Codes for statistical analysis

Answer Choice (R) > model.binom = glmer(PctNoun1 ~ VerbType_factor*Structure_factor*Group_factor + (1 | Participant) + (1 | Item), data = all, family = binomial()) > summary(model.all) > source("centerfactor.r") > > contrasts(all$VerbType_factor) > centerfactor(all$VerbType_factor) > contrasts(all$VerbType_factor) = centerfactor(all$VerbType_factor) > contrasts(all$VerbType_factor) > > contrasts(all$Structure_factor) > centerfactor(all$Structure_factor) > contrasts(all$Structure_factor) = centerfactor(all$Structure_factor) > contrasts(all$Structure_factor) > > contrasts(all$Group_factor) > contrasts(all$Group_factor) = centerfactor(all$Group_factor) > contrasts(all$Group_factor) Reading and Response Time (R) Head Nouns > model.all = lmer(log_RT_N1 ~ VerbType_factor*Structure_factor*Group_factor + (1 | Participant) + (1 | Item), data = all, REML = FALSE) > summary(model.all) > model.all = lmer(log_RT_N2 ~ VerbType_factor*Structure_factor*Group_factor + (1 | Participant) + (1 | Item), data = all, REML = FALSE) > summary(model.all) Embedded Verb > model.all = lmer(log_RT_verb ~ VerbType_factor*Structure_factor*Group_factor + (1 | Participant) + (1 | Item), data = all, REML = FALSE) > summary(model.all) Reponse Time > model.all = lmer(log_RespTime ~ VerbType_factor*Structure_factor*Group_factor + (1 | Participant) + (1 | Item), data = all, REML = FALSE) > summary(model.all) Log_RT (python) import pandas as pd import numpy as np df_log = pd.read_excel('all.xlsx') df_ log["log_RespTime"] = np.log(df_ log["RespTime"]) df_ log["log_RT_N1"] = np.log(df_ log["RT_N1"]) df_ log["log_RT_N2"] = np.log(df_ log["RT_N2"]) df_ log["log_RT_verb"] = np.log(df_ log["RT_verb"]) display(df_ log) df_ log.to_excel("all_done.xlsx") Backtransform: Log_RT (R) emm