levels <- read.csv("/Users/andesgomez/Documents/Stanford/Autumn2013-Masters/PayedWork/andres_data/scale_plus_6stimuli_3levels_no_fam_24_january_SCAL.csv", header=TRUE, sep="\t", row.names=NULL, stringsAsFactors = FALSE) levels$correct <- levels$Answer.choice == "\"target\"" level_0_bool <- levels$Answer.scale_and_levels_condition == 2 level_1_bool <- levels$Answer.scale_and_levels_condition == 3 level_2_bool <- levels$Answer.scale_and_levels_condition == 4 level_0 <- subset(levels, level_0_bool) level_1 <- subset(levels, level_1_bool) level_2 <- subset(levels, level_2_bool) level_0$target <- level_0$Answer.choice=="\"target\"" level_0$logical <- level_0$Answer.choice=="\"logical\"" level_0$foil <- level_0$Answer.choice=="\"foil\"" level_1$target <- level_1$Answer.choice=="\"target\"" level_1$logical <- level_1$Answer.choice=="\"logical\"" level_1$foil <- level_1$Answer.choice=="\"foil\"" level_2$target <- level_2$Answer.choice=="\"target\"" level_2$logical <- level_2$Answer.choice=="\"logical\"" level_2$foil <- level_2$Answer.choice=="\"foil\"" level_0_by_item <- aggregate(cbind(target, logical, foil) ~ Answer.item, data=level_0, sum) level_0_pragmatic <- mean(level_0$target) level_1_by_item <- aggregate(cbind(target, logical, foil) ~ Answer.item, data=level_1, sum) level_1_pragmatic <- mean(level_1$target) level_2_by_item <- aggregate(cbind(target, logical, foil) ~ Answer.item, data=level_2, sum) level_2_pragmatic <- mean(level_2$target) # Printing level_0_by_item level_0_pragmatic level_1_by_item level_1_pragmatic level_2_by_item level_2_pragmatic sum(level_2_by_item[,2:4]) sum(level_2_by_item[,2]) summary(aov(correct ~ as.factor(Answer.item) * Answer.scale_and_levels_condition, data = levels)) summary(glm(correct ~ as.factor(Answer.item) * as.factor(Answer.scale_and_levels_condition), data = levels)) # Separate by gender and age levels$males = levels$Answer.gender == "\"m\"" | levels$Answer.gender == "\"male\"" | levels$Answer.gender == "\"M\"" | levels$Answer.gender == "\"Male\"" | levels$Answer.gender == "\"MALE\"" levels$females = levels$Answer.gender == "\"f\"" | levels$Answer.gender == "\"female\"" | levels$Answer.gender == "\"F\"" | levels$Answer.gender == "\"Female\"" | levels$Answer.gender == "\"FEMALE\"" levels$twenties = levels$Answer.age == "\"20\"" | levels$Answer.age == "\"21\"" | levels$Answer.age == "\"22\"" | levels$Answer.age == "\"23\"" | levels$Answer.age == "\"24\"" | levels$Answer.age == "\"25\"" | levels$Answer.age == "\"26\"" | levels$Answer.age == "\"27\"" | levels$Answer.age == "\"28\"" | levels$Answer.age == "\"29\"" levels$thirties = levels$Answer.age == "\"30\"" | levels$Answer.age == "\"31\"" | levels$Answer.age == "\"32\"" | levels$Answer.age == "\"33\"" | levels$Answer.age == "\"34\"" | levels$Answer.age == "\"35\"" | levels$Answer.age == "\"36\"" | levels$Answer.age == "\"37\"" | levels$Answer.age == "\"38\"" | levels$Answer.age == "\"39\"" levels$fourties = levels$Answer.age == "\"40\"" | levels$Answer.age == "\"41\"" | levels$Answer.age == "\"42\"" | levels$Answer.age == "\"43\"" | levels$Answer.age == "\"44\"" | levels$Answer.age == "\"45\"" | levels$Answer.age == "\"46\"" | levels$Answer.age == "\"47\"" | levels$Answer.age == "\"48\"" | levels$Answer.age == "\"49\"" levels$fifties = levels$Answer.age == "\"50\"" | levels$Answer.age == "\"51\"" | levels$Answer.age == "\"52\"" | levels$Answer.age == "\"53\"" | levels$Answer.age == "\"54\"" | levels$Answer.age == "\"55\"" | levels$Answer.age == "\"56\"" | levels$Answer.age == "\"57\"" | levels$Answer.age == "\"58\"" | levels$Answer.age == "\"59\"" summary(glm(correct ~ twenties + thirties + fourties + fifties + males + females + as.factor(Answer.item) + as.factor(Answer.scale_and_levels_condition) + as.factor(Answer.target_position), data = levels)) ###### Scale ########### Scale ########### Scale ########### Scale ########### Scale ########### Scale ########### Scale ##### levels_scale <- read.csv("/Users/andesgomez/Documents/Stanford/Autumn2013-Masters/PayedWork/andres_data/scales_6stimuli_3levels_no_fam_25_january_OSCA.csv", header=TRUE, sep="\t", row.names=NULL, stringsAsFactors = FALSE) levels_scale <- levels_scale[1:180,] levels_scale$correct <- levels_scale$Answer.choice == "\"target\"" level_0_bool_scale <- levels_scale$Answer.scale_and_levels_condition == 0 level_1_bool_scale <- levels_scale$Answer.scale_and_levels_condition == 1 level_0_scale <- subset(levels_scale, level_0_bool_scale) level_1_scale <- subset(levels_scale, level_1_bool_scale) level_0_scale$target <- level_0_scale$Answer.choice=="\"target\"" level_0_scale$logical <- level_0_scale$Answer.choice=="\"logical\"" level_0_scale$foil <- level_0_scale$Answer.choice=="\"foil\"" level_1_scale$target <- level_1_scale$Answer.choice=="\"target\"" level_1_scale$logical <- level_1_scale$Answer.choice=="\"logical\"" level_1_scale$foil <- level_1_scale$Answer.choice=="\"foil\"" level_0_by_item_scale <- aggregate(cbind(target, logical, foil) ~ Answer.item, data=level_0_scale, sum) level_0_pragmatic_scale <- mean(level_0_scale$target) level_1_by_item_scale <- aggregate(cbind(target, logical, foil) ~ Answer.item, data=level_1_scale, sum) level_1_pragmatic_scale <- mean(level_1_scale$target) # Printing level_0_by_item_scale level_0_pragmatic_scale level_1_by_item_scale level_1_pragmatic_scale sum(level_0_by_item_scale[,2:4]) sum(level_0_by_item_scale[,2]) summary(aov(correct ~ as.factor(Answer.item) * as.factor(Answer.scale_and_levels_condition), data = levels_scale)) summary(glm(correct ~ as.factor(Answer.item) * as.factor(Answer.scale_and_levels_condition), data = levels_scale)) # Separate by gender and age levels_scale$males = levels_scale$Answer.gender == "\"m\"" | levels_scale$Answer.gender == "\"male\"" | levels_scale$Answer.gender == "\"M\"" | levels_scale$Answer.gender == "\"Male\"" | levels_scale$Answer.gender == "\"MALE\"" levels_scale$females = levels_scale$Answer.gender == "\"f\"" | levels_scale$Answer.gender == "\"female\"" | levels_scale$Answer.gender == "\"F\"" | levels_scale$Answer.gender == "\"Female\"" | levels_scale$Answer.gender == "\"FEMALE\"" levels_scale$twenties = levels_scale$Answer.age == "\"20\"" | levels_scale$Answer.age == "\"21\"" | levels_scale$Answer.age == "\"22\"" | levels_scale$Answer.age == "\"23\"" | levels_scale$Answer.age == "\"24\"" | levels_scale$Answer.age == "\"25\"" | levels_scale$Answer.age == "\"26\"" | levels_scale$Answer.age == "\"27\"" | levels_scale$Answer.age == "\"28\"" | levels_scale$Answer.age == "\"29\"" levels_scale$thirties = levels_scale$Answer.age == "\"30\"" | levels_scale$Answer.age == "\"31\"" | levels_scale$Answer.age == "\"32\"" | levels_scale$Answer.age == "\"33\"" | levels_scale$Answer.age == "\"34\"" | levels_scale$Answer.age == "\"35\"" | levels_scale$Answer.age == "\"36\"" | levels_scale$Answer.age == "\"37\"" | levels_scale$Answer.age == "\"38\"" | levels_scale$Answer.age == "\"39\"" levels_scale$fourties = levels_scale$Answer.age == "\"40\"" | levels_scale$Answer.age == "\"41\"" | levels_scale$Answer.age == "\"42\"" | levels_scale$Answer.age == "\"43\"" | levels_scale$Answer.age == "\"44\"" | levels_scale$Answer.age == "\"45\"" | levels_scale$Answer.age == "\"46\"" | levels_scale$Answer.age == "\"47\"" | levels_scale$Answer.age == "\"48\"" | levels_scale$Answer.age == "\"49\"" levels_scale$fifties = levels_scale$Answer.age == "\"50\"" | levels_scale$Answer.age == "\"51\"" | levels_scale$Answer.age == "\"52\"" | levels_scale$Answer.age == "\"53\"" | levels_scale$Answer.age == "\"54\"" | levels_scale$Answer.age == "\"55\"" | levels_scale$Answer.age == "\"56\"" | levels_scale$Answer.age == "\"57\"" | levels_scale$Answer.age == "\"58\"" | levels_scale$Answer.age == "\"59\"" summary(aov(correct ~ males + females + as.factor(Answer.target_position) + as.factor(Answer.logical_position), data = levels_scale)) total_group = rbind(levels_scale, levels) sum(levels_scale$females) + sum(levels$females)