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 studentGrowthProjections
DEMO_Data <- abcSGP(sgpData_LONG)
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