#checking missing data on merged final file
descriptives(mhl, vars = vars(mhl, sds, loc, age), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────
## mhl sds loc age
## ────────────────────────────────────────────────────────────
## N 228 225 225 251
## Missing 26 29 29 3
## Mean 3.091381 3.321905 0.7634680 32.23506
## Median 3.129032 3.428571 0.7500000 24.00000
## Minimum 1.875000 1.142857 0.1666667 18.00000
## Maximum 4.034483 4.000000 1.083333 85.00000
## ────────────────────────────────────────────────────────────
#correlations
mhl.scales<- mhl[,c("mhl", "sds", "loc", "age", "recog", "know.risk", "know.treat", "know.help", "attitude")]
#calculating correlations and CIs
cor1 <- cor.mtest(mhl.scales, use="pairwise.complete.obs", conf.level = 0.95)
cor1
## $p
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 6.252025e-17 3.561775e-05 1.427965e-03 4.826137e-05
## [2,] 6.252025e-17 0.000000e+00 1.301469e-09 7.719588e-05 3.331047e-01
## [3,] 3.561775e-05 1.301469e-09 0.000000e+00 8.354590e-03 3.518631e-01
## [4,] 1.427965e-03 7.719588e-05 8.354590e-03 0.000000e+00 1.270493e-01
## [5,] 4.826137e-05 3.331047e-01 3.518631e-01 1.270493e-01 0.000000e+00
## [6,] 2.331737e-04 9.225866e-01 2.799762e-01 1.355157e-01 9.536904e-02
## [7,] 4.236035e-04 9.230429e-02 1.379222e-01 4.072120e-01 4.449214e-01
## [8,] 5.184637e-07 1.866431e-02 4.891378e-04 1.460791e-01 3.985796e-01
## [9,] 2.051132e-65 8.165309e-17 4.406215e-03 1.654037e-02 6.072543e-02
## [,6] [,7] [,8] [,9]
## [1,] 0.0002331737 0.0004236035 5.184637e-07 2.051132e-65
## [2,] 0.9225865947 0.0923042870 1.866431e-02 8.165309e-17
## [3,] 0.2799761677 0.1379221577 4.891378e-04 4.406215e-03
## [4,] 0.1355156780 0.4072120053 1.460791e-01 1.654037e-02
## [5,] 0.0953690412 0.4449213889 3.985796e-01 6.072543e-02
## [6,] 0.0000000000 0.4986582231 1.656669e-01 7.689222e-01
## [7,] 0.4986582231 0.0000000000 2.931437e-02 2.772504e-01
## [8,] 0.1656669331 0.0293143721 0.000000e+00 1.112848e-01
## [9,] 0.7689222303 0.2772504417 1.112848e-01 0.000000e+00
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.41670590 0.14633099 -0.33157005 0.14067460 0.12340552
## [2,] 0.4167059 1.00000000 0.27366946 -0.37909888 -0.06653630 -0.13295746
## [3,] 0.1463310 0.27366946 1.00000000 -0.29999556 -0.06900105 -0.06312810
## [4,] -0.3315701 -0.37909888 -0.29999556 1.00000000 -0.22875886 -0.24229871
## [5,] 0.1406746 -0.06653630 -0.06900105 -0.22875886 1.00000000 -0.02088236
## [6,] 0.1234055 -0.13295746 -0.06312810 -0.24229871 -0.02088236 1.00000000
## [7,] 0.1052345 -0.01856940 -0.03199248 -0.18496808 -0.18036296 -0.09169776
## [8,] 0.2047199 0.02646578 0.10289907 -0.03400986 -0.07462905 -0.04110654
## [9,] 0.8128596 0.41481778 0.05986130 -0.28389345 -0.25082269 -0.11878035
## [,7] [,8] [,9]
## [1,] 0.10523455 0.20471995 0.81285956
## [2,] -0.01856940 0.02646578 0.41481778
## [3,] -0.03199248 0.10289907 0.05986130
## [4,] -0.18496808 -0.03400986 -0.28389345
## [5,] -0.18036296 -0.07462905 -0.25082269
## [6,] -0.09169776 -0.04110654 -0.11878035
## [7,] 1.00000000 0.01477671 -0.05847314
## [8,] 0.01477671 1.00000000 -0.02462478
## [9,] -0.05847314 -0.02462478 1.00000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.00000000 0.6086583 0.38887797 -0.08249212 0.382463658 0.38339412
## [2,] 0.60865835 1.0000000 0.49594531 -0.13450394 0.193964700 0.14662066
## [3,] 0.38887797 0.4959453 1.00000000 -0.04579977 0.191580593 0.21482302
## [4,] -0.08249212 -0.1345039 -0.04579977 1.00000000 0.029023656 0.03348459
## [5,] 0.38246366 0.1939647 0.19158059 0.02902366 1.000000000 0.25346239
## [6,] 0.38339412 0.1466207 0.21482302 0.03348459 0.253462392 1.00000000
## [7,] 0.35234919 0.2397575 0.22705761 0.07582063 0.079968842 0.18654558
## [8,] 0.43832199 0.2817301 0.35079753 0.22514127 0.185555025 0.23510104
## [9,] 0.88476923 0.6072193 0.31223623 -0.02940549 0.005620605 0.15998191
## [,7] [,8] [,9]
## [1,] 0.35234919 0.4383220 0.884769225
## [2,] 0.23975750 0.2817301 0.607219313
## [3,] 0.22705761 0.3507975 0.312236229
## [4,] 0.07582063 0.2251413 -0.029405493
## [5,] 0.07996884 0.1855550 0.005620605
## [6,] 0.18654558 0.2351010 0.159981910
## [7,] 1.00000000 0.2703817 0.201176019
## [8,] 0.27038169 1.0000000 0.233481011
## [9,] 0.20117602 0.2334810 1.000000000
#correlation Matrix
corrplot(cor(mhl.scales, use="pairwise.complete.obs"), method="color",col = "#8C91A4", type="upper",
addCoef.col = "black", tl.col="black", tl.srt=30, p.mat = cor1$p,tl.cex = 1,
sig.level = 0.05, insig = "blank", diag=FALSE, number.cex= 10/ncol(mhl.scales))
chart.Correlation(mhl.scales, histogram=TRUE)
hist(mhl$mhl)
ggplot(mhl, aes(x=age, y=mhl)) +
geom_point(alpha=.4)
## Warning: Removed 27 rows containing missing values (geom_point).
ggplot(mhl, aes(x=age, y=sds)) +
geom_point(alpha=.4)
## Warning: Removed 30 rows containing missing values (geom_point).
ggplot(mhl, aes(x=age, y=loc)) +
geom_point(alpha=.4)
## Warning: Removed 30 rows containing missing values (geom_point).
ggplot(mhl, aes(x=mhl, y=sds)) +
geom_point(alpha=.4)
## Warning: Removed 29 rows containing missing values (geom_point).
ggplot(mhl, aes(x=mhl, y=loc)) +
geom_point(alpha=.4)
## Warning: Removed 29 rows containing missing values (geom_point).
#####################################################################################
######################### FACTOR ANALYSIS & RELIABILITIES ###########################
#####################################################################################
m1 <- 'mhl.neg =~ mhl16R + mhl17R + mhl18R + mhl19R + mhl20R + mhl21R + mhl22R + mhl23R + mhl24R
mhl.pos =~ mhl25R + mhl26R + mhl27R + mhl28R + mhl29R + mhl30R + mhl31R'
m1.fit <- lavaan::cfa(m1, data=mhl,
ordered = c("mhl16R", "mhl17R", "mhl18R", "mhl19R", "mhl20R", "mhl21R", "mhl22R",
"mhl23R", "mhl24R", "mhl25R", "mhl26R", "mhl27R", "mhl28R", "mhl29R",
"mhl30R", "mhl31R"), se = "robust")
## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.543911e-16) is smaller than zero. This may be a symptom that
## the model is not identified.
summary(m1.fit, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-8 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 80
##
## Used Total
## Number of observations 222 254
##
## Model Test User Model:
## Standard Robust
## Test Statistic 167.799 197.959
## Degrees of freedom 103 103
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.121
## Shift parameter 48.325
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 17008.130 7270.398
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 2.362
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.996 0.987
## Tucker-Lewis Index (TLI) 0.996 0.985
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.065
## 90 Percent confidence interval - lower 0.038 0.051
## 90 Percent confidence interval - upper 0.068 0.078
## P-value RMSEA <= 0.05 0.340 0.040
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.078 0.078
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mhl.neg =~
## mhl16R 1.000 0.771 0.771
## mhl17R 1.136 0.108 10.551 0.000 0.877 0.877
## mhl18R 1.036 0.120 8.647 0.000 0.799 0.799
## mhl19R 0.486 0.093 5.225 0.000 0.375 0.375
## mhl20R 1.115 0.119 9.366 0.000 0.860 0.860
## mhl21R 0.416 0.100 4.148 0.000 0.321 0.321
## mhl22R 0.863 0.109 7.893 0.000 0.666 0.666
## mhl23R 0.686 0.090 7.644 0.000 0.530 0.530
## mhl24R 0.734 0.086 8.530 0.000 0.566 0.566
## mhl.pos =~
## mhl25R 1.000 0.844 0.844
## mhl26R 1.130 0.034 33.296 0.000 0.954 0.954
## mhl27R 1.125 0.038 29.972 0.000 0.950 0.950
## mhl28R 1.105 0.035 31.287 0.000 0.933 0.933
## mhl29R 0.985 0.034 29.341 0.000 0.832 0.832
## mhl30R 0.968 0.040 23.981 0.000 0.817 0.817
## mhl31R 1.061 0.037 28.492 0.000 0.896 0.896
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mhl.neg ~~
## mhl.pos -0.385 0.047 -8.119 0.000 -0.592 -0.592
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mhl16R 0.000 0.000 0.000
## .mhl17R 0.000 0.000 0.000
## .mhl18R 0.000 0.000 0.000
## .mhl19R 0.000 0.000 0.000
## .mhl20R 0.000 0.000 0.000
## .mhl21R 0.000 0.000 0.000
## .mhl22R 0.000 0.000 0.000
## .mhl23R 0.000 0.000 0.000
## .mhl24R 0.000 0.000 0.000
## .mhl25R 0.000 0.000 0.000
## .mhl26R 0.000 0.000 0.000
## .mhl27R 0.000 0.000 0.000
## .mhl28R 0.000 0.000 0.000
## .mhl29R 0.000 0.000 0.000
## .mhl30R 0.000 0.000 0.000
## .mhl31R 0.000 0.000 0.000
## mhl.neg 0.000 0.000 0.000
## mhl.pos 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mhl16R|t1 1.082 0.105 10.310 0.000 1.082 1.082
## mhl16R|t2 1.607 0.139 11.591 0.000 1.607 1.607
## mhl16R|t3 2.211 0.224 9.855 0.000 2.211 2.211
## mhl17R|t1 1.190 0.110 10.813 0.000 1.190 1.190
## mhl17R|t2 1.530 0.132 11.585 0.000 1.530 1.530
## mhl17R|t3 1.744 0.152 11.455 0.000 1.744 1.744
## mhl17R|t4 2.365 0.261 9.053 0.000 2.365 2.365
## mhl18R|t1 1.368 0.120 11.374 0.000 1.368 1.368
## mhl18R|t2 1.695 0.147 11.525 0.000 1.695 1.695
## mhl18R|t3 1.926 0.175 11.017 0.000 1.926 1.926
## mhl18R|t4 2.211 0.224 9.855 0.000 2.211 2.211
## mhl19R|t1 -0.334 0.086 -3.877 0.000 -0.334 -0.334
## mhl19R|t2 0.468 0.088 5.335 0.000 0.468 0.468
## mhl19R|t3 1.368 0.120 11.374 0.000 1.368 1.368
## mhl19R|t4 2.612 0.342 7.639 0.000 2.612 2.612
## mhl20R|t1 1.213 0.111 10.906 0.000 1.213 1.213
## mhl20R|t2 1.799 0.158 11.354 0.000 1.799 1.799
## mhl20R|t3 2.211 0.224 9.855 0.000 2.211 2.211
## mhl20R|t4 2.612 0.342 7.639 0.000 2.612 2.612
## mhl21R|t1 -0.310 0.086 -3.610 0.000 -0.310 -0.310
## mhl21R|t2 0.456 0.088 5.203 0.000 0.456 0.456
## mhl21R|t3 1.042 0.103 10.094 0.000 1.042 1.042
## mhl21R|t4 1.926 0.175 11.017 0.000 1.926 1.926
## mhl22R|t1 1.062 0.104 10.203 0.000 1.062 1.062
## mhl22R|t2 1.494 0.129 11.562 0.000 1.494 1.494
## mhl22R|t3 1.799 0.158 11.354 0.000 1.799 1.799
## mhl22R|t4 2.097 0.202 10.381 0.000 2.097 2.097
## mhl23R|t1 0.182 0.085 2.142 0.032 0.182 0.182
## mhl23R|t2 0.770 0.094 8.185 0.000 0.770 0.770
## mhl23R|t3 1.213 0.111 10.906 0.000 1.213 1.213
## mhl23R|t4 1.859 0.166 11.212 0.000 1.859 1.859
## mhl24R|t1 0.800 0.095 8.435 0.000 0.800 0.800
## mhl24R|t2 1.368 0.120 11.374 0.000 1.368 1.368
## mhl24R|t3 1.799 0.158 11.354 0.000 1.799 1.799
## mhl24R|t4 2.004 0.186 10.750 0.000 2.004 2.004
## mhl25R|t1 -2.004 0.186 -10.750 0.000 -2.004 -2.004
## mhl25R|t2 -1.494 0.129 -11.562 0.000 -1.494 -1.494
## mhl25R|t3 -0.740 0.093 -7.933 0.000 -0.740 -0.740
## mhl25R|t4 -0.125 0.085 -1.473 0.141 -0.125 -0.125
## mhl26R|t1 -2.211 0.224 -9.855 0.000 -2.211 -2.211
## mhl26R|t2 -1.859 0.166 -11.212 0.000 -1.859 -1.859
## mhl26R|t3 -1.368 0.120 -11.374 0.000 -1.368 -1.368
## mhl26R|t4 -0.456 0.088 -5.203 0.000 -0.456 -0.456
## mhl27R|t1 -2.365 0.261 -9.053 0.000 -2.365 -2.365
## mhl27R|t2 -2.004 0.186 -10.750 0.000 -2.004 -2.004
## mhl27R|t3 -1.494 0.129 -11.562 0.000 -1.494 -1.494
## mhl27R|t4 -0.506 0.088 -5.731 0.000 -0.506 -0.506
## mhl28R|t1 -2.004 0.186 -10.750 0.000 -2.004 -2.004
## mhl28R|t2 -1.799 0.158 -11.354 0.000 -1.799 -1.799
## mhl28R|t3 -1.102 0.106 -10.416 0.000 -1.102 -1.102
## mhl28R|t4 -0.286 0.086 -3.344 0.001 -0.286 -0.286
## mhl29R|t1 -1.926 0.175 -11.017 0.000 -1.926 -1.926
## mhl29R|t2 -1.567 0.135 -11.595 0.000 -1.567 -1.567
## mhl29R|t3 -0.848 0.096 -8.806 0.000 -0.848 -0.848
## mhl29R|t4 -0.170 0.085 -2.008 0.045 -0.170 -0.170
## mhl30R|t1 -2.211 0.224 -9.855 0.000 -2.211 -2.211
## mhl30R|t2 -1.428 0.124 -11.485 0.000 -1.428 -1.428
## mhl30R|t3 -0.785 0.094 -8.311 0.000 -0.785 -0.785
## mhl30R|t4 0.182 0.085 2.142 0.032 0.182 0.182
## mhl31R|t1 -2.211 0.224 -9.855 0.000 -2.211 -2.211
## mhl31R|t2 -1.428 0.124 -11.485 0.000 -1.428 -1.428
## mhl31R|t3 -1.004 0.102 -9.871 0.000 -1.004 -1.004
## mhl31R|t4 0.011 0.084 0.134 0.893 0.011 0.011
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mhl16R 0.405 0.405 0.405
## .mhl17R 0.231 0.231 0.231
## .mhl18R 0.361 0.361 0.361
## .mhl19R 0.859 0.859 0.859
## .mhl20R 0.260 0.260 0.260
## .mhl21R 0.897 0.897 0.897
## .mhl22R 0.557 0.557 0.557
## .mhl23R 0.720 0.720 0.720
## .mhl24R 0.679 0.679 0.679
## .mhl25R 0.287 0.287 0.287
## .mhl26R 0.089 0.089 0.089
## .mhl27R 0.097 0.097 0.097
## .mhl28R 0.129 0.129 0.129
## .mhl29R 0.307 0.307 0.307
## .mhl30R 0.332 0.332 0.332
## .mhl31R 0.197 0.197 0.197
## mhl.neg 0.595 0.096 6.186 0.000 1.000 1.000
## mhl.pos 0.713 0.046 15.643 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mhl16R 1.000 1.000 1.000
## mhl17R 1.000 1.000 1.000
## mhl18R 1.000 1.000 1.000
## mhl19R 1.000 1.000 1.000
## mhl20R 1.000 1.000 1.000
## mhl21R 1.000 1.000 1.000
## mhl22R 1.000 1.000 1.000
## mhl23R 1.000 1.000 1.000
## mhl24R 1.000 1.000 1.000
## mhl25R 1.000 1.000 1.000
## mhl26R 1.000 1.000 1.000
## mhl27R 1.000 1.000 1.000
## mhl28R 1.000 1.000 1.000
## mhl29R 1.000 1.000 1.000
## mhl30R 1.000 1.000 1.000
## mhl31R 1.000 1.000 1.000
## scale reliability
mhl.scale<- mhl[,c(85:115)]
psych::alpha(mhl.scale, check.keys=TRUE)
## Warning in psych::alpha(mhl.scale, check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: psych::alpha(x = mhl.scale, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.87 0.12 4.3 0.016 4 0.31 0.096
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## mhl1R 0.82 0.82 0.87 0.13 4.4 0.016 0.026 0.104
## mhl2R 0.81 0.81 0.87 0.12 4.2 0.016 0.027 0.095
## mhl3R 0.82 0.82 0.87 0.13 4.4 0.015 0.027 0.104
## mhl4R 0.81 0.81 0.87 0.12 4.3 0.016 0.027 0.096
## mhl5R 0.81 0.81 0.86 0.12 4.2 0.016 0.027 0.096
## mhl6R 0.81 0.81 0.87 0.13 4.3 0.016 0.027 0.101
## mhl7R 0.81 0.81 0.87 0.12 4.2 0.016 0.027 0.096
## mhl8R 0.81 0.81 0.87 0.13 4.3 0.016 0.027 0.102
## mhl9R- 0.82 0.82 0.87 0.13 4.6 0.015 0.026 0.105
## mhl10R 0.82 0.82 0.87 0.13 4.5 0.016 0.027 0.105
## mhl11R 0.82 0.82 0.87 0.13 4.4 0.016 0.027 0.104
## mhl12R 0.81 0.81 0.87 0.13 4.4 0.016 0.027 0.102
## mhl13R 0.81 0.81 0.87 0.13 4.3 0.016 0.027 0.102
## mhl14R 0.81 0.81 0.87 0.12 4.2 0.016 0.027 0.096
## mhl15R 0.81 0.81 0.87 0.13 4.4 0.016 0.027 0.102
## mhl16R- 0.80 0.80 0.86 0.12 4.0 0.017 0.026 0.095
## mhl17R- 0.80 0.80 0.86 0.12 4.0 0.017 0.026 0.093
## mhl18R- 0.80 0.80 0.86 0.12 4.1 0.017 0.026 0.093
## mhl19R- 0.81 0.81 0.87 0.12 4.2 0.016 0.027 0.096
## mhl20R- 0.80 0.80 0.86 0.12 4.1 0.017 0.026 0.095
## mhl21R- 0.82 0.81 0.87 0.13 4.3 0.015 0.027 0.102
## mhl22R- 0.81 0.81 0.87 0.12 4.1 0.016 0.027 0.096
## mhl23R- 0.81 0.81 0.87 0.12 4.2 0.016 0.027 0.096
## mhl24R- 0.81 0.81 0.87 0.12 4.3 0.016 0.027 0.098
## mhl25R 0.79 0.80 0.86 0.12 4.0 0.018 0.023 0.095
## mhl26R 0.79 0.79 0.85 0.11 3.8 0.018 0.022 0.092
## mhl27R 0.80 0.80 0.86 0.12 3.9 0.017 0.023 0.095
## mhl28R 0.79 0.79 0.86 0.11 3.9 0.018 0.022 0.093
## mhl29R 0.80 0.80 0.86 0.12 4.0 0.018 0.024 0.096
## mhl30R 0.80 0.80 0.86 0.12 4.0 0.017 0.024 0.096
## mhl31R 0.79 0.80 0.86 0.12 3.9 0.018 0.023 0.092
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mhl1R 225 0.132 0.171 0.124 0.067 3.2 0.64
## mhl2R 224 0.347 0.387 0.350 0.290 3.6 0.55
## mhl3R 226 0.133 0.177 0.117 0.063 3.1 0.78
## mhl4R 221 0.285 0.321 0.290 0.222 3.7 0.64
## mhl5R 141 0.300 0.347 0.324 0.252 3.5 0.64
## mhl6R 180 0.226 0.254 0.204 0.162 3.6 0.66
## mhl7R 224 0.348 0.380 0.345 0.295 3.8 0.40
## mhl8R 217 0.250 0.274 0.228 0.178 3.7 0.62
## mhl9R- 195 0.058 0.041 -0.019 -0.052 3.4 0.95
## mhl10R 189 0.129 0.133 0.068 0.049 3.7 0.61
## mhl11R 226 0.165 0.160 0.100 0.084 3.4 0.68
## mhl12R 226 0.183 0.208 0.149 0.132 3.8 0.55
## mhl13R 191 0.213 0.261 0.203 0.170 3.6 0.55
## mhl14R 223 0.337 0.379 0.347 0.296 3.9 0.43
## mhl15R 221 0.229 0.223 0.171 0.143 3.7 0.78
## mhl16R- 227 0.513 0.528 0.513 0.454 4.8 0.64
## mhl17R- 227 0.542 0.562 0.554 0.491 4.8 0.72
## mhl18R- 227 0.480 0.512 0.501 0.423 4.8 0.64
## mhl19R- 226 0.364 0.335 0.296 0.260 4.0 0.99
## mhl20R- 226 0.505 0.510 0.502 0.451 4.8 0.54
## mhl21R- 227 0.298 0.243 0.193 0.173 3.9 1.13
## mhl22R- 227 0.398 0.433 0.411 0.335 4.7 0.78
## mhl23R- 225 0.411 0.378 0.348 0.304 4.2 1.11
## mhl24R- 226 0.315 0.307 0.259 0.230 4.7 0.82
## mhl25R 226 0.647 0.602 0.612 0.578 4.2 1.02
## mhl26R 226 0.757 0.725 0.759 0.711 4.5 0.79
## mhl27R 226 0.695 0.660 0.688 0.647 4.6 0.72
## mhl28R 226 0.746 0.703 0.732 0.695 4.4 0.89
## mhl29R 226 0.649 0.603 0.614 0.570 4.3 1.00
## mhl30R 225 0.614 0.579 0.589 0.559 4.1 0.96
## mhl31R 225 0.694 0.644 0.665 0.630 4.2 0.95
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## mhl1R 0.02 0.08 0.61 0.30 0.00 0.11
## mhl2R 0.00 0.04 0.30 0.67 0.00 0.12
## mhl3R 0.02 0.20 0.45 0.33 0.00 0.11
## mhl4R 0.02 0.04 0.19 0.75 0.00 0.13
## mhl5R 0.01 0.04 0.40 0.55 0.00 0.44
## mhl6R 0.01 0.06 0.28 0.64 0.00 0.29
## mhl7R 0.00 0.01 0.13 0.86 0.00 0.12
## mhl8R 0.01 0.04 0.22 0.73 0.00 0.15
## mhl9R 0.14 0.29 0.38 0.18 0.00 0.23
## mhl10R 0.03 0.00 0.23 0.74 0.00 0.26
## mhl11R 0.03 0.02 0.48 0.47 0.00 0.11
## mhl12R 0.02 0.00 0.15 0.83 0.00 0.11
## mhl13R 0.01 0.01 0.38 0.60 0.00 0.25
## mhl14R 0.01 0.01 0.06 0.91 0.00 0.12
## mhl15R 0.06 0.00 0.14 0.79 0.00 0.13
## mhl16R 0.86 0.08 0.04 0.00 0.01 0.11
## mhl17R 0.88 0.06 0.02 0.03 0.01 0.11
## mhl18R 0.92 0.04 0.02 0.01 0.01 0.11
## mhl19R 0.38 0.31 0.23 0.08 0.00 0.11
## mhl20R 0.89 0.08 0.02 0.01 0.00 0.11
## mhl21R 0.38 0.30 0.17 0.12 0.03 0.11
## mhl22R 0.85 0.07 0.03 0.02 0.02 0.11
## mhl23R 0.58 0.20 0.11 0.08 0.03 0.11
## mhl24R 0.79 0.12 0.05 0.01 0.02 0.11
## mhl25R 0.02 0.04 0.16 0.23 0.55 0.11
## mhl26R 0.01 0.02 0.05 0.24 0.67 0.11
## mhl27R 0.01 0.01 0.04 0.24 0.69 0.11
## mhl28R 0.02 0.01 0.10 0.26 0.61 0.11
## mhl29R 0.03 0.03 0.14 0.23 0.57 0.11
## mhl30R 0.01 0.06 0.14 0.36 0.43 0.11
## mhl31R 0.01 0.07 0.08 0.35 0.49 0.11
# recognition<- mhl[,c(85:92)]
# psych::alpha(recognition, check.keys=TRUE)
# risk<- mhl[,c(93:94)]
# psych::alpha(risk, check.keys=TRUE)
# treat<- mhl[,c(95:96)]
# psych::alpha(treat, check.keys=TRUE)
# help<- mhl[,c(97:98)]
# psych::alpha(help, check.keys=TRUE)
attitudes<- mhl[,c(99:115)]
psych::alpha(attitudes, check.keys=TRUE)
## Warning in psych::alpha(attitudes, check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: psych::alpha(x = attitudes, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.89 0.26 5.8 0.014 4.4 0.46 0.18
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## mhl15R 0.85 0.86 0.90 0.28 6.2 0.013 0.044 0.21
## mhl16R- 0.84 0.85 0.88 0.25 5.5 0.014 0.047 0.17
## mhl17R- 0.84 0.84 0.88 0.25 5.4 0.015 0.046 0.17
## mhl18R- 0.84 0.85 0.89 0.26 5.6 0.014 0.045 0.18
## mhl19R- 0.85 0.86 0.89 0.27 5.9 0.013 0.046 0.20
## mhl20R- 0.84 0.84 0.88 0.25 5.4 0.014 0.046 0.17
## mhl21R- 0.85 0.86 0.89 0.28 6.1 0.013 0.044 0.21
## mhl22R- 0.84 0.85 0.89 0.26 5.7 0.014 0.046 0.19
## mhl23R- 0.85 0.85 0.89 0.27 5.8 0.013 0.046 0.18
## mhl24R- 0.85 0.86 0.90 0.27 6.0 0.014 0.045 0.21
## mhl25R 0.82 0.84 0.88 0.24 5.2 0.016 0.039 0.18
## mhl26R 0.82 0.83 0.87 0.24 5.0 0.016 0.037 0.17
## mhl27R 0.83 0.84 0.87 0.24 5.1 0.015 0.037 0.18
## mhl28R 0.82 0.83 0.87 0.24 5.0 0.016 0.036 0.17
## mhl29R 0.83 0.84 0.88 0.25 5.2 0.016 0.039 0.18
## mhl30R 0.83 0.84 0.88 0.25 5.2 0.016 0.040 0.18
## mhl31R 0.82 0.84 0.87 0.24 5.1 0.016 0.038 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mhl15R 221 0.25 0.26 0.17 0.16 3.7 0.78
## mhl16R- 227 0.52 0.56 0.52 0.46 4.8 0.64
## mhl17R- 227 0.55 0.58 0.55 0.48 4.8 0.72
## mhl18R- 227 0.45 0.50 0.46 0.38 4.8 0.64
## mhl19R- 226 0.36 0.35 0.28 0.24 4.0 0.99
## mhl20R- 226 0.52 0.56 0.54 0.47 4.8 0.54
## mhl21R- 227 0.33 0.29 0.21 0.20 3.9 1.13
## mhl22R- 227 0.40 0.44 0.38 0.32 4.7 0.78
## mhl23R- 225 0.42 0.40 0.35 0.30 4.2 1.11
## mhl24R- 226 0.31 0.31 0.23 0.21 4.7 0.82
## mhl25R 226 0.72 0.70 0.70 0.64 4.2 1.02
## mhl26R 226 0.79 0.79 0.81 0.74 4.5 0.79
## mhl27R 226 0.72 0.72 0.73 0.67 4.6 0.72
## mhl28R 226 0.79 0.78 0.79 0.74 4.4 0.89
## mhl29R 226 0.69 0.68 0.67 0.62 4.3 1.00
## mhl30R 225 0.69 0.66 0.66 0.62 4.1 0.96
## mhl31R 225 0.75 0.73 0.73 0.69 4.2 0.95
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## mhl15R 0.06 0.00 0.14 0.79 0.00 0.13
## mhl16R 0.86 0.08 0.04 0.00 0.01 0.11
## mhl17R 0.88 0.06 0.02 0.03 0.01 0.11
## mhl18R 0.92 0.04 0.02 0.01 0.01 0.11
## mhl19R 0.38 0.31 0.23 0.08 0.00 0.11
## mhl20R 0.89 0.08 0.02 0.01 0.00 0.11
## mhl21R 0.38 0.30 0.17 0.12 0.03 0.11
## mhl22R 0.85 0.07 0.03 0.02 0.02 0.11
## mhl23R 0.58 0.20 0.11 0.08 0.03 0.11
## mhl24R 0.79 0.12 0.05 0.01 0.02 0.11
## mhl25R 0.02 0.04 0.16 0.23 0.55 0.11
## mhl26R 0.01 0.02 0.05 0.24 0.67 0.11
## mhl27R 0.01 0.01 0.04 0.24 0.69 0.11
## mhl28R 0.02 0.01 0.10 0.26 0.61 0.11
## mhl29R 0.03 0.03 0.14 0.23 0.57 0.11
## mhl30R 0.01 0.06 0.14 0.36 0.43 0.11
## mhl31R 0.01 0.07 0.08 0.35 0.49 0.11
sds.scale<- mhl[,c(78:84)]
psych::alpha(sds.scale, check.keys=TRUE)
##
## Reliability analysis
## Call: psych::alpha(x = sds.scale, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.91 0.61 11 0.0085 3.3 0.56 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sds1 0.90 0.91 0.90 0.62 9.9 0.0095 0.0054 0.63
## sds2 0.90 0.90 0.90 0.61 9.4 0.0096 0.0056 0.61
## sds3 0.90 0.90 0.90 0.60 9.1 0.0099 0.0056 0.61
## sds4 0.90 0.91 0.90 0.62 9.7 0.0093 0.0040 0.61
## sds5 0.89 0.90 0.89 0.59 8.6 0.0108 0.0053 0.60
## sds6 0.90 0.90 0.89 0.60 9.0 0.0098 0.0031 0.60
## sds7 0.89 0.90 0.90 0.60 8.9 0.0103 0.0063 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sds1 225 0.78 0.77 0.72 0.69 3.1 0.71
## sds2 225 0.79 0.80 0.76 0.71 3.5 0.63
## sds3 225 0.81 0.82 0.79 0.74 3.6 0.62
## sds4 224 0.81 0.78 0.74 0.70 2.7 0.86
## sds5 224 0.86 0.85 0.83 0.80 3.2 0.79
## sds6 225 0.81 0.83 0.80 0.74 3.6 0.58
## sds7 224 0.83 0.84 0.81 0.77 3.4 0.63
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## sds1 0.02 0.15 0.53 0.30 0.11
## sds2 0.01 0.05 0.33 0.61 0.11
## sds3 0.01 0.04 0.31 0.64 0.11
## sds4 0.08 0.29 0.44 0.19 0.12
## sds5 0.04 0.12 0.45 0.40 0.12
## sds6 0.01 0.03 0.29 0.68 0.11
## sds7 0.00 0.07 0.42 0.51 0.12
loc.scale<- mhl[,c(116:127)]
psych::alpha(loc.scale, check.keys=TRUE)
##
## Reliability analysis
## Call: psych::alpha(x = loc.scale, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.66 0.7 0.14 2 0.032 0.76 0.17 0.12
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.64 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## loc1R 0.63 0.65 0.69 0.14 1.8 0.034 0.017 0.12
## loc2R 0.63 0.65 0.67 0.15 1.9 0.034 0.014 0.12
## loc3R 0.62 0.63 0.66 0.13 1.7 0.034 0.017 0.12
## loc4R 0.60 0.63 0.67 0.13 1.7 0.036 0.017 0.10
## loc5R 0.63 0.65 0.69 0.15 1.9 0.034 0.017 0.12
## loc6R 0.61 0.64 0.68 0.14 1.8 0.035 0.019 0.10
## loc7R 0.66 0.69 0.72 0.17 2.2 0.031 0.015 0.15
## loc8R 0.63 0.66 0.67 0.15 1.9 0.033 0.014 0.14
## loc9R 0.60 0.62 0.65 0.13 1.6 0.036 0.014 0.10
## loc10R 0.59 0.61 0.64 0.13 1.6 0.037 0.013 0.12
## loc11R 0.64 0.66 0.69 0.15 1.9 0.033 0.018 0.14
## loc12R 0.62 0.65 0.68 0.14 1.8 0.034 0.016 0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## loc1R 225 0.35 0.44 0.346 0.267 0.96 0.20
## loc2R 225 0.46 0.42 0.368 0.270 0.27 0.45
## loc3R 225 0.45 0.55 0.495 0.367 0.96 0.21
## loc4R 224 0.56 0.55 0.492 0.394 0.76 0.43
## loc5R 225 0.48 0.41 0.307 0.265 0.44 0.50
## loc6R 225 0.52 0.50 0.407 0.354 0.80 0.40
## loc7R 224 0.17 0.22 0.056 0.027 1.90 0.30
## loc8R 225 0.41 0.40 0.333 0.243 0.18 0.39
## loc9R 225 0.56 0.59 0.572 0.426 0.86 0.35
## loc10R 224 0.60 0.62 0.607 0.473 0.85 0.36
## loc11R 225 0.39 0.39 0.275 0.201 0.75 0.43
## loc12R 225 0.51 0.46 0.373 0.300 0.44 0.50
##
## Non missing response frequency for each item
## 0 1 2 miss
## loc1R 0.04 0.96 0.0 0.11
## loc2R 0.73 0.27 0.0 0.11
## loc3R 0.04 0.96 0.0 0.11
## loc4R 0.24 0.76 0.0 0.12
## loc5R 0.56 0.44 0.0 0.11
## loc6R 0.20 0.80 0.0 0.11
## loc7R 0.00 0.10 0.9 0.12
## loc8R 0.82 0.18 0.0 0.11
## loc9R 0.14 0.86 0.0 0.11
## loc10R 0.15 0.85 0.0 0.12
## loc11R 0.25 0.75 0.0 0.11
## loc12R 0.56 0.44 0.0 0.11
##########################################################################
######################### REGRESSION MODELS ##############################
##########################################################################
summary(m1 <- lm(mhl ~ sex + age + hh.conserve + conserve + sample, na.action=na.exclude, data = mhl))
##
## Call:
## lm(formula = mhl ~ sex + age + hh.conserve + conserve + sample,
## data = mhl, na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85343 -0.09168 0.01458 0.12241 0.91899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.993514 0.150607 19.876 <2e-16 ***
## sex -0.013580 0.030440 -0.446 0.6560
## age -0.001506 0.001202 -1.253 0.2115
## hh.conserve 0.030725 0.015265 2.013 0.0454 *
## conserve 0.022674 0.017576 1.290 0.1984
## sample -0.012218 0.037500 -0.326 0.7449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2061 on 217 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.07697, Adjusted R-squared: 0.0557
## F-statistic: 3.619 on 5 and 217 DF, p-value: 0.003644
tab_model(m1)
|
|
mhl
|
|
Predictors
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
2.99
|
2.70 – 3.29
|
<0.001
|
|
sex
|
-0.01
|
-0.07 – 0.05
|
0.656
|
|
age
|
-0.00
|
-0.00 – 0.00
|
0.211
|
|
hh.conserve
|
0.03
|
0.00 – 0.06
|
0.045
|
|
conserve
|
0.02
|
-0.01 – 0.06
|
0.198
|
|
sample
|
-0.01
|
-0.09 – 0.06
|
0.745
|
|
Observations
|
223
|
|
R2 / R2 adjusted
|
0.077 / 0.056
|
summary(m2 <- lm(mhl ~ sex + age + hh.conserve + conserve + sample + sds, na.action=na.exclude, data = mhl))
##
## Call:
## lm(formula = mhl ~ sex + age + hh.conserve + conserve + sample +
## sds, data = mhl, na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86655 -0.09332 0.00663 0.11092 0.76782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.211080 0.161580 13.684 < 2e-16 ***
## sex 0.008864 0.026719 0.332 0.7404
## age 0.001264 0.001124 1.125 0.2620
## hh.conserve 0.029598 0.013330 2.220 0.0274 *
## conserve 0.014548 0.015462 0.941 0.3478
## sample 0.035979 0.033548 1.072 0.2847
## sds 0.191175 0.022953 8.329 9.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1797 on 214 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.3046, Adjusted R-squared: 0.2851
## F-statistic: 15.62 on 6 and 214 DF, p-value: 7.542e-15
tab_model(m1,m2)
|
|
mhl
|
mhl
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
2.99
|
2.70 – 3.29
|
<0.001
|
2.21
|
1.89 – 2.53
|
<0.001
|
|
sex
|
-0.01
|
-0.07 – 0.05
|
0.656
|
0.01
|
-0.04 – 0.06
|
0.740
|
|
age
|
-0.00
|
-0.00 – 0.00
|
0.211
|
0.00
|
-0.00 – 0.00
|
0.262
|
|
hh.conserve
|
0.03
|
0.00 – 0.06
|
0.045
|
0.03
|
0.00 – 0.06
|
0.027
|
|
conserve
|
0.02
|
-0.01 – 0.06
|
0.198
|
0.01
|
-0.02 – 0.05
|
0.348
|
|
sample
|
-0.01
|
-0.09 – 0.06
|
0.745
|
0.04
|
-0.03 – 0.10
|
0.285
|
|
sds
|
|
|
|
0.19
|
0.15 – 0.24
|
<0.001
|
|
Observations
|
223
|
221
|
|
R2 / R2 adjusted
|
0.077 / 0.056
|
0.305 / 0.285
|