An extended version of the NELS:88 discipline dataset (disc) with
2,000 students, including standardized test scores in reading and math,
a composite SES (socioeconomic status) measure, and additional derived
variables. This version uses more intuitive variable names and includes
both base-year and follow-up reading scores, making it suitable for
analyses involving academic achievement as both a predictor and outcome.
Format
A tibble with 2,000 rows and 15 columns:
- male
Sex of student. Type: numeric. Binary indicator (0/1) where 1 = male, 0 = female. NA = 20.
- notengl
Non-English home language. Type: numeric. Binary indicator (0/1) where 1 = primary home language is not English, 0 = English is primary. NA = 23.
- bedroom
Has own bedroom. Type: numeric. Binary indicator (0/1) where 1 = has own bedroom, 0 = does not. NA = 33.
- osentoff
Ordinal version of office referral. Type: numeric. Values: 0, 1, 2. Where 0 = never, 1 = once or twice, 2 = more than twice. NA = 28.
- ses
Composite socioeconomic status score. Type: numeric. Range: (-2.89, 1.85). Mean = -0.06. Standardized composite of family income, parental education, and parental occupation. NA = 1.
- reading1
Base-year (8th grade) standardized reading score. Type: numeric. Range: (31.95, 70.55). Mean = 50.95. Standardized with mean approximately 50 and SD approximately 10. NA = 77.
- math1
Base-year (8th grade) standardized math score. Type: numeric. Range: (34.10, 77.20). Mean = 51.49. Standardized with mean approximately 50 and SD approximately 10. NA = 75.
- reading2
Follow-up (10th grade) standardized reading score. Type: numeric. Range: (30.82, 68.91). Mean = 51.24. NA = 218.
- fath_coll
Father has college education. Type: numeric. Binary indicator (0/1) where 1 = father has a college degree, 0 = no college degree. NA = 282.
- fath_ed
Father has at least a high school education. Type: numeric. Binary indicator (0/1) where 1 = high school or above, 0 = less than high school. NA = 282.
- moth_coll
Mother has college education. Type: numeric. Binary indicator (0/1) where 1 = mother has a college degree, 0 = no college degree. NA = 208.
- moth_ed
Mother has at least a high school education. Type: numeric. Binary indicator (0/1) where 1 = high school or above, 0 = less than high school. NA = 208.
- minority
Minority status. Type: numeric. Binary indicator (0/1) where 1 = racial/ethnic minority (non-White), 0 = White. NA = 24.
- race
Race/ethnicity. Type: numeric. Values: 1, 2, 3, 4. Where 1 = Asian, 2 = Hispanic, 3 = Black, 4 = White. NA = 92.
- sentoff
Ever sent to the office for misbehavior. Type: numeric. Binary indicator (0/1) where 1 = yes, 0 = no. NA = 28.
Source
National Center for Education Statistics (1988). National
Education Longitudinal Study of 1988 (NELS:88). U.S. Department of
Education. Original data file: disc2.dta
Details
This dataset is used alongside disc in Chapters 9-13 (Logistic
Regression and Ordinal Response Models). Key analyses include: examining
the relationship between SES, test scores, and discipline outcomes;
building multiple logistic regression models with continuous and
categorical predictors; and comparing base-year and follow-up reading
scores. The SES composite and standardized test scores provide continuous
predictors for logistic regression, complementing the categorical
predictors in disc.
Examples
data(disc2)
head(disc2)
#> # A tibble: 6 × 15
#> male notengl bedroom osentoff ses reading1 math1 reading2 fath_coll
#> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0 0 1 0 -0.601 66.8 58.6 50.2 0
#> 2 1 0 1 0 -0.0700 58.6 59.8 61.6 0
#> 3 1 0 1 0 0.0960 49.5 49.7 44 0
#> 4 1 0 1 0 -0.715 55.6 49.1 40.7 0
#> 5 0 0 0 1 0.205 56.3 44.5 53.7 0
#> 6 1 0 1 0 0.355 52.2 54.8 43.3 0
#> # ℹ 6 more variables: fath_ed <int>, moth_coll <int>, moth_ed <int>,
#> # minority <int>, race <fct>, sentoff <int>
# Logistic regression: SES effect on office referral
glm(sentoff ~ ses, data = disc2, family = binomial)
#>
#> Call: glm(formula = sentoff ~ ses, family = binomial, data = disc2)
#>
#> Coefficients:
#> (Intercept) ses
#> -0.9879 -0.3921
#>
#> Degrees of Freedom: 1970 Total (i.e. Null); 1969 Residual
#> (29 observations deleted due to missingness)
#> Null Deviance: 2336
#> Residual Deviance: 2297 AIC: 2301
# Multiple logistic regression with achievement and demographics
glm(sentoff ~ ses + reading1 + male + minority, data = disc2,
family = binomial)
#>
#> Call: glm(formula = sentoff ~ ses + reading1 + male + minority, family = binomial,
#> data = disc2)
#>
#> Coefficients:
#> (Intercept) ses reading1 male minority
#> 0.51829 -0.23894 -0.04409 1.23041 -0.01177
#>
#> Degrees of Freedom: 1870 Total (i.e. Null); 1866 Residual
#> (129 observations deleted due to missingness)
#> Null Deviance: 2200
#> Residual Deviance: 1977 AIC: 1987