prepareSGP, SGP data analysis, analyzeSGP,
data combining, combineSGP, and data summarization, summarizeSGP. Calculating and using
student growth percentiles is as easy as ABC.abcSGP(sgp_object=sgpData_LONG,
state="DEMO",
years,
content_areas,
grades,
sgp.config,
sgp.percentiles=TRUE,
sgp.projections=TRUE,
sgp.projections.lagged=TRUE,
simulate.sgps=TRUE,
sgp.summaries=list(MEDIAN_SGP="median_na(SGP)",
MEDIAN_SGP_COUNT="num_non_missing(SGP)",
PERCENT_AT_ABOVE_PROFICIENT=
"percent_in_category(ACHIEVEMENT_LEVEL, list(c(1,4)), list(1:5))",
PERCENT_AT_ABOVE_PROFICIENT_COUNT=
"num_non_missing(ACHIEVEMENT_LEVEL)"),
summary.groups=list(institution=c("STATE", "SCHOOL_NUMBER"),
content="CONTENT_AREA",
time="YEAR",
institution_level="GRADE",
demographic=c("GENDER", "ETHNICITY", "FREE_REDUCED_LUNCH_STATUS",
"ELL_STATUS", "CATCH_KEEP_UP"),
institution_inclusion=list(STATE="OCTOBER_ENROLLMENT_STATUS",
SCHOOL_NUMBER="OCTOBER_ENROLLMENT_STATUS")),
confidence.interval.groups=list(institution="SCHOOL_NUMBER",
content="CONTENT_AREA",
time="YEAR",
institution_level= NULL,
demographic=NULL,
institution_inclusion=list(STATE=NULL,
SCHOOL_NUMBER="OCTOBER_ENROLLMENT_STATUS")))Student (from prepareSGP) slot.years, content_areas, and grades are missing, user can directly specify a list containing three vectors: sgp.content.areas,
sgp.panel.years, and sgp.grade.sequences. This adsummary.group argument.institution, content, time, institution_level,
demographic, and institution_inclusion. Summaries gensummary.groups argument indicating which groups to provide confidence intervals for. See documentation for summarizeSGP and Student slot as a data.table keyed using VALID_CASE, CONTENT_AREA,
YEAR, ID, SGP results including student growth percentile and student growth projections/trajectories in the SGP slot, and summary results in the
Summary slot.sgpData_LONG, prepareSGP, analyzeSGP, combineSGP, summarizeSGP,
studentGrowthPercentiles, and studentGrowthProjectionsDEMO_Data <- abcSGP(sgpData_LONG)Run the code above in your browser using DataLab