Parenting Project Attitudes Questionnaire Uptake with Control Group
Emily & Mike
2016-02-10
Data analysis of basic parenting values/attitudes survey.
1 Data preprocessing
Preliminaries.
## [1] "dplyr" "langcog" "tidyr" "ggplot2" "lme4"##
## Attaching package: 'langcog'## The following object is masked from 'package:base':
##
## scale##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Warning: package 'ggplot2' was built under R version 3.2.3##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: boot##
## Attaching package: 'boot'## The following object is masked from 'package:psych':
##
## logit## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:boot':
##
## melanoma##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
##
## parallel## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expandRead in files and consolidate to the same directory.
files <- dir("../production-results/uptake_e2/")
d.raw <- data.frame()
for (f in files) {
jf <- paste("../production-results/uptake_e2/",f,sep="")
jd <- fromJSON(paste(readLines(jf), collapse=""))
# clean up different tasks
trial_type <- jd$answer$data$trial_type
#parenting questionnaire
sent <- jd$answers$data$sentence[trial_type != "uptake"]
rating <- as.numeric(jd$answers$data$rating[trial_type != "uptake"])
#uptake experiment
control_recall_1 = as.character(jd$answers$data$answer[1])
control_recall_2 = as.character(jd$answers$data$answer[2])
control_recall_3 = as.character(jd$answers$data$answer[3])
control_recall_4 = as.character(jd$answers$data$answer[4])
control_recall_5 = as.character(jd$answers$data$answer[5])
target_generalize_1 = as.character(jd$answers$data$answer[6])
target_generalize_2 = as.character(jd$answers$data$answer[7])
target_generalize_3 = as.character(jd$answers$data$answer[8])
target_generalize_4 = as.character(jd$answers$data$answer[9])
target_generalize_5 = as.character(jd$answers$data$answer[10])
target_recall_1 = as.character(jd$answers$data$answer[11])
target_recall_2 = as.character(jd$answers$data$answer[12])
target_recall_3 = as.character(jd$answers$data$answer[13])
target_recall_4 = as.character(jd$answers$data$answer[14])
target_recall_5 = as.character(jd$answers$data$answer[15])
reading_time_target = jd$answers$data$target_rt[1]
reading_time_control = jd$answers$data$control_rt[1]
time_questionnaire = jd$answers$data$questionnaire_rt[1]
#demographics
race <- as.character(jd$answers$data$race[1])
id <- data.frame(workerid = jd$WorkerId,
sent = sent,
rating = rating,
enjoy_target = jd$answers$data$enjoy_target,
enjoy_control = jd$answers$data$enjoy_control,
reading_ease_target = jd$answers$data$reading_ease_target,
reading_ease_control = jd$answers$data$reading_ease_control,
target_recall_1 = target_recall_1,
target_recall_2 = target_recall_2,
target_recall_3 = target_recall_3,
target_recall_4 = target_recall_4,
target_recall_5 = target_recall_5,
target_generalize_1 = target_generalize_1,
target_generalize_2 = target_generalize_2,
target_generalize_3 = target_generalize_3,
target_generalize_4 = target_generalize_4,
target_generalize_5 = target_generalize_5,
control_recall_1 = control_recall_1,
control_recall_2 = control_recall_2,
control_recall_3 = control_recall_3,
control_recall_4 = control_recall_4,
control_recall_5 = control_recall_5,
children = jd$answers$data$children,
language = jd$answers$data$homelang,
ses = jd$answers$data$ladder,
gender = jd$answers$data$gender,
age = jd$answers$data$age,
education = jd$answers$data$education,
ethnicity = jd$answers$data$ethnicity,
childAgeYoung = jd$answers$data$childAgeYoung,
childAgeOld = jd$answers$data$childAgeOld,
race = race,
reading_time_target = reading_time_target,
reading_time_control = reading_time_control,
time_questionnaire = time_questionnaire,
group = "reading"
)
d.raw <- bind_rows(d.raw, id)
}files <- dir("../production-results/uptake_control_e1/")
for (f in files) {
jf <- paste("../production-results/uptake_control_e1/",f,sep="")
jd <- fromJSON(paste(readLines(jf), collapse=""))
# clean up different tasks
trial_type <- jd$answer$data$trial_type
#parenting questionnaire
sent <- jd$answers$data$sentence[trial_type != "uptake"]
rating <- as.numeric(jd$answers$data$rating[trial_type != "uptake"])
#uptake experiment
control_recall_1 = as.character(jd$answers$data$answer[1])
control_recall_2 = as.character(jd$answers$data$answer[2])
control_recall_3 = as.character(jd$answers$data$answer[3])
control_recall_4 = as.character(jd$answers$data$answer[4])
control_recall_5 = as.character(jd$answers$data$answer[5])
target_generalize_1 = as.character(jd$answers$data$answer[6])
target_generalize_2 = as.character(jd$answers$data$answer[7])
target_generalize_3 = as.character(jd$answers$data$answer[8])
target_generalize_4 = as.character(jd$answers$data$answer[9])
target_generalize_5 = as.character(jd$answers$data$answer[10])
target_recall_1 = as.character(jd$answers$data$answer[11])
target_recall_2 = as.character(jd$answers$data$answer[12])
target_recall_3 = as.character(jd$answers$data$answer[13])
target_recall_4 = as.character(jd$answers$data$answer[14])
target_recall_5 = as.character(jd$answers$data$answer[15])
#demographics
race <- as.character(jd$answers$data$race[1])
id <- data.frame(workerid = jd$WorkerId,
sent = sent,
rating = rating,
target_recall_1 = target_recall_1,
target_recall_2 = target_recall_2,
target_recall_3 = target_recall_3,
target_recall_4 = target_recall_4,
target_recall_5 = target_recall_5,
target_generalize_1 = target_generalize_1,
target_generalize_2 = target_generalize_2,
target_generalize_3 = target_generalize_3,
target_generalize_4 = target_generalize_4,
target_generalize_5 = target_generalize_5,
control_recall_1 = control_recall_1,
control_recall_2 = control_recall_2,
control_recall_3 = control_recall_3,
control_recall_4 = control_recall_4,
control_recall_5 = control_recall_5,
children = jd$answers$data$children,
language = jd$answers$data$homelang,
ses = jd$answers$data$ladder,
gender = jd$answers$data$gender,
age = jd$answers$data$age,
education = jd$answers$data$education,
ethnicity = jd$answers$data$ethnicity,
childAgeYoung = jd$answers$data$childAgeYoung,
childAgeOld = jd$answers$data$childAgeOld,
race = race,
group = "control"
)
d.raw <- bind_rows(d.raw, id)
}
d.raw <- distinct(d.raw, workerid, sent)NOTE: 21 participants were dropped from Control group because they had already participated in the Reading experiment.
Map on question short forms so that we can use these instead.
labels <- read.csv("sent_forms_e7.csv")
labels$sent <- as.character(labels$sent)
answers <- read.csv("uptake_key.csv")
answers$tar_gen <-as.character(answers$tar_gen)
answers$tar_rec <-as.character(answers$tar_rec)
answers$con_rec <-as.character(answers$con_rec)Clean up labels.
d.raw$sent <- as.character(d.raw$sent)
d.raw$sent <- str_replace_all(d.raw$sent, "'", "")
d.raw$sent <- str_replace_all(d.raw$sent, "’", "")
d.raw$sent <- str_replace_all(d.raw$sent, "“", "")
d.raw$sent <- str_replace_all(d.raw$sent, "”", "")
d.raw$sent <- str_replace_all(d.raw$sent, "‘", "")
d.raw$sent <- str_replace_all(d.raw$sent, "â", "")Merge. Recode uptake answers by accuracy.
d <- left_join(d.raw, labels)
d$rating[d$reverse_code == 1] <- 6 - d$rating[d$reverse_code == 1]
d$target_generalize_1[d$target_generalize_1 != answers$tar_gen[1]] <- 0
d$target_generalize_1[d$target_generalize_1 == answers$tar_gen[1]] <- 1
d$target_generalize_2[d$target_generalize_2 != answers$tar_gen[2]] <- 0
d$target_generalize_2[d$target_generalize_2 == answers$tar_gen[2]] <- 1
d$target_generalize_3[d$target_generalize_3 != answers$tar_gen[3]] <- 0
d$target_generalize_3[d$target_generalize_3 == answers$tar_gen[3]] <- 1
d$target_generalize_4[d$target_generalize_4 != answers$tar_gen[4]] <- 0
d$target_generalize_4[d$target_generalize_4 == answers$tar_gen[4]] <- 1
d$target_generalize_5[d$target_generalize_5 != answers$tar_gen[5]] <- 0
d$target_generalize_5[d$target_generalize_5 == answers$tar_gen[5]] <- 1
d$target_recall_1[d$target_recall_1 != answers$tar_rec[1]] <- 0
d$target_recall_1[d$target_recall_1 == answers$tar_rec[1]] <- 1
d$target_recall_2[d$target_recall_2 != answers$tar_rec[2]] <- 0
d$target_recall_2[d$target_recall_2 == answers$tar_rec[2]] <- 1
d$target_recall_3[d$target_recall_3 != answers$tar_rec[3]] <- 0
d$target_recall_3[d$target_recall_3 == answers$tar_rec[3]] <- 1
d$target_recall_4[d$target_recall_4 != answers$tar_rec[4]] <- 0
d$target_recall_4[d$target_recall_4 == answers$tar_rec[4]] <- 1
d$target_recall_5[d$target_recall_5 != answers$tar_rec[5]] <- 0
d$target_recall_5[d$target_recall_5 == answers$tar_rec[5]] <- 1
d$control_recall_1[d$control_recall_1 != answers$con_rec[1]] <- 0
d$control_recall_1[d$control_recall_1 == answers$con_rec[1]] <- 1
d$control_recall_2[d$control_recall_2 != answers$con_rec[2]] <- 0
d$control_recall_2[d$control_recall_2 == answers$con_rec[2]] <- 1
d$control_recall_3[d$control_recall_3 != answers$con_rec[3]] <- 0
d$control_recall_3[d$control_recall_3 == answers$con_rec[3]] <- 1
d$control_recall_4[d$control_recall_4 != answers$con_rec[4]] <- 0
d$control_recall_4[d$control_recall_4 == answers$con_rec[4]] <- 1
d$control_recall_5[d$control_recall_5 != answers$con_rec[5]] <- 0
d$control_recall_5[d$control_recall_5 == answers$con_rec[5]] <- 1Plot demographic info.
subinfo <- d %>%
group_by(workerid) %>%
select(-short_sent, -category, -instrument, -reverse_code) %>%
distinct
questions <- subinfo %>%
select(workerid, starts_with("target"), starts_with("control")) %>%
gather(question, correct, starts_with("target"), starts_with("control")) %>%
separate(question, c("passage","trial_type","q_num"), sep = "_") %>%
group_by(workerid, passage, trial_type) %>%
summarise(correct = mean(as.numeric(correct))) %>%
unite(trialtype, passage, trial_type) %>%
spread(trialtype, correct)
subinfo <- subinfo %>%
select(-starts_with("target"), -starts_with("control")) %>%
left_join(questions) %>%
select(-sent, -rating)subinfo$education <- factor(subinfo$education,
levels = c("highSchool","someCollege","4year","someGrad","Grad"))
subinfo$gender <- str_replace_all(subinfo$gender,
"female|FEMALE|F$|f$|Femal$|Females|Females","Female")
subinfo$gender <- str_replace_all(subinfo$gender,
"^male|^Male|^MALE|^M$|^m$|^Maleq|Make", "Male")
subinfo$gender <- str_replace_all(subinfo$gender,
"29|24|25|28|32|33|45", "")
subinfo$gender <- str_replace_all(subinfo$gender,
"males", "male")
subinfo$gender <- str_replace_all(subinfo$gender, " ", "")
subinfo$language <- str_replace_all(subinfo$language, "english|eNGLISH|Engliah|ENGLISH|^eng$|Enlgish", "English")
subinfo$language <- str_replace_all(subinfo$language," ", "")
subinfo$language <- str_replace_all(subinfo$language,"arabic", "Arabic")
subinfo$language <- str_replace_all(subinfo$language,"chinese", "Chinese")
subinfo$language <- str_replace_all(subinfo$language,"german", "German")
subinfo$language <- str_replace_all(subinfo$language,"tagalog", "Tagalog")
subinfo$youngestChildAge <- factor(subinfo$childAgeYoung, levels = c("","0to6mo","7to12mo","1y","2y","3y","4y","5y","6y","7y","8y","9y","10y","olderthan10"))
subinfo$oldestChildAge <- factor(subinfo$childAgeOld, levels = c("","0to6mo","7to12mo","1y","2y","3y","4y","5y","6y","7y","8y","9y","10y","olderthan10"))
subinfo$reading_ease_target <- factor(subinfo$reading_ease_target, levels = c("Very Difficult", "Somewhat Difficult", "Somewhat Easy", "Very Easy"))
subinfo$reading_ease_control <- factor(subinfo$reading_ease_control, levels = c("Very Difficult", "Somewhat Difficult", "Somewhat Easy", "Very Easy"))
subinfo$enjoy_target <- factor(subinfo$enjoy_target, levels = c("Very Unenjoyable", "Somewhat Unenjoyable", "Somewhat Enjoyable", "Very Enjoyable"))
subinfo$enjoy_control <- factor(subinfo$enjoy_control, levels = c("Very Unenjoyable", "Somewhat Unenjoyable", "Somewhat Enjoyable", "Very Enjoyable"))qplot(ses, data=subinfo)
qplot(children, data=subinfo)
qplot(gender, data=subinfo)
qplot(education, data=subinfo)
qplot(age, data=subinfo)
qplot(language, data=subinfo)
qplot(ethnicity, data=subinfo)
qplot(race, data=subinfo)
qplot(youngestChildAge, data=subinfo)
qplot(oldestChildAge, data=subinfo)
qplot(reading_ease_target, data=subinfo)
qplot(reading_ease_control, data=subinfo)
qplot(enjoy_target, data=subinfo)
qplot(enjoy_control, data=subinfo)
qplot(group, data=subinfo)
2 Basic analyses
Now look at mean ratings across sentences.
rating_count <- table(d$rating)
rating_count##
## 0 1 2 3 4 5 6
## 149 253 394 954 1374 1922 3576prop.table(rating_count) ##
## 0 1 2 3 4 5
## 0.01728137 0.02934354 0.04569705 0.11064718 0.15935978 0.22291812
## 6
## 0.41475296ms <- d %>%
group_by(category, instrument, short_sent, reverse_code) %>%
multi_boot_standard(col = "rating") %>%
arrange(instrument, category, desc(mean))
ms$short_sent_ord <- factor(ms$short_sent,
levels = ms$short_sent)Plot attitude.
qplot(short_sent_ord, mean, col = category,
ymin = ci_lower, ymax = ci_upper, pch = factor(reverse_code),
geom = "pointrange",
data = filter(ms, instrument == "attitudes")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) +
xlab("") +
ylab("Mean Rating") +
ylim(c(0,6)) +
scale_colour_solarized()
Plot mean subscale scores.
mc <- d %>%
group_by(category) %>%
multi_boot_standard(col = "rating") %>%
arrange(category, desc(mean))
ggplot(mc, aes(x = category, y = mean)) +
geom_bar(stat="identity") +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper),
position = position_dodge(width = .9))
mcl <- d %>%
group_by(category, workerid) %>%
multi_boot_standard(col = "rating") %>%
arrange(category, desc(mean))%>%
spread(category, mean) 3 Scale reliability
3.1 Whole Scale
wide.attitudes <- d %>%
dplyr::select(workerid, short_sent, rating) %>%
spread(short_sent, rating)
alpha.mat <- as.matrix(select(wide.attitudes, -workerid))
summary(psych::alpha(x = alpha.mat))##
## Reliability analysis
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.82 0.84 0.87 0.23 5.3 0.015 4.7 0.683.2 Rules and Respect Subscale
wide.rules_respect <- d %>%
filter(category == "rules_respect") %>%
select(workerid, short_sent, rating) %>%
spread(short_sent, rating)
alpha.rr <- as.matrix(select(wide.rules_respect, -workerid))
summary(psych::alpha(x = alpha.rr))##
## Reliability analysis
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.7 0.71 0.7 0.29 2.4 0.032 4.2 0.913.3 Affection and Attachment Subscale
wide.affection <- d %>%
filter(category == "affection") %>%
select(workerid, short_sent, rating) %>%
spread(short_sent, rating)
alpha.af <- as.matrix(select(wide.affection, -workerid))
summary(psych::alpha(x = alpha.af))##
## Reliability analysis
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.76 0.78 0.79 0.37 3.5 0.028 4.8 0.953.4 Active Learning Subscale
wide.active_learning <- d %>%
filter(category == "active_learning") %>%
select(workerid, short_sent, rating) %>%
spread(short_sent, rating)
alpha.al <- as.matrix(select(wide.active_learning, -workerid))
summary(psych::alpha(x = alpha.al))##
## Reliability analysis
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.74 0.75 0.73 0.33 2.9 0.029 5 0.824 Information Uptake Analyses
Create a data frame that has subscale scores.
Standardize ratings within subscale.
ds <- d
ds$srating <- ave(ds$rating, ds$category, FUN=scale)
ss <- ds %>%
group_by(workerid, category) %>%
summarize(srating = mean(srating)) %>%
spread(category, srating) %>%
left_join(subinfo) %>%
gather(trial_type, correct, control_recall, target_recall, target_generalize)
ss.long <- ss %>%
gather(subscale, srating, active_learning, affection, rules_respect) 4.1 Plots
ggplot(ss.long, aes(x = srating, y = correct, col = subscale)) +
geom_jitter() +
geom_smooth(method="lm", se=FALSE) +
facet_wrap(~trial_type)
Setting up exclusion based on reading time.
exclude <- subinfo$workerid[subinfo$reading_time_target < 30 | subinfo$reading_time_control < 30]
length(exclude)## [1] 277qplot(subinfo$reading_time_target, binwidth = 15) +
geom_vline(xintercept =15, lty =2, col="red")
qplot(subinfo$reading_time_control, binwidth = 15) +
geom_vline(xintercept =15, lty =2, col="red")
Both groups with exclusions.
ggplot(filter(ss.long, !workerid %in% exclude),
aes(x = srating, y = correct, colour = subscale)) +
geom_jitter() +
geom_smooth(method="lm", se=FALSE) +
facet_grid(trial_type ~ group)
4.2 Stats
subscales <- ds %>%
group_by(workerid, category) %>%
summarize(srating = mean(srating)) %>%
spread(category, srating)
ratings <- d %>%
select(workerid, starts_with("target"), starts_with("control"), group) %>%
gather(question, correct, starts_with("target"), starts_with("control")) %>%
separate(question, c("passage","trial_type","q_num"), sep = "_") %>%
mutate(correct = as.numeric(correct),
q_num = as.numeric(q_num) +
as.numeric(factor(passage)) * 10 +
as.numeric(factor(trial_type)) * 100)
d.reg <- left_join(ratings, subscales) %>%
unite(question_type, passage, trial_type)summary(glmer(correct ~ question_type * rules_respect +
question_type * active_learning +
question_type * group * active_learning +
question_type * group * rules_respect +
(1|workerid) +
(1|q_num),
data = filter(d.reg, !workerid %in% exclude),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## correct ~ question_type * rules_respect + question_type * active_learning +
## question_type * group * active_learning + question_type *
## group * rules_respect + (1 | workerid) + (1 | q_num)
## Data: filter(d.reg, !workerid %in% exclude)
##
## AIC BIC logLik deviance df.resid
## 111476.9 111670.2 -55718.5 111436.9 116350
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.6203 -0.6445 0.2408 0.5746 4.2573
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.2598 1.1224
## q_num (Intercept) 0.5498 0.7415
## Number of obs: 116370, groups: workerid, 431; q_num, 15
##
## Fixed effects:
## Estimate
## (Intercept) -0.20737
## question_typetarget_generalize 0.27567
## question_typetarget_recall 1.03102
## rules_respect 0.21339
## active_learning 0.19764
## groupreading 1.21837
## question_typetarget_generalize:rules_respect -0.34686
## question_typetarget_recall:rules_respect -0.30468
## question_typetarget_generalize:active_learning 0.30318
## question_typetarget_recall:active_learning 0.49712
## question_typetarget_generalize:groupreading 1.38292
## question_typetarget_recall:groupreading 0.31912
## active_learning:groupreading 0.03264
## rules_respect:groupreading -0.32495
## question_typetarget_generalize:active_learning:groupreading 0.37783
## question_typetarget_recall:active_learning:groupreading 0.06797
## question_typetarget_generalize:rules_respect:groupreading 0.25850
## question_typetarget_recall:rules_respect:groupreading 0.60658
## Std. Error
## (Intercept) 0.29393
## question_typetarget_generalize 0.40453
## question_typetarget_recall 0.39538
## rules_respect 0.13332
## active_learning 0.13002
## groupreading 0.11161
## question_typetarget_generalize:rules_respect 0.03911
## question_typetarget_recall:rules_respect 0.04171
## question_typetarget_generalize:active_learning 0.03791
## question_typetarget_recall:active_learning 0.03987
## question_typetarget_generalize:groupreading 0.03961
## question_typetarget_recall:groupreading 0.03875
## active_learning:groupreading 0.19640
## rules_respect:groupreading 0.19328
## question_typetarget_generalize:active_learning:groupreading 0.06736
## question_typetarget_recall:active_learning:groupreading 0.06626
## question_typetarget_generalize:rules_respect:groupreading 0.07309
## question_typetarget_recall:rules_respect:groupreading 0.07002
## z value
## (Intercept) -0.71
## question_typetarget_generalize 0.68
## question_typetarget_recall 2.61
## rules_respect 1.60
## active_learning 1.52
## groupreading 10.92
## question_typetarget_generalize:rules_respect -8.87
## question_typetarget_recall:rules_respect -7.30
## question_typetarget_generalize:active_learning 8.00
## question_typetarget_recall:active_learning 12.47
## question_typetarget_generalize:groupreading 34.91
## question_typetarget_recall:groupreading 8.24
## active_learning:groupreading 0.17
## rules_respect:groupreading -1.68
## question_typetarget_generalize:active_learning:groupreading 5.61
## question_typetarget_recall:active_learning:groupreading 1.03
## question_typetarget_generalize:rules_respect:groupreading 3.54
## question_typetarget_recall:rules_respect:groupreading 8.66
## Pr(>|z|)
## (Intercept) 0.480500
## question_typetarget_generalize 0.495584
## question_typetarget_recall 0.009117 **
## rules_respect 0.109460
## active_learning 0.128498
## groupreading < 2e-16 ***
## question_typetarget_generalize:rules_respect < 2e-16 ***
## question_typetarget_recall:rules_respect 2.78e-13 ***
## question_typetarget_generalize:active_learning 1.27e-15 ***
## question_typetarget_recall:active_learning < 2e-16 ***
## question_typetarget_generalize:groupreading < 2e-16 ***
## question_typetarget_recall:groupreading < 2e-16 ***
## active_learning:groupreading 0.868000
## rules_respect:groupreading 0.092718 .
## question_typetarget_generalize:active_learning:groupreading 2.03e-08 ***
## question_typetarget_recall:active_learning:groupreading 0.305014
## question_typetarget_generalize:rules_respect:groupreading 0.000406 ***
## question_typetarget_recall:rules_respect:groupreading < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) qstn_typtrgt_g qstn_typtrgt_r rls_rs actv_l
## qstn_typtrgt_g -0.609
## qstn_typtrgt_r -0.606 0.397
## rules_rspct -0.001 -0.005 -0.004
## activ_lrnng -0.009 0.002 0.002 -0.344
## groupreadng -0.154 -0.007 -0.011 0.013 0.025
## qstn_typtrgt_gnrlz:r_ 0.006 -0.004 -0.007 -0.139 0.045
## qstn_typtrgt_rcll:r_ 0.005 -0.004 -0.008 -0.131 0.043
## qstn_typtrgt_gnrlz:c_ -0.001 -0.002 0.002 0.043 -0.142
## qstn_typtrgt_rcll:c_ -0.002 0.000 0.004 0.040 -0.135
## qstn_typtrgt_g: 0.012 -0.021 -0.007 -0.002 -0.005
## qstn_typtrgt_r: 0.012 -0.008 -0.023 -0.002 -0.006
## actv_lrnng: -0.003 0.004 0.011 0.217 -0.645
## rls_rspct:g 0.004 0.000 -0.002 -0.674 0.228
## qstn_typtrgt_gnrlz:c_: 0.005 -0.002 -0.005 -0.022 0.077
## qstn_typtrgt_rcll:c_: 0.007 -0.003 -0.008 -0.022 0.080
## qstn_typtrgt_gnrlz:r_: -0.006 0.004 0.007 0.071 -0.022
## qstn_typtrgt_rcll:r_: -0.005 0.004 0.007 0.075 -0.025
## grprdn qstn_typtrgt_gnrlz:r_ qstn_typtrgt_rcll:r_
## qstn_typtrgt_g
## qstn_typtrgt_r
## rules_rspct
## activ_lrnng
## groupreadng
## qstn_typtrgt_gnrlz:r_ -0.004
## qstn_typtrgt_rcll:r_ -0.004 0.471
## qstn_typtrgt_gnrlz:c_ -0.006 -0.345 -0.157
## qstn_typtrgt_rcll:c_ -0.005 -0.159 -0.367
## qstn_typtrgt_g: -0.127 0.017 0.010
## qstn_typtrgt_r: -0.132 0.011 0.023
## actv_lrnng: -0.106 -0.028 -0.028
## rls_rspct:g 0.004 0.093 0.088
## qstn_typtrgt_gnrlz:c_: 0.014 0.192 0.086
## qstn_typtrgt_rcll:c_: 0.014 0.094 0.218
## qstn_typtrgt_gnrlz:r_: -0.002 -0.532 -0.249
## qstn_typtrgt_rcll:r_: -0.001 -0.278 -0.592
## qstn_typtrgt_gnrlz:c_ qstn_typtrgt_rcll:c_
## qstn_typtrgt_g
## qstn_typtrgt_r
## rules_rspct
## activ_lrnng
## groupreadng
## qstn_typtrgt_gnrlz:r_
## qstn_typtrgt_rcll:r_
## qstn_typtrgt_gnrlz:c_
## qstn_typtrgt_rcll:c_ 0.476
## qstn_typtrgt_g: 0.033 0.018
## qstn_typtrgt_r: 0.017 0.008
## actv_lrnng: 0.092 0.088
## rls_rspct:g -0.027 -0.025
## qstn_typtrgt_gnrlz:c_: -0.560 -0.266
## qstn_typtrgt_rcll:c_: -0.285 -0.595
## qstn_typtrgt_gnrlz:r_: 0.183 0.084
## qstn_typtrgt_rcll:r_: 0.092 0.217
## qstn_typtrgt_g: qstn_typtrgt_r: actv_: rls_r:
## qstn_typtrgt_g
## qstn_typtrgt_r
## rules_rspct
## activ_lrnng
## groupreadng
## qstn_typtrgt_gnrlz:r_
## qstn_typtrgt_rcll:r_
## qstn_typtrgt_gnrlz:c_
## qstn_typtrgt_rcll:c_
## qstn_typtrgt_g:
## qstn_typtrgt_r: 0.410
## actv_lrnng: 0.014 0.014
## rls_rspct:g -0.003 -0.002 -0.286
## qstn_typtrgt_gnrlz:c_: 0.015 -0.031 -0.130 0.037
## qstn_typtrgt_rcll:c_: -0.032 -0.020 -0.136 0.039
## qstn_typtrgt_gnrlz:r_: -0.022 0.011 0.035 -0.122
## qstn_typtrgt_rcll:r_: 0.012 0.026 0.040 -0.131
## qstn_typtrgt_gnrlz:c_: qstn_typtrgt_rcll:c_:
## qstn_typtrgt_g
## qstn_typtrgt_r
## rules_rspct
## activ_lrnng
## groupreadng
## qstn_typtrgt_gnrlz:r_
## qstn_typtrgt_rcll:r_
## qstn_typtrgt_gnrlz:c_
## qstn_typtrgt_rcll:c_
## qstn_typtrgt_g:
## qstn_typtrgt_r:
## actv_lrnng:
## rls_rspct:g
## qstn_typtrgt_gnrlz:c_:
## qstn_typtrgt_rcll:c_: 0.434
## qstn_typtrgt_gnrlz:r_: -0.370 -0.130
## qstn_typtrgt_rcll:r_: -0.134 -0.344
## qstn_typtrgt_gnrlz:r_:
## qstn_typtrgt_g
## qstn_typtrgt_r
## rules_rspct
## activ_lrnng
## groupreadng
## qstn_typtrgt_gnrlz:r_
## qstn_typtrgt_rcll:r_
## qstn_typtrgt_gnrlz:c_
## qstn_typtrgt_rcll:c_
## qstn_typtrgt_g:
## qstn_typtrgt_r:
## actv_lrnng:
## rls_rspct:g
## qstn_typtrgt_gnrlz:c_:
## qstn_typtrgt_rcll:c_:
## qstn_typtrgt_gnrlz:r_:
## qstn_typtrgt_rcll:r_: 0.399