pprof (version 1.0.2)

ecls_data: Early Childhood Longitudinal Study Dataset

Description

The Early Childhood Longitudinal Study (ECLS) dataset tracks more than 18,000 children from kindergarten through fifth grade, providing comprehensive student-level information. See Tourangeau et al. (2015) for details.

Usage

data(ecls_data)

Format

A data frame with 9,101 observations, including:

Child_ID

Unique identifier for each child in the dataset.

School_ID

Identifier for each school in the dataset.

Math_Score

Continuous variable representing the consolidated math proficiency score.

Income

Household income, categorized into 18 ordinal levels, from $5,000 or less to $200,000 or more. Treated as a continuous variable in the analysis.

Child_Sex

Binary variable indicating the gender of each student.

Details

The dataset includes fifth-grade cross-sectional data, focusing on students' mathematical assessment scores as the primary outcome measure. The mathematical assessment covers 18 topics such as number sense, properties, operations, measurement, geometry, data analysis, and algebra. These items evaluate students' competencies in conceptual knowledge, procedural knowledge, and problem-solving, consolidated into a single "Math score." A higher score indicates greater proficiency. The primary predictors are household income and gender, where gender is a categorical variable. Household income is categorized into 18 ordinal levels, ranging from $5,000 or less (level 1) to $200,000 or more (level 18). In the analysis, the income variable is treated as a continuous variable. The dataset removes all records with missing values and consists of 9,101 complete observations from 2,275 schools.

References

Tourangeau, K., Nord, C., Lê, T., Sorongon, A. G., Hagedorn, M. C., Daly, P., & Najarian, M. (2015). Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011): User’s manual for the ECLS-K:2011 kindergarten data file and electronic codebook, public version (NCES 2015-074). National Center for Education Statistics.

Examples

data(ecls_data)
formula_FE <- as.formula("Math_Score ~ Income + id(School_ID) + Child_Sex")
fit_FE <- linear_fe(formula = formula_FE, data = ecls_data)

formula_RE <- as.formula("Math_Score ~ Income + (1|School_ID) + Child_Sex")
fit_RE <- linear_re(formula = formula_RE, data = ecls_data)