Descriptives, alpha, correlations
safe_log <- function(x) {
ifelse(x == 0, NA, log(x))
}
#
# d <- d %>%
# mutate(posemo_l = log(posemo), # affective
# cogproc_l = log(cogproc), # cognitive
# cogproc_prop_l = log(cogproc_prop))
#
# d <- mutate(d, cogproc_prop_s = cogproc_prop_l,
# Tone_prop_s = Tone_prop /100,
# emo_prop_s = emo_prop/100)
d <- d %>%
mutate(cogproc_prop_l = safe_log(cogproc_prop),
posemo_prop_l = safe_log(posemo_prop),
ts_60_sq = sqrt(ts_60))
d %>%
select(pre_uv, pre_int, cogproc_prop, cogproc_prop_l, posemo_prop, posemo_prop_l, emo_prop, beh_eng, ts_60, ts_60_sq, final_grade) %>%
psych::describe()
## vars n mean sd median trimmed mad min
## pre_uv 1 628 3.76 0.74 3.67 3.78 0.49 1.00
## pre_int 2 623 4.31 0.59 4.40 4.37 0.59 1.80
## cogproc_prop 3 620 921.75 392.32 855.89 884.62 314.55 0.00
## cogproc_prop_l 4 619 6.75 0.41 6.75 6.76 0.36 5.01
## posemo_prop 5 620 228.35 92.31 210.92 218.37 71.33 0.00
## posemo_prop_l 6 617 5.37 0.37 5.35 5.36 0.35 4.13
## emo_prop 7 620 159.97 86.18 147.58 153.34 69.17 -100.28
## beh_eng 8 620 0.01 1.49 0.09 0.00 1.55 -3.31
## ts_60 9 634 31.60 22.35 26.58 28.82 17.72 0.01
## ts_60_sq 10 634 5.27 1.96 5.16 5.23 1.73 0.11
## final_grade 11 634 77.37 21.44 83.69 81.69 13.97 0.90
## max range skew kurtosis se
## pre_uv 5.00 4.00 -0.28 -0.13 0.03
## pre_int 5.00 3.20 -0.78 0.57 0.02
## cogproc_prop 3916.91 3916.91 1.76 7.69 15.76
## cogproc_prop_l 8.27 3.26 -0.36 1.51 0.02
## posemo_prop 816.63 816.63 1.59 5.19 3.71
## posemo_prop_l 6.71 2.58 0.09 0.68 0.01
## emo_prop 675.16 775.44 1.26 4.71 3.46
## beh_eng 5.30 8.61 0.12 -0.10 0.06
## ts_60 147.85 147.84 1.44 2.89 0.89
## ts_60_sq 12.16 12.05 0.25 0.35 0.08
## final_grade 100.00 99.10 -1.86 3.16 0.85
hist(d$cogproc_prop_l)
hist(d$emo_prop)
hist(d$posemo_prop)
hist(d$posemo_prop_l)
hist(d$beh_eng)
hist(d$ts_60)
hist(d$ts_60_sq)
pre_uv_d <- select(d, q1, q4, q5, q8, q10)
psych::alpha(pre_uv_d)
##
## Reliability analysis
## Call: psych::alpha(x = pre_uv_d)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.85 0.58 6.9 0.0083 4.3 0.59 0.57
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q1 0.83 0.84 0.80 0.57 5.2 0.0109 0.0040 0.54
## q4 0.85 0.86 0.83 0.61 6.3 0.0093 0.0021 0.61
## q5 0.84 0.85 0.81 0.58 5.6 0.0103 0.0027 0.57
## q8 0.83 0.83 0.79 0.55 4.9 0.0111 0.0012 0.55
## q10 0.84 0.85 0.81 0.58 5.6 0.0104 0.0046 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q1 630 0.82 0.83 0.78 0.73 4.3 0.65
## q4 627 0.78 0.77 0.67 0.63 4.3 0.80
## q5 629 0.80 0.81 0.74 0.69 4.3 0.67
## q8 629 0.84 0.85 0.82 0.75 4.4 0.64
## q10 631 0.83 0.81 0.74 0.69 4.2 0.86
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## q1 0.00 0.01 0.07 0.48 0.44 0.01
## q4 0.01 0.03 0.08 0.38 0.50 0.01
## q5 0.00 0.01 0.07 0.52 0.39 0.01
## q8 0.00 0.01 0.06 0.49 0.44 0.01
## q10 0.00 0.04 0.15 0.38 0.42 0.00
d %>%
select(pre_uv, pre_int, cogproc_prop_l, emo_prop, beh_eng, final_grade) %>%
corrr::correlate() %>%
slice() %>%
fashion()
## rowname pre_uv pre_int cogproc_prop_l emo_prop beh_eng
## 1 pre_uv .51 -.15 -.09 .02
## 2 pre_int .51 .01 -.10 .20
## 3 cogproc_prop_l -.15 .01 .43 .06
## 4 emo_prop -.09 -.10 .43 -.13
## 5 beh_eng .02 .20 .06 -.13
## 6 final_grade .05 .15 .25 -.02 .59
## final_grade
## 1 .05
## 2 .15
## 3 .25
## 4 -.02
## 5 .59
## 6
d %>%
select(pre_uv, pre_int, cogproc, posemo, beh_eng, final_grade)
## # A tibble: 634 x 6
## pre_uv pre_int cogproc posemo beh_eng final_grade
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3.67 4.2 15.1 3.56 1.07 93.5
## 2 3 4 7.17 19.0 -2.11 81.7
## 3 3 4.2 15.2 2.91 0.812 88.5
## 4 3.67 4 14.5 5.59 -1.34 81.9
## 5 3.67 3.8 16.7 3.79 -1.43 84
## 6 4 3.6 15.0 5.69 -0.589 83.6
## 7 3.67 4.2 15.1 5.15 -0.800 97.8
## 8 2 3.8 18.0 6.22 0.401 96.1
## 9 3.33 4.8 15.7 8.48 1.21 81.0
## 10 4.67 4.6 12.3 6.30 0.121 93.9
## # … with 624 more rows
SEM
Affective
m <-
'
pre_int =~ q1 + q4 + q5 + q8 + q10
posemo_prop_l ~ a*pre_int
final_grade_s ~ c*pre_int + b*posemo_prop_l
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
m_est <- sem(m, data = d)
group <- svydesign(ids = ~course_ID, data = d)
out_robust <- lavaan.survey(m_est, group)
summary(out_robust, standardized = T, fit.measures = T)
## lavaan 0.6-3 ended normally after 54 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Number of observations 605
##
## Estimator ML Robust
## Model Fit Test Statistic 25.710 16.841
## Degrees of freedom 13 13
## P-value (Chi-square) 0.019 0.207
## Scaling correction factor 1.527
## for the Satorra-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1478.272 729.167
## Degrees of freedom 21 21
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.991 0.995
## Tucker-Lewis Index (TLI) 0.986 0.991
##
## Robust Comparative Fit Index (CFI) 0.996
## Robust Tucker-Lewis Index (TLI) 0.993
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3642.723 -3642.723
## Loglikelihood unrestricted model (H1) -3629.868 -3629.868
##
## Number of free parameters 22 22
## Akaike (AIC) 7329.446 7329.446
## Bayesian (BIC) 7426.361 7426.361
## Sample-size adjusted Bayesian (BIC) 7356.516 7356.516
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.040 0.022
## 90 Percent Confidence Interval 0.016 0.063 0.000 0.044
## P-value RMSEA <= 0.05 0.736 0.986
##
## Robust RMSEA 0.027
## 90 Percent Confidence Interval 0.000 0.060
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.019 0.019
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_int =~
## q1 1.000 0.517 0.791
## q4 1.036 0.070 14.737 0.000 0.535 0.668
## q5 0.979 0.069 14.276 0.000 0.506 0.769
## q8 1.045 0.078 13.475 0.000 0.540 0.844
## q10 1.237 0.060 20.618 0.000 0.639 0.748
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## posemo_prop_l ~
## pre_int (a) -0.007 0.038 -0.176 0.860 -0.003 -0.009
## final_grade_s ~
## pre_int (c) 0.276 0.104 2.653 0.008 0.143 0.149
## posm_prp_l (b) 0.241 0.194 1.239 0.215 0.241 0.093
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 4.354 0.051 85.599 0.000 4.354 6.663
## .q4 4.355 0.044 99.693 0.000 4.355 5.435
## .q5 4.302 0.038 114.674 0.000 4.302 6.543
## .q8 4.362 0.047 93.244 0.000 4.362 6.817
## .q10 4.187 0.060 69.935 0.000 4.187 4.899
## .posemo_prop_l 5.370 0.030 179.131 0.000 5.370 14.539
## .final_grade_s -1.262 1.045 -1.207 0.227 -1.262 -1.320
## pre_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 0.160 0.017 9.571 0.000 0.160 0.375
## .q4 0.356 0.052 6.900 0.000 0.356 0.554
## .q5 0.177 0.026 6.902 0.000 0.177 0.409
## .q8 0.118 0.016 7.266 0.000 0.118 0.287
## .q10 0.322 0.029 11.078 0.000 0.322 0.441
## .posemo_prop_l 0.136 0.013 10.431 0.000 0.136 1.000
## .final_grade_s 0.885 0.075 11.867 0.000 0.885 0.969
## pre_int 0.267 0.039 6.793 0.000 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ab -0.002 0.009 -0.171 0.864 -0.001 -0.001
## total 0.275 0.104 2.630 0.009 0.142 0.148
Cognitive
m <-
'
pre_int =~ q1 + q4 + q5 + q8 + q10
cogproc_prop_l ~ a*pre_int
final_grade_s ~ c*pre_int + b*cogproc_prop_l
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
m_est <- sem(m, data = d)
group <- svydesign(ids = ~course_ID, data = d)
out_robust <- lavaan.survey(m_est, group)
summary(out_robust, standardized = T, fit.measures = T)
## lavaan 0.6-3 ended normally after 60 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Number of observations 607
##
## Estimator ML Robust
## Model Fit Test Statistic 29.122 19.030
## Degrees of freedom 13 13
## P-value (Chi-square) 0.006 0.122
## Scaling correction factor 1.530
## for the Satorra-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1505.927 751.864
## Degrees of freedom 21 21
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.989 0.992
## Tucker-Lewis Index (TLI) 0.982 0.987
##
## Robust Comparative Fit Index (CFI) 0.994
## Robust Tucker-Lewis Index (TLI) 0.990
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3722.053 -3722.053
## Loglikelihood unrestricted model (H1) -3707.492 -3707.492
##
## Number of free parameters 22 22
## Akaike (AIC) 7488.106 7488.106
## Bayesian (BIC) 7585.093 7585.093
## Sample-size adjusted Bayesian (BIC) 7515.249 7515.249
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045 0.028
## 90 Percent Confidence Interval 0.023 0.067 0.000 0.048
## P-value RMSEA <= 0.05 0.607 0.967
##
## Robust RMSEA 0.034
## 90 Percent Confidence Interval 0.000 0.065
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.020 0.020
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_int =~
## q1 1.000 0.516 0.790
## q4 1.034 0.072 14.424 0.000 0.533 0.666
## q5 0.981 0.069 14.141 0.000 0.506 0.770
## q8 1.044 0.078 13.411 0.000 0.539 0.839
## q10 1.237 0.061 20.415 0.000 0.638 0.747
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cogproc_prop_l ~
## pre_int (a) 0.015 0.038 0.393 0.695 0.008 0.019
## final_grade_s ~
## pre_int (c) 0.259 0.105 2.468 0.014 0.133 0.138
## cgprc_prp_ (b) 0.578 0.151 3.827 0.000 0.578 0.247
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 4.356 0.051 85.634 0.000 4.356 6.667
## .q4 4.356 0.044 100.070 0.000 4.356 5.440
## .q5 4.303 0.037 115.171 0.000 4.303 6.547
## .q8 4.361 0.047 93.068 0.000 4.361 6.795
## .q10 4.189 0.060 69.935 0.000 4.189 4.903
## .cogproc_prop_l 6.745 0.043 158.598 0.000 6.745 16.302
## .final_grade_s -3.875 1.020 -3.798 0.000 -3.875 -4.006
## pre_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 0.161 0.017 9.622 0.000 0.161 0.376
## .q4 0.357 0.052 6.915 0.000 0.357 0.556
## .q5 0.176 0.026 6.868 0.000 0.176 0.407
## .q8 0.122 0.017 6.971 0.000 0.122 0.296
## .q10 0.323 0.029 11.066 0.000 0.323 0.442
## .cogproc_prop_l 0.171 0.020 8.538 0.000 0.171 1.000
## .final_grade_s 0.859 0.057 15.071 0.000 0.859 0.919
## pre_int 0.266 0.039 6.743 0.000 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ab 0.009 0.022 0.392 0.695 0.004 0.005
## total 0.267 0.107 2.498 0.012 0.138 0.143
Behavioral
m <-
'
pre_int =~ q1 + q4 + q5 + q8 + q10
ts_60_sq ~ a*pre_int
final_grade_s ~ c*pre_int + b*ts_60_sq
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
m_est <- sem(m, data = d)
group <- svydesign(ids = ~course_ID, data = d)
out_robust <- lavaan.survey(m_est, group)
summary(out_robust, standardized = T, fit.measures = T)
## lavaan 0.6-3 ended normally after 54 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Number of observations 622
##
## Estimator ML Robust
## Model Fit Test Statistic 21.569 13.490
## Degrees of freedom 13 13
## P-value (Chi-square) 0.062 0.411
## Scaling correction factor 1.599
## for the Satorra-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1644.789 839.386
## Degrees of freedom 21 21
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.995 0.999
## Tucker-Lewis Index (TLI) 0.991 0.999
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4757.268 -4757.268
## Loglikelihood unrestricted model (H1) -4746.484 -4746.484
##
## Number of free parameters 22 22
## Akaike (AIC) 9558.536 9558.536
## Bayesian (BIC) 9656.061 9656.061
## Sample-size adjusted Bayesian (BIC) 9586.215 9586.215
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.033 0.008
## 90 Percent Confidence Interval 0.000 0.056 0.000 0.035
## P-value RMSEA <= 0.05 0.879 0.999
##
## Robust RMSEA 0.010
## 90 Percent Confidence Interval 0.000 0.052
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.014 0.014
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_int =~
## q1 1.000 0.515 0.789
## q4 1.035 0.069 14.989 0.000 0.533 0.669
## q5 0.989 0.072 13.736 0.000 0.509 0.770
## q8 1.041 0.074 14.118 0.000 0.536 0.837
## q10 1.259 0.060 21.007 0.000 0.649 0.752
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ts_60_sq ~
## pre_int (a) 0.434 0.182 2.378 0.017 0.223 0.114
## final_grade_s ~
## pre_int (c) 0.209 0.090 2.313 0.021 0.108 0.107
## ts_60_sq (b) 0.224 0.017 12.814 0.000 0.224 0.436
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 4.350 0.051 85.038 0.000 4.350 6.658
## .q4 4.349 0.044 99.740 0.000 4.349 5.455
## .q5 4.297 0.037 116.502 0.000 4.297 6.499
## .q8 4.359 0.047 92.214 0.000 4.359 6.807
## .q10 4.178 0.062 67.582 0.000 4.178 4.842
## .ts_60_sq 5.258 0.151 34.903 0.000 5.258 2.679
## .final_grade_s -1.181 0.122 -9.717 0.000 -1.181 -1.175
## pre_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 0.161 0.016 9.888 0.000 0.161 0.378
## .q4 0.351 0.050 6.992 0.000 0.351 0.552
## .q5 0.178 0.025 7.060 0.000 0.178 0.406
## .q8 0.122 0.017 7.381 0.000 0.122 0.299
## .q10 0.324 0.028 11.428 0.000 0.324 0.435
## .ts_60_sq 3.801 0.393 9.668 0.000 3.801 0.987
## .final_grade_s 0.796 0.065 12.310 0.000 0.796 0.787
## pre_int 0.266 0.038 6.964 0.000 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ab 0.097 0.043 2.230 0.026 0.050 0.050
## total 0.306 0.109 2.817 0.005 0.158 0.157
All variables
m <-
'
pre_int =~ q1 + q4 + q5 + q8 + q10
ts_60_sq ~ pre_int
cogproc_prop_l ~ pre_int
posemo_prop_l ~ pre_int
final_grade_s ~ pre_int + ts_60_sq + cogproc_prop_l + posemo_prop_l
'
m_est <- sem(m, data = d)
group <- svydesign(ids = ~course_ID, data = d)
out_robust <- lavaan.survey(m_est, group)
summary(out_robust, standardized = T, fit.measures = T)
## lavaan 0.6-3 ended normally after 68 iterations
##
## Optimization method NLMINB
## Number of free parameters 30
##
## Number of observations 605
##
## Estimator ML Robust
## Model Fit Test Statistic 366.570 252.923
## Degrees of freedom 24 24
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.449
## for the Satorra-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1980.520 1062.415
## Degrees of freedom 36 36
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.824 0.777
## Tucker-Lewis Index (TLI) 0.736 0.665
##
## Robust Comparative Fit Index (CFI) 0.827
## Robust Tucker-Lewis Index (TLI) 0.740
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5145.897 -5145.897
## Loglikelihood unrestricted model (H1) -4962.612 -4962.612
##
## Number of free parameters 30 30
## Akaike (AIC) 10351.794 10351.794
## Bayesian (BIC) 10483.951 10483.951
## Sample-size adjusted Bayesian (BIC) 10388.708 10388.708
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.154 0.126
## 90 Percent Confidence Interval 0.140 0.168 0.114 0.137
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA 0.151
## 90 Percent Confidence Interval 0.135 0.168
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.097 0.097
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_int =~
## q1 1.000 0.516 0.790
## q4 1.036 0.070 14.780 0.000 0.535 0.668
## q5 0.979 0.068 14.319 0.000 0.505 0.769
## q8 1.047 0.077 13.577 0.000 0.541 0.845
## q10 1.238 0.060 20.731 0.000 0.639 0.748
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ts_60_sq ~
## pre_int 0.394 0.187 2.103 0.035 0.204 0.105
## cogproc_prop_l ~
## pre_int 0.013 0.038 0.337 0.736 0.007 0.016
## posemo_prop_l ~
## pre_int -0.005 0.038 -0.129 0.898 -0.003 -0.007
## final_grade_s ~
## pre_int 0.185 0.088 2.117 0.034 0.096 0.097
## ts_60_sq 0.196 0.015 12.681 0.000 0.196 0.389
## cogproc_prop_l 0.743 0.139 5.331 0.000 0.743 0.313
## posemo_prop_l -0.375 0.175 -2.138 0.032 -0.375 -0.141
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 4.354 0.051 85.599 0.000 4.354 6.663
## .q4 4.355 0.044 99.693 0.000 4.355 5.435
## .q5 4.302 0.038 114.674 0.000 4.302 6.543
## .q8 4.362 0.047 93.244 0.000 4.362 6.817
## .q10 4.187 0.060 69.935 0.000 4.187 4.899
## .ts_60_sq 5.295 0.148 35.730 0.000 5.295 2.724
## .cogproc_prop_l 6.746 0.043 158.079 0.000 6.746 16.340
## .posemo_prop_l 5.370 0.030 179.131 0.000 5.370 14.539
## .final_grade_s -4.009 1.776 -2.257 0.024 -4.009 -4.085
## pre_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q1 0.160 0.017 9.604 0.000 0.160 0.376
## .q4 0.356 0.052 6.904 0.000 0.356 0.554
## .q5 0.177 0.026 6.890 0.000 0.177 0.409
## .q8 0.117 0.016 7.300 0.000 0.117 0.286
## .q10 0.322 0.029 11.146 0.000 0.322 0.441
## .ts_60_sq 3.738 0.403 9.280 0.000 3.738 0.989
## .cogproc_prop_l 0.170 0.020 8.392 0.000 0.170 1.000
## .posemo_prop_l 0.136 0.013 10.430 0.000 0.136 1.000
## .final_grade_s 0.686 0.057 11.941 0.000 0.686 0.712
## pre_int 0.267 0.039 6.810 0.000 1.000 1.000