) : 2 (0.3)

1	:	60	(	3.1%	)
2	:	124	(	6.4%	)
3	:	748	(	38.9%	)
4	:	404	(	21.0%	)
5	:	512	(	26.6%	)
6	:	76	(	4.0%	)

1924 (71.4%)

772 (28.6%)

p18_o2 [numeric]

¿Cuál es el último curso o nivel de estudios que completó tu padre?

Mean (sd) : 3.8 (1.1)
min ≤ med ≤ max:
1 ≤ 4 ≤ 6
IQR (CV) : 2 (0.3)

1	:	40	(	2.4%	)
2	:	116	(	6.9%	)
3	:	600	(	35.5%	)
4	:	356	(	21.0%	)
5	:	512	(	30.3%	)
6	:	68	(	4.0%	)

1692 (62.8%)

1004 (37.2%)

genero [factor]

1. Hombre
2. Mujer

1372	(	52.6%	)
1236	(	47.4%	)

2608 (96.7%)

88 (3.3%)

curso [factor]

1. Básica
2. Media

1308	(	48.5%	)
1388	(	51.5%	)

2696 (100.0%)

0 (0.0%)

libros_hogar [factor]

1. Menos de 25 libros
2. Más de 25 libros

2268	(	84.2%	)
424	(	15.8%	)

2692 (99.9%)

4 (0.1%)

merit_esfuerzo [numeric]

En Chile, las personas son recompensadas por sus esfuerzos

Mean (sd) : 2.7 (0.8)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 1 (0.3)

1	:	244	(	9.1%	)
2	:	792	(	29.4%	)
3	:	1244	(	46.1%	)
4	:	416	(	15.4%	)

2696 (100.0%)

0 (0.0%)

merit_talento [numeric]

En Chile, las personas son recompensadas por su inteligencia y habilidad

Mean (sd) : 2.8 (0.8)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 1 (0.3)

1	:	144	(	5.4%	)
2	:	664	(	24.7%	)
3	:	1400	(	52.1%	)
4	:	480	(	17.9%	)

2688 (99.7%)

8 (0.3%)

school_esfuerzo [numeric]

En esta escuela, quienes se esfuerzan obtienen buenas notas

Mean (sd) : 3.1 (0.8)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 1 (0.2)

1	:	116	(	4.3%	)
2	:	320	(	11.9%	)
3	:	1344	(	49.9%	)
4	:	912	(	33.9%	)

2692 (99.9%)

4 (0.1%)

school_talento [numeric]

En esta escuela, quienes son inteligentes obtienen buenas notas

Mean (sd) : 3.2 (0.7)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 1 (0.2)

1	:	64	(	2.4%	)
2	:	264	(	9.8%	)
3	:	1552	(	57.6%	)
4	:	816	(	30.3%	)

2696 (100.0%)

0 (0.0%)

school_merecimiento [numeric]

En esta escuela, los/as estudiantes obtienen las notas que merecen

Mean (sd) : 2.9 (0.7)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 0 (0.3)

1	:	104	(	3.9%	)
2	:	528	(	19.6%	)
3	:	1492	(	55.5%	)
4	:	564	(	21.0%	)

2688 (99.7%)

8 (0.3%)

exp_t2_a [numeric]

[Texto 2] Estudiante A (nota = 3.7) Se esfuerza más que la mayoría. Décimas a regalar:

Mean (sd) : 3.4 (1)
min ≤ med ≤ max:
0 ≤ 3 ≤ 5
IQR (CV) : 1 (0.3)

0	:	72	(	2.7%	)
1	:	20	(	0.7%	)
2	:	124	(	4.6%	)
3	:	1428	(	53.0%	)
4	:	596	(	22.1%	)
5	:	456	(	16.9%	)

2696 (100.0%)

0 (0.0%)

exp_t2_b [numeric]

[Texto 2] Estudiante B (nota = 3.7) Se esfuerza menos que la mayoría. Décimas a regalar:

Mean (sd) : 1.5 (0.9)
min ≤ med ≤ max:
0 ≤ 2 ≤ 5
IQR (CV) : 1 (0.6)

0	:	512	(	19.0%	)
1	:	596	(	22.1%	)
2	:	1428	(	53.0%	)
3	:	124	(	4.6%	)
4	:	20	(	0.7%	)
5	:	16	(	0.6%	)

2696 (100.0%)

0 (0.0%)

exp_t3_a [numeric]

[Texto 3] Estudiante A (nota = 3.7) Se esfuerza más que la mayoría - Su casa es pequeña, no tiene un espacio cómodo para estudiar. Décimas a regalar:

Mean (sd) : 3.7 (1.1)
min ≤ med ≤ max:
0 ≤ 4 ≤ 5
IQR (CV) : 2 (0.3)

0	:	56	(	2.1%	)
1	:	4	(	0.1%	)
2	:	108	(	4.0%	)
3	:	1140	(	42.3%	)
4	:	616	(	22.8%	)
5	:	772	(	28.6%	)

2696 (100.0%)

0 (0.0%)

exp_t3_b [numeric]

[Texto 3] Estudiante B (nota = 3.7) Se esfuerza menos que la mayoría - Su casa es grande, tiene un espacio cómodo para estudiar. Décimas a regalar:

Mean (sd) : 1.2 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 5
IQR (CV) : 2 (0.8)

0	:	804	(	29.8%	)
1	:	616	(	22.8%	)
2	:	1140	(	42.3%	)
3	:	108	(	4.0%	)
4	:	4	(	0.1%	)
5	:	24	(	0.9%	)

2696 (100.0%)

0 (0.0%)

id_estudiante [numeric]

Identificador único estudiante

Mean (sd) : 208376105 (1070076)
min ≤ med ≤ max:
207508736 ≤ 207885689 ≤ 211723557
IQR (CV) : 1521691 (0)

674 distinct values

2696 (100.0%)

0 (0.0%)

exp_t1_a [numeric]

[Texto 1] Estudiante A (nota = 3.7). Décimas a regalar:

Mean (sd) : 2.7 (1)
min ≤ med ≤ max:
0 ≤ 3 ≤ 5
IQR (CV) : 1 (0.4)

0	:	172	(	6.4%	)
1	:	12	(	0.4%	)
2	:	712	(	26.4%	)
3	:	1600	(	59.3%	)
4	:	76	(	2.8%	)
5	:	124	(	4.6%	)

2696 (100.0%)

0 (0.0%)

exp_t1_b [numeric]

[Texto 1] Estudiante B (nota = 3.7). Décimas a regalar:

Mean (sd) : 2.1 (0.9)
min ≤ med ≤ max:
0 ≤ 2 ≤ 5
IQR (CV) : 1 (0.4)

0	:	272	(	10.1%	)
1	:	76	(	2.8%	)
2	:	1600	(	59.3%	)
3	:	712	(	26.4%	)
4	:	12	(	0.4%	)
5	:	24	(	0.9%	)

2696 (100.0%)

0 (0.0%)

dependencia [factor]

1. Colegio Particular Subven
2. Colegio Municipal

2568	(	95.3%	)
128	(	4.7%	)

2696 (100.0%)

0 (0.0%)

dependencia_rec [numeric]

Min : 0
Mean : 0
Max : 1

0	:	2568	(	95.3%	)
1	:	128	(	4.7%	)

2696 (100.0%)

0 (0.0%)

educ_max [factor]

1. Enseñanza media o menos
2. Estudios superiores

732	(	36.0%	)
1300	(	64.0%	)

2032 (75.4%)

664 (24.6%)

educ_max_rec [numeric]

Min : 0
Mean : 0.6
Max : 1

0	:	732	(	36.0%	)
1	:	1300	(	64.0%	)

2032 (75.4%)

664 (24.6%)

libros_rec [numeric]

Min : 0
Mean : 0.2
Max : 1

0	:	2268	(	84.2%	)
1	:	424	(	15.8%	)

2692 (99.9%)

4 (0.1%)

genero_rec [numeric]

Min : 0
Mean : 0.5
Max : 1

0	:	1372	(	52.6%	)
1	:	1236	(	47.4%	)

2608 (96.7%)

88 (3.3%)

curso_rec [numeric]

Min : 0
Mean : 0.5
Max : 1

0	:	1308	(	48.5%	)
1	:	1388	(	51.5%	)

2696 (100.0%)

0 (0.0%)

justicia_nota [numeric]

Mean (sd) : 0.8 (0.4)
min ≤ med ≤ max:
-0.3 ≤ 1 ≤ 1.9
IQR (CV) : 0 (0.5)

79 distinct values

2692 (99.9%)

4 (0.1%)

variable [character]

1. exp_t4_a
2. exp_t4_b
3. exp_t4_c
4. exp_t4_d

674	(	25.0%	)
674	(	25.0%	)
674	(	25.0%	)
674	(	25.0%	)

2696 (100.0%)

0 (0.0%)

decimas_asignadas [numeric]

Mean (sd) : 2.5 (1.5)
min ≤ med ≤ max:
0 ≤ 3 ≤ 10
IQR (CV) : 1 (0.6)

0	:	304	(	11.3%	)
1	:	361	(	13.4%	)
2	:	654	(	24.3%	)
3	:	864	(	32.0%	)
4	:	308	(	11.4%	)
5	:	169	(	6.3%	)
6	:	5	(	0.2%	)
7	:	12	(	0.4%	)
8	:	1	(	0.0%	)
10	:	18	(	0.7%	)

2696 (100.0%)

0 (0.0%)

esfuerzo [character]

1. Se esfuerza más que la ma
2. Se esfuerza menos que la

1348	(	50.0%	)
1348	(	50.0%	)

2696 (100.0%)

0 (0.0%)

contexto_hogar [character]

1. Su casa es grande, tiene
2. Su casa es pequeña, no ti

1348	(	50.0%	)
1348	(	50.0%	)

2696 (100.0%)

0 (0.0%)

total_decimas [numeric]

Min : 0
Mean : 9.9
Max : 10

0	:	40	(	1.5%	)
10	:	2656	(	98.5%	)

2696 (100.0%)

0 (0.0%)

prop_decimas [numeric]

Mean (sd) : 0.2 (0.1)
min ≤ med ≤ max:
0 ≤ 0.3 ≤ 1
IQR (CV) : 0.1 (0.6)

10 distinct values

2656 (98.5%)

40 (1.5%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2025-06-08

Parte 1

Se trabaja con un primer subset denominado datos_viñetas_1, el cual contiene solo las décimas asignadas del experimento 1.

Error : Can't find datos_vi

Data Frame Summary

Dimensions: 1348 x 1
Duplicates: 1342

decimas_asignadas [numeric]

Mean (sd) : 2.4 (1)
min ≤ med ≤ max:
0 ≤ 2 ≤ 5
IQR (CV) : 1 (0.4)

0	:	111	(	8.2%	)
1	:	22	(	1.6%	)
2	:	578	(	42.9%	)
3	:	578	(	42.9%	)
4	:	22	(	1.6%	)
5	:	37	(	2.7%	)

1348 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2025-06-08

Correlaciones

	genero	curso	merit_esfuerzo	merit_talento	school_esfuerzo	school_talento	school_merecimiento	decimas_asignadas	justicia_nota	nota
genero
curso	-0.003
merit_esfuerzo	0.007	-0.208
merit_talento	-0.046	-0.133	0.501
school_esfuerzo	-0.042	-0.039	0.238	0.109
school_talento	-0.142	0.049	0.096	0.107	0.216
school_merecimiento	-0.092	-0.039	0.086	0.183	0.283	0.196
decimas_asignadas	-0.022	0.013	-0.018	0.024	0.033	0.037	0.007
justicia_nota	0.025	-0.049	0.097	0.124	0.131	0.037	0.103	0.003
nota	0.053	-0.041	0.045	0.090	0.141	0.018	0.019	0.043	0.105
Computed correlation used pearson-method with listwise-deletion.

Modelos simples

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.39^***	2.35^***	2.38^***	2.36^***	2.23^***
	(0.04)	(0.04)	(0.03)	(0.09)	(0.10)
generoMujer	-0.04
	(0.05)
cursoMedia		0.02
		(0.05)
libros_hogarMás de 25 libros			-0.07
			(0.07)
merit_esfuerzo				0.00
				(0.03)
merit_talento					0.05
					(0.03)
AIC	3631.92	3781.03	3764.37	3782.15	3760.66
BIC	3652.61	3801.85	3785.19	3802.98	3781.47
Log Likelihood	-1811.96	-1886.51	-1878.18	-1887.08	-1876.33
Num. obs.	1304	1348	1346	1348	1344
Num. groups: id_estudiante	652	674	673	674	672
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	0.94	0.96	0.95	0.96	0.95
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.24^***	2.17^***	2.31^***	1.87^***	2.38^***
	(0.11)	(0.13)	(0.11)	(0.27)	(0.06)
school_esfuerzo	0.04
	(0.03)
school_talento		0.06
		(0.04)
school_merecimiento			0.02
			(0.04)
nota				0.08
				(0.04)
justicia_nota					-0.01
					(0.06)
AIC	3765.21	3779.23	3762.21	3778.06	3765.44
BIC	3786.03	3800.05	3783.02	3798.89	3786.26
Log Likelihood	-1878.61	-1885.61	-1877.10	-1885.03	-1878.72
Num. obs.	1346	1348	1344	1348	1346
Num. groups: id_estudiante	673	674	672	674	673
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	0.95	0.95	0.95	0.95	0.95
^*p < 0.001; ^p < 0.01; ^*p < 0.05

ICC

Linear mixed model fit by REML ['lmerMod']
Formula: decimas_asignadas ~ 1 + (1 | id_estudiante)
   Data: datos_viñetas_1

REML criterion at convergence: 3769.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4166 -0.3710 -0.3710  0.6518  2.6974 

Random effects:
 Groups        Name        Variance Std.Dev.
 id_estudiante (Intercept) 0.0000   0.0000  
 Residual                  0.9559   0.9777  
Number of obs: 1348, groups:  id_estudiante, 674

Fixed effects:
            Estimate Std. Error t value
(Intercept)  2.36276    0.02663   88.73
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

[1] 0

Modelos multinivel

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	2.66^***	2.67^***	2.45^***	2.11^***	2.45^***	2.11^***
	(0.04)	(0.05)	(0.19)	(0.30)	(0.19)	(0.30)
descripcion_viñetaEstudiante B	-0.59^***	-0.58^***	-0.59^***	-0.59^***	-0.59^***	-0.59^***
	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
generoMujer		-0.04	-0.03	-0.03	-0.03	-0.03
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
cursoMedia		0.02	0.02	0.02	0.02	0.02
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
libros_hogarMás de 25 libros		-0.00	-0.01	-0.02	-0.01	-0.02
		(0.07)	(0.07)	(0.07)	(0.07)	(0.07)
merit_esfuerzo			-0.06	-0.05	-0.06	-0.05
			(0.04)	(0.04)	(0.04)	(0.04)
merit_talento			0.05	0.05	0.06	0.05
			(0.04)	(0.04)	(0.04)	(0.04)
school_esfuerzo			0.05	0.04	0.05	0.04
			(0.04)	(0.04)	(0.04)	(0.04)
school_talento			0.04	0.04	0.04	0.04
			(0.04)	(0.04)	(0.04)	(0.04)
school_merecimiento			-0.02	-0.02	-0.02	-0.02
			(0.04)	(0.04)	(0.04)	(0.04)
nota				0.06		0.06
				(0.04)		(0.04)
justicia_nota					-0.00	-0.01
					(0.06)	(0.06)
AIC	3654.33	3524.93	3527.91	3532.28	3533.67	3538.01
BIC	3675.16	3561.14	3589.94	3599.47	3600.86	3610.37
Log Likelihood	-1823.17	-1755.47	-1751.96	-1753.14	-1753.83	-1755.00
Num. obs.	1348	1304	1298	1298	1298	1298
Num. groups: id_estudiante	674	652	649	649	649	649
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00	0.00
Var: Residual	0.87	0.85	0.85	0.85	0.85	0.85
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Parte 2

Se trabaja con un primer subset denominado datos_viñetas_2, el cual contiene solo las décimas asignadas del experimento 2.

Error : Can't find datos_vi

Data Frame Summary

Dimensions: 1348 x 1
Duplicates: 1342

decimas_asignadas [numeric]

Mean (sd) : 2.4 (1.4)
min ≤ med ≤ max:
0 ≤ 2 ≤ 5
IQR (CV) : 1 (0.6)

0	:	146	(	10.8%	)
1	:	154	(	11.4%	)
2	:	388	(	28.8%	)
3	:	388	(	28.8%	)
4	:	154	(	11.4%	)
5	:	118	(	8.8%	)

1348 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2025-06-08

Correlaciones

	genero	curso	merit_esfuerzo	merit_talento	school_esfuerzo	school_talento	school_merecimiento	decimas_asignadas	justicia_nota	nota
genero
curso	-0.003
merit_esfuerzo	0.007	-0.208
merit_talento	-0.046	-0.133	0.501
school_esfuerzo	-0.042	-0.039	0.238	0.109
school_talento	-0.142	0.049	0.096	0.107	0.216
school_merecimiento	-0.092	-0.039	0.086	0.183	0.283	0.196
decimas_asignadas	0.004	0.001	-0.010	-0.006	0.009	0.019	0.009
justicia_nota	0.025	-0.049	0.097	0.124	0.131	0.037	0.103	0.001
nota	0.053	-0.041	0.045	0.090	0.141	0.018	0.019	0.023	0.105
Computed correlation used pearson-method with listwise-deletion.

Modelos simples

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.45^***	2.45^***	2.46^***	2.44^***	2.44^***
	(0.05)	(0.05)	(0.04)	(0.12)	(0.14)
generoMujer	0.02
	(0.08)
cursoMedia		-0.01
		(0.07)
libros_hogarMás de 25 libros			-0.05
			(0.10)
merit_esfuerzo				0.00
				(0.04)
merit_talento					0.00
					(0.05)
AIC	4550.39	4692.01	4685.95	4693.07	4676.38
BIC	4571.08	4712.83	4706.77	4713.90	4697.19
Log Likelihood	-2271.20	-2342.00	-2338.97	-2342.54	-2334.19
Num. obs.	1304	1348	1346	1348	1344
Num. groups: id_estudiante	652	674	673	674	672
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	1.90	1.88	1.88	1.88	1.88
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.39^***	2.29^***	2.37^***	2.10^***	2.46^***
	(0.15)	(0.18)	(0.15)	(0.37)	(0.08)
school_esfuerzo	0.02
	(0.05)
school_talento		0.05
		(0.05)
school_merecimiento			0.03
			(0.05)
nota				0.06
				(0.06)
justicia_nota					-0.01
					(0.09)
AIC	4687.58	4691.86	4679.99	4691.54	4686.48
BIC	4708.40	4712.69	4700.80	4712.37	4707.30
Log Likelihood	-2339.79	-2341.93	-2335.99	-2341.77	-2339.24
Num. obs.	1346	1348	1344	1348	1346
Num. groups: id_estudiante	673	674	672	674	673
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	1.88	1.88	1.88	1.88	1.88
^*p < 0.001; ^p < 0.01; ^*p < 0.05

ICC

Linear mixed model fit by REML ['lmerMod']
Formula: decimas_asignadas ~ 1 + (1 | id_estudiante)
   Data: datos_viñetas_2

REML criterion at convergence: 4680.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.7851 -0.3267 -0.3267  0.4025  1.8608 

Random effects:
 Groups        Name        Variance Std.Dev.
 id_estudiante (Intercept) 0.000    0.000   
 Residual                  1.881    1.371   
Number of obs: 1348, groups:  id_estudiante, 674

Fixed effects:
            Estimate Std. Error t value
(Intercept)  2.44807    0.03735   65.54
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

[1] 0

Modelos multinivel

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	3.42^***	3.44^***	3.33^***	3.04^***	3.33^***	3.04^***
	(0.04)	(0.05)	(0.19)	(0.32)	(0.19)	(0.32)
descripcion_viñetaSe esfuerza menos que la mayoría	-1.94^***	-1.98^***	-1.98^***	-1.98^***	-1.98^***	-1.98^***
	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
generoMujer		0.02	0.02	0.02	0.02	0.02
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
cursoMedia		-0.00	-0.01	-0.00	-0.01	-0.00
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
libros_hogarMás de 25 libros		0.02	0.01	0.00	0.01	0.00
		(0.07)	(0.07)	(0.08)	(0.07)	(0.08)
merit_esfuerzo			-0.02	-0.02	-0.02	-0.02
			(0.04)	(0.04)	(0.04)	(0.04)
merit_talento			-0.01	-0.01	-0.01	-0.01
			(0.04)	(0.04)	(0.04)	(0.04)
school_esfuerzo			0.01	0.00	0.01	0.01
			(0.04)	(0.04)	(0.04)	(0.04)
school_talento			0.04	0.04	0.04	0.04
			(0.04)	(0.04)	(0.04)	(0.04)
school_merecimiento			0.01	0.01	0.01	0.01
			(0.04)	(0.04)	(0.04)	(0.04)
nota				0.05		0.05
				(0.05)		(0.05)
justicia_nota					0.00	-0.00
					(0.06)	(0.06)
AIC	3756.39	3624.17	3625.04	3630.05	3630.72	3635.72
BIC	3777.22	3660.38	3687.06	3697.25	3697.91	3708.08
Log Likelihood	-1874.20	-1805.08	-1800.52	-1802.03	-1802.36	-1803.86
Num. obs.	1348	1304	1298	1298	1298	1298
Num. groups: id_estudiante	674	652	649	649	649	649
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00	0.00
Var: Residual	0.94	0.92	0.91	0.91	0.91	0.91
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Parte 3

Se trabaja con un primer subset denominado datos_viñetas_3, el cual contiene solo las décimas asignadas del experimento 3.

Error : Can't find datos_vi

Data Frame Summary

Dimensions: 1348 x 1
Duplicates: 1342

decimas_asignadas [numeric]

Mean (sd) : 2.5 (1.6)
min ≤ med ≤ max:
0 ≤ 2 ≤ 5
IQR (CV) : 3 (0.6)

0	:	215	(	15.9%	)
1	:	155	(	11.5%	)
2	:	312	(	23.1%	)
3	:	312	(	23.1%	)
4	:	155	(	11.5%	)
5	:	199	(	14.8%	)

1348 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2025-06-08

Correlaciones

	genero	curso	merit_esfuerzo	merit_talento	school_esfuerzo	school_talento	school_merecimiento	decimas_asignadas	justicia_nota	nota
genero
curso	-0.003
merit_esfuerzo	0.007	-0.208
merit_talento	-0.046	-0.133	0.501
school_esfuerzo	-0.042	-0.039	0.238	0.109
school_talento	-0.142	0.049	0.096	0.107	0.216
school_merecimiento	-0.092	-0.039	0.086	0.183	0.283	0.196
decimas_asignadas	0.011	0.003	-0.000	-0.013	0.003	0.015	-0.001
justicia_nota	0.025	-0.049	0.097	0.124	0.131	0.037	0.103	-0.002
nota	0.053	-0.041	0.045	0.090	0.141	0.018	0.019	0.007	0.105
Computed correlation used pearson-method with listwise-deletion.

Modelos simples

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.46^***	2.47^***	2.47^***	2.46^***	2.55^***
	(0.06)	(0.06)	(0.05)	(0.15)	(0.16)
generoMujer	0.04
	(0.09)
cursoMedia		0.00
		(0.09)
libros_hogarMás de 25 libros			-0.02
			(0.12)
merit_esfuerzo				0.00
				(0.05)
merit_talento					-0.03
					(0.06)
AIC	4934.88	5103.73	5095.75	5104.77	5092.88
BIC	4955.57	5124.56	5116.57	5125.60	5113.70
Log Likelihood	-2463.44	-2547.87	-2543.88	-2548.39	-2542.44
Num. obs.	1304	1348	1346	1348	1344
Num. groups: id_estudiante	652	674	673	674	672
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	2.55	2.56	2.56	2.56	2.56
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.45^***	2.31^***	2.44^***	2.31^***	2.48^***
	(0.18)	(0.20)	(0.18)	(0.44)	(0.09)
school_esfuerzo	0.01
	(0.06)
school_talento		0.05
		(0.06)
school_merecimiento			0.01
			(0.06)
nota				0.03
				(0.07)
justicia_nota					-0.01
					(0.10)
AIC	5097.30	5103.76	5086.74	5103.95	5096.09
BIC	5118.12	5124.59	5107.55	5124.78	5116.91
Log Likelihood	-2544.65	-2547.88	-2539.37	-2547.98	-2544.05
Num. obs.	1346	1348	1344	1348	1346
Num. groups: id_estudiante	673	674	672	674	673
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	2.56	2.55	2.55	2.56	2.56
^*p < 0.001; ^p < 0.01; ^*p < 0.05

ICC

Modelos multinivel

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

boundary (singular) fit: see help('isSingular')

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	1.24^***	1.23^***	1.16^***	1.06^**	1.17^***	1.06^**
	(0.04)	(0.06)	(0.21)	(0.34)	(0.21)	(0.34)
esfuerzoSe esfuerza más que la mayoría	2.45^***	2.45^***	2.45^***	2.45^***	2.45^***	2.45^***
	(0.06)	(0.06)	(0.06)	(0.06)	(0.06)	(0.06)
generoMujer		0.04	0.04	0.04	0.04	0.04
		(0.06)	(0.06)	(0.06)	(0.06)	(0.06)
cursoMedia		0.01	0.00	0.01	0.00	0.01
		(0.06)	(0.06)	(0.06)	(0.06)	(0.06)
libros_hogarMás de 25 libros		-0.00	-0.00	-0.01	-0.00	-0.01
		(0.08)	(0.08)	(0.08)	(0.08)	(0.08)
merit_esfuerzo			0.01	0.01	0.01	0.01
			(0.04)	(0.04)	(0.04)	(0.04)
merit_talento			-0.04	-0.04	-0.03	-0.04
			(0.04)	(0.04)	(0.04)	(0.04)
school_esfuerzo			0.00	-0.00	0.00	-0.00
			(0.04)	(0.04)	(0.04)	(0.04)
school_talento			0.04	0.04	0.04	0.04
			(0.04)	(0.04)	(0.04)	(0.04)
school_merecimiento			-0.00	-0.00	-0.00	-0.00
			(0.04)	(0.04)	(0.04)	(0.04)
nota				0.02		0.02
				(0.05)		(0.05)
justicia_nota					-0.00	-0.01
					(0.07)	(0.07)
AIC	3908.34	3794.20	3809.26	3815.31	3814.79	3820.83
BIC	3929.17	3830.41	3871.28	3882.50	3881.98	3893.19
Log Likelihood	-1950.17	-1890.10	-1892.63	-1894.66	-1894.39	-1896.42
Num. obs.	1348	1304	1298	1298	1298	1298
Num. groups: id_estudiante	674	652	649	649	649	649
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00	0.00
Var: Residual	1.05	1.05	1.05	1.05	1.05	1.06
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Experimento

Error : Can't find datos_vi

Data Frame Summary

Dimensions: 2696 x 1
Duplicates: 2686

decimas_asignadas [numeric]

Mean (sd) : 2.5 (1.5)
min ≤ med ≤ max:
0 ≤ 3 ≤ 10
IQR (CV) : 1 (0.6)

0	:	304	(	11.3%	)
1	:	361	(	13.4%	)
2	:	654	(	24.3%	)
3	:	864	(	32.0%	)
4	:	308	(	11.4%	)
5	:	169	(	6.3%	)
6	:	5	(	0.2%	)
7	:	12	(	0.4%	)
8	:	1	(	0.0%	)
10	:	18	(	0.7%	)

2696 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2025-06-08

Correlaciones

	genero	curso	merit_esfuerzo	merit_talento	school_esfuerzo	school_talento	school_merecimiento	decimas_asignadas	justicia_nota	nota
genero
curso	-0.003
merit_esfuerzo	0.007	-0.208
merit_talento	-0.046	-0.133	0.501
school_esfuerzo	-0.042	-0.039	0.238	0.109
school_talento	-0.142	0.049	0.096	0.107	0.216
school_merecimiento	-0.092	-0.039	0.086	0.183	0.283	0.196
decimas_asignadas	0.004	0.011	0.002	0.009	0.007	0.001	0.009
justicia_nota	0.025	-0.049	0.097	0.124	0.131	0.037	0.103	0.004
nota	0.053	-0.041	0.045	0.090	0.141	0.018	0.019	0.017	0.105
Computed correlation used pearson-method with listwise-deletion.

Modelos simples

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.46^***	2.45^***	2.47^***	2.45^***	2.42^***
	(0.04)	(0.04)	(0.03)	(0.10)	(0.11)
generoMujer	0.01
	(0.06)
cursoMedia		0.03
		(0.06)
libros_hogarMás de 25 libros			-0.07
			(0.08)
merit_esfuerzo				0.00
				(0.03)
merit_talento					0.01
					(0.04)
AIC	9583.91	9906.03	9890.38	9907.35	9884.84
BIC	9607.37	9929.63	9913.97	9930.95	9908.43
Log Likelihood	-4787.95	-4949.01	-4941.19	-4949.68	-4938.42
Num. obs.	2608	2696	2692	2696	2688
Num. groups: id_estudiante	652	674	673	674	672
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	2.30	2.30	2.30	2.30	2.30
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5
(Intercept)	2.44^***	2.42^***	2.38^***	2.16^***	2.46^***
	(0.12)	(0.14)	(0.12)	(0.29)	(0.06)
school_esfuerzo	0.01
	(0.04)
school_talento		0.01
		(0.04)
school_merecimiento			0.03
			(0.04)
nota				0.05
				(0.05)
justicia_nota					0.00
					(0.07)
AIC	9892.57	9906.88	9877.37	9905.59	9891.41
BIC	9916.17	9930.47	9900.96	9929.19	9915.00
Log Likelihood	-4942.29	-4949.44	-4934.69	-4948.80	-4941.71
Num. obs.	2692	2696	2688	2696	2692
Num. groups: id_estudiante	673	674	672	674	673
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00
Var: Residual	2.30	2.30	2.30	2.29	2.30
^*p < 0.001; ^p < 0.01; ^*p < 0.05

ICC

results_0 = lmer(decimas_asignadas ~ 1 + (1 | id_estudiante), data = datos_viñetas)

boundary (singular) fit: see help('isSingular')

summary(results_0)

Linear mixed model fit by REML ['lmerMod']
Formula: decimas_asignadas ~ 1 + (1 | id_estudiante)
   Data: datos_viñetas

REML criterion at convergence: 9894.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6259 -0.3056  0.3546  0.3546  4.9755 

Random effects:
 Groups        Name        Variance Std.Dev.
 id_estudiante (Intercept) 0.000    0.000   
 Residual                  2.295    1.515   
Number of obs: 2696, groups:  id_estudiante, 674

Fixed effects:
            Estimate Std. Error t value
(Intercept)  2.46291    0.02917   84.42
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

reghelper::ICC(results_0)

[1] 0

Modelos multinivel


Se esfuerza menos que la mayoría   Se esfuerza más que la mayoría 
                            1348                             1348

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Statistical models
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	1.29^***	1.27^***	1.15^***	0.91^**	1.15^***	0.91^**
	(0.04)	(0.05)	(0.18)	(0.29)	(0.18)	(0.29)
esfuerzoSe esfuerza más que la mayoría	1.63^***	1.66^***	1.66^***	1.66^***	1.66^***	1.66^***
	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
contexto_hogarSu casa es pequeña, no tiene un espacio cómodo para estudiar	0.71^***	0.71^***	0.71^***	0.71^***	0.71^***	0.71^***
	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
generoMujer		0.01	0.02	0.02	0.02	0.02
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
cursoMedia		0.04	0.04	0.04	0.04	0.04
		(0.05)	(0.05)	(0.05)	(0.05)	(0.05)
libros_hogarMás de 25 libros		-0.06	-0.06	-0.07	-0.06	-0.07
		(0.07)	(0.07)	(0.07)	(0.07)	(0.07)
merit_esfuerzo			-0.01	-0.00	-0.01	-0.00
			(0.03)	(0.03)	(0.03)	(0.03)
merit_talento			0.02	0.02	0.02	0.02
			(0.04)	(0.04)	(0.04)	(0.04)
school_esfuerzo			0.01	0.01	0.01	0.01
			(0.03)	(0.03)	(0.03)	(0.03)
school_talento			-0.00	-0.00	-0.00	-0.00
			(0.04)	(0.04)	(0.04)	(0.04)
school_merecimiento			0.01	0.02	0.01	0.02
			(0.03)	(0.03)	(0.03)	(0.03)
nota				0.04		0.04
				(0.04)		(0.04)
justicia_nota					0.01	0.00
					(0.06)	(0.06)
AIC	8774.15	8462.77	8465.46	8470.87	8471.33	8476.74
BIC	8803.64	8509.70	8541.67	8552.93	8553.39	8564.67
Log Likelihood	-4382.07	-4223.38	-4219.73	-4221.43	-4221.66	-4223.37
Num. obs.	2696	2608	2596	2596	2596	2596
Num. groups: id_estudiante	674	652	649	649	649	649
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00	0.00	0.00
Var: Residual	1.50	1.48	1.49	1.49	1.49	1.49
^*p < 0.001; ^p < 0.01; ^*p < 0.05

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Statistical models
	Model 1	Model 2	Model 3
(Intercept)	0.13^***	0.13^***	0.13^***
	(0.00)	(0.01)	(0.02)
esfuerzoSe esfuerza más que la mayoría	0.17^***	0.17^***	0.17^***
	(0.00)	(0.00)	(0.00)
contexto_hogarSu casa es pequeña, no tiene un espacio cómodo para estudiar	0.07^***	0.07^***	0.07^***
	(0.00)	(0.00)	(0.00)
generoMujer		-0.00	0.00
		(0.00)	(0.00)
cursoMedia		0.00	0.00
		(0.00)	(0.00)
libros_hogarMás de 25 libros		-0.00	-0.00
		(0.01)	(0.01)
merit_esfuerzo			-0.00
			(0.00)
merit_talento			0.00
			(0.00)
school_esfuerzo			0.00
			(0.00)
school_talento			0.00
			(0.00)
school_merecimiento			-0.00
			(0.00)
AIC	-3722.37	-3596.63	-3514.39
BIC	-3692.95	-3549.80	-3438.35
Log Likelihood	1866.19	1806.31	1770.19
Num. obs.	2656	2576	2564
Num. groups: id_estudiante	664	644	641
Var: id_estudiante (Intercept)	0.00	0.00	0.00
Var: Residual	0.01	0.01	0.01
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Pendiente aleatoria

boundary (singular) fit: see help('isSingular')

boundary (singular) fit: see help('isSingular')

boundary (singular) fit: see help('isSingular')

boundary (singular) fit: see help('isSingular')

boundary (singular) fit: see help('isSingular')

boundary (singular) fit: see help('isSingular')

Modelos con todas las fases del experimento

Statistical models
	Exp 1	Exp 2	Exp 3	Exp 4
Intercepto	2.11^***	3.04^***	1.06^**	0.91^**
	(0.30)	(0.32)	(0.34)	(0.29)
Viñeta exp 1	-0.59^***
	(0.05)
Mujer	-0.03	0.02	0.04	0.02
	(0.05)	(0.05)	(0.06)	(0.05)
Media	0.02	-0.00	0.01	0.04
	(0.05)	(0.05)	(0.06)	(0.05)
Más de 25 libros	-0.02	0.00	-0.01	-0.07
	(0.07)	(0.08)	(0.08)	(0.07)
Esfuerzo social	-0.05	-0.02	0.01	-0.00
	(0.04)	(0.04)	(0.04)	(0.03)
Talento social	0.05	-0.01	-0.04	0.02
	(0.04)	(0.04)	(0.04)	(0.04)
Esfuerzo escuela	0.04	0.01	-0.00	0.01
	(0.04)	(0.04)	(0.04)	(0.03)
Talento escuela	0.04	0.04	0.04	-0.00
	(0.04)	(0.04)	(0.04)	(0.04)
Merecimiento escuela	-0.02	0.01	-0.00	0.02
	(0.04)	(0.04)	(0.04)	(0.03)
Rendimiento	0.06	0.05	0.02	0.04
	(0.04)	(0.05)	(0.05)	(0.04)
Justicia	-0.01	-0.00	-0.01	0.00
	(0.06)	(0.06)	(0.07)	(0.06)
Viñeta exp 2		-1.98^***
		(0.05)
Se esfuerza más			2.45^***	1.66^***
			(0.06)	(0.05)
Casa pequeña				0.71^***
				(0.05)
AIC	3538.01	3635.72	3820.83	8476.74
BIC	3610.37	3708.08	3893.19	8564.67
Log Likelihood	-1755.00	-1803.86	-1896.42	-4223.37
Num. obs.	1298	1298	1298	2596
Num. groups: id_estudiante	649	649	649	649
Var: id_estudiante (Intercept)	0.00	0.00	0.00	0.00
Var: Residual	0.85	0.91	1.06	1.49
^*p < 0.001; ^p < 0.01; ^*p < 0.05