SGP (version 0.7-1.0)

abcSGP: Perform 4 step sequence: prepareSGP, analyzeSGP, combineSGP, and summarizeSGP

Description

Utility function to perform sequence of steps going from data preparation, prepareSGP, SGP data analysis, analyzeSGP, data combining, combineSGP, and data summarization, summarizeSGP. Calculating and using student growth percentiles is as easy as ABC.

Usage

abcSGP(sgp_object,
	state,
	steps=c("prepareSGP", "analyzeSGP", "combineSGP", "summarizeSGP", "visualizeSGP"),
	years,
	content_areas,
	grades,
	sgp.percentiles=TRUE,
	sgp.projections=TRUE,
	sgp.projections.lagged=TRUE,
        sgp.percentiles.baseline=TRUE,
        sgp.projections.baseline=TRUE,
        sgp.projections.lagged.baseline=TRUE,
	simulate.sgps=TRUE,
	parallel.config,
	save.intermediate.results=FALSE,
	sgp.summaries=list(MEDIAN_SGP="median_na(SGP)",
		MEDIAN_SGP_TARGET="median_na(SGP_TARGET)",
		PERCENT_CATCHING_UP_KEEPING_UP=
			"percent_in_category(CATCH_UP_KEEP_UP_STATUS, list(c('Catch Up: Yes', 'Keep Up: Yes')), list(c('Catch Up: Yes', 'Catch Up: No', 'Keep Up: Yes', 'Keep Up: No')))",
		MEDIAN_SGP_COUNT="num_non_missing(SGP)",
		PERCENT_AT_ABOVE_PROFICIENT="percent_in_category(ACHIEVEMENT_LEVEL, list(c('Proficient', 'Advanced')), list(c('Unsatisfactory', 'Partially Proficient', 'Proficient', 'Advanced')))",
		PERCENT_AT_ABOVE_PROFICIENT_COUNT="num_non_missing(ACHIEVEMENT_LEVEL)"),
	summary.groups=list(institution=c("STATE", "DISTRICT_NUMBER", "SCHOOL_NUMBER"),
		content="CONTENT_AREA",
		time="YEAR",
		institution_level="GRADE",
		demographic=c("GENDER", "ETHNICITY", "FREE_REDUCED_LUNCH_STATUS", "ELL_STATUS", "IEP_STATUS", "GIFTED_AND_TALENTED_PROGRAM_STATUS", "CATCH_UP_KEEP_UP_STATUS_INITIAL"),
		institution_inclusion=list(STATE="STATE_ENROLLMENT_STATUS", DISTRICT_NUMBER="DISTRICT_ENROLLMENT_STATUS", SCHOOL_NUMBER="SCHOOL_ENROLLMENT_STATUS")),
        confidence.interval.groups=list(TYPE="Bootstrap",
                VARIABLES=c("SGP"),
                QUANTILES=c(0.025, 0.975),
                GROUPS=list(institution="SCHOOL_NUMBER",
                content="CONTENT_AREA",
                time="YEAR",
		institution_level= NULL,
                demographic=NULL,
                institution_inclusion=list(STATE=NULL, DISTRICT_NUMBER=NULL, SCHOOL_NUMBER="SCHOOL_ENROLLMENT_STATUS"))),
	plot.types=c("bubblePlot", "studentGrowthPlot", "growthAchievementPlot"))

Arguments

sgp_object

A list containing LONG formatted data. See sgpData_LONG for an exemplar.

state

Acronym indicating state associated with the data for access to embedded knot and boundaries, cutscores, CSEMs, and other relevant state level data.

steps

Vector containing all or some subset of prepareSGP, analyzeSGP, combineSGP,

years

A vector indicating year(s) in which to produce student growth percentiles and/or student growth projections/trajectories. If missing the function will use the data to infer the year(s) in which to perform growth percentile analyses based upon the assum

content_areas

A vector indicating content area(s) in which to produce student growth percentiles and/or student growth projections/trajectories. If missing the function will use the data to infer the content area(s) available for analyses.

grades

A vector indicating grades for which to calculate student growth percentiles and/or student growth projections/trajectories. If missing the function will use the data to infer all the grade progressions for student growth percentile and student growth pr

sgp.percentiles

Boolean variable indicating whether to calculate student growth percentiles. Defaults to TRUE.

sgp.projections

Boolean variable indicating whether to calculate student growth projections. Defaults to TRUE.

sgp.projections.lagged

Boolean variable indicating whether to calculate lagged student growth projections often used for growth to standard analyses. Defaults to TRUE.

sgp.percentiles.baseline

Boolean variable indicating whether to calculate baseline student growth percentiles and/or coefficient matrices. Defaults to TRUE.

sgp.projections.baseline

Boolean variable indicating whether to calculate baseline student growth projections. Defaults to TRUE.

sgp.projections.lagged.baseline

Boolean variable indicating whether to calculate lagged baseline student growth projections. Defaults to TRUE.

simulate.sgps

Boolean variable indicating whether to simulate SGP values for students based on test-specific Conditional Standard Errors of Measurement (CSEM). Test CSEM data must be available for simulation. Must be set to TRUE for confidence interval construction.

parallel.config

A named list indicating, at a minimum, the TYPE of the back end to be used for parallel computation. TYPE = one of FOREACH, SNOW, MULTICORE. List may also contain WORKERS for the number of cores or nodes used in

save.intermediate.results

Should intermediate results of abcSGP be saved after each of prepareSGP, analyzeSGP, combineSGP

sgp.summaries

A list giving the summaries requested for each group analyzed based upon the summary.group argument.

summary.groups

A list consisting of 5 types of groups across which all summaries are taken: institution, content, time, institution_level, demographic, and institution_inclusion. Summaries gen

confidence.interval.groups

A subset of the groups provided in the summary.groups argument indicating which groups to provide confidence intervals for. See documentation for summarizeSGP and

plot.types

A character vector passed to visualizeSGP indicating the types of plots to produce. Currently supported plots include bubblePlots,

Value

• Function returns a list containing the input long data set in the 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.

See Also

sgpData_LONG, prepareSGP, analyzeSGP, combineSGP, summarizeSGP, studentGrowthPercentiles, and studentGrowthProjections

Examples

## Runs all 5 steps
Demonstration_Data <- abcSGP(sgp_object=sgpData_LONG, state="DEMO")


## Or letting the function detect the state

Demonstration_Data <- sgpData_LONG
Demonstration_Data <- abcSGP(Demonstration_Data)