> # ============================================================ > # Pre-task_Grammar_Instruction_LREs_Young_Learners – Table 6 > # Pearson correlations between Flyers & MLAT-ES scores and > # target-form LRE production by group (Control / Experimental) > # ============================================================ > # ---- 1. Load libraries ---- > library(readxl) > library(dplyr) > # ---- 2. Import Excel data ---- > datos <- read_excel("C:/Users/dmard/Documents/PTGI_LRE_repro_package/data/PTGI_LRE_data_1.xlsx", + sheet = "D1+D2+IDs") > # ---- 3. Clean and convert numeric variables ---- > datos <- datos %>% + mutate( + Proficicency_Level_Q = as.numeric(gsub(",", ".", Proficicency_Level_Q)), + MLAT_Q = as.numeric(gsub(",", ".", MLAT_Q)) + ) > # ---- 4. Split data by group ---- > ctrl <- subset(datos, Group == "Control") > exp <- subset(datos, Group == "Experimental") > # ---- 5. Pearson correlations ---- > cat("\n==============================\nCONTROL GROUP\n==============================\n") ============================== CONTROL GROUP ============================== > # 3ps -s > cat("\n3ps -s × Flyers\n") 3ps -s × Flyers > print(cor.test(ctrl$Proficicency_Level_Q, ctrl$`3ps_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$Proficicency_Level_Q and ctrl$`3ps_SUM` t = -0.30702, df = 3, p-value = 0.7789 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9157841 0.8365469 sample estimates: cor -0.17454 > cat("\n3ps -s × MLAT-ES\n") 3ps -s × MLAT-ES > print(cor.test(ctrl$MLAT_Q, ctrl$`3ps_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$MLAT_Q and ctrl$`3ps_SUM` t = -1.3606, df = 3, p-value = 0.2668 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9708718 0.5813808 sample estimates: cor -0.6177497 > # HE/SHE > cat("\nHE/SHE × Flyers\n") HE/SHE × Flyers > print(cor.test(ctrl$Proficicency_Level_Q, ctrl$`HE/SHE_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$Proficicency_Level_Q and ctrl$`HE/SHE_SUM` t = 0.57688, df = 3, p-value = 0.6045 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.7851706 0.9370262 sample estimates: cor 0.3159949 > cat("\nHE/SHE × MLAT-ES\n") HE/SHE × MLAT-ES > print(cor.test(ctrl$MLAT_Q, ctrl$`HE/SHE_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$MLAT_Q and ctrl$`HE/SHE_SUM` t = -0.12187, df = 3, p-value = 0.9107 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.8969137 0.8656853 sample estimates: cor -0.07018931 > # Total Target LREs > cat("\nTotal Target LREs × Flyers\n") Total Target LREs × Flyers > print(cor.test(ctrl$Proficicency_Level_Q, ctrl$`Target LREs_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$Proficicency_Level_Q and ctrl$`Target LREs_SUM` t = 0.069332, df = 3, p-value = 0.9491 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.8730787 0.8908278 sample estimates: cor 0.0399968 > cat("\nTotal Target LREs × MLAT-ES\n") Total Target LREs × MLAT-ES > print(cor.test(ctrl$MLAT_Q, ctrl$`Target LREs_SUM`, method = "pearson")) Pearson's product-moment correlation data: ctrl$MLAT_Q and ctrl$`Target LREs_SUM` t = -0.76451, df = 3, p-value = 0.5002 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9482456 0.7432584 sample estimates: cor -0.403802 > cat("\n==============================\nEXPERIMENTAL GROUP\n==============================\n") ============================== EXPERIMENTAL GROUP ============================== > # 3ps -s > cat("\n3ps -s × Flyers\n") 3ps -s × Flyers > print(cor.test(exp$Proficicency_Level_Q, exp$`3ps_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$Proficicency_Level_Q and exp$`3ps_SUM` t = 1.0392, df = 3, p-value = 0.3751 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.6734769 0.9606856 sample estimates: cor 0.5144958 > cat("\n3ps -s × MLAT-ES\n") 3ps -s × MLAT-ES > print(cor.test(exp$MLAT_Q, exp$`3ps_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$MLAT_Q and exp$`3ps_SUM` t = 0.73804, df = 3, p-value = 0.514 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.7494667 0.9468133 sample estimates: cor 0.3920048 > # HE/SHE > cat("\nHE/SHE × Flyers\n") HE/SHE × Flyers > print(cor.test(exp$Proficicency_Level_Q, exp$`HE/SHE_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$Proficicency_Level_Q and exp$`HE/SHE_SUM` t = -0.70556, df = 3, p-value = 0.5313 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9449913 0.7569555 sample estimates: cor -0.3772567 > cat("\nHE/SHE × MLAT-ES\n") HE/SHE × MLAT-ES > print(cor.test(exp$MLAT_Q, exp$`HE/SHE_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$MLAT_Q and exp$`HE/SHE_SUM` t = -2.0288, df = 3, p-value = 0.1355 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9831280 0.3699963 sample estimates: cor -0.7605359 > # Total Target LREs > cat("\nTotal Target LREs × Flyers\n") Total Target LREs × Flyers > print(cor.test(exp$Proficicency_Level_Q, exp$`Target LREs_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$Proficicency_Level_Q and exp$`Target LREs_SUM` t = -0.3922, df = 3, p-value = 0.7211 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9232259 0.8214831 sample estimates: cor -0.2208433 > cat("\nTotal Target LREs × MLAT-ES\n") Total Target LREs × MLAT-ES > print(cor.test(exp$MLAT_Q, exp$`Target LREs_SUM`, method = "pearson")) Pearson's product-moment correlation data: exp$MLAT_Q and exp$`Target LREs_SUM` t = -1.3128, df = 3, p-value = 0.2806 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9695899 0.5956678 sample estimates: cor -0.6040493