Trabajo adeim

MÁSTER DE PSICODIDÁCTICA 2013-2014 EUSKAL HERRIKO UNIBERTSITATEA 22/01/2014

TRABAJO ANÁLISIS DE DATOS Y EVALUACIÓN DE INSTRUMENTOS DE MEDIDA

IRATXE ANTONIO AGIRRE ANE ARROYO GAUNA IONE ECHARRI CARASATORRE DIEGO DÍEZ IBÁÑEZ

Este este trabajo empleamos la base de datos ANSIEDAD para la primera y tercera parte, mientras que en la segunda parte usamos la base de datos EJEMPLO. A continuación detallamos las secuencias de comandos ejecutadas en los siguientes tres apartados:

1. FIABILIDAD 1.1.FACTORIZACIÓN DATOS/MODIFICAR VARIABLE/CONVERTIR EN FACTOR > ANSIEDAD$f1 <- factor(ANSIEDAD$y1, labels=c('siempre','casi siempre','a veces','nunca')) > ANSIEDAD$f2 <- factor(ANSIEDAD$y2, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f3 <- factor(ANSIEDAD$y3, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f4 <- factor(ANSIEDAD$y4, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f5 <- factor(ANSIEDAD$y5, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f6 <- factor(ANSIEDAD$y6, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f7 <- factor(ANSIEDAD$y7, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f8 <- factor(ANSIEDAD$y8, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f9 <- factor(ANSIEDAD$y9, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f10 <- factor(ANSIEDAD$y10, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f11 <- factor(ANSIEDAD$y11, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f12 <- factor(ANSIEDAD$y12, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f13 <- factor(ANSIEDAD$y13, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f14 <- factor(ANSIEDAD$y14, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f15 <- factor(ANSIEDAD$y15, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f16 <- factor(ANSIEDAD$y16, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f17 <- factor(ANSIEDAD$y17, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f18 <- factor(ANSIEDAD$y18, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f19 <- factor(ANSIEDAD$y19, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f20 <- factor(ANSIEDAD$y20, labels=c('nunca','a veces','casi siempre','siempre'))

1.2. DISTRIBUCIÓN DE FRECUENCIAS ESTADÍSTICOS/RESÚMENES/DISTRIBUCIÓN DE FRECUENCIAS > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30)

> .Table <- table(ANSIEDAD$f1) > .Table # counts for f1 siempre casi siempre 46 66

a veces nunca 161 170

> round(100*.Table/sum(.Table), 2) # percentages for f1 siempre casi siempre 10.38 14.90

a veces 36.34

nunca 38.37

> .Table <- table(ANSIEDAD$f2) > .Table # counts for f2 nunca 84

a veces casi siempre siempre 139 92 128

> round(100*.Table/sum(.Table), 2) # percentages for f2 nunca 18.96

a veces casi siempre 31.38 20.77

siempre 28.89

> .Table <- table(ANSIEDAD$f3) > .Table # counts for f3 nunca 189

a veces casi siempre 116 64

siempre 74

> round(100*.Table/sum(.Table), 2) # percentages for f3 nunca 42.66

a veces casi siempre 26.19 14.45

siempre 16.70

> .Table <- table(ANSIEDAD$f4) > .Table # counts for f4 nunca 157

a veces casi siempre 136 80

siempre 70

> round(100*.Table/sum(.Table), 2) # percentages for f4 nunca 35.44

a veces casi siempre 30.70 18.06

siempre 15.80

> .Table <- table(ANSIEDAD$f5) > .Table # counts for f5 nunca 255

a veces casi siempre 98 44

siempre 46

> round(100*.Table/sum(.Table), 2) # percentages for f5 nunca 57.56

a veces casi siempre 22.12 9.93

siempre 10.38

> .Table <- table(ANSIEDAD$f6) > .Table # counts for f6 nunca 189

a veces casi siempre 114 74

siempre 66

> round(100*.Table/sum(.Table), 2) # percentages for f6 nunca 42.66

a veces casi siempre 25.73 16.70

siempre 14.90

> .Table <- table(ANSIEDAD$f7) > .Table # counts for f7 nunca 99

a veces casi siempre 152 94

siempre 98

> round(100*.Table/sum(.Table), 2) # percentages for f7 nunca 22.35

a veces casi siempre 34.31 21.22

siempre 22.12

> .Table <- table(ANSIEDAD$f8) > .Table # counts for f8 nunca 52

a veces casi siempre 129 100

siempre 162

> round(100*.Table/sum(.Table), 2) # percentages for f8 nunca 11.74

a veces casi siempre 29.12 22.57

siempre 36.57

> .Table <- table(ANSIEDAD$f9) > .Table # counts for f9 nunca 74

a veces casi siempre 111 116

siempre 142

> round(100*.Table/sum(.Table), 2) # percentages for f9 nunca 16.70

a veces casi siempre 25.06 26.19

siempre 32.05

> .Table <- table(ANSIEDAD$f10) > .Table # counts for f10 nunca 42

a veces casi siempre siempre 57 98 246

> round(100*.Table/sum(.Table), 2) # percentages for f10 nunca 9.48

a veces casi siempre 12.87 22.12

siempre 55.53

> .Table <- table(ANSIEDAD$f11) > .Table # counts for f11 nunca 90

a veces casi siempre 149 110

siempre 94

> round(100*.Table/sum(.Table), 2) # percentages for f11 nunca 20.32

a veces casi siempre 33.63 24.83

siempre 21.22

> .Table <- table(ANSIEDAD$f12) > .Table # counts for f12 nunca 62

a veces casi siempre siempre 78 75 228

> round(100*.Table/sum(.Table), 2) # percentages for f12 nunca 14.00

a veces casi siempre 17.61 16.93

siempre 51.47

> .Table <- table(ANSIEDAD$f13) > .Table # counts for f13 nunca 241

a veces casi siempre 78 52

siempre 72

> round(100*.Table/sum(.Table), 2) # percentages for f13 nunca 54.40

a veces casi siempre 17.61 11.74

siempre 16.25

> .Table <- table(ANSIEDAD$f14) > .Table # counts for f14 nunca 151

a veces casi siempre 156 62

siempre 74

> round(100*.Table/sum(.Table), 2) # percentages for f14 nunca 34.09

a veces casi siempre 35.21 14.00

siempre 16.70

> .Table <- table(ANSIEDAD$f15) > .Table # counts for f15 nunca 106

a veces casi siempre 123 86

siempre 128

> round(100*.Table/sum(.Table), 2) # percentages for f15 nunca 23.93

a veces casi siempre 27.77 19.41

siempre 28.89

> .Table <- table(ANSIEDAD$f16) > .Table # counts for f16 nunca 38

a veces casi siempre 122 113

siempre 170

> round(100*.Table/sum(.Table), 2) # percentages for f16 nunca 8.58

a veces casi siempre 27.54 25.51

siempre 38.37

> .Table <- table(ANSIEDAD$f17) > .Table # counts for f17 nunca 126

a veces casi siempre 115 94

siempre 108

> round(100*.Table/sum(.Table), 2) # percentages for f17 nunca 28.44

a veces casi siempre 25.96 21.22

siempre 24.38

> .Table <- table(ANSIEDAD$f18) > .Table # counts for f18 nunca 134

a veces casi siempre 103 92

siempre 114

> round(100*.Table/sum(.Table), 2) # percentages for f18 nunca 30.25

a veces casi siempre 23.25 20.77

siempre 25.73

> .Table <- table(ANSIEDAD$f19) > .Table # counts for f19 nunca 134

a veces casi siempre 123 100

siempre 86

> round(100*.Table/sum(.Table), 2) # percentages for f19 nunca 30.25

a veces casi siempre 27.77 22.57

siempre 19.41

> .Table <- table(ANSIEDAD$f20) > .Table # counts for f20 nunca 95

a veces casi siempre siempre 134 82 132

> round(100*.Table/sum(.Table), 2) # percentages for f20 nunca 21.44

a veces casi siempre 30.25 18.51

siempre 29.80

1.3. ESTADĂ?STICOS DESCRIPTIVOS > describe(ANSIEDAD) var n mean sd median y1 1 443 3.03 0.97 3 y2 2 443 2.60 1.10 2 y3 3 443 2.05 1.11 2 y4 4 443 2.14 1.07 2 y5 5 443 1.73 1.01 1 y6 6 443 2.04 1.09 2 y7 7 443 2.43 1.07 2 y8 8 443 2.84 1.05 3 y9 9 443 2.74 1.08 3 y10 10 443 3.24 1.00 4 y11 11 443 2.47 1.04 2 y12 12 443 3.06 1.12 4 y13 13 443 1.90 1.14 1 y14 14 443 2.13 1.06 2 y15 15 443 2.53 1.14 2 y16 16 443 2.94 1.00 3 y17 17 443 2.42 1.14 2 y18 18 443 2.42 1.17 2 y19 19 443 2.31 1.10 2 y20 20 443 2.57 1.13 2 f1* 21 443 3.03 0.97 3 f2* 22 443 2.60 1.10 2 f3* 23 443 2.05 1.11 2 f4* 24 443 2.14 1.07 2 f5* 25 443 1.73 1.01 1 f6* 26 443 2.04 1.09 2 f7* 27 443 2.43 1.07 2 f8* 28 443 2.84 1.05 3 f9* 29 443 2.74 1.08 3 f10* 30 443 3.24 1.00 4 f11* 31 443 2.47 1.04 2 f12* 32 443 3.06 1.12 4 f13* 33 443 1.90 1.14 1 f14* 34 443 2.13 1.06 2 f15* 35 443 2.53 1.14 2 f16* 36 443 2.94 1.00 3 f17* 37 443 2.42 1.14 2 f18* 38 443 2.42 1.17 2 f19* 39 443 2.31 1.10 2 f20* 40 443 2.57 1.13 2

trimmed mad min 3.16 1.48 1 4 2.62 1.48 1 4 1.94 1.48 1 4 2.05 1.48 1 4 1.54 0.00 1 4 1.92 1.48 1 4 2.41 1.48 1 4 2.92 1.48 1 4 2.79 1.48 1 4 3.41 0.00 1 4 2.46 1.48 1 4 3.20 0.00 1 4 1.75 0.00 1 4 2.04 1.48 1 4 2.54 1.48 1 4 3.03 1.48 1 4 2.39 1.48 1 4 2.40 1.48 1 4 2.26 1.48 1 4 2.58 1.48 1 4 3.16 1.48 1 4 2.62 1.48 1 4 1.94 1.48 1 4 2.05 1.48 1 4 1.54 0.00 1 4 1.92 1.48 1 4 2.41 1.48 1 4 2.92 1.48 1 4 2.79 1.48 1 4 3.41 0.00 1 4 2.46 1.48 1 4 3.20 0.00 1 4 1.75 0.00 1 4 2.04 1.48 1 4 2.54 1.48 1 4 3.03 1.48 1 4 2.39 1.48 1 4 2.40 1.48 1 4 2.26 1.48 1 4 2.58 1.48 1 4

max range skew kurtosis se 3 -0.73 -0.49 0.05 3 -0.02 -1.34 0.05 3 0.62 -1.02 0.05 3 0.49 -1.04 0.05 3 1.16 0.05 0.05 3 0.61 -0.99 0.05 3 0.17 -1.21 0.05 3 -0.29 -1.23 0.05 3 -0.25 -1.25 0.05 3 -1.05 -0.19 0.05 3 0.11 -1.17 0.05 3 -0.72 -0.99 0.05 3 0.85 -0.83 0.05 3 0.56 -0.92 0.05 3 0.02 -1.43 0.05 3 -0.39 -1.09 0.05 3 0.13 -1.40 0.05 3 0.11 -1.47 0.06 3 0.24 -1.28 0.05 3 0.01 -1.40 0.05 3 -0.73 -0.49 0.05 3 -0.02 -1.34 0.05 3 0.62 -1.02 0.05 3 0.49 -1.04 0.05 3 1.16 0.05 0.05 3 0.61 -0.99 0.05 3 0.17 -1.21 0.05 3 -0.29 -1.23 0.05 3 -0.25 -1.25 0.05 3 -1.05 -0.19 0.05 3 0.11 -1.17 0.05 3 -0.72 -0.99 0.05 3 0.85 -0.83 0.05 3 0.56 -0.92 0.05 3 0.02 -1.43 0.05 3 -0.39 -1.09 0.05 3 0.13 -1.40 0.05 3 0.11 -1.47 0.06 3 0.24 -1.28 0.05 3 0.01 -1.40 0.05

> barplot(table(ANSIEDAD[,1]),ylim=range(0,400), ylab="Frecuencias", border="blue", col=heat.colors(5),main= "1. Cuando voy a un examen me siento confiado/a y relajado/a", sub="Item1")

1.4. ÍNDICE

DE

HOMOGENEIDAD/DISCRIMINACIÓN:

CORRELACIÓN

ENTRE ÍTEMS ESTADÍSTICOS/ANÁLISIS DIMENSIONAL/FIABILIDAD DE LA ESCALA >reliability(cov(ANSIEDAD[,c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10","y11","y12","y13","y14","y15","y16"," y17","y18","y19","y20")], use="complete.obs")) Alpha reliability = 0.9122 Standardized alpha = 0.9119 Reliability deleting each item in turn: Alpha Std.Alpha r(item, total) y1 0.9115 0.9115 0.4000 y2 0.9058 0.9055 0.6463 y3 0.9078 0.9075 0.5664 y4 0.9077 0.9074 0.5688 y5 0.9115 0.9115 0.4010 y6 0.9087 0.9085 0.5280 y7 0.9076 0.9073 0.5750 y8 0.9037 0.9031 0.7408 y9 0.9059 0.9055 0.6436 y10 0.9124 0.9124 0.3606 y11 0.9053 0.9049 0.6721 y12 0.9109 0.9105 0.4403 y13 0.9085 0.9082 0.5388 y14 0.9042 0.9038 0.7174 y15 0.9043 0.9042 0.7009 y16 0.9077 0.9073 0.5726 y17 0.9095 0.9092 0.4983 y18 0.9077 0.9074 0.5702 y19 0.9111 0.9107 0.4315 y20 0.9075 0.9072 0.5794

1.5. INTERVALO DE CONFIANZA > alfa <- 0.9122 > Se<-(sqrt(1-alfa)) > print (Se) 0.2963106

DATOS/MODIFICAR VARIABLE DE CONJUNTO DE DATOS ACTIVO/ CALCULAR UNA NUEVA VARIABLE PT > ANSIEDAD$PT <- with(ANSIEDAD, y1+ y2+ y3+ y4+ y5+ y6+ y7+ y8+ y9+ y10+ y11+ y12+ y13+ y14+ y15+ y16+ y17+ y18+ y19+ y20) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30)

LI > ANSIEDAD$LI <- with(ANSIEDAD, PT-1.96*Se) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30)

LS > ANSIEDAD$LS <- with(ANSIEDAD, PT+1.96*Se) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30)

1.6. PUNTUACIONES VERDADERAS CÁLCULO DE LA MEDIA ESTADÍSTICOS/RESÚMENES/CONJUNTO DE DATOS ACTIVO > summary(ANSIEDAD) y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000 Median :3.000 Median :2.000 Median :2.000 Median :2.000 Median :1.000 Median :2.000 Median :2.000 Median :3.00 Median :3.000 Median :4.000 Median :2.00 Median :4.000 Mean :3.027 Mean :2.596 Mean :2.052 Mean :2.142 Mean :1.731 Mean :2.038 Mean :2.431 Mean :2.84 Mean :2.736 Mean :3.237 Mean :2.47 Mean :3.059 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 y13 y14 y15 y16 y17 y18 y19 y20 f1 f2 f3 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 siempre : 46 nunca : 84 nunca :189 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2.000 casi siempre: 66 a veces :139 a veces :116 Median :1.000 Median :2.000 Median :2.000 Median :3.000 Median :2.000 Median :2.00 Median :2.000 Median :2.000 a veces :161 casi siempre: 92 casi siempre: 64

Mean :1.898 Mean :2.133 Mean :2.533 Mean :2.937 Mean :2.415 Mean :2.42 Mean :2.312 Mean :2.567 nunca :170 siempre :128 siempre : 74 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 nunca :157 nunca :255 nunca :189 nunca : 99 nunca : 52 nunca : 74 nunca : 42 nunca : 90 nunca : 62 nunca :241 a veces :136 a veces : 98 a veces :114 a veces :152 a veces :129 a veces :111 a veces : 57 a veces :149 a veces : 78 a veces : 78 casi siempre: 80 casi siempre: 44 casi siempre: 74 casi siempre: 94 casi siempre:100 casi siempre:116 casi siempre: 98 casi siempre:110 casi siempre: 75 casi siempre: 52 siempre : 70 siempre : 46 siempre : 66 siempre : 98 siempre :162 siempre :142 siempre :246 siempre : 94 siempre :228 siempre : 72 f14 f15 f16 f17 f18 f19 f20 PT LI LS nunca :151 nunca :106 nunca : 38 nunca :126 nunca :134 nunca :134 nunca : 95 Min. :26.00 Min. :25.42 Min. :26.58 a veces :156 a veces :123 a veces :122 a veces :115 a veces :103 a veces :123 a veces :134 1st Qu.:38.00 1st Qu.:37.42 1st Qu.:38.58 casi siempre: 62 casi siempre: 86 casi siempre:113 casi siempre: 94 casi siempre: 92 casi siempre:100 casi siempre: 82 Median :50.00 Median :49.42 Median :50.58 siempre : 74 siempre :128 siempre :170 siempre :108 siempre :114 siempre : 86 siempre :132 Mean :49.57 Mean :48.99 Mean :50.15 3rd Qu.:61.00 3rd Qu.:60.42 3rd Qu.:61.58 Max. :77.00 Max. :76.42 Max. :77.58

PUNTUACIĂ&#x201C;N VERDADERA=V > ANSIEDAD$V <- with(ANSIEDAD, 0.9122*PT+(1-0.9122)*49.57) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30)

2. VALIDEZ 2.1. DESCRIPTIVOS BÁSICOS > describe (EJEMPLO) var n mean sd median trimmed mad min max range skew kurtosis se MOTIVACION 1 1517 68.26 14.61 69 69.05 13.34 0 100 100 -0.64 0.82 0.38 METACOGNICION 2 1517 11.22 2.47 11 11.26 2.97 3 17 14 -0.21 -0.09 0.06 VERBAL 3 1517 48.23 7.86 49 48.41 8.90 26 70 44 -0.18 -0.37 0.20 RAZONAMIENTO 4 1517 48.45 9.25 49 48.68 8.90 17 74 57 -0.25 -0.12 0.24 MATEMATICAS 5 1517 50.48 9.90 50 50.78 8.90 17 86 69 -0.24 0.35 0.25

GRÁFICAS/DISPERSIÓN > scatterplot(RAZONAMIENTO~MATEMATICAS, reg.line=lm, smooth=TRUE, spread=TRUE, id.method='mahal', id.n = 2, boxplots='xy', span=0.5, data=EJEMPLO) 342 556 342 556

2.2. COEFICIENTE DE VALIDEZ ESTADÍSTICOS/RESÚMENES/TEST DE CORRELACIÓN > cor.test(EJEMPLO$MATEMATICAS, EJEMPLO$RAZONAMIENTO, alternative="two.sided", method="pearson") Pearson's product-moment correlation data: EJEMPLO$MATEMATICAS and EJEMPLO$RAZONAMIENTO t = 28.3631, df = 1515, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.5550474 0.6208517 sample estimates: cor 0.5889247

2.3. REGRESIÓN LINEAL

ESTADÍSTICOS/AJUSTE DE MODELO/REGRESIÓN LINEAL > RegModel.1 <- lm(MATEMATICAS~RAZONAMIENTO, data=EJEMPLO) > summary(RegModel.1) Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -31.2908 -4.6148 0.3225 5.2145 23.3678 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 19.92822 1.09675 18.17 <2e-16 *** RAZONAMIENTO 0.63067 0.02224 28.36 <2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 8.004 on 1515 degrees of freedom Multiple R-squared: 0.3468, Adjusted R-squared: 0.3464 F-statistic: 804.5 on 1 and 1515 DF, p-value: < 2.2e-16

GRÁFICOS/ MATRIZ DE DIAGRAMAS DE DISPERSIÓN

2.4. MATRIZ DE CORRELACIÓN PEARSON ESTADÍSTICOS/RESÚMENES/MATRIZ DE CORRELACIÓN > cor(EJEMPLO[,c("MATEMATICAS","METACOGNICION","MOTIVACION","RAZONAMIENTO","VERBAL")], use="complete") MATEMATICAS METACOGNICION MOTIVACION RAZONAMIENTO VERBAL MATEMATICAS 1.0000000 0.4474878 0.1905037 0.5889247 0.5913707 METACOGNICION 0.4474878 1.0000000 0.1984354 0.4972449 0.4485804 MOTIVACION 0.1905037 0.1984354 1.0000000 0.2552468 0.2025340 RAZONAMIENTO 0.5889247 0.4972449 0.2552468 1.0000000 0.5926421 VERBAL 0.5913707 0.4485804 0.2025340 0.5926421 1.0000000

2.5. REGRESIÓN LINEAL MÚLTIPLE ESTADÍSTICOS/AJUSTE DE MODELOS/MODELO LINEAL Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + MOTIVACION + VERBAL, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -27.8188 -4.2870 0.3778 4.6772 22.1370 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.663131 1.391696 4.788 1.85e-06 *** RAZONAMIENTO 0.339096 0.027028 12.546 < 2e-16 *** METACOGNICION 0.538525 0.090416 5.956 3.21e-09 *** MOTIVACION 0.009503 0.013425 0.708 0.479 VERBAL 0.429171 0.030608 14.021 < 2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.347 on 1512 degrees of freedom Multiple R-squared: 0.4507, Adjusted R-squared: 0.4493 F-statistic: 310.2 on 4 and 1512 DF, p-value: < 2.2e-16

2.6. REGRESIÓN LINEAL MÚLTIPLE CORREGIDA ESTADÍSTICOS/AJUSTE DE MODELOS/MODELO LINEAL Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + VERBAL, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -27.7647 -4.3220 0.3694 4.6986 21.9989 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.07883 1.26149 5.611 2.38e-08 *** RAZONAMIENTO 0.34178 0.02676 12.774 < 2e-16 *** METACOGNICION 0.54318 0.09016 6.024 2.13e-09 *** VERBAL 0.43022 0.03057 14.074 < 2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.346 on 1513 degrees of freedom Multiple R-squared: 0.4505, Adjusted R-squared: 0.4495 F-statistic: 413.5 on 3 and 1513 DF, p-value: < 2.2e-16

2.7. MODELO FINAL Y’ = 7.07883 + 0.34178 * RAZONAM + 0.54318 * METACOG + 0.43022 * VERB

3. ANÁLISIS FACTORIAL 3.1. ESTADÍSTICOS DESCRIPTIVOS ESTADÍSTICOS/RESÚMENES/RESÚMENES NUMÉRICOS > numSummary(ANSIEDAD[,c("y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8", "y9", "y10", "y11", "y12", "y13", "y14", "y15", "y16", "y17", "y18", "y19", "y20")], statistics=c("mean", "sd", "IQR", + "quantiles"), quantiles=c(0,.25,.5,.75,1)) mean sd IQR 0% 25% 50% 75% 100% n y1 3.027088 0.9744182 2 1 2 3 4 4 443 y2 2.595937 1.0957267 2 1 2 2 4 4 443 y3 2.051919 1.1132744 2 1 1 2 3 4 443 y4 2.142212 1.0721069 2 1 1 2 3 4 443 y5 1.731377 1.0101676 1 1 1 1 2 4 443 y6 2.038375 1.0912524 2 1 1 2 3 4 443 y7 2.431151 1.0664040 1 1 2 2 3 4 443 y8 2.839729 1.0503401 2 1 2 3 4 4 443 y9 2.735892 1.0826682 2 1 2 3 4 4 443 y10 3.237020 1.0046418 1 1 3 4 4 4 443 y11 2.469526 1.0402952 1 1 2 2 3 4 443 y12 3.058691 1.1180077 2 1 2 4 4 4 443 y13 1.898420 1.1419885 2 1 1 1 3 4 443 y14 2.133183 1.0648032 2 1 1 2 3 4 443 y15 2.532731 1.1438161 2 1 2 2 4 4 443 y16 2.936795 1.0002604 2 1 2 3 4 4 443 y17 2.415350 1.1411430 2 1 1 2 3 4 443 y18 2.419865 1.1688889 3 1 1 2 4 4 443 y19 2.311512 1.1001967 2 1 1 2 3 4 443 y20 2.566591 1.1283929 2 1 2 2 4 4 443 > describe (ANSIEDAD) var n mean sd median trimmed mad min max range skew kurtosis se y1 1 443 3.03 0.97 3.00 3.16 1.48 1.00 4.00 3.00 -0.73 -0.49 0.05 y2 2 443 2.60 1.10 2.00 2.62 1.48 1.00 4.00 3.00 -0.02 -1.34 0.05 y3 3 443 2.05 1.11 2.00 1.94 1.48 1.00 4.00 3.00 0.62 -1.02 0.05 y4 4 443 2.14 1.07 2.00 2.05 1.48 1.00 4.00 3.00 0.49 -1.04 0.05 y5 5 443 1.73 1.01 1.00 1.54 0.00 1.00 4.00 3.00 1.16 0.05 0.05 y6 6 443 2.04 1.09 2.00 1.92 1.48 1.00 4.00 3.00 0.61 -0.99 0.05 y7 7 443 2.43 1.07 2.00 2.41 1.48 1.00 4.00 3.00 0.17 -1.21 0.05 y8 8 443 2.84 1.05 3.00 2.92 1.48 1.00 4.00 3.00 -0.29 -1.23 0.05 y9 9 443 2.74 1.08 3.00 2.79 1.48 1.00 4.00 3.00 -0.25 -1.25 0.05 y10 10 443 3.24 1.00 4.00 3.41 0.00 1.00 4.00 3.00 -1.05 -0.19 0.05 y11 11 443 2.47 1.04 2.00 2.46 1.48 1.00 4.00 3.00 0.11 -1.17 0.05 y12 12 443 3.06 1.12 4.00 3.20 0.00 1.00 4.00 3.00 -0.72 -0.99 0.05 y13 13 443 1.90 1.14 1.00 1.75 0.00 1.00 4.00 3.00 0.85 -0.83 0.05 y14 14 443 2.13 1.06 2.00 2.04 1.48 1.00 4.00 3.00 0.56 -0.92 0.05 y15 15 443 2.53 1.14 2.00 2.54 1.48 1.00 4.00 3.00 0.02 -1.43 0.05 y16 16 443 2.94 1.00 3.00 3.03 1.48 1.00 4.00 3.00 -0.39 -1.09 0.05 y17 17 443 2.42 1.14 2.00 2.39 1.48 1.00 4.00 3.00 0.13 -1.40 0.05 y18 18 443 2.42 1.17 2.00 2.40 1.48 1.00 4.00 3.00 0.11 -1.47 0.06 y19 19 443 2.31 1.10 2.00 2.26 1.48 1.00 4.00 3.00 0.24 -1.28 0.05 y20 20 443 2.57 1.13 2.00 2.58 1.48 1.00 4.00 3.00 0.01 -1.40 0.05 f1* 21 443 3.03 0.97 3.00 3.16 1.48 1.00 4.00 3.00 -0.73 -0.49 0.05 f2* 22 443 2.60 1.10 2.00 2.62 1.48 1.00 4.00 3.00 -0.02 -1.34 0.05 f3* 23 443 2.05 1.11 2.00 1.94 1.48 1.00 4.00 3.00 0.62 -1.02 0.05 f4* 24 443 2.14 1.07 2.00 2.05 1.48 1.00 4.00 3.00 0.49 -1.04 0.05 f5* 25 443 1.73 1.01 1.00 1.54 0.00 1.00 4.00 3.00 1.16 0.05 0.05 f6* 26 443 2.04 1.09 2.00 1.92 1.48 1.00 4.00 3.00 0.61 -0.99 0.05 f7* 27 443 2.43 1.07 2.00 2.41 1.48 1.00 4.00 3.00 0.17 -1.21 0.05 f8* 28 443 2.84 1.05 3.00 2.92 1.48 1.00 4.00 3.00 -0.29 -1.23 0.05 f9* 29 443 2.74 1.08 3.00 2.79 1.48 1.00 4.00 3.00 -0.25 -1.25 0.05 f10* 30 443 3.24 1.00 4.00 3.41 0.00 1.00 4.00 3.00 -1.05 -0.19 0.05 f11* 31 443 2.47 1.04 2.00 2.46 1.48 1.00 4.00 3.00 0.11 -1.17 0.05 f12* 32 443 3.06 1.12 4.00 3.20 0.00 1.00 4.00 3.00 -0.72 -0.99 0.05 f13* 33 443 1.90 1.14 1.00 1.75 0.00 1.00 4.00 3.00 0.85 -0.83 0.05 f14* 34 443 2.13 1.06 2.00 2.04 1.48 1.00 4.00 3.00 0.56 -0.92 0.05 f15* 35 443 2.53 1.14 2.00 2.54 1.48 1.00 4.00 3.00 0.02 -1.43 0.05

f16* 36 443 2.94 1.00 f17* 37 443 2.42 1.14 f18* 38 443 2.42 1.17 f19* 39 443 2.31 1.10 f20* 40 443 2.57 1.13 PT 41 443 49.57 13.25 LI 42 443 48.99 13.25 LS 43 443 50.15 13.25 V 44 443 49.57 12.08

3.00 2.00 2.00 2.00 2.00 50.00 49.42 50.58 49.96

3.03 1.48 1.00 4.00 3.00 -0.39 -1.09 0.05 2.39 1.48 1.00 4.00 3.00 0.13 -1.40 0.05 2.40 1.48 1.00 4.00 3.00 0.11 -1.47 0.06 2.26 1.48 1.00 4.00 3.00 0.24 -1.28 0.05 2.58 1.48 1.00 4.00 3.00 0.01 -1.40 0.05 49.48 16.31 26.00 77.00 51.00 0.07 -1.04 0.63 48.90 16.31 25.42 76.42 51.00 0.07 -1.04 0.63 50.06 16.31 26.58 77.58 51.00 0.07 -1.04 0.63 49.49 14.88 28.07 74.59 46.52 0.07 -1.04 0.57

3.2. MATRIZ DE CORRELACIONES ESTADÍSTICOS/RESÚMENES/MATRIZ DE CORRELACIÓN + P-VALORES PAREADOS > rcorr.adjust(ANSIEDAD[,c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10","y11","y12","y13","y14","y15","y16","y17 ","y18","y19","y20")], type="pearson", use="complete") Pearson correlations: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 1.0000 0.5061 0.1614 0.1912 0.0488 0.0586 0.1499 0.4508 0.3585 0.2060 0.3668 0.2851 0.1570 0.2756 0.3646 0.3082 0.1648 0.2204 0.1820 0.2700 y2 0.5061 1.0000 0.3307 0.3360 0.2431 0.3025 0.3372 0.6926 0.5449 0.2537 0.6074 0.2743 0.3559 0.4748 0.5584 0.4308 0.2757 0.4260 0.2173 0.3411 y3 0.1614 0.3307 1.0000 0.4241 0.4409 0.4956 0.4671 0.3883 0.2798 0.2216 0.3384 0.2302 0.3191 0.4388 0.3833 0.2915 0.3783 0.3101 0.2934 0.3962 y4 0.1912 0.3360 0.4241 1.0000 0.4260 0.5697 0.4429 0.4563 0.3852 0.1094 0.3619 0.1214 0.2835 0.5442 0.3809 0.2405 0.3252 0.2592 0.1388 0.6159 y5 0.0488 0.2431 0.4409 0.4260 1.0000 0.4424 0.4375 0.2643 0.2060 0.0116 0.1978 0.0981 0.1901 0.3804 0.2220 0.1399 0.4110 0.0689 0.1447 0.3303 y6 0.0586 0.3025 0.4956 0.5697 0.4424 1.0000 0.5048 0.3508 0.2939 0.0639 0.3269 0.1243 0.3245 0.5077 0.3135 0.1888 0.3106 0.2392 0.2048 0.5206 y7 0.1499 0.3372 0.4671 0.4429 0.4375 0.5048 1.0000 0.4396 0.3046 0.1895 0.3677 0.2406 0.3277 0.5331 0.4234 0.2716 0.3285 0.2501 0.2864 0.3813 y8 0.4508 0.6926 0.3883 0.4563 0.2643 0.3508 0.4396 1.0000 0.6690 0.2912 0.6612 0.4165 0.3995 0.5067 0.5910 0.5115 0.3048 0.4751 0.2547 0.3956 y9 0.3585 0.5449 0.2798 0.3852 0.2060 0.2939 0.3046 0.6690 1.0000 0.3676 0.5442 0.4091 0.3021 0.4290 0.5889 0.4525 0.2538 0.3488 0.3085 0.4209 y10 0.2060 0.2537 0.2216 0.1094 0.0116 0.0639 0.1895 0.2912 0.3676 1.0000 0.2613 0.1568 0.2636 0.2475 0.3663 0.3617 0.2178 0.2696 0.2892 0.1148 y11 0.3668 0.6074 0.3384 0.3619 0.1978 0.3269 0.3677 0.6612 0.5442 0.2613 1.0000 0.3497 0.4744 0.5031 0.5308 0.4591 0.2546 0.4924 0.2752 0.3819 y12 0.2851 0.2743 0.2302 0.1214 0.0981 0.1243 0.2406 0.4165 0.4091 0.1568 0.3497 1.0000 0.1819 0.2671 0.4107 0.3311 0.3320 0.2650 0.3566 0.3251 y13 0.1570 0.3559 0.3191 0.2835 0.1901 0.3245 0.3277 0.3995 0.3021 0.2636 0.4744 0.1819 1.0000 0.4261 0.4226 0.3469 0.2963 0.5371 0.3242 0.3309 y14 0.2756 0.4748 0.4388 0.5442 0.3804 0.5077 0.5331 0.5067 0.4290 0.2475 0.5031 0.2671 0.4261 1.0000 0.5286 0.4328 0.4422 0.4748 0.3701 0.4549 y15 0.3646 0.5584 0.3833 0.3809 0.2220 0.3135 0.4234 0.5910 0.5889 0.3663 0.5308 0.4107 0.4226 0.5286 1.0000 0.5436 0.2981 0.5261 0.3353 0.2950 y16 0.3082 0.4308 0.2915 0.2405 0.1399 0.1888 0.2716 0.5115 0.4525 0.3617 0.4591 0.3311 0.3469 0.4328 0.5436 1.0000 0.2569 0.4872 0.3633 0.2403 y17 0.1648 0.2757 0.3783 0.3252 0.4110 0.3106 0.3285 0.3048 0.2538 0.2178 0.2546 0.3320 0.2963 0.4422 0.2981 0.2569 1.0000 0.3642 0.2067 0.4002 y18 0.2204 0.4260 0.3101 0.2592 0.0689 0.2392 0.2501 0.4751 0.3488 0.2696 0.4924 0.2650 0.5371 0.4748 0.5261 0.4872 0.3642 1.0000 0.3062 0.2961 y19 0.1820 0.2173 0.2934 0.1388 0.1447 0.2048 0.2864 0.2547 0.3085 0.2892 0.2752 0.3566 0.3242 0.3701 0.3353 0.3633 0.2067 0.3062 1.0000 0.2183 y20 0.2700 0.3411 0.3962 0.6159 0.3303 0.5206 0.3813 0.3956 0.4209 0.1148 0.3819 0.3251 0.3309 0.4549 0.2950 0.2403 0.4002 0.2961 0.2183 1.0000 Number of observations: 443 Pairwise two-sided p-values:

y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 <.0001 0.0007 <.0001 0.3056 0.2184 0.0016 <.0001 <.0001 <.0001 <.0001 <.0001 0.0009 <.0001 <.0001 <.0001 0.0005 <.0001 0.0001 <.0001 y2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y3 0.0007 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0213 <.0001 0.0106 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0034 <.0001 y5 0.3056 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.8076 <.0001 0.0390 <.0001 <.0001 <.0001 0.0032 <.0001 0.1476 0.0023 <.0001 y6 0.2184 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1793 <.0001 0.0088 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y7 0.0016 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y9 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y10 <.0001 <.0001 <.0001 0.0213 0.8076 0.1793 <.0001 <.0001 <.0001 <.0001 0.0009 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0157 y11 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y12 <.0001 <.0001 <.0001 0.0106 0.0390 0.0088 <.0001 <.0001 <.0001 0.0009 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y13 0.0009 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y14 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y16 <.0001 <.0001 <.0001 <.0001 0.0032 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y17 0.0005 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y18 <.0001 <.0001 <.0001 <.0001 0.1476 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y19 0.0001 <.0001 <.0001 0.0034 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y20 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0157 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted p-values (Holm's method) y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 <.0001 0.0111 0.0012 0.7381 0.7381 0.0219 <.0001 <.0001 0.0003 <.0001 <.0001 0.0146 <.0001 <.0001 <.0001 0.0089 <.0001 0.0023 <.0001 y2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 y3 0.0111 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y4 0.0012 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1492 <.0001 0.0951 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0381 <.0001 y5 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 0.8076 0.0007 0.2338 0.0013 <.0001 <.0001 0.0381 <.0001 0.7381 0.0295 <.0001 y6 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.7381 <.0001 0.0885 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 0.0004 <.0001 y7 0.0219 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y9 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y10 0.0003 <.0001 <.0001 0.1492 0.8076 0.7381 0.0013 <.0001 <.0001 <.0001 0.0146 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 0.1253 y11 <.0001 <.0001 <.0001 <.0001 0.0007 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y12 <.0001 <.0001 <.0001 0.0951 0.2338 0.0885 <.0001 <.0001 <.0001 0.0146 <.0001 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y13 0.0146 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

y14 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y16 <.0001 <.0001 <.0001 <.0001 0.0381 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y17 0.0089 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 y18 <.0001 <.0001 <.0001 <.0001 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y19 0.0023 0.0001 <.0001 0.0381 0.0295 0.0004 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 0.0001 y20 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1253 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

3.3. PRUEBA DE ESFERICIDAD DE BARTLETT > bartlett.test (ANSIEDAD) Bartlett test of homogeneity of variances data: ANSIEDAD Bartlett's K-squared = 42553.95, df = 43, p-value < 2.2e-16

3.4. EXTRACCIÓN DE FACTORES ESTADÍSTICOS/ANÁLISIS DIMENSIONAL/ANÁLISIS DE COMPONENTES PRINCIPALES OPCIONES: GRÁFICOS DE SEDIMENTACIÓN + AÑADIR COMPONENTES PRINCIPALES AL CONJUNTO DE DATOS > .PC$sd^2 # component variances Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 7.6969949 2.0437618 1.2191539 1.0270841 0.8770322 0.8040020 0.7694692 0.6918792 0.6075237 0.5694914 0.5588023 0.4672406 0.4480902 0.3987410 0.3731815 0.3290076 0.3154573 0.3116517 0.3018474 Comp.20 0.1895880 > summary(.PC) # proportions of variance Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Standard deviation 2.7743458 1.4296020 1.10415301 1.01345159 0.93649998 0.8966616 0.87719392 0.83179278 0.77943805 0.75464656 0.74753079 0.68354999 0.66939540 0.63145939 0.61088583 Proportion of Variance 0.3848497 0.1021881 0.06095769 0.05135421 0.04385161 0.0402001 0.03847346 0.03459396 0.03037618 0.02847457 0.02794011 0.02336203 0.02240451 0.01993705 0.01865908 Cumulative Proportion 0.3848497 0.4870378 0.54799553 0.59934973 0.64320134 0.6834014 0.72187490 0.75646886 0.78684505 0.81531962 0.84325973 0.86662176 0.88902627 0.90896332 0.92762240 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Standard deviation 0.57359186 0.56165589 0.55825772 0.54940644 0.435417030 Proportion of Variance 0.01645038 0.01577287 0.01558258 0.01509237 0.009479399 Cumulative Proportion 0.94407278 0.95984564 0.97542823 0.99052060 1.000000000

3.5. GRÁFICO DE SEDIMENTACIÓN

3.6. ANÁLISIS FACTORIAL: ROTACIÓN VARIMAX > fit <- principal(ANSIEDAD, nfactors = 4, rotate = "varimax") > fit

Al darnos error este comando utilizamos la siguiente orden: ESTADÍSTICOS/ANÁLISIS DIMENSIONALES/ANÁLISIS FACTORIAL OPCIONES: VARIMAX + 2 FACTORES Call: factanal(x = ~y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 + y13 + y14 + y15 + y16 + y17 + y18 + y19 + y20, factors = 2, data = ANSIEDAD, scores = "none", rotation = "varimax") Uniquenesses: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 0.715 0.438 0.585 0.453 0.617 0.406 0.552 0.310 0.464 0.808 0.431 0.757 0.694 0.426 0.416 0.568 0.725 0.611 0.820 0.546 Loadings: Factor1 Factor2 y1 0.531 y2 0.701 0.267 y3 0.258 0.590 y4 0.226 0.704 y5 0.617 y6 0.115 0.762 y7 0.276 0.610 y8 0.758 0.340 y9 0.682 0.265 y10 0.434 y11 0.692 0.301 y12 0.472 0.140 y13 0.439 0.336 y14 0.460 0.602 y15 0.701 0.304 y16 0.636 0.168 y17 0.254 0.459

y18 0.579 0.233 y19 0.364 0.218 y20 0.266 0.618 Factor1 Factor2 SS loadings 4.816 3.842 Proportion Var 0.241 0.192 Cumulative Var 0.241 0.433 Test of the hypothesis that 2 factors are sufficient.

The chi square statistic is 660.13 on 151 degrees of freedom.

The p-value is 8.79e-65

3.7. AÑADIR PUNTUACIONES AL CONJUNTO DE DATOS

3.8. DISTRIBUCIÓN DE ÍTEMS EN UN ESPACIO BIDIMENSIONAL El comando indicado da error y no es posible obtener la gráfica. > # plot factor 1 by factor 2 > load <- fit$loadings[,1:2] > plot(load,type="n") # set up plot > text(load,labels=names(ANSIEDAD),cex=.7)

ANEXOS ANEXO 1. Archivo .txt de la sesi贸n.

> load("F:/IONE DOCS/Master Didactica/B1 Analisis/ANSIEDAD.RData") > ANSIEDAD$f1 <- factor(ANSIEDAD$y1, labels=c('siempre','casi siempre','a veces','nunca')) > ANSIEDAD$f2 <- factor(ANSIEDAD$y2, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f3 <- factor(ANSIEDAD$y3, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f4 <- factor(ANSIEDAD$y4, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f5 <- factor(ANSIEDAD$y5, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f6 <- factor(ANSIEDAD$y6, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f7 <- factor(ANSIEDAD$y7, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f8 <- factor(ANSIEDAD$y8, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f9 <- factor(ANSIEDAD$y9, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f10 <- factor(ANSIEDAD$y10, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f11 <- factor(ANSIEDAD$y11, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f12 <- factor(ANSIEDAD$y12, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f13 <- factor(ANSIEDAD$y13, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f14 <- factor(ANSIEDAD$y14, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f15 <- factor(ANSIEDAD$y15, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f16 <- factor(ANSIEDAD$y16, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f17 <- factor(ANSIEDAD$y17, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f18 <- factor(ANSIEDAD$y18, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f19 <- factor(ANSIEDAD$y19, labels=c('nunca','a veces','casi siempre','siempre')) > ANSIEDAD$f20 <- factor(ANSIEDAD$y20, labels=c('nunca','a veces','casi siempre','siempre')) > library(relimp, pos=4) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > .Table <- table(ANSIEDAD$f1) > .Table # counts for f1 siempre casi siempre 46 66

a veces nunca 161 170

> round(100*.Table/sum(.Table), 2) # percentages for f1 siempre casi siempre 10.38 14.90

a veces 36.34

nunca 38.37

> .Table <- table(ANSIEDAD$f2) > .Table # counts for f2 nunca 84

a veces casi siempre siempre 139 92 128

> round(100*.Table/sum(.Table), 2) # percentages for f2 nunca 18.96

a veces casi siempre 31.38 20.77

> .Table <- table(ANSIEDAD$f3)

siempre 28.89

> .Table # counts for f3 nunca 189

a veces casi siempre 116 64

siempre 74

> round(100*.Table/sum(.Table), 2) # percentages for f3 nunca 42.66

a veces casi siempre 26.19 14.45

siempre 16.70

> .Table <- table(ANSIEDAD$f4) > .Table # counts for f4 nunca 157

a veces casi siempre 136 80

siempre 70

> round(100*.Table/sum(.Table), 2) # percentages for f4 nunca 35.44

a veces casi siempre 30.70 18.06

siempre 15.80

> .Table <- table(ANSIEDAD$f5) > .Table # counts for f5 nunca 255

a veces casi siempre 98 44

siempre 46

> round(100*.Table/sum(.Table), 2) # percentages for f5 nunca 57.56

a veces casi siempre 22.12 9.93

siempre 10.38

> .Table <- table(ANSIEDAD$f6) > .Table # counts for f6 nunca 189

a veces casi siempre 114 74

siempre 66

> round(100*.Table/sum(.Table), 2) # percentages for f6 nunca 42.66

a veces casi siempre 25.73 16.70

siempre 14.90

> .Table <- table(ANSIEDAD$f7) > .Table # counts for f7 nunca 99

a veces casi siempre 152 94

siempre 98

> round(100*.Table/sum(.Table), 2) # percentages for f7 nunca 22.35

a veces casi siempre 34.31 21.22

siempre 22.12

> .Table <- table(ANSIEDAD$f8) > .Table # counts for f8 nunca 52

a veces casi siempre 129 100

siempre 162

> round(100*.Table/sum(.Table), 2) # percentages for f8 nunca 11.74

a veces casi siempre 29.12 22.57

> .Table <- table(ANSIEDAD$f9)

siempre 36.57

> .Table # counts for f9 nunca 74

a veces casi siempre 111 116

siempre 142

> round(100*.Table/sum(.Table), 2) # percentages for f9 nunca 16.70

a veces casi siempre 25.06 26.19

siempre 32.05

> .Table <- table(ANSIEDAD$f10) > .Table # counts for f10 nunca 42

a veces casi siempre siempre 57 98 246

> round(100*.Table/sum(.Table), 2) # percentages for f10 nunca 9.48

a veces casi siempre 12.87 22.12

siempre 55.53

> .Table <- table(ANSIEDAD$f11) > .Table # counts for f11 nunca 90

a veces casi siempre 149 110

siempre 94

> round(100*.Table/sum(.Table), 2) # percentages for f11 nunca 20.32

a veces casi siempre 33.63 24.83

siempre 21.22

> .Table <- table(ANSIEDAD$f12) > .Table # counts for f12 nunca 62

a veces casi siempre siempre 78 75 228

> round(100*.Table/sum(.Table), 2) # percentages for f12 nunca 14.00

a veces casi siempre 17.61 16.93

siempre 51.47

> .Table <- table(ANSIEDAD$f13) > .Table # counts for f13 nunca 241

a veces casi siempre 78 52

siempre 72

> round(100*.Table/sum(.Table), 2) # percentages for f13 nunca 54.40

a veces casi siempre 17.61 11.74

siempre 16.25

> .Table <- table(ANSIEDAD$f14) > .Table # counts for f14 nunca 151

a veces casi siempre 156 62

siempre 74

> round(100*.Table/sum(.Table), 2) # percentages for f14 nunca 34.09

a veces casi siempre 35.21 14.00

> .Table <- table(ANSIEDAD$f15)

siempre 16.70

> .Table # counts for f15 nunca 106

a veces casi siempre 123 86

siempre 128

> round(100*.Table/sum(.Table), 2) # percentages for f15 nunca 23.93

a veces casi siempre 27.77 19.41

siempre 28.89

> .Table <- table(ANSIEDAD$f16) > .Table # counts for f16 nunca 38

a veces casi siempre 122 113

siempre 170

> round(100*.Table/sum(.Table), 2) # percentages for f16 nunca 8.58

a veces casi siempre 27.54 25.51

siempre 38.37

> .Table <- table(ANSIEDAD$f17) > .Table # counts for f17 nunca 126

a veces casi siempre 115 94

siempre 108

> round(100*.Table/sum(.Table), 2) # percentages for f17 nunca 28.44

a veces casi siempre 25.96 21.22

siempre 24.38

> .Table <- table(ANSIEDAD$f18) > .Table # counts for f18 nunca 134

a veces casi siempre 103 92

siempre 114

> round(100*.Table/sum(.Table), 2) # percentages for f18 nunca 30.25

a veces casi siempre 23.25 20.77

siempre 25.73

> .Table <- table(ANSIEDAD$f19) > .Table # counts for f19 nunca 134

a veces casi siempre 123 100

siempre 86

> round(100*.Table/sum(.Table), 2) # percentages for f19 nunca 30.25

a veces casi siempre 27.77 22.57

siempre 19.41

> .Table <- table(ANSIEDAD$f20) > .Table # counts for f20 nunca 95

a veces casi siempre siempre 134 82 132

> round(100*.Table/sum(.Table), 2) # percentages for f20 nunca 21.44

a veces casi siempre 30.25 18.51

> remove(.Table)

siempre 29.80

> describe(ANSIEDAD) var n mean sd median y1 1 443 3.03 0.97 3 y2 2 443 2.60 1.10 2 y3 3 443 2.05 1.11 2 y4 4 443 2.14 1.07 2 y5 5 443 1.73 1.01 1 y6 6 443 2.04 1.09 2 y7 7 443 2.43 1.07 2 y8 8 443 2.84 1.05 3 y9 9 443 2.74 1.08 3 y10 10 443 3.24 1.00 4 y11 11 443 2.47 1.04 2 y12 12 443 3.06 1.12 4 y13 13 443 1.90 1.14 1 y14 14 443 2.13 1.06 2 y15 15 443 2.53 1.14 2 y16 16 443 2.94 1.00 3 y17 17 443 2.42 1.14 2 y18 18 443 2.42 1.17 2 y19 19 443 2.31 1.10 2 y20 20 443 2.57 1.13 2 f1* 21 443 3.03 0.97 3 f2* 22 443 2.60 1.10 2 f3* 23 443 2.05 1.11 2 f4* 24 443 2.14 1.07 2 f5* 25 443 1.73 1.01 1 f6* 26 443 2.04 1.09 2 f7* 27 443 2.43 1.07 2 f8* 28 443 2.84 1.05 3 f9* 29 443 2.74 1.08 3 f10* 30 443 3.24 1.00 4 f11* 31 443 2.47 1.04 2 f12* 32 443 3.06 1.12 4 f13* 33 443 1.90 1.14 1 f14* 34 443 2.13 1.06 2 f15* 35 443 2.53 1.14 2 f16* 36 443 2.94 1.00 3 f17* 37 443 2.42 1.14 2 f18* 38 443 2.42 1.17 2 f19* 39 443 2.31 1.10 2 f20* 40 443 2.57 1.13 2

trimmed mad min 3.16 1.48 1 4 2.62 1.48 1 4 1.94 1.48 1 4 2.05 1.48 1 4 1.54 0.00 1 4 1.92 1.48 1 4 2.41 1.48 1 4 2.92 1.48 1 4 2.79 1.48 1 4 3.41 0.00 1 4 2.46 1.48 1 4 3.20 0.00 1 4 1.75 0.00 1 4 2.04 1.48 1 4 2.54 1.48 1 4 3.03 1.48 1 4 2.39 1.48 1 4 2.40 1.48 1 4 2.26 1.48 1 4 2.58 1.48 1 4 3.16 1.48 1 4 2.62 1.48 1 4 1.94 1.48 1 4 2.05 1.48 1 4 1.54 0.00 1 4 1.92 1.48 1 4 2.41 1.48 1 4 2.92 1.48 1 4 2.79 1.48 1 4 3.41 0.00 1 4 2.46 1.48 1 4 3.20 0.00 1 4 1.75 0.00 1 4 2.04 1.48 1 4 2.54 1.48 1 4 3.03 1.48 1 4 2.39 1.48 1 4 2.40 1.48 1 4 2.26 1.48 1 4 2.58 1.48 1 4

max range skew kurtosis se 3 -0.73 -0.49 0.05 3 -0.02 -1.34 0.05 3 0.62 -1.02 0.05 3 0.49 -1.04 0.05 3 1.16 0.05 0.05 3 0.61 -0.99 0.05 3 0.17 -1.21 0.05 3 -0.29 -1.23 0.05 3 -0.25 -1.25 0.05 3 -1.05 -0.19 0.05 3 0.11 -1.17 0.05 3 -0.72 -0.99 0.05 3 0.85 -0.83 0.05 3 0.56 -0.92 0.05 3 0.02 -1.43 0.05 3 -0.39 -1.09 0.05 3 0.13 -1.40 0.05 3 0.11 -1.47 0.06 3 0.24 -1.28 0.05 3 0.01 -1.40 0.05 3 -0.73 -0.49 0.05 3 -0.02 -1.34 0.05 3 0.62 -1.02 0.05 3 0.49 -1.04 0.05 3 1.16 0.05 0.05 3 0.61 -0.99 0.05 3 0.17 -1.21 0.05 3 -0.29 -1.23 0.05 3 -0.25 -1.25 0.05 3 -1.05 -0.19 0.05 3 0.11 -1.17 0.05 3 -0.72 -0.99 0.05 3 0.85 -0.83 0.05 3 0.56 -0.92 0.05 3 0.02 -1.43 0.05 3 -0.39 -1.09 0.05 3 0.13 -1.40 0.05 3 0.11 -1.47 0.06 3 0.24 -1.28 0.05 3 0.01 -1.40 0.05

> barplot(table(ANSIEDAD[,1]),ylim=range(0,400), ylab="Frecuencias", border="blue", "1. Cuando voy a un examen me siento confiado/a y relajado/a", sub="Item1")

col=heat.colors(5),main=

> reliability(cov(ANSIEDAD[,c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10","y11","y12","y13","y14","y15","y16","y1 7","y18","y19","y20")], use="complete.obs")) Alpha reliability = 0.9122 Standardized alpha = 0.9119 Reliability deleting each item in turn: Alpha Std.Alpha r(item, total) y1 0.9115 0.9115 0.4000 y2 0.9058 0.9055 0.6463 y3 0.9078 0.9075 0.5664 y4 0.9077 0.9074 0.5688 y5 0.9115 0.9115 0.4010 y6 0.9087 0.9085 0.5280 y7 0.9076 0.9073 0.5750 y8 0.9037 0.9031 0.7408 y9 0.9059 0.9055 0.6436 y10 0.9124 0.9124 0.3606 y11 0.9053 0.9049 0.6721 y12 0.9109 0.9105 0.4403 y13 0.9085 0.9082 0.5388 y14 0.9042 0.9038 0.7174 y15 0.9043 0.9042 0.7009 y16 0.9077 0.9073 0.5726 y17 0.9095 0.9092 0.4983 y18 0.9077 0.9074 0.5702

y19 0.9111 y20 0.9075

0.9107 0.9072

0.4315 0.5794

> alfa <- 0.9122 > Se<-(sqrt(1-alfa)) > print (Se) [1] 0.2963106 > ANSIEDAD$PT <- with(ANSIEDAD, y1+ y2+ y3+ y4+ y5+ y6+ y7+ y8+ y9+ y10+ y11+ y12+ y13+ y14+ y15+ y16+ y17+ y18+ y19+ y20) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > ANSIEDAD$LI <- with(ANSIEDAD, PT-1.96*Se) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > ANSIEDAD$LS <- with(ANSIEDAD, PT+1.96*Se) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > summary(ANSIEDAD) y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000 Median :3.000 Median :2.000 Median :2.000 Median :2.000 Median :1.000 Median :2.000 Median :2.000 Median :3.00 Median :3.000 Median :4.000 Median :2.00 Median :4.000 Mean :3.027 Mean :2.596 Mean :2.052 Mean :2.142 Mean :1.731 Mean :2.038 Mean :2.431 Mean :2.84 Mean :2.736 Mean :3.237 Mean :2.47 Mean :3.059 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 y13 y14 y15 y16 y17 y18 y19 y20 f1 f2 f3 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 siempre : 46 nunca : 84 nunca :189 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2.000 casi siempre: 66 a veces :139 a veces :116 Median :1.000 Median :2.000 Median :2.000 Median :3.000 Median :2.000 Median :2.00 Median :2.000 Median :2.000 a veces :161 casi siempre: 92 casi siempre: 64 Mean :1.898 Mean :2.133 Mean :2.533 Mean :2.937 Mean :2.415 Mean :2.42 Mean :2.312 Mean :2.567 nunca :170 siempre :128 siempre : 74 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 nunca :157 nunca :255 nunca :189 nunca : 99 nunca : 52 nunca : 74 nunca : 42 nunca : 90 nunca : 62 nunca :241 a veces :136 a veces : 98 a veces :114 a veces :152 a veces :129 a veces :111 a veces : 57 a veces :149 a veces : 78 a veces : 78 casi siempre: 80 casi siempre: 44 casi siempre: 74 casi siempre: 94 casi siempre:100 casi siempre:116 casi siempre: 98 casi siempre:110 casi siempre: 75 casi siempre: 52 siempre : 70 siempre : 46 siempre : 66 siempre : 98 siempre :162 siempre :142 siempre :246 siempre : 94 siempre :228 siempre : 72 f14 f15 f16 f17 f18 f19 f20 PT LI LS nunca :151 nunca :106 nunca : 38 nunca :126 nunca :134 nunca :134 nunca : 95 Min. :26.00 Min. :25.42 Min. :26.58 a veces :156 a veces :123 a veces :122 a veces :115 a veces :103 a veces :123 a veces :134 1st Qu.:38.00 1st Qu.:37.42 1st Qu.:38.58 casi siempre: 62 casi siempre: 86 casi siempre:113 casi siempre: 94 casi siempre: 92 casi siempre:100 casi siempre: 82 Median :50.00 Median :49.42 Median :50.58

siempre : 74 siempre :128 siempre :170 siempre :132 Mean :49.57 Mean :48.99 Mean :50.15

:108 siempre

:114 siempre

Qu.:60.42 3rd Qu.:61.58

: 86 siempre

3rd Qu.:61.00 Max.

:77.00

3rd Max.

:76.42 Max. :77.58 > ANSIEDAD$V <- with(ANSIEDAD, 0.9122*PT+(1-0.9122)*49.57) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > save("ANSIEDAD", file="F:/IONE DOCS/Master Didactica/B1 Analisis/ANSIEDADtrabajo.RData") > load("F:/IONE DOCS/Master Didactica/B1 Analisis/EJEMPLO.RData") > describe (EJEMPLO) var n mean sd median trimmed mad min max range skew kurtosis se MOTIVACION 1 1517 68.26 14.61 69 69.05 13.34 0 100 100 -0.64 0.82 0.38 METACOGNICION 2 1517 11.22 2.47 11 11.26 2.97 3 17 14 -0.21 -0.09 0.06 VERBAL 3 1517 48.23 7.86 49 48.41 8.90 26 70 44 -0.18 -0.37 0.20 RAZONAMIENTO 4 1517 48.45 9.25 49 48.68 8.90 17 74 57 -0.25 -0.12 0.24 MATEMATICAS 5 1517 50.48 9.90 50 50.78 8.90 17 86 69 -0.24 0.35 0.25 > scatterplot(RAZONAMIENTO~MATEMATICAS, reg.line=lm, smooth=TRUE, spread=TRUE, id.method='mahal', id.n = 2, boxplots='xy', span=0.5, data=EJEMPLO) 342 556 342 556 > RegModel.1 <- lm(MATEMATICAS~RAZONAMIENTO, data=EJEMPLO) > summary(RegModel.1) Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -31.2908 -4.6148 0.3225 5.2145 23.3678 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 19.92822 1.09675 18.17 <2e-16 *** RAZONAMIENTO 0.63067 0.02224 28.36 <2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 8.004 on 1515 degrees of freedom Multiple R-squared: 0.3468, Adjusted R-squared: 0.3464 F-statistic: 804.5 on 1 and 1515 DF, p-value: < 2.2e-16 > cor.test(EJEMPLO$MATEMATICAS, EJEMPLO$RAZONAMIENTO, alternative="two.sided", method="pearson") Pearson's product-moment correlation data: EJEMPLO$MATEMATICAS and EJEMPLO$RAZONAMIENTO t = 28.3631, df = 1515, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.5550474 0.6208517 sample estimates: cor 0.5889247 > scatterplotMatrix(~MATEMATICAS+METACOGNICION+MOTIVACION+RAZONAMIENTO, smooth=TRUE, spread=FALSE, span=0.5, id.n=0, diagonal = 'density', data=EJEMPLO)

reg.line=lm,

> scatterplotMatrix(~MATEMATICAS+METACOGNICION+MOTIVACION+RAZONAMIENTO+VERBAL, smooth=TRUE, spread=FALSE, span=0.5, id.n=0, diagonal = 'density', data=EJEMPLO)

reg.line=lm,

> LinearModel.2 <- lm(MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + MOTIVACION + VERBAL, data=EJEMPLO) > summary(LinearModel.2) Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + MOTIVACION + VERBAL, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -27.8188 -4.2870 0.3778 4.6772 22.1370 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.663131 1.391696 4.788 1.85e-06 *** RAZONAMIENTO 0.339096 0.027028 12.546 < 2e-16 *** METACOGNICION 0.538525 0.090416 5.956 3.21e-09 *** MOTIVACION 0.009503 0.013425 0.708 0.479 VERBAL 0.429171 0.030608 14.021 < 2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.347 on 1512 degrees of freedom Multiple R-squared: 0.4507, Adjusted R-squared: 0.4493 F-statistic: 310.2 on 4 and 1512 DF, p-value: < 2.2e-16 > cor(EJEMPLO[,c("MATEMATICAS","METACOGNICION","MOTIVACION","RAZONAMIENTO","VERBAL")], use="complete") MATEMATICAS METACOGNICION MOTIVACION RAZONAMIENTO VERBAL MATEMATICAS 1.0000000 0.4474878 0.1905037 0.5889247 0.5913707 METACOGNICION 0.4474878 1.0000000 0.1984354 0.4972449 0.4485804 MOTIVACION 0.1905037 0.1984354 1.0000000 0.2552468 0.2025340 RAZONAMIENTO 0.5889247 0.4972449 0.2552468 1.0000000 0.5926421 VERBAL 0.5913707 0.4485804 0.2025340 0.5926421 1.0000000 > LinearModel.3 <- lm(MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + VERBAL, data=EJEMPLO) > summary(LinearModel.3) Call: lm(formula = MATEMATICAS ~ RAZONAMIENTO + METACOGNICION + VERBAL, data = EJEMPLO) Residuals: Min 1Q Median 3Q Max -27.7647 -4.3220 0.3694 4.6986 21.9989 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.07883 1.26149 5.611 2.38e-08 *** RAZONAMIENTO 0.34178 0.02676 12.774 < 2e-16 *** METACOGNICION 0.54318 0.09016 6.024 2.13e-09 *** VERBAL 0.43022 0.03057 14.074 < 2e-16 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.346 on 1513 degrees of freedom Multiple R-squared: 0.4505, Adjusted R-squared: 0.4495 F-statistic: 413.5 on 3 and 1513 DF, p-value: < 2.2e-16 > save("EJEMPLO", file="F:/IONE DOCS/Master Didactica/B1 Analisis/EJEMPLOtrabajo.RData") > load("F:/IONE DOCS/Master Didactica/B1 Analisis/ANSIEDADtrabajo.RData") > library(abind, pos=4) > library(e1071, pos=4)

> numSummary(ANSIEDAD[,c("y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8", "y9", "y10", "y11", "y12", "y13", "y14", "y15", "y16", "y17", "y18", "y19", "y20")], statistics=c("mean", "sd", "IQR", + "quantiles"), quantiles=c(0,.25,.5,.75,1)) mean sd IQR 0% 25% 50% 75% 100% n y1 3.027088 0.9744182 2 1 2 3 4 4 443 y2 2.595937 1.0957267 2 1 2 2 4 4 443 y3 2.051919 1.1132744 2 1 1 2 3 4 443 y4 2.142212 1.0721069 2 1 1 2 3 4 443 y5 1.731377 1.0101676 1 1 1 1 2 4 443 y6 2.038375 1.0912524 2 1 1 2 3 4 443 y7 2.431151 1.0664040 1 1 2 2 3 4 443 y8 2.839729 1.0503401 2 1 2 3 4 4 443 y9 2.735892 1.0826682 2 1 2 3 4 4 443 y10 3.237020 1.0046418 1 1 3 4 4 4 443 y11 2.469526 1.0402952 1 1 2 2 3 4 443 y12 3.058691 1.1180077 2 1 2 4 4 4 443 y13 1.898420 1.1419885 2 1 1 1 3 4 443 y14 2.133183 1.0648032 2 1 1 2 3 4 443 y15 2.532731 1.1438161 2 1 2 2 4 4 443 y16 2.936795 1.0002604 2 1 2 3 4 4 443 y17 2.415350 1.1411430 2 1 1 2 3 4 443 y18 2.419865 1.1688889 3 1 1 2 4 4 443 y19 2.311512 1.1001967 2 1 1 2 3 4 443 y20 2.566591 1.1283929 2 1 2 2 4 4 443 > describe (ANSIEDAD) var n mean sd median trimmed mad min max range skew kurtosis se y1 1 443 3.03 0.97 3.00 3.16 1.48 1.00 4.00 3.00 -0.73 -0.49 0.05 y2 2 443 2.60 1.10 2.00 2.62 1.48 1.00 4.00 3.00 -0.02 -1.34 0.05 y3 3 443 2.05 1.11 2.00 1.94 1.48 1.00 4.00 3.00 0.62 -1.02 0.05 y4 4 443 2.14 1.07 2.00 2.05 1.48 1.00 4.00 3.00 0.49 -1.04 0.05 y5 5 443 1.73 1.01 1.00 1.54 0.00 1.00 4.00 3.00 1.16 0.05 0.05 y6 6 443 2.04 1.09 2.00 1.92 1.48 1.00 4.00 3.00 0.61 -0.99 0.05 y7 7 443 2.43 1.07 2.00 2.41 1.48 1.00 4.00 3.00 0.17 -1.21 0.05 y8 8 443 2.84 1.05 3.00 2.92 1.48 1.00 4.00 3.00 -0.29 -1.23 0.05 y9 9 443 2.74 1.08 3.00 2.79 1.48 1.00 4.00 3.00 -0.25 -1.25 0.05 y10 10 443 3.24 1.00 4.00 3.41 0.00 1.00 4.00 3.00 -1.05 -0.19 0.05 y11 11 443 2.47 1.04 2.00 2.46 1.48 1.00 4.00 3.00 0.11 -1.17 0.05 y12 12 443 3.06 1.12 4.00 3.20 0.00 1.00 4.00 3.00 -0.72 -0.99 0.05 y13 13 443 1.90 1.14 1.00 1.75 0.00 1.00 4.00 3.00 0.85 -0.83 0.05 y14 14 443 2.13 1.06 2.00 2.04 1.48 1.00 4.00 3.00 0.56 -0.92 0.05 y15 15 443 2.53 1.14 2.00 2.54 1.48 1.00 4.00 3.00 0.02 -1.43 0.05 y16 16 443 2.94 1.00 3.00 3.03 1.48 1.00 4.00 3.00 -0.39 -1.09 0.05 y17 17 443 2.42 1.14 2.00 2.39 1.48 1.00 4.00 3.00 0.13 -1.40 0.05 y18 18 443 2.42 1.17 2.00 2.40 1.48 1.00 4.00 3.00 0.11 -1.47 0.06 y19 19 443 2.31 1.10 2.00 2.26 1.48 1.00 4.00 3.00 0.24 -1.28 0.05 y20 20 443 2.57 1.13 2.00 2.58 1.48 1.00 4.00 3.00 0.01 -1.40 0.05 f1* 21 443 3.03 0.97 3.00 3.16 1.48 1.00 4.00 3.00 -0.73 -0.49 0.05 f2* 22 443 2.60 1.10 2.00 2.62 1.48 1.00 4.00 3.00 -0.02 -1.34 0.05 f3* 23 443 2.05 1.11 2.00 1.94 1.48 1.00 4.00 3.00 0.62 -1.02 0.05 f4* 24 443 2.14 1.07 2.00 2.05 1.48 1.00 4.00 3.00 0.49 -1.04 0.05 f5* 25 443 1.73 1.01 1.00 1.54 0.00 1.00 4.00 3.00 1.16 0.05 0.05 f6* 26 443 2.04 1.09 2.00 1.92 1.48 1.00 4.00 3.00 0.61 -0.99 0.05 f7* 27 443 2.43 1.07 2.00 2.41 1.48 1.00 4.00 3.00 0.17 -1.21 0.05 f8* 28 443 2.84 1.05 3.00 2.92 1.48 1.00 4.00 3.00 -0.29 -1.23 0.05 f9* 29 443 2.74 1.08 3.00 2.79 1.48 1.00 4.00 3.00 -0.25 -1.25 0.05 f10* 30 443 3.24 1.00 4.00 3.41 0.00 1.00 4.00 3.00 -1.05 -0.19 0.05 f11* 31 443 2.47 1.04 2.00 2.46 1.48 1.00 4.00 3.00 0.11 -1.17 0.05 f12* 32 443 3.06 1.12 4.00 3.20 0.00 1.00 4.00 3.00 -0.72 -0.99 0.05 f13* 33 443 1.90 1.14 1.00 1.75 0.00 1.00 4.00 3.00 0.85 -0.83 0.05 f14* 34 443 2.13 1.06 2.00 2.04 1.48 1.00 4.00 3.00 0.56 -0.92 0.05 f15* 35 443 2.53 1.14 2.00 2.54 1.48 1.00 4.00 3.00 0.02 -1.43 0.05 f16* 36 443 2.94 1.00 3.00 3.03 1.48 1.00 4.00 3.00 -0.39 -1.09 0.05 f17* 37 443 2.42 1.14 2.00 2.39 1.48 1.00 4.00 3.00 0.13 -1.40 0.05 f18* 38 443 2.42 1.17 2.00 2.40 1.48 1.00 4.00 3.00 0.11 -1.47 0.06 f19* 39 443 2.31 1.10 2.00 2.26 1.48 1.00 4.00 3.00 0.24 -1.28 0.05 f20* 40 443 2.57 1.13 2.00 2.58 1.48 1.00 4.00 3.00 0.01 -1.40 0.05 PT 41 443 49.57 13.25 50.00 49.48 16.31 26.00 77.00 51.00 0.07 -1.04 0.63 LI 42 443 48.99 13.25 49.42 48.90 16.31 25.42 76.42 51.00 0.07 -1.04 0.63 LS 43 443 50.15 13.25 50.58 50.06 16.31 26.58 77.58 51.00 0.07 -1.04 0.63 V 44 443 49.57 12.08 49.96 49.49 14.88 28.07 74.59 46.52 0.07 -1.04 0.57

> library(Hmisc, pos=4) > rcorr.adjust(ANSIEDAD[,c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10","y11","y12","y13","y14","y15","y16","y17 ","y18","y19","y20")], type="pearson", use="complete") Pearson correlations: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 1.0000 0.5061 0.1614 0.1912 0.0488 0.0586 0.1499 0.4508 0.3585 0.2060 0.3668 0.2851 0.1570 0.2756 0.3646 0.3082 0.1648 0.2204 0.1820 0.2700 y2 0.5061 1.0000 0.3307 0.3360 0.2431 0.3025 0.3372 0.6926 0.5449 0.2537 0.6074 0.2743 0.3559 0.4748 0.5584 0.4308 0.2757 0.4260 0.2173 0.3411 y3 0.1614 0.3307 1.0000 0.4241 0.4409 0.4956 0.4671 0.3883 0.2798 0.2216 0.3384 0.2302 0.3191 0.4388 0.3833 0.2915 0.3783 0.3101 0.2934 0.3962 y4 0.1912 0.3360 0.4241 1.0000 0.4260 0.5697 0.4429 0.4563 0.3852 0.1094 0.3619 0.1214 0.2835 0.5442 0.3809 0.2405 0.3252 0.2592 0.1388 0.6159 y5 0.0488 0.2431 0.4409 0.4260 1.0000 0.4424 0.4375 0.2643 0.2060 0.0116 0.1978 0.0981 0.1901 0.3804 0.2220 0.1399 0.4110 0.0689 0.1447 0.3303 y6 0.0586 0.3025 0.4956 0.5697 0.4424 1.0000 0.5048 0.3508 0.2939 0.0639 0.3269 0.1243 0.3245 0.5077 0.3135 0.1888 0.3106 0.2392 0.2048 0.5206 y7 0.1499 0.3372 0.4671 0.4429 0.4375 0.5048 1.0000 0.4396 0.3046 0.1895 0.3677 0.2406 0.3277 0.5331 0.4234 0.2716 0.3285 0.2501 0.2864 0.3813 y8 0.4508 0.6926 0.3883 0.4563 0.2643 0.3508 0.4396 1.0000 0.6690 0.2912 0.6612 0.4165 0.3995 0.5067 0.5910 0.5115 0.3048 0.4751 0.2547 0.3956 y9 0.3585 0.5449 0.2798 0.3852 0.2060 0.2939 0.3046 0.6690 1.0000 0.3676 0.5442 0.4091 0.3021 0.4290 0.5889 0.4525 0.2538 0.3488 0.3085 0.4209 y10 0.2060 0.2537 0.2216 0.1094 0.0116 0.0639 0.1895 0.2912 0.3676 1.0000 0.2613 0.1568 0.2636 0.2475 0.3663 0.3617 0.2178 0.2696 0.2892 0.1148 y11 0.3668 0.6074 0.3384 0.3619 0.1978 0.3269 0.3677 0.6612 0.5442 0.2613 1.0000 0.3497 0.4744 0.5031 0.5308 0.4591 0.2546 0.4924 0.2752 0.3819 y12 0.2851 0.2743 0.2302 0.1214 0.0981 0.1243 0.2406 0.4165 0.4091 0.1568 0.3497 1.0000 0.1819 0.2671 0.4107 0.3311 0.3320 0.2650 0.3566 0.3251 y13 0.1570 0.3559 0.3191 0.2835 0.1901 0.3245 0.3277 0.3995 0.3021 0.2636 0.4744 0.1819 1.0000 0.4261 0.4226 0.3469 0.2963 0.5371 0.3242 0.3309 y14 0.2756 0.4748 0.4388 0.5442 0.3804 0.5077 0.5331 0.5067 0.4290 0.2475 0.5031 0.2671 0.4261 1.0000 0.5286 0.4328 0.4422 0.4748 0.3701 0.4549 y15 0.3646 0.5584 0.3833 0.3809 0.2220 0.3135 0.4234 0.5910 0.5889 0.3663 0.5308 0.4107 0.4226 0.5286 1.0000 0.5436 0.2981 0.5261 0.3353 0.2950 y16 0.3082 0.4308 0.2915 0.2405 0.1399 0.1888 0.2716 0.5115 0.4525 0.3617 0.4591 0.3311 0.3469 0.4328 0.5436 1.0000 0.2569 0.4872 0.3633 0.2403 y17 0.1648 0.2757 0.3783 0.3252 0.4110 0.3106 0.3285 0.3048 0.2538 0.2178 0.2546 0.3320 0.2963 0.4422 0.2981 0.2569 1.0000 0.3642 0.2067 0.4002 y18 0.2204 0.4260 0.3101 0.2592 0.0689 0.2392 0.2501 0.4751 0.3488 0.2696 0.4924 0.2650 0.5371 0.4748 0.5261 0.4872 0.3642 1.0000 0.3062 0.2961 y19 0.1820 0.2173 0.2934 0.1388 0.1447 0.2048 0.2864 0.2547 0.3085 0.2892 0.2752 0.3566 0.3242 0.3701 0.3353 0.3633 0.2067 0.3062 1.0000 0.2183 y20 0.2700 0.3411 0.3962 0.6159 0.3303 0.5206 0.3813 0.3956 0.4209 0.1148 0.3819 0.3251 0.3309 0.4549 0.2950 0.2403 0.4002 0.2961 0.2183 1.0000 Number of observations: 443 Pairwise two-sided p-values: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 <.0001 0.0007 <.0001 0.3056 0.2184 0.0016 <.0001 <.0001 <.0001 <.0001 <.0001 0.0009 <.0001 <.0001 <.0001 0.0005 <.0001 0.0001 <.0001 y2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y3 0.0007 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0213 <.0001 0.0106 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0034 <.0001 y5 0.3056 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.8076 <.0001 0.0390 <.0001 <.0001 <.0001 0.0032 <.0001 0.1476 0.0023 <.0001 y6 0.2184 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1793 <.0001 0.0088 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y7 0.0016 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

y9 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y10 <.0001 <.0001 <.0001 0.0213 0.8076 0.1793 <.0001 <.0001 <.0001 <.0001 0.0009 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0157 y11 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y12 <.0001 <.0001 <.0001 0.0106 0.0390 0.0088 <.0001 <.0001 <.0001 0.0009 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y13 0.0009 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y14 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y16 <.0001 <.0001 <.0001 <.0001 0.0032 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y17 0.0005 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y18 <.0001 <.0001 <.0001 <.0001 0.1476 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y19 0.0001 <.0001 <.0001 0.0034 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y20 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0157 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Adjusted p-values (Holm's method) y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y1 <.0001 0.0111 0.0012 0.7381 0.7381 0.0219 <.0001 <.0001 0.0003 <.0001 <.0001 0.0146 <.0001 <.0001 <.0001 0.0089 <.0001 0.0023 <.0001 y2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 y3 0.0111 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y4 0.0012 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1492 <.0001 0.0951 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0381 <.0001 y5 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 0.8076 0.0007 0.2338 0.0013 <.0001 <.0001 0.0381 <.0001 0.7381 0.0295 <.0001 y6 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.7381 <.0001 0.0885 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 0.0004 <.0001 y7 0.0219 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y9 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y10 0.0003 <.0001 <.0001 0.1492 0.8076 0.7381 0.0013 <.0001 <.0001 <.0001 0.0146 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 0.1253 y11 <.0001 <.0001 <.0001 <.0001 0.0007 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y12 <.0001 <.0001 <.0001 0.0951 0.2338 0.0885 <.0001 <.0001 <.0001 0.0146 <.0001 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y13 0.0146 <.0001 <.0001 <.0001 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0023 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y14 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y16 <.0001 <.0001 <.0001 <.0001 0.0381 0.0013 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y17 0.0089 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 y18 <.0001 <.0001 <.0001 <.0001 0.7381 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 y19 0.0023 0.0001 <.0001 0.0381 0.0295 0.0004 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 0.0001 y20 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.1253 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 > bartlett.test (ANSIEDAD) Bartlett test of homogeneity of variances

data: ANSIEDAD Bartlett's K-squared = 42553.95, df = 43, p-value < 2.2e-16 > .PC <- princomp(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, cor=TRUE, data=ANSIEDAD) > unclass(loadings(.PC)) # component loadings Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 y1 -0.1661740 0.25381628 -0.38798805 0.18985183 0.009763335 0.188748836 0.03857039 0.682853515 0.067046479 0.082171462 -0.110764895 -0.184602760 0.14423488 -0.09087411 -0.08217807 y2 -0.2561822 0.16415729 -0.31036491 -0.12845951 0.072601873 0.152814022 -0.20142994 0.131300551 0.109365664 -0.075255086 0.084753973 0.289193153 -0.07760557 -0.21387121 0.19086049 y3 -0.2204848 -0.23572668 0.15130672 0.06985412 0.189452090 0.079198471 -0.12086905 0.134375717 0.199799562 0.692180559 0.342515712 0.105128730 0.03732976 0.23775114 -0.02859027 y4 -0.2262414 -0.31418753 -0.21630472 -0.11333001 0.001793975 -0.107287877 0.34292677 -0.040625053 0.266083296 -0.014379970 -0.006641945 -0.075362049 0.17915988 0.39201795 -0.04737473 y5 -0.1623564 -0.40374393 -0.01802677 0.15139011 0.202441261 0.279981104 -0.32709573 -0.049032270 0.008764389 -0.385396836 0.351879593 -0.230195312 0.14611050 0.05846924 -0.08951004 y6 -0.2102906 -0.38541946 -0.02426409 -0.12538254 0.030273270 -0.246007012 0.07343417 -0.004376198 0.023496435 0.143116386 0.011421731 0.209252407 -0.08685951 -0.71716128 -0.16320563 y7 -0.2257107 -0.24087374 0.07480401 0.03275959 0.261180401 -0.139050654 -0.26038766 0.001825353 0.114726304 0.027045680 -0.642197923 -0.251496002 -0.15868310 0.06439932 0.40066369 y8 -0.2865734 0.14329702 -0.25762597 -0.04762498 0.049396260 -0.004542832 -0.12039492 -0.162046061 0.112584333 -0.001894144 0.033691934 0.026880404 -0.12850478 0.11351093 0.24791011 y9 -0.2532205 0.17530845 -0.20804738 0.10972438 0.144510391 -0.146496971 0.19645811 -0.354605651 0.090250418 -0.235930108 0.195258482 0.150568015 0.18001485 -0.04605716 0.20444188 y10 -0.1476890 0.23867398 0.30219242 0.01953041 0.525650659 0.245125894 0.54484844 -0.035970831 0.235683255 -0.001198991 -0.114633086 -0.016580185 -0.10454748 -0.01476516 -0.13597298 y11 -0.2639360 0.15236318 -0.14263205 -0.19732117 -0.098320779 -0.095985308 -0.13929645 -0.062015267 0.262125446 -0.085987030 0.060679213 0.058947965 -0.44622095 0.28135273 -0.40425634 y12 -0.1775164 0.17164982 0.03525377 0.61711662 -0.330820862 -0.170360062 -0.11739415 -0.261277854 0.169232778 0.168542207 -0.143043884 -0.115149435 0.03496752 -0.03340820 -0.26608357 y13 -0.2139181 0.03450751 0.30206396 -0.39572409 -0.268542661 -0.036949317 -0.00120408 0.158111919 0.421634808 -0.208458738 0.055283114 -0.421358520 0.23178111 -0.11588344 -0.09561021 y14 -0.2746895 -0.11811856 0.07824749 -0.09293831 -0.002989795 -0.009086314 -0.02873651 0.108718325 0.343218455 -0.177955059 -0.308679947 0.282045005 -0.07621654 0.14114313 -0.43479480 y15 -0.2714416 0.17189299 0.02195706 -0.04451791 0.124407118 -0.018590604 -0.12043316 -0.206332028 0.154557208 0.118871439 -0.154797629 0.002406319 0.63296690 -0.08781980 -0.14925865 y16 -0.2264249 0.24218246 0.15093902 -0.02970572 0.092812896 0.021248340 -0.06954047 -0.134028060 0.573884248 0.117537354 0.252750633 -0.474531085 -0.35855588 -0.20854726 0.01651010 y17 -0.1946771 -0.16378858 0.20912546 0.27737931 -0.293312774 0.631767275 0.08234800 -0.067379093 0.013735292 -0.144452340 -0.100156386 0.196320821 -0.11161694 -0.10294559 0.10352997 y18 -0.2270906 0.17142775 0.26010478 -0.32478619 -0.373630701 0.131698792 -0.02762926 -0.029706315 0.165794297 0.188644052 -0.017166116 0.199506322 0.10438532 0.12121111 0.30994404 y19 -0.1711911 0.10210797 0.44133701 0.29688876 0.078361210 -0.453056694 -0.05572392 0.398362677 0.085557011 -0.294591569 0.215018882 0.253604721 0.00370452 0.09194916 0.18237226 y20 -0.2264070 -0.23211691 -0.16610218 0.12997305 -0.319209478 -0.145638362 0.48123992 0.086351919 0.038909832 0.027299991 0.086774391 -0.200475937 -0.11538904 0.01060309 0.20451793 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 y1 -0.010130146 0.05957815 0.339605135 -0.03423518 0.065739409 y2 0.106138802 -0.17979219 -0.627113125 0.03109384 0.271473987 y3 -0.206544397 0.18369402 -0.074188524 -0.06034243 0.039074619 y4 -0.147283290 -0.46207817 0.005023093 0.23254956 0.325026188 y5 0.380465008 -0.05463650 0.111937518 -0.19970747 -0.016496927 y6 0.051256878 -0.15547775 0.288074641 0.02875897 0.002339459 y7 0.038573464 0.13592050 0.037475119 0.06321612 0.155648343 y8 -0.278336417 -0.38943850 0.178414179 -0.19657500 -0.616836186 y9 -0.242254022 0.44717620 0.230669190 -0.16496709 0.321144464 y10 0.222961765 -0.18761507 -0.017596630 -0.10470395 -0.013694021 y11 0.213667966 0.17200504 0.203420894 0.40399237 0.030471578 y12 0.077665893 -0.27784620 -0.090062167 -0.20109247 0.201865473 y13 -0.325263012 -0.03013859 -0.108164951 -0.07884517 0.063113472 y14 -0.178771986 0.21234702 -0.191048525 -0.46786591 -0.107570246 y15 0.161125573 0.14333773 -0.130046584 0.39967062 -0.325005608 y16 -0.117440238 0.03931902 -0.076242651 0.05498227 0.076337753 y17 -0.284781700 0.04870054 0.070238833 0.35407144 -0.030702817 y18 0.426228251 -0.07108367 0.296060435 -0.24788022 0.163297834 y19 -0.002545087 -0.11405061 0.016947933 0.19695078 -0.075338563 y20 0.312357711 0.29849054 -0.300515179 -0.03862459 -0.323303971

> .PC$sd^2 # component variances Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 7.6969949 2.0437618 1.2191539 1.0270841 0.8770322 0.8040020 0.7694692 0.6918792 0.6075237 0.5694914 0.5588023 0.4672406 0.4480902 0.3987410 0.3731815 0.3290076 0.3154573 0.3116517 0.3018474 Comp.20 0.1895880 > summary(.PC) # proportions of variance Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Standard deviation 2.7743458 1.4296020 1.10415301 1.01345159 0.93649998 0.8966616 0.87719392 0.83179278 0.77943805 0.75464656 0.74753079 0.68354999 0.66939540 0.63145939 0.61088583 Proportion of Variance 0.3848497 0.1021881 0.06095769 0.05135421 0.04385161 0.0402001 0.03847346 0.03459396 0.03037618 0.02847457 0.02794011 0.02336203 0.02240451 0.01993705 0.01865908 Cumulative Proportion 0.3848497 0.4870378 0.54799553 0.59934973 0.64320134 0.6834014 0.72187490 0.75646886 0.78684505 0.81531962 0.84325973 0.86662176 0.88902627 0.90896332 0.92762240 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Standard deviation 0.57359186 0.56165589 0.55825772 0.54940644 0.435417030 Proportion of Variance 0.01645038 0.01577287 0.01558258 0.01509237 0.009479399 Cumulative Proportion 0.94407278 0.95984564 0.97542823 0.99052060 1.000000000 > remove(.PC) > .PC <- princomp(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, cor=TRUE, data=ANSIEDAD) > unclass(loadings(.PC)) # component loadings Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 y1 -0.1661740 0.25381628 -0.38798805 0.18985183 0.009763335 0.188748836 0.03857039 0.682853515 0.067046479 0.082171462 -0.110764895 -0.184602760 0.14423488 -0.09087411 -0.08217807 y2 -0.2561822 0.16415729 -0.31036491 -0.12845951 0.072601873 0.152814022 -0.20142994 0.131300551 0.109365664 -0.075255086 0.084753973 0.289193153 -0.07760557 -0.21387121 0.19086049 y3 -0.2204848 -0.23572668 0.15130672 0.06985412 0.189452090 0.079198471 -0.12086905 0.134375717 0.199799562 0.692180559 0.342515712 0.105128730 0.03732976 0.23775114 -0.02859027 y4 -0.2262414 -0.31418753 -0.21630472 -0.11333001 0.001793975 -0.107287877 0.34292677 -0.040625053 0.266083296 -0.014379970 -0.006641945 -0.075362049 0.17915988 0.39201795 -0.04737473 y5 -0.1623564 -0.40374393 -0.01802677 0.15139011 0.202441261 0.279981104 -0.32709573 -0.049032270 0.008764389 -0.385396836 0.351879593 -0.230195312 0.14611050 0.05846924 -0.08951004 y6 -0.2102906 -0.38541946 -0.02426409 -0.12538254 0.030273270 -0.246007012 0.07343417 -0.004376198 0.023496435 0.143116386 0.011421731 0.209252407 -0.08685951 -0.71716128 -0.16320563 y7 -0.2257107 -0.24087374 0.07480401 0.03275959 0.261180401 -0.139050654 -0.26038766 0.001825353 0.114726304 0.027045680 -0.642197923 -0.251496002 -0.15868310 0.06439932 0.40066369 y8 -0.2865734 0.14329702 -0.25762597 -0.04762498 0.049396260 -0.004542832 -0.12039492 -0.162046061 0.112584333 -0.001894144 0.033691934 0.026880404 -0.12850478 0.11351093 0.24791011 y9 -0.2532205 0.17530845 -0.20804738 0.10972438 0.144510391 -0.146496971 0.19645811 -0.354605651 0.090250418 -0.235930108 0.195258482 0.150568015 0.18001485 -0.04605716 0.20444188 y10 -0.1476890 0.23867398 0.30219242 0.01953041 0.525650659 0.245125894 0.54484844 -0.035970831 0.235683255 -0.001198991 -0.114633086 -0.016580185 -0.10454748 -0.01476516 -0.13597298 y11 -0.2639360 0.15236318 -0.14263205 -0.19732117 -0.098320779 -0.095985308 -0.13929645 -0.062015267 0.262125446 -0.085987030 0.060679213 0.058947965 -0.44622095 0.28135273 -0.40425634 y12 -0.1775164 0.17164982 0.03525377 0.61711662 -0.330820862 -0.170360062 -0.11739415 -0.261277854 0.169232778 0.168542207 -0.143043884 -0.115149435 0.03496752 -0.03340820 -0.26608357 y13 -0.2139181 0.03450751 0.30206396 -0.39572409 -0.268542661 -0.036949317 -0.00120408 0.158111919 0.421634808 -0.208458738 0.055283114 -0.421358520 0.23178111 -0.11588344 -0.09561021 y14 -0.2746895 -0.11811856 0.07824749 -0.09293831 -0.002989795 -0.009086314 -0.02873651 0.108718325 0.343218455 -0.177955059 -0.308679947 0.282045005 -0.07621654 0.14114313 -0.43479480 y15 -0.2714416 0.17189299 0.02195706 -0.04451791 0.124407118 -0.018590604 -0.12043316 -0.206332028 0.154557208 0.118871439 -0.154797629 0.002406319 0.63296690 -0.08781980 -0.14925865 y16 -0.2264249 0.24218246 0.15093902 -0.02970572 0.092812896 0.021248340 -0.06954047 -0.134028060 0.573884248 0.117537354 0.252750633 -0.474531085 -0.35855588 -0.20854726 0.01651010 y17 -0.1946771 -0.16378858 0.20912546 0.27737931 -0.293312774 0.631767275 0.08234800 -0.067379093 0.013735292 -0.144452340 -0.100156386 0.196320821 -0.11161694 -0.10294559 0.10352997 y18 -0.2270906 0.17142775 0.26010478 -0.32478619 -0.373630701 0.131698792 -0.02762926 -0.029706315 0.165794297 0.188644052 -0.017166116 0.199506322 0.10438532 0.12121111 0.30994404 y19 -0.1711911 0.10210797 0.44133701 0.29688876 0.078361210 -0.453056694 -0.05572392 0.398362677 0.085557011 -0.294591569 0.215018882 0.253604721 0.00370452 0.09194916 0.18237226 y20 -0.2264070 -0.23211691 -0.16610218 0.12997305 -0.319209478 -0.145638362 0.48123992 0.086351919 0.038909832 0.027299991 0.086774391 -0.200475937 -0.11538904 0.01060309 0.20451793

Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 y1 -0.010130146 0.05957815 0.339605135 -0.03423518 0.065739409 y2 0.106138802 -0.17979219 -0.627113125 0.03109384 0.271473987 y3 -0.206544397 0.18369402 -0.074188524 -0.06034243 0.039074619 y4 -0.147283290 -0.46207817 0.005023093 0.23254956 0.325026188 y5 0.380465008 -0.05463650 0.111937518 -0.19970747 -0.016496927 y6 0.051256878 -0.15547775 0.288074641 0.02875897 0.002339459 y7 0.038573464 0.13592050 0.037475119 0.06321612 0.155648343 y8 -0.278336417 -0.38943850 0.178414179 -0.19657500 -0.616836186 y9 -0.242254022 0.44717620 0.230669190 -0.16496709 0.321144464 y10 0.222961765 -0.18761507 -0.017596630 -0.10470395 -0.013694021 y11 0.213667966 0.17200504 0.203420894 0.40399237 0.030471578 y12 0.077665893 -0.27784620 -0.090062167 -0.20109247 0.201865473 y13 -0.325263012 -0.03013859 -0.108164951 -0.07884517 0.063113472 y14 -0.178771986 0.21234702 -0.191048525 -0.46786591 -0.107570246 y15 0.161125573 0.14333773 -0.130046584 0.39967062 -0.325005608 y16 -0.117440238 0.03931902 -0.076242651 0.05498227 0.076337753 y17 -0.284781700 0.04870054 0.070238833 0.35407144 -0.030702817 y18 0.426228251 -0.07108367 0.296060435 -0.24788022 0.163297834 y19 -0.002545087 -0.11405061 0.016947933 0.19695078 -0.075338563 y20 0.312357711 0.29849054 -0.300515179 -0.03862459 -0.323303971 > .PC$sd^2 # component variances Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 7.6969949 2.0437618 1.2191539 1.0270841 0.8770322 0.8040020 0.7694692 0.6918792 0.6075237 0.5694914 0.5588023 0.4672406 0.4480902 0.3987410 0.3731815 0.3290076 0.3154573 0.3116517 0.3018474 Comp.20 0.1895880 > summary(.PC) # proportions of variance Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Standard deviation 2.7743458 1.4296020 1.10415301 1.01345159 0.93649998 0.8966616 0.87719392 0.83179278 0.77943805 0.75464656 0.74753079 0.68354999 0.66939540 0.63145939 0.61088583 Proportion of Variance 0.3848497 0.1021881 0.06095769 0.05135421 0.04385161 0.0402001 0.03847346 0.03459396 0.03037618 0.02847457 0.02794011 0.02336203 0.02240451 0.01993705 0.01865908 Cumulative Proportion 0.3848497 0.4870378 0.54799553 0.59934973 0.64320134 0.6834014 0.72187490 0.75646886 0.78684505 0.81531962 0.84325973 0.86662176 0.88902627 0.90896332 0.92762240 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Standard deviation 0.57359186 0.56165589 0.55825772 0.54940644 0.435417030 Proportion of Variance 0.01645038 0.01577287 0.01558258 0.01509237 0.009479399 Cumulative Proportion 0.94407278 0.95984564 0.97542823 0.99052060 1.000000000 > screeplot(.PC) > remove(.PC) > > fit <- principal(ANSIEDAD, nfactors = 2, rotate = "varimax") + > fit > > fit <- principal(ANSIEDAD, nfactors = 2, rotate = "varimax") > fit <- principal(ANSIEDAD, nfactors = 2, rotate = "varimax") > fit > fit <- principal(ANSIEDAD, nfactors = 4, rotate = "varimax") > fit > > fit <- principal(ANSIEDAD, nfactors = 4, rotate = "varimax") + > fit > > fit <- principal(ANSIEDAD, nfactors = 4, rotate = "varimax") > .FA <- factanal(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, factors=2, rotation="varimax", scores="none", data=ANSIEDAD) > .FA

Call: factanal(x = ~y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 + y13 + y14 + y15 + y16 + y17 + y18 + y19 + y20, factors = 2, data = ANSIEDAD, scores = "none", rotation = "varimax") Uniquenesses: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 0.715 0.438 0.585 0.453 0.617 0.406 0.552 0.310 0.464 0.808 0.431 0.757 0.694 0.426 0.416 0.568 0.725 0.611 0.820 0.546 Loadings: Factor1 Factor2 y1 0.531 y2 0.701 0.267 y3 0.258 0.590 y4 0.226 0.704 y5 0.617 y6 0.115 0.762 y7 0.276 0.610 y8 0.758 0.340 y9 0.682 0.265 y10 0.434 y11 0.692 0.301 y12 0.472 0.140 y13 0.439 0.336 y14 0.460 0.602 y15 0.701 0.304 y16 0.636 0.168 y17 0.254 0.459 y18 0.579 0.233 y19 0.364 0.218 y20 0.266 0.618 Factor1 Factor2 SS loadings 4.816 3.842 Proportion Var 0.241 0.192 Cumulative Var 0.241 0.433 Test of the hypothesis that 2 factors are sufficient.

The chi square statistic is 660.13 on 151 degrees of freedom.

The p-value is 8.79e-65 > remove(.FA) > .FA <- factanal(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, factors=4, rotation="varimax", scores="none", data=ANSIEDAD) > .FA Call: factanal(x = ~y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 + y13 + y14 + y15 + y16 + y17 + y18 + y19 + y20, factors = 4, data = ANSIEDAD, scores = "none", rotation = "varimax") Uniquenesses: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 0.677 0.380 0.562 0.397 0.597 0.410 0.527 0.233 0.417 0.736 0.427 0.698 0.616 0.412 0.406 0.529 0.680 0.050 0.606 0.534 Loadings: Factor1 Factor2 Factor3 Factor4 y1 0.548 0.140 y2 0.251 0.712 0.127 0.183 y3 0.570 0.147 0.266 0.143 y4 0.717 0.273 0.110 y5 0.622 y6 0.751 0.111 0.111 y7 0.599 0.200 0.266 y8 0.321 0.772 0.173 0.194 y9 0.253 0.659 0.283 y10 0.257 0.430 0.106 y11 0.280 0.622 0.193 0.266 y12 0.118 0.348 0.404 y13 0.298 0.233 0.257 0.417

y14 y15 y16 y17 y18 y19 y20

0.577 0.272 0.131 0.432 0.135 0.186 0.613

0.310 0.526 0.432 0.102 0.280 0.111 0.261

0.293 0.411 0.438 0.257 0.235 0.575

0.271 0.273 0.275 0.238 0.894 0.129 0.142

Factor1 Factor2 Factor3 Factor4 SS loadings 3.603 3.346 1.653 1.505 Proportion Var 0.180 0.167 0.083 0.075 Cumulative Var 0.180 0.347 0.430 0.505 Test of the hypothesis that 4 factors are sufficient.

The chi square statistic is 377.37 on 116 degrees of freedom.

The p-value is 2.08e-29 > remove(.FA) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > library(sem, pos=4) > .model <- c('Factor.1: y1, y2, y3, y4, y5, y6, y7, y8, y9, y10, y11, y12, y13, y14, y15, y16, y17, y18, y19, y20') > .model <- cfa(file=textConnection(.model), reference.indicators=FALSE) > .Data <- ANSIEDAD[, c('y1', 'y2', 'y3', 'y4', 'y5', 'y6', 'y7', 'y8', 'y9', 'y10', 'y11', 'y12', 'y13', 'y14', 'y15', 'y16', 'y17', 'y18', 'y19', 'y20')] > summary(sem(.model, data=.Data), robust=FALSE) Model Chisquare = 1210.74 Df = 170 Pr(>Chisq) = 2.115068e-156 AIC = 1290.74 BIC = 174.8328 Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -3.74400 -1.32400 -0.06665 0.04901 1.02200 5.54000 R-square for Endogenous Variables y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 0.2071 0.5132 0.3094 0.3450 0.1586 0.2823 0.3379 0.6467 0.4913 0.1485 0.5375 0.2174 0.3123 0.5291 0.5536 0.3734 0.2321 0.3715 0.1841 0.3306 Parameter Estimates Estimate Std Error z value Pr(>|z|) lam[y1:Factor.1] 0.4434543 0.04541882 9.763670 1.612141e-22 y1 <--- Factor.1 lam[y2:Factor.1] 0.7849429 0.04623010 16.979043 1.173851e-64 y2 <--- Factor.1 lam[y3:Factor.1] 0.6192287 0.05029785 12.311236 7.880689e-35 y3 <--- Factor.1 lam[y4:Factor.1] 0.6297281 0.04789233 13.148830 1.728283e-39 y4 <--- Factor.1 lam[y5:Factor.1] 0.4022784 0.04775621 8.423583 3.651422e-17 y5 <--- Factor.1 lam[y6:Factor.1] 0.5797946 0.04972122 11.660907 2.018813e-31 y6 <--- Factor.1 lam[y7:Factor.1] 0.6199345 0.04774562 12.984113 1.505722e-38 y7 <--- Factor.1 lam[y8:Factor.1] 0.8446526 0.04214687 20.040693 2.433551e-89 y8 <--- Factor.1 lam[y9:Factor.1] 0.7588890 0.04603628 16.484587 4.735164e-61 y9 <--- Factor.1 lam[y10:Factor.1] 0.3871381 0.04763263 8.127581 4.379421e-16 y10 <--- Factor.1 lam[y11:Factor.1] 0.7626981 0.04350653 17.530657 8.359742e-69 y11 <--- Factor.1 lam[y12:Factor.1] 0.5213120 0.05195256 10.034384 1.076290e-23 y12 <--- Factor.1 lam[y13:Factor.1] 0.6382069 0.05154751 12.380945 3.314190e-35 y13 <--- Factor.1 lam[y14:Factor.1] 0.7745429 0.04466780 17.340074 2.344338e-67 y14 <--- Factor.1 lam[y15:Factor.1] 0.8510692 0.04755402 17.896895 1.246833e-71 y15 <--- Factor.1 lam[y16:Factor.1] 0.6112366 0.04427299 13.806083 2.342256e-43 y16 <--- Factor.1 lam[y17:Factor.1] 0.5497110 0.05279631 10.411921 2.187620e-25 y17 <--- Factor.1 lam[y18:Factor.1] 0.7124284 0.05176949 13.761550 4.341186e-43 y18 <--- Factor.1 lam[y19:Factor.1] 0.4720420 0.05162957 9.142861 6.082148e-20 y19 <--- Factor.1 lam[y20:Factor.1] 0.6487767 0.05064010 12.811520 1.413378e-37 y20 <--- Factor.1 V[y1] 0.7528391 0.05169568 14.562901 4.836086e-48 y1 <--> y1 V[y2] 0.5844818 0.04285215 13.639499 2.331573e-42 y2 <--> y2 V[y3] 0.8559356 0.05966449 14.345813 1.131666e-46 y3 <--> y3 V[y4] 0.7528555 0.05281643 14.254192 4.221572e-46 y4 <--> y4

V[y5] V[y6] V[y7] V[y8] V[y9] V[y10] V[y11] V[y12] V[y13] V[y14] V[y15] V[y16] V[y17] V[y18] V[y19] V[y20]

0.8586108 0.05861876 14.647373 1.400249e-48 y5 <--> y5 0.8546701 0.05931349 14.409373 4.517925e-47 y6 <--> y6 0.7528984 0.05274931 14.273142 3.217485e-46 y7 <--> y7 0.3897762 0.03061083 12.733275 3.862873e-37 y8 <--> y8 0.5962571 0.04338781 13.742504 5.648806e-43 y9 <--> y9 0.8594293 0.05860920 14.663728 1.100607e-48 y10 <--> y10 0.5005054 0.03703790 13.513331 1.304813e-41 y11 <--> y11 0.9781750 0.06725811 14.543599 6.413073e-48 y12 <--> y12 0.8968299 0.06254645 14.338623 1.255211e-46 y13 <--> y13 0.5338890 0.03937711 13.558358 7.070099e-42 y14 <--> y14 0.5839969 0.04350973 13.422213 4.481308e-41 y15 <--> y15 0.6269110 0.04423080 14.173631 1.334199e-45 y16 <--> y16 1.0000250 0.06889448 14.515314 9.691301e-48 y17 <--> y17 0.8587471 0.06056321 14.179351 1.229785e-45 y18 <--> y18 0.9876086 0.06762477 14.604243 2.638964e-48 y19 <--> y19 0.8523594 0.05963686 14.292495 2.437202e-46 y20 <--> y20

Iterations = 15 > remove('.model', '.Data') > .PC <- princomp(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, cor=TRUE, data=ANSIEDAD) > unclass(loadings(.PC)) # component loadings Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 y1 -0.1661740 0.25381628 -0.38798805 0.18985183 0.009763335 0.188748836 0.03857039 0.682853515 0.067046479 0.082171462 -0.110764895 -0.184602760 0.14423488 -0.09087411 -0.08217807 y2 -0.2561822 0.16415729 -0.31036491 -0.12845951 0.072601873 0.152814022 -0.20142994 0.131300551 0.109365664 -0.075255086 0.084753973 0.289193153 -0.07760557 -0.21387121 0.19086049 y3 -0.2204848 -0.23572668 0.15130672 0.06985412 0.189452090 0.079198471 -0.12086905 0.134375717 0.199799562 0.692180559 0.342515712 0.105128730 0.03732976 0.23775114 -0.02859027 y4 -0.2262414 -0.31418753 -0.21630472 -0.11333001 0.001793975 -0.107287877 0.34292677 -0.040625053 0.266083296 -0.014379970 -0.006641945 -0.075362049 0.17915988 0.39201795 -0.04737473 y5 -0.1623564 -0.40374393 -0.01802677 0.15139011 0.202441261 0.279981104 -0.32709573 -0.049032270 0.008764389 -0.385396836 0.351879593 -0.230195312 0.14611050 0.05846924 -0.08951004 y6 -0.2102906 -0.38541946 -0.02426409 -0.12538254 0.030273270 -0.246007012 0.07343417 -0.004376198 0.023496435 0.143116386 0.011421731 0.209252407 -0.08685951 -0.71716128 -0.16320563 y7 -0.2257107 -0.24087374 0.07480401 0.03275959 0.261180401 -0.139050654 -0.26038766 0.001825353 0.114726304 0.027045680 -0.642197923 -0.251496002 -0.15868310 0.06439932 0.40066369 y8 -0.2865734 0.14329702 -0.25762597 -0.04762498 0.049396260 -0.004542832 -0.12039492 -0.162046061 0.112584333 -0.001894144 0.033691934 0.026880404 -0.12850478 0.11351093 0.24791011 y9 -0.2532205 0.17530845 -0.20804738 0.10972438 0.144510391 -0.146496971 0.19645811 -0.354605651 0.090250418 -0.235930108 0.195258482 0.150568015 0.18001485 -0.04605716 0.20444188 y10 -0.1476890 0.23867398 0.30219242 0.01953041 0.525650659 0.245125894 0.54484844 -0.035970831 0.235683255 -0.001198991 -0.114633086 -0.016580185 -0.10454748 -0.01476516 -0.13597298 y11 -0.2639360 0.15236318 -0.14263205 -0.19732117 -0.098320779 -0.095985308 -0.13929645 -0.062015267 0.262125446 -0.085987030 0.060679213 0.058947965 -0.44622095 0.28135273 -0.40425634 y12 -0.1775164 0.17164982 0.03525377 0.61711662 -0.330820862 -0.170360062 -0.11739415 -0.261277854 0.169232778 0.168542207 -0.143043884 -0.115149435 0.03496752 -0.03340820 -0.26608357 y13 -0.2139181 0.03450751 0.30206396 -0.39572409 -0.268542661 -0.036949317 -0.00120408 0.158111919 0.421634808 -0.208458738 0.055283114 -0.421358520 0.23178111 -0.11588344 -0.09561021 y14 -0.2746895 -0.11811856 0.07824749 -0.09293831 -0.002989795 -0.009086314 -0.02873651 0.108718325 0.343218455 -0.177955059 -0.308679947 0.282045005 -0.07621654 0.14114313 -0.43479480 y15 -0.2714416 0.17189299 0.02195706 -0.04451791 0.124407118 -0.018590604 -0.12043316 -0.206332028 0.154557208 0.118871439 -0.154797629 0.002406319 0.63296690 -0.08781980 -0.14925865 y16 -0.2264249 0.24218246 0.15093902 -0.02970572 0.092812896 0.021248340 -0.06954047 -0.134028060 0.573884248 0.117537354 0.252750633 -0.474531085 -0.35855588 -0.20854726 0.01651010 y17 -0.1946771 -0.16378858 0.20912546 0.27737931 -0.293312774 0.631767275 0.08234800 -0.067379093 0.013735292 -0.144452340 -0.100156386 0.196320821 -0.11161694 -0.10294559 0.10352997 y18 -0.2270906 0.17142775 0.26010478 -0.32478619 -0.373630701 0.131698792 -0.02762926 -0.029706315 0.165794297 0.188644052 -0.017166116 0.199506322 0.10438532 0.12121111 0.30994404 y19 -0.1711911 0.10210797 0.44133701 0.29688876 0.078361210 -0.453056694 -0.05572392 0.398362677 0.085557011 -0.294591569 0.215018882 0.253604721 0.00370452 0.09194916 0.18237226 y20 -0.2264070 -0.23211691 -0.16610218 0.12997305 -0.319209478 -0.145638362 0.48123992 0.086351919 0.038909832 0.027299991 0.086774391 -0.200475937 -0.11538904 0.01060309 0.20451793 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 y1 -0.010130146 0.05957815 0.339605135 -0.03423518 0.065739409 y2 0.106138802 -0.17979219 -0.627113125 0.03109384 0.271473987 y3 -0.206544397 0.18369402 -0.074188524 -0.06034243 0.039074619 y4 -0.147283290 -0.46207817 0.005023093 0.23254956 0.325026188

y5 0.380465008 -0.05463650 0.111937518 -0.19970747 -0.016496927 y6 0.051256878 -0.15547775 0.288074641 0.02875897 0.002339459 y7 0.038573464 0.13592050 0.037475119 0.06321612 0.155648343 y8 -0.278336417 -0.38943850 0.178414179 -0.19657500 -0.616836186 y9 -0.242254022 0.44717620 0.230669190 -0.16496709 0.321144464 y10 0.222961765 -0.18761507 -0.017596630 -0.10470395 -0.013694021 y11 0.213667966 0.17200504 0.203420894 0.40399237 0.030471578 y12 0.077665893 -0.27784620 -0.090062167 -0.20109247 0.201865473 y13 -0.325263012 -0.03013859 -0.108164951 -0.07884517 0.063113472 y14 -0.178771986 0.21234702 -0.191048525 -0.46786591 -0.107570246 y15 0.161125573 0.14333773 -0.130046584 0.39967062 -0.325005608 y16 -0.117440238 0.03931902 -0.076242651 0.05498227 0.076337753 y17 -0.284781700 0.04870054 0.070238833 0.35407144 -0.030702817 y18 0.426228251 -0.07108367 0.296060435 -0.24788022 0.163297834 y19 -0.002545087 -0.11405061 0.016947933 0.19695078 -0.075338563 y20 0.312357711 0.29849054 -0.300515179 -0.03862459 -0.323303971 > .PC$sd^2 # component variances Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 7.6969949 2.0437618 1.2191539 1.0270841 0.8770322 0.8040020 0.7694692 0.6918792 0.6075237 0.5694914 0.5588023 0.4672406 0.4480902 0.3987410 0.3731815 0.3290076 0.3154573 0.3116517 0.3018474 Comp.20 0.1895880 > summary(.PC) # proportions of variance Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Standard deviation 2.7743458 1.4296020 1.10415301 1.01345159 0.93649998 0.8966616 0.87719392 0.83179278 0.77943805 0.75464656 0.74753079 0.68354999 0.66939540 0.63145939 0.61088583 Proportion of Variance 0.3848497 0.1021881 0.06095769 0.05135421 0.04385161 0.0402001 0.03847346 0.03459396 0.03037618 0.02847457 0.02794011 0.02336203 0.02240451 0.01993705 0.01865908 Cumulative Proportion 0.3848497 0.4870378 0.54799553 0.59934973 0.64320134 0.6834014 0.72187490 0.75646886 0.78684505 0.81531962 0.84325973 0.86662176 0.88902627 0.90896332 0.92762240 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Standard deviation 0.57359186 0.56165589 0.55825772 0.54940644 0.435417030 Proportion of Variance 0.01645038 0.01577287 0.01558258 0.01509237 0.009479399 Cumulative Proportion 0.94407278 0.95984564 0.97542823 0.99052060 1.000000000 > screeplot(.PC) > ANSIEDAD$PC1 <- .PC$scores[,1] > ANSIEDAD$PC2 <- .PC$scores[,2] > remove(.PC) > showData(ANSIEDAD, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80, maxheight=30) > save("ANSIEDAD", file="F:/IONE DOCS/Master Didactica/B1 Analisis/ANSIEDADtrabajo.RData") > .FA <- factanal(~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16+y17+y18+y19+y20, factors=2, rotation="varimax", scores="none", data=ANSIEDAD) > .FA Call: factanal(x = ~y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 + y13 + y14 + y15 + y16 + y17 + y18 + y19 + y20, factors = 2, data = ANSIEDAD, scores = "none", rotation = "varimax") Uniquenesses: y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 0.715 0.438 0.585 0.453 0.617 0.406 0.552 0.310 0.464 0.808 0.431 0.757 0.694 0.426 0.416 0.568 0.725 0.611 0.820 0.546 Loadings: Factor1 y1 0.531 y2 0.701 y3 0.258 y4 0.226

Factor2 0.267 0.590 0.704

y5 0.617 y6 0.115 0.762 y7 0.276 0.610 y8 0.758 0.340 y9 0.682 0.265 y10 0.434 y11 0.692 0.301 y12 0.472 0.140 y13 0.439 0.336 y14 0.460 0.602 y15 0.701 0.304 y16 0.636 0.168 y17 0.254 0.459 y18 0.579 0.233 y19 0.364 0.218 y20 0.266 0.618 Factor1 Factor2 SS loadings 4.816 3.842 Proportion Var 0.241 0.192 Cumulative Var 0.241 0.433 Test of the hypothesis that 2 factors are sufficient.

The chi square statistic is 660.13 on 151 degrees of freedom.

The p-value is 8.79e-65 > remove(.FA) > # plot factor 1 by factor 2 > load <- fit$loadings[,1:2] > plot(load,type="n") # set up plot > text(load,labels=names(ANSIEDAD),cex=.7) > # plot factor 1 by factor 2 > load <- fit$loadings[,1:2] > plot(load,type="n") # set up plot > text(load,labels=names(ANSIEDAD),cex=.7) > # plot factor 1 by factor 2 > load <- fit$loadings[,1:2] > plot(load,type="n") > # plot factor 1 by factor 2 > load <- fit$loadings[,1:2] > plot(load,type="n") # set up plot > text(load,labels=names(ANSIEDAD),cex=.7)