8 Sesgo de Deseabilidad Social
Las medidas de autoinforme son centrales en la investigación en psicología, educación, salud y ciencias sociales. Son eficientes y, con frecuencia, permiten acceder a conductas, evaluaciones, sentimientos y experiencias que no son directamente observables (Lange & Dewitte, 2019; Peterson & Kerin, 1981). Al mismo tiempo, las respuestas a los ítems de autoinforme pueden reflejar más que el constructo que el investigador pretende medir.
Entre estas influencias adicionales se encuentran los estilos de respuesta y los sesgos de respuesta. La deseabilidad social, la aquiescencia y la respuesta extrema son ejemplos frecuentes. Estas influencias importan porque pueden modificar las medias de los ítems, las asociaciones entre ítems, las estructuras factoriales y las relaciones con variables externas.
NotaMapa del capítulo
Este capítulo distingue la manipulación intencional de respuestas (faking) de la respuesta socialmente deseable, examina cómo la deseabilidad social puede incorporarse a un modelo psicométrico y revisa varias estrategias para controlarla:
- escalas de deseabilidad social;
- modelos factoriales de sesgo de respuesta;
- modelos de mezcla factorial;
- modelos de factor de método común; y
- cuádruples equilibrados al estilo de Peabody y modelado MIMIC.
La sección final implementa dos de estos enfoques en R.
8.1 Faking: Contexto, Motivación y Oportunidad
El faking se entiende mejor como una conducta de respuesta dependiente del contexto. Una persona modifica deliberadamente sus respuestas para producir una autopresentación que sirve a algún objetivo (Ziegler et al., 2011). Por tanto, la conducta no está determinada solamente por la persona ni solamente por el cuestionario. Surge de una interacción entre características individuales, demandas situacionales y características de la propia evaluación.
Suelen distinguirse dos formas generales:
| Forma | Propósito general |
|---|---|
| Faking good | Presentarse de manera más favorable de lo que ocurriría en condiciones ordinarias de respuesta |
| Faking bad | Fabricar o exagerar características o síntomas indeseables para obtener un resultado deseado |
El faking good ha sido ampliamente estudiado en contextos de personalidad y selección (Zickar & Robie, 1999). El faking bad es especialmente relevante cuando la persona puede beneficiarse de parecer más afectada o sintomática (Ziegler et al., 2011).
8.1.1 ¿Por qué una persona manipularía sus respuestas?
La explicación basada en expectativas resumida por Ziegler et al. (2011) destaca tres tipos de creencias:
- ¿Puedo hacerlo? — la persona cree que es posible modificar la impresión pretendida;
- ¿Tendrá algún efecto? — cree que las respuestas alteradas pueden afectar el resultado; y
- ¿Valoro ese resultado? — el beneficio resultante es suficientemente importante.
Estas creencias pueden estar influidas por la personalidad, la habilidad cognitiva, el conocimiento del constructo evaluado, la transparencia de los ítems, la experiencia previa con tests y características de la situación de evaluación (McFarland & Ryan, 2000; Raymark & Tafero, 2009; Riggio et al., 1988; Snell et al., 1999).
ImportanteFaking no es lo mismo que deseabilidad social
Faking se refiere a un proceso intencional de respuesta en un contexto particular. La deseabilidad social es más amplia: se refiere a la tendencia de las respuestas a estar relacionadas con la evaluación social del contenido. Por tanto, una puntuación alta en una escala de deseabilidad social no debe interpretarse automáticamente como prueba de que una persona mintió.
8.6 Cuádruples de Peabody: Separar Contenido Descriptivo y Evaluativo
Una estrategia diferente aborda la deseabilidad social durante la construcción de ítems. Peabody (1967) distinguió el contenido descriptivo de un término de rasgo de su deseabilidad evaluativa. Al cruzar contenido descriptivo bajo frente a alto con contenido evaluativo bajo frente a alto, pueden construirse cuádruples equilibrados.
| Baja deseabilidad | Alta deseabilidad | |
|---|---|---|
| Polo descriptivo bajo | Retraído | Introspectivo |
| Polo descriptivo alto | Hablador | Comunicativo |
El diseño también puede ayudar a separar aquiescencia porque los polos descriptivos positivo y negativo se representan dentro del mismo conjunto equilibrado (Mirowsky & Ross, 1991).
Para un cuádruple de cuatro ítems, una representación simplificada de dos factores es
\[ \begin{bmatrix} x_1\\ x_2\\ x_3\\ x_4 \end{bmatrix} = \begin{bmatrix} -\lambda_{1c} & +\lambda_{1d}\\ -\lambda_{2c} & -\lambda_{2d}\\ +\lambda_{3c} & +\lambda_{3d}\\ +\lambda_{4c} & -\lambda_{4d} \end{bmatrix} \begin{bmatrix} \eta_c\\ \eta_d \end{bmatrix} + \begin{bmatrix} \epsilon_1\\ \epsilon_2\\ \epsilon_3\\ \epsilon_4 \end{bmatrix}, \]
donde \(\eta_c\) representa contenido descriptivo y \(\eta_d\) representa deseabilidad evaluativa. Enfoques relacionados basados en adjetivos han usado esta lógica para distinguir varianza evaluativa y descriptiva de personalidad (Pettersson et al., 2012, 2014; Saucier et al., 2001).
8.6.1 Extender el factor de deseabilidad con un modelo MIMIC
Degobi & Valentini (2023) evaluó si un factor de deseabilidad identificado a partir de cuádruples manipulados podía predecir efectos de deseabilidad en ítems adicionales mediante un modelo de Múltiples Indicadores y Múltiples Causas (MIMIC). Sus estudios de simulación consideraron versiones tipo Likert y de elección forzada y evaluaron la recuperación mediante criterios de sesgo y cobertura.
La idea práctica es:
- identificar el factor de deseabilidad mediante ítems en los que el contenido evaluativo fue manipulado deliberadamente;
- estimar los factores sustantivos de contenido a partir de todos los ítems relevantes; y
- regresar los ítems adicionales que no forman parte de cuádruples sobre el factor de deseabilidad.
Esto evita exigir que cada ítem de una escala se redacte como parte de un cuádruple, aunque el método sigue dependiendo de la calidad y el número de los cuádruples manipulados.
NotaHerramientas de diseño asociadas con el enfoque MIMIC-cuádruple
Se desarrollaron dos herramientas de simulación para planificar estos modelos: quadSimple, destinada a etapas tempranas del diseño cuando se dispone de poca información sobre parámetros, y quadSim, pensada para situaciones en las que los investigadores cuentan con información más detallada sobre los parámetros esperados del modelo.
9 Controlar la Deseabilidad Social en R
9.1 Procedimiento de Ferrando et al. con vampyr
El paquete vampyr implementa procedimientos factoriales para controlar sesgos de respuesta (Navarro-Gonzalez et al., 2021).
La instalación solo es necesaria una vez:
Cargue el paquete e inspeccione su conjunto de datos de ejemplo:
V2 V8 V13 V21
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
Median :4.000 Median :4.000 Median :2.000 Median :3.000
Mean :3.667 Mean :3.263 Mean :2.317 Mean :2.947
3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
V1 V6 V17 V19 V20
Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:1.000
Median :4.000 Median :2.000 Median :4.00 Median :4.000 Median :2.000
Mean :3.643 Mean :2.467 Mean :3.71 Mean :3.493 Mean :1.997
3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:3.000
Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
V25
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.687
3rd Qu.:2.000
Max. :5.000 El ejemplo contiene 10 variables de 300 participantes. Cuatro variables son marcadores de deseabilidad, mientras que las seis restantes representan agresión física. Los ítems de contenido incluyen indicadores codificados tanto positiva como negativamente.
DETAILS OF ANALYSIS
Number of participants : 300
Number of items : 10
Items selected as SD items : 1, 2, 3, 4
Items selected as unbalanced : 0
Dispersion Matrix : Polychoric Correlations
Method for factor extraction : Unweighted Least Squares (ULS)
Rotation Method : none
-----------------------------------------------------------------------
Univariate item descriptives
Item Mean Variance Skewness Kurtosis (Zero centered)
Item 1 3.667 1.260 -0.555 -0.566
Item 2 3.263 1.760 -0.379 -1.005
Item 3 2.317 1.695 0.601 -0.880
Item 4 2.947 1.924 -0.033 -1.284
Item 5 3.643 1.374 -0.565 -0.535
Item 6 2.467 1.802 0.487 -0.967
Item 7 3.710 1.678 -0.652 -0.716
Item 8 3.493 1.629 -0.411 -0.862
Item 9 1.997 1.515 1.041 -0.011
Item 10 1.687 0.925 1.293 0.838
Polychoric correlation is advised when the univariate distributions of ordinal items are
asymmetric or with excess of kurtosis. If both indices are lower than one in absolute value,
then Pearson correlation is advised. You can read more about this subject in:
Muthen, B., & Kaplan D. (1985). A Comparison of Some Methodologies for the Factor Analysis of
Non-Normal Likert Variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
Muthen, B., & Kaplan D. (1992). A Comparison of Some Methodologies for the Factor Analysis of
Non-Normal Likert Variables: A Note on the Size of the Model. British Journal of Mathematical
and Statistical Psychology, 45, 19-30.
-----------------------------------------------------------------------
Adequacy of the dispersion matrix
Determinant of the matrix = 0.047816437916936
Bartlett's statistic = 896.4 (df = 45; P = 0.000000)
Kaiser-Meyer-Olkin (KMO) test = 0.76664 (fair)
-----------------------------------------------------------------------
EXPLORATORY FACTOR ANALYSIS CONTROLLING SOCIAL DESIRABILITY AND ACQUIESCENCE
-----------------------------------------------------------------------
Robust Goodness of Fit statistics
Root Mean Square Error of Approximation (RMSEA) = 0.032
Robust Mean-Scaled Chi Square with 23 degrees of freedom = 30.146
Non-Normed Fit Index (NNFI; Tucker & Lewis) = 0.989
Comparative Fit Index (CFI) = 0.994
Goodness of Fit Index (GFI) = 0.977
-----------------------------------------------------------------------
Root Mean Square Residuals (RMSR) = 0.0452
Expected mean value of RMSR for an acceptable model = 0.0578 (Kelley's criterion)
-----------------------------------------------------------------------
Unrotated loading matrix
Factor SD Factor AC Factor 1
Item 1 0.60256 0.00000 0.00000
Item 2 0.51526 0.00000 0.00000
Item 3 0.72709 0.00000 0.00000
Item 4 0.71129 0.00000 0.00000
Item 5 -0.07851 0.23761 -0.54830
Item 6 0.27519 0.00222 0.49053
Item 7 -0.16412 0.57419 -0.70140
Item 8 -0.14319 0.54062 -0.59105
Item 9 0.26559 0.19603 0.66802
Item 10 0.31732 0.06247 0.68220Aquí, contAC = TRUE solicita el control simultáneo de la aquiescencia. Si la aquiescencia no forma parte del análisis, cámbielo a FALSE.
La salida debe interpretarse en tres niveles:
- la solución de factores de contenido después de considerar los componentes de sesgo de respuesta;
- las cargas asociadas con los componentes de deseabilidad social y aquiescencia; y
- la adecuación e interpretabilidad de la solución factorial resultante.
También pueden solicitarse puntuaciones factoriales:
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DETAILS OF ANALYSIS
Number of participants : 300
Number of items : 10
Items selected as SD items : 1, 2, 3, 4
Items selected as unbalanced : 0
Dispersion Matrix : Polychoric Correlations
Method for factor extraction : Unweighted Least Squares (ULS)
Rotation Method : none
-----------------------------------------------------------------------
Univariate item descriptives
Item Mean Variance Skewness Kurtosis (Zero centered)
Item 1 3.667 1.260 -0.555 -0.566
Item 2 3.263 1.760 -0.379 -1.005
Item 3 2.317 1.695 0.601 -0.880
Item 4 2.947 1.924 -0.033 -1.284
Item 5 3.643 1.374 -0.565 -0.535
Item 6 2.467 1.802 0.487 -0.967
Item 7 3.710 1.678 -0.652 -0.716
Item 8 3.493 1.629 -0.411 -0.862
Item 9 1.997 1.515 1.041 -0.011
Item 10 1.687 0.925 1.293 0.838
Polychoric correlation is advised when the univariate distributions of ordinal items are
asymmetric or with excess of kurtosis. If both indices are lower than one in absolute value,
then Pearson correlation is advised. You can read more about this subject in:
Muthen, B., & Kaplan D. (1985). A Comparison of Some Methodologies for the Factor Analysis of
Non-Normal Likert Variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
Muthen, B., & Kaplan D. (1992). A Comparison of Some Methodologies for the Factor Analysis of
Non-Normal Likert Variables: A Note on the Size of the Model. British Journal of Mathematical
and Statistical Psychology, 45, 19-30.
-----------------------------------------------------------------------
Adequacy of the dispersion matrix
Determinant of the matrix = 0.047816437916936
Bartlett's statistic = 896.4 (df = 45; P = 0.000000)
Kaiser-Meyer-Olkin (KMO) test = 0.76664 (fair)
-----------------------------------------------------------------------
EXPLORATORY FACTOR ANALYSIS CONTROLLING SOCIAL DESIRABILITY AND ACQUIESCENCE
-----------------------------------------------------------------------
Robust Goodness of Fit statistics
Root Mean Square Error of Approximation (RMSEA) = 0.032
Robust Mean-Scaled Chi Square with 23 degrees of freedom = 30.146
Non-Normed Fit Index (NNFI; Tucker & Lewis) = 0.989
Comparative Fit Index (CFI) = 0.994
Goodness of Fit Index (GFI) = 0.977
-----------------------------------------------------------------------
Root Mean Square Residuals (RMSR) = 0.0452
Expected mean value of RMSR for an acceptable model = 0.0578 (Kelley's criterion)
-----------------------------------------------------------------------
Unrotated loading matrix
Factor SD Factor AC Factor 1
Item 1 0.60259 0.00000 0.00000
Item 2 0.51525 0.00000 0.00000
Item 3 0.72712 0.00000 0.00000
Item 4 0.71129 0.00000 0.00000
Item 5 -0.07851 0.23767 -0.54830
Item 6 0.27520 0.00209 0.49046
Item 7 -0.16413 0.57419 -0.70134
Item 8 -0.14320 0.54074 -0.59101
Item 9 0.26560 0.19586 0.66794
Item 10 0.31733 0.06247 0.68224
-----------------------------------------------------------------------
RELIABILITY OF EAP SCORES
Factor EAP Reliability estimate
SD : 0.6741
Acquiescence : 0.4110
Factor 1 : 0.6588
PARTICIPANTS'S SCORES ON FACTORS:
Rescaled to mean = 50 and standard deviation = 10 in the sample
Factor SD Factor AC Factor 1
[1,] 44.30702 48.53063 48.55156
[2,] 59.62101 38.14813 49.43151
[3,] 61.18560 42.28581 42.33472
[4,] 55.02621 43.35043 43.33027
[5,] 48.22865 38.63845 60.28253
[6,] 44.51583 64.27810 45.87970
[7,] 44.73212 65.04428 56.23778
[8,] 39.64260 50.43314 71.98675
[9,] 61.34243 36.08920 45.46330
[10,] 52.16778 54.33486 54.69947
[11,] 55.76826 44.66406 56.05639
[12,] 53.40442 58.26992 58.40598
[13,] 46.67244 51.20814 51.35356
[14,] 78.49696 78.23436 55.24197
[15,] 55.73240 54.95574 55.71051
[16,] 44.79375 64.48780 37.53629
[17,] 45.01419 46.09085 56.32604
[18,] 44.88607 59.48052 55.32799
[19,] 43.98174 35.51063 67.03734
[20,] 56.02789 34.92491 54.53837
[21,] 43.26936 33.50301 43.18442
[22,] 55.84757 64.01373 66.76853
[23,] 28.12213 58.07306 33.26714
[24,] 49.33847 42.43554 50.63448
[25,] 39.89180 45.29836 47.25516
[26,] 37.03596 57.53551 42.78104
[27,] 55.01237 47.35809 47.48023
[28,] 55.91154 62.54042 50.80875
[29,] 43.96957 45.20954 56.56510
[30,] 55.62015 44.38344 55.72269
[31,] 37.85553 65.93646 41.52298
[32,] 44.88443 43.34237 45.22750
[33,] 46.71701 40.35556 62.79169
[34,] 65.13094 49.65776 49.86502
[35,] 47.81881 48.87526 50.86982
[36,] 67.06673 41.08028 40.17831
[37,] 44.91462 53.52431 53.53017
[38,] 54.25245 57.87887 67.98931
[39,] 55.62406 44.10900 56.01387
[40,] 43.18127 54.50697 56.11618
[41,] 52.52845 46.39444 58.65295
[42,] 64.90993 50.83754 48.65833
[43,] 65.34152 53.50174 42.94816
[44,] 54.85296 42.88695 54.41221
[45,] 55.73982 55.70860 55.73464
[46,] 52.50598 44.32337 55.29851
[47,] 34.77523 41.69434 52.74365
[48,] 45.26276 45.00814 56.27016
[49,] 32.23588 36.27289 50.71351
[50,] 55.73953 43.90068 44.00606
[51,] 32.22464 56.28823 56.28840
[52,] 49.72672 51.33873 41.26803
[53,] 46.28992 38.95309 51.22768
[54,] 50.75016 58.68693 30.04927
[55,] 56.36416 50.67661 67.06378
[56,] 34.49177 41.63850 38.07691
[57,] 44.14524 50.12867 45.75348
[58,] 44.05206 53.41568 53.43104
[59,] 78.32474 61.79532 45.64725
[60,] 46.94967 53.90651 54.39278
[61,] 78.53938 71.24517 43.46458
[62,] 55.64334 47.45751 55.71921
[63,] 43.98727 53.49776 55.68996
[64,] 57.69291 47.71394 64.03488
[65,] 55.68323 42.58360 53.78106
[66,] 55.60383 43.40725 55.72966
[67,] 55.75805 56.92106 56.40426
[68,] 64.71046 52.12223 56.32263
[69,] 45.40341 60.14048 33.71362
[70,] 48.54641 62.54351 37.06576
[71,] 43.40342 44.67986 45.23517
[72,] 43.79686 58.55205 31.07596
[73,] 37.83035 58.38129 31.93033
[74,] 72.68600 68.25723 40.04981
[75,] 38.54091 60.71628 46.17017
[76,] 44.39775 77.51877 66.33543
[77,] 34.99100 42.99819 66.00216
[78,] 42.29040 37.14596 47.71149
[79,] 55.43487 33.81891 64.89668
[80,] 41.19056 67.25621 43.43235
[81,] 44.31979 50.67810 55.39795
[82,] 78.46786 61.63070 30.43675
[83,] 53.24748 71.49708 72.34526
[84,] 41.16971 50.34979 43.28405
[85,] 55.38985 62.00281 61.93926
[86,] 44.96860 74.34196 78.48571
[87,] 54.44490 39.62713 67.51139
[88,] 51.48115 52.15968 45.84761
[89,] 45.35948 43.61600 59.95273
[90,] 55.82140 45.90314 45.89842
[91,] 37.66133 71.90497 48.64906
[92,] 44.12231 53.92683 53.94090
[93,] 34.83255 66.23167 61.57493
[94,] 54.82542 53.86264 54.17040
[95,] 66.64026 51.15414 39.68461
[96,] 67.81891 78.46782 55.73773
[97,] 56.12513 51.65547 55.46941
[98,] 33.52510 58.25472 32.54171
[99,] 48.00066 51.28869 44.86843
[100,] 54.34978 53.20056 56.70568
[101,] 44.91284 68.09690 44.32226
[102,] 66.49185 44.36721 56.00901
[103,] 49.86868 66.59138 50.03746
[104,] 44.95703 65.11683 67.23725
[105,] 33.06394 48.68094 50.14210
[106,] 75.49454 42.91675 46.03403
[107,] 56.44165 55.56915 65.96104
[108,] 70.56545 54.85236 53.73929
[109,] 56.47416 66.36547 47.59948
[110,] 54.43103 50.10506 59.49525
[111,] 44.33444 46.82774 46.59609
[112,] 55.87863 50.68218 50.67407
[113,] 40.23119 46.74343 46.49407
[114,] 60.99349 46.11764 44.07638
[115,] 55.65058 34.95038 54.73320
[116,] 66.93613 44.63701 43.63742
[117,] 55.99748 56.99160 57.68665
[118,] 55.54187 58.42299 58.53718
[119,] 44.58458 49.91510 49.93807
[120,] 75.70586 57.32689 33.60968
[121,] 53.27232 48.44112 48.44180
[122,] 46.07399 37.19359 55.68549
[123,] 67.24763 55.06465 55.28643
[124,] 38.76115 50.00204 55.43122
[125,] 64.49819 52.49580 52.50690
[126,] 41.73277 58.49243 31.37129
[127,] 43.14225 46.40909 77.98967
[128,] 41.30360 45.13414 67.11730
[129,] 42.51561 62.30427 43.75131
[130,] 31.75714 35.56310 47.12916
[131,] 37.36708 75.92734 66.44226
[132,] 46.14624 58.59357 30.72651
[133,] 43.81892 41.37324 52.70075
[134,] 66.99862 55.76778 57.01011
[135,] 44.32073 36.68535 67.10882
[136,] 42.22618 58.50715 31.30097
[137,] 43.89436 48.57774 48.73739
[138,] 65.19089 49.37079 37.87722
[139,] 42.66222 71.28776 56.59399
[140,] 33.86134 58.26594 32.49618
[141,] 42.69045 46.67895 36.45778
[142,] 44.93873 52.98415 42.23162
[143,] 55.74583 46.76377 55.17194
[144,] 49.67984 58.66476 30.20514
[145,] 32.17211 47.61089 56.68785
[146,] 47.70235 40.14501 40.12142
[147,] 29.64901 58.02852 45.31675
[148,] 33.28955 40.06240 53.67498
[149,] 55.81775 46.35247 57.66756
[150,] 48.66164 58.64813 30.35851
[151,] 47.97475 55.06809 56.18135
[152,] 43.65899 38.23072 61.94442
[153,] 54.17824 46.08125 57.77563
[154,] 61.57265 49.03210 37.04453
[155,] 43.33197 58.53868 31.14252
[156,] 55.71433 58.77543 29.30800
[157,] 53.68852 55.24007 55.22002
[158,] 44.74136 57.76118 41.50918
[159,] 55.04736 47.07009 50.58505
[160,] 44.81184 58.57611 30.92818
[161,] 55.75065 63.31878 43.50666
[162,] 60.58813 46.33231 45.31090
[163,] 68.79705 42.98699 42.84300
[164,] 56.44025 64.92207 64.83487
[165,] 43.76933 43.80124 42.24419
[166,] 55.74947 52.57010 55.52129
[167,] 55.72438 45.39474 55.63690
[168,] 55.67130 49.90520 51.34215
[169,] 56.73557 68.11468 42.76938
[170,] 68.26724 40.71806 46.55656
[171,] 62.50423 67.16133 39.98412
[172,] 53.84692 47.51366 48.21851
[173,] 67.01736 55.69181 55.57940
[174,] 42.47350 57.66593 41.61325
[175,] 67.06213 47.50322 55.69083
[176,] 50.89359 63.06823 52.59901
[177,] 46.05396 60.55529 44.43560
[178,] 54.32972 55.21099 44.13067
[179,] 56.59217 46.35856 44.10441
[180,] 44.87846 49.54326 38.58936
[181,] 58.31624 44.61286 44.32355
[182,] 54.62460 47.85486 48.69422
[183,] 60.12518 58.57013 55.69574
[184,] 34.35336 58.28051 32.42649
[185,] 47.21515 58.62122 30.57263
[186,] 60.62014 46.21286 34.25919
[187,] 78.42766 58.70306 34.76349
[188,] 37.86659 57.56004 42.64914
[189,] 55.41234 42.21247 54.81339
[190,] 47.63832 54.36088 64.35439
[191,] 55.69734 44.73816 44.73018
[192,] 62.74595 44.58547 46.68316
[193,] 41.00067 49.14055 39.38552
[194,] 56.91915 47.76296 76.15838
[195,] 41.66055 40.40874 68.90198
[196,] 55.68580 39.83292 55.11887
[197,] 68.74316 61.61321 55.83636
[198,] 55.57530 56.17685 44.81855
[199,] 55.71430 69.79176 41.83262
[200,] 44.21239 56.43005 56.46239
[201,] 55.18684 47.82398 56.76784
[202,] 44.77241 69.69404 46.29153
[203,] 51.10568 41.50086 47.76216
[204,] 56.46256 77.80103 55.65606
[205,] 39.54434 60.63963 45.90853
[206,] 44.14111 60.15041 33.95615
[207,] 61.72351 43.41733 55.69254
[208,] 54.38111 46.28940 66.08432
[209,] 50.23539 46.52423 46.56346
[210,] 44.67930 48.55260 43.72548
[211,] 58.57982 57.01706 65.11867
[212,] 55.73807 58.77937 29.31033
[213,] 54.03646 32.77144 53.46291
[214,] 55.87397 58.77899 29.28537
[215,] 41.35609 50.67616 50.25031
[216,] 47.10344 52.43007 74.55882
[217,] 50.98010 67.98313 42.35284
[218,] 55.74764 51.40932 42.28507
[219,] 58.51323 43.21274 67.12062
[220,] 51.74077 42.12935 45.85924
[221,] 77.23746 50.45609 78.38559
[222,] 55.68601 66.10248 44.69221
[223,] 45.46708 48.19169 48.15182
[224,] 29.00400 62.70776 47.09194
[225,] 67.09952 44.87649 44.67415
[226,] 44.36781 66.29620 67.22525
[227,] 39.95185 36.14198 58.53892
[228,] 56.17716 36.70255 44.14874
[229,] 45.74817 60.13778 33.64687
[230,] 67.13421 46.00349 45.95336
[231,] 44.94922 55.42742 55.56321
[232,] 55.60053 44.88317 44.95182
[233,] 55.71750 43.85545 55.71205
[234,] 67.10076 44.62062 44.58481
[235,] 67.23630 35.23199 46.58197
[236,] 48.49817 58.64503 30.38265
[237,] 55.69185 44.55027 44.54256
[238,] 43.46622 45.45644 36.05285
[239,] 43.86760 33.04204 44.11000
[240,] 66.99797 44.58240 44.52715
[241,] 56.14275 44.02387 55.15403
[242,] 61.02457 38.24590 45.62964
[243,] 42.85284 51.89394 64.26144
[244,] 55.70343 59.37916 47.98791
[245,] 55.73565 55.37298 44.02424
[246,] 58.90367 58.04775 37.69678
[247,] 55.73357 44.57373 56.41505
[248,] 44.26891 38.82533 52.67862
[249,] 36.53949 75.30860 51.80707
[250,] 78.49792 54.79526 44.19312
[251,] 65.91045 45.74000 75.74079
[252,] 55.80855 44.30158 55.23352
[253,] 71.61711 44.53033 32.26638
[254,] 70.39145 51.52355 53.04289
[255,] 66.84939 36.94889 67.05292
[256,] 66.65347 60.65207 49.67645
[257,] 45.58416 45.56202 35.66298
[258,] 55.70546 47.17811 50.33811
[259,] 48.96193 51.78187 52.40554
[260,] 75.67200 46.46760 46.04763
[261,] 45.70151 46.84141 35.91825
[262,] 51.98408 43.83765 43.83546
[263,] 55.51939 71.16084 43.94505
[264,] 34.67673 29.94341 46.18303
[265,] 44.45840 67.56917 43.65450
[266,] 41.74798 58.49336 31.36939
[267,] 33.97252 58.64181 44.63295
[268,] 50.60884 58.90655 51.48737
[269,] 35.31015 63.30636 39.73473
[270,] 34.33516 34.58743 68.97472
[271,] 53.11265 42.91490 45.88614
[272,] 67.40953 45.39201 55.76110
[273,] 53.08535 60.07964 32.25757
[274,] 55.68588 72.87799 54.23314
[275,] 55.47561 57.60431 46.24323
[276,] 45.34838 58.58646 30.84894
[277,] 37.00667 70.17243 55.61635
[278,] 40.28535 53.76049 55.37342
[279,] 44.44595 56.11118 67.36083
[280,] 45.25804 59.73069 50.73288
[281,] 55.31349 60.06191 31.83555
[282,] 44.27977 62.83796 38.28175
[283,] 55.54194 47.69397 66.13047
[284,] 55.28981 38.82184 57.40900
[285,] 51.52488 58.70088 29.93387
[286,] 43.30857 57.66602 41.45808
[287,] 67.66619 69.66415 56.13246
[288,] 54.75796 65.93035 46.60417
[289,] 46.98775 37.41871 49.88539
[290,] 55.54859 44.00550 55.56583
[291,] 45.58144 46.66561 55.23614
[292,] 62.80562 55.73295 43.75449
[293,] 45.74826 51.33156 51.41169
[294,] 52.38594 44.70648 34.71245
[295,] 57.57635 50.81772 52.64558
[296,] 75.39581 65.89118 37.66226
[297,] 67.16578 45.44243 55.99969
[298,] 58.57261 36.37440 46.69614
[299,] 55.86651 42.68121 44.49191
[300,] 73.41695 45.75375 57.08540
NOTE: The precision matrices for the 3 factors were not printed for preventing console spacing issues.
These matrices are stored in $Precision_matrix in the output variable.
AdvertenciaLas puntuaciones factoriales son estimaciones dependientes del modelo
Las puntuaciones resultantes no son rasgos observados libres de sesgo. Son estimaciones condicionadas al modelo de sesgo de respuesta y a sus supuestos, incluido el tratamiento de los marcadores de deseabilidad.
9.2 Modelado MIMIC con Cuádruples de Peabody
El segundo ejemplo sigue la lógica MIMIC-cuádruple evaluada por Degobi & Valentini (2023). Utilizamos lavaan (Rosseel, 2012) para simular y estimar el modelo y semPlot (Epskamp, 2022) para el diagrama de rutas.
This is lavaan 0.7-2
lavaan is FREE software! Please report any bugs.9.2.1 1. Simular un diseño con cuádruples
El código siguiente crea dos factores sustantivos, un factor de deseabilidad social identificado por 16 ítems de cuádruples y 10 ítems adicionales cuyas respuestas también están influidas por la deseabilidad social.
Mostrar código de simulación de datos
# Cargas factoriales de los cuádruples en Deseabilidad Social
FactorLoadingsSDQ<- rep(0.3, 16)*c(1,-1,1,-1)
# Cargas factoriales de los cuádruples en el constructo objetivo
RandomFactorLoadingsQ<-rep(0.7, 16)*c(-1,-1,1,1)
# Carga factorial del ítem adicional en el constructo objetivo
set.seed(2021)
RandomFactorLoadings <- round(runif((10), min = .3, max = .8), 3)
# Regresiones de deseabilidad para los ítems del constructo objetivo
set.seed(2021)
RandomSDregression <- round(runif((10), min = .1, max = .5), 3)
# Umbrales de los ítems
set.seed(2020)
thld1Vet<-round(runif(26, min=-2, max=.5),3)
thld2Vet<-round(thld1Vet +.5,3)
thld3Vet<-round(thld1Vet + 1,3)
thld4Vet<-round(thld1Vet + 1.5,3)
# Modelo simulado
simModel <- paste0("fator1 =~",RandomFactorLoadings[1],"*it1 +",
RandomFactorLoadings[2],"*it2 +",
RandomFactorLoadings[3],"*it3 +",
RandomFactorLoadings[4],"*it4 +",
RandomFactorLoadings[5],"*it5 +",
RandomFactorLoadingsQ[1],"*sd1 +",
RandomFactorLoadingsQ[2],"*sd2 +",
RandomFactorLoadingsQ[3],"*sd3 +",
RandomFactorLoadingsQ[4],"*sd4 +",
RandomFactorLoadingsQ[5],"*sd5 +",
RandomFactorLoadingsQ[6],"*sd6 +",
RandomFactorLoadingsQ[7],"*sd7 +",
RandomFactorLoadingsQ[8],"*sd8\n",
"fator2 =~", RandomFactorLoadingsQ[6],"*it6 +",
RandomFactorLoadingsQ[7],"*it7 +",
RandomFactorLoadingsQ[8],"*it8 +",
RandomFactorLoadingsQ[9],"*it9 +",
RandomFactorLoadingsQ[10],"*it10 +",
RandomFactorLoadingsQ[9],"*sd9 +",
RandomFactorLoadingsQ[10],"*sd10 +",
RandomFactorLoadingsQ[11],"*sd11 +",
RandomFactorLoadingsQ[12],"*sd12 +",
RandomFactorLoadingsQ[13],"*sd13 +",
RandomFactorLoadingsQ[14],"*sd14 +",
RandomFactorLoadingsQ[15],"*sd15 +",
RandomFactorLoadingsQ[16],"*sd16\n",
"SD =~", FactorLoadingsSDQ[1], "*sd1 +",
FactorLoadingsSDQ[2],"*sd2 +",
FactorLoadingsSDQ[3],"*sd3 +",
FactorLoadingsSDQ[4],"*sd4 +",
FactorLoadingsSDQ[5], "*sd5 +",
FactorLoadingsSDQ[6],"*sd6 +",
FactorLoadingsSDQ[7],"*sd7 +",
FactorLoadingsSDQ[8],"*sd8 +",
FactorLoadingsSDQ[9], "*sd9 +",
FactorLoadingsSDQ[10],"*sd10 +",
FactorLoadingsSDQ[11],"*sd11 +",
FactorLoadingsSDQ[12],"*sd12 +",
FactorLoadingsSDQ[13], "*sd13 +",
FactorLoadingsSDQ[14],"*sd14 +",
FactorLoadingsSDQ[15],"*sd15 +",
FactorLoadingsSDQ[16],"*sd16\n",
"SD ~~ 1*SD\n",
"fator1 ~~ 1*fator1\n",
"fator2 ~~ 1*fator2\n",
"fator1 ~~ 0*SD\n",
"fator2 ~~ 0*SD\n",
"fator1 ~~ .3*fator2\n",
"it1 ~",RandomSDregression[1],"*SD\n",
"it2 ~",RandomSDregression[2],"*SD\n",
"it3 ~",RandomSDregression[3],"*SD\n",
"it4 ~",RandomSDregression[4],"*SD\n",
"it5 ~",RandomSDregression[5],"*SD\n",
"it6 ~",RandomSDregression[6],"*SD\n",
"it7 ~",RandomSDregression[7],"*SD\n",
"it8 ~",RandomSDregression[8],"*SD\n",
"it9 ~",RandomSDregression[9],"*SD\n",
"it10 ~",RandomSDregression[10],"*SD\n",
"sd1 |",thld1Vet[1],"*t1 +", thld2Vet[1], "*t2 +",
thld3Vet[1],"*t3 +",thld4Vet[1],"*t4\n",
"sd2 |",thld1Vet[2],"*t1 +", thld2Vet[2], "*t2 +",
thld3Vet[2],"*t3 +",thld4Vet[2],"*t4\n",
"sd3 |",thld1Vet[3],"*t1 +", thld2Vet[3], "*t2 +",
thld3Vet[3],"*t3 +",thld4Vet[3],"*t4\n",
"sd4 |",thld1Vet[4],"*t1 +", thld2Vet[4], "*t2 +",
thld3Vet[4],"*t3 +",thld4Vet[4],"*t4\n",
"it1 |",thld1Vet[5],"*t1 +", thld2Vet[5], "*t2 +",
thld3Vet[5],"*t3 +",thld4Vet[5],"*t4\n",
"it2 |",thld1Vet[6],"*t1 +", thld2Vet[6], "*t2 +",
thld3Vet[6],"*t3 +",thld4Vet[6],"*t4\n",
"it3 |",thld1Vet[7],"*t1 +", thld2Vet[7], "*t2 +",
thld3Vet[7],"*t3 +",thld4Vet[7],"*t4\n",
"it4 |",thld1Vet[8],"*t1 +", thld2Vet[8], "*t2 +",
thld3Vet[8],"*t3 +",thld4Vet[8],"*t4\n",
"it5 |",thld1Vet[9],"*t1 +", thld2Vet[9], "*t2 +",
thld3Vet[9],"*t3 +",thld4Vet[9],"*t4\n",
"it6 |",thld1Vet[10],"*t1 +", thld2Vet[10], "*t2 +",
thld3Vet[10],"*t3 +",thld4Vet[10],"*t4\n",
"it7 |",thld1Vet[11],"*t1 +", thld2Vet[11], "*t2 +",
thld3Vet[11],"*t3 +",thld4Vet[11],"*t4\n",
"it8 |",thld1Vet[12],"*t1 +", thld2Vet[12], "*t2 +",
thld3Vet[12],"*t3 +",thld4Vet[12],"*t4\n",
"it9 |",thld1Vet[13],"*t1 +", thld2Vet[13], "*t2 +",
thld3Vet[13],"*t3 +",thld4Vet[13],"*t4\n",
"it10 |",thld1Vet[14],"*t1 +", thld2Vet[14], "*t2 +",
thld3Vet[14],"*t3 +",thld4Vet[14],"*t4\n",
"sd5 |",thld1Vet[15],"*t1 +", thld2Vet[15], "*t2 +",
thld3Vet[15],"*t3 +",thld4Vet[15],"*t4\n",
"sd6 |",thld1Vet[16],"*t1 +", thld2Vet[16], "*t2 +",
thld3Vet[16],"*t3 +",thld4Vet[16],"*t4\n",
"sd7 |",thld1Vet[17],"*t1 +", thld2Vet[17], "*t2 +",
thld3Vet[17],"*t3 +",thld4Vet[17],"*t4\n",
"sd8 |",thld1Vet[18],"*t1 +", thld2Vet[18], "*t2 +",
thld3Vet[18],"*t3 +",thld4Vet[18],"*t4\n",
"sd9 |",thld1Vet[19],"*t1 +", thld2Vet[19], "*t2 +",
thld3Vet[19],"*t3 +",thld4Vet[19],"*t4\n",
"sd10 |",thld1Vet[20],"*t1 +", thld2Vet[20], "*t2 +",
thld3Vet[20],"*t3 +",thld4Vet[20],"*t4\n",
"sd11 |",thld1Vet[21],"*t1 +", thld2Vet[21], "*t2 +",
thld3Vet[21],"*t3 +",thld4Vet[21],"*t4\n",
"sd12 |",thld1Vet[22],"*t1 +", thld2Vet[22], "*t2 +",
thld3Vet[22],"*t3 +",thld4Vet[22],"*t4\n",
"sd13 |",thld1Vet[23],"*t1 +", thld2Vet[23], "*t2 +",
thld3Vet[23],"*t3 +",thld4Vet[23],"*t4\n",
"sd14 |",thld1Vet[24],"*t1 +", thld2Vet[24], "*t2 +",
thld3Vet[24],"*t3 +",thld4Vet[24],"*t4\n",
"sd15 |",thld1Vet[25],"*t1 +", thld2Vet[25], "*t2 +",
thld3Vet[25],"*t3 +",thld4Vet[25],"*t4\n",
"sd16 |",thld1Vet[26],"*t1 +", thld2Vet[26], "*t2 +",
thld3Vet[26],"*t3 +",thld4Vet[26],"*t4")
# Simulación de los datos
simulatedData <- lavaan::simulateData(model = simModel,
model.type = "sem",
sample.nobs = 4000,
seed = 2024,
return.type = "data.frame",
standardized = TRUE
)El conjunto de datos simulado contiene 26 ítems de cinco categorías: 16 ítems organizados como cuatro cuádruples y 10 ítems adicionales fuera del formato de cuádruples.
it1 it2 it3 it4
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:2.000
Median :5.000 Median :5.000 Median :5.000 Median :4.000
Mean :4.142 Mean :4.297 Mean :4.151 Mean :3.356
3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
it5 sd1 sd2 sd3
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
Median :5.000 Median :2.000 Median :4.000 Median :2.000
Mean :4.436 Mean :2.507 Mean :3.344 Mean :2.606
3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
sd4 sd5 sd6 sd7 sd8
Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :3.000 Median :3.00 Median :3.000 Median :1.000 Median :2.000
Mean :3.082 Mean :3.31 Mean :2.857 Mean :1.639 Mean :2.485
3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:4.000
Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
it6 it7 it8 it9 it10
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00
Median :2.000 Median :2.000 Median :2.000 Median :1.000 Median :3.00
Mean :2.589 Mean :2.133 Mean :2.196 Mean :1.961 Mean :3.26
3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:5.00
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
sd9 sd10 sd11 sd12
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
Median :3.000 Median :4.000 Median :5.000 Median :5.000
Mean :2.835 Mean :3.772 Mean :3.957 Mean :4.281
3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
sd13 sd14 sd15 sd16
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000
Median :1.000 Median :1.000 Median :1.000 Median :5.000
Mean :1.982 Mean :1.679 Mean :1.813 Mean :4.061
3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:5.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 9.2.2 2. Especificar el modelo MIMIC empírico
Todos los ítems relevantes para el contenido definen su factor sustantivo. El factor de deseabilidad se identifica únicamente mediante los ítems de los cuádruples manipulados, y los ítems adicionales se regresan sobre ese factor de deseabilidad.
Los factores sustantivos se restringen a ser ortogonales al factor de deseabilidad para identificar esta demostración. Esa restricción es un supuesto de modelado y debe considerarse cuidadosamente en aplicaciones empíricas.
Mostrar sintaxis del modelo MIMIC-cuádruple
empiricalModel <- "
factor1 =~ NA*it1 + it2 + it3 + it4 + it5 + sd1 + sd2 + sd3 +
sd4 + sd5 + sd6 + sd7 + sd8
factor2 =~ NA*it6 + it7 + it8 + it9 + it10 + sd9 + sd10 + sd11 +
sd12 + sd13 + sd14 + sd15 + sd16
SD =~ NA*sd1 + sd2 + sd3 + sd4 + sd5 + sd6 + sd7 + sd8 + sd9 +
sd10 + sd11 + sd12 + sd13 + sd14 + sd15 + sd16
SD ~~ 1*SD
factor1 ~~ 1*factor1
factor2 ~~ 1*factor2
factor1 ~~ 0*SD
factor2 ~~ 0*SD
factor1 ~~ factor2
it1 ~ SD
it2 ~ SD
it3 ~ SD
it4 ~ SD
it5 ~ SD
it6 ~ SD
it7 ~ SD
it8 ~ SD
it9 ~ SD
it10 ~SD"9.2.3 3. Ajustar el modelo para respuestas ordinales
Como los ítems simulados son ordinales, el modelo se estima con WLSMV.
lavaan 0.7-2 ended normally after 43 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 157
Number of observations 4000
Model Test User Model:
Standard Scaled
Test Statistic 134.313 262.042
Degrees of freedom 272 272
P-value (Unknown) NA 0.657
Scaling correction factor 0.829
Shift parameter 100.035
simple second-order correction
Model Test Baseline Model:
Test statistic 182910.056 68572.439
Degrees of freedom 325 325
P-value NA 0.000
Scaling correction factor 2.675
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 1.000
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.000
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.000 0.005
P-value H_0: RMSEA <= 0.050 1.000 1.000
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.009
P-value H_0: Robust RMSEA <= 0.050 1.000
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.011 0.011
Goodness of Fit Index:
Goodness of Fit Index (GFI) 1.000
90 Percent confidence interval - lower 1.000
90 Percent confidence interval - upper 1.000
Robust GFI 1.000
90 Percent confidence interval - lower 0.998
90 Percent confidence interval - upper 1.000
Parameter Estimates:
Parameterization Delta
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
it1 0.533 0.015 34.817 0.000 0.533 0.533
it2 0.710 0.013 54.436 0.000 0.710 0.710
it3 0.657 0.013 49.952 0.000 0.657 0.657
it4 0.469 0.015 31.176 0.000 0.469 0.469
it5 0.629 0.015 42.507 0.000 0.629 0.629
sd1 -0.702 0.011 -63.668 0.000 -0.702 -0.702
sd2 -0.710 0.011 -66.074 0.000 -0.710 -0.710
sd3 0.702 0.011 64.968 0.000 0.702 0.702
sd4 0.695 0.011 62.271 0.000 0.695 0.695
sd5 -0.686 0.011 -60.281 0.000 -0.686 -0.686
sd6 -0.711 0.011 -66.961 0.000 -0.711 -0.711
sd7 0.691 0.013 51.760 0.000 0.691 0.691
sd8 0.684 0.012 58.123 0.000 0.684 0.684
factor2 =~
it6 0.706 0.011 64.652 0.000 0.706 0.706
it7 -0.693 0.012 -57.861 0.000 -0.693 -0.693
it8 -0.703 0.011 -63.503 0.000 -0.703 -0.703
it9 0.694 0.012 56.075 0.000 0.694 0.694
it10 0.701 0.012 59.331 0.000 0.701 0.701
sd9 0.674 0.011 59.334 0.000 0.674 0.674
sd10 0.697 0.011 61.714 0.000 0.697 0.697
sd11 -0.720 0.012 -62.435 0.000 -0.720 -0.720
sd12 -0.688 0.013 -54.044 0.000 -0.688 -0.688
sd13 0.709 0.011 61.910 0.000 0.709 0.709
sd14 0.702 0.013 55.003 0.000 0.702 0.702
sd15 -0.692 0.013 -54.627 0.000 -0.692 -0.692
sd16 -0.688 0.012 -58.482 0.000 -0.688 -0.688
SD =~
sd1 0.313 0.019 16.640 0.000 0.313 0.313
sd2 -0.259 0.019 -13.382 0.000 -0.259 -0.259
sd3 0.298 0.019 16.043 0.000 0.298 0.298
sd4 -0.307 0.019 -16.431 0.000 -0.307 -0.307
sd5 0.303 0.019 15.838 0.000 0.303 0.303
sd6 -0.318 0.018 -17.525 0.000 -0.318 -0.318
sd7 0.310 0.021 14.747 0.000 0.310 0.310
sd8 -0.309 0.019 -16.377 0.000 -0.309 -0.309
sd9 0.356 0.018 19.303 0.000 0.356 0.356
sd10 -0.292 0.020 -14.681 0.000 -0.292 -0.292
sd11 0.308 0.020 15.377 0.000 0.308 0.308
sd12 -0.331 0.020 -16.296 0.000 -0.331 -0.331
sd13 0.290 0.020 14.403 0.000 0.290 0.290
sd14 -0.302 0.021 -14.363 0.000 -0.302 -0.302
sd15 0.312 0.021 15.141 0.000 0.312 0.312
sd16 -0.295 0.020 -14.743 0.000 -0.295 -0.295
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
it1 ~
SD 0.278 0.020 14.008 0.000 0.278 0.278
it2 ~
SD 0.413 0.020 21.023 0.000 0.413 0.413
it3 ~
SD 0.371 0.020 18.908 0.000 0.371 0.371
it4 ~
SD 0.280 0.019 15.075 0.000 0.280 0.280
it5 ~
SD 0.364 0.020 18.041 0.000 0.364 0.364
it6 ~
SD 0.394 0.018 21.530 0.000 0.394 0.394
it7 ~
SD 0.382 0.019 19.824 0.000 0.382 0.382
it8 ~
SD 0.213 0.020 10.436 0.000 0.213 0.213
it9 ~
SD 0.424 0.019 22.368 0.000 0.424 0.424
it10 ~
SD 0.502 0.017 29.643 0.000 0.502 0.502
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
SD 0.000 0.000 0.000
factor2 ~~
SD 0.000 0.000 0.000
factor1 ~~
factor2 -0.289 0.017 -17.396 0.000 -0.289 -0.289
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
it1|t1 -1.655 0.034 -49.192 0.000 -1.655 -1.655
it1|t2 -1.141 0.025 -45.092 0.000 -1.141 -1.141
it1|t3 -0.665 0.021 -30.938 0.000 -0.665 -0.665
it1|t4 -0.180 0.020 -9.007 0.000 -0.180 -0.180
it2|t1 -1.780 0.037 -48.455 0.000 -1.780 -1.780
it2|t2 -1.314 0.027 -47.808 0.000 -1.314 -1.314
it2|t3 -0.818 0.022 -36.464 0.000 -0.818 -0.818
it2|t4 -0.347 0.020 -17.134 0.000 -0.347 -0.347
it3|t1 -1.672 0.034 -49.127 0.000 -1.672 -1.672
it3|t2 -1.144 0.025 -45.163 0.000 -1.144 -1.144
it3|t3 -0.685 0.022 -31.696 0.000 -0.685 -0.685
it3|t4 -0.180 0.020 -9.007 0.000 -0.180 -0.180
it4|t1 -0.997 0.024 -41.791 0.000 -0.997 -0.997
it4|t2 -0.515 0.021 -24.758 0.000 -0.515 -0.515
it4|t3 -0.012 0.020 -0.601 0.548 -0.012 -0.012
it4|t4 0.486 0.021 23.483 0.000 0.486 0.486
it5|t1 -1.986 0.043 -46.008 0.000 -1.986 -1.986
it5|t2 -1.497 0.030 -49.192 0.000 -1.497 -1.497
it5|t3 -0.995 0.024 -41.764 0.000 -0.995 -0.995
it5|t4 -0.485 0.021 -23.452 0.000 -0.485 -0.485
sd1|t1 -0.383 0.020 -18.828 0.000 -0.383 -0.383
sd1|t2 0.113 0.020 5.690 0.000 0.113 0.113
sd1|t3 0.622 0.021 29.229 0.000 0.622 0.622
sd1|t4 1.100 0.025 44.246 0.000 1.100 1.100
sd2|t1 -0.997 0.024 -41.791 0.000 -0.997 -0.997
sd2|t2 -0.475 0.021 -22.985 0.000 -0.475 -0.475
sd2|t3 -0.019 0.020 -0.980 0.327 -0.019 -0.019
sd2|t4 0.486 0.021 23.483 0.000 0.486 0.486
sd3|t1 -0.456 0.021 -22.144 0.000 -0.456 -0.456
sd3|t2 0.035 0.020 1.771 0.077 0.035 0.035
sd3|t3 0.537 0.021 25.718 0.000 0.537 0.537
sd3|t4 1.044 0.024 42.979 0.000 1.044 1.044
sd4|t1 -0.803 0.022 -35.966 0.000 -0.803 -0.803
sd4|t2 -0.325 0.020 -16.097 0.000 -0.325 -0.325
sd4|t3 0.182 0.020 9.133 0.000 0.182 0.182
sd4|t4 0.713 0.022 32.752 0.000 0.713 0.713
sd5|t1 -0.974 0.024 -41.196 0.000 -0.974 -0.974
sd5|t2 -0.466 0.021 -22.580 0.000 -0.466 -0.466
sd5|t3 0.033 0.020 1.644 0.100 0.033 0.033
sd5|t4 0.500 0.021 24.105 0.000 0.500 0.500
sd6|t1 -0.647 0.021 -30.238 0.000 -0.647 -0.647
sd6|t2 -0.129 0.020 -6.512 0.000 -0.129 -0.129
sd6|t3 0.346 0.020 17.071 0.000 0.346 0.346
sd6|t4 0.843 0.023 37.308 0.000 0.843 0.843
sd7|t1 0.404 0.020 19.768 0.000 0.404 0.404
sd7|t2 0.895 0.023 38.915 0.000 0.895 0.895
sd7|t3 1.405 0.029 48.691 0.000 1.405 1.405
sd7|t4 1.881 0.040 47.440 0.000 1.881 1.881
sd8|t1 -0.374 0.020 -18.389 0.000 -0.374 -0.374
sd8|t2 0.135 0.020 6.765 0.000 0.135 0.135
sd8|t3 0.633 0.021 29.688 0.000 0.633 0.633
sd8|t4 1.129 0.025 44.854 0.000 1.129 1.129
it6|t1 -0.440 0.021 -21.426 0.000 -0.440 -0.440
it6|t2 0.058 0.020 2.909 0.004 0.058 0.058
it6|t3 0.548 0.021 26.152 0.000 0.548 0.548
it6|t4 1.036 0.024 42.797 0.000 1.036 1.036
it7|t1 -0.073 0.020 -3.699 0.000 -0.073 -0.073
it7|t2 0.421 0.020 20.551 0.000 0.421 0.421
it7|t3 0.890 0.023 38.773 0.000 0.890 0.890
it7|t4 1.403 0.029 48.678 0.000 1.403 1.403
it8|t1 -0.148 0.020 -7.428 0.000 -0.148 -0.148
it8|t2 0.348 0.020 17.165 0.000 0.348 0.348
it8|t3 0.872 0.023 38.230 0.000 0.872 0.872
it8|t4 1.390 0.029 48.573 0.000 1.390 1.390
it9|t1 0.056 0.020 2.814 0.005 0.056 0.056
it9|t2 0.564 0.021 26.831 0.000 0.564 0.564
it9|t3 1.069 0.025 43.570 0.000 1.069 1.069
it9|t4 1.603 0.033 49.310 0.000 1.603 1.603
it10|t1 -0.929 0.023 -39.928 0.000 -0.929 -0.929
it10|t2 -0.434 0.021 -21.176 0.000 -0.434 -0.434
it10|t3 0.045 0.020 2.276 0.023 0.045 0.045
it10|t4 0.564 0.021 26.831 0.000 0.564 0.564
sd9|t1 -0.655 0.021 -30.542 0.000 -0.655 -0.655
sd9|t2 -0.121 0.020 -6.069 0.000 -0.121 -0.121
sd9|t3 0.364 0.020 17.919 0.000 0.364 0.364
sd9|t4 0.896 0.023 38.943 0.000 0.896 0.896
sd10|t1 -1.339 0.028 -48.095 0.000 -1.339 -1.339
sd10|t2 -0.840 0.023 -37.192 0.000 -0.840 -0.840
sd10|t3 -0.330 0.020 -16.317 0.000 -0.330 -0.330
sd10|t4 0.167 0.020 8.375 0.000 0.167 0.167
sd11|t1 -1.467 0.030 -49.069 0.000 -1.467 -1.467
sd11|t2 -0.979 0.024 -41.332 0.000 -0.979 -0.979
sd11|t3 -0.485 0.021 -23.452 0.000 -0.485 -0.485
sd11|t4 -0.014 0.020 -0.727 0.467 -0.014 -0.014
sd12|t1 -1.881 0.040 -47.440 0.000 -1.881 -1.881
sd12|t2 -1.323 0.028 -47.912 0.000 -1.323 -1.323
sd12|t3 -0.798 0.022 -35.790 0.000 -0.798 -0.798
sd12|t4 -0.297 0.020 -14.745 0.000 -0.297 -0.297
sd13|t1 0.054 0.020 2.719 0.007 0.054 0.054
sd13|t2 0.532 0.021 25.502 0.000 0.532 0.532
sd13|t3 1.047 0.024 43.057 0.000 1.047 1.047
sd13|t4 1.565 0.032 49.324 0.000 1.565 1.565
sd14|t1 0.352 0.020 17.385 0.000 0.352 0.352
sd14|t2 0.857 0.023 37.742 0.000 0.857 0.857
sd14|t3 1.356 0.028 48.269 0.000 1.356 1.356
sd14|t4 1.835 0.038 47.947 0.000 1.835 1.835
sd15|t1 0.206 0.020 10.301 0.000 0.206 0.206
sd15|t2 0.717 0.022 32.903 0.000 0.717 0.717
sd15|t3 1.198 0.026 46.141 0.000 1.198 1.198
sd15|t4 1.728 0.035 48.838 0.000 1.728 1.728
sd16|t1 -1.616 0.033 -49.290 0.000 -1.616 -1.616
sd16|t2 -1.090 0.025 -44.047 0.000 -1.090 -1.090
sd16|t3 -0.579 0.021 -27.479 0.000 -0.579 -0.579
sd16|t4 -0.083 0.020 -4.205 0.000 -0.083 -0.083
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
SD 1.000 1.000 1.000
factor1 1.000 1.000 1.000
factor2 1.000 1.000 1.000
.it1 0.639 0.639 0.639
.it2 0.326 0.326 0.326
.it3 0.431 0.431 0.431
.it4 0.701 0.701 0.701
.it5 0.471 0.471 0.471
.sd1 0.409 0.409 0.409
.sd2 0.429 0.429 0.429
.sd3 0.419 0.419 0.419
.sd4 0.422 0.422 0.422
.sd5 0.438 0.438 0.438
.sd6 0.392 0.392 0.392
.sd7 0.427 0.427 0.427
.sd8 0.437 0.437 0.437
.it6 0.346 0.346 0.346
.it7 0.374 0.374 0.374
.it8 0.460 0.460 0.460
.it9 0.339 0.339 0.339
.it10 0.257 0.257 0.257
.sd9 0.419 0.419 0.419
.sd10 0.428 0.428 0.428
.sd11 0.387 0.387 0.387
.sd12 0.417 0.417 0.417
.sd13 0.413 0.413 0.413
.sd14 0.415 0.415 0.415
.sd15 0.424 0.424 0.424
.sd16 0.439 0.439 0.439Inspeccione el ajuste global, las cargas estandarizadas de contenido y deseabilidad y las regresiones de SD sobre it1–it10. En una simulación, la recuperación también puede evaluarse frente a los valores de los parámetros utilizados para generar los datos.
9.2.4 4. Estimar puntuaciones latentes
El objeto resultante contiene estimaciones de puntuaciones latentes basadas en el modelo añadidas a los datos.
9.2.5 5. Visualizar el modelo
En modelos grandes, los diagramas de rutas pueden volverse visualmente densos. Resultan más útiles para comprobar la parametrización prevista que para sustituir la inspección numérica del modelo ajustado.
10 Elegir una Estrategia
Ningún control de deseabilidad social está libre de supuestos. Una elección útil depende de la información que el diseño del estudio puede proporcionar.
| Situación | Estrategia especialmente relevante |
|---|---|
| Conjunto de datos existente con una escala separada de DS | Análisis de sensibilidad; control basado en factores si los supuestos de los marcadores son plausibles |
| Hipótesis de que solo algunas personas manipulan sus respuestas | Modelo de mezcla factorial |
| Manipulación experimental del contexto de respuesta | Factor de método común / diseño de medidas repetidas |
| Nuevo instrumento en desarrollo | Contenido equilibrado al estilo de Peabody u otras estrategias de diseño de ítems |
| Deseo de extender un factor evaluativo identificado a ítems ordinarios | Modelo MIMIC-cuádruple |
ImportanteControlar no es automáticamente mejor que no controlar
Un ajuste por sesgo de respuesta es útil solo cuando el modelo separa de forma creíble la varianza molesta de la varianza sustantiva. Un factor de deseabilidad mal identificado puede eliminar varianza significativa del constructo y producir puntuaciones que parecen más limpias pero son menos válidas.
11 Consideraciones Finales
La deseabilidad social no es una única molestia estadística con una corrección universal. Puede reflejar autopresentación intencional, creencias autofavorables, rasgos sustantivos socialmente valorados, evaluación de los ítems o varianza de método compartida. Diferentes procedimientos de control se dirigen a diferentes partes del problema.
La lección práctica es hacer explícito el proceso de respuesta supuesto. Si la deseabilidad social se representa como un factor latente de método, el investigador debe explicar por qué ese factor es identificable y distinguible del contenido sustantivo. Si se aborda mediante diseño de ítems, las manipulaciones evaluativas y descriptivas deben ser defendibles. Si se utiliza una escala separada de deseabilidad, su puntuación no debe tratarse automáticamente como una medida pura de deshonestidad.
El capítulo siguiente aborda otra tendencia de respuesta —la aquiescencia— y muestra por qué limitarse a invertir la redacción de los ítems no siempre basta para eliminarla.