An Analysis of Upper Elementary (Grades 3-5) Students Social-Emotional Skills during the COVID-19 Pandemic
Jake Reynolds
library(tidyverse)
library(corrr)
library(psych)library(haven)
sel <- read_csv("35sel2.csv")Parsed with column specification:
cols(
.default = col_double(),
submitted = col_character()
)
See spec(...) for full column specifications.Explore your data with glimpse
describe(sel)str(sel)tibble [382 x 37] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ Student ID: num [1:382] 2781 2852 4324 2251 2054 ...
$ submitted : chr [1:382] "2020-09-16 13:36:02 UTC" "2020-09-10 17:53:24 UTC" "2020-09-10 15:01:18 UTC" "2020-09-10 17:47:51 UTC" ...
$ Grade : num [1:382] 3 3 3 3 3 3 3 3 3 3 ...
$ Race : num [1:382] 7 7 7 2 3 3 3 3 3 3 ...
$ Sex : num [1:382] 1 1 1 1 1 1 1 1 1 1 ...
$ Q1 : num [1:382] 3 2 2 3 3 3 3 3 3 3 ...
$ Q2 : num [1:382] 2 2 3 3 3 1 2 3 1 3 ...
$ Q3 : num [1:382] 2 2 2 2 2 2 3 2 2 3 ...
$ Q4 : num [1:382] 2 2 2 3 2 3 3 2 2 3 ...
$ Q5 : num [1:382] 3 3 3 2 2 2 1 3 2 3 ...
$ Q6 : num [1:382] 2 1 2 3 1 2 3 1 1 2 ...
$ Q7 : num [1:382] 3 2 NA 1 2 3 3 2 3 3 ...
$ Q8 : num [1:382] 2 2 2 1 2 2 1 2 2 3 ...
$ Q9 : num [1:382] 2 2 1 3 3 2 2 3 1 2 ...
$ Q10 : num [1:382] 3 2 2 2 2 2 3 2 2 2 ...
$ Q11 : num [1:382] 1 3 1 1 1 1 1 1 1 1 ...
$ Q12 : num [1:382] 3 2 3 3 2 2 2 2 2 3 ...
$ Q13 : num [1:382] 3 3 3 2 3 1 1 3 1 1 ...
$ Q14 : num [1:382] 3 2 2 3 2 2 2 2 2 2 ...
$ Q15 : num [1:382] 3 2 2 3 2 2 2 2 2 3 ...
$ Q16 : num [1:382] 1 1 1 1 1 1 1 1 1 1 ...
$ Q17 : num [1:382] 2 2 2 2 2 3 1 2 2 2 ...
$ Q18 : num [1:382] 3 2 2 2 3 2 2 1 2 3 ...
$ Q19 : num [1:382] 3 3 3 3 3 3 3 1 1 1 ...
$ Q20 : num [1:382] 2 2 3 2 2 2 3 2 2 2 ...
$ Q21 : num [1:382] 3 2 3 1 3 3 1 2 2 3 ...
$ Q22 : num [1:382] 1 2 1 1 1 1 1 1 1 1 ...
$ Q23 : num [1:382] 3 2 2 2 2 2 2 2 2 2 ...
$ Q24 : num [1:382] 2 2 2 3 2 3 3 2 2 2 ...
$ Q25 : num [1:382] 3 2 1 1 1 1 2 1 1 2 ...
$ Q26 : num [1:382] 2 3 3 3 3 3 3 3 3 1 ...
$ Q27 : num [1:382] 2 2 3 3 2 2 3 2 2 2 ...
$ Q28 : num [1:382] 3 2 3 3 2 2 2 2 2 2 ...
$ Q29 : num [1:382] 2 2 2 2 2 2 3 2 2 2 ...
$ Q30 : num [1:382] 2 3 1 2 2 2 2 2 2 3 ...
$ Q31 : num [1:382] 2 2 2 2 3 3 1 2 2 2 ...
$ Q32 : num [1:382] 2 2 2 2 2 2 2 2 2 2 ...
- attr(*, "spec")=
.. cols(
.. `Student ID` = col_double(),
.. submitted = col_character(),
.. Grade = col_double(),
.. Race = col_double(),
.. Sex = col_double(),
.. Q1 = col_double(),
.. Q2 = col_double(),
.. Q3 = col_double(),
.. Q4 = col_double(),
.. Q5 = col_double(),
.. Q6 = col_double(),
.. Q7 = col_double(),
.. Q8 = col_double(),
.. Q9 = col_double(),
.. Q10 = col_double(),
.. Q11 = col_double(),
.. Q12 = col_double(),
.. Q13 = col_double(),
.. Q14 = col_double(),
.. Q15 = col_double(),
.. Q16 = col_double(),
.. Q17 = col_double(),
.. Q18 = col_double(),
.. Q19 = col_double(),
.. Q20 = col_double(),
.. Q21 = col_double(),
.. Q22 = col_double(),
.. Q23 = col_double(),
.. Q24 = col_double(),
.. Q25 = col_double(),
.. Q26 = col_double(),
.. Q27 = col_double(),
.. Q28 = col_double(),
.. Q29 = col_double(),
.. Q30 = col_double(),
.. Q31 = col_double(),
.. Q32 = col_double()
.. )glimpse(sel)Rows: 382
Columns: 37
$ `Student ID` <dbl> 2781, 2852, 4324, 2251, 2054, 2312, 2385, 2416, 2443, 2451, 2597, 3409, 4522, 2331, 2639, 2654, 2671, 2768, 2823, 3710, 2289, 2437, 2447, 2575, 3832, 1...
$ submitted <chr> "2020-09-16 13:36:02 UTC", "2020-09-10 17:53:24 UTC", "2020-09-10 15:01:18 UTC", "2020-09-10 17:47:51 UTC", "2020-09-09 18:44:55 UTC", "2020-09-10 16:4...
$ Grade <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3...
$ Race <dbl> 7, 7, 7, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5...
$ Sex <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ Q1 <dbl> 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 1, 2, 1, 1, 3, 3, 2, 2, 3, 2, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 2, 3, 3, 2, 3, 2...
$ Q2 <dbl> 2, 2, 3, 3, 3, 1, 2, 3, 1, 3, 3, 3, 1, 1, 2, 3, 3, 2, 3, 1, 3, 2, 3, 1, 3, 3, 3, 2, 2, 3, 3, 3, 3, 1, 3, 2, 1, 3, 3, 1, 3, 3, 3, 3, 1, 3, 2, 3, 1, 3, 3...
$ Q3 <dbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2...
$ Q4 <dbl> 2, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 1, 3, 3, 2, 3, 1, 3, 2, 3, 2, 3, 3, 3, 1, 3, 3, 2, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2...
$ Q5 <dbl> 3, 3, 3, 2, 2, 2, 1, 3, 2, 3, 2, 3, 1, 1, 3, 2, 3, 3, 2, 2, 1, 1, 2, 2, 2, 1, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 1, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 2...
$ Q6 <dbl> 2, 1, 2, 3, 1, 2, 3, 1, 1, 2, 2, 1, 1, 3, 3, 1, 2, 3, 2, 1, 3, 3, 2, 3, 2, 3, 3, 2, 3, 3, 1, 3, 1, 1, 1, 3, 2, 1, 1, 1, 3, 3, 2, 1, 1, 2, 3, NA, 1, 3, ...
$ Q7 <dbl> 3, 2, NA, 1, 2, 3, 3, 2, 3, 3, 3, 3, 3, NA, 3, 3, 3, 3, 3, 2, 1, 1, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3, 1, 2, 3, 2, 2, 3, 2, 3, 2, 2, 1, 2, 2, 3,...
$ Q8 <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 3, 1, 1, 1, 2, 1, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3...
$ Q9 <dbl> 2, 2, 1, 3, 3, 2, 2, 3, 1, 2, 2, 1, 2, 3, 3, 1, 2, 3, 3, 1, 2, 3, 1, 3, 1, 3, 2, 3, 2, 2, 1, 2, 3, 2, 1, 3, 2, 1, 3, 3, 1, 3, 3, 1, 3, 3, 3, 2, 1, 3, 2...
$ Q10 <dbl> 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, NA, 2, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, ...
$ Q11 <dbl> 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 2, 1, 1, 2, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1...
$ Q12 <dbl> 3, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, NA, 2, 2, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, ...
$ Q13 <dbl> 3, 3, 3, 2, 3, 1, 1, 3, 1, 1, 1, 1, 3, 1, 2, 1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 1, 2, 3, 3, 1, 3, 2, 1, 3, 2, 1, 2, 2, 3, 3, 2, 3, 3, 3, 1, 3, 1, 2, 2...
$ Q14 <dbl> 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, NA, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, NA, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 3, 2, 2, NA, 3, ...
$ Q15 <dbl> 3, 2, 2, 3, 2, 2, 2, 2, 2, 3, NA, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 1, 2, NA, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 3, 1, 2, 3, 3, 2, 2,...
$ Q16 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, NA, 1, 1, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1,...
$ Q17 <dbl> 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, NA, 2, 1, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, NA, 3, NA, 2, 2, NA, 2, 1, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, NA, 2,...
$ Q18 <dbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 3, NA, 2, 2, 2, 1, 2, 1, 3, 3, 2, 2, 3, 3, 1, NA, 3, NA, 1, NA, 3, 3, 2, 3, 1, 3, 2, 2, 2, 2, 3, 3, 3, 3, NA, 3, 2, 1, 2, 1,...
$ Q19 <dbl> 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, NA, 1, 2, 1, 3, 1, 3, 3, 3, 1, 2, 2, 3, 3, NA, 1, NA, 1, NA, 1, 3, 3, 3, 2, 3, 1, 1, 1, 3, 3, 2, 1, 3, 1, 1, 1, 2, 3, 1, ...
$ Q20 <dbl> 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 1, 2, NA, 3, NA, 2, NA, NA, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 3, 2, 2,...
$ Q21 <dbl> 3, 2, 3, 1, 3, 3, 1, 2, 2, 3, NA, 3, 2, 2, NA, 3, 2, 3, 3, 2, 3, 2, 3, 2, NA, 3, NA, 2, NA, 2, 2, 3, 3, 2, 3, 3, 2, 1, 3, 3, 3, 3, 1, 2, 1, 2, 3, 3, 2,...
$ Q22 <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 3, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, NA, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, ...
$ Q23 <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 3, NA, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
$ Q24 <dbl> 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, NA, 2, 2, 3, NA, 2, 2, 3, 2, 2, 2, 2, 2, 2, NA, 3, NA, 3, 3, NA, 2, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 2,...
$ Q25 <dbl> 3, 2, 1, 1, 1, 1, 2, 1, 1, 2, NA, 3, 2, 2, NA, 1, 2, 3, 1, 1, 2, 1, 1, 2, NA, 1, NA, 1, 1, 1, 3, 3, 2, 2, 3, 1, 3, 2, 3, 3, 1, 1, 2, 3, 3, 3, 3, NA, 1,...
$ Q26 <dbl> 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, NA, 1, 1, 3, NA, 3, 1, 1, 3, 1, 2, 2, 3, 3, NA, 3, NA, 1, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 1, 3, 1, 3, 3, 2, NA, 1,...
$ Q27 <dbl> 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, NA, 2, 3, 2, NA, 2, 2, 2, 2, 2, 2, 3, 3, 2, NA, 1, NA, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, ...
$ Q28 <dbl> 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, NA, 2, 2, 3, NA, 2, 3, 2, 2, 2, 2, 2, 1, 2, NA, 3, NA, 3, 3, NA, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 1, 2, 2,...
$ Q29 <dbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 2, 3, 2, NA, 3, NA, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, ...
$ Q30 <dbl> 2, 3, 1, 2, 2, 2, 2, 2, 2, 3, NA, 2, 1, 3, NA, 2, NA, 3, 1, 2, 2, 2, 3, 2, NA, 3, NA, 3, 3, 1, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 1, 3, 2,...
$ Q31 <dbl> 2, 2, 2, 2, 3, 3, 1, 2, 2, 2, NA, 1, 2, 2, NA, 2, 3, 2, 2, 2, 2, 3, 3, 2, NA, 1, NA, 3, 3, 2, 2, 3, 1, 1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, ...
$ Q32 <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 2, 3, 2, NA, 3, NA, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, ...summary(sel) Student ID submitted Grade Race Sex Q1 Q2 Q3 Q4 Q5
Min. :1076 Length:382 Min. :3.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2056 Class :character 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2341 Mode :character Median :4.000 Median :5.000 Median :1.000 Median :3.00 Median :3.000 Median :2.000 Median :3.000 Median :2.000
Mean :2520 Mean :4.183 Mean :4.513 Mean :1.026 Mean :2.61 Mean :2.241 Mean :2.086 Mean :2.508 Mean :2.393
3rd Qu.:2789 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:1.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
Max. :4530 Max. :5.000 Max. :8.000 Max. :3.000 Max. :3.00 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
NA's :1 NA's :1 NA's :2 NA's :3
Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16
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. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
Median :2.000 Median :3.000 Median :2.000 Median :2.000 Median :2.000 Median :1.00 Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :1.000
Mean :1.932 Mean :2.435 Mean :2.393 Mean :1.984 Mean :2.168 Mean :1.45 Mean :2.286 Mean :2.171 Mean :2.173 Mean :2.263 Mean :1.185
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:1.000
Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.00 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
NA's :1 NA's :3 NA's :2 NA's :1 NA's :2 NA's :5 NA's :2 NA's :6 NA's :2 NA's :3
Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :2.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :3.000 Median :1.000 Median :2.000 Median :2.000 Median :2.000 Median :3.000 Median :2.000
Mean :2.358 Mean :2.256 Mean :1.981 Mean :2.109 Mean :2.413 Mean :1.069 Mean :2.091 Mean :2.419 Mean :2.064 Mean :2.397 Mean :2.198
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
NA's :10 NA's :11 NA's :6 NA's :7 NA's :7 NA's :6 NA's :7 NA's :10 NA's :8 NA's :9 NA's :9
Q28 Q29 Q30 Q31 Q32
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :2.000
Mean :2.181 Mean :2.225 Mean :2.409 Mean :2.278 Mean :2.045
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
NA's :11 NA's :8 NA's :8 NA's :8 NA's :7 corr_matrix <- correlate(sel[,6:35])
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'corr_matrixsel.clean <-sel[,4:36] %>%
mutate(.,
Q2R = 4 - Q2,
Q6R = 4 - Q6,
Q9R = 4 - Q9,
Q11R = 4 - Q11,
Q13R = 4 - Q13,
Q16R = 4 - Q16,
Q19R = 4 - Q19,
Q22R = 4 - Q22,
Q25R = 4 - Q25,
Q26R = 4 - Q26,) %>%
select(.,
-Q2,
-Q6,
-Q9,
-Q11,
-Q13,
-Q16,
-Q19,
-Q22,
-Q25,
-Q26,
-Q31)Double check
glimpse(sel.clean)Rows: 382
Columns: 32
$ Race <dbl> 7, 7, 7, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 3, ...
$ Sex <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
$ Q1 <dbl> 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 1, 2, 1, 1, 3, 3, 2, 2, 3, 2, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 2, 3, 3, 2, 3, 2, 3, 2, ...
$ Q3 <dbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, ...
$ Q4 <dbl> 2, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 1, 3, 3, 2, 3, 1, 3, 2, 3, 2, 3, 3, 3, 1, 3, 3, 2, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, ...
$ Q5 <dbl> 3, 3, 3, 2, 2, 2, 1, 3, 2, 3, 2, 3, 1, 1, 3, 2, 3, 3, 2, 2, 1, 1, 2, 2, 2, 1, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 1, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 2, 2, 3, ...
$ Q7 <dbl> 3, 2, NA, 1, 2, 3, 3, 2, 3, 3, 3, 3, 3, NA, 3, 3, 3, 3, 3, 2, 1, 1, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3, 1, 2, 3, 2, 2, 3, 2, 3, 2, 2, 1, 2, 2, 3, 2, 3, 2...
$ Q8 <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 3, 1, 1, 1, 2, 1, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 1, 3, ...
$ Q10 <dbl> 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, NA, 2, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,...
$ Q12 <dbl> 3, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, NA, 2, 2, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2,...
$ Q14 <dbl> 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, NA, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, NA, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 3, 2, 2, NA, 3, 2, 2, 2,...
$ Q15 <dbl> 3, 2, 2, 3, 2, 2, 2, 2, 2, 3, NA, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 1, 2, NA, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 3, 1, 2, 3, 3, 2, 2, 2, 2, 2...
$ Q17 <dbl> 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, NA, 2, 1, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, NA, 3, NA, 2, 2, NA, 2, 1, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, NA, 2, 3, 1, 2...
$ Q18 <dbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 3, NA, 2, 2, 2, 1, 2, 1, 3, 3, 2, 2, 3, 3, 1, NA, 3, NA, 1, NA, 3, 3, 2, 3, 1, 3, 2, 2, 2, 2, 3, 3, 3, 3, NA, 3, 2, 1, 2, 1, 2, 1, 2...
$ Q20 <dbl> 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 1, 2, NA, 3, NA, 2, NA, NA, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 3, 2, 2, 2, 2, 2...
$ Q21 <dbl> 3, 2, 3, 1, 3, 3, 1, 2, 2, 3, NA, 3, 2, 2, NA, 3, 2, 3, 3, 2, 3, 2, 3, 2, NA, 3, NA, 2, NA, 2, 2, 3, 3, 2, 3, 3, 2, 1, 3, 3, 3, 3, 1, 2, 1, 2, 3, 3, 2, 2, 2, 1...
$ Q23 <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, 3, NA, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ Q24 <dbl> 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, NA, 2, 2, 3, NA, 2, 2, 3, 2, 2, 2, 2, 2, 2, NA, 3, NA, 3, 3, NA, 2, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 2, 3, 1, 2...
$ Q27 <dbl> 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, NA, 2, 3, 2, NA, 2, 2, 2, 2, 2, 2, 3, 3, 2, NA, 1, NA, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,...
$ Q28 <dbl> 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, NA, 2, 2, 3, NA, 2, 3, 2, 2, 2, 2, 2, 1, 2, NA, 3, NA, 3, 3, NA, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 1, 2, 2, 2, 2, 2...
$ Q29 <dbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 2, 3, 2, NA, 3, NA, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 2,...
$ Q30 <dbl> 2, 3, 1, 2, 2, 2, 2, 2, 2, 3, NA, 2, 1, 3, NA, 2, NA, 3, 1, 2, 2, 2, 3, 2, NA, 3, NA, 3, 3, 1, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 1, 3, 2, 3, 3, 2...
$ Q2R <dbl> 2, 2, 1, 1, 1, 3, 2, 1, 3, 1, 1, 1, 3, 3, 2, 1, 1, 2, 1, 3, 1, 2, 1, 3, 1, 1, 1, 2, 2, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 3, 1, 1, 1, 1, 3, 1, 2, 1, 3, 1, 1, 3, 3, ...
$ Q6R <dbl> 2, 3, 2, 1, 3, 2, 1, 3, 3, 2, 2, 3, 3, 1, 1, 3, 2, 1, 2, 3, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 3, 1, 3, 3, 3, 1, 2, 3, 3, 3, 1, 1, 2, 3, 3, 2, 1, NA, 3, 1, 1, 3, 3,...
$ Q9R <dbl> 2, 2, 3, 1, 1, 2, 2, 1, 3, 2, 2, 3, 2, 1, 1, 3, 2, 1, 1, 3, 2, 1, 3, 1, 3, 1, 2, 1, 2, 2, 3, 2, 1, 2, 3, 1, 2, 3, 1, 1, 3, 1, 1, 3, 1, 1, 1, 2, 3, 1, 2, 2, 3, ...
$ Q11R <dbl> 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 3, 1, 1, 3, 3, 1, 1, 3, 1, 3, 3, 1, 2, 3, 3, 2, 2, 3, 3, 3, 3, 1, 3, 3, 3, 3, 1, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3, ...
$ Q13R <dbl> 1, 1, 1, 2, 1, 3, 3, 1, 3, 3, 3, 3, 1, 3, 2, 3, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 3, 2, 1, 1, 3, 1, 2, 3, 1, 2, 3, 2, 2, 1, 1, 2, 1, 1, 1, 3, 1, 3, 2, 2, 2, 3, ...
$ Q16R <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, NA, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3, NA, 3, 3, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3...
$ Q19R <dbl> 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, NA, 3, 2, 3, 1, 3, 1, 1, 1, 3, 2, 2, 1, 1, NA, 3, NA, 3, NA, 3, 1, 1, 1, 2, 1, 3, 3, 3, 1, 1, 2, 3, 1, 3, 3, 3, 2, 1, 3, 1, 3, 3,...
$ Q22R <dbl> 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, NA, 3, 1, 3, NA, 3, 3, 3, 3, 3, 3, 3, 3, 3, NA, 3, NA, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3,...
$ Q25R <dbl> 1, 2, 3, 3, 3, 3, 2, 3, 3, 2, NA, 1, 2, 2, NA, 3, 2, 1, 3, 3, 2, 3, 3, 2, NA, 3, NA, 3, 3, 3, 1, 1, 2, 2, 1, 3, 1, 2, 1, 1, 3, 3, 2, 1, 1, 1, 1, NA, 3, 2, 2, 2...
$ Q26R <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, NA, 3, 3, 1, NA, 1, 3, 3, 1, 3, 2, 2, 1, 1, NA, 1, NA, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 3, 1, 3, 1, 1, 2, NA, 3, 1, 3, 3...corr_matrix <- correlate(sel.clean)
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'corr_matrixCFA for latent measures
cfa.model0 <-
'
health =~ Q1 + Q4 + Q19R + Q26R
social =~ Q3 + Q12 + Q14 + Q15 + Q16R + Q18 + Q22R + Q23 + Q28 + Q30
emotions =~ Q2R + Q5 + Q10 + Q11R + Q13R + Q21 + Q25R + Q29
personal =~ Q6R + Q7 + Q8 + Q9R + Q17 + Q18 + Q19R + Q20 + Q27
'
cfa_fit <- cfa(cfa.model0, data=sel.clean, estimator = "MLR", missing = "ML", fixed.x = FALSE)lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.summary(cfa_fit, fit.measures=TRUE, standardized=TRUE)lavaan 0.6-8 ended normally after 198 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 95
Number of observations 382
Number of missing patterns 28
Model Test User Model:
Standard Robust
Test Statistic 588.070 515.890
Degrees of freedom 369 369
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.140
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1004.142 859.988
Degrees of freedom 406 406
P-value 0.000 0.000
Scaling correction factor 1.168
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.634 0.676
Tucker-Lewis Index (TLI) 0.597 0.644
Robust Comparative Fit Index (CFI) 0.684
Robust Tucker-Lewis Index (TLI) 0.652
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9215.761 -9215.761
Scaling correction factor 1.334
for the MLR correction
Loglikelihood unrestricted model (H1) -8921.726 -8921.726
Scaling correction factor 1.180
for the MLR correction
Akaike (AIC) 18621.522 18621.522
Bayesian (BIC) 18996.337 18996.337
Sample-size adjusted Bayesian (BIC) 18694.918 18694.918
Root Mean Square Error of Approximation:
RMSEA 0.039 0.032
90 Percent confidence interval - lower 0.033 0.026
90 Percent confidence interval - upper 0.045 0.038
P-value RMSEA <= 0.05 0.999 1.000
Robust RMSEA 0.034
90 Percent confidence interval - lower 0.027
90 Percent confidence interval - upper 0.041
Standardized Root Mean Square Residual:
SRMR 0.056 0.056
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
health =~
Q1 1.000 0.179 0.339
Q4 0.711 0.269 2.646 0.008 0.128 0.201
Q19R -2.490 0.705 -3.531 0.000 -0.447 -0.464
Q26R -3.101 1.109 -2.796 0.005 -0.556 -0.659
social =~
Q3 1.000 0.093 0.312
Q12 1.988 0.722 2.754 0.006 0.186 0.362
Q14 1.580 0.654 2.416 0.016 0.148 0.343
Q15 2.221 0.753 2.951 0.003 0.207 0.453
Q16R -0.734 0.546 -1.343 0.179 -0.069 -0.122
Q18 1.514 1.237 1.224 0.221 0.141 0.223
Q22R -0.362 0.451 -0.804 0.421 -0.034 -0.097
Q23 0.898 0.289 3.113 0.002 0.084 0.292
Q28 2.498 0.826 3.024 0.002 0.233 0.500
Q30 2.392 0.860 2.781 0.005 0.223 0.401
emotions =~
Q2R 1.000 0.158 0.184
Q5 -0.564 0.495 -1.139 0.255 -0.089 -0.144
Q10 -0.954 0.480 -1.986 0.047 -0.151 -0.359
Q11R 1.250 0.661 1.892 0.059 0.198 0.250
Q13R 0.419 0.373 1.123 0.262 0.066 0.086
Q21 -0.014 0.441 -0.033 0.974 -0.002 -0.003
Q25R 0.533 0.448 1.190 0.234 0.084 0.095
Q29 -1.349 0.651 -2.072 0.038 -0.213 -0.453
personal =~
Q6R 1.000 0.289 0.334
Q7 -0.348 0.242 -1.436 0.151 -0.100 -0.151
Q8 -0.210 0.179 -1.176 0.239 -0.061 -0.092
Q9R 0.628 0.197 3.185 0.001 0.181 0.217
Q17 -0.815 0.309 -2.635 0.008 -0.235 -0.419
Q18 0.104 0.395 0.263 0.792 0.030 0.047
Q19R 0.030 0.287 0.103 0.918 0.009 0.009
Q20 -0.445 0.167 -2.660 0.008 -0.128 -0.343
Q27 -0.608 0.258 -2.355 0.019 -0.175 -0.380
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
health ~~
social 0.001 0.002 0.605 0.545 0.065 0.065
emotions -0.004 0.007 -0.649 0.516 -0.152 -0.152
personal -0.011 0.011 -0.998 0.318 -0.216 -0.216
social ~~
emotions -0.009 0.004 -2.069 0.039 -0.607 -0.607
personal -0.022 0.010 -2.165 0.030 -0.798 -0.798
emotions ~~
personal 0.046 0.022 2.146 0.032 1.017 1.017
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q1 2.610 0.027 96.437 0.000 2.610 4.934
.Q4 2.508 0.033 77.057 0.000 2.508 3.952
.Q19R 2.017 0.050 40.624 0.000 2.017 2.094
.Q26R 1.601 0.044 36.778 0.000 1.601 1.898
.Q3 2.086 0.015 136.385 0.000 2.086 6.978
.Q12 2.286 0.026 86.593 0.000 2.286 4.459
.Q14 2.173 0.022 98.017 0.000 2.173 5.046
.Q15 2.263 0.023 96.387 0.000 2.263 4.942
.Q16R 2.815 0.029 97.763 0.000 2.815 5.023
.Q18 2.256 0.033 68.733 0.000 2.256 3.563
.Q22R 2.931 0.018 162.232 0.000 2.931 8.361
.Q23 2.090 0.015 141.411 0.000 2.090 7.282
.Q28 2.179 0.024 90.373 0.000 2.179 4.673
.Q30 2.409 0.029 83.631 0.000 2.409 4.320
.Q2R 1.759 0.044 39.903 0.000 1.759 2.045
.Q5 2.393 0.032 75.280 0.000 2.393 3.877
.Q10 2.168 0.022 100.756 0.000 2.168 5.160
.Q11R 2.550 0.041 62.798 0.000 2.550 3.222
.Q13R 1.829 0.039 46.446 0.000 1.829 2.383
.Q21 2.413 0.037 65.818 0.000 2.413 3.400
.Q25R 1.936 0.046 42.340 0.000 1.936 2.189
.Q29 2.224 0.024 91.518 0.000 2.224 4.720
.Q6R 2.068 0.044 46.739 0.000 2.068 2.395
.Q7 2.435 0.034 71.403 0.000 2.435 3.667
.Q8 2.393 0.034 70.706 0.000 2.393 3.618
.Q9R 2.015 0.043 46.950 0.000 2.015 2.408
.Q17 2.357 0.029 81.100 0.000 2.357 4.199
.Q20 2.109 0.019 109.309 0.000 2.109 5.638
.Q27 2.198 0.024 92.370 0.000 2.198 4.769
health 0.000 0.000 0.000
social 0.000 0.000 0.000
emotions 0.000 0.000 0.000
personal 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q1 0.248 0.023 10.708 0.000 0.248 0.885
.Q4 0.386 0.032 11.974 0.000 0.386 0.960
.Q19R 0.727 0.076 9.616 0.000 0.727 0.783
.Q26R 0.403 0.131 3.076 0.002 0.403 0.566
.Q3 0.081 0.011 7.120 0.000 0.081 0.902
.Q12 0.228 0.019 11.997 0.000 0.228 0.869
.Q14 0.164 0.018 9.336 0.000 0.164 0.883
.Q15 0.167 0.017 9.869 0.000 0.167 0.795
.Q16R 0.309 0.044 7.002 0.000 0.309 0.985
.Q18 0.387 0.027 14.353 0.000 0.387 0.965
.Q22R 0.122 0.032 3.787 0.000 0.122 0.991
.Q23 0.075 0.011 6.872 0.000 0.075 0.915
.Q28 0.163 0.017 9.349 0.000 0.163 0.750
.Q30 0.261 0.028 9.200 0.000 0.261 0.839
.Q2R 0.715 0.031 22.859 0.000 0.715 0.966
.Q5 0.373 0.031 12.226 0.000 0.373 0.979
.Q10 0.154 0.017 9.306 0.000 0.154 0.871
.Q11R 0.587 0.047 12.462 0.000 0.587 0.938
.Q13R 0.585 0.026 22.097 0.000 0.585 0.993
.Q21 0.504 0.030 16.684 0.000 0.504 1.000
.Q25R 0.775 0.025 30.395 0.000 0.775 0.991
.Q29 0.177 0.022 8.148 0.000 0.177 0.795
.Q6R 0.663 0.051 13.050 0.000 0.663 0.888
.Q7 0.431 0.032 13.374 0.000 0.431 0.977
.Q8 0.434 0.027 16.216 0.000 0.434 0.992
.Q9R 0.667 0.032 21.092 0.000 0.667 0.953
.Q17 0.260 0.027 9.576 0.000 0.260 0.824
.Q20 0.124 0.016 7.888 0.000 0.124 0.882
.Q27 0.182 0.021 8.813 0.000 0.182 0.855
health 0.032 0.014 2.324 0.020 1.000 1.000
social 0.009 0.005 1.689 0.091 1.000 1.000
emotions 0.025 0.022 1.132 0.258 1.000 1.000
personal 0.083 0.048 1.740 0.082 1.000 1.000cfa.model <- ' health =~ Q1 + Q19R + Q26R
social =~ Q3 + Q12 + Q14 + Q15 + Q28 + Q30
emotions =~ Q10 + Q29
personal =~ Q6R + Q17 + Q20 + Q27
goals =~ Q24'
cfa_fit <- cfa(cfa.model, data=sel.clean, estimator = "MLR", missing = "ML", fixed.x = FALSE)
summary(cfa_fit, fit.measures=TRUE, standardized=TRUE)lavaan 0.6-8 ended normally after 151 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 57
Number of observations 382
Number of missing patterns 20
Model Test User Model:
Standard Robust
Test Statistic 154.331 119.766
Degrees of freedom 95 95
P-value (Chi-square) 0.000 0.044
Scaling correction factor 1.289
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 539.569 412.438
Degrees of freedom 120 120
P-value 0.000 0.000
Scaling correction factor 1.308
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.859 0.915
Tucker-Lewis Index (TLI) 0.821 0.893
Robust Comparative Fit Index (CFI) 0.917
Robust Tucker-Lewis Index (TLI) 0.895
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4431.451 -4431.451
Scaling correction factor 1.189
for the MLR correction
Loglikelihood unrestricted model (H1) -4354.285 -4354.285
Scaling correction factor 1.251
for the MLR correction
Akaike (AIC) 8976.901 8976.901
Bayesian (BIC) 9201.790 9201.790
Sample-size adjusted Bayesian (BIC) 9020.939 9020.939
Root Mean Square Error of Approximation:
RMSEA 0.040 0.026
90 Percent confidence interval - lower 0.028 0.009
90 Percent confidence interval - upper 0.052 0.038
P-value RMSEA <= 0.05 0.915 1.000
Robust RMSEA 0.030
90 Percent confidence interval - lower 0.005
90 Percent confidence interval - upper 0.045
Standardized Root Mean Square Residual:
SRMR 0.044 0.044
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
health =~
Q1 1.000 0.168 0.319
Q19R -2.428 0.629 -3.862 0.000 -0.409 -0.425
Q26R -3.685 1.475 -2.498 0.012 -0.621 -0.736
social =~
Q3 1.000 0.078 0.262
Q12 2.233 0.918 2.432 0.015 0.175 0.341
Q14 2.026 0.872 2.324 0.020 0.159 0.369
Q15 2.637 1.003 2.630 0.009 0.207 0.451
Q28 2.987 1.187 2.517 0.012 0.234 0.502
Q30 3.161 1.214 2.604 0.009 0.248 0.444
emotions =~
Q10 1.000 0.138 0.329
Q29 1.737 0.463 3.750 0.000 0.240 0.509
personal =~
Q6R 1.000 0.250 0.290
Q17 -0.917 0.304 -3.021 0.003 -0.229 -0.409
Q20 -0.541 0.190 -2.856 0.004 -0.135 -0.362
Q27 -0.865 0.311 -2.783 0.005 -0.217 -0.470
goals =~
Q24 1.000 0.540 1.000
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
health ~~
social 0.001 0.001 0.844 0.399 0.080 0.080
emotions 0.000 0.003 0.178 0.859 0.020 0.020
personal -0.003 0.006 -0.518 0.605 -0.072 -0.072
goals 0.016 0.009 1.765 0.078 0.178 0.178
social ~~
emotions 0.007 0.004 1.927 0.054 0.641 0.641
personal -0.015 0.007 -2.005 0.045 -0.746 -0.746
goals 0.015 0.005 2.902 0.004 0.347 0.347
emotions ~~
personal -0.033 0.013 -2.463 0.014 -0.961 -0.961
goals 0.027 0.010 2.831 0.005 0.367 0.367
personal ~~
goals -0.056 0.016 -3.549 0.000 -0.415 -0.415
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q1 2.610 0.027 96.437 0.000 2.610 4.934
.Q19R 2.018 0.050 40.618 0.000 2.018 2.095
.Q26R 1.602 0.044 36.756 0.000 1.602 1.899
.Q3 2.086 0.015 136.385 0.000 2.086 6.978
.Q12 2.287 0.026 86.561 0.000 2.287 4.458
.Q14 2.173 0.022 97.963 0.000 2.173 5.045
.Q15 2.263 0.023 96.396 0.000 2.263 4.942
.Q28 2.179 0.024 90.311 0.000 2.179 4.669
.Q30 2.409 0.029 83.560 0.000 2.409 4.319
.Q10 2.168 0.022 100.773 0.000 2.168 5.160
.Q29 2.224 0.024 91.441 0.000 2.224 4.719
.Q6R 2.068 0.044 46.743 0.000 2.068 2.395
.Q17 2.357 0.029 81.071 0.000 2.357 4.198
.Q20 2.109 0.019 109.367 0.000 2.109 5.637
.Q27 2.198 0.024 92.329 0.000 2.198 4.769
.Q24 2.419 0.028 86.538 0.000 2.419 4.477
health 0.000 0.000 0.000
social 0.000 0.000 0.000
emotions 0.000 0.000 0.000
personal 0.000 0.000 0.000
goals 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q1 0.251 0.023 10.951 0.000 0.251 0.899
.Q19R 0.760 0.066 11.506 0.000 0.760 0.820
.Q26R 0.326 0.147 2.212 0.027 0.326 0.458
.Q3 0.083 0.012 7.135 0.000 0.083 0.931
.Q12 0.233 0.019 12.456 0.000 0.233 0.884
.Q14 0.160 0.016 9.785 0.000 0.160 0.864
.Q15 0.167 0.017 9.623 0.000 0.167 0.796
.Q28 0.163 0.018 9.217 0.000 0.163 0.748
.Q30 0.250 0.028 8.844 0.000 0.250 0.803
.Q10 0.157 0.017 9.398 0.000 0.157 0.892
.Q29 0.165 0.027 6.170 0.000 0.165 0.741
.Q6R 0.683 0.037 18.440 0.000 0.683 0.916
.Q17 0.263 0.027 9.555 0.000 0.263 0.833
.Q20 0.122 0.016 7.774 0.000 0.122 0.869
.Q27 0.165 0.023 7.304 0.000 0.165 0.779
.Q24 0.000 0.000 0.000
health 0.028 0.014 1.975 0.048 1.000 1.000
social 0.006 0.004 1.398 0.162 1.000 1.000
emotions 0.019 0.010 1.845 0.065 1.000 1.000
personal 0.063 0.032 1.980 0.048 1.000 1.000
goals 0.292 0.015 19.894 0.000 1.000 1.000Path diagram
semPlot::semPaths(cfa_fit, whatLabels = "standardized", residuals = FALSE)
SEM Model
social (p-e-s) #Structural Model ## direct effect social ~ cpersonal ## mediator social ~ bemotions emotions ~ apersonal ## indirect effect (ab) ab := ab ## total effect total := c + (ab)
social (e-p-s) #Structural Model ## direct effect social ~ cemotions ## mediator social ~ bpersonal personal ~ aemotions ## indirect effect (ab) ab := ab ## total effect total := c + (ab)
personal (e-s-p) #Structural Model ## direct effect personal ~ cemotions ## mediator personal ~ bsocial social ~ aemotions ## indirect effect (ab) ab := ab ## total effect total := c + (ab)
personal (s-e-p) #Structural Model ## direct effect personal ~ csocial ## mediator personal ~ bemotions emotions ~ asocial ## indirect effect (ab) ab := ab ## total effect total := c + (a*b)
emotions (s-p-e) #Structural Model ## direct effect emotions ~ csocial ## mediator emotions ~ bpersonal personal ~ asocial ## indirect effect (ab) ab := ab ## total effect total := c + (ab)
emotions (p-s-e) #Structural Model ## direct effect emotions ~ cpersonal ## mediator personal ~ bemotions emotions ~ asocial ## indirect effect (ab) ab := ab ## total effect total := c + (ab)
sem.model <- '
#Measurement Model
social =~ Q3 + Q12 + Q14 + Q15 + Q28 + Q30
emotions =~ Q10 + Q29 + Q24
personal =~ Q6R + Q17 + Q20 + Q27
health =~ Q1 + Q4 + Q19R + Q26R
#Structural Model
## direct effect
personal ~ c*social
## mediator
personal ~ b*emotions
emotions ~ a*social
## indirect effect (a*b)
ab := a*b
## total effect
total := c + (a*b)
'
sem_fit <- sem(sem.model, data=sel.clean, estimator = "MLR", missing = "ML", fixed.x = FALSE)lavaan WARNING: some estimated lv variances are negativesummary(sem_fit, fit.measures=TRUE, standardized=TRUE)lavaan 0.6-8 ended normally after 151 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 55
Number of observations 382
Number of missing patterns 21
Model Test User Model:
Standard Robust
Test Statistic 185.793 147.615
Degrees of freedom 115 115
P-value (Chi-square) 0.000 0.022
Scaling correction factor 1.259
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 571.352 445.996
Degrees of freedom 136 136
P-value 0.000 0.000
Scaling correction factor 1.281
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.837 0.895
Tucker-Lewis Index (TLI) 0.808 0.876
Robust Comparative Fit Index (CFI) 0.897
Robust Tucker-Lewis Index (TLI) 0.878
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4797.608 -4797.608
Scaling correction factor 1.174
for the MLR correction
Loglikelihood unrestricted model (H1) -4704.712 -4704.712
Scaling correction factor 1.231
for the MLR correction
Akaike (AIC) 9705.216 9705.216
Bayesian (BIC) 9922.215 9922.215
Sample-size adjusted Bayesian (BIC) 9747.709 9747.709
Root Mean Square Error of Approximation:
RMSEA 0.040 0.027
90 Percent confidence interval - lower 0.029 0.013
90 Percent confidence interval - upper 0.051 0.038
P-value RMSEA <= 0.05 0.941 1.000
Robust RMSEA 0.031
90 Percent confidence interval - lower 0.012
90 Percent confidence interval - upper 0.044
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
social =~
Q3 1.000 0.080 0.269
Q12 2.150 0.865 2.487 0.013 0.173 0.337
Q14 1.976 0.835 2.366 0.018 0.159 0.369
Q15 2.556 0.939 2.721 0.007 0.206 0.449
Q28 2.881 1.097 2.625 0.009 0.232 0.496
Q30 3.098 1.152 2.688 0.007 0.249 0.447
emotions =~
Q10 1.000 0.132 0.313
Q29 1.659 0.413 4.013 0.000 0.218 0.463
Q24 1.742 0.486 3.585 0.000 0.229 0.424
personal =~
Q6R 1.000 0.249 0.289
Q17 -0.921 0.301 -3.065 0.002 -0.230 -0.409
Q20 -0.541 0.186 -2.917 0.004 -0.135 -0.361
Q27 -0.871 0.306 -2.844 0.004 -0.217 -0.471
health =~
Q1 1.000 0.169 0.319
Q4 0.679 0.250 2.720 0.007 0.115 0.181
Q19R -2.447 0.615 -3.977 0.000 -0.412 -0.428
Q26R -3.656 1.211 -3.018 0.003 -0.616 -0.731
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
personal ~
social (c) 0.076 1.362 0.056 0.956 0.024 0.024
emotions (b) -1.965 1.111 -1.768 0.077 -1.036 -1.036
emotions ~
social (a) 1.222 0.486 2.514 0.012 0.747 0.747
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
social ~~
health 0.002 0.001 1.134 0.257 0.115 0.115
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q3 2.086 0.015 136.385 0.000 2.086 6.978
.Q12 2.287 0.026 86.553 0.000 2.287 4.458
.Q14 2.173 0.022 97.965 0.000 2.173 5.045
.Q15 2.263 0.023 96.395 0.000 2.263 4.942
.Q28 2.179 0.024 90.296 0.000 2.179 4.669
.Q30 2.409 0.029 83.556 0.000 2.409 4.319
.Q10 2.168 0.022 100.784 0.000 2.168 5.160
.Q29 2.224 0.024 91.477 0.000 2.224 4.719
.Q24 2.420 0.028 86.488 0.000 2.420 4.478
.Q6R 2.068 0.044 46.749 0.000 2.068 2.395
.Q17 2.357 0.029 81.089 0.000 2.357 4.198
.Q20 2.109 0.019 109.372 0.000 2.109 5.637
.Q27 2.198 0.024 92.330 0.000 2.198 4.769
.Q1 2.610 0.027 96.437 0.000 2.610 4.934
.Q4 2.508 0.033 77.059 0.000 2.508 3.952
.Q19R 2.017 0.050 40.614 0.000 2.017 2.094
.Q26R 1.601 0.044 36.760 0.000 1.601 1.898
social 0.000 0.000 0.000
.emotions 0.000 0.000 0.000
.personal 0.000 0.000 0.000
health 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q3 0.083 0.012 7.129 0.000 0.083 0.928
.Q12 0.233 0.019 12.400 0.000 0.233 0.886
.Q14 0.160 0.016 9.725 0.000 0.160 0.864
.Q15 0.167 0.017 9.735 0.000 0.167 0.799
.Q28 0.164 0.018 9.298 0.000 0.164 0.754
.Q30 0.249 0.028 8.787 0.000 0.249 0.801
.Q10 0.159 0.016 9.959 0.000 0.159 0.902
.Q29 0.174 0.019 9.047 0.000 0.174 0.786
.Q24 0.239 0.023 10.454 0.000 0.239 0.820
.Q6R 0.684 0.037 18.702 0.000 0.684 0.917
.Q17 0.262 0.027 9.636 0.000 0.262 0.833
.Q20 0.122 0.015 7.872 0.000 0.122 0.870
.Q27 0.165 0.022 7.352 0.000 0.165 0.778
.Q1 0.251 0.022 11.181 0.000 0.251 0.898
.Q4 0.389 0.031 12.664 0.000 0.389 0.967
.Q19R 0.758 0.056 13.438 0.000 0.758 0.817
.Q26R 0.332 0.118 2.813 0.005 0.332 0.466
social 0.006 0.004 1.456 0.145 1.000 1.000
.emotions 0.008 0.005 1.409 0.159 0.443 0.443
.personal -0.002 0.020 -0.113 0.910 -0.037 -0.037
health 0.028 0.013 2.209 0.027 1.000 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ab -2.400 1.698 -1.414 0.157 -0.774 -0.774
total -2.324 0.943 -2.466 0.014 -0.749 -0.749Visualize it with a path diagram
semPlot::semPaths(sem_fit, whatLabels = "standardized", residuals = FALSE)
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RkFMU0UpDQpzdW1tYXJ5KHNlbV9maXQsIGZpdC5tZWFzdXJlcz1UUlVFLCBzdGFuZGFyZGl6ZWQ9VFJVRSkNCmBgYA0KDQojIyBWaXN1YWxpemUgaXQgd2l0aCBhIHBhdGggZGlhZ3JhbQ0KYGBge3J9DQpzZW1QbG90OjpzZW1QYXRocyhzZW1fZml0LCB3aGF0TGFiZWxzID0gInN0YW5kYXJkaXplZCIsIHJlc2lkdWFscyA9IEZBTFNFKQ0KYGBgDQoNCg==