library(haven)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2 v purrr 0.3.4
v tibble 3.0.3 v dplyr 1.0.2
v tidyr 1.1.1 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x tidyr::expand() masks Matrix::expand()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x tidyr::pack() masks Matrix::pack()
x tidyr::unpack() masks Matrix::unpack()
library(lme4)
library(lmerTest)
str(projectSTAR)
tibble [6,325 x 27] (S3: tbl_df/tbl/data.frame)
$ stdntid : num [1:6325] 10001 10133 10246 10263 10266 ...
..- attr(*, "label")= chr "STUDENT ID"
..- attr(*, "format.stata")= chr "%10.0g"
$ race : dbl+lbl [1:6325] 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1...
..@ label : chr "STUDENT RACE/ETHNICITY"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:6] 1 2 3 4 5 6
.. ..- attr(*, "names")= chr [1:6] "WHITE" "BLACK" "ASIAN" "HISPANIC" ...
$ gender : dbl+lbl [1:6325] 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1...
..@ label : chr "STUDENT GENDER"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "MALE" "FEMALE"
$ FLAGSGK : dbl+lbl [1:6325] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
..@ label : chr "IN STAR IN KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "NO" "YES"
$ flaggk : dbl+lbl [1:6325] 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0...
..@ label : chr "ACHIEVEMENT DATA AVAILABLE GRADE K"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "NO" "YES"
$ gkclasstype : dbl+lbl [1:6325] 3, 3, 3, 1, 2, 3, 1, 3, 1, 2, 3, 3, 2, 3, 3, 3...
..@ label : chr "CLASSROOM TYPE KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:3] 1 2 3
.. ..- attr(*, "names")= chr [1:3] "SMALL CLASS" "REGULAR CLASS" "REGULAR + AIDE CLASS"
$ gkschid : num [1:6325] 169229 169280 218562 205492 257899 ...
..- attr(*, "label")= chr "KINDERGARTEN SCHOOL ID"
..- attr(*, "format.stata")= chr "%10.0g"
$ gksurban : dbl+lbl [1:6325] 2, 2, 4, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2, 2, 2, 3...
..@ label : chr "SCHOOL URBANICITY KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "INNER CITY" "SUBURBAN" "RURAL" "URBAN"
$ gktchid : num [1:6325] 16922904 16928003 21856202 20549204 25789904 ...
..- attr(*, "label")= chr "KINDERGARTEN TEACHER ID"
..- attr(*, "format.stata")= chr "%10.0g"
$ gktgen : dbl+lbl [1:6325] 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
..@ label : chr "TEACHER GENDER KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "MALE" "FEMALE"
$ gktrace : dbl+lbl [1:6325] 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
..@ label : chr "TEACHER RACE/ETHNICITY KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:6] 1 2 3 4 5 6
.. ..- attr(*, "names")= chr [1:6] "WHITE" "BLACK" "ASIAN" "HISPANIC" ...
$ gkthighdegree: dbl+lbl [1:6325] 2, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2...
..@ label : chr "TEACHER HIGHEST DEGREE KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:6] 1 2 3 4 5 6
.. ..- attr(*, "names")= chr [1:6] "ASSOCIATES" "BACHELORS" "MASTERS" "MASTERS +" ...
$ gktcareer : dbl+lbl [1:6325] 4, 4, 4, 4, 4, NA, 4, 4, 4, 4, 4, 4...
..@ label : chr "TEACHER CAREER LADDER LEVEL KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:7] 1 2 3 4 5 6 7
.. ..- attr(*, "names")= chr [1:7] "CHOSE NO TO BE ON CAREER LADDER" "APPRENTICE" "PROBATION" "LADDER LEVEL 1" ...
$ gktyears : num [1:6325] 5 7 8 3 12 2 7 14 4 6 ...
..- attr(*, "label")= chr "YEARS OF TOTAL TEACHING EXPERIENCE KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkclasssize : num [1:6325] 24 22 17 17 24 24 13 24 14 23 ...
..- attr(*, "label")= chr "CLASS SIZE KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkfreelunch : dbl+lbl [1:6325] 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2...
..@ label : chr "FREE/REDUCED LUNCH STATUS KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "FREE LUNCH" "NON-FREE LUNCH"
$ gkrepeat : dbl+lbl [1:6325] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
..@ label : chr "REPEATING KINDERGARTEN IN 1985-1986 SCHOOL YEAR"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "YES, PROMOTION RECOMMENDED" "NO, PROMOTION NOT RECOMMENDED"
$ gkspeced : dbl+lbl [1:6325] 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
..@ label : chr "SPECIAL EDUCATION STATUS KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "YES" "NO"
$ gkspecin : dbl+lbl [1:6325] 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2...
..@ label : chr "PULLED OUT FOR SPECIAL INSTRUCTION KINDERGARTEN"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "YES" "NO"
$ gkpresent : num [1:6325] 161 175 132 178 170 94 160 154 172 95 ...
..- attr(*, "label")= chr "DAYS PRESENT AT SCHOOL KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkabsent : num [1:6325] 19 5 28 2 10 3 2 7 8 2 ...
..- attr(*, "label")= chr "DAYS ABSENT FROM SCHOOL KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gktreadss : num [1:6325] NA 427 450 483 456 411 443 448 463 472 ...
..- attr(*, "label")= chr "TOTAL READING SCALE SCORE SAT KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gktmathss : num [1:6325] NA 478 494 513 513 468 473 449 520 536 ...
..- attr(*, "label")= chr "TOTAL MATH SCALE SCORE SAT KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gktlistss : num [1:6325] NA 509 549 554 520 571 595 540 565 595 ...
..- attr(*, "label")= chr "TOTAL LISTENING SCALE SCORE SAT KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkwordskillss: num [1:6325] NA 418 444 431 468 396 444 444 480 486 ...
..- attr(*, "label")= chr "WORD STUDY SKILLS SCALE SCORE SAT KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkmotivraw : num [1:6325] 23 24 28 27 25 24 NA NA 26 27 ...
..- attr(*, "label")= chr "MOTIVATION RAW SCORE SCAMIN KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
$ gkselfconcraw: num [1:6325] 52 53 56 61 54 55 NA NA 52 61 ...
..- attr(*, "label")= chr "SELF-CONCEPT RAW SCORE SCAMIN KINDERGARTEN"
..- attr(*, "format.stata")= chr "%10.0g"
star.clean <- projectSTAR %>%
mutate(.,
schoolid = gkschid,
classid = gktchid,
math = gktmathss,
classtype = as_factor(gkclasstype),
years_exp = gktyears,
degree = as_factor(gkthighdegree),
self_concept = gkselfconcraw
) %>%
group_by(schoolid) %>%
mutate(.,
school_math = mean(gktmathss, na.rm = TRUE)
) %>%
ungroup() %>%
select(.,
schoolid,
classid,
math,
classtype,
years_exp,
degree,
self_concept,
school_math)
glimpse(star.clean)
Rows: 6,325
Columns: 8
$ schoolid <dbl> 169229, 169280, 218562, 205492, 257899, 161176,...
$ classid <dbl> 16922904, 16928003, 21856202, 20549204, 2578990...
$ math <dbl> NA, 478, 494, 513, 513, 468, 473, 449, 520, 536...
$ classtype <fct> REGULAR + AIDE CLASS, REGULAR + AIDE CLASS, REG...
$ years_exp <dbl> 5, 7, 8, 3, 12, 2, 7, 14, 4, 6, 11, 16, 12, 5, ...
$ degree <fct> BACHELORS, BACHELORS, MASTERS, BACHELORS, MASTE...
$ self_concept <dbl> 52, 53, 56, 61, 54, 55, NA, NA, 52, 61, 55, 49,...
$ school_math <dbl> 498.8768, 486.9811, 501.1071, 502.6133, 476.280...
table(star.clean$degree)
ASSOCIATES BACHELORS MASTERS MASTERS + SPECIALIST DOCTORAL
0 4119 1981 161 43 0
model.0<-lmer(math~(1|classid)+(1|schoolid), REML=FALSE, data=star.clean)
summary(model.0)
Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula: math ~ (1 | classid) + (1 | schoolid)
Data: star.clean
AIC BIC logLik deviance df.resid
60618.8 60645.5 -30305.4 60610.8 5867
Scaled residuals:
Min 1Q Median 3Q Max
-6.2037 -0.6273 -0.0635 0.5846 3.9171
Random effects:
Groups Name Variance Std.Dev.
classid (Intercept) 287.6 16.96
schoolid (Intercept) 375.8 19.39
Residual 1612.4 40.15
Number of obs: 5871, groups: classid, 325; schoolid, 79
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 486.00 2.45 78.31 198.4 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ICC.2 <-287.6/(287.6+381.9+1612.4)
ICC.2
[1] 0.1260353
ICC.3 <- 381.9/(287.6+381.9+1612.4)
ICC.3
[1] 0.1673605
model.1 <- lmer(math~self_concept + classtype + degree + school_math + (1|classid) + (1|schoolid), REML=FALSE, data=star.clean)
boundary (singular) fit: see ?isSingular
summary(model.1)
Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula: math ~ self_concept + classtype + degree + school_math + (1 |
classid) + (1 | schoolid)
Data: star.clean
AIC BIC logLik deviance df.resid
48768.2 48839.3 -24373.1 48746.2 4745
Scaled residuals:
Min 1Q Median 3Q Max
-3.7976 -0.6435 -0.0603 0.5904 4.0060
Random effects:
Groups Name Variance Std.Dev.
classid (Intercept) 174.4 13.21
schoolid (Intercept) 0.0 0.00
Residual 1552.8 39.41
Number of obs: 4756, groups: classid, 301; schoolid, 73
Fixed effects:
Estimate Std. Error df t value
(Intercept) -14.15268 22.09078 317.69097 -0.641
self_concept 0.43544 0.11725 4755.84428 3.714
classtypeREGULAR CLASS -8.20633 2.35152 293.16090 -3.490
classtypeREGULAR + AIDE CLASS -8.69961 2.35099 288.49370 -3.700
degreeMASTERS -0.33621 2.10936 280.44980 -0.159
degreeMASTERS + -2.37697 5.96377 275.53135 -0.399
degreeSPECIALIST 19.25123 10.64910 405.76184 1.808
school_math 0.99386 0.04374 275.01862 22.724
Pr(>|t|)
(Intercept) 0.522205
self_concept 0.000206 ***
classtypeREGULAR CLASS 0.000558 ***
classtypeREGULAR + AIDE CLASS 0.000258 ***
degreeMASTERS 0.873477
degreeMASTERS + 0.690520
degreeSPECIALIST 0.071381 .
school_math < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slf_cn cREGUC cRE+AC dMASTE dMAST+ dSPECI
self_concpt -0.274
cREGULARCLA -0.039 0.030
cREGULAR+AC -0.059 0.027 0.479
degrMASTERS 0.019 -0.011 -0.072 -0.064
dgrMASTERS+ 0.021 -0.007 -0.021 0.000 0.114
dSPECIALIST 0.003 -0.008 0.101 0.101 0.054 0.021
school_math -0.951 -0.025 -0.019 0.002 -0.043 -0.030 -0.014
convergence code: 0
boundary (singular) fit: see ?isSingular
rand(model.1)
boundary (singular) fit: see ?isSingular
ANOVA-like table for random-effects: Single term deletions
Model:
math ~ self_concept + classtype + degree + school_math + (1 |
classid) + (1 | schoolid)
npar logLik AIC LRT Df Pr(>Chisq)
<none> 11 -24373 48768
(1 | classid) 10 -24465 48949 183.04 1 <2e-16 ***
(1 | schoolid) 10 -24373 48766 0.00 1 1
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
model.2 <- lmer(math~self_concept + classtype + degree + school_math + (self_concept|classid) + (1|schoolid), REML=FALSE, data=star.clean)
Model failed to converge with max|grad| = 3.22002 (tol = 0.002, component 1)Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
summary(model.2)
Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula:
math ~ self_concept + classtype + degree + school_math + (self_concept |
classid) + (1 | schoolid)
Data: star.clean
AIC BIC logLik deviance df.resid
48766.2 48850.3 -24370.1 48740.2 4743
Scaled residuals:
Min 1Q Median 3Q Max
-3.7936 -0.6469 -0.0605 0.5859 4.0123
Random effects:
Groups Name Variance Std.Dev. Corr
classid (Intercept) 1.140e+03 33.75818
self_concept 3.159e-01 0.56204 -0.92
schoolid (Intercept) 3.346e-03 0.05784
Residual 1.545e+03 39.30534
Number of obs: 4756, groups: classid, 301; schoolid, 73
Fixed effects:
Estimate Std. Error df t value
(Intercept) -14.12538 22.04803 322.68698 -0.641
self_concept 0.43505 0.12361 125.78425 3.520
classtypeREGULAR CLASS -8.17896 2.33745 295.50182 -3.499
classtypeREGULAR + AIDE CLASS -8.67467 2.33612 290.20731 -3.713
degreeMASTERS -0.33759 2.09558 281.69626 -0.161
degreeMASTERS + -2.21992 5.93015 277.84282 -0.374
degreeSPECIALIST 19.36573 10.60500 399.16136 1.826
school_math 0.99387 0.04347 276.96504 22.863
Pr(>|t|)
(Intercept) 0.522196
self_concept 0.000603 ***
classtypeREGULAR CLASS 0.000539 ***
classtypeREGULAR + AIDE CLASS 0.000245 ***
degreeMASTERS 0.872135
degreeMASTERS + 0.708433
degreeSPECIALIST 0.068583 .
school_math < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slf_cn cREGUC cRE+AC dMASTE dMAST+ dSPECI
self_concpt -0.288
cREGULARCLA -0.040 0.031
cREGULAR+AC -0.060 0.028 0.480
degrMASTERS 0.019 -0.009 -0.072 -0.064
dgrMASTERS+ 0.020 -0.006 -0.022 -0.001 0.114
dSPECIALIST 0.003 -0.008 0.101 0.101 0.053 0.021
school_math -0.946 -0.028 -0.019 0.002 -0.044 -0.030 -0.014
convergence code: 0
Model failed to converge with max|grad| = 3.22002 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
model.3 <- lmer(math~self_concept + classtype + degree + school_math + (1|classid) + (self_concept|schoolid), REML=FALSE, data=star.clean)
boundary (singular) fit: see ?isSingular
summary(model.3)
Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula: math ~ self_concept + classtype + degree + school_math + (1 |
classid) + (self_concept | schoolid)
Data: star.clean
AIC BIC logLik deviance df.resid
48763.3 48847.4 -24368.6 48737.3 4743
Scaled residuals:
Min 1Q Median 3Q Max
-3.7454 -0.6503 -0.0607 0.5866 4.0344
Random effects:
Groups Name Variance Std.Dev. Corr
classid (Intercept) 169.4817 13.0185
schoolid (Intercept) 1733.5612 41.6361
self_concept 0.5549 0.7449 -1.00
Residual 1540.9570 39.2550
Number of obs: 4756, groups: classid, 301; schoolid, 73
Fixed effects:
Estimate Std. Error df t value
(Intercept) -14.19106 22.49511 324.23626 -0.631
self_concept 0.41450 0.14949 64.96742 2.773
classtypeREGULAR CLASS -8.28684 2.33591 298.05015 -3.548
classtypeREGULAR + AIDE CLASS -8.67911 2.33323 292.28934 -3.720
degreeMASTERS -0.59336 2.09262 283.66571 -0.284
degreeMASTERS + -2.55123 5.92240 279.49950 -0.431
degreeSPECIALIST 19.55020 10.59636 414.03966 1.845
school_math 0.99669 0.04336 277.31610 22.988
Pr(>|t|)
(Intercept) 0.528582
self_concept 0.007245 **
classtypeREGULAR CLASS 0.000451 ***
classtypeREGULAR + AIDE CLASS 0.000239 ***
degreeMASTERS 0.776964
degreeMASTERS + 0.666963
degreeSPECIALIST 0.065753 .
school_math < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slf_cn cREGUC cRE+AC dMASTE dMAST+ dSPECI
self_concpt -0.352
cREGULARCLA -0.040 0.027
cREGULAR+AC -0.058 0.023 0.481
degrMASTERS 0.019 -0.011 -0.072 -0.064
dgrMASTERS+ 0.020 -0.005 -0.021 -0.001 0.114
dSPECIALIST 0.004 -0.010 0.100 0.101 0.052 0.021
school_math -0.925 -0.023 -0.019 0.002 -0.043 -0.029 -0.014
convergence code: 0
boundary (singular) fit: see ?isSingular
anova(model.1, model.2)
Data: star.clean
Models:
model.1: math ~ self_concept + classtype + degree + school_math + (1 |
model.1: classid) + (1 | schoolid)
model.2: math ~ self_concept + classtype + degree + school_math + (self_concept |
model.2: classid) + (1 | schoolid)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.1 11 48768 48839 -24373 48746
model.2 13 48766 48850 -24370 48740 5.939 2 0.05133 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(model.1, model.3)
Data: star.clean
Models:
model.1: math ~ self_concept + classtype + degree + school_math + (1 |
model.1: classid) + (1 | schoolid)
model.3: math ~ self_concept + classtype + degree + school_math + (1 |
model.3: classid) + (self_concept | schoolid)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.1 11 48768 48839 -24373 48746
model.3 13 48763 48847 -24369 48737 8.8782 2 0.01181 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
model.2 <- lmer(math~self_concept + classtype + degree + school_math + (self_concept|classid) + (1|schoolid), REML=FALSE, data=star.clean)
Model failed to converge with max|grad| = 3.22002 (tol = 0.002, component 1)Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
summary(model.2)
Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula:
math ~ self_concept + classtype + degree + school_math + (self_concept |
classid) + (1 | schoolid)
Data: star.clean
AIC BIC logLik deviance df.resid
48766.2 48850.3 -24370.1 48740.2 4743
Scaled residuals:
Min 1Q Median 3Q Max
-3.7936 -0.6469 -0.0605 0.5859 4.0123
Random effects:
Groups Name Variance Std.Dev. Corr
classid (Intercept) 1.140e+03 33.75818
self_concept 3.159e-01 0.56204 -0.92
schoolid (Intercept) 3.346e-03 0.05784
Residual 1.545e+03 39.30534
Number of obs: 4756, groups: classid, 301; schoolid, 73
Fixed effects:
Estimate Std. Error df t value
(Intercept) -14.12538 22.04803 322.68698 -0.641
self_concept 0.43505 0.12361 125.78425 3.520
classtypeREGULAR CLASS -8.17896 2.33745 295.50182 -3.499
classtypeREGULAR + AIDE CLASS -8.67467 2.33612 290.20731 -3.713
degreeMASTERS -0.33759 2.09558 281.69626 -0.161
degreeMASTERS + -2.21992 5.93015 277.84282 -0.374
degreeSPECIALIST 19.36573 10.60500 399.16136 1.826
school_math 0.99387 0.04347 276.96504 22.863
Pr(>|t|)
(Intercept) 0.522196
self_concept 0.000603 ***
classtypeREGULAR CLASS 0.000539 ***
classtypeREGULAR + AIDE CLASS 0.000245 ***
degreeMASTERS 0.872135
degreeMASTERS + 0.708433
degreeSPECIALIST 0.068583 .
school_math < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slf_cn cREGUC cRE+AC dMASTE dMAST+ dSPECI
self_concpt -0.288
cREGULARCLA -0.040 0.031
cREGULAR+AC -0.060 0.028 0.480
degrMASTERS 0.019 -0.009 -0.072 -0.064
dgrMASTERS+ 0.020 -0.006 -0.022 -0.001 0.114
dSPECIALIST 0.003 -0.008 0.101 0.101 0.053 0.021
school_math -0.946 -0.028 -0.019 0.002 -0.044 -0.030 -0.014
convergence code: 0
Model failed to converge with max|grad| = 3.22002 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
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