8 Viés de Desejabilidade Social
Medidas de autorrelato são centrais para pesquisas em psicologia, educação, saúde e ciências sociais. Elas são eficientes e frequentemente fornecem acesso a comportamentos, avaliações, sentimentos e experiências que não são diretamente observáveis (Lange & Dewitte, 2019; Peterson & Kerin, 1981). Ao mesmo tempo, as respostas a itens de autorrelato podem refletir mais do que o construto que o pesquisador pretende medir.
Entre essas influências adicionais estão estilos de resposta e vieses de resposta. Desejabilidade social, aquiescência e respostas extremas são exemplos comuns. Essas influências importam porque podem alterar médias dos itens, associações entre itens, estruturas fatoriais e relações com variáveis externas.
NotaMapa do capítulo
Este capítulo distingue faking de respostas socialmente desejáveis, examina como a desejabilidade social pode entrar em um modelo psicométrico e revisa várias estratégias para controlá-la:
- escalas de desejabilidade social;
- modelos fatoriais de viés de resposta;
- modelos fatoriais de mistura;
- modelos com fator de método comum; e
- quádruplas balanceadas no estilo Peabody e modelagem MIMIC.
A seção final implementa duas dessas abordagens em R.
8.1 Faking: contexto, motivação e oportunidade
Faking é melhor compreendido como um comportamento de resposta dependente do contexto. O respondente altera deliberadamente suas respostas para produzir uma autoapresentação que atende a algum objetivo (Ziegler et al., 2011). O comportamento, portanto, não é determinado apenas pela pessoa nem apenas pelo questionário. Ele emerge de uma interação entre características individuais, demandas situacionais e características da própria avaliação.
Duas formas amplas são frequentemente distinguidas:
| Forma | Finalidade geral |
|---|---|
| Faking good | Apresentar-se de maneira mais favorável do que em condições comuns de resposta |
| Faking bad | Fabricar ou exagerar características ou sintomas indesejáveis para obter um resultado desejado |
O faking good tem sido amplamente estudado em contextos de personalidade e seleção (Zickar & Robie, 1999). O faking bad é especialmente relevante quando o respondente pode se beneficiar de parecer mais comprometido ou sintomático (Ziegler et al., 2011).
8.1.1 Por que uma pessoa faria faking?
A explicação baseada em expectativa resumida por Ziegler et al. (2011) enfatiza três tipos de crença:
- Eu consigo fazer isso? — o respondente acredita ser possível alterar a impressão pretendida;
- Isso fará diferença? — o respondente acredita que respostas alteradas podem afetar o resultado; e
- Eu valorizo esse resultado? — o benefício resultante é suficientemente importante.
Essas crenças podem ser influenciadas por personalidade, capacidade cognitiva, conhecimento sobre o construto avaliado, transparência dos itens, experiência prévia com testes e características da situação de avaliação (McFarland & Ryan, 2000; Raymark & Tafero, 2009; Riggio et al., 1988; Snell et al., 1999).
ImportanteFaking não é idêntico à desejabilidade social
Faking refere-se a um processo intencional de resposta em um contexto específico. Desejabilidade social é mais ampla: diz respeito à tendência de as respostas se relacionarem à avaliação social do conteúdo. Portanto, um escore alto em uma escala de desejabilidade social não deve ser interpretado automaticamente como prova de que o respondente mentiu.
8.6 Quádruplas de Peabody: separando conteúdo descritivo e avaliativo
Uma estratégia diferente aborda a desejabilidade social durante a construção dos itens. Peabody (1967) distinguiu o conteúdo descritivo de um termo de traço de sua desejabilidade avaliativa. Ao cruzar conteúdo descritivo baixo versus alto com conteúdo avaliativo baixo versus alto, pesquisadores podem construir quádruplas balanceadas.
| Baixa desejabilidade | Alta desejabilidade | |
|---|---|---|
| Polo descritivo baixo | Retraído | Introspectivo |
| Polo descritivo alto | Tagarela | Comunicativo |
O delineamento também pode ajudar a separar aquiescência porque polos descritivos positivos e negativos são representados no mesmo conjunto balanceado (Mirowsky & Ross, 1991).
Para uma quádrupla de quatro itens, uma representação simplificada de dois fatores é
\[ \begin{bmatrix} x_1\\ x_2\\ x_3\\ x_4 \end{bmatrix} = \begin{bmatrix} -\lambda_{1c} & +\lambda_{1d}\\ -\lambda_{2c} & -\lambda_{2d}\\ +\lambda_{3c} & +\lambda_{3d}\\ +\lambda_{4c} & -\lambda_{4d} \end{bmatrix} \begin{bmatrix} \eta_c\\ \eta_d \end{bmatrix} + \begin{bmatrix} \epsilon_1\\ \epsilon_2\\ \epsilon_3\\ \epsilon_4 \end{bmatrix}, \]
em que \(\eta_c\) representa conteúdo descritivo e \(\eta_d\) representa desejabilidade avaliativa. Abordagens relacionadas baseadas em adjetivos usaram essa lógica para distinguir variância avaliativa e descritiva de personalidade (Pettersson et al., 2012, 2014; Saucier et al., 2001).
8.6.1 Estendendo o fator de desejabilidade com um modelo MIMIC
Degobi & Valentini (2023) avaliaram se um fator de desejabilidade identificado a partir de quádruplas manipuladas poderia predizer efeitos de desejabilidade em itens adicionais por meio de um modelo de Múltiplos Indicadores e Múltiplas Causas (Multiple Indicators Multiple Causes; MIMIC). Seus estudos de simulação consideraram versões do tipo Likert e de escolha forçada da abordagem e avaliaram a recuperação por meio de critérios de viés e cobertura.
A ideia prática é:
- identificar o fator de desejabilidade usando itens nos quais o conteúdo avaliativo foi deliberadamente manipulado;
- estimar os fatores de conteúdo substantivo a partir de todos os itens relevantes; e
- regredir itens adicionais, que não fazem parte de quádruplas, no fator de desejabilidade.
Isso evita exigir que todos os itens de uma escala sejam escritos como quádruplas, embora o método ainda dependa da qualidade e da quantidade de quádruplas manipuladas.
NotaFerramentas de delineamento associadas à abordagem MIMIC com quádruplas
Duas ferramentas de simulação foram desenvolvidas para planejar esses modelos: quadSimple, destinada ao planejamento em estágio inicial quando há pouca informação sobre os parâmetros, e quadSim, destinada a situações em que os pesquisadores já dispõem de informações mais detalhadas sobre os parâmetros esperados do modelo.
9 Controlando desejabilidade social em R
9.1 Procedimento de Ferrando et al. com vampyr
O pacote vampyr implementa procedimentos de análise fatorial para controlar viés de resposta (Navarro-Gonzalez et al., 2021).
A instalação é necessária apenas uma vez:
Carregue o pacote e inspecione seu conjunto de dados de exemplo:
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 O exemplo contém 10 variáveis de 300 respondentes. Quatro variáveis são marcadores de desejabilidade, enquanto as seis restantes representam agressão física. Os itens de conteúdo incluem indicadores com chave positiva e negativa.
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 0.60260 0.00000 0.00000
Item 2 0.51525 0.00000 0.00000
Item 3 0.72715 0.00000 0.00000
Item 4 0.71130 0.00000 0.00000
Item 5 -0.07851 0.23771 0.54830
Item 6 0.27521 0.00203 -0.49043
Item 7 -0.16413 0.57418 0.70131
Item 8 -0.14320 0.54080 0.59098
Item 9 0.26561 0.19576 -0.66789
Item 10 0.31734 0.06246 -0.68227Aqui, contAC = TRUE solicita controle simultâneo da aquiescência. Se a aquiescência não fizer parte da análise, altere para FALSE.
A saída deve ser interpretada em três níveis:
- a solução do fator de conteúdo depois que os componentes de viés de resposta são considerados;
- cargas associadas aos componentes de desejabilidade social e aquiescência; e
- a adequação e interpretabilidade da solução fatorial resultante.
Também é possível solicitar escores fatoriais:
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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 0.60253 0.00000 0.00000
Item 2 0.51526 0.00000 0.00000
Item 3 0.72703 0.00000 0.00000
Item 4 0.71131 0.00000 0.00000
Item 5 -0.07850 0.23754 -0.54832
Item 6 0.27518 0.00261 0.49070
Item 7 -0.16411 0.57383 -0.70153
Item 8 -0.14319 0.54039 -0.59124
Item 9 0.26558 0.19656 0.66826
Item 10 0.31730 0.06260 0.68213
-----------------------------------------------------------------------
RELIABILITY OF EAP SCORES
Factor EAP Reliability estimate
SD : 0.6740
Acquiescence : 0.4113
Factor 1 : 0.6595
PARTICIPANTS'S SCORES ON FACTORS:
Rescaled to mean = 50 and standard deviation = 10 in the sample
Factor SD Factor AC Factor 1
[1,] 44.30689 48.53366 48.55481
[2,] 59.62676 38.14300 49.42822
[3,] 61.19149 42.28901 42.33887
[4,] 55.02774 43.35157 43.33143
[5,] 48.23150 38.65223 60.29584
[6,] 44.51622 64.27224 45.86318
[7,] 44.73169 65.05792 56.23595
[8,] 39.62658 50.47164 72.00526
[9,] 61.35179 36.07697 45.46004
[10,] 52.16252 54.34160 54.70672
[11,] 55.76839 44.66178 56.05370
[12,] 53.40909 58.25883 58.39425
[13,] 46.66440 51.23089 51.37671
[14,] 78.49638 78.23461 55.23978
[15,] 55.73233 54.95501 55.71025
[16,] 44.79377 64.49204 37.53692
[17,] 45.01518 46.09337 56.32182
[18,] 44.88459 59.49744 55.32995
[19,] 43.98233 35.50938 67.03810
[20,] 56.02714 34.92406 54.54780
[21,] 43.26990 33.50549 43.19109
[22,] 55.84811 64.01990 66.76669
[23,] 28.12252 58.06664 33.26303
[24,] 49.33861 42.44275 50.64104
[25,] 39.88174 45.29486 47.26120
[26,] 37.03493 57.52833 42.77424
[27,] 55.00994 47.35622 47.47955
[28,] 55.91196 62.54238 50.80747
[29,] 43.96928 45.20919 56.56382
[30,] 55.62033 44.38423 55.72283
[31,] 37.85434 65.94467 41.52543
[32,] 44.88414 43.32472 45.22757
[33,] 46.71564 40.36763 62.80616
[34,] 65.13272 49.64082 49.84926
[35,] 47.81287 48.87295 50.87582
[36,] 67.06700 41.07172 40.17404
[37,] 44.91538 53.52503 53.53092
[38,] 54.26612 57.86664 67.96809
[39,] 55.62403 44.10916 56.01325
[40,] 43.18481 54.51043 56.11400
[41,] 52.53560 46.38410 58.64348
[42,] 64.92065 50.81588 48.63477
[43,] 65.34004 53.49938 42.95120
[44,] 54.85168 42.89260 54.41923
[45,] 55.73981 55.70843 55.73435
[46,] 52.50929 44.32594 55.30058
[47,] 34.77906 41.69107 52.74006
[48,] 45.26547 45.00772 56.26828
[49,] 32.23231 36.26805 50.72370
[50,] 55.73959 43.90233 44.00926
[51,] 32.22039 56.28458 56.28415
[52,] 49.72481 51.33284 41.26988
[53,] 46.29046 38.95463 51.23626
[54,] 50.74920 58.67881 30.04546
[55,] 56.36090 50.67849 67.06410
[56,] 34.49186 41.64036 38.08394
[57,] 44.14586 50.12878 45.75154
[58,] 44.05125 53.41687 53.43233
[59,] 78.32619 61.80413 45.63028
[60,] 46.94607 53.90147 54.39022
[61,] 78.53929 71.25219 43.46473
[62,] 55.64359 47.47046 55.71931
[63,] 43.98610 53.49423 55.68944
[64,] 57.69024 47.71729 64.04295
[65,] 55.68321 42.59639 53.79380
[66,] 55.60400 43.40663 55.73033
[67,] 55.75841 56.92418 56.39676
[68,] 64.71987 52.13566 56.31960
[69,] 45.40473 60.13684 33.70991
[70,] 48.54842 62.54469 37.06291
[71,] 43.40090 44.68478 45.24363
[72,] 43.79680 58.54428 31.07182
[73,] 37.82947 58.37393 31.92628
[74,] 72.69029 68.24328 40.03023
[75,] 38.54384 60.71215 46.16012
[76,] 44.39551 77.52377 66.33669
[77,] 34.98421 43.00715 66.01090
[78,] 42.29027 37.15704 47.72289
[79,] 55.43521 33.83009 64.90789
[80,] 41.18879 67.26527 43.43337
[81,] 44.31979 50.69456 55.40043
[82,] 78.46787 61.62733 30.43706
[83,] 53.24682 71.48759 72.32344
[84,] 41.16935 50.33895 43.28248
[85,] 55.38686 62.01921 61.95396
[86,] 44.96613 74.35606 78.48432
[87,] 54.44572 39.64727 67.51259
[88,] 51.48684 52.15478 45.84323
[89,] 45.36174 43.60087 59.93814
[90,] 55.82180 45.90917 45.90440
[91,] 37.66052 71.91832 48.64405
[92,] 44.12145 53.92852 53.94266
[93,] 34.84195 66.23620 61.55310
[94,] 54.82184 53.86888 54.17804
[95,] 66.64009 51.14908 39.67990
[96,] 67.81444 78.46934 55.73703
[97,] 56.12434 51.66272 55.47112
[98,] 33.52546 58.24773 32.53746
[99,] 48.00070 51.28483 44.86747
[100,] 54.35165 53.20657 56.70502
[101,] 44.91261 68.10634 44.32024
[102,] 66.49295 44.37014 56.00981
[103,] 49.86738 66.59855 50.03031
[104,] 44.95376 65.14172 67.23573
[105,] 33.06017 48.67854 50.14665
[106,] 75.49254 42.91801 46.04166
[107,] 56.44582 55.56877 65.95531
[108,] 70.56665 54.84693 53.73218
[109,] 56.47437 66.37083 47.59083
[110,] 54.42766 50.12085 59.49523
[111,] 44.33567 46.81400 46.58155
[112,] 55.87867 50.70256 50.69427
[113,] 40.22991 46.74677 46.49519
[114,] 60.99933 46.11097 44.07103
[115,] 55.65069 34.94943 54.74184
[116,] 66.93616 44.63486 43.63797
[117,] 55.99764 56.97438 57.66519
[118,] 55.54236 58.38847 58.50287
[119,] 44.58541 49.90723 49.93050
[120,] 75.70730 57.31465 33.60132
[121,] 53.27656 48.42646 48.42720
[122,] 46.07510 37.19603 55.68692
[123,] 67.24753 55.06612 55.28875
[124,] 38.76118 50.00655 55.43316
[125,] 64.49861 52.48452 52.49580
[126,] 41.73147 58.48478 31.36732
[127,] 43.13873 46.42577 77.99326
[128,] 41.30261 45.13719 67.11745
[129,] 42.51628 62.30556 43.74788
[130,] 31.75869 35.57343 47.14107
[131,] 37.35594 75.96052 66.44645
[132,] 46.14815 58.58574 30.72216
[133,] 43.81907 41.37603 52.70343
[134,] 66.99958 55.76005 56.99068
[135,] 44.32081 36.68753 67.10929
[136,] 42.22611 58.49950 31.29683
[137,] 43.89315 48.57283 48.73408
[138,] 65.19365 49.36534 37.87222
[139,] 42.66188 71.27167 56.56496
[140,] 33.86175 58.25891 32.49193
[141,] 42.68658 46.67658 36.46465
[142,] 44.93806 52.99398 42.24209
[143,] 55.74570 46.77768 55.17834
[144,] 49.67856 58.65668 30.20135
[145,] 32.17731 47.60987 56.68094
[146,] 47.70732 40.15241 40.12900
[147,] 29.64873 58.01781 45.30541
[148,] 33.28773 40.07796 53.69124
[149,] 55.81807 46.33202 57.64751
[150,] 48.66496 58.64019 30.35401
[151,] 47.96922 55.06033 56.17641
[152,] 43.65849 38.22632 61.94381
[153,] 54.17557 46.08693 57.77961
[154,] 61.57751 49.03677 37.04873
[155,] 43.33152 58.53093 31.13844
[156,] 55.71491 58.76707 29.30408
[157,] 53.68391 55.24081 55.22047
[158,] 44.74147 57.75207 41.50153
[159,] 55.04725 47.04892 50.57626
[160,] 44.81198 58.56828 30.92404
[161,] 55.75041 63.31863 43.50637
[162,] 60.59524 46.32542 45.30288
[163,] 68.80127 42.99082 42.84719
[164,] 56.44457 64.90120 64.81169
[165,] 43.77053 43.80530 42.24620
[166,] 55.74922 52.56173 55.51813
[167,] 55.72438 45.39900 55.63765
[168,] 55.67114 49.89998 51.34563
[169,] 56.73593 68.12089 42.77033
[170,] 68.26646 40.70164 46.55629
[171,] 62.50614 67.14448 39.96452
[172,] 53.84267 47.51389 48.22268
[173,] 67.01736 55.69444 55.58084
[174,] 42.47457 57.65678 41.60421
[175,] 67.06230 47.50581 55.69113
[176,] 50.88600 63.05368 52.58494
[177,] 46.05292 60.55397 44.43013
[178,] 54.32708 55.21020 44.13169
[179,] 56.59296 46.35509 44.10219
[180,] 44.87831 49.54914 38.59968
[181,] 58.32223 44.61159 44.32222
[182,] 54.62167 47.86086 48.70429
[183,] 60.13832 58.59295 55.69562
[184,] 34.35295 58.27342 32.42234
[185,] 47.21473 58.61327 30.56863
[186,] 60.62885 46.21396 34.26061
[187,] 78.42787 58.69116 34.75254
[188,] 37.86727 57.55273 42.64201
[189,] 55.41218 42.20943 54.81677
[190,] 47.64696 54.34411 64.33228
[191,] 55.69738 44.73492 44.72698
[192,] 62.74137 44.59770 46.69845
[193,] 40.99717 49.14468 39.39612
[194,] 56.91784 47.76795 76.16123
[195,] 41.66013 40.42168 68.90308
[196,] 55.68603 39.82933 55.12158
[197,] 68.74856 61.65364 55.83574
[198,] 55.57649 56.17268 44.81451
[199,] 55.71428 69.80106 41.83683
[200,] 44.21214 56.42276 56.45497
[201,] 55.18583 47.83983 56.76683
[202,] 44.77234 69.69557 46.27593
[203,] 51.10366 41.48116 47.75605
[204,] 56.46080 77.80944 55.65573
[205,] 39.54496 60.63615 45.89959
[206,] 44.14103 60.14688 33.95262
[207,] 61.72446 43.40979 55.68969
[208,] 54.37952 46.29801 66.09402
[209,] 50.24045 46.51836 46.55814
[210,] 44.67886 48.54723 43.72615
[211,] 58.58796 57.01554 65.10313
[212,] 55.73808 58.77104 29.30652
[213,] 54.03665 32.77644 53.47494
[214,] 55.87381 58.77063 29.28157
[215,] 41.35781 50.66138 50.23333
[216,] 47.11956 52.41802 74.53548
[217,] 50.97314 67.98220 42.34982
[218,] 55.74758 51.39887 42.28736
[219,] 58.51417 43.21548 67.12077
[220,] 51.73236 42.10955 45.85938
[221,] 77.23644 50.46768 78.38563
[222,] 55.68615 66.10584 44.68712
[223,] 45.47026 48.19153 48.15139
[224,] 29.00475 62.70800 47.08451
[225,] 67.09955 44.87984 44.67610
[226,] 44.36710 66.30914 67.22400
[227,] 39.95337 36.14781 58.54429
[228,] 56.17840 36.70710 44.15466
[229,] 45.74831 60.13412 33.64341
[230,] 67.13421 46.00863 45.95821
[231,] 44.94677 55.42946 55.56509
[232,] 55.60030 44.88338 44.95280
[233,] 55.71751 43.85505 55.71227
[234,] 67.10090 44.62113 44.58526
[235,] 67.23705 35.23532 46.58676
[236,] 48.49626 58.63699 30.37892
[237,] 55.69196 44.54699 44.53933
[238,] 43.46552 45.45131 36.05727
[239,] 43.86884 33.04787 44.11644
[240,] 66.99836 44.58159 44.52623
[241,] 56.14215 44.02883 55.15853
[242,] 61.02612 38.23016 45.62707
[243,] 42.84676 51.91540 64.28075
[244,] 55.70342 59.35681 47.96595
[245,] 55.73564 55.37031 44.02250
[246,] 58.90726 58.03001 37.68314
[247,] 55.73360 44.57259 56.41409
[248,] 44.26830 38.83234 52.68983
[249,] 36.53412 75.32536 51.81381
[250,] 78.49797 54.79306 44.19435
[251,] 65.90609 45.75924 75.74762
[252,] 55.80809 44.31014 55.24007
[253,] 71.62354 44.53340 32.27070
[254,] 70.38607 51.52456 53.04823
[255,] 66.84971 36.94303 67.05323
[256,] 66.65255 60.64398 49.66884
[257,] 45.58434 45.55717 35.66693
[258,] 55.70543 47.15575 50.32891
[259,] 48.95974 51.77100 52.39655
[260,] 75.67549 46.47139 46.05039
[261,] 45.70171 46.84115 35.92416
[262,] 51.98984 43.83725 43.83524
[263,] 55.51899 71.16610 43.94455
[264,] 34.67718 29.95450 46.19106
[265,] 44.45825 67.55856 43.64307
[266,] 41.74674 58.48570 31.36542
[267,] 33.97292 58.63687 44.62569
[268,] 50.60911 58.92299 51.49472
[269,] 35.30883 63.29743 39.72525
[270,] 34.33532 34.60090 68.98077
[271,] 53.10484 42.89792 45.88752
[272,] 67.41028 45.39586 55.76102
[273,] 53.08483 60.07560 32.25501
[274,] 55.68562 72.88794 54.22598
[275,] 55.47510 57.59831 46.23721
[276,] 45.34842 58.57860 30.84482
[277,] 36.99518 70.18308 55.61469
[278,] 40.28336 53.76799 55.37751
[279,] 44.44520 56.11681 67.36091
[280,] 45.25829 59.73136 50.72984
[281,] 55.31371 60.05775 31.83308
[282,] 44.27963 62.84050 38.28069
[283,] 55.54116 47.68332 66.12800
[284,] 55.28903 38.82915 57.41257
[285,] 51.52606 58.69276 29.92977
[286,] 43.30872 57.65683 41.44927
[287,] 67.66598 69.67662 56.12747
[288,] 54.75928 65.90921 46.58073
[289,] 46.98791 37.41641 49.89089
[290,] 55.54857 44.00507 55.56651
[291,] 45.58097 46.66730 55.23550
[292,] 62.80600 55.74113 43.75941
[293,] 45.74397 51.35807 51.43838
[294,] 52.38125 44.70087 34.71793
[295,] 57.57954 50.80372 52.63398
[296,] 75.39493 65.89785 37.66038
[297,] 67.16584 45.44713 55.99976
[298,] 58.57972 36.36585 46.69176
[299,] 55.86650 42.66667 44.49111
[300,] 73.41976 45.74252 57.07646
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.
AvisoEscores fatoriais são estimativas dependentes do modelo
Os escores resultantes não são traços observados livres de viés. São estimativas condicionais ao modelo de viés de resposta e aos seus pressupostos, incluindo o tratamento dos marcadores de desejabilidade.
9.2 Modelagem MIMIC com quádruplas de Peabody
O segundo exemplo segue a lógica MIMIC com quádruplas avaliada por Degobi & Valentini (2023). Usamos lavaan (Rosseel, 2012) para simulação e estimação do modelo e semPlot (Epskamp, 2022) para um diagrama de caminhos.
This is lavaan 0.7-2
lavaan is FREE software! Please report any bugs.9.2.1 1. Simular um delineamento com quádruplas
O código a seguir cria dois fatores substantivos, um fator de desejabilidade social identificado por 16 itens de quádruplas e 10 itens adicionais cujas respostas também são influenciadas pela desejabilidade social.
Mostrar código de simulação dos dados
# Cargas fatoriais das quádruplas em Desejabilidade Social
FactorLoadingsSDQ<- rep(0.3, 16)*c(1,-1,1,-1)
# Cargas fatoriais das quádruplas no construto-alvo
RandomFactorLoadingsQ<-rep(0.7, 16)*c(-1,-1,1,1)
# Cargas fatoriais dos itens adicionais no construto-alvo
set.seed(2021)
RandomFactorLoadings <- round(runif((10), min = .3, max = .8), 3)
# Regressões de desejabilidade para os itens do construto-alvo
set.seed(2021)
RandomSDregression <- round(runif((10), min = .1, max = .5), 3)
# Limiares dos itens
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")
# Simulando os dados
simulatedData <- lavaan::simulateData(model = simModel,
model.type = "sem",
sample.nobs = 4000,
seed = 2024,
return.type = "data.frame",
standardized = TRUE
)O conjunto de dados simulado contém 26 itens com cinco categorias: 16 itens organizados em quatro quádruplas e 10 itens adicionais fora do formato de quádrupla.
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 o modelo MIMIC empírico
Todos os itens relevantes para o conteúdo definem seu fator substantivo. O fator de desejabilidade é identificado apenas pelos itens das quádruplas manipuladas, e os itens adicionais são regredidos nesse fator de desejabilidade.
Os fatores substantivos são restringidos a serem ortogonais ao fator de desejabilidade para fins de identificação nesta demonstração. Essa restrição é uma suposição de modelagem e deve ser considerada cuidadosamente em aplicações empíricas.
Mostrar sintaxe do modelo MIMIC com quádruplas
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 o modelo para respostas ordinais
Como os itens simulados são ordinais, o modelo é estimado com 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 0.439Inspecione o ajuste global, as cargas padronizadas de conteúdo e desejabilidade e as regressões de SD para it1–it10. Em uma simulação, a recuperação também pode ser avaliada em relação aos valores dos parâmetros usados para gerar os dados.
9.2.4 4. Estimar escores latentes
O objeto resultante contém estimativas de escores latentes baseadas no modelo anexadas aos dados.
9.2.5 5. Visualizar o modelo
Para modelos grandes, diagramas de caminhos podem se tornar visualmente densos. Eles são mais úteis para verificar a parametrização pretendida do que para substituir a inspeção numérica do modelo ajustado.
10 Escolhendo uma estratégia
Nenhum controle de desejabilidade social é livre de pressupostos. Uma escolha útil depende de qual informação o delineamento do estudo pode fornecer.
| Situação | Estratégia especialmente relevante |
|---|---|
| Conjunto de dados existente com uma escala separada de DS | Análises de sensibilidade; controle baseado em fatores se os pressupostos sobre marcadores forem plausíveis |
| Hipótese de que apenas alguns respondentes fazem faking | Modelo fatorial de mistura |
| Manipulação experimental do contexto de resposta | Fator de método comum / delineamento de medidas repetidas |
| Novo instrumento em desenvolvimento | Conteúdo balanceado no estilo Peabody ou estratégias relacionadas de elaboração de itens |
| Desejo de estender um fator avaliativo identificado a itens comuns | Modelo MIMIC com quádruplas |
ImportanteControlar não é automaticamente melhor do que não controlar
Um ajuste de viés de resposta é útil apenas quando o modelo separa de forma plausível a variância de incômodo da variância substantiva. Um fator de desejabilidade mal identificado pode remover variância significativa do construto e produzir escores que parecem mais limpos, mas são menos válidos.
11 Considerações finais
Desejabilidade social não é um único incômodo estatístico com uma correção universal. Ela pode refletir autoapresentação intencional, crenças autofavorecedoras, traços substantivos socialmente valorizados, avaliação dos itens ou variância compartilhada de método. Diferentes procedimentos de controle têm como alvo diferentes partes desse problema.
A lição prática, portanto, é tornar explícito o processo de resposta pressuposto. Se a desejabilidade social for representada como um fator latente de método, o pesquisador deve explicar por que esse fator é identificável e por que é distinguível do conteúdo substantivo. Se for tratada por meio do delineamento dos itens, as manipulações avaliativas e descritivas devem ser defensáveis. Se uma escala separada de desejabilidade for usada, seu escore não deve ser tratado automaticamente como medida pura de desonestidade.
O próximo capítulo trata de outra tendência de resposta — aquiescência — e mostra por que simplesmente inverter a redação dos itens nem sempre é suficiente para removê-la.