# ANALYSES for: # Wotschack, C., & Kliegl, R. (2013). # Reading strategy modulates parafoveal-on-foveal effects in sentence reading. # The Quarterly Journal of Experimental Psychology, 66(3), 548-562. # Christiane Wotschack, November 2011 # adapted to current version of lme4; estimates differ in decimals from tables in publication, # but AFAICS differences do not affect the interpretation. # Reinhold Kliegl, March 2013 # --------------------------------------------------------------------------------- # This script uses different dataframes: # dataframe "em_id.rda" contains means of variables of word based summary statistics per ID per group # (easy young, hard young, easy old, hard old) # # "young_bd3.rda"/ "old_bd3.rda" contains three dataframes for single fixation cases for each age group (young/old): # - b contains fixation based variables in original metric # - b.id contains means of fixation based variables in original metric # - d contains fixations based variables in centered within subject metric # # coding: # id: subjects ID # cnd: reading condition (0 = easy-question, 1 = hard-question) # age: age group (0 = young, 1= old) #------------------------------------- # (1) SUMMARY STATISTICS (TABLE 1) #------------------------------------- # 09/2009 rm(list=ls()) load("em_id.rda") # YOUNG SUBJECTS data <- em_id em_id <- em_id[which(em_id$age==0),] # FIXATION PROBABILITIES # skipping summary(p0.aov <- aov(p0 ~ cnd, data=em_id)) print(model.tables(p0.aov,"means"),digits=5) # single fixation summary(p1.aov <- aov(p1 ~ cnd, data=em_id)) print(model.tables(p1.aov,"means"),digits=5) # double fixation summary(p2.aov <- aov(p2 ~ cnd, data=em_id)) print(model.tables(p2.aov,"means"),digits=5) # three-plus fixation summary(p3.aov <- aov(p3 ~ cnd, data=em_id)) print(model.tables(p3.aov,"means"),digits=5) # regression summary(roc.aov <- aov(roc ~ cnd, data=em_id)) print(model.tables(roc.aov,"means"),digits=5) # FIXATION DURATIONS # single fixation summary(d0.aov <- aov(d0 ~ cnd, data=em_id)) print(model.tables(d0.aov,"means"),digits=5) # 1st of multiple summary(d1.aov <- aov(d1 ~ cnd, data=em_id)) print(model.tables(d1.aov,"means"),digits=5) # 2nd of multiple summary(d2.aov <- aov(d2 ~ cnd, data=em_id)) print(model.tables(d2.aov,"means"),digits=5) # gaze summary(gz.aov <- aov(gz ~ cnd, data=em_id)) print(model.tables(gz.aov,"means"),digits=5) # total reading time summary(trt.aov <- aov(trt ~ cnd, data=em_id)) print(model.tables(trt.aov,"means"),digits=5) # RELATIVE FIXATION POSITION # single fixation summary(o0.aov <- aov(o0 ~ cnd, data=em_id)) print(model.tables(o0.aov,"means"),digits=5) # 1st of multiple summary(o1.aov <- aov(o1 ~ cnd, data=em_id)) print(model.tables(o1.aov,"means"),digits=5) # 2nd of multiple summary(o2.aov <- aov(o2 ~ cnd, data=em_id)) print(model.tables(o2.aov,"means"),digits=5) # OLD SUBJECTS em_id <- data em_id <- em_id[which(em_id$age==1),] # FIXATION PROBABILITIES # skipping summary(p0.aov <- aov(p0 ~ cnd, data=em_id)) print(model.tables(p0.aov,"means"),digits=5) # single fixation summary(p1.aov <- aov(p1 ~ cnd, data=em_id)) print(model.tables(p1.aov,"means"),digits=5) # double fixation summary(p2.aov <- aov(p2 ~ cnd, data=em_id)) print(model.tables(p2.aov,"means"),digits=5) # three-plus fixation summary(p3.aov <- aov(p3 ~ cnd, data=em_id)) print(model.tables(p3.aov,"means"),digits=5) # regression summary(roc.aov <- aov(roc ~ cnd, data=em_id)) print(model.tables(roc.aov,"means"),digits=5) # FIXATION DURATIONS # single fixation summary(d0.aov <- aov(d0 ~ cnd, data=em_id)) print(model.tables(d0.aov,"means"),digits=5) # 1st of multiple summary(d1.aov <- aov(d1 ~ cnd, data=em_id)) print(model.tables(d1.aov,"means"),digits=5) # 2nd of multiple summary(d2.aov <- aov(d2 ~ cnd, data=em_id)) print(model.tables(d2.aov,"means"),digits=5) # gaze summary(gz.aov <- aov(gz ~ cnd, data=em_id)) print(model.tables(gz.aov,"means"),digits=5) # total reading time summary(trt.aov <- aov(trt ~ cnd, data=em_id)) print(model.tables(trt.aov,"means"),digits=5) # RELATIVE FIXATION POSITION # single fixation summary(o0.aov <- aov(o0 ~ cnd, data=em_id)) print(model.tables(o0.aov,"means"),digits=5) # 1st of multiple summary(o1.aov <- aov(o1 ~ cnd, data=em_id)) print(model.tables(o1.aov,"means"),digits=5) # 2nd of multiple summary(o2.aov <- aov(o2 ~ cnd, data=em_id)) print(model.tables(o2.aov,"means"),digits=5) #----------------------------------------------- # (2) MEANS OF SINGLE FIXATION CASES (TABLE 2) #----------------------------------------------- # 10/2008 # YOUNG SUBJECTS rm(list=ls()) load("yng_bd3.rda") # use varibale CND as group variable (1 = easy-question, 2 = hard-question) # frequency summary(f.id.aov <- aov(f.id ~ CND, data=b.id)) print(model.tables(f.id.aov,"means"),digits=5) # predictability summary(p.id.aov <- aov(p.id ~ CND, data=b.id)) print(model.tables(p.id.aov,"means"),digits=5) # length (= 1/length) summary(l.id.aov <- aov(l.id ~ CND, data=b.id)) print(model.tables(l.id.aov,"means"),digits=5) # function word proportion summary(x.id.aov <- aov(x.id ~ CND, data=b.id)) print(model.tables(x.id.aov,"means"),digits=5) # single fixation duration summary(dur.id.aov <- aov(dur.id ~ CND, data=b.id)) print(model.tables(dur.id.aov,"means"),digits=5) # OLD SUBJECTS rm(list=ls()) load("old_bd3.rda") # use varibale CND as group variable (1 = easy-question, 2 = hard-question) # frequency summary(f.id.aov <- aov(f.id ~ CND, data=b.id)) print(model.tables(f.id.aov,"means"),digits=5) # predictability summary(p.id.aov <- aov(p.id ~ CND, data=b.id)) print(model.tables(p.id.aov,"means"),digits=5) # length (= 1/length) summary(l.id.aov <- aov(l.id ~ CND, data=b.id)) print(model.tables(l.id.aov,"means"),digits=5) # function word proportion summary(x.id.aov <- aov(x.id ~ CND, data=b.id)) print(model.tables(x.id.aov,"means"),digits=5) # single fixation duration summary(dur.id.aov <- aov(dur.id ~ CND, data=b.id)) print(model.tables(dur.id.aov,"means"),digits=5) #------------------------------------------------------------ # (3) LMM ANALYSES FOR LOG SINGLE FIXATION DURATION (cf. APPENDIX) #------------------------------------------------------------ # 09/2008 rm(list=ls()) library(lme4) # YOUNG SUBJECTS (TABLES A1 AND A2) load("yng_bd3.rda") print(lmer.yng <- lmer(dur ~ trial + wst + cnd + pc1 + pc2 + poly(f,3) + p + l + f1 + p1 + l1 + f2 + p2 + l2 + ao1 + o + oq + ao + lf + l67 + f1f + ff2 + cnd:(poly(f,3) + l + p2 + ao1 + o + ao + lf + trial) + (1|id) + (1|wid) + (1|sn), REML=TRUE, data=d), cor=FALSE) # OLD SUBJECTS (TABLES A3 AND A4) load("old_bd3.rda") print(lmer.old <- lmer(dur ~ trial + wst + cnd + pc1 + pc2 + poly(f,3) + p + l + f1 + p1 + l1 + f2 + p2 + l2 + ao1 + o + oq + ao + f1f + lf + l67 + f1f + ff2 + cnd:(poly(f,3) + f1 + f2 + ao1 + ao + trial) + (1|id) + (1|wid) + (1|sn), REML=TRUE, data=d), cor=FALSE)