National Youth Tobacco Survey (NYTS) 2018

Source: R/dataset.r

This dataset includes 5 manifest items about abortion and several covariates. From the original 2018 National Youth Tobacco Survey data, the Non Hispanic, white students are selected and schools with 30-50 students were selected. Thus, the dataset has 1743 respondents. The covariates include the sex of the respondents and the school ID to which the respondnets belong, and the level of the corresponding school.

Format

A data frame with 1734 observations on the following 8 variables.

ECIGT

Whether to have tried cigarette smoking, even one or two puffs

ECIGAR

Whether to have ever tried cigar smoking, even one or two puffs

ESLT

Whether to have used chewing tobacco, snuff, or dip

EELCIGT

Whether to have used electronic cigarettes or e-cigarettes

EHOOKAH

Whether to have tried smoking tobacco from a hookah or a waterpipe

SEX

Respondent's Sex

SCH_ID

School ID to which the respondent belongs

SCH_LEV

Level of the corresponding school

Examples

data("nyts18")
# \donttest{
# Model 1: LCA
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
           data = nyts18, nclass = 3)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -2087.91 
#> SET : 2  .....loglik : -2088.307 
#> SET : 3  .....loglik : -2119.035 
#> SET : 4  .....loglik : -2118.658 
#> SET : 5  .....loglik : -2088.554 
#> SET : 6  .....loglik : -2086.869 
#> SET : 7  .....loglik : -2100.749 
#> SET : 8  .....loglik : -2094.886 
#> SET : 9  .....loglik : -2088.368 
#> SET : 10 .....loglik : -2109.467 
#> 
#> Start with SET 6 (-2086.869)
#> 
#> Latent class analysis Fitting...
#> 
#> .. 231 iteration 
#> 
#> Converged at 231 iteration (loglik :-2086.857)
summary(lca)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     1, data = nyts18, nclass = 3)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of observations : 1734 
#> Number of parameters : 17 
#> 
#> log-likelihood : -2086.857 
#>      G-squared : 29.52011 
#>            AIC : 4207.714 
#>            BIC : 4300.503 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.12572 0.04460 0.82968 
#> 
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.5308 0.3655 0.1901  0.8717  0.0618
#> Class 2 0.9340 0.9777 0.6542  0.9767  0.6776
#> Class 3 0.0125 0.0074 0.0102  0.0843  0.0071
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.4692 0.6345 0.8099  0.1283  0.9382
#> Class 2 0.0660 0.0223 0.3458  0.0233  0.3224
#> Class 3 0.9875 0.9926 0.9898  0.9157  0.9929

# Model 2: LCR
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
           data = nyts18, nclass = 3)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Covariates (Level 1) : 
#>  SEX 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -2085.498 
#> SET : 2  .....loglik : -2083.957 
#> SET : 3  .....loglik : -2112.708 
#> SET : 4  .....loglik : -2089.61 
#> SET : 5  .....loglik : -2098.521 
#> SET : 6  .....loglik : -2085.283 
#> SET : 7  .....loglik : -2086.116 
#> SET : 8  .....loglik : -2083.78 
#> SET : 9  .....loglik : -2084.815 
#> SET : 10 .....loglik : -2111.966 
#> 
#> Start with SET 8 (-2083.78)
#> 
#> Latent class analysis Fitting...
#> 
#> .. 283 iteration 
#> 
#> Converged at 283 iteration (loglik :-2083.746)
summary(lca)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     SEX, data = nyts18, nclass = 3)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Covariates (Level 1) : SEX 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of observations : 1734 
#> Number of parameters : 19 
#> 
#> log-likelihood : -2083.746 
#>      G-squared : 98.82476 
#>            AIC : 4205.492 
#>            BIC : 4309.197 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.12821 0.82078 0.05102 
#> 
#> Logistic regression coefficients :
#>             Class 1/3 Class 2/3
#> (Intercept)    1.3004    3.1542
#> SEXFemale     -0.6691   -0.6633
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.4939 0.3160 0.1632  0.8466  0.0554
#> Class 2 0.0109 0.0069 0.0099  0.0787  0.0069
#> Class 3 0.9102 0.9695 0.6368  0.9786  0.6113
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.5061 0.6840 0.8368  0.1534  0.9446
#> Class 2 0.9891 0.9931 0.9901  0.9213  0.9931
#> Class 3 0.0898 0.0305 0.3632  0.0214  0.3887
coef(lca)
#> Class 1 / 3 :
#>             Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)    
#> (Intercept)     3.6707      1.3004      0.3032    4.289  9.68e-05 ***
#> SEXFemale       0.5121     -0.6691      0.3607   -1.855    0.0703 .  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Class 2 / 3 :
#>             Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)    
#> (Intercept)    23.4332      3.1542      0.2765   11.408  9.86e-15 ***
#> SEXFemale       0.5151     -0.6633      0.2855   -2.323    0.0249 *  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# Model 3: MGLCA
mglca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
             group = SEX, data = nyts18, nclass = 3)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SEX 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -2085.195 
#> SET : 2  .....loglik : -2107.331 
#> SET : 3  .....loglik : -2085.416 
#> SET : 4  .....loglik : -2084.726 
#> SET : 5  .....loglik : -2113.222 
#> SET : 6  .....loglik : -2113.414 
#> SET : 7  .....loglik : -2086.518 
#> SET : 8  .....loglik : -2083.899 
#> SET : 9  .....loglik : -2114.25 
#> SET : 10 .....loglik : -2092.189 
#> 
#> Start with SET 8 (-2083.899)
#> 
#> Multiple-group latent class analysis Fitting...
#> 
#> .. 249 iteration 
#> 
#> Converged at 249 iteration (loglik :-2083.746)
summary(mglca)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     1, group = SEX, data = nyts18, nclass = 3)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SEX 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Multiple-group latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of groups : 2 
#> Number of observations : 1734 
#> Number of parameters : 19 
#> 
#> log-likelihood : -2083.746 
#>      G-squared : 98.82476 
#>            AIC : 4205.492 
#>            BIC : 4309.197 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.12820 0.82079 0.05101 
#> 
#> Class prevalences by group :
#>        Class 1 Class 2 Class 3
#> Male   0.13060 0.83382 0.03558
#> Female 0.12573 0.80739 0.06688
#> 
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.4940 0.3161 0.1632  0.8467  0.0554
#> Class 2 0.0109 0.0069 0.0099  0.0788  0.0069
#> Class 3 0.9102 0.9695 0.6368  0.9786  0.6114
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.5060 0.6839 0.8368  0.1533  0.9446
#> Class 2 0.9891 0.9931 0.9901  0.9212  0.9931
#> Class 3 0.0898 0.0305 0.3632  0.0214  0.3886

# Model 4: MLCA
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
   group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -1955.488 
#> SET : 2  .....loglik : -1968.406 
#> SET : 3  .....loglik : -1955.593 
#> SET : 4  .....loglik : -1957.32 
#> SET : 5  .....loglik : -1957.826 
#> SET : 6  .....loglik : -1977.273 
#> SET : 7  .....loglik : -1955.499 
#> SET : 8  .....loglik : -1958.045 
#> SET : 9  .....loglik : -1955.491 
#> SET : 10 .....loglik : -1958.555 
#> 
#> Start with SET 1 (-1955.488)
#> 
#> Nonparametric multilevel latent class analysis Fitting...
#> 
#>  69 iteration 
#> 
#> Converged at 69 iteration (loglik :-1955.487)
summary(mlca)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     1, group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Nonparametric multilevel latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of latent clusters : 2 
#> Number of groups : 45 
#> Number of observations : 1734 
#> Number of parameters : 20 
#> 
#> log-likelihood : -1955.487 
#>      G-squared : 768.5035 
#>            AIC : 3950.973 
#>            BIC : 4060.137 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.76961 0.05960 0.17079 
#> 
#> Marginal prevalences for latent clusters :
#> Cluster 1 Cluster 2 
#>   0.62069   0.37931 
#> 
#> Class prevalences by cluster :
#>           Class 1 Class 2 Class 3
#> Cluster 1 0.92995 0.00876 0.06129
#> Cluster 2 0.51177 0.14137 0.34687
#> 
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.0062 0.0043 0.0088  0.0413  0.0057
#> Class 2 0.9112 0.9750 0.5651  0.9778  0.5364
#> Class 3 0.3488 0.2006 0.1236  0.7783  0.0443
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.9938 0.9957 0.9912  0.9587  0.9943
#> Class 2 0.0888 0.0250 0.4349  0.0222  0.4636
#> Class 3 0.6512 0.7994 0.8764  0.2217  0.9557
#> 

# Model 5: MLCA with level-1 covariate(s) only
mlcr = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> Covariates (Level 1) : 
#>  SEX 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -1951.914 
#> SET : 2  .....loglik : -1960.453 
#> SET : 3  .....loglik : -1953.765 
#> SET : 4  .....loglik : -1953.064 
#> SET : 5  .....loglik : -1951.982 
#> SET : 6  .....loglik : -2010.17 
#> SET : 7  .....loglik : -1952.027 
#> SET : 8  .....loglik : -1969.89 
#> SET : 9  .....loglik : -1965.47 
#> SET : 10 .....loglik : -1951.901 
#> 
#> Start with SET 10 (-1951.901)
#> 
#> Nonparametric multilevel latent class analysis Fitting...
#> 
#> . 168 iteration 
#> 
#> Converged at 168 iteration (loglik :-1951.879)
summary(mlcr)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     SEX, group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> Covariates (Level 1) : SEX 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Nonparametric multilevel latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of latent clusters : 2 
#> Number of groups : 45 
#> Number of observations : 1734 
#> Number of parameters : 22 
#> 
#> log-likelihood : -1951.879 
#>      G-squared : 1116.424 
#>            AIC : 3947.757 
#>            BIC : 4067.837 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.06018 0.17105 0.76877 
#> 
#> Marginal prevalences for latent clusters :
#> Cluster 1 Cluster 2 
#>   0.61682   0.38318 
#> 
#> Class prevalences by cluster :
#>           Class 1 Class 2 Class 3
#> Cluster 1 0.00923 0.06023 0.93054
#> Cluster 2 0.14098 0.34682 0.51220
#> 
#> 
#> Logistic regression coefficients (level 1) :
#> Cluster 1 
#>             Class 1/3 Class 2/3
#> (Intercept)   -4.9893   -2.7664
#> SEXFemale      0.6576    0.0578
#> 
#> Cluster 2 
#>             Class 1/3 Class 2/3
#> (Intercept)   -1.6489   -0.4171
#> SEXFemale      0.6576    0.0578
#> 
#> 
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.9025 0.9712 0.5744  0.9782  0.5341
#> Class 2 0.3502 0.1987 0.1188  0.7726  0.0438
#> Class 3 0.0059 0.0042 0.0087  0.0416  0.0057
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.0975 0.0288 0.4256  0.0218  0.4659
#> Class 2 0.6498 0.8013 0.8812  0.2274  0.9562
#> Class 3 0.9941 0.9958 0.9913  0.9584  0.9943
#> 
coef(mlcr)
#> 
#> Level 1 Coefficients :
#> 
#> Class 1 / 3 :
#>           Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)   
#> SEXFemale     1.9301      0.6576      0.2071    3.176   0.00152 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Class 2 / 3 :
#>           Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)
#> SEXFemale    1.05946     0.05776     0.16491     0.35     0.726
#> 

# Model 6: MLCA with level-1 and level-2 covariate(s)
# (SEX: level-1 covariate, PARTY: level-2 covariate)
mlcr2 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX + SCH_LEV,
             group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
#> Manifest items :
#>  ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> Covariates (Level 2) : 
#>  SCH_LEV 
#> Covariates (Level 1) : 
#>  SEX 
#> 
#> Deleted observation(s) : 
#> 0 observation(s) for missing all manifest items
#> 0 observation(s) for missing at least 1 covariates
#> 
#> SET : 1  .....loglik : -1921.21 
#> SET : 2  .....loglik : -1919.94 
#> SET : 3  .....loglik : -1921.655 
#> SET : 4  .....loglik : -1919.938 
#> SET : 5  .....loglik : -1944.429 
#> SET : 6  .....loglik : -1942.334 
#> SET : 7  .....loglik : -1944.573 
#> SET : 8  .....loglik : -1942.551 
#> SET : 9  .....loglik : -1920.384 
#> SET : 10 .....loglik : -1921.833 
#> 
#> Start with SET 4 (-1919.938)
#> 
#> Nonparametric multilevel latent class analysis Fitting...
#> 
#> . 178 iteration 
#> 
#> Converged at 178 iteration (loglik :-1919.937)
summary(mlcr2)
#> 
#> Call:
#> glca(formula = item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 
#>     SEX + SCH_LEV, group = SCH_ID, data = nyts18, nclass = 3, 
#>     ncluster = 2)
#> 
#> Manifest items : ECIGT ECIGAR ESLT EELCIGT EHOOKAH 
#> Grouping variable : SCH_ID 
#> Covariates (Level 1) : SEX 
#> Covariates (Level 2) : SCH_LEV 
#> 
#> Categories for manifest items :
#>         Y = 1 Y = 2
#> ECIGT     Yes    No
#> ECIGAR    Yes    No
#> ESLT      Yes    No
#> EELCIGT   Yes    No
#> EHOOKAH   Yes    No
#> 
#> Model : Nonparametric multilevel latent class analysis 
#> 
#> Number of latent classes : 3 
#> Number of latent clusters : 2 
#> Number of groups : 45 
#> Number of observations : 1734 
#> Number of parameters : 24 
#> 
#> log-likelihood : -1919.937 
#>      G-squared : 1052.541 
#>            AIC : 3887.874 
#>            BIC : 4018.87 
#> 
#> Marginal prevalences for latent classes :
#> Class 1 Class 2 Class 3 
#> 0.18159 0.06455 0.75385 
#> 
#> Marginal prevalences for latent clusters :
#> Cluster 1 Cluster 2 
#>   0.45917   0.54083 
#> 
#> Class prevalences by cluster :
#>           Class 1 Class 2 Class 3
#> Cluster 1 0.29044  0.1175 0.59206
#> Cluster 2 0.08978  0.0199 0.89032
#> 
#> 
#> Logistic regression coefficients (level 1) :
#> Cluster 1 
#>             Class 1/3 Class 2/3
#> (Intercept)    0.1671   -0.8997
#> SEXFemale      0.1159    0.6307
#> 
#> Cluster 2 
#>             Class 1/3 Class 2/3
#> (Intercept)   -1.0654   -2.6345
#> SEXFemale      0.1159    0.6307
#> 
#> Logistic regression coefficients (level 2) :
#>                      Class 1/3 Class 2/3
#> SCH_LEVMiddle School   -1.9522   -2.5656
#> 
#> 
#> Item-response probabilities (Y = 1) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.3227 0.1696 0.1127  0.7266  0.0394
#> Class 2 0.8914 0.9657 0.5507  0.9782  0.5049
#> Class 3 0.0033 0.0034 0.0074  0.0372  0.0056
#> 
#> Item-response probabilities (Y = 2) :
#>          ECIGT ECIGAR   ESLT EELCIGT EHOOKAH
#> Class 1 0.6773 0.8304 0.8873  0.2734  0.9606
#> Class 2 0.1086 0.0343 0.4493  0.0218  0.4951
#> Class 3 0.9967 0.9966 0.9926  0.9628  0.9944
#> 
coef(mlcr2)
#> 
#> Level 1 Coefficients :
#> 
#> Class 1 / 3 :
#>           Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)
#> SEXFemale     1.1228      0.1159      0.1716    0.675       0.5
#> 
#> Class 2 / 3 :
#>           Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)   
#> SEXFemale     1.8788      0.6307      0.2189    2.881   0.00402 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> 
#> Level 2 Coefficients :
#> 
#> Class 1 / 3 :
#>                      Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)    
#> SCH_LEVMiddle School     0.1420     -1.9522      0.4185   -4.665  3.33e-06 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Class 2 / 3 :
#>                      Odds Ratio Coefficient  Std. Error  t value  Pr(>|t|)    
#> SCH_LEVMiddle School    0.07687    -2.56558     0.59246    -4.33  1.57e-05 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
# }