####################################################################################################### ### ### Script to run 2025 analyses ### STEP 1: Percentiles (cohort and baseline referenced) ### STEP 2: Projections (cohort and baseline referenced/lagged and current) using adjusted grades ### ####################################################################################################### ### Load packages require(SGP) require(data.table) require(SGPmatrices) options(error=recover) ### Load Data load("Data/WIDA_MA_SGP.Rdata") load("Data/WIDA_MA_Data_LONG_2025.Rdata") ### Add single-cohort baseline matrices to SGPstateData SGPstateData <- SGPmatrices::addBaselineMatrices("WIDA_MA", "2025") ### STEP 1: Run analyses to calculate SGPs WIDA_MA_SGP <- updateSGP( what_sgp_object=WIDA_MA_SGP, with_sgp_data_LONG=WIDA_MA_Data_LONG_2025, steps=c("prepareSGP", "analyzeSGP", "combineSGP"), sgp.percentiles=TRUE, sgp.projections=FALSE, sgp.projections.lagged=FALSE, sgp.percentiles.baseline=TRUE, sgp.projections.baseline=FALSE, sgp.projections.lagged.baseline=FALSE, get.cohort.data.info=TRUE, #parallel.config=list(BACKEND="PARALLEL", WORKERS=list(PERCENTILES=4, BASELINE_PERCENTILES=4, PROJECTIONS=4, LAGGED_PROJECTIONS=4, SGP_SCALE_SCORE_TARGETS=4, GA_PLOTS=1, SG_PLOTS=1))); Helps "speed things up" - can throw issues with windows; not necessary save.intermediate.results=FALSE) ### STEP 2: Run analyses to calculate projections (swapping GRADE and GRADE_ADJUSTED) setnames(WIDA_MA_SGP@Data, c("GRADE", "GRADE_ADJUSTED"), c("GRADE_ADJUSTED", "GRADE")) WIDA_MA_SGP <- abcSGP( sgp_object=WIDA_MA_SGP, steps=c("prepareSGP", "analyzeSGP", "combineSGP", "outputSGP"), years="2025", sgp.percentiles=FALSE, sgp.projections=TRUE, sgp.projections.lagged=TRUE, sgp.percentiles.baseline=FALSE, sgp.projections.baseline=TRUE, sgp.projections.lagged.baseline=TRUE, sgp.target.scale.scores=TRUE) #parallel.config=list(BACKEND="PARALLEL", WORKERS=list(PERCENTILES=4, BASELINE_PERCENTILES=4, PROJECTIONS=4, LAGGED_PROJECTIONS=4, SGP_SCALE_SCORE_TARGETS=4, GA_PLOTS=1, SG_PLOTS=1))); Helps "speed things up" - can throw issues with windows; not necessary setnames(WIDA_MA_SGP@Data, c("GRADE", "GRADE_ADJUSTED"), c("GRADE_ADJUSTED", "GRADE")) ####################################### ### Add in additional variables ####################################### tmp.data <- copy(WIDA_MA_SGP@Data) ### Convert CURRENT SCALE_SCORE_TARGETS to PROFICIENCY_TARGETS tmp.data[YEAR == "2025", PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_5_YEAR_PROJ_YEAR_1_CURRENT:=SGPstateData[['WIDA']][['SGP_Configuration']][['ss_to_pl_function']][['value']](as.character(as.numeric(GRADE)+1), ceiling(SCALE_SCORE_SGP_TARGET_BASELINE_5_YEAR_PROJ_YEAR_1_CURRENT))] tmp.data[YEAR == "2025", PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_4_YEAR_PROJ_YEAR_1_CURRENT:=SGPstateData[['WIDA']][['SGP_Configuration']][['ss_to_pl_function']][['value']](as.character(as.numeric(GRADE)+1), ceiling(SCALE_SCORE_SGP_TARGET_BASELINE_4_YEAR_PROJ_YEAR_1_CURRENT))] tmp.data[YEAR == "2025", PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_3_YEAR_PROJ_YEAR_1_CURRENT:=SGPstateData[['WIDA']][['SGP_Configuration']][['ss_to_pl_function']][['value']](as.character(as.numeric(GRADE)+1), ceiling(SCALE_SCORE_SGP_TARGET_BASELINE_3_YEAR_PROJ_YEAR_1_CURRENT))] tmp.data[YEAR == "2025", PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_2_YEAR_PROJ_YEAR_1_CURRENT:=SGPstateData[['WIDA']][['SGP_Configuration']][['ss_to_pl_function']][['value']](as.character(as.numeric(GRADE)+1), ceiling(SCALE_SCORE_SGP_TARGET_BASELINE_2_YEAR_PROJ_YEAR_1_CURRENT))] tmp.data[YEAR == "2025", PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_1_YEAR_PROJ_YEAR_1_CURRENT:=SGPstateData[['WIDA']][['SGP_Configuration']][['ss_to_pl_function']][['value']](as.character(as.numeric(GRADE)+1), ceiling(SCALE_SCORE_SGP_TARGET_BASELINE_1_YEAR_PROJ_YEAR_1_CURRENT))] ### Select CURRENT PROFICIENCY_TARGETS based upon YEARS_IN_MASS tmp.data[YEAR == "2025" & YEARS_IN_MA==1 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_5_YEAR_PROJ_YEAR_1_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==2 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_4_YEAR_PROJ_YEAR_1_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==3 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_3_YEAR_PROJ_YEAR_1_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==4 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=PROFICIENCY_LEVEL_SGP_TARGET_BASELINE_2_YEAR_PROJ_YEAR_1_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA>=5 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=4.2] tmp.data[YEAR == "2025" & ACHIEVEMENT_LEVEL_ORIGINAL >= "4.2", ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT:=4.2] ### Select CURRENT SGP_TARGETS based upon YEARS_IN_MASS tmp.data[YEAR == "2025" & YEARS_IN_MA==1 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_5_YEAR_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==2 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_4_YEAR_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==3 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_3_YEAR_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA==4 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_2_YEAR_CURRENT] tmp.data[YEAR == "2025" & YEARS_IN_MA>=5 & ACHIEVEMENT_LEVEL_ORIGINAL < "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_1_YEAR_CURRENT] tmp.data[YEAR == "2025" & ACHIEVEMENT_LEVEL_ORIGINAL >= "4.2", ACCOUNTABILITY_SGP_TARGET_CURRENT:=SGP_TARGET_BASELINE_1_YEAR_CURRENT] ### Add to SGP object WIDA_MA_SGP@Data <- tmp.data ### Define variables in final file variables.to.keep <- c("ID", "CONTENT_AREA", "YEAR", "GRADE", "ACHIEVEMENT_LEVEL", "ACHIEVEMENT_LEVEL_ORIGINAL", "SCALE_SCORE", "SGP_BASELINE", "ACCOUNTABILITY_SGP_TARGET_CURRENT", "ACCOUNTABILITY_PROFICIENCY_LEVEL_TARGET_CURRENT", "YEARS_IN_MA") WIDA_MA_Formatted_Output_2025 <- WIDA_MA_SGP@Data[ YEAR == "2025" & VALID_CASE=="VALID_CASE", ..variables.to.keep] ### Save Formatted Results save(WIDA_MA_Formatted_Output_2025, file="Data/WIDA_MA_Formatted_Output_2025.Rdata") ### Save SGP object setkey(WIDA_MA_SGP@Data, VALID_CASE, CONTENT_AREA, YEAR, GRADE, ID) save(WIDA_MA_SGP, file="Data/WIDA_MA_SGP.Rdata")