Jordan subset of PISA 2006 data
jordan.RdThe data stem from the large-scale assessment study PISA 2006 (Organisation for Economic Co-Operation and Development, 2009) where competencies of 15-year-old students in reading, mathematics, and science are assessed using nationally representative samples in 3-year cycles. In this example, data from the student background questionnaire from the Jordan sample of PISA 2006 were used. Only data of students with complete responses to all 15 items (N = 6,038) were considered.
Format
A data frame of fifteen variables and 6,038 observations:
- enjoy1
indicator for enjoyment of science, item ST16Q01: I generally have fun when I am learning <broad science> topics.
- enjoy2
indicator for enjoyment of science, item ST16Q02: I like reading about <broad science>.
- enjoy3
indicator for enjoyment of science, item ST16Q03: I am happy doing <broad science> problems.
- enjoy4
indicator for enjoyment of science, item ST16Q04: I enjoy acquiring new knowledge in <broad science>.
- enjoy5
indicator for enjoyment of science, item ST16Q05: I am interested in learning about <broad science>.
- academic1
indicator for academic self-concept in science, item ST37Q01: I can easily understand new ideas in <school science>.
- academic2
indicator for academic self-concept in science, item ST37Q02: Learning advanced <school science> topics would be easy for me.
- academic3
indicator for academic self-concept in science, item ST37Q03: I can usually give good answers to <test questions> on <school science> topics.
- academic4
indicator for academic self-concept in science, item ST37Q04: I learn <school science> topics quickly.
- academic5
indicator for academic self-concept in science, item ST37Q05: <School science> topics are easy for me.
- academic6
indicator for academic self-concept in science, item ST37Q06: When I am being taught <school science>, I can understand the concepts very well.
- career1
indicator for career aspirations in science, item ST29Q01: I would like to work in a career involving <broad science>.
- career2
indicator for career aspirations in science, item ST29Q02: I would like to study <broad science> after <secondary school>.
- career3
indicator for career aspirations in science, item ST29Q03: I would like to spend my life doing advanced <broad science>.
- career4
indicator for career aspirations in science, item ST29Q04: I would like to work on <broad science> projects as an adult.
Source
This version of the dataset, as well as the description was gathered from the documentation of the 'nlsem' package (https://cran.r-project.org/package=nlsem), where the only difference is that the names of the variables were changed
Originally the dataset was gathered by the Organisation for Economic Co-Operation and Development (2009). Pisa 2006: Science competencies for tomorrow's world (Tech. Rep.). Paris, France. Obtained from: https://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm
Examples
# \dontrun{
m1 <- "
ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
CAREER =~ career1 + career2 + career3 + career4
SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
"
est <- modsem(m1, data = jordan, method = "qml")
summary(est)
#>
#> modsem (1.0.13) ended normally after 48 iterations
#>
#> Estimator QML
#> Optimization method NLMINB
#> Number of model parameters 51
#>
#> Number of observations 6038
#>
#> Loglikelihood and Information Criteria:
#> Loglikelihood -110519.99
#> Akaike (AIC) 221141.98
#> Bayesian (BIC) 221483.98
#>
#> Fit Measures for Baseline Model (H0):
#> Standard
#> Chi-square 1016.34
#> Degrees of Freedom (Chi-square) 87
#> P-value (Chi-square) 0.000
#> RMSEA 0.042
#>
#> Loglikelihood -110521.29
#> Akaike (AIC) 221138.58
#> Bayesian (BIC) 221460.46
#>
#> Comparative Fit to H0 (LRT test):
#> Loglikelihood change 1.30
#> Difference test (D) 2.59
#> Degrees of freedom (D) 3
#> P-value (D) 0.458
#>
#> R-Squared Interaction Model (H1):
#> CAREER 0.513
#> R-Squared Baseline Model (H0):
#> CAREER 0.510
#> R-Squared Change (H1 - H0):
#> CAREER 0.003
#>
#> Parameter Estimates:
#> Coefficients unstandardized
#> Information observed
#> Standard errors standard
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> ENJ =~
#> enjoy1 1.000
#> enjoy2 1.002 0.020 50.574 0.000
#> enjoy3 0.894 0.020 43.665 0.000
#> enjoy4 0.999 0.021 48.219 0.000
#> enjoy5 1.047 0.021 50.391 0.000
#> SC =~
#> academic1 1.000
#> academic2 1.104 0.028 38.951 0.000
#> academic3 1.235 0.030 41.727 0.000
#> academic4 1.254 0.030 41.836 0.000
#> academic5 1.113 0.029 38.653 0.000
#> academic6 1.198 0.030 40.364 0.000
#> CAREER =~
#> career1 1.000
#> career2 1.040 0.016 65.185 0.000
#> career3 0.952 0.016 57.843 0.000
#> career4 0.818 0.017 48.361 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> CAREER ~
#> ENJ 0.526 0.020 26.309 0.000
#> SC 0.464 0.023 20.004 0.000
#> ENJ:ENJ 0.029 0.022 1.341 0.180
#> ENJ:SC -0.046 0.045 -1.015 0.310
#> SC:SC 0.001 0.036 0.025 0.980
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .enjoy1 0.000 0.011 -0.010 0.992
#> .enjoy2 0.000 0.013 0.011 0.991
#> .enjoy3 0.000 0.017 -0.018 0.986
#> .enjoy4 0.000 0.016 0.005 0.996
#> .enjoy5 0.000 0.016 0.020 0.984
#> .academic1 0.000 0.014 -0.011 0.991
#> .academic2 0.000 0.013 -0.011 0.992
#> .academic3 0.000 0.013 -0.034 0.973
#> .academic4 0.000 0.014 -0.018 0.986
#> .academic5 -0.001 0.013 -0.049 0.961
#> .academic6 0.001 0.015 0.046 0.964
#> .career1 -0.005 0.020 -0.231 0.817
#> .career2 -0.005 0.020 -0.270 0.787
#> .career3 -0.005 0.019 -0.240 0.811
#> .career4 -0.005 0.018 -0.257 0.798
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> ENJ ~~
#> SC 0.218 0.009 25.479 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .enjoy1 0.487 0.011 44.350 0.000
#> .enjoy2 0.489 0.011 44.421 0.000
#> .enjoy3 0.596 0.012 48.234 0.000
#> .enjoy4 0.488 0.011 44.568 0.000
#> .enjoy5 0.442 0.010 42.478 0.000
#> .academic1 0.644 0.013 49.812 0.000
#> .academic2 0.566 0.012 47.864 0.000
#> .academic3 0.473 0.011 44.320 0.000
#> .academic4 0.455 0.010 43.582 0.000
#> .academic5 0.565 0.012 47.684 0.000
#> .academic6 0.502 0.011 45.441 0.000
#> .career1 0.373 0.009 40.396 0.000
#> .career2 0.328 0.009 37.017 0.000
#> .career3 0.436 0.010 43.275 0.000
#> .career4 0.576 0.012 48.375 0.000
#> ENJ 0.500 0.017 29.545 0.000
#> SC 0.338 0.015 23.199 0.000
#> .CAREER 0.302 0.010 29.725 0.000
#>
# }