minneapolis.csv

The data in minneapolis.csv were provided in Long (2012). They constitute a sample of $n=22$ students taken from a much larger dataset collected by the Minneapolis Public School District. The data were collected to comply with the No Child Left Behind Act of 2001 and began during the 2004-05 school year. The variables included in the data are:

• student_id: De-identified student ID number
• reading_score: Reading achievement score${}^{†}$.
• grade: Grade-level.
• special_ed: Is the student recieving special education services?
• attendance: Proportion of attendance based on the number of school days the student attended school during the four year of the study.

${}^{†}$The reading achievement scores are based on the reading section of th Northwest Achievement Levels Test (NALT), a multiple-choice, adaptive assessment of students’ academic achievement. The NALT raw scores were converted to vertically equated scaled scores using an IRT model. Higher scale scores indicate more reading achievement.

Preview

# Import data
mpls = readr::read_csv(file = "https://raw.githubusercontent.com/zief0002/bespectacled-antelope/main/data/minneapolis.csv")

# View data
mpls

# A tibble: 88 × 5
   student_id reading_score grade special_ed attendance
        <dbl>         <dbl> <dbl> <chr>           <dbl>
 1          1           172     5 No               0.94
 2          1           185     6 No               0.94
 3          1           179     7 No               0.94
 4          1           194     8 No               0.94
 5          2           200     5 No               0.91
 6          2           210     6 No               0.91
 7          2           209     7 No               0.91
 8          2           210     8 No               0.91
 9          3           191     5 No               0.97
10          3           199     6 No               0.97
# … with 78 more rows

References

Long, J. D. (2012). Longitudinal data analysis for the behavioral sciences using R. Thousand Oaks, CA: Sage Publications, Inc.