Experimental Analyses_TA support effects on academic self-perceptions
Chelsea Taylor
2025-02-24
Hypotheses
Based on conflicting previous evidence and student interviews, it is unclear if TA support would affect student outcomes regardless of the type of support issued, or if particular types of support would affect certain outcomes more than other types (Federici & Skaalvik, 2014; Malecki & Demaray, 2003; Perna et al., 2009; Sandstrom, 2023; Thompson, 2008; van Gijn-Grosvenor & Huisman, 2020; Zeldin & Pajares, 2000). Because of this, I had two competing hypotheses:
4a) Global support effects:
There will be no difference in relationships between type of support and outcomes, where any type of support will affect each outcome to the same degree. Given findings in my own and others’ research indicating that types of supportive behaviors are either highly correlated (Federici & Skaalvik, 2014) or all load onto the same global construct (Malecki & Demaray, 2003), we expected that the presence of any support will generalize to all outcomes, regardless of its type. However, given that women tend to be more vigilant to environmental cues than men in STEM courses (Canning et al., 2022; Murphy et al., 2007), I expected that women will experience stronger effects of support on all student outcomes, regardless of support type.
4b) Differential support type effects:
Effects of support on certain outcomes will depend the support type. Based on student interviews, I expected the following:
Nurturant support: because this support is social in nature and conveys to the student that they are worthy and esteemed, social outcomes like sense of belonging and ability-evaluative outcomes like sense of self-efficacy will be affected, while actual performance will not. Further, based on one of very few studies exploring TA behaviors where interviewed students indicated that course interest in part results from nurturant support via encouragement (O’Neal et al., 2007), I expected that nurturant support would also impact interest and identification.
Practical support: because this type of support is action-facilitative towards achieving academic goals, I predicted that it would only impact performance-evaluative and performance-based outcomes, such as self-efficacy, anticipated grade, and actual performance.
Supplemental support: because this type of support conveys that a student is worthy of attention and resources, and is action-facilitative towards achieving academic goals, I predicted that it would have an impact on all student outcomes: social, performance-evaluative, performance, and interest and identification.
Packages
suppressPackageStartupMessages({
source('https://raw.githubusercontent.com/joshuacorrell/teachR/main/mcSummaryLm.R')
library(car)
library(carData)
library(reshape2)
library(tidyr)
library(ggplot2)
library(tidyverse)
library(readxl)
library(psych)
library(writexl)
library(sjPlot)
library(lme4)
library(lmerTest)
library(psychTools)
library(vtable)
library(corrplot)
library(lavaan)
library(semTools)
library(likert)
library(modelsummary)
library(see)
library(paletteer)
library(viridis)
})Data
d <- readxl::read_excel("Study2_Wrangled_N496.xlsx")
names<-as.data.frame(colnames(d))Variable coding
d$Condition<- as.factor(d$Condition)
levels(d$Condition)## [1] "ALLsupp" "NOsupp" "NURTURANT" "PRACTICAL" "SUPPLEMENTAL"d$Condition.ordered<- factor(d$Condition, levels = c('NOsupp', 'ALLsupp', 'NURTURANT','PRACTICAL','SUPPLEMENTAL'))
d[1:10, c("Condition.ordered", "Condition")]## # A tibble: 10 × 2
## Condition.ordered Condition
## <fct> <fct>
## 1 NURTURANT NURTURANT
## 2 NOsupp NOsupp
## 3 SUPPLEMENTAL SUPPLEMENTAL
## 4 PRACTICAL PRACTICAL
## 5 PRACTICAL PRACTICAL
## 6 ALLsupp ALLsupp
## 7 NOsupp NOsupp
## 8 PRACTICAL PRACTICAL
## 9 NURTURANT NURTURANT
## 10 NOsupp NOsuppIV centering
d$TAsup.av_c <-d$TAsup.av-mean(d$TAsup.av,na.rm=T)d$TTAcomm.av_c <-d$TAcomm.av-mean(d$TAcomm.av,na.rm=T)Condition helmert codes
Practical vs Supplemental
d$NOvSupport <- -(4/5)*(d$Condition == "NOsupp") + (1/5)*(d$Condition == "ALLsupp") + (1/5)*(d$Condition == "NURTURANT") + (1/5)*(d$Condition == "PRACTICAL")+ (1/5)*(d$Condition == "SUPPLEMENTAL")
d$ALLvOther <- 0*(d$Condition == "NOsupp") -(3/4)*(d$Condition == "ALLsupp") + (1/4)*(d$Condition == "NURTURANT") + (1/4)*(d$Condition == "PRACTICAL")+ (1/4)*(d$Condition == "SUPPLEMENTAL")
d$NURTvPrSu <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") -(2/3)*(d$Condition == "NURTURANT") + (1/3)*(d$Condition == "PRACTICAL")+ (1/3)*(d$Condition == "SUPPLEMENTAL")
d$PRACTvSUPP <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") + 0*(d$Condition == "NURTURANT") - (.5)*(d$Condition == "PRACTICAL")+ (.5)*(d$Condition == "SUPPLEMENTAL")
d[1:10,c("Condition", "NOvSupport","ALLvOther","NURTvPrSu","PRACTvSUPP")]## # A tibble: 10 × 5
## Condition NOvSupport ALLvOther NURTvPrSu PRACTvSUPP
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 NURTURANT 0.2 0.25 -0.667 0
## 2 NOsupp -0.8 0 0 0
## 3 SUPPLEMENTAL 0.2 0.25 0.333 0.5
## 4 PRACTICAL 0.2 0.25 0.333 -0.5
## 5 PRACTICAL 0.2 0.25 0.333 -0.5
## 6 ALLsupp 0.2 -0.75 0 0
## 7 NOsupp -0.8 0 0 0
## 8 PRACTICAL 0.2 0.25 0.333 -0.5
## 9 NURTURANT 0.2 0.25 -0.667 0
## 10 NOsupp -0.8 0 0 0Practical vs Nurturant
d$SUPvPrNu <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") + (1/3)*(d$Condition == "NURTURANT") + (1/3)*(d$Condition == "PRACTICAL")+ -(2/3)*(d$Condition == "SUPPLEMENTAL")
d$PRACTvNURT <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") + .5*(d$Condition == "NURTURANT") - (.5)*(d$Condition == "PRACTICAL")+ 0*(d$Condition == "SUPPLEMENTAL")
d[1:10,c("Condition", "NOvSupport","ALLvOther","SUPvPrNu","PRACTvNURT")]## # A tibble: 10 × 5
## Condition NOvSupport ALLvOther SUPvPrNu PRACTvNURT
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 NURTURANT 0.2 0.25 0.333 0.5
## 2 NOsupp -0.8 0 0 0
## 3 SUPPLEMENTAL 0.2 0.25 -0.667 0
## 4 PRACTICAL 0.2 0.25 0.333 -0.5
## 5 PRACTICAL 0.2 0.25 0.333 -0.5
## 6 ALLsupp 0.2 -0.75 0 0
## 7 NOsupp -0.8 0 0 0
## 8 PRACTICAL 0.2 0.25 0.333 -0.5
## 9 NURTURANT 0.2 0.25 0.333 0.5
## 10 NOsupp -0.8 0 0 0Supplemental vs Nurturant
d$PRAvSuNu <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") + (1/3)*(d$Condition == "NURTURANT") - (2/3)*(d$Condition == "PRACTICAL")+ (1/3)*(d$Condition == "SUPPLEMENTAL")
d$SUPPvNURT <- 0*(d$Condition == "NOsupp") + 0*(d$Condition == "ALLsupp") + .5*(d$Condition == "NURTURANT") +0*(d$Condition == "PRACTICAL")+ -.5*(d$Condition == "SUPPLEMENTAL")
d[1:10,c("Condition", "NOvSupport","ALLvOther","PRAvSuNu","SUPPvNURT")]## # A tibble: 10 × 5
## Condition NOvSupport ALLvOther PRAvSuNu SUPPvNURT
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 NURTURANT 0.2 0.25 0.333 0.5
## 2 NOsupp -0.8 0 0 0
## 3 SUPPLEMENTAL 0.2 0.25 0.333 -0.5
## 4 PRACTICAL 0.2 0.25 -0.667 0
## 5 PRACTICAL 0.2 0.25 -0.667 0
## 6 ALLsupp 0.2 -0.75 0 0
## 7 NOsupp -0.8 0 0 0
## 8 PRACTICAL 0.2 0.25 -0.667 0
## 9 NURTURANT 0.2 0.25 0.333 0.5
## 10 NOsupp -0.8 0 0 0Course dummy codes
already in data set
Persistence dummy codes by quiz
Quiz 1
table(d$Q1allCORRECT.dummy);table(d$Q1retry.CATS)##
## 0 1
## 314 182##
## ALLcorrect NOretry retry
## 314 108 74Quiz1.retry<-d[d$Q1allCORRECT.dummy == 1,]
table(Quiz1.retry$Q1retry.CATS)##
## NOretry retry
## 108 74Quiz1.retry <- Quiz1.retry %>%
mutate( Q1noretry.d = if_else(Q1retry.CATS == "NOretry", 0, 1))
#Quiz1.retry[1:20, c("Q1retry.CATS", "Q1noretry.d")]Quiz 2
table(d$Q2allCORRECT.dummy);table(d$Q2retry.CATS)##
## 0 1
## 123 373##
## ALLcorrect NOretry retry
## 123 197 176Quiz2.retry<-d[d$Q2allCORRECT.dummy == 1,]
table(Quiz2.retry$Q2retry.CATS)##
## NOretry retry
## 197 176Quiz2.retry <- Quiz2.retry %>%
mutate( Q2noretry.d = if_else(Q2retry.CATS == "NOretry", 0, 1))
#Quiz2.retry[1:20, c("Q2retry.CATS", "Q2noretry.d")]Quiz 3
table(d$Q3allCORRECT.dummy);table(d$Q3retry.CATS)##
## 0 1
## 153 343##
## ALLcorrect NOretry retry
## 153 234 109Quiz3.retry<-d[d$Q3allCORRECT.dummy == 1,]
table(Quiz3.retry$Q3retry.CATS)##
## NOretry retry
## 234 109Quiz3.retry <- Quiz3.retry %>%
mutate( Q3noretry.d = if_else(Q3retry.CATS == "NOretry", 0, 1))
Quiz3.retry[3:20, c("Q3retry.CATS", "Q3noretry.d")]## # A tibble: 18 × 2
## Q3retry.CATS Q3noretry.d
## <chr> <dbl>
## 1 retry 1
## 2 NOretry 0
## 3 retry 1
## 4 NOretry 0
## 5 NOretry 0
## 6 retry 1
## 7 retry 1
## 8 retry 1
## 9 NOretry 0
## 10 retry 1
## 11 NOretry 0
## 12 retry 1
## 13 retry 1
## 14 retry 1
## 15 NOretry 0
## 16 NOretry 0
## 17 NOretry 0
## 18 NOretry 0Analysis Strategy
Look at Mahalanobis distance and univariate outliers
Get descriptives & Correlations of ASPS, performance, and persistence
Calcuate reliabilities & belonging EFA/CFA
From pre-reg:To determine if some types of support may have stronger effects than others overall on each outcome variable and if these effects are stronger for women, each outcome variable will be tested separately with linear regressions in which the outcome is regressed on TA support conditions (contrast coded), participant gender (contrast coded), and the interaction of TA support condition and participant gender. In the event that interactions are found for any outcome variable, simple effects will be examined.
- TA support
- TA communality
- Social Belonging
- Ability Belonging
- Self-Efficacy
- Q1 total
- Q1 persistence (did students who missed ≥ 1 q retake quiz?)
- Q2 total
- Q2 persistence (did students who missed ≥ 1 q retake quiz?)
- Q3 total
- Q3 persistence (did students who missed ≥ 1 q retake quiz?)
- Time spent on each quiz (first + second attempts, as well as first attempts only)
- Exam score
- Time spent on exam
- Anticipated grade
- To determine if previous experience in statistics moderates effects of gender and TA support condition on outcome variables, additional analyses will be conducted with the addition of previous experience (dummy coded) included in the model.
Data Screening
Univariate outliers
d$SBz <- (d$SB.av - mean(d$SB.av, na.rm = T))/sd(d$SB.av, na.rm = T)
d$ABz <- (d$AB.av - mean(d$AB.av, na.rm = T))/sd(d$AB.av, na.rm = T)
d$SEz <- (d$SE.av - mean(d$SE.av, na.rm = T))/sd(d$SE.av, na.rm = T)
d$Quiz1.totalz <- (d$Quiz1.total - mean(d$Quiz1.total, na.rm = T))/sd(d$Quiz1.total, na.rm = T)
d$Quiz2.totalz <- (d$Quiz2.total - mean(d$Quiz2.total, na.rm = T))/sd(d$Quiz2.total, na.rm = T)
d$Quiz3.totalz <- (d$Quiz3.total - mean(d$Quiz3.total, na.rm = T))/sd(d$Quiz3.total, na.rm = T)
d$Exam.totalz <- (d$Exam.total - mean(d$Exam.total, na.rm = T))/sd(d$Exam.total, na.rm = T)
d$GRADEz <- (d$Ant.grade - mean(d$Ant.grade, na.rm = T))/sd(d$Ant.grade, na.rm = T)
d$SUPz <- (d$TAsup.av - mean(d$TAsup.av, na.rm = T))/sd(d$TAsup.av, na.rm = T)
d$COMz <- (d$TAcomm.av - mean(d$TAcomm.av, na.rm = T))/sd(d$TAcomm.av, na.rm = T)
boop<- data.frame(id = d$id, gender = d$Gen.name,SUPz = d$SUPz, COMz = d$COMz,
SBz = d$SBz,ABz = d$ABz, SEz = d$SEz, GRADEz = d$GRADEz,Quiz1z = d$Quiz1.totalz, Quiz2z = d$Quiz2.totalz,Quiz3z = d$Quiz3.totalz,Examz = d$Exam.totalz,
TAsup = d$TAsup.av, TAcom = d$TAcomm.av, SB = d$SB.av, AB = d$AB.av, SE= d$SE.av, Quiz1 = d$Quiz1.total, Quiz2 = d$Quiz2.total, Quiz3 = d$Quiz3.total, Exam = d$Exam.total, Grade = d$Ant.grade)
Outliers<- boop[boop$SBz >= 3.29| boop$SBz <= -3.29 |boop$ABz >= 3.29| boop$ABz <= -3.29 |boop$SEz >= 3.29| boop$SEz <= -3.29 |boop$Quiz1z >= 3.29| boop$Quiz1z <= -3.29 | boop$Quiz2 >= 3.29| boop$Quiz2z <= -3.29 |boop$Quiz3z >= 3.29| boop$Quiz3z <= -3.29 | boop$Examz >= 3.29| boop$Examz <= -3.29 |boop$GRADEz >= 3.29| boop$GRADEz <= -3.29 |boop$SUPz >= 3.29| boop$SUPz <= -3.29 |boop$COMz >= 3.29| boop$COMz <= -3.29 ,]
Outliers<- Outliers[order(Outliers$SUPz, decreasing = F),]
writexl::write_xlsx(Outliers, "Study2_outliers.xlsx")summary(d$SUPz);summary(d$TAsup.av)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.06002 -0.61867 0.01581 0.00000 0.65029 1.70775 1## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.000 4.625 5.000 4.991 5.375 6.000 1Outliers[Outliers$SUPz <= -3.59, c("id","SUPz", "TAsup")]## id SUPz TAsup
## 351 450 -5.06002 2
## NA NA NA NA
## NA.1 NA NA NA
## NA.2 NA NA NAMahalanobis distance
Mahalanobis distance (Mahalanobis, 1930) is often used for multivariate outliers detection as this distance takes into account the shape of the observations. The default threshold is often arbitrarily set to some deviation (in terms of SD or MAD) from the mean (or median) of the Mahalanobis distance. However, as the Mahalanobis distance can be approximated by a Chi squared distribution (Rousseeuw & Van Zomeren, 1990), we can use the alpha quantile of the chi-square distribution with k degrees of freedom (k being the number of columns). By default, the alpha threshold is set to 0.025 (corresponding to the 2.5 Cabana, 2019). This criterion is a natural extension of the median plus or minus a coefficient times the MAD method (Leys et al., 2013).
https://easystats.github.io/performance/reference/check_outliers.html
d[is.na(d$SB.av) | is.na(d$AB.av) | is.na(d$SE.av) | is.na(d$Quiz1.total) | is.na(d$Quiz2.total) | is.na(d$Quiz3.total) | is.na(d$Exam.total) | is.na(d$Ant.grade) ,c("id", "SB.av","AB.av","SE.av", "Quiz1.total","Quiz2.total","Quiz3.total","Exam.total","Ant.grade") ]## # A tibble: 3 × 9
## id SB.av AB.av SE.av Quiz1.total Quiz2.total Quiz3.total Exam.total
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 168 6 NA 5 4 2 2 7
## 2 314 4.4 2.75 4.4 3 2 3 8
## 3 450 NA 4 3.5 4 3 3 5
## # ℹ 1 more variable: Ant.grade <dbl>DD <-d[d$id != 168 &
d$id !=314 &
d$id !=450,]Mahal.SM<- DD[ ,c("SB.av","AB.av","SE.av", "Quiz1.total","Quiz2.total","Quiz3.total","Exam.total","Ant.grade")]
performance::check_outliers(Mahal.SM, method = "mahalanobis")## 4 outliers detected: cases 294, 401, 464, 478.
## - Based on the following method and threshold: mahalanobis (30).
## - For variables: SB.av, AB.av, SE.av, Quiz1.total, Quiz2.total,
## Quiz3.total, Exam.total, Ant.grade.DD[c(294,401,464, 478), c("id","SBz","ABz","SEz","Quiz1.totalz","Quiz2.totalz","Quiz3.totalz", "Exam.totalz","GRADEz")]## # A tibble: 4 × 9
## id SBz ABz SEz Quiz1.totalz Quiz2.totalz Quiz3.totalz Exam.totalz
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 383 -0.928 -1.72 -3.24 -1.77 -2.60 -1.48 -2.85
## 2 513 0.326 0.661 -2.04 -0.554 1.26 -0.591 -1.79
## 3 583 -0.928 -1.25 -1.25 -2.98 1.26 0.294 0.327
## 4 605 -1.68 -0.293 -2.24 -2.98 0.293 -2.36 0.856
## # ℹ 1 more variable: GRADEz <dbl>4 multivariate outliers removed: id = c(383,513,583,605)
N = 492
dat<- d[d$id != 383 &
d$id !=513 &
d$id !=583&
d$id !=605,]remove(DD)
remove(d)
remove(boop)
remove(Mahal.SM)dat<- dplyr::rename(dat,
Quiz1.time = `Quiz1.timing_Page Submit`,
Quiz2.time = `Quiz2.timing_Page Submit`,
Quiz3.time = `Quiz3.timing_Page Submit`,
Quiz1retry.time =` Quiz1retry.timing _Page Submit`,
Quiz2retry.time =`Quiz2.retry_Page Submit`,
Quiz3retry.time =`Quiz3retry.timing_Page Submit`,
Exam.time = `Exam_time_spent_Page Submit` ) #new name = old name (new name first)Descriptives
Marginal Means
descripts<- dat[,c("TAsup.av","TAcomm.av", "SB.av", "AB.av","SE.av", "Quiz1.total","Quiz1.time","Quiz1.retry.total","Quiz1retry.time", "Quiz2.total","Quiz2.time", "Quiz2.retry.total","Quiz2retry.time","Quiz3.total","Quiz3.time","Quiz3.retry.total","Quiz3retry.time", "Exam.total","Exam.time","Ant.grade","Gen.name", "Condition","Condition.ordered")]
modelsummary::datasummary_skim(descripts)| Unique | Missing Pct. | Mean | SD | Min | Median | Max | Histogram | |
|---|---|---|---|---|---|---|---|---|
| TAsup.av | 24 | 0 | 5.0 | 0.6 | 2.0 | 5.0 | 6.0 |  |
| TAcomm.av | 31 | 0 | 5.3 | 0.6 | 1.0 | 5.3 | 6.0 |  |
| SB.av | 25 | 0 | 3.9 | 0.8 | 1.4 | 4.0 | 6.0 |  |
| AB.av | 22 | 0 | 3.8 | 1.0 | 1.2 | 3.8 | 6.0 |  |
| SE.av | 25 | 0 | 4.5 | 1.0 | 1.2 | 4.6 | 6.0 |  |
| Quiz1.total | 5 | 0 | 3.5 | 0.8 | 0.0 | 4.0 | 4.0 |  |
| Quiz1.time | 491 | 0 | 97.6 | 33.0 | 5.3 | 91.4 | 180.1 |  |
| Quiz1.retry.total | 5 | 85 | 3.1 | 1.0 | 1.0 | 3.0 | 4.0 |  |
| Quiz1retry.time | 75 | 85 | 63.8 | 33.8 | 10.0 | 56.3 | 161.9 |  |
| Quiz2.total | 5 | 0 | 2.7 | 1.0 | 0.0 | 3.0 | 4.0 |  |
| Quiz2.time | 492 | 0 | 74.2 | 32.8 | 7.1 | 66.9 | 180.1 |  |
| Quiz2.retry.total | 6 | 64 | 2.9 | 1.0 | 0.0 | 3.0 | 4.0 |  |
| Quiz2retry.time | 176 | 64 | 31.5 | 26.2 | 6.1 | 22.1 | 153.8 |  |
| Quiz3.total | 5 | 0 | 2.7 | 1.1 | 0.0 | 3.0 | 4.0 |  |
| Quiz3.time | 492 | 0 | 84.0 | 37.9 | 5.2 | 79.0 | 180.1 |  |
| Quiz3.retry.total | 5 | 78 | 2.6 | 1.0 | 1.0 | 3.0 | 4.0 |  |
| Quiz3retry.time | 110 | 78 | 52.3 | 30.9 | 7.3 | 48.0 | 160.2 |  |
| Exam.total | 10 | 0 | 7.4 | 1.9 | 1.0 | 8.0 | 10.0 |  |
| Exam.time | 492 | 0 | 140.5 | 64.2 | 12.9 | 125.4 | 478.7 |  |
| Ant.grade | 11 | 0 | 3.3 | 0.6 | 1.0 | 3.3 | 4.0 |  |
| N | % | |||||||
| Gen.name | Man | 114 | 23.2 | |||||
| Woman | 378 | 76.8 | ||||||
| Condition | ALLsupp | 102 | 20.7 | |||||
| NOsupp | 106 | 21.5 | ||||||
| NURTURANT | 91 | 18.5 | ||||||
| PRACTICAL | 97 | 19.7 | ||||||
| SUPPLEMENTAL | 96 | 19.5 | ||||||
| Condition.ordered | NOsupp | 106 | 21.5 | |||||
| ALLsupp | 102 | 20.7 | ||||||
| NURTURANT | 91 | 18.5 | ||||||
| PRACTICAL | 97 | 19.7 | ||||||
| SUPPLEMENTAL | 96 | 19.5 |
sumtable(descripts, digits = 3)| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| TAsup.av | 491 | 5 | 0.584 | 2 | 4.62 | 5.38 | 6 |
| TAcomm.av | 492 | 5.26 | 0.649 | 1 | 4.9 | 5.8 | 6 |
| SB.av | 491 | 3.95 | 0.796 | 1.4 | 3.4 | 4.4 | 6 |
| AB.av | 491 | 3.81 | 1.05 | 1.25 | 3 | 4.5 | 6 |
| SE.av | 492 | 4.47 | 0.988 | 1.2 | 4 | 5.2 | 6 |
| Quiz1.total | 492 | 3.47 | 0.808 | 0 | 3 | 4 | 4 |
| Quiz1.time | 492 | 97.6 | 33 | 5.34 | 74.8 | 119 | 180 |
| Quiz1.retry.total | 74 | 3.11 | 1.01 | 1 | 2 | 4 | 4 |
| Quiz1retry.time | 74 | 63.8 | 33.8 | 9.98 | 36.4 | 88.7 | 162 |
| Quiz2.total | 492 | 2.7 | 1.03 | 0 | 2 | 3 | 4 |
| Quiz2.time | 492 | 74.2 | 32.8 | 7.13 | 50.6 | 91 | 180 |
| Quiz2.retry.total | 176 | 2.9 | 0.99 | 0 | 2 | 4 | 4 |
| Quiz2retry.time | 176 | 31.5 | 26.2 | 6.06 | 14.1 | 39.8 | 154 |
| Quiz3.total | 492 | 2.68 | 1.13 | 0 | 2 | 4 | 4 |
| Quiz3.time | 492 | 84 | 37.9 | 5.17 | 57.4 | 109 | 180 |
| Quiz3.retry.total | 109 | 2.57 | 1.05 | 1 | 2 | 3 | 4 |
| Quiz3retry.time | 109 | 52.3 | 30.9 | 7.31 | 31.9 | 66.5 | 160 |
| Exam.total | 492 | 7.4 | 1.87 | 1 | 6 | 9 | 10 |
| Exam.time | 492 | 140 | 64.2 | 13 | 98.7 | 163 | 479 |
| Ant.grade | 491 | 3.25 | 0.648 | 1 | 3 | 3.7 | 4 |
| Gen.name | 492 | ||||||
| … Man | 114 | 23.2% | |||||
| … Woman | 378 | 76.8% | |||||
| Condition | 492 | ||||||
| … ALLsupp | 102 | 20.7% | |||||
| … NOsupp | 106 | 21.5% | |||||
| … NURTURANT | 91 | 18.5% | |||||
| … PRACTICAL | 97 | 19.7% | |||||
| … SUPPLEMENTAL | 96 | 19.5% | |||||
| Condition.ordered | 492 | ||||||
| … NOsupp | 106 | 21.5% | |||||
| … ALLsupp | 102 | 20.7% | |||||
| … NURTURANT | 91 | 18.5% | |||||
| … PRACTICAL | 97 | 19.7% | |||||
| … SUPPLEMENTAL | 96 | 19.5% |
By Gender
sumtable(descripts,group = c("Gen.name"), digits = 3)| Variable | N | Mean | SD | N | Mean | SD |
|---|---|---|---|---|---|---|
| TAsup.av | 114 | 4.92 | 0.525 | 377 | 5.02 | 0.6 |
| TAcomm.av | 114 | 5.16 | 0.65 | 378 | 5.28 | 0.646 |
| SB.av | 114 | 4.15 | 0.742 | 377 | 3.88 | 0.803 |
| AB.av | 114 | 4.18 | 0.977 | 377 | 3.7 | 1.04 |
| SE.av | 114 | 4.84 | 0.885 | 378 | 4.36 | 0.992 |
| Quiz1.total | 114 | 3.57 | 0.752 | 378 | 3.44 | 0.823 |
| Quiz1.time | 114 | 99.7 | 33.2 | 378 | 97 | 32.9 |
| Quiz1.retry.total | 17 | 3.06 | 0.966 | 57 | 3.12 | 1.04 |
| Quiz1retry.time | 17 | 61.5 | 30.4 | 57 | 64.4 | 35 |
| Quiz2.total | 114 | 2.89 | 0.906 | 378 | 2.63 | 1.06 |
| Quiz2.time | 114 | 71.5 | 30.3 | 378 | 75 | 33.4 |
| Quiz2.retry.total | 41 | 3 | 0.949 | 135 | 2.87 | 1 |
| Quiz2retry.time | 41 | 34.2 | 28.7 | 135 | 30.7 | 25.5 |
| Quiz3.total | 114 | 2.86 | 1.12 | 378 | 2.62 | 1.12 |
| Quiz3.time | 114 | 84.2 | 40.5 | 378 | 83.9 | 37.1 |
| Quiz3.retry.total | 27 | 2.59 | 0.888 | 82 | 2.56 | 1.1 |
| Quiz3retry.time | 27 | 53.3 | 29.9 | 82 | 51.9 | 31.4 |
| Exam.total | 114 | 7.62 | 1.82 | 378 | 7.33 | 1.89 |
| Exam.time | 114 | 134 | 55.1 | 378 | 142 | 66.7 |
| Ant.grade | 114 | 3.4 | 0.59 | 377 | 3.21 | 0.659 |
| Condition | 114 | 378 | ||||
| … ALLsupp | 26 | 22.8% | 76 | 20.1% | ||
| … NOsupp | 25 | 21.9% | 81 | 21.4% | ||
| … NURTURANT | 17 | 14.9% | 74 | 19.6% | ||
| … PRACTICAL | 24 | 21.1% | 73 | 19.3% | ||
| … SUPPLEMENTAL | 22 | 19.3% | 74 | 19.6% | ||
| Condition.ordered | 114 | 378 | ||||
| … NOsupp | 25 | 21.9% | 81 | 21.4% | ||
| … ALLsupp | 26 | 22.8% | 76 | 20.1% | ||
| … NURTURANT | 17 | 14.9% | 74 | 19.6% | ||
| … PRACTICAL | 24 | 21.1% | 73 | 19.3% | ||
| … SUPPLEMENTAL | 22 | 19.3% | 74 | 19.6% |
Gender diffs
t.test(dat$TAsup.av~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$TAsup.av by dat$Gen.name
## t = -1.6631, df = 489, p-value = 0.09693
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## -0.22618377 0.01881023
## sample estimates:
## mean in group Man mean in group Woman
## 4.918860 5.022546t.test(dat$TAcomm.av~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$TAcomm.av by dat$Gen.name
## t = -1.7451, df = 490, p-value = 0.08159
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## -0.25660489 0.01519582
## sample estimates:
## mean in group Man mean in group Woman
## 5.163158 5.283862t.test(dat$SB.av~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$SB.av by dat$Gen.name
## t = 3.096, df = 489, p-value = 0.002074
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 0.09545432 0.42707348
## sample estimates:
## mean in group Man mean in group Woman
## 4.145614 3.884350t.test(dat$AB.av~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$AB.av by dat$Gen.name
## t = 4.3144, df = 489, p-value = 1.938e-05
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 0.2584850 0.6908069
## sample estimates:
## mean in group Man mean in group Woman
## 4.176901 3.702255t.test(dat$SE.av~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$SE.av by dat$Gen.name
## t = 4.5821, df = 490, p-value = 5.847e-06
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 0.2708308 0.6774566
## sample estimates:
## mean in group Man mean in group Woman
## 4.836842 4.362698t.test(dat$Ant.grade~dat$Gen.name, var.equal = T)##
## Two Sample t-test
##
## data: dat$Ant.grade by dat$Gen.name
## t = 2.7274, df = 489, p-value = 0.006612
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 0.05249291 0.32298291
## sample estimates:
## mean in group Man mean in group Woman
## 3.396491 3.208753By Condition
sumtable(descripts,group = c("Condition"), digits = 3)| Variable | N | Mean | SD | N | Mean | SD | N | Mean | SD | N | Mean | SD | N | Mean | SD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TAsup.av | 102 | 4.98 | 0.635 | 106 | 5 | 0.548 | 91 | 4.98 | 0.542 | 96 | 4.95 | 0.61 | 96 | 5.08 | 0.584 |
| TAcomm.av | 102 | 5.23 | 0.753 | 106 | 5.3 | 0.546 | 91 | 5.29 | 0.593 | 97 | 5.24 | 0.563 | 96 | 5.22 | 0.765 |
| SB.av | 101 | 3.96 | 0.732 | 106 | 3.86 | 0.805 | 91 | 3.89 | 0.797 | 97 | 3.93 | 0.819 | 96 | 4.09 | 0.823 |
| AB.av | 102 | 3.84 | 1.1 | 106 | 3.74 | 1 | 91 | 3.7 | 1.04 | 97 | 3.76 | 1.06 | 95 | 4.02 | 1.03 |
| SE.av | 102 | 4.4 | 1.1 | 106 | 4.46 | 0.947 | 91 | 4.3 | 0.997 | 97 | 4.56 | 0.936 | 96 | 4.65 | 0.936 |
| Quiz1.total | 102 | 3.47 | 0.817 | 106 | 3.46 | 0.83 | 91 | 3.4 | 0.893 | 97 | 3.61 | 0.67 | 96 | 3.41 | 0.815 |
| Quiz1.time | 102 | 96.6 | 33.8 | 106 | 102 | 33.5 | 91 | 98.1 | 33.5 | 97 | 98.9 | 32.3 | 96 | 92.3 | 31.4 |
| Quiz1.retry.total | 17 | 3.18 | 0.883 | 14 | 2.79 | 1.25 | 15 | 3.2 | 1.08 | 11 | 3.55 | 0.688 | 17 | 2.94 | 1.03 |
| Quiz1retry.time | 17 | 69.6 | 34.7 | 14 | 54.2 | 32.9 | 15 | 53.5 | 32.9 | 11 | 72 | 38.1 | 17 | 69.6 | 31.1 |
| Quiz2.total | 102 | 2.7 | 0.993 | 106 | 2.67 | 0.963 | 91 | 2.76 | 1.04 | 97 | 2.69 | 1 | 96 | 2.67 | 1.18 |
| Quiz2.time | 102 | 75.1 | 37.1 | 106 | 70 | 30.1 | 91 | 74.1 | 32.2 | 97 | 75.6 | 29.6 | 96 | 76.6 | 34.4 |
| Quiz2.retry.total | 37 | 2.89 | 0.875 | 35 | 2.8 | 0.964 | 40 | 2.6 | 1.17 | 32 | 3.22 | 0.751 | 32 | 3.09 | 1.03 |
| Quiz2retry.time | 37 | 31.6 | 20.6 | 35 | 25 | 28.4 | 40 | 35 | 31.9 | 32 | 35.8 | 26.6 | 32 | 30.2 | 20.8 |
| Quiz3.total | 102 | 2.71 | 1.17 | 106 | 2.74 | 1.12 | 91 | 2.51 | 1.15 | 97 | 2.84 | 1.03 | 96 | 2.58 | 1.15 |
| Quiz3.time | 102 | 82.9 | 41.6 | 106 | 86.1 | 39.9 | 91 | 83.4 | 34.7 | 97 | 85.5 | 36 | 96 | 81.7 | 37 |
| Quiz3.retry.total | 20 | 2.6 | 1.1 | 25 | 2.76 | 0.97 | 28 | 2.43 | 1.1 | 16 | 2.88 | 1.09 | 20 | 2.25 | 0.967 |
| Quiz3retry.time | 20 | 68.5 | 39.3 | 25 | 47.7 | 30.1 | 28 | 43.6 | 25.8 | 16 | 47.4 | 26.1 | 20 | 57.7 | 28.4 |
| Exam.total | 102 | 7.19 | 2.04 | 106 | 7.35 | 1.84 | 91 | 7.16 | 1.98 | 97 | 8.03 | 1.38 | 96 | 7.25 | 1.95 |
| Exam.time | 102 | 141 | 73 | 106 | 139 | 62.6 | 91 | 147 | 69.9 | 97 | 136 | 50.8 | 96 | 140 | 63.6 |
| Ant.grade | 102 | 3.27 | 0.641 | 106 | 3.25 | 0.668 | 90 | 3.13 | 0.715 | 97 | 3.27 | 0.63 | 96 | 3.33 | 0.578 |
| Gen.name | 102 | 106 | 91 | 97 | 96 | ||||||||||
| … Man | 26 | 25.5% | 25 | 23.6% | 17 | 18.7% | 24 | 24.7% | 22 | 22.9% | |||||
| … Woman | 76 | 74.5% | 81 | 76.4% | 74 | 81.3% | 73 | 75.3% | 74 | 77.1% | |||||
| Condition.ordered | 102 | 106 | 91 | 97 | 96 | ||||||||||
| … NOsupp | 0 | 0% | 106 | 100% | 0 | 0% | 0 | 0% | 0 | 0% | |||||
| … ALLsupp | 102 | 100% | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% | |||||
| … NURTURANT | 0 | 0% | 0 | 0% | 91 | 100% | 0 | 0% | 0 | 0% | |||||
| … PRACTICAL | 0 | 0% | 0 | 0% | 0 | 0% | 97 | 100% | 0 | 0% | |||||
| … SUPPLEMENTAL | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% | 96 | 100% |
Correlations
modelsummary::datasummary_correlation(descripts[,c("TAsup.av","TAcomm.av", "SB.av", "AB.av","SE.av","Ant.grade", "Quiz1.total", "Quiz2.total","Quiz3.total","Exam.total","Exam.time")])| TAsup.av | TAcomm.av | SB.av | AB.av | SE.av | Ant.grade | Quiz1.total | Quiz2.total | Quiz3.total | Exam.total | Exam.time | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| TAsup.av | 1 | . | . | . | . | . | . | . | . | . | . |
| TAcomm.av | .61 | 1 | . | . | . | . | . | . | . | . | . |
| SB.av | .30 | .23 | 1 | . | . | . | . | . | . | . | . |
| AB.av | .17 | .09 | .76 | 1 | . | . | . | . | . | . | . |
| SE.av | .24 | .19 | .74 | .81 | 1 | . | . | . | . | . | . |
| Ant.grade | .16 | .11 | .65 | .73 | .79 | 1 | . | . | . | . | . |
| Quiz1.total | .07 | .03 | .20 | .31 | .32 | .37 | 1 | . | . | . | . |
| Quiz2.total | .06 | .06 | .21 | .28 | .24 | .28 | .19 | 1 | . | . | . |
| Quiz3.total | .07 | .07 | .29 | .36 | .33 | .38 | .23 | .31 | 1 | . | . |
| Exam.total | .10 | .09 | .26 | .39 | .40 | .43 | .43 | .36 | .49 | 1 | . |
| Exam.time | .01 | -.01 | -.07 | -.13 | -.10 | -.09 | -.12 | .04 | .00 | -.03 | 1 |
Hmisc::rcorr(as.matrix(descripts[,c("TAsup.av","TAcomm.av", "SB.av", "AB.av","SE.av","Ant.grade", "Quiz1.total", "Quiz2.total","Quiz3.total","Exam.total","Exam.time")]),type="pearson")## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 1.00 0.61 0.30 0.17 0.24 0.16 0.07
## TAcomm.av 0.61 1.00 0.23 0.09 0.19 0.11 0.03
## SB.av 0.30 0.23 1.00 0.76 0.74 0.65 0.20
## AB.av 0.17 0.09 0.76 1.00 0.81 0.73 0.31
## SE.av 0.24 0.19 0.74 0.81 1.00 0.79 0.32
## Ant.grade 0.16 0.11 0.65 0.73 0.79 1.00 0.37
## Quiz1.total 0.07 0.03 0.20 0.31 0.32 0.37 1.00
## Quiz2.total 0.06 0.06 0.21 0.28 0.24 0.28 0.19
## Quiz3.total 0.07 0.07 0.29 0.36 0.33 0.38 0.23
## Exam.total 0.10 0.09 0.26 0.39 0.40 0.43 0.43
## Exam.time 0.01 -0.01 -0.07 -0.13 -0.10 -0.09 -0.12
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 0.06 0.07 0.10 0.01
## TAcomm.av 0.06 0.07 0.09 -0.01
## SB.av 0.21 0.29 0.26 -0.07
## AB.av 0.28 0.36 0.39 -0.13
## SE.av 0.24 0.33 0.40 -0.10
## Ant.grade 0.28 0.38 0.43 -0.09
## Quiz1.total 0.19 0.23 0.43 -0.12
## Quiz2.total 1.00 0.31 0.36 0.04
## Quiz3.total 0.31 1.00 0.49 0.00
## Exam.total 0.36 0.49 1.00 -0.03
## Exam.time 0.04 0.00 -0.03 1.00
##
## n
## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 491 491 490 490 491 490 491
## TAcomm.av 491 492 491 491 492 491 492
## SB.av 490 491 491 490 491 490 491
## AB.av 490 491 490 491 491 490 491
## SE.av 491 492 491 491 492 491 492
## Ant.grade 490 491 490 490 491 491 491
## Quiz1.total 491 492 491 491 492 491 492
## Quiz2.total 491 492 491 491 492 491 492
## Quiz3.total 491 492 491 491 492 491 492
## Exam.total 491 492 491 491 492 491 492
## Exam.time 491 492 491 491 492 491 492
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 491 491 491 491
## TAcomm.av 492 492 492 492
## SB.av 491 491 491 491
## AB.av 491 491 491 491
## SE.av 492 492 492 492
## Ant.grade 491 491 491 491
## Quiz1.total 492 492 492 492
## Quiz2.total 492 492 492 492
## Quiz3.total 492 492 492 492
## Exam.total 492 492 492 492
## Exam.time 492 492 492 492
##
## P
## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 0.0000 0.0000 0.0001 0.0000 0.0004 0.1359
## TAcomm.av 0.0000 0.0000 0.0412 0.0000 0.0178 0.4446
## SB.av 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## AB.av 0.0001 0.0412 0.0000 0.0000 0.0000 0.0000
## SE.av 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Ant.grade 0.0004 0.0178 0.0000 0.0000 0.0000 0.0000
## Quiz1.total 0.1359 0.4446 0.0000 0.0000 0.0000 0.0000
## Quiz2.total 0.1897 0.2178 0.0000 0.0000 0.0000 0.0000 0.0000
## Quiz3.total 0.1105 0.1210 0.0000 0.0000 0.0000 0.0000 0.0000
## Exam.total 0.0346 0.0402 0.0000 0.0000 0.0000 0.0000 0.0000
## Exam.time 0.8482 0.7501 0.1184 0.0046 0.0324 0.0416 0.0061
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 0.1897 0.1105 0.0346 0.8482
## TAcomm.av 0.2178 0.1210 0.0402 0.7501
## SB.av 0.0000 0.0000 0.0000 0.1184
## AB.av 0.0000 0.0000 0.0000 0.0046
## SE.av 0.0000 0.0000 0.0000 0.0324
## Ant.grade 0.0000 0.0000 0.0000 0.0416
## Quiz1.total 0.0000 0.0000 0.0000 0.0061
## Quiz2.total 0.0000 0.0000 0.3656
## Quiz3.total 0.0000 0.0000 0.9343
## Exam.total 0.0000 0.0000 0.4753
## Exam.time 0.3656 0.9343 0.4753Women only
modelsummary::datasummary_correlation(descripts[descripts$Gen.name=="Woman",c("TAsup.av","TAcomm.av", "SB.av", "AB.av","SE.av","Ant.grade", "Quiz1.total", "Quiz2.total","Quiz3.total","Exam.total","Exam.time")])| TAsup.av | TAcomm.av | SB.av | AB.av | SE.av | Ant.grade | Quiz1.total | Quiz2.total | Quiz3.total | Exam.total | Exam.time | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| TAsup.av | 1 | . | . | . | . | . | . | . | . | . | . |
| TAcomm.av | .60 | 1 | . | . | . | . | . | . | . | . | . |
| SB.av | .30 | .23 | 1 | . | . | . | . | . | . | . | . |
| AB.av | .18 | .08 | .76 | 1 | . | . | . | . | . | . | . |
| SE.av | .26 | .21 | .75 | .80 | 1 | . | . | . | . | . | . |
| Ant.grade | .16 | .10 | .66 | .74 | .80 | 1 | . | . | . | . | . |
| Quiz1.total | .04 | .00 | .21 | .32 | .33 | .39 | 1 | . | . | . | . |
| Quiz2.total | .05 | .03 | .18 | .25 | .21 | .28 | .20 | 1 | . | . | . |
| Quiz3.total | .04 | .05 | .27 | .32 | .31 | .39 | .22 | .28 | 1 | . | . |
| Exam.total | .09 | .07 | .24 | .37 | .38 | .45 | .40 | .38 | .50 | 1 | . |
| Exam.time | -.01 | -.05 | -.10 | -.13 | -.11 | -.08 | -.10 | .05 | .01 | -.02 | 1 |
Hmisc::rcorr(as.matrix(descripts[descripts$Gen.name=="Woman",c("TAsup.av","TAcomm.av", "SB.av", "AB.av","SE.av","Ant.grade", "Quiz1.total", "Quiz2.total","Quiz3.total","Exam.total","Exam.time")]),type="pearson")## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 1.00 0.60 0.30 0.18 0.26 0.16 0.04
## TAcomm.av 0.60 1.00 0.23 0.08 0.21 0.10 0.00
## SB.av 0.30 0.23 1.00 0.76 0.75 0.66 0.21
## AB.av 0.18 0.08 0.76 1.00 0.80 0.74 0.32
## SE.av 0.26 0.21 0.75 0.80 1.00 0.80 0.33
## Ant.grade 0.16 0.10 0.66 0.74 0.80 1.00 0.39
## Quiz1.total 0.04 0.00 0.21 0.32 0.33 0.39 1.00
## Quiz2.total 0.05 0.03 0.18 0.25 0.21 0.28 0.20
## Quiz3.total 0.04 0.05 0.27 0.32 0.31 0.39 0.22
## Exam.total 0.09 0.07 0.24 0.37 0.38 0.45 0.40
## Exam.time -0.01 -0.05 -0.10 -0.13 -0.11 -0.08 -0.10
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 0.05 0.04 0.09 -0.01
## TAcomm.av 0.03 0.05 0.07 -0.05
## SB.av 0.18 0.27 0.24 -0.10
## AB.av 0.25 0.32 0.37 -0.13
## SE.av 0.21 0.31 0.38 -0.11
## Ant.grade 0.28 0.39 0.45 -0.08
## Quiz1.total 0.20 0.22 0.40 -0.10
## Quiz2.total 1.00 0.28 0.38 0.05
## Quiz3.total 0.28 1.00 0.50 0.01
## Exam.total 0.38 0.50 1.00 -0.02
## Exam.time 0.05 0.01 -0.02 1.00
##
## n
## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 377 377 376 376 377 376 377
## TAcomm.av 377 378 377 377 378 377 378
## SB.av 376 377 377 376 377 376 377
## AB.av 376 377 376 377 377 376 377
## SE.av 377 378 377 377 378 377 378
## Ant.grade 376 377 376 376 377 377 377
## Quiz1.total 377 378 377 377 378 377 378
## Quiz2.total 377 378 377 377 378 377 378
## Quiz3.total 377 378 377 377 378 377 378
## Exam.total 377 378 377 377 378 377 378
## Exam.time 377 378 377 377 378 377 378
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 377 377 377 377
## TAcomm.av 378 378 378 378
## SB.av 377 377 377 377
## AB.av 377 377 377 377
## SE.av 378 378 378 378
## Ant.grade 377 377 377 377
## Quiz1.total 378 378 378 378
## Quiz2.total 378 378 378 378
## Quiz3.total 378 378 378 378
## Exam.total 378 378 378 378
## Exam.time 378 378 378 378
##
## P
## TAsup.av TAcomm.av SB.av AB.av SE.av Ant.grade Quiz1.total
## TAsup.av 0.0000 0.0000 0.0005 0.0000 0.0024 0.4171
## TAcomm.av 0.0000 0.0000 0.1058 0.0000 0.0460 0.9521
## SB.av 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## AB.av 0.0005 0.1058 0.0000 0.0000 0.0000 0.0000
## SE.av 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Ant.grade 0.0024 0.0460 0.0000 0.0000 0.0000 0.0000
## Quiz1.total 0.4171 0.9521 0.0000 0.0000 0.0000 0.0000
## Quiz2.total 0.3013 0.5398 0.0003 0.0000 0.0000 0.0000 0.0000
## Quiz3.total 0.4633 0.3076 0.0000 0.0000 0.0000 0.0000 0.0000
## Exam.total 0.0829 0.1769 0.0000 0.0000 0.0000 0.0000 0.0000
## Exam.time 0.9060 0.3792 0.0545 0.0117 0.0327 0.1256 0.0468
## Quiz2.total Quiz3.total Exam.total Exam.time
## TAsup.av 0.3013 0.4633 0.0829 0.9060
## TAcomm.av 0.5398 0.3076 0.1769 0.3792
## SB.av 0.0003 0.0000 0.0000 0.0545
## AB.av 0.0000 0.0000 0.0000 0.0117
## SE.av 0.0000 0.0000 0.0000 0.0327
## Ant.grade 0.0000 0.0000 0.0000 0.1256
## Quiz1.total 0.0000 0.0000 0.0000 0.0468
## Quiz2.total 0.0000 0.0000 0.3224
## Quiz3.total 0.0000 0.0000 0.9215
## Exam.total 0.0000 0.0000 0.6522
## Exam.time 0.3224 0.9215 0.6522Sup vs. comm EFA (2 factors supported)
exog<-cor(dat[,c("TA1", "TA2", "TA3","TA4","TA5","TA6","TA_facil1", "TA_facil3", "TAcomm1","TAcomm2","TAcomm3","TAcomm4","TAcomm5","TAcomm6","TAcomm7","TAcomm8","TAcomm9","TAcomm10")],use="pairwise.complete.obs")
cormat<-round(exog,2) #rounding this to the hundredth place
corrplot::corrplot(exog) #This function comes from the package by the same name
Item Correlations
upper<-cormat
upper[upper.tri(cormat)] <-""
upper<-as.data.frame(upper)
upper## TA1 TA2 TA3 TA4 TA5 TA6 TA_facil1 TA_facil3 TAcomm1 TAcomm2
## TA1 1
## TA2 0.5 1
## TA3 0.4 0.42 1
## TA4 0.39 0.4 0.36 1
## TA5 0.49 0.57 0.45 0.44 1
## TA6 0.32 0.33 0.27 0.35 0.36 1
## TA_facil1 0.27 0.28 0.21 0.18 0.21 0.23 1
## TA_facil3 0.44 0.56 0.45 0.43 0.48 0.35 0.27 1
## TAcomm1 0.43 0.43 0.34 0.32 0.36 0.36 0.22 0.43 1
## TAcomm2 0.35 0.35 0.25 0.32 0.34 0.33 0.13 0.4 0.68 1
## TAcomm3 0.43 0.45 0.31 0.28 0.4 0.33 0.29 0.43 0.68 0.58
## TAcomm4 0.43 0.37 0.31 0.43 0.41 0.38 0.18 0.41 0.64 0.67
## TAcomm5 0.36 0.38 0.26 0.33 0.32 0.35 0.2 0.44 0.69 0.77
## TAcomm6 0.37 0.38 0.32 0.39 0.31 0.39 0.2 0.43 0.62 0.74
## TAcomm7 0.42 0.51 0.35 0.28 0.47 0.35 0.26 0.43 0.7 0.62
## TAcomm8 0.39 0.43 0.35 0.31 0.38 0.35 0.24 0.45 0.68 0.68
## TAcomm9 0.38 0.42 0.36 0.33 0.4 0.33 0.19 0.46 0.67 0.72
## TAcomm10 0.35 0.38 0.3 0.32 0.33 0.32 0.21 0.39 0.62 0.7
## TAcomm3 TAcomm4 TAcomm5 TAcomm6 TAcomm7 TAcomm8 TAcomm9 TAcomm10
## TA1
## TA2
## TA3
## TA4
## TA5
## TA6
## TA_facil1
## TA_facil3
## TAcomm1
## TAcomm2
## TAcomm3 1
## TAcomm4 0.62 1
## TAcomm5 0.6 0.69 1
## TAcomm6 0.55 0.63 0.74 1
## TAcomm7 0.73 0.64 0.66 0.61 1
## TAcomm8 0.66 0.61 0.68 0.73 0.73 1
## TAcomm9 0.64 0.71 0.71 0.68 0.68 0.72 1
## TAcomm10 0.54 0.62 0.71 0.71 0.6 0.67 0.68 1Hmisc::rcorr(as.matrix(dat[,c("TA1", "TA2", "TA3","TA4","TA5","TA6","TA_facil1", "TA_facil3", "TAcomm1","TAcomm2","TAcomm3","TAcomm4","TAcomm5","TAcomm6","TAcomm7","TAcomm8","TAcomm9","TAcomm10")]),type="pearson")## TA1 TA2 TA3 TA4 TA5 TA6 TA_facil1 TA_facil3 TAcomm1 TAcomm2
## TA1 1.00 0.50 0.40 0.39 0.49 0.32 0.27 0.44 0.43 0.35
## TA2 0.50 1.00 0.42 0.40 0.57 0.33 0.28 0.56 0.43 0.35
## TA3 0.40 0.42 1.00 0.36 0.45 0.27 0.21 0.45 0.34 0.25
## TA4 0.39 0.40 0.36 1.00 0.44 0.35 0.18 0.43 0.32 0.32
## TA5 0.49 0.57 0.45 0.44 1.00 0.36 0.21 0.48 0.36 0.34
## TA6 0.32 0.33 0.27 0.35 0.36 1.00 0.23 0.35 0.36 0.33
## TA_facil1 0.27 0.28 0.21 0.18 0.21 0.23 1.00 0.27 0.22 0.13
## TA_facil3 0.44 0.56 0.45 0.43 0.48 0.35 0.27 1.00 0.43 0.40
## TAcomm1 0.43 0.43 0.34 0.32 0.36 0.36 0.22 0.43 1.00 0.68
## TAcomm2 0.35 0.35 0.25 0.32 0.34 0.33 0.13 0.40 0.68 1.00
## TAcomm3 0.43 0.45 0.31 0.28 0.40 0.33 0.29 0.43 0.68 0.58
## TAcomm4 0.43 0.37 0.31 0.43 0.41 0.38 0.18 0.41 0.64 0.67
## TAcomm5 0.36 0.38 0.26 0.33 0.32 0.35 0.20 0.44 0.69 0.77
## TAcomm6 0.37 0.38 0.32 0.39 0.31 0.39 0.20 0.43 0.62 0.74
## TAcomm7 0.42 0.51 0.35 0.28 0.47 0.35 0.26 0.43 0.70 0.62
## TAcomm8 0.39 0.43 0.35 0.31 0.38 0.35 0.24 0.45 0.68 0.68
## TAcomm9 0.38 0.42 0.36 0.33 0.40 0.33 0.19 0.46 0.67 0.72
## TAcomm10 0.35 0.38 0.30 0.32 0.33 0.32 0.21 0.39 0.62 0.70
## TAcomm3 TAcomm4 TAcomm5 TAcomm6 TAcomm7 TAcomm8 TAcomm9 TAcomm10
## TA1 0.43 0.43 0.36 0.37 0.42 0.39 0.38 0.35
## TA2 0.45 0.37 0.38 0.38 0.51 0.43 0.42 0.38
## TA3 0.31 0.31 0.26 0.32 0.35 0.35 0.36 0.30
## TA4 0.28 0.43 0.33 0.39 0.28 0.31 0.33 0.32
## TA5 0.40 0.41 0.32 0.31 0.47 0.38 0.40 0.33
## TA6 0.33 0.38 0.35 0.39 0.35 0.35 0.33 0.32
## TA_facil1 0.29 0.18 0.20 0.20 0.26 0.24 0.19 0.21
## TA_facil3 0.43 0.41 0.44 0.43 0.43 0.45 0.46 0.39
## TAcomm1 0.68 0.64 0.69 0.62 0.70 0.68 0.67 0.62
## TAcomm2 0.58 0.67 0.77 0.74 0.62 0.68 0.72 0.70
## TAcomm3 1.00 0.62 0.60 0.55 0.73 0.66 0.64 0.54
## TAcomm4 0.62 1.00 0.69 0.63 0.64 0.61 0.71 0.62
## TAcomm5 0.60 0.69 1.00 0.74 0.66 0.68 0.71 0.71
## TAcomm6 0.55 0.63 0.74 1.00 0.61 0.73 0.68 0.71
## TAcomm7 0.73 0.64 0.66 0.61 1.00 0.73 0.68 0.60
## TAcomm8 0.66 0.61 0.68 0.73 0.73 1.00 0.72 0.67
## TAcomm9 0.64 0.71 0.71 0.68 0.68 0.72 1.00 0.68
## TAcomm10 0.54 0.62 0.71 0.71 0.60 0.67 0.68 1.00
##
## n
## TA1 TA2 TA3 TA4 TA5 TA6 TA_facil1 TA_facil3 TAcomm1 TAcomm2 TAcomm3
## TA1 488 488 487 488 488 487 487 488 488 488 488
## TA2 488 488 487 488 488 487 487 488 488 488 488
## TA3 487 487 488 487 488 487 487 487 488 488 488
## TA4 488 488 487 490 488 487 487 488 490 490 490
## TA5 488 488 488 488 489 487 487 488 489 489 489
## TA6 487 487 487 487 487 487 487 487 487 487 487
## TA_facil1 487 487 487 487 487 487 487 487 487 487 487
## TA_facil3 488 488 487 488 488 487 487 488 488 488 488
## TAcomm1 488 488 488 490 489 487 487 488 492 492 492
## TAcomm2 488 488 488 490 489 487 487 488 492 492 492
## TAcomm3 488 488 488 490 489 487 487 488 492 492 492
## TAcomm4 488 488 488 490 489 487 487 488 492 492 492
## TAcomm5 488 488 488 490 489 487 487 488 492 492 492
## TAcomm6 488 488 488 490 489 487 487 488 492 492 492
## TAcomm7 488 488 488 490 489 487 487 488 492 492 492
## TAcomm8 488 488 488 490 489 487 487 488 492 492 492
## TAcomm9 488 488 488 490 489 487 487 488 492 492 492
## TAcomm10 488 488 488 490 489 487 487 488 492 492 492
## TAcomm4 TAcomm5 TAcomm6 TAcomm7 TAcomm8 TAcomm9 TAcomm10
## TA1 488 488 488 488 488 488 488
## TA2 488 488 488 488 488 488 488
## TA3 488 488 488 488 488 488 488
## TA4 490 490 490 490 490 490 490
## TA5 489 489 489 489 489 489 489
## TA6 487 487 487 487 487 487 487
## TA_facil1 487 487 487 487 487 487 487
## TA_facil3 488 488 488 488 488 488 488
## TAcomm1 492 492 492 492 492 492 492
## TAcomm2 492 492 492 492 492 492 492
## TAcomm3 492 492 492 492 492 492 492
## TAcomm4 492 492 492 492 492 492 492
## TAcomm5 492 492 492 492 492 492 492
## TAcomm6 492 492 492 492 492 492 492
## TAcomm7 492 492 492 492 492 492 492
## TAcomm8 492 492 492 492 492 492 492
## TAcomm9 492 492 492 492 492 492 492
## TAcomm10 492 492 492 492 492 492 492
##
## P
## TA1 TA2 TA3 TA4 TA5 TA6 TA_facil1 TA_facil3 TAcomm1
## TA1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA_facil1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA_facil3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0044 0.0000 0.0000
## TAcomm3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm2 TAcomm3 TAcomm4 TAcomm5 TAcomm6 TAcomm7 TAcomm8 TAcomm9
## TA1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA_facil1 0.0044 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TA_facil3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## TAcomm10
## TA1 0.0000
## TA2 0.0000
## TA3 0.0000
## TA4 0.0000
## TA5 0.0000
## TA6 0.0000
## TA_facil1 0.0000
## TA_facil3 0.0000
## TAcomm1 0.0000
## TAcomm2 0.0000
## TAcomm3 0.0000
## TAcomm4 0.0000
## TAcomm5 0.0000
## TAcomm6 0.0000
## TAcomm7 0.0000
## TAcomm8 0.0000
## TAcomm9 0.0000
## TAcomm10Eigs & scree
#The number of objects in the last line of the output is a rough estimate of how many factors there might be. Look for number of values ≥ 1.00
ev<-eigen(exog) #Where the object "exog" is the correlation matrix we computed earlier
round(ev$values,2)## [1] 8.93 1.80 0.94 0.83 0.68 0.63 0.58 0.53 0.49 0.43 0.36 0.33 0.30 0.27 0.26
## [16] 0.23 0.21 0.19ev$values[ev$values>1]## [1] 8.931096 1.800936#Look for the largest "elbow", as well as amount of obvious elbows
plot(ev$values, type = "b", las = 1)
2 factor EFA
EFA
xx<-fa(exog,nfactors=2, fm = "pa", rotate="Promax", SMC=TRUE, scores="regression",use="pairwise",cor="poly")
print(xx$loadings,cut=.25)##
## Loadings:
## PA1 PA2
## TA1 0.657
## TA2 0.777
## TA3 0.651
## TA4 0.572
## TA5 0.806
## TA6 0.397
## TA_facil1 0.367
## TA_facil3 0.664
## TAcomm1 0.735
## TAcomm2 0.928
## TAcomm3 0.610
## TAcomm4 0.709
## TAcomm5 0.918
## TAcomm6 0.835
## TAcomm7 0.659
## TAcomm8 0.788
## TAcomm9 0.810
## TAcomm10 0.815
##
## PA1 PA2
## SS loadings 6.240 3.302
## Proportion Var 0.347 0.183
## Cumulative Var 0.347 0.530Factor Correlations
round(xx$Phi,2)## PA1 PA2
## PA1 1.00 0.67
## PA2 0.67 1.00cor.test(dat$TAcomm.av,dat$TAsup.av)##
## Pearson's product-moment correlation
##
## data: dat$TAcomm.av and dat$TAsup.av
## t = 16.924, df = 489, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5487792 0.6607139
## sample estimates:
## cor
## 0.6077565CFA
1 factor
CFA.1 <- '
#latent variables
TAfactor =~ TA1 + TA2 + TA3 + TA4 + TA5 + TA6 + TA_facil1 +TA_facil3 + TAcomm1 +TAcomm2 +TAcomm3 +TAcomm4 +TAcomm5 +TAcomm6 +TAcomm7 +TAcomm8 +TAcomm9 +TAcomm10
# factor variances
#Note: Multiplying by 1 specifies that factor has unit variance, which allows all indicator variances to be freely estimated
TAfactor ~~ 1*TAfactor
'DWLS Global Fit
On the fence. All statistics are “good-ish” fit.
CFA.1.fit <- cfa(CFA.1, data = dat, std.lv=TRUE, estimator = "DWLS") ## Warning: lavaan->lav_options_est_dwls():
## estimator "DWLS" is not recommended for continuous data. Did you forget to
## set the ordered= argument?# summary(CFA.1.fit,fit.measures=TRUE, standardized=T, rsquare=T)Fit stats only
fitMeasures(CFA.1.fit,c("chisq","pvalue", "cfi","rmsea", "rmsea.ci.lower", "rmsea.ci.upper","srmr"))## chisq pvalue cfi rmsea rmsea.ci.lower
## 294.690 0.000 0.980 0.049 0.042
## rmsea.ci.upper srmr
## 0.057 0.0832 factors
CFA.2 <- '
#latent variables
TAsup =~ TA1 + TA2 + TA3 + TA4 + TA5 + TA6 + TA_facil1 + TA_facil3
TAcom =~ TAcomm1 +TAcomm2 +TAcomm3 +TAcomm4 +TAcomm5 +TAcomm6 +TAcomm7 +TAcomm8 +TAcomm9 +TAcomm10
# factor variances
#Note: Multiplying by 1 specifies that factor has unit variance
TAsup ~~ 1*TAsup
TAcom ~~ 1*TAcom
TAsup ~~TAcom
'DWLS global fit
CFA.2.fit <- cfa(CFA.2, data = dat, std.lv=TRUE, estimator = "DWLS") ## Warning: lavaan->lav_options_est_dwls():
## estimator "DWLS" is not recommended for continuous data. Did you forget to
## set the ordered= argument?#summary(CFA.2.fit,fit.measures=TRUE, standardized=T, rsquare=T)Fit stats only
Ooooh, crazy excellent fit.
fitMeasures(CFA.2.fit,c("chisq","pvalue", "cfi","rmsea", "rmsea.ci.lower", "rmsea.ci.upper","srmr"))## chisq pvalue cfi rmsea rmsea.ci.lower
## 77.037 1.000 1.000 0.000 0.000
## rmsea.ci.upper srmr
## 0.000 0.040Fit compare
fitMeasures(CFA.1.fit,c("chisq","df", "pvalue", "cfi","rmsea", "rmsea.ci.lower", "rmsea.ci.upper","srmr")); fitMeasures(CFA.2.fit,c("chisq","df", "pvalue", "cfi","rmsea", "rmsea.ci.lower", "rmsea.ci.upper","srmr"))## chisq df pvalue cfi rmsea
## 294.690 135.000 0.000 0.980 0.049
## rmsea.ci.lower rmsea.ci.upper srmr
## 0.042 0.057 0.083## chisq df pvalue cfi rmsea
## 77.037 134.000 1.000 1.000 0.000
## rmsea.ci.lower rmsea.ci.upper srmr
## 0.000 0.000 0.040Test difference in fit
anova(CFA.2.fit,CFA.1.fit)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## CFA.2.fit 134 77.037
## CFA.1.fit 135 294.690 217.65 0.66699 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ASPs EFA (total mess)
exog<-cor(dat[,c("SB1","SB2","SB3.r","SB4","SB5","AB1.r","AB2.r","AB3","AB4.r","SE1","SE2","SE3","SE4","SE5")],use="pairwise.complete.obs")
cormat<-round(exog,2) #rounding this to the hundredth place
corrplot::corrplot(exog) #This function comes from the package by the same name
upper<-cormat
upper[upper.tri(cormat)] <-""
upper<-as.data.frame(upper)
upper## SB1 SB2 SB3.r SB4 SB5 AB1.r AB2.r AB3 AB4.r SE1 SE2 SE3 SE4 SE5
## SB1 1
## SB2 0.51 1
## SB3.r 0.53 0.4 1
## SB4 0.44 0.49 0.27 1
## SB5 0.51 0.43 0.27 0.51 1
## AB1.r 0.45 0.26 0.57 0.26 0.28 1
## AB2.r 0.65 0.46 0.7 0.36 0.42 0.61 1
## AB3 0.64 0.53 0.55 0.47 0.48 0.44 0.65 1
## AB4.r 0.51 0.37 0.57 0.29 0.28 0.54 0.65 0.53 1
## SE1 0.55 0.46 0.5 0.32 0.35 0.45 0.61 0.65 0.51 1
## SE2 0.67 0.52 0.57 0.42 0.42 0.47 0.73 0.72 0.56 0.69 1
## SE3 0.63 0.51 0.54 0.39 0.41 0.47 0.67 0.68 0.52 0.68 0.77 1
## SE4 0.57 0.47 0.49 0.35 0.35 0.39 0.65 0.65 0.53 0.64 0.69 0.65 1
## SE5 0.61 0.49 0.58 0.34 0.37 0.48 0.69 0.7 0.58 0.71 0.76 0.73 0.68 1Hmisc::rcorr(as.matrix(dat[,c("SB1","SB2","SB3.r","SB4","SB5","AB1.r","AB2.r","AB3","AB4.r","SE1","SE2","SE3","SE4","SE5")]),type="pearson")## SB1 SB2 SB3.r SB4 SB5 AB1.r AB2.r AB3 AB4.r SE1 SE2 SE3 SE4 SE5
## SB1 1.00 0.51 0.53 0.44 0.51 0.45 0.65 0.64 0.51 0.55 0.67 0.63 0.57 0.61
## SB2 0.51 1.00 0.40 0.49 0.43 0.26 0.46 0.53 0.37 0.46 0.52 0.51 0.47 0.49
## SB3.r 0.53 0.40 1.00 0.27 0.27 0.57 0.70 0.55 0.57 0.50 0.57 0.54 0.49 0.58
## SB4 0.44 0.49 0.27 1.00 0.51 0.26 0.36 0.47 0.29 0.32 0.42 0.39 0.35 0.34
## SB5 0.51 0.43 0.27 0.51 1.00 0.28 0.42 0.48 0.28 0.35 0.42 0.41 0.35 0.37
## AB1.r 0.45 0.26 0.57 0.26 0.28 1.00 0.61 0.44 0.54 0.45 0.47 0.47 0.39 0.48
## AB2.r 0.65 0.46 0.70 0.36 0.42 0.61 1.00 0.65 0.65 0.61 0.73 0.67 0.65 0.69
## AB3 0.64 0.53 0.55 0.47 0.48 0.44 0.65 1.00 0.53 0.65 0.72 0.68 0.65 0.70
## AB4.r 0.51 0.37 0.57 0.29 0.28 0.54 0.65 0.53 1.00 0.51 0.56 0.52 0.53 0.58
## SE1 0.55 0.46 0.50 0.32 0.35 0.45 0.61 0.65 0.51 1.00 0.69 0.68 0.64 0.71
## SE2 0.67 0.52 0.57 0.42 0.42 0.47 0.73 0.72 0.56 0.69 1.00 0.77 0.69 0.76
## SE3 0.63 0.51 0.54 0.39 0.41 0.47 0.67 0.68 0.52 0.68 0.77 1.00 0.65 0.73
## SE4 0.57 0.47 0.49 0.35 0.35 0.39 0.65 0.65 0.53 0.64 0.69 0.65 1.00 0.68
## SE5 0.61 0.49 0.58 0.34 0.37 0.48 0.69 0.70 0.58 0.71 0.76 0.73 0.68 1.00
##
## n
## SB1 SB2 SB3.r SB4 SB5 AB1.r AB2.r AB3 AB4.r SE1 SE2 SE3 SE4 SE5
## SB1 489 489 488 489 488 489 488 489 489 488 488 489 489 488
## SB2 489 489 488 489 488 489 488 489 489 488 488 489 489 488
## SB3.r 488 488 488 488 488 488 488 488 488 488 488 488 488 488
## SB4 489 489 488 491 488 489 489 489 489 488 488 489 490 489
## SB5 488 488 488 488 488 488 488 488 488 488 488 488 488 488
## AB1.r 489 489 488 489 488 489 488 489 489 488 488 489 489 488
## AB2.r 488 488 488 489 488 488 489 488 488 488 488 488 489 488
## AB3 489 489 488 489 488 489 488 490 489 488 489 490 489 488
## AB4.r 489 489 488 489 488 489 488 489 489 488 488 489 489 488
## SE1 488 488 488 488 488 488 488 488 488 488 488 488 488 488
## SE2 488 488 488 488 488 488 488 489 488 488 489 489 488 488
## SE3 489 489 488 489 488 489 488 490 489 488 489 490 489 488
## SE4 489 489 488 490 488 489 489 489 489 488 488 489 490 488
## SE5 488 488 488 489 488 488 488 488 488 488 488 488 488 489
##
## P
## SB1 SB2 SB3.r SB4 SB5 AB1.r AB2.r AB3 AB4.r SE1 SE2 SE3 SE4 SE5
## SB1 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB2 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB3.r 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB4 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB5 0 0 0 0 0 0 0 0 0 0 0 0 0
## AB1.r 0 0 0 0 0 0 0 0 0 0 0 0 0
## AB2.r 0 0 0 0 0 0 0 0 0 0 0 0 0
## AB3 0 0 0 0 0 0 0 0 0 0 0 0 0
## AB4.r 0 0 0 0 0 0 0 0 0 0 0 0 0
## SE1 0 0 0 0 0 0 0 0 0 0 0 0 0
## SE2 0 0 0 0 0 0 0 0 0 0 0 0 0
## SE3 0 0 0 0 0 0 0 0 0 0 0 0 0
## SE4 0 0 0 0 0 0 0 0 0 0 0 0 0
## SE5 0 0 0 0 0 0 0 0 0 0 0 0 0Eigs & scree
#The number of objects in the last line of the output is a rough estimate of how many factors there might be. Look for number of values ≥ 1.00
ev<-eigen(exog) #Where the object "exog" is the correlation matrix we computed earlier
round(ev$values,2)## [1] 7.93 1.23 0.85 0.57 0.49 0.46 0.42 0.38 0.36 0.33 0.30 0.25 0.24 0.19ev$values[ev$values>1]## [1] 7.927581 1.234333#Look for the largest "elbow", as well as amount of obvious elbows
plot(ev$values, type = "b", las = 1)
3 factor EFA
EFA
xx<-fa(exog,nfactors=3, fm = "pa", rotate="Promax", SMC=TRUE, scores="regression",use="pairwise",cor="poly")
print(xx$loadings,cut=.25)##
## Loadings:
## PA1 PA3 PA2
## SB1 0.269 0.369
## SB2 0.284 0.482
## SB3.r 0.749
## SB4 0.827
## SB5 0.762
## AB1.r 0.816
## AB2.r 0.651
## AB3 0.559 0.276
## AB4.r 0.628
## SE1 0.810
## SE2 0.773
## SE3 0.761
## SE4 0.762
## SE5 0.851
##
## PA1 PA3 PA2
## SS loadings 3.729 2.139 1.735
## Proportion Var 0.266 0.153 0.124
## Cumulative Var 0.266 0.419 0.543Factor Correlations
round(xx$Phi,2)## PA1 PA3 PA2
## PA1 1.00 0.77 0.70
## PA3 0.77 1.00 0.58
## PA2 0.70 0.58 1.002 factor EFA
EFA
xx<-fa(exog,nfactors=2, fm = "pa", rotate="Promax", SMC=TRUE, scores="regression",use="pairwise",cor="poly")
print(xx$loadings,cut=.25)##
## Loadings:
## PA1 PA2
## SB1 0.460 0.400
## SB2 0.564
## SB3.r 0.800
## SB4 0.757
## SB5 0.706
## AB1.r 0.707
## AB2.r 0.857
## AB3 0.508 0.407
## AB4.r 0.760
## SE1 0.665
## SE2 0.677 0.255
## SE3 0.643
## SE4 0.629
## SE5 0.772
##
## PA1 PA2
## SS loadings 5.266 1.936
## Proportion Var 0.376 0.138
## Cumulative Var 0.376 0.514Factor Correlations
round(xx$Phi,2)## PA1 PA2
## PA1 1.00 0.65
## PA2 0.65 1.00Manipulation Check
TA support
summary(aov(dat$TAsup.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 1.0 0.2504 0.732 0.571
## Residuals 486 166.3 0.3422
## 1 observation deleted due to missingnessTukeyHSD(aov(dat$TAsup.av~dat$Condition))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$TAsup.av ~ dat$Condition)
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp 0.029226785 -0.1929303 0.2513839 0.9963956
## NURTURANT-ALLsupp 0.001158156 -0.2298033 0.2321196 1.0000000
## PRACTICAL-ALLsupp -0.023667279 -0.2514278 0.2040933 0.9985648
## SUPPLEMENTAL-ALLsupp 0.107843137 -0.1199174 0.3356037 0.6935518
## NURTURANT-NOsupp -0.028068629 -0.2569662 0.2008289 0.9972577
## PRACTICAL-NOsupp -0.052894064 -0.2785614 0.1727733 0.9681109
## SUPPLEMENTAL-NOsupp 0.078616352 -0.1470510 0.3042837 0.8754309
## PRACTICAL-NURTURANT -0.024825435 -0.2591653 0.2095144 0.9984519
## SUPPLEMENTAL-NURTURANT 0.106684982 -0.1276548 0.3410248 0.7239946
## SUPPLEMENTAL-PRACTICAL 0.131510417 -0.0996753 0.3626961 0.5255073model<- lm(TAsup.av~NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP, data = dat)
mcSummary(model)## lm(formula = TAsup.av ~ NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 1.002 4 0.250 0.006 0.732 0.571
## Error 166.310 486 0.342
## Corr Total 167.311 490 0.341
##
## RMSE AdjEtaSq
## 0.585 -0.002
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 4.998 0.026 189.068 12232.626 0.987 NA 4.946 5.050 0.000
## NOvSupport -0.008 0.064 -0.123 0.005 0.000 1 -0.134 0.118 0.902
## ALLvOther 0.028 0.068 0.421 0.061 0.000 1 -0.104 0.161 0.674
## NURTvPrSu 0.041 0.074 0.550 0.103 0.001 1 -0.105 0.187 0.583
## PRACTvSUPP 0.132 0.084 1.558 0.830 0.005 1 -0.034 0.297 0.120Graphs
boxplot(dat$TAsup.av~ dat$Condition.ordered, ylab = "Perceived TA Support", names = c("NO Support", "All Support", "Nurturant", "Practical", "Supplemental"))
library(see)
ggplot(dat, aes(Condition.ordered,TAsup.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = .2, y = 0)) + geom_jitter(alpha = 0.01, width = 0.15) +
theme(legend.position = "none")
p <- ggplot(dat, aes(Condition.ordered, TAsup.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = .2, y = 0)) +geom_boxplot(width=.1)+
theme(legend.position = "none") +
xlab('Condition') +
ylab('Perceived TA Support')
(p = p + scale_fill_grey(start = 0.3, end = .9) + xlab('Condition') +
ylab('Perceived TA Support'))+ scale_x_discrete(labels=c('No Support', 'All Support', 'Nurturant', 'Practical','Supplemental'))
p <- ggplot(dat, aes(Condition.ordered, TAsup.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = .2, y = 0)) +geom_boxplot(width=.1)+
theme(legend.position = "none") +
xlab('Condition') +
ylab('Perceived TA Support')
(p = p +scale_fill_viridis(discrete = TRUE, alpha=0.6) + xlab('Condition') +
ylab('Anticipated TA Support'))+ scale_x_discrete(labels=c('No Support', 'All Support', 'Nurturant', 'Practical','Supplemental'))
p <- ggplot(dat, aes(Condition.ordered, TAsup.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
pairwise.t.test(dat$TAsup.av, interaction(dat$Condition), p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$TAsup.av and interaction(dat$Condition)
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1 - - -
## NURTURANT 1 1 - -
## PRACTICAL 1 1 1 -
## SUPPLEMENTAL 1 1 1 1
##
## P value adjustment method: bonferroniTA communality
summary(aov(dat$TAcomm.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 0.45 0.1120 0.265 0.901
## Residuals 487 206.14 0.4233TukeyHSD(aov(dat$TAcomm.av~dat$Condition))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$TAcomm.av ~ dat$Condition)
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp 0.065834258 -0.1812459 0.3129144 0.9495783
## NURTURANT-ALLsupp 0.056421030 -0.2004511 0.3132932 0.9748392
## PRACTICAL-ALLsupp 0.011875884 -0.2407627 0.2645145 0.9999381
## SUPPLEMENTAL-ALLsupp -0.007475490 -0.2607877 0.2458367 0.9999903
## NURTURANT-NOsupp -0.009413228 -0.2639899 0.2451635 0.9999762
## PRACTICAL-NOsupp -0.053958374 -0.3042627 0.1963460 0.9765127
## SUPPLEMENTAL-NOsupp -0.073309748 -0.3242939 0.1776744 0.9306158
## PRACTICAL-NURTURANT -0.044545146 -0.3045201 0.2154298 0.9900553
## SUPPLEMENTAL-NURTURANT -0.063896520 -0.3245261 0.1967330 0.9624997
## SUPPLEMENTAL-PRACTICAL -0.019351375 -0.2758094 0.2371067 0.9995938model<- lm(TAcomm.av~NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP, data = dat)
mcSummary(model)## lm(formula = TAcomm.av ~ NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.448 4 0.112 0.002 0.265 0.901
## Error 206.145 487 0.423
## Corr Total 206.593 491 0.421
##
## RMSE AdjEtaSq
## 0.651 -0.006
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 5.256 0.029 178.936 13553.036 0.985 NA 5.198 5.313 0.000
## NOvSupport -0.051 0.071 -0.710 0.213 0.001 1 -0.191 0.090 0.478
## ALLvOther 0.020 0.075 0.270 0.031 0.000 1 -0.127 0.168 0.787
## NURTvPrSu -0.054 0.083 -0.655 0.182 0.001 1 -0.217 0.108 0.513
## PRACTvSUPP -0.019 0.094 -0.207 0.018 0.000 1 -0.203 0.165 0.836Graphs
boxplot(dat$TAcomm.av~ dat$Condition)
pairwise.t.test(dat$TAcomm.av, interaction(dat$Condition), p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$TAcomm.av and interaction(dat$Condition)
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1 - - -
## NURTURANT 1 1 - -
## PRACTICAL 1 1 1 -
## SUPPLEMENTAL 1 1 1 1
##
## P value adjustment method: bonferroniSB
Prac v. Supp (marginal)
model<- lm(SB.av~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SB.av ~ Gen.con * (NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 12.720 9 1.413 0.041 2.281 0.016
## Error 298.075 481 0.620
## Corr Total 310.795 490 0.634
##
## RMSE AdjEtaSq
## 0.787 0.023
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.027 0.042 94.862 5576.580 0.949 NA 3.944 4.110
## Gen.con -0.285 0.085 -3.351 6.958 0.023 0.982 -0.451 -0.118
## NOvSupport 0.191 0.102 1.875 2.180 0.007 0.716 -0.009 0.392
## ALLvOther 0.096 0.106 0.905 0.507 0.002 0.740 -0.112 0.304
## NURTvPrSu -0.012 0.125 -0.097 0.006 0.000 0.641 -0.258 0.234
## PRACTvSUPP 0.191 0.133 1.436 1.277 0.004 0.725 -0.070 0.453
## Gen.con:NOvSupport -0.300 0.204 -1.472 1.342 0.004 0.715 -0.702 0.101
## Gen.con:ALLvOther -0.278 0.212 -1.311 1.064 0.004 0.740 -0.694 0.139
## Gen.con:NURTvPrSu 0.393 0.250 1.570 1.528 0.005 0.641 -0.099 0.884
## Gen.con:PRACTvSUPP -0.092 0.266 -0.346 0.074 0.000 0.725 -0.615 0.431
## p
## (Intercept) 0.000
## Gen.con 0.001
## NOvSupport 0.061
## ALLvOther 0.366
## NURTvPrSu 0.923
## PRACTvSUPP 0.152
## Gen.con:NOvSupport 0.142
## Gen.con:ALLvOther 0.191
## Gen.con:NURTvPrSu 0.117
## Gen.con:PRACTvSUPP 0.729Prac v. Nurt (n.s.)
model<- lm(SB.av ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SB.av ~ Gen.con * (NOvSupport + ALLvOther + SUPvPrNu +
## PRACTvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 12.720 9 1.413 0.041 2.281 0.016
## Error 298.075 481 0.620
## Corr Total 310.795 490 0.634
##
## RMSE AdjEtaSq
## 0.787 0.023
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.027 0.042 94.862 5576.580 0.949 NA 3.944 4.110
## Gen.con -0.285 0.085 -3.351 6.958 0.023 0.982 -0.451 -0.118
## NOvSupport 0.191 0.102 1.875 2.180 0.007 0.716 -0.009 0.392
## ALLvOther 0.096 0.106 0.905 0.507 0.002 0.740 -0.112 0.304
## SUPvPrNu -0.137 0.119 -1.157 0.829 0.003 0.692 -0.370 0.096
## PRACTvNURT 0.108 0.141 0.765 0.363 0.001 0.667 -0.169 0.384
## Gen.con:NOvSupport -0.300 0.204 -1.472 1.342 0.004 0.715 -0.702 0.101
## Gen.con:ALLvOther -0.278 0.212 -1.311 1.064 0.004 0.740 -0.694 0.139
## Gen.con:SUPvPrNu -0.127 0.237 -0.536 0.178 0.001 0.692 -0.594 0.339
## Gen.con:PRACTvNURT -0.439 0.281 -1.560 1.507 0.005 0.667 -0.991 0.114
## p
## (Intercept) 0.000
## Gen.con 0.001
## NOvSupport 0.061
## ALLvOther 0.366
## SUPvPrNu 0.248
## PRACTvNURT 0.445
## Gen.con:NOvSupport 0.142
## Gen.con:ALLvOther 0.191
## Gen.con:SUPvPrNu 0.592
## Gen.con:PRACTvNURT 0.120Supp v. Nurt (n.s.)
model<- lm(SB.av ~Gen.con*(NOvSupport + ALLvOther + dat$PRAvSuNu + SUPPvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SB.av ~ Gen.con * (NOvSupport + ALLvOther + dat$PRAvSuNu +
## SUPPvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 12.720 9 1.413 0.041 2.281 0.016
## Error 298.075 481 0.620
## Corr Total 310.795 490 0.634
##
## RMSE AdjEtaSq
## 0.787 0.023
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.027 0.042 94.862 5576.580 0.949 NA 3.944 4.110
## Gen.con -0.285 0.085 -3.351 6.958 0.023 0.982 -0.451 -0.118
## NOvSupport 0.191 0.102 1.875 2.180 0.007 0.716 -0.009 0.392
## ALLvOther 0.096 0.106 0.905 0.507 0.002 0.740 -0.112 0.304
## dat$PRAvSuNu 0.149 0.117 1.277 1.011 0.003 0.710 -0.080 0.379
## SUPPvNURT -0.083 0.143 -0.585 0.212 0.001 0.652 -0.364 0.197
## Gen.con:NOvSupport -0.300 0.204 -1.472 1.342 0.004 0.715 -0.702 0.101
## Gen.con:ALLvOther -0.278 0.212 -1.311 1.064 0.004 0.740 -0.694 0.139
## Gen.con:dat$PRAvSuNu -0.265 0.234 -1.135 0.799 0.003 0.710 -0.725 0.194
## Gen.con:SUPPvNURT -0.347 0.285 -1.215 0.915 0.003 0.652 -0.907 0.214
## p
## (Intercept) 0.000
## Gen.con 0.001
## NOvSupport 0.061
## ALLvOther 0.366
## dat$PRAvSuNu 0.202
## SUPPvNURT 0.559
## Gen.con:NOvSupport 0.142
## Gen.con:ALLvOther 0.191
## Gen.con:dat$PRAvSuNu 0.257
## Gen.con:SUPPvNURT 0.225Tukey’s (sig gender diff in NURT)
model<-aov(dat$SB.av~ dat$Gen.name*dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Gen.name 1 5.97 5.975 9.641 0.00201 **
## dat$Condition 4 3.08 0.769 1.242 0.29231
## dat$Gen.name:dat$Condition 4 3.67 0.917 1.479 0.20722
## Residuals 481 298.08 0.620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$SB.av ~ dat$Gen.name * dat$Condition)
##
## $`dat$Gen.name`
## diff lwr upr p adj
## Woman-Man -0.2612639 -0.4265934 -0.09593436 0.0020146
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp -0.09249468 -0.39221761 0.2072283 0.9163530
## NURTURANT-ALLsupp -0.05623324 -0.36777599 0.2553095 0.9878866
## PRACTICAL-ALLsupp -0.02788569 -0.33431794 0.2785465 0.9991482
## SUPPLEMENTAL-ALLsupp 0.13865370 -0.16859158 0.4458990 0.7304728
## NURTURANT-NOsupp 0.03626144 -0.27177912 0.3443020 0.9976611
## PRACTICAL-NOsupp 0.06460899 -0.23826198 0.3674799 0.9773982
## SUPPLEMENTAL-NOsupp 0.23114838 -0.07254516 0.5348419 0.2286269
## PRACTICAL-NURTURANT 0.02834755 -0.28622497 0.3429201 0.9991804
## SUPPLEMENTAL-NURTURANT 0.19488695 -0.12047763 0.5102515 0.4397788
## SUPPLEMENTAL-PRACTICAL 0.16653940 -0.14377762 0.4768564 0.5828586
##
## $`dat$Gen.name:dat$Condition`
## diff lwr upr
## Woman:ALLsupp-Man:ALLsupp -0.13620513 -0.70568481 0.433274553
## Man:NOsupp-Man:ALLsupp -0.16553846 -0.86645008 0.535373161
## Woman:NOsupp-Man:ALLsupp -0.20968661 -0.77371057 0.354337355
## Man:NURTURANT-Man:ALLsupp 0.37375566 -0.40671745 1.154228767
## Woman:NURTURANT-Man:ALLsupp -0.30207900 -0.87254836 0.268390351
## Man:PRACTICAL-Man:ALLsupp 0.04679487 -0.66152196 0.755111699
## Woman:PRACTICAL-Man:ALLsupp -0.19030558 -0.76178994 0.381178769
## Man:SUPPLEMENTAL-Man:ALLsupp 0.28391608 -0.44094946 1.008781628
## Woman:SUPPLEMENTAL-Man:ALLsupp -0.04532225 -0.61579160 0.525147108
## Man:NOsupp-Woman:ALLsupp -0.02933333 -0.60720874 0.548542078
## Woman:NOsupp-Woman:ALLsupp -0.07348148 -0.47446268 0.327499717
## Man:NURTURANT-Woman:ALLsupp 0.50996078 -0.16220078 1.182122346
## Woman:NURTURANT-Woman:ALLsupp -0.16587387 -0.57587164 0.244123896
## Man:PRACTICAL-Woman:ALLsupp 0.18300000 -0.40383525 0.769835255
## Woman:PRACTICAL-Woman:ALLsupp -0.05410046 -0.46550932 0.357308410
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.42012121 -0.18658543 1.026827850
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.09088288 -0.31911489 0.500880653
## Woman:NOsupp-Man:NOsupp -0.04414815 -0.61664786 0.528351561
## Man:NURTURANT-Man:NOsupp 0.53929412 -0.24732597 1.325914204
## Woman:NURTURANT-Man:NOsupp -0.13654054 -0.71539127 0.442310189
## Man:PRACTICAL-Man:NOsupp 0.21233333 -0.50275101 0.927417672
## Woman:PRACTICAL-Man:NOsupp -0.02476712 -0.60461818 0.555083937
## Man:SUPPLEMENTAL-Man:NOsupp 0.44945455 -0.28202542 1.180934511
## Woman:SUPPLEMENTAL-Man:NOsupp 0.12021622 -0.45863451 0.699066946
## Man:NURTURANT-Woman:NOsupp 0.58344227 -0.08410331 1.250987838
## Woman:NURTURANT-Woman:NOsupp -0.09239239 -0.49477790 0.309993116
## Man:PRACTICAL-Woman:NOsupp 0.25648148 -0.32506090 0.838023864
## Woman:PRACTICAL-Woman:NOsupp 0.01938102 -0.38444218 0.423204231
## Man:SUPPLEMENTAL-Woman:NOsupp 0.49360269 -0.10798593 1.095191316
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.16436436 -0.23802114 0.566749873
## Woman:NURTURANT-Man:NURTURANT -0.67583466 -1.34883491 -0.002834405
## Man:PRACTICAL-Man:NURTURANT -0.32696078 -1.12018634 0.466264773
## Woman:PRACTICAL-Man:NURTURANT -0.56406124 -1.23792208 0.109799594
## Man:SUPPLEMENTAL-Man:NURTURANT -0.08983957 -0.89787677 0.718197626
## Woman:SUPPLEMENTAL-Man:NURTURANT -0.41907790 -1.09207815 0.253922352
## Man:PRACTICAL-Woman:NURTURANT 0.34887387 -0.23892183 0.936669580
## Woman:PRACTICAL-Woman:NURTURANT 0.11177342 -0.30100429 0.524551120
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.58599509 -0.02164059 1.193630767
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.25675676 -0.15461454 0.668128057
## Woman:PRACTICAL-Man:PRACTICAL -0.23710046 -0.82588130 0.351680382
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.23712121 -0.50145753 0.975699959
## Woman:SUPPLEMENTAL-Man:PRACTICAL -0.09211712 -0.67991282 0.495678589
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.47422167 -0.13436703 1.082810368
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.14498334 -0.26779436 0.557761042
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL -0.32923833 -0.93687401 0.278397352
## p adj
## Woman:ALLsupp-Man:ALLsupp 0.9990449
## Man:NOsupp-Man:ALLsupp 0.9991365
## Woman:NOsupp-Man:ALLsupp 0.9748770
## Man:NURTURANT-Man:ALLsupp 0.8828968
## Woman:NURTURANT-Man:ALLsupp 0.8046413
## Man:PRACTICAL-Man:ALLsupp 1.0000000
## Woman:PRACTICAL-Man:ALLsupp 0.9882460
## Man:SUPPLEMENTAL-Man:ALLsupp 0.9646066
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.9999999
## Man:NOsupp-Woman:ALLsupp 1.0000000
## Woman:NOsupp-Woman:ALLsupp 0.9998926
## Man:NURTURANT-Woman:ALLsupp 0.3211379
## Woman:NURTURANT-Woman:ALLsupp 0.9564842
## Man:PRACTICAL-Woman:ALLsupp 0.9926795
## Woman:PRACTICAL-Woman:ALLsupp 0.9999937
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.4574175
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.9994822
## Woman:NOsupp-Man:NOsupp 0.9999999
## Man:NURTURANT-Man:NOsupp 0.4725790
## Woman:NURTURANT-Man:NOsupp 0.9991451
## Man:PRACTICAL-Man:NOsupp 0.9949037
## Woman:PRACTICAL-Man:NOsupp 1.0000000
## Man:SUPPLEMENTAL-Man:NOsupp 0.6324387
## Woman:SUPPLEMENTAL-Man:NOsupp 0.9996960
## Man:NURTURANT-Woman:NOsupp 0.1468886
## Woman:NURTURANT-Woman:NOsupp 0.9993115
## Man:PRACTICAL-Woman:NOsupp 0.9263927
## Woman:PRACTICAL-Woman:NOsupp 1.0000000
## Man:SUPPLEMENTAL-Woman:NOsupp 0.2166193
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.9537796
## Woman:NURTURANT-Man:NURTURANT 0.0480507
## Man:PRACTICAL-Man:NURTURANT 0.9510936
## Woman:PRACTICAL-Man:NURTURANT 0.1929726
## Man:SUPPLEMENTAL-Man:NURTURANT 0.9999985
## Woman:SUPPLEMENTAL-Man:NURTURANT 0.6140320
## Man:PRACTICAL-Woman:NURTURANT 0.6782090
## Woman:PRACTICAL-Woman:NURTURANT 0.9974689
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.0693300
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.6107901
## Woman:PRACTICAL-Man:PRACTICAL 0.9577424
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.9909539
## Woman:SUPPLEMENTAL-Man:PRACTICAL 0.9999715
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.2836679
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.9829231
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL 0.7824104Differences within condition:
Sig Gender within Nurturant
+ Communality
model<- lm(SB.av~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SB.av ~ Gen.con * (NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP) + TAcomm.av, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 31.014 10 3.101 0.1 5.321 0
## Error 279.781 480 0.583
## Corr Total 310.795 490 0.634
##
## RMSE AdjEtaSq
## 0.763 0.081
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 2.443 0.286 8.551 42.624 0.132 NA 1.882 3.005 0.000
## Gen.con -0.317 0.083 -3.843 8.607 0.030 0.977 -0.479 -0.155 0.000
## NOvSupport 0.168 0.099 1.700 1.684 0.006 0.714 -0.026 0.363 0.090
## ALLvOther 0.104 0.103 1.016 0.601 0.002 0.740 -0.098 0.306 0.310
## NURTvPrSu 0.034 0.122 0.277 0.045 0.000 0.638 -0.205 0.272 0.782
## PRACTvSUPP 0.217 0.129 1.682 1.649 0.006 0.724 -0.037 0.471 0.093
## TAcomm.av 0.303 0.054 5.602 18.294 0.061 0.965 0.197 0.409 0.000
## Gen.con:NOvSupport -0.157 0.200 -0.786 0.360 0.001 0.703 -0.549 0.235 0.432
## Gen.con:ALLvOther -0.337 0.206 -1.635 1.557 0.006 0.738 -0.741 0.068 0.103
## Gen.con:NURTvPrSu 0.292 0.243 1.199 0.838 0.003 0.637 -0.186 0.769 0.231
## Gen.con:PRACTvSUPP -0.166 0.258 -0.643 0.241 0.001 0.723 -0.674 0.342 0.521Graphs
Condition main effect
p <- ggplot(dat, aes(Condition.ordered, SB.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
Violin condition x gender
p <- ggplot(dat, aes(Condition.ordered, SB.av, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0, y = 0))
theme(legend.position = "none") +
xlab('Gender') +
ylab('Anticipated Social Belonging')## List of 3
## $ legend.position: chr "none"
## $ x : chr "Gender"
## $ y : chr "Anticipated Social Belonging"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE(p = p + scale_fill_grey(start = 0, end = .9) + xlab('Student Gender') +
ylab('Anticipated Social Belonging'))
p
Gender main effect
p <- ggplot(dat, aes(Gen.name, SB.av, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0, y = 0))
theme(legend.position = "none") +
xlab('Gender') +
ylab('Anticipated Social Belonging')## List of 3
## $ legend.position: chr "none"
## $ x : chr "Gender"
## $ y : chr "Anticipated Social Belonging"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE(p = p + scale_fill_grey(start = 0, end = .9) + xlab('Student Gender') +
ylab('Anticipated Social Belonging'))
p
p <- ggplot(dat, aes(Gen.name, SB.av, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("suffrager::classic") + ylab("") + xlab("")
p
Violin gender x condition
AB
Prac v. Supp (n.s.)
model<- lm(AB.av~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = AB.av ~ Gen.con * (NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 31.295 9 3.477 0.058 3.302 0.001
## Error 506.484 481 1.053
## Corr Total 537.779 490 1.098
##
## RMSE AdjEtaSq
## 1.026 0.041
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 3.953 0.055 71.432 5372.859 0.914 NA 3.844 4.062
## Gen.con -0.501 0.111 -4.526 21.572 0.041 0.982 -0.718 -0.283
## NOvSupport 0.188 0.133 1.413 2.103 0.004 0.716 -0.073 0.449
## ALLvOther 0.121 0.138 0.880 0.815 0.002 0.737 -0.150 0.393
## NURTvPrSu 0.027 0.163 0.166 0.029 0.000 0.642 -0.293 0.347
## PRACTvSUPP 0.245 0.174 1.414 2.104 0.004 0.728 -0.096 0.587
## Gen.con:NOvSupport -0.341 0.266 -1.280 1.726 0.003 0.715 -0.864 0.182
## Gen.con:ALLvOther -0.439 0.276 -1.591 2.666 0.005 0.736 -0.982 0.103
## Gen.con:NURTvPrSu 0.429 0.326 1.316 1.824 0.004 0.642 -0.211 1.070
## Gen.con:PRACTvSUPP 0.091 0.347 0.263 0.073 0.000 0.728 -0.591 0.774
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.158
## ALLvOther 0.379
## NURTvPrSu 0.868
## PRACTvSUPP 0.158
## Gen.con:NOvSupport 0.201
## Gen.con:ALLvOther 0.112
## Gen.con:NURTvPrSu 0.189
## Gen.con:PRACTvSUPP 0.793Prac v. Nurt (n.s.)
model<- lm(AB.av ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = AB.av ~ Gen.con * (NOvSupport + ALLvOther + SUPvPrNu +
## PRACTvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 31.295 9 3.477 0.058 3.302 0.001
## Error 506.484 481 1.053
## Corr Total 537.779 490 1.098
##
## RMSE AdjEtaSq
## 1.026 0.041
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 3.953 0.055 71.432 5372.859 0.914 NA 3.844 4.062
## Gen.con -0.501 0.111 -4.526 21.572 0.041 0.982 -0.718 -0.283
## NOvSupport 0.188 0.133 1.413 2.103 0.004 0.716 -0.073 0.449
## ALLvOther 0.121 0.138 0.880 0.815 0.002 0.737 -0.150 0.393
## SUPvPrNu -0.198 0.155 -1.276 1.716 0.003 0.696 -0.502 0.107
## PRACTvNURT 0.096 0.183 0.521 0.286 0.001 0.667 -0.265 0.456
## Gen.con:NOvSupport -0.341 0.266 -1.280 1.726 0.003 0.715 -0.864 0.182
## Gen.con:ALLvOther -0.439 0.276 -1.591 2.666 0.005 0.736 -0.982 0.103
## Gen.con:SUPvPrNu -0.283 0.310 -0.914 0.880 0.002 0.696 -0.892 0.325
## Gen.con:PRACTvNURT -0.384 0.367 -1.046 1.152 0.002 0.667 -1.104 0.337
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.158
## ALLvOther 0.379
## SUPvPrNu 0.202
## PRACTvNURT 0.602
## Gen.con:NOvSupport 0.201
## Gen.con:ALLvOther 0.112
## Gen.con:SUPvPrNu 0.361
## Gen.con:PRACTvNURT 0.296Supp v. Nurt (n.s.)
model<- lm(AB.av ~Gen.con*(NOvSupport + ALLvOther + dat$PRAvSuNu + SUPPvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = AB.av ~ Gen.con * (NOvSupport + ALLvOther + dat$PRAvSuNu +
## SUPPvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 31.295 9 3.477 0.058 3.302 0.001
## Error 506.484 481 1.053
## Corr Total 537.779 490 1.098
##
## RMSE AdjEtaSq
## 1.026 0.041
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 3.953 0.055 71.432 5372.859 0.914 NA 3.844 4.062
## Gen.con -0.501 0.111 -4.526 21.572 0.041 0.982 -0.718 -0.283
## NOvSupport 0.188 0.133 1.413 2.103 0.004 0.716 -0.073 0.449
## ALLvOther 0.121 0.138 0.880 0.815 0.002 0.737 -0.150 0.393
## dat$PRAvSuNu 0.171 0.152 1.119 1.318 0.003 0.711 -0.129 0.470
## SUPPvNURT -0.150 0.186 -0.805 0.683 0.001 0.654 -0.515 0.216
## Gen.con:NOvSupport -0.341 0.266 -1.280 1.726 0.003 0.715 -0.864 0.182
## Gen.con:ALLvOther -0.439 0.276 -1.591 2.666 0.005 0.736 -0.982 0.103
## Gen.con:dat$PRAvSuNu -0.146 0.305 -0.479 0.242 0.000 0.711 -0.745 0.453
## Gen.con:SUPPvNURT -0.475 0.372 -1.276 1.715 0.003 0.654 -1.206 0.256
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.158
## ALLvOther 0.379
## dat$PRAvSuNu 0.264
## SUPPvNURT 0.421
## Gen.con:NOvSupport 0.201
## Gen.con:ALLvOther 0.112
## Gen.con:dat$PRAvSuNu 0.632
## Gen.con:SUPPvNURT 0.203Tukey’s (sig gender diff in Nurturant)
model<-aov(dat$AB.av~ dat$Gen.name*dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Gen.name 1 19.7 19.720 18.728 1.84e-05 ***
## dat$Condition 4 5.8 1.453 1.380 0.240
## dat$Gen.name:dat$Condition 4 5.8 1.441 1.368 0.244
## Residuals 481 506.5 1.053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$AB.av ~ dat$Gen.name * dat$Condition)
##
## $`dat$Gen.name`
## diff lwr upr p adj
## Woman-Man -0.4746459 -0.6901574 -0.2591345 1.84e-05
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp -0.08950453 -0.4792194 0.3002104 0.9703783
## NURTURANT-ALLsupp -0.10781876 -0.5129785 0.2973410 0.9497913
## PRACTICAL-ALLsupp -0.07682699 -0.4753092 0.3216553 0.9844801
## SUPPLEMENTAL-ALLsupp 0.19143653 -0.2091899 0.5920630 0.6861273
## NURTURANT-NOsupp -0.01831424 -0.4198534 0.3832249 0.9999451
## PRACTICAL-NOsupp 0.01267754 -0.3821229 0.4074780 0.9999865
## SUPPLEMENTAL-NOsupp 0.28094106 -0.1160235 0.6779056 0.2987039
## PRACTICAL-NURTURANT 0.03099178 -0.3790619 0.4410455 0.9995910
## SUPPLEMENTAL-NURTURANT 0.29925529 -0.1128824 0.7113930 0.2732862
## SUPPLEMENTAL-PRACTICAL 0.26826352 -0.1373116 0.6738387 0.3682695
##
## $`dat$Gen.name:dat$Condition`
## diff lwr upr
## Woman:ALLsupp-Man:ALLsupp -0.23962551 -0.98069927 0.501448253
## Man:NOsupp-Man:ALLsupp -0.10256410 -1.01622119 0.811092989
## Woman:NOsupp-Man:ALLsupp -0.33095916 -1.06617952 0.404261196
## Man:NURTURANT-Man:ALLsupp 0.46606335 -0.55130428 1.483430972
## Woman:NURTURANT-Man:ALLsupp -0.49896050 -1.24258259 0.244661597
## Man:PRACTICAL-Man:ALLsupp 0.17868590 -0.74462408 1.101995871
## Woman:PRACTICAL-Man:ALLsupp -0.40279241 -1.14773759 0.342152764
## Man:SUPPLEMENTAL-Man:ALLsupp 0.37849650 -0.56638517 1.323378173
## Woman:SUPPLEMENTAL-Man:ALLsupp -0.11169652 -0.85664170 0.633248655
## Man:NOsupp-Woman:ALLsupp 0.13706140 -0.61497473 0.889097534
## Woman:NOsupp-Woman:ALLsupp -0.09133366 -0.61223484 0.429567521
## Man:NURTURANT-Woman:ALLsupp 0.70568885 -0.16942596 1.580803671
## Woman:NURTURANT-Woman:ALLsupp -0.25933499 -0.79202898 0.273358992
## Man:PRACTICAL-Woman:ALLsupp 0.41831140 -0.34542308 1.182045892
## Woman:PRACTICAL-Woman:ALLsupp -0.16316691 -0.69770631 0.371372500
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.61812201 -0.17155541 1.407799431
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.12792898 -0.40661042 0.662468391
## Woman:NOsupp-Man:NOsupp -0.22839506 -0.97466378 0.517873658
## Man:NURTURANT-Man:NOsupp 0.56862745 -0.45675292 1.594007823
## Woman:NURTURANT-Man:NOsupp -0.39639640 -1.15094384 0.358151048
## Man:PRACTICAL-Man:NOsupp 0.28125000 -0.65088161 1.213381607
## Woman:PRACTICAL-Man:NOsupp -0.30022831 -1.05607971 0.455623092
## Man:SUPPLEMENTAL-Man:NOsupp 0.48106061 -0.47244314 1.434564353
## Woman:SUPPLEMENTAL-Man:NOsupp -0.00913242 -0.76498382 0.746718982
## Man:NURTURANT-Woman:NOsupp 0.79702251 -0.07314104 1.667186063
## Woman:NURTURANT-Woman:NOsupp -0.16800133 -0.69252163 0.356518964
## Man:PRACTICAL-Woman:NOsupp 0.50964506 -0.24841102 1.267701148
## Woman:PRACTICAL-Woman:NOsupp -0.07183325 -0.59822762 0.454561127
## Man:SUPPLEMENTAL-Woman:NOsupp 0.70945567 -0.07473123 1.493642566
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.21926264 -0.30713173 0.745657018
## Woman:NURTURANT-Man:NURTURANT -0.96502385 -1.84229772 -0.087749975
## Man:PRACTICAL-Man:NURTURANT -0.28737745 -1.32136823 0.746613330
## Woman:PRACTICAL-Man:NURTURANT -0.86885576 -1.74725143 0.009539903
## Man:SUPPLEMENTAL-Man:NURTURANT -0.08756684 -1.14086500 0.965731307
## Woman:SUPPLEMENTAL-Man:NURTURANT -0.57775987 -1.45615554 0.300635794
## Man:PRACTICAL-Woman:NURTURANT 0.67764640 -0.08856106 1.443853858
## Woman:PRACTICAL-Woman:NURTURANT 0.09616809 -0.44189872 0.634234887
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.87745700 0.08538760 1.669526403
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.38726398 -0.15080283 0.925330778
## Woman:PRACTICAL-Man:PRACTICAL -0.58147831 -1.34896992 0.186013298
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.19981061 -0.76294659 1.162567804
## Woman:SUPPLEMENTAL-Man:PRACTICAL -0.29038242 -1.05787403 0.477109189
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.78128892 -0.01202277 1.574600603
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.29109589 -0.24879797 0.830989749
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL -0.49019303 -1.28350471 0.303118660
## p adj
## Woman:ALLsupp-Man:ALLsupp 0.9904762
## Man:NOsupp-Man:ALLsupp 0.9999984
## Woman:NOsupp-Man:ALLsupp 0.9170998
## Man:NURTURANT-Man:ALLsupp 0.9083779
## Woman:NURTURANT-Man:ALLsupp 0.5051310
## Man:PRACTICAL-Man:ALLsupp 0.9998305
## Woman:PRACTICAL-Man:ALLsupp 0.7845069
## Man:SUPPLEMENTAL-Man:ALLsupp 0.9591340
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.9999805
## Man:NOsupp-Woman:ALLsupp 0.9998974
## Woman:NOsupp-Woman:ALLsupp 0.9999259
## Man:NURTURANT-Woman:ALLsupp 0.2382345
## Woman:NURTURANT-Woman:ALLsupp 0.8721023
## Man:PRACTICAL-Woman:ALLsupp 0.7713877
## Woman:PRACTICAL-Woman:ALLsupp 0.9937440
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.2774797
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.9990401
## Woman:NOsupp-Man:NOsupp 0.9936217
## Man:NURTURANT-Man:NOsupp 0.7582799
## Woman:NURTURANT-Man:NOsupp 0.8119310
## Man:PRACTICAL-Man:NOsupp 0.9942586
## Woman:PRACTICAL-Man:NOsupp 0.9612754
## Man:SUPPLEMENTAL-Man:NOsupp 0.8460674
## Woman:SUPPLEMENTAL-Man:NOsupp 1.0000000
## Man:NURTURANT-Woman:NOsupp 0.1051810
## Woman:NURTURANT-Woman:NOsupp 0.9911067
## Man:PRACTICAL-Woman:NOsupp 0.5021735
## Woman:PRACTICAL-Woman:NOsupp 0.9999913
## Man:SUPPLEMENTAL-Woman:NOsupp 0.1153210
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.9478142
## Woman:NURTURANT-Man:NURTURANT 0.0182922
## Man:PRACTICAL-Man:NURTURANT 0.9969085
## Woman:PRACTICAL-Man:NURTURANT 0.0553396
## Man:SUPPLEMENTAL-Man:NURTURANT 0.9999999
## Woman:SUPPLEMENTAL-Man:NURTURANT 0.5349612
## Man:PRACTICAL-Woman:NURTURANT 0.1355283
## Woman:PRACTICAL-Woman:NURTURANT 0.9999129
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.0168289
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.3988668
## Woman:PRACTICAL-Man:PRACTICAL 0.3231295
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.9996977
## Woman:SUPPLEMENTAL-Man:PRACTICAL 0.9717693
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.0575803
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.7873166
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL 0.6247387Differences:
Significant gender in Nurturant
Graphs
Gender Main effect
p <- ggplot(dat, aes(Gen.name, AB.av, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("suffrager::classic") + ylab("") + xlab("")
p
Condition null
boxplot(dat$AB.av~ dat$Condition)
p <- ggplot(dat, aes(Condition.ordered, AB.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
Condition x gender null
SE
Prac v. Supp (n.s.)
model<- lm(SE.av~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 30.422 9 3.380 0.063 3.63 0
## Error 448.798 482 0.931
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.965 0.046
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.610 0.052 88.610 7310.865 0.942 NA 4.507 4.712
## Gen.con -0.494 0.104 -4.750 21.007 0.045 0.982 -0.699 -0.290
## NOvSupport 0.108 0.125 0.867 0.700 0.002 0.715 -0.137 0.354
## ALLvOther 0.223 0.130 1.720 2.755 0.006 0.736 -0.032 0.478
## NURTvPrSu 0.182 0.153 1.187 1.313 0.003 0.641 -0.119 0.483
## PRACTvSUPP 0.163 0.163 0.998 0.927 0.002 0.725 -0.158 0.483
## Gen.con:NOvSupport -0.305 0.250 -1.218 1.380 0.003 0.715 -0.796 0.187
## Gen.con:ALLvOther -0.377 0.260 -1.452 1.964 0.004 0.736 -0.887 0.133
## Gen.con:NURTvPrSu 0.293 0.307 0.957 0.853 0.002 0.641 -0.309 0.896
## Gen.con:PRACTvSUPP -0.235 0.326 -0.720 0.483 0.001 0.725 -0.876 0.406
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.386
## ALLvOther 0.086
## NURTvPrSu 0.236
## PRACTvSUPP 0.319
## Gen.con:NOvSupport 0.224
## Gen.con:ALLvOther 0.147
## Gen.con:NURTvPrSu 0.339
## Gen.con:PRACTvSUPP 0.472Prac v. Nurt (n.s.)
model<- lm(SE.av ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + SUPvPrNu +
## PRACTvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 30.422 9 3.380 0.063 3.63 0
## Error 448.798 482 0.931
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.965 0.046
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.610 0.052 88.610 7310.865 0.942 NA 4.507 4.712
## Gen.con -0.494 0.104 -4.750 21.007 0.045 0.982 -0.699 -0.290
## NOvSupport 0.108 0.125 0.867 0.700 0.002 0.715 -0.137 0.354
## ALLvOther 0.223 0.130 1.720 2.755 0.006 0.736 -0.032 0.478
## SUPvPrNu -0.213 0.145 -1.465 1.998 0.004 0.692 -0.499 0.073
## PRACTvNURT -0.101 0.172 -0.583 0.317 0.001 0.667 -0.439 0.238
## Gen.con:NOvSupport -0.305 0.250 -1.218 1.380 0.003 0.715 -0.796 0.187
## Gen.con:ALLvOther -0.377 0.260 -1.452 1.964 0.004 0.736 -0.887 0.133
## Gen.con:SUPvPrNu 0.030 0.291 0.102 0.010 0.000 0.692 -0.542 0.601
## Gen.con:PRACTvNURT -0.411 0.345 -1.192 1.323 0.003 0.667 -1.089 0.267
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.386
## ALLvOther 0.086
## SUPvPrNu 0.144
## PRACTvNURT 0.560
## Gen.con:NOvSupport 0.224
## Gen.con:ALLvOther 0.147
## Gen.con:SUPvPrNu 0.919
## Gen.con:PRACTvNURT 0.234Supp v. Nurt (n.s.)
model<- lm(SE.av ~Gen.con*(NOvSupport + ALLvOther + dat$PRAvSuNu + SUPPvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + dat$PRAvSuNu +
## SUPPvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 30.422 9 3.380 0.063 3.63 0
## Error 448.798 482 0.931
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.965 0.046
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.610 0.052 88.610 7310.865 0.942 NA 4.507 4.712
## Gen.con -0.494 0.104 -4.750 21.007 0.045 0.982 -0.699 -0.290
## NOvSupport 0.108 0.125 0.867 0.700 0.002 0.715 -0.137 0.354
## ALLvOther 0.223 0.130 1.720 2.755 0.006 0.736 -0.032 0.478
## dat$PRAvSuNu 0.031 0.143 0.217 0.044 0.000 0.710 -0.250 0.313
## SUPPvNURT -0.263 0.175 -1.506 2.113 0.005 0.652 -0.607 0.080
## Gen.con:NOvSupport -0.305 0.250 -1.218 1.380 0.003 0.715 -0.796 0.187
## Gen.con:ALLvOther -0.377 0.260 -1.452 1.964 0.004 0.736 -0.887 0.133
## Gen.con:dat$PRAvSuNu -0.323 0.287 -1.127 1.183 0.003 0.710 -0.886 0.240
## Gen.con:SUPPvNURT -0.176 0.350 -0.503 0.236 0.001 0.652 -0.863 0.511
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.386
## ALLvOther 0.086
## dat$PRAvSuNu 0.828
## SUPPvNURT 0.133
## Gen.con:NOvSupport 0.224
## Gen.con:ALLvOther 0.147
## Gen.con:dat$PRAvSuNu 0.260
## Gen.con:SUPPvNURT 0.615Tukey’s (sig gender diff in Nurt)
model<-aov(dat$SE.av~ dat$Gen.name*dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Gen.name 1 19.7 19.690 21.147 5.44e-06 ***
## dat$Condition 4 6.4 1.604 1.722 0.144
## dat$Gen.name:dat$Condition 4 4.3 1.079 1.159 0.328
## Residuals 482 448.8 0.931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$SE.av ~ dat$Gen.name * dat$Condition)
##
## $`dat$Gen.name`
## diff lwr upr p adj
## Woman-Man -0.4741437 -0.6767374 -0.27155 5.4e-06
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp 0.06857876 -0.29788824 0.4350458 0.9861228
## NURTURANT-ALLsupp -0.06367606 -0.44466652 0.3173144 0.9909511
## PRACTICAL-ALLsupp 0.16318846 -0.21152286 0.5378998 0.7557524
## SUPPLEMENTAL-ALLsupp 0.26306007 -0.11265025 0.6387704 0.3095365
## NURTURANT-NOsupp -0.13225482 -0.50984067 0.2453310 0.8732405
## PRACTICAL-NOsupp 0.09460970 -0.27663943 0.4658588 0.9569252
## SUPPLEMENTAL-NOsupp 0.19448131 -0.17777611 0.5667387 0.6082258
## PRACTICAL-NURTURANT 0.22686452 -0.15872799 0.6124570 0.4910291
## SUPPLEMENTAL-NURTURANT 0.32673613 -0.05982726 0.7132995 0.1420435
## SUPPLEMENTAL-PRACTICAL 0.09987161 -0.28050464 0.4802479 0.9521181
##
## $`dat$Gen.name:dat$Condition`
## diff lwr upr
## Woman:ALLsupp-Man:ALLsupp -0.27236842 -0.96923309 0.42449625
## Man:NOsupp-Man:ALLsupp 0.04800000 -0.81115246 0.90715246
## Woman:NOsupp-Man:ALLsupp -0.20246914 -0.89382959 0.48889132
## Man:NURTURANT-Man:ALLsupp 0.38823529 -0.56844080 1.34491139
## Woman:NURTURANT-Man:ALLsupp -0.45675676 -1.15601774 0.24250423
## Man:PRACTICAL-Man:ALLsupp 0.28333333 -0.58489617 1.15156283
## Woman:PRACTICAL-Man:ALLsupp -0.15068493 -0.85119007 0.54982020
## Man:SUPPLEMENTAL-Man:ALLsupp 0.56363636 -0.32487796 1.45215069
## Woman:SUPPLEMENTAL-Man:ALLsupp -0.10540541 -0.80466639 0.59385558
## Man:NOsupp-Woman:ALLsupp 0.32036842 -0.38680465 1.02754150
## Woman:NOsupp-Woman:ALLsupp 0.06989929 -0.41992730 0.55972587
## Man:NURTURANT-Woman:ALLsupp 0.66060372 -0.16230573 1.48351316
## Woman:NURTURANT-Woman:ALLsupp -0.18438834 -0.68530422 0.31652755
## Man:PRACTICAL-Woman:ALLsupp 0.55570175 -0.16247181 1.27387532
## Woman:PRACTICAL-Woman:ALLsupp 0.12168349 -0.38096773 0.62433471
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.83600478 0.09343593 1.57857364
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.16696302 -0.33395287 0.66787890
## Woman:NOsupp-Man:NOsupp -0.25046914 -0.95221886 0.45128059
## Man:NURTURANT-Man:NOsupp 0.34023529 -0.62397555 1.30444614
## Woman:NURTURANT-Man:NOsupp -0.50475676 -1.21429133 0.20477782
## Man:PRACTICAL-Man:NOsupp 0.23533333 -0.64119154 1.11185821
## Woman:PRACTICAL-Man:NOsupp -0.19868493 -0.90944568 0.51207581
## Man:SUPPLEMENTAL-Man:NOsupp 0.51563636 -0.38098569 1.41225841
## Woman:SUPPLEMENTAL-Man:NOsupp -0.15340541 -0.86293998 0.55612917
## Man:NURTURANT-Woman:NOsupp 0.59070443 -0.22754912 1.40895798
## Woman:NURTURANT-Woman:NOsupp -0.25428762 -0.74751742 0.23894218
## Man:PRACTICAL-Woman:NOsupp 0.48580247 -0.22703144 1.19863638
## Woman:PRACTICAL-Woman:NOsupp 0.05178420 -0.44320788 0.54677629
## Man:SUPPLEMENTAL-Woman:NOsupp 0.76610550 0.02869963 1.50351137
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.09706373 -0.39616607 0.59029353
## Woman:NURTURANT-Man:NURTURANT -0.84499205 -1.66993175 -0.02005235
## Man:PRACTICAL-Man:NURTURANT -0.10490196 -1.07720955 0.86740563
## Woman:PRACTICAL-Man:NURTURANT -0.53892023 -1.36491480 0.28707435
## Man:SUPPLEMENTAL-Man:NURTURANT 0.17540107 -0.81506210 1.16586424
## Woman:SUPPLEMENTAL-Man:NURTURANT -0.49364070 -1.31858040 0.33129900
## Man:PRACTICAL-Woman:NURTURANT 0.74009009 0.01959108 1.46058910
## Woman:PRACTICAL-Woman:NURTURANT 0.30607183 -0.19989636 0.81204001
## Man:SUPPLEMENTAL-Woman:NURTURANT 1.02039312 0.27557498 1.76521126
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.35135135 -0.15289291 0.85559562
## Woman:PRACTICAL-Man:PRACTICAL -0.43401826 -1.15572481 0.28768828
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.28030303 -0.62502045 1.18562651
## Woman:SUPPLEMENTAL-Man:PRACTICAL -0.38873874 -1.10923775 0.33176027
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.71432130 -0.03166502 1.46030761
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.04527953 -0.46068866 0.55124771
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL -0.66904177 -1.41385991 0.07577637
## p adj
## Woman:ALLsupp-Man:ALLsupp 0.9650929
## Man:NOsupp-Man:ALLsupp 1.0000000
## Woman:NOsupp-Man:ALLsupp 0.9954054
## Man:NURTURANT-Man:ALLsupp 0.9556383
## Woman:NURTURANT-Man:ALLsupp 0.5452485
## Man:PRACTICAL-Man:ALLsupp 0.9898270
## Woman:PRACTICAL-Man:ALLsupp 0.9995946
## Man:SUPPLEMENTAL-Man:ALLsupp 0.5878426
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.9999796
## Man:NOsupp-Woman:ALLsupp 0.9140182
## Woman:NOsupp-Woman:ALLsupp 0.9999872
## Man:NURTURANT-Woman:ALLsupp 0.2440230
## Woman:NURTURANT-Woman:ALLsupp 0.9764937
## Man:PRACTICAL-Woman:ALLsupp 0.2933578
## Woman:PRACTICAL-Woman:ALLsupp 0.9989484
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.0138330
## Woman:SUPPLEMENTAL-Woman:ALLsupp 0.9881685
## Woman:NOsupp-Man:NOsupp 0.9809323
## Man:NURTURANT-Man:NOsupp 0.9823720
## Woman:NURTURANT-Man:NOsupp 0.4164831
## Man:PRACTICAL-Man:NOsupp 0.9976305
## Woman:PRACTICAL-Man:NOsupp 0.9967696
## Man:SUPPLEMENTAL-Man:NOsupp 0.7171246
## Woman:SUPPLEMENTAL-Man:NOsupp 0.9995774
## Man:NURTURANT-Woman:NOsupp 0.3943328
## Woman:NURTURANT-Woman:NOsupp 0.8284384
## Man:PRACTICAL-Woman:NOsupp 0.4816329
## Woman:PRACTICAL-Woman:NOsupp 0.9999991
## Man:SUPPLEMENTAL-Woman:NOsupp 0.0343213
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.9998052
## Woman:NURTURANT-Man:NURTURANT 0.0396195
## Man:PRACTICAL-Man:NURTURANT 0.9999989
## Woman:PRACTICAL-Man:NURTURANT 0.5469430
## Man:SUPPLEMENTAL-Man:NURTURANT 0.9999194
## Woman:SUPPLEMENTAL-Man:NURTURANT 0.6677059
## Man:PRACTICAL-Woman:NURTURANT 0.0385213
## Woman:PRACTICAL-Woman:NURTURANT 0.6534668
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.0006803
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.4479635
## Woman:PRACTICAL-Man:PRACTICAL 0.6612196
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.9930547
## Woman:SUPPLEMENTAL-Man:PRACTICAL 0.7866516
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.0736735
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.9999998
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL 0.1214671Differences within condition:
Significant Gender diff in Nurturant
+ Communality
model<- lm(SE.av~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + NURTvPrSu +
## PRACTvSUPP) + TAcomm.av, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 52.078 10 5.208 0.109 5.865 0
## Error 427.141 481 0.888
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.942 0.09
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 2.886 0.353 8.186 59.514 0.122 NA 2.194 3.579 0.000
## Gen.con -0.530 0.102 -5.199 24.006 0.053 0.977 -0.730 -0.329 0.000
## NOvSupport 0.084 0.122 0.683 0.414 0.001 0.714 -0.157 0.324 0.495
## ALLvOther 0.232 0.127 1.831 2.977 0.007 0.736 -0.017 0.481 0.068
## NURTvPrSu 0.232 0.150 1.545 2.119 0.005 0.638 -0.063 0.527 0.123
## PRACTvSUPP 0.191 0.159 1.200 1.279 0.003 0.724 -0.122 0.505 0.231
## TAcomm.av 0.330 0.067 4.938 21.657 0.048 0.965 0.198 0.461 0.000
## Gen.con:NOvSupport -0.148 0.246 -0.602 0.321 0.001 0.703 -0.632 0.336 0.548
## Gen.con:ALLvOther -0.442 0.254 -1.740 2.689 0.006 0.734 -0.940 0.057 0.082
## Gen.con:NURTvPrSu 0.183 0.300 0.611 0.332 0.001 0.637 -0.406 0.773 0.541
## Gen.con:PRACTvSUPP -0.316 0.319 -0.989 0.869 0.002 0.723 -0.943 0.311 0.323Graphs
Gender main effect
p <- ggplot(dat, aes(Gen.name, SE.av, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("suffrager::classic") + ylab("") + xlab("")
p
Condition null
boxplot(dat$SE.av~ dat$Condition)
p <- ggplot(dat, aes(Condition.ordered, SE.av, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
Condition x gender null
Anticipated Grade
Prac v. Supp (n.s.)
model<- lm(Ant.grade~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Ant.grade ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 6.107 9 0.679 0.03 1.634 0.103
## Error 199.778 481 0.415
## Corr Total 205.885 490 0.420
##
## RMSE AdjEtaSq
## 0.644 0.012
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 3.305 0.035 95.092 3755.701 0.949 NA 3.237 3.373
## Gen.con -0.194 0.070 -2.797 3.249 0.016 0.982 -0.331 -0.058
## NOvSupport 0.039 0.084 0.471 0.092 0.000 0.716 -0.125 0.204
## ALLvOther 0.035 0.087 0.401 0.067 0.000 0.737 -0.136 0.205
## NURTvPrSu 0.129 0.102 1.261 0.660 0.003 0.644 -0.072 0.331
## PRACTvSUPP 0.085 0.109 0.780 0.253 0.001 0.725 -0.129 0.299
## Gen.con:NOvSupport -0.137 0.167 -0.817 0.277 0.001 0.715 -0.465 0.192
## Gen.con:ALLvOther -0.229 0.173 -1.323 0.727 0.004 0.736 -0.570 0.111
## Gen.con:NURTvPrSu 0.111 0.205 0.540 0.121 0.001 0.644 -0.292 0.513
## Gen.con:PRACTvSUPP -0.106 0.218 -0.486 0.098 0.000 0.725 -0.534 0.322
## p
## (Intercept) 0.000
## Gen.con 0.005
## NOvSupport 0.638
## ALLvOther 0.689
## NURTvPrSu 0.208
## PRACTvSUPP 0.436
## Gen.con:NOvSupport 0.414
## Gen.con:ALLvOther 0.186
## Gen.con:NURTvPrSu 0.589
## Gen.con:PRACTvSUPP 0.627Prac v. Nurt ()
model<- lm(SE.av ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + SUPvPrNu +
## PRACTvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 30.422 9 3.380 0.063 3.63 0
## Error 448.798 482 0.931
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.965 0.046
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.610 0.052 88.610 7310.865 0.942 NA 4.507 4.712
## Gen.con -0.494 0.104 -4.750 21.007 0.045 0.982 -0.699 -0.290
## NOvSupport 0.108 0.125 0.867 0.700 0.002 0.715 -0.137 0.354
## ALLvOther 0.223 0.130 1.720 2.755 0.006 0.736 -0.032 0.478
## SUPvPrNu -0.213 0.145 -1.465 1.998 0.004 0.692 -0.499 0.073
## PRACTvNURT -0.101 0.172 -0.583 0.317 0.001 0.667 -0.439 0.238
## Gen.con:NOvSupport -0.305 0.250 -1.218 1.380 0.003 0.715 -0.796 0.187
## Gen.con:ALLvOther -0.377 0.260 -1.452 1.964 0.004 0.736 -0.887 0.133
## Gen.con:SUPvPrNu 0.030 0.291 0.102 0.010 0.000 0.692 -0.542 0.601
## Gen.con:PRACTvNURT -0.411 0.345 -1.192 1.323 0.003 0.667 -1.089 0.267
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.386
## ALLvOther 0.086
## SUPvPrNu 0.144
## PRACTvNURT 0.560
## Gen.con:NOvSupport 0.224
## Gen.con:ALLvOther 0.147
## Gen.con:SUPvPrNu 0.919
## Gen.con:PRACTvNURT 0.234Supp v. Nurt (n.s.)
model<- lm(SE.av ~Gen.con*(NOvSupport + ALLvOther + dat$PRAvSuNu + SUPPvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = SE.av ~ Gen.con * (NOvSupport + ALLvOther + dat$PRAvSuNu +
## SUPPvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 30.422 9 3.380 0.063 3.63 0
## Error 448.798 482 0.931
## Corr Total 479.220 491 0.976
##
## RMSE AdjEtaSq
## 0.965 0.046
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 4.610 0.052 88.610 7310.865 0.942 NA 4.507 4.712
## Gen.con -0.494 0.104 -4.750 21.007 0.045 0.982 -0.699 -0.290
## NOvSupport 0.108 0.125 0.867 0.700 0.002 0.715 -0.137 0.354
## ALLvOther 0.223 0.130 1.720 2.755 0.006 0.736 -0.032 0.478
## dat$PRAvSuNu 0.031 0.143 0.217 0.044 0.000 0.710 -0.250 0.313
## SUPPvNURT -0.263 0.175 -1.506 2.113 0.005 0.652 -0.607 0.080
## Gen.con:NOvSupport -0.305 0.250 -1.218 1.380 0.003 0.715 -0.796 0.187
## Gen.con:ALLvOther -0.377 0.260 -1.452 1.964 0.004 0.736 -0.887 0.133
## Gen.con:dat$PRAvSuNu -0.323 0.287 -1.127 1.183 0.003 0.710 -0.886 0.240
## Gen.con:SUPPvNURT -0.176 0.350 -0.503 0.236 0.001 0.652 -0.863 0.511
## p
## (Intercept) 0.000
## Gen.con 0.000
## NOvSupport 0.386
## ALLvOther 0.086
## dat$PRAvSuNu 0.828
## SUPPvNURT 0.133
## Gen.con:NOvSupport 0.224
## Gen.con:ALLvOther 0.147
## Gen.con:dat$PRAvSuNu 0.260
## Gen.con:SUPPvNURT 0.615Tukey’s (n.s.)
model<-aov(dat$Ant.grade~ dat$Gen.name*dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Gen.name 1 3.09 3.0851 7.428 0.00666 **
## dat$Condition 4 1.88 0.4707 1.133 0.34001
## dat$Gen.name:dat$Condition 4 1.14 0.2847 0.685 0.60231
## Residuals 481 199.78 0.4153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessTukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$Ant.grade ~ dat$Gen.name * dat$Condition)
##
## $`dat$Gen.name`
## diff lwr upr p adj
## Woman-Man -0.1877379 -0.3230889 -0.05238695 0.0066562
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp -0.019989455 -0.2647481 0.2247691 0.9994443
## NURTURANT-ALLsupp -0.136561092 -0.3917657 0.1186436 0.5856614
## PRACTICAL-ALLsupp 0.001121145 -0.2491437 0.2513860 1.0000000
## SUPPLEMENTAL-ALLsupp 0.057405020 -0.1935271 0.3083371 0.9707985
## NURTURANT-NOsupp -0.116571637 -0.3695091 0.1363658 0.7146967
## PRACTICAL-NOsupp 0.021110600 -0.2268419 0.2690631 0.9993450
## SUPPLEMENTAL-NOsupp 0.077394475 -0.1712315 0.3260204 0.9138801
## PRACTICAL-NURTURANT 0.137682237 -0.1205872 0.3959517 0.5892446
## SUPPLEMENTAL-NURTURANT 0.193966112 -0.0649499 0.4528821 0.2433349
## SUPPLEMENTAL-PRACTICAL 0.056283875 -0.1977645 0.3103323 0.9740260
##
## $`dat$Gen.name:dat$Condition`
## diff lwr upr
## Woman:ALLsupp-Man:ALLsupp -0.049696356 -0.515124196 0.4157315
## Man:NOsupp-Man:ALLsupp 0.004461538 -0.569356440 0.5782795
## Woman:NOsupp-Man:ALLsupp -0.080674264 -0.542425905 0.3810774
## Man:NURTURANT-Man:ALLsupp 0.100226244 -0.538726640 0.7391791
## Woman:NURTURANT-Man:ALLsupp -0.252634352 -0.720493618 0.2152249
## Man:PRACTICAL-Man:ALLsupp 0.105128205 -0.474752221 0.6850086
## Woman:PRACTICAL-Man:ALLsupp -0.084141201 -0.552000467 0.3837181
## Man:SUPPLEMENTAL-Man:ALLsupp 0.243006993 -0.350421433 0.8364354
## Woman:SUPPLEMENTAL-Man:ALLsupp -0.052079002 -0.519107313 0.4149493
## Man:NOsupp-Woman:ALLsupp 0.054157895 -0.418154810 0.5264706
## Woman:NOsupp-Woman:ALLsupp -0.030977908 -0.358127409 0.2961716
## Man:NURTURANT-Woman:ALLsupp 0.149922601 -0.399689087 0.6995343
## Woman:NURTURANT-Woman:ALLsupp -0.202937996 -0.538652921 0.1327769
## Man:PRACTICAL-Woman:ALLsupp 0.154824561 -0.324835242 0.6344844
## Woman:PRACTICAL-Woman:ALLsupp -0.034444845 -0.370159770 0.3012701
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.292703349 -0.203249789 0.7886565
## Woman:SUPPLEMENTAL-Woman:ALLsupp -0.002382646 -0.336938563 0.3321733
## Woman:NOsupp-Man:NOsupp -0.085135802 -0.553826313 0.3835547
## Man:NURTURANT-Man:NOsupp 0.095764706 -0.548220546 0.7397500
## Woman:NURTURANT-Man:NOsupp -0.257095890 -0.731804759 0.2176130
## Man:PRACTICAL-Man:NOsupp 0.100666667 -0.484754144 0.6860875
## Woman:PRACTICAL-Man:NOsupp -0.088602740 -0.563311608 0.3861061
## Man:SUPPLEMENTAL-Man:NOsupp 0.238545455 -0.360298026 0.8373889
## Woman:SUPPLEMENTAL-Man:NOsupp -0.056540541 -0.530430465 0.4173494
## Man:NURTURANT-Woman:NOsupp 0.180900508 -0.365601559 0.7274026
## Woman:NURTURANT-Woman:NOsupp -0.171960088 -0.502559565 0.1586394
## Man:PRACTICAL-Woman:NOsupp 0.185802469 -0.290291040 0.6618960
## Woman:PRACTICAL-Woman:NOsupp -0.003466937 -0.334066415 0.3271325
## Man:SUPPLEMENTAL-Woman:NOsupp 0.323681257 -0.168823585 0.8161861
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.028595262 -0.300827210 0.3580177
## Woman:NURTURANT-Man:NURTURANT -0.352860596 -0.904532804 0.1988116
## Man:PRACTICAL-Man:NURTURANT 0.004901961 -0.644491018 0.6542949
## Woman:PRACTICAL-Man:NURTURANT -0.184367446 -0.736039654 0.3673048
## Man:SUPPLEMENTAL-Man:NURTURANT 0.142780749 -0.518738132 0.8042996
## Woman:SUPPLEMENTAL-Man:NURTURANT -0.152305246 -0.703272918 0.3986624
## Man:PRACTICAL-Woman:NURTURANT 0.357762557 -0.124256887 0.8397820
## Woman:PRACTICAL-Woman:NURTURANT 0.168493151 -0.170584612 0.5075709
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.495641345 -0.002594276 0.9938770
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.200555350 -0.137374939 0.5384856
## Woman:PRACTICAL-Man:PRACTICAL -0.189269406 -0.671288851 0.2927500
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.137878788 -0.466776279 0.7425339
## Woman:SUPPLEMENTAL-Man:PRACTICAL -0.157207207 -0.638420149 0.3240057
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.327148194 -0.171087427 0.8253838
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.032062199 -0.305868089 0.3699925
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL -0.295085995 -0.792541405 0.2023694
## p adj
## Woman:ALLsupp-Man:ALLsupp 0.9999990
## Man:NOsupp-Man:ALLsupp 1.0000000
## Woman:NOsupp-Man:ALLsupp 0.9999281
## Man:NURTURANT-Man:ALLsupp 0.9999712
## Woman:NURTURANT-Man:ALLsupp 0.7858353
## Man:PRACTICAL-Man:ALLsupp 0.9999019
## Woman:PRACTICAL-Man:ALLsupp 0.9999083
## Man:SUPPLEMENTAL-Man:ALLsupp 0.9530519
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.9999985
## Man:NOsupp-Woman:ALLsupp 0.9999981
## Woman:NOsupp-Woman:ALLsupp 0.9999996
## Man:NURTURANT-Woman:ALLsupp 0.9973217
## Woman:NURTURANT-Woman:ALLsupp 0.6543923
## Man:PRACTICAL-Woman:ALLsupp 0.9905963
## Woman:PRACTICAL-Woman:ALLsupp 0.9999993
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.6853825
## Woman:SUPPLEMENTAL-Woman:ALLsupp 1.0000000
## Woman:NOsupp-Man:NOsupp 0.9999002
## Man:NURTURANT-Man:NOsupp 0.9999818
## Woman:NURTURANT-Man:NOsupp 0.7828708
## Man:PRACTICAL-Man:NOsupp 0.9999372
## Woman:PRACTICAL-Man:NOsupp 0.9998747
## Man:SUPPLEMENTAL-Man:NOsupp 0.9605616
## Woman:SUPPLEMENTAL-Man:NOsupp 0.9999973
## Man:NURTURANT-Woman:NOsupp 0.9887358
## Woman:NURTURANT-Woman:NOsupp 0.8207894
## Man:PRACTICAL-Woman:NOsupp 0.9654256
## Woman:PRACTICAL-Woman:NOsupp 1.0000000
## Man:SUPPLEMENTAL-Woman:NOsupp 0.5361588
## Woman:SUPPLEMENTAL-Woman:NOsupp 0.9999998
## Woman:NURTURANT-Man:NURTURANT 0.5759497
## Man:PRACTICAL-Man:NURTURANT 1.0000000
## Woman:PRACTICAL-Man:NURTURANT 0.9879433
## Man:SUPPLEMENTAL-Man:NURTURANT 0.9995832
## Woman:SUPPLEMENTAL-Man:NURTURANT 0.9970337
## Man:PRACTICAL-Woman:NURTURANT 0.3530217
## Woman:PRACTICAL-Woman:NURTURANT 0.8576025
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.0525026
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.6783116
## Woman:PRACTICAL-Man:PRACTICAL 0.9640327
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.9993489
## Woman:SUPPLEMENTAL-Man:PRACTICAL 0.9897463
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.5375168
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.9999996
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL 0.6789365+ Communality
model<- lm(Ant.grade~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Ant.grade ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 9.026 10 0.903 0.044 2.201 0.017
## Error 196.858 480 0.410
## Corr Total 205.885 490 0.420
##
## RMSE AdjEtaSq
## 0.64 0.024
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 2.671 0.240 11.136 50.855 0.205 NA 2.200 3.143 0.000
## Gen.con -0.207 0.069 -2.992 3.672 0.018 0.977 -0.343 -0.071 0.003
## NOvSupport 0.030 0.083 0.365 0.055 0.000 0.714 -0.133 0.194 0.715
## ALLvOther 0.038 0.086 0.444 0.081 0.000 0.737 -0.131 0.208 0.657
## NURTvPrSu 0.147 0.102 1.439 0.850 0.004 0.642 -0.054 0.347 0.151
## PRACTvSUPP 0.095 0.108 0.881 0.318 0.002 0.724 -0.117 0.308 0.379
## TAcomm.av 0.121 0.045 2.668 2.920 0.015 0.965 0.032 0.210 0.008
## Gen.con:NOvSupport -0.079 0.167 -0.470 0.091 0.000 0.703 -0.408 0.250 0.638
## Gen.con:ALLvOther -0.253 0.173 -1.465 0.880 0.004 0.734 -0.592 0.086 0.144
## Gen.con:NURTvPrSu 0.069 0.204 0.338 0.047 0.000 0.640 -0.332 0.470 0.736
## Gen.con:PRACTvSUPP -0.135 0.217 -0.625 0.160 0.001 0.723 -0.561 0.291 0.532Graphs
Gender main effect
p <- ggplot(dat, aes(Gen.name, Ant.grade, fill = Gen.name)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("suffrager::classic") + ylab("") + xlab("")
p
Condition null
boxplot(dat$Ant.grade~ dat$Condition)
p <- ggplot(dat, aes(Condition.ordered, Ant.grade, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
Condition x gender null
Exam grade
Prac v. Supp (sig)
model<- lm(Exam.total~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Exam.total ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 66.510 9 7.390 0.039 2.152 0.024
## Error 1655.204 482 3.434
## Corr Total 1721.713 491 3.507
##
## RMSE AdjEtaSq
## 1.853 0.021
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 7.487 0.100 74.944 19287.767 0.921 NA 7.291 7.683
## Gen.con -0.312 0.200 -1.559 8.351 0.005 0.982 -0.704 0.081
## NOvSupport 0.064 0.240 0.267 0.245 0.000 0.715 -0.408 0.536
## ALLvOther 0.466 0.249 1.870 12.015 0.007 0.736 -0.024 0.956
## NURTvPrSu 0.263 0.294 0.894 2.742 0.002 0.641 -0.315 0.841
## PRACTvSUPP -0.829 0.313 -2.644 24.015 0.014 0.725 -1.444 -0.213
## Gen.con:NOvSupport 0.021 0.481 0.044 0.007 0.000 0.715 -0.923 0.965
## Gen.con:ALLvOther -0.606 0.498 -1.215 5.067 0.003 0.736 -1.585 0.374
## Gen.con:NURTvPrSu 0.635 0.589 1.079 4.001 0.002 0.641 -0.521 1.792
## Gen.con:PRACTvSUPP 0.199 0.627 0.318 0.347 0.000 0.725 -1.032 1.430
## p
## (Intercept) 0.000
## Gen.con 0.120
## NOvSupport 0.790
## ALLvOther 0.062
## NURTvPrSu 0.372
## PRACTvSUPP 0.008
## Gen.con:NOvSupport 0.965
## Gen.con:ALLvOther 0.225
## Gen.con:NURTvPrSu 0.281
## Gen.con:PRACTvSUPP 0.751Prac v. Nurt (sig)
model<- lm(Exam.total ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Exam.total ~ Gen.con * (NOvSupport + ALLvOther +
## SUPvPrNu + PRACTvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 66.510 9 7.390 0.039 2.152 0.024
## Error 1655.204 482 3.434
## Corr Total 1721.713 491 3.507
##
## RMSE AdjEtaSq
## 1.853 0.021
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 7.487 0.100 74.944 19287.767 0.921 NA 7.291 7.683
## Gen.con -0.312 0.200 -1.559 8.351 0.005 0.982 -0.704 0.081
## NOvSupport 0.064 0.240 0.267 0.245 0.000 0.715 -0.408 0.536
## ALLvOther 0.466 0.249 1.870 12.015 0.007 0.736 -0.024 0.956
## SUPvPrNu 0.490 0.279 1.754 10.561 0.006 0.692 -0.059 1.039
## PRACTvNURT -0.677 0.331 -2.045 14.367 0.009 0.667 -1.328 -0.027
## Gen.con:NOvSupport 0.021 0.481 0.044 0.007 0.000 0.715 -0.923 0.965
## Gen.con:ALLvOther -0.606 0.498 -1.215 5.067 0.003 0.736 -1.585 0.374
## Gen.con:SUPvPrNu -0.467 0.559 -0.836 2.399 0.001 0.692 -1.565 0.631
## Gen.con:PRACTvNURT -0.536 0.662 -0.809 2.249 0.001 0.667 -1.837 0.765
## p
## (Intercept) 0.000
## Gen.con 0.120
## NOvSupport 0.790
## ALLvOther 0.062
## SUPvPrNu 0.080
## PRACTvNURT 0.041
## Gen.con:NOvSupport 0.965
## Gen.con:ALLvOther 0.225
## Gen.con:SUPvPrNu 0.404
## Gen.con:PRACTvNURT 0.419Supp v. Nurt (n.s.)
model<- lm(Exam.total ~Gen.con*(NOvSupport + ALLvOther + dat$PRAvSuNu + SUPPvNURT), data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Exam.total ~ Gen.con * (NOvSupport + ALLvOther +
## dat$PRAvSuNu + SUPPvNURT), data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 66.510 9 7.390 0.039 2.152 0.024
## Error 1655.204 482 3.434
## Corr Total 1721.713 491 3.507
##
## RMSE AdjEtaSq
## 1.853 0.021
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 7.487 0.100 74.944 19287.767 0.921 NA 7.291 7.683
## Gen.con -0.312 0.200 -1.559 8.351 0.005 0.982 -0.704 0.081
## NOvSupport 0.064 0.240 0.267 0.245 0.000 0.715 -0.408 0.536
## ALLvOther 0.466 0.249 1.870 12.015 0.007 0.736 -0.024 0.956
## dat$PRAvSuNu -0.753 0.275 -2.736 25.709 0.015 0.710 -1.294 -0.212
## SUPPvNURT 0.151 0.336 0.450 0.697 0.000 0.652 -0.508 0.811
## Gen.con:NOvSupport 0.021 0.481 0.044 0.007 0.000 0.715 -0.923 0.965
## Gen.con:ALLvOther -0.606 0.498 -1.215 5.067 0.003 0.736 -1.585 0.374
## Gen.con:dat$PRAvSuNu -0.168 0.550 -0.306 0.322 0.000 0.710 -1.250 0.913
## Gen.con:SUPPvNURT -0.735 0.671 -1.094 4.114 0.002 0.652 -2.054 0.584
## p
## (Intercept) 0.000
## Gen.con 0.120
## NOvSupport 0.790
## ALLvOther 0.062
## dat$PRAvSuNu 0.006
## SUPPvNURT 0.653
## Gen.con:NOvSupport 0.965
## Gen.con:ALLvOther 0.225
## Gen.con:dat$PRAvSuNu 0.760
## Gen.con:SUPPvNURT 0.274Tukey’s w/gender (sig PRAC v others)
model<-aov(dat$Exam.total~ dat$Gen.name*dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Gen.name 1 7.6 7.610 2.216 0.13724
## dat$Condition 4 49.9 12.483 3.635 0.00624 **
## dat$Gen.name:dat$Condition 4 9.0 2.242 0.653 0.62515
## Residuals 482 1655.2 3.434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$Exam.total ~ dat$Gen.name * dat$Condition)
##
## $`dat$Gen.name`
## diff lwr upr p adj
## Woman-Man -0.2947647 -0.6838335 0.0943041 0.1372367
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp 0.168398217 -0.53537910 0.87217554 0.9656344
## NURTURANT-ALLsupp -0.001369179 -0.73303790 0.73029955 1.0000000
## PRACTICAL-ALLsupp 0.846857953 0.12724793 1.56646798 0.0118050
## SUPPLEMENTAL-ALLsupp 0.071311346 -0.65021719 0.79283988 0.9988210
## NURTURANT-NOsupp -0.169767396 -0.89489778 0.55536299 0.9682394
## PRACTICAL-NOsupp 0.678459736 -0.03450137 1.39142084 0.0708989
## SUPPLEMENTAL-NOsupp -0.097086871 -0.81198433 0.61781059 0.9959228
## PRACTICAL-NURTURANT 0.848227132 0.10772045 1.58873381 0.0155357
## SUPPLEMENTAL-NURTURANT 0.072680525 -0.66969067 0.81505172 0.9988641
## SUPPLEMENTAL-PRACTICAL -0.775546607 -1.50603579 -0.04505742 0.0311200
##
## $`dat$Gen.name:dat$Condition`
## diff lwr upr
## Woman:ALLsupp-Man:ALLsupp 0.146761134 -1.19152457 1.4850468
## Man:NOsupp-Man:ALLsupp 0.523076923 -1.12687250 2.1730263
## Woman:NOsupp-Man:ALLsupp 0.194681861 -1.13303334 1.5223971
## Man:NURTURANT-Man:ALLsupp 0.805429864 -1.03180770 2.6426674
## Woman:NURTURANT-Man:ALLsupp -0.076923077 -1.41981076 1.2659646
## Man:PRACTICAL-Man:ALLsupp 1.214743590 -0.45263772 2.8821249
## Woman:PRACTICAL-Man:ALLsupp 0.868282403 -0.47699459 2.2135594
## Man:SUPPLEMENTAL-Man:ALLsupp 0.286713287 -1.41962379 1.9930504
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.139293139 -1.20359454 1.4821808
## Man:NOsupp-Woman:ALLsupp 0.376315789 -0.98176657 1.7343982
## Woman:NOsupp-Woman:ALLsupp 0.047920728 -0.89276108 0.9886025
## Man:NURTURANT-Woman:ALLsupp 0.658668731 -0.92167821 2.2390157
## Woman:NURTURANT-Woman:ALLsupp -0.223684211 -1.18566234 0.7382939
## Man:PRACTICAL-Woman:ALLsupp 1.067982456 -0.31122567 2.4471906
## Woman:PRACTICAL-Woman:ALLsupp 0.721521269 -0.24378945 1.6868320
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.139952153 -1.28610563 1.5660099
## Woman:SUPPLEMENTAL-Woman:ALLsupp -0.007467994 -0.96944612 0.9545101
## Woman:NOsupp-Man:NOsupp -0.328395062 -1.67606221 1.0192721
## Man:NURTURANT-Man:NOsupp 0.282352941 -1.56935463 2.1340605
## Woman:NURTURANT-Man:NOsupp -0.600000000 -1.96261748 0.7626175
## Man:PRACTICAL-Man:NOsupp 0.691666667 -0.99164540 2.3749787
## Woman:PRACTICAL-Man:NOsupp 0.345205479 -1.01976678 1.7101777
## Man:SUPPLEMENTAL-Man:NOsupp -0.236363636 -1.95827109 1.4855438
## Woman:SUPPLEMENTAL-Man:NOsupp -0.383783784 -1.74640126 0.9788337
## Man:NURTURANT-Woman:NOsupp 0.610748003 -0.96065758 2.1821536
## Woman:NURTURANT-Woman:NOsupp -0.271604938 -1.21882242 0.6756125
## Man:PRACTICAL-Woman:NOsupp 1.020061728 -0.34889192 2.3890154
## Woman:PRACTICAL-Woman:NOsupp 0.673600541 -0.27700128 1.6242024
## Man:SUPPLEMENTAL-Woman:NOsupp 0.092031425 -1.32411117 1.5081740
## Woman:SUPPLEMENTAL-Woman:NOsupp -0.055388722 -1.00260620 0.8918288
## Woman:NURTURANT-Man:NURTURANT -0.882352941 -2.46659887 0.7018930
## Man:PRACTICAL-Man:NURTURANT 0.409313725 -1.45794316 2.2765706
## Woman:PRACTICAL-Man:NURTURANT 0.062852538 -1.52341920 1.6491243
## Man:SUPPLEMENTAL-Man:NURTURANT -0.518716578 -2.42084014 1.3834070
## Woman:SUPPLEMENTAL-Man:NURTURANT -0.666136725 -2.25038265 0.9181092
## Man:PRACTICAL-Woman:NURTURANT 1.291666667 -0.09200733 2.6753407
## Woman:PRACTICAL-Woman:NURTURANT 0.945205479 -0.02647527 1.9168862
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.363636364 -1.06674103 1.7940138
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.216216216 -0.75215386 1.1845863
## Woman:PRACTICAL-Man:PRACTICAL -0.346461187 -1.73245419 1.0395318
## Man:SUPPLEMENTAL-Man:PRACTICAL -0.928030303 -2.66664833 0.8105877
## Woman:SUPPLEMENTAL-Man:PRACTICAL -1.075450450 -2.45912445 0.3082235
## Man:SUPPLEMENTAL-Woman:PRACTICAL -0.581569116 -2.01418992 0.8510517
## Woman:SUPPLEMENTAL-Woman:PRACTICAL -0.728989263 -1.70067002 0.2426915
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL -0.147420147 -1.57779755 1.2829573
## p adj
## Woman:ALLsupp-Man:ALLsupp 0.9999987
## Man:NOsupp-Man:ALLsupp 0.9917451
## Woman:NOsupp-Man:ALLsupp 0.9999839
## Man:NURTURANT-Man:ALLsupp 0.9289579
## Woman:NURTURANT-Man:ALLsupp 1.0000000
## Man:PRACTICAL-Man:ALLsupp 0.3806347
## Woman:PRACTICAL-Man:ALLsupp 0.5628245
## Man:SUPPLEMENTAL-Man:ALLsupp 0.9999483
## Woman:SUPPLEMENTAL-Man:ALLsupp 0.9999992
## Man:NOsupp-Woman:ALLsupp 0.9969793
## Woman:NOsupp-Woman:ALLsupp 1.0000000
## Man:NURTURANT-Woman:ALLsupp 0.9476247
## Woman:NURTURANT-Woman:ALLsupp 0.9992381
## Man:PRACTICAL-Woman:ALLsupp 0.2923285
## Woman:PRACTICAL-Woman:ALLsupp 0.3427342
## Man:SUPPLEMENTAL-Woman:ALLsupp 0.9999995
## Woman:SUPPLEMENTAL-Woman:ALLsupp 1.0000000
## Woman:NOsupp-Man:NOsupp 0.9988921
## Man:NURTURANT-Man:NOsupp 0.9999774
## Woman:NURTURANT-Man:NOsupp 0.9270891
## Man:PRACTICAL-Man:NOsupp 0.9520460
## Woman:PRACTICAL-Man:NOsupp 0.9985137
## Man:SUPPLEMENTAL-Man:NOsupp 0.9999909
## Woman:SUPPLEMENTAL-Man:NOsupp 0.9965787
## Man:NURTURANT-Woman:NOsupp 0.9663375
## Woman:NURTURANT-Woman:NOsupp 0.9960810
## Man:PRACTICAL-Woman:NOsupp 0.3472606
## Woman:PRACTICAL-Woman:NOsupp 0.4224299
## Man:SUPPLEMENTAL-Woman:NOsupp 1.0000000
## Woman:SUPPLEMENTAL-Woman:NOsupp 1.0000000
## Woman:NURTURANT-Man:NURTURANT 0.7536107
## Man:PRACTICAL-Man:NURTURANT 0.9995272
## Woman:PRACTICAL-Man:NURTURANT 1.0000000
## Man:SUPPLEMENTAL-Man:NURTURANT 0.9973277
## Woman:SUPPLEMENTAL-Man:NURTURANT 0.9447144
## Man:PRACTICAL-Woman:NURTURANT 0.0907743
## Woman:PRACTICAL-Woman:NURTURANT 0.0643037
## Man:SUPPLEMENTAL-Woman:NURTURANT 0.9984515
## Woman:SUPPLEMENTAL-Woman:NURTURANT 0.9994509
## Woman:PRACTICAL-Man:PRACTICAL 0.9986441
## Man:SUPPLEMENTAL-Man:PRACTICAL 0.7971106
## Woman:SUPPLEMENTAL-Man:PRACTICAL 0.2871619
## Man:SUPPLEMENTAL-Woman:PRACTICAL 0.9555483
## Woman:SUPPLEMENTAL-Woman:PRACTICAL 0.3373157
## Woman:SUPPLEMENTAL-Man:SUPPLEMENTAL 0.9999993Tukey’s w/out gender
model<-aov(dat$Exam.total~ dat$Condition)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 50.7 12.683 3.696 0.00562 **
## Residuals 487 1671.0 3.431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dat$Exam.total ~ dat$Condition)
##
## $`dat$Condition`
## diff lwr upr p adj
## NOsupp-ALLsupp 0.16278209 -0.54067461 0.86623880 0.9695619
## NURTURANT-ALLsupp -0.02143934 -0.75277474 0.70989605 0.9999906
## PRACTICAL-ALLsupp 0.84465333 0.12537113 1.56393552 0.0120708
## SUPPLEMENTAL-ALLsupp 0.06372549 -0.65747434 0.78492532 0.9992418
## NURTURANT-NOsupp -0.18422144 -0.90902148 0.54057860 0.9573357
## PRACTICAL-NOsupp 0.68187123 -0.03076507 1.39450753 0.0683459
## SUPPLEMENTAL-NOsupp -0.09905660 -0.81362838 0.61551517 0.9955864
## PRACTICAL-NURTURANT 0.86609267 0.12592334 1.60626200 0.0125246
## SUPPLEMENTAL-NURTURANT 0.08516484 -0.65686815 0.82719782 0.9978818
## SUPPLEMENTAL-PRACTICAL -0.78092784 -1.51108423 -0.05077144 0.0292280+ Communality
model<- lm(Exam.total~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = Exam.total ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP) + TAcomm.av, data = dat)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 83.624 10 8.362 0.049 2.455 0.007
## Error 1638.089 481 3.406
## Corr Total 1721.713 491 3.507
##
## RMSE AdjEtaSq
## 1.845 0.029
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 5.955 0.690 8.625 253.337 0.134 NA 4.599 7.312 0.000
## Gen.con -0.343 0.199 -1.720 10.073 0.006 0.977 -0.735 0.049 0.086
## NOvSupport 0.042 0.239 0.175 0.105 0.000 0.714 -0.429 0.513 0.861
## ALLvOther 0.474 0.248 1.910 12.421 0.008 0.736 -0.014 0.962 0.057
## NURTvPrSu 0.307 0.294 1.046 3.724 0.002 0.638 -0.270 0.885 0.296
## PRACTvSUPP -0.803 0.312 -2.573 22.539 0.014 0.724 -1.417 -0.190 0.010
## TAcomm.av 0.293 0.131 2.242 17.114 0.010 0.965 0.036 0.550 0.025
## Gen.con:NOvSupport 0.160 0.483 0.332 0.375 0.000 0.703 -0.788 1.108 0.740
## Gen.con:ALLvOther -0.663 0.497 -1.334 6.059 0.004 0.734 -1.640 0.314 0.183
## Gen.con:NURTvPrSu 0.538 0.588 0.915 2.848 0.002 0.637 -0.617 1.693 0.361
## Gen.con:PRACTvSUPP 0.127 0.625 0.204 0.142 0.000 0.723 -1.100 1.355 0.838Graphs
Condition main effect
boxplot(dat$Exam.total~ dat$Condition)
p <- ggplot(dat, aes(Condition.ordered, Exam.total, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = .2, y = 0))
theme(legend.position = "none") +
xlab('Condition') +
ylab('Perceived TA Support')## List of 3
## $ legend.position: chr "none"
## $ x : chr "Condition"
## $ y : chr "Perceived TA Support"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE(p = p + scale_fill_grey(start = 0, end = .9) + xlab('Condition') +
ylab('Exam Score') + scale_x_discrete(labels=c('No Support', 'All Support', 'Nurturant', 'Practical','Supplemental')))
p
p <- ggplot(dat, aes(Condition.ordered, Exam.total, fill = Condition.ordered)) +
geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
geom_boxplot(width=0.4, color="black", alpha=0.75) +
geom_jitter(color="black", size=0.4, alpha=0.5)+
coord_flip()+
theme(legend.position = "none",
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 14,face = "bold"))
p = p + scale_fill_paletteer_d("PNWColors::Bay") + ylab('Anticipated Grade') + xlab("") + ylab("")
p
Condition x gender interaction null
Persistance
Quiz 1
Proportions within gender
table(dat$Q1retry.CATS)##
## ALLcorrect NOretry retry
## 314 104 74table(dat$Gen.name)##
## Man Woman
## 114 378table(dat$Q1retry.CATS, dat$Gen.name)##
## Man Woman
## ALLcorrect 80 234
## NOretry 17 87
## retry 17 57table(Quiz1.retry$Q1retry.CATS, Quiz1.retry$Condition.ordered)##
## NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 25 20 23 17 23
## retry 14 17 15 11 17Helmerts
Prac v. Supp (n.s.)
m0CC <- glm(Q1noretry.d ~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP),
family = binomial("logit"), data = Quiz1.retry)
tab_model(m0CC, title = "P v S", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q1noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 3.36 | 282.71 | 0.988 |
| Gen con | 0.04 | 6.09 | 0.984 |
| NOvSupport | 9.11 | 957.01 | 0.983 |
| ALLvOther | 9.49 | 1328.65 | 0.987 |
| NURTvPrSu | 0.00 | 0.16 | 0.985 |
| PRACTvSUPP | 1.29 | 0.80 | 0.678 |
| Gen con × NOvSupport | 0.02 | 3.70 | 0.985 |
| Gen con × ALLvOther | 0.01 | 2.26 | 0.986 |
| Gen con × NURTvPrSu | 12338408.42 | 10367649447.21 | 0.984 |
| Gen con × PRACTvSUPP | 0.65 | 0.80 | 0.729 |
| Observations | 182 | ||
| R2 Tjur | 0.035 | ||
Prac v. Nurt (n.s.)
m0CC <- glm(Q1noretry.d ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT),
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## SUPvPrNu + PRACTvNURT), family = binomial("logit"), data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.213 84.028 0.014 0.988
## Gen.con -3.318 168.055 -0.020 0.984
## NOvSupport 2.210 105.035 0.021 0.983
## ALLvOther 2.250 140.046 0.016 0.987
## SUPvPrNu 3.738 210.069 0.018 0.986
## PRACTvNURT 7.988 420.137 0.019 0.985
## Gen.con:NOvSupport -4.039 210.070 -0.019 0.985
## Gen.con:ALLvOther -4.820 280.093 -0.017 0.986
## Gen.con:SUPvPrNu -7.843 420.138 -0.019 0.985
## Gen.con:PRACTvNURT -16.542 840.275 -0.020 0.984
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 238.46 on 172 degrees of freedom
## AIC: 258.46
##
## Number of Fisher Scoring iterations: 14tab_model(m0CC, title = "p v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q1noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 3.36 | 282.71 | 0.988 |
| Gen con | 0.04 | 6.09 | 0.984 |
| NOvSupport | 9.11 | 957.01 | 0.983 |
| ALLvOther | 9.49 | 1328.65 | 0.987 |
| SUPvPrNu | 42.01 | 8825.24 | 0.986 |
| PRACTvNURT | 2945.92 | 1237691.01 | 0.985 |
| Gen con × NOvSupport | 0.02 | 3.70 | 0.985 |
| Gen con × ALLvOther | 0.01 | 2.26 | 0.986 |
| Gen con × SUPvPrNu | 0.00 | 0.16 | 0.985 |
| Gen con × PRACTvNURT | 0.00 | 0.00 | 0.984 |
| Observations | 182 | ||
| R2 Tjur | 0.035 | ||
Supp v. Nurt (n.s.)
m0CC <- glm(Q1noretry.d ~Gen.con*(NOvSupport + ALLvOther + PRAvSuNu + SUPPvNURT),
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## PRAvSuNu + SUPPvNURT), family = binomial("logit"), data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.213 84.028 0.014 0.988
## Gen.con -3.318 168.055 -0.020 0.984
## NOvSupport 2.210 105.035 0.021 0.983
## ALLvOther 2.250 140.046 0.016 0.987
## PRAvSuNu 4.122 210.069 0.020 0.984
## SUPPvNURT 7.732 420.137 0.018 0.985
## Gen.con:NOvSupport -4.039 210.070 -0.019 0.985
## Gen.con:ALLvOther -4.820 280.093 -0.017 0.986
## Gen.con:PRAvSuNu -8.485 420.138 -0.020 0.984
## Gen.con:SUPPvNURT -16.114 840.275 -0.019 0.985
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 238.46 on 172 degrees of freedom
## AIC: 258.46
##
## Number of Fisher Scoring iterations: 14tab_model(m0CC, title = "S v N",show.ci = F, show.se = T, string.se = "SE") #output in Odds Ratios so you dont have to convert| Q1noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | SE | p |
| (Intercept) | 3.36 | 282.71 | 0.988 |
| Gen con | 0.04 | 6.09 | 0.984 |
| NOvSupport | 9.11 | 957.01 | 0.983 |
| ALLvOther | 9.49 | 1328.65 | 0.987 |
| PRAvSuNu | 61.69 | 12959.72 | 0.984 |
| SUPPvNURT | 2280.21 | 958001.72 | 0.985 |
| Gen con × NOvSupport | 0.02 | 3.70 | 0.985 |
| Gen con × ALLvOther | 0.01 | 2.26 | 0.986 |
| Gen con × PRAvSuNu | 0.00 | 0.09 | 0.984 |
| Gen con × SUPPvNURT | 0.00 | 0.00 | 0.985 |
| Observations | 182 | ||
| R2 Tjur | 0.035 | ||
Dummies by condition
m0CC <- glm(Q1noretry.d ~Gen.con*NOsup_dummy,
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * NOsup_dummy, family = binomial("logit"),
## data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.55433 0.41056 -1.350 0.177
## Gen.con -0.08701 0.82112 -0.106 0.916
## NOsup_dummy 0.38146 0.46331 0.823 0.410
## Gen.con:NOsup_dummy -0.40695 0.92662 -0.439 0.661
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 244.11 on 178 degrees of freedom
## AIC: 252.11
##
## Number of Fisher Scoring iterations: 4m0CC <- glm(Q1noretry.d ~Gen.con*NURT_dummy,
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * NURT_dummy, family = binomial("logit"),
## data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.458 420.137 0.018 0.986
## Gen.con -16.217 840.274 -0.019 0.985
## NURT_dummy -7.782 420.137 -0.019 0.985
## Gen.con:NURT_dummy 16.070 840.274 0.019 0.985
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 239.78 on 178 degrees of freedom
## AIC: 247.78
##
## Number of Fisher Scoring iterations: 14m0CC <- glm(Q1noretry.d ~Gen.con*PRAC_dummy,
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * PRAC_dummy, family = binomial("logit"),
## data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5304 0.4843 -1.095 0.273
## Gen.con 0.3254 0.9685 0.336 0.737
## PRAC_dummy 0.3286 0.5268 0.624 0.533
## Gen.con:PRAC_dummy -0.8670 1.0535 -0.823 0.411
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 244.07 on 178 degrees of freedom
## AIC: 252.07
##
## Number of Fisher Scoring iterations: 4m0CC <- glm(Q1noretry.d ~Gen.con*SUPP_dummy,
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * SUPP_dummy, family = binomial("logit"),
## data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.27428 0.38160 -0.719 0.472
## Gen.con -0.10228 0.76320 -0.134 0.893
## SUPP_dummy 0.02804 0.43959 0.064 0.949
## Gen.con:SUPP_dummy -0.39020 0.87917 -0.444 0.657
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 244.56 on 178 degrees of freedom
## AIC: 252.56
##
## Number of Fisher Scoring iterations: 4m0CC <- glm(Q1noretry.d ~Gen.con*ALL_dummy,
family = binomial("logit"), data = Quiz1.retry)
summary(m0CC)##
## Call:
## glm(formula = Q1noretry.d ~ Gen.con * ALL_dummy, family = binomial("logit"),
## data = Quiz1.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03227 0.38595 -0.084 0.933
## Gen.con -0.51083 0.77190 -0.662 0.508
## ALL_dummy -0.29351 0.44338 -0.662 0.508
## Gen.con:ALL_dummy 0.16757 0.88675 0.189 0.850
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 245.92 on 181 degrees of freedom
## Residual deviance: 244.33 on 178 degrees of freedom
## AIC: 252.33
##
## Number of Fisher Scoring iterations: 4Quiz 2
Proportions within gender
table(dat$Q2retry.CATS)##
## ALLcorrect NOretry retry
## 121 195 176table(dat$Gen.name)##
## Man Woman
## 114 378table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Gen.name)##
## Man Woman
## NOretry 41 156
## retry 41 135table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Condition.ordered)##
## NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 47 43 29 43 35
## retry 35 37 40 32 32table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Condition,Quiz2.retry$Gen.name)## , , = Man
##
##
## ALLsupp NOsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 11 11 1 9 9
## retry 9 7 11 7 7
##
## , , = Woman
##
##
## ALLsupp NOsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 32 36 28 34 26
## retry 28 28 29 25 25table(Quiz2.retry$Q2retry.CATS); table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Condition.ordered, Quiz2.retry$Gen.name)##
## NOretry retry
## 197 176## , , = Man
##
##
## NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 11 11 1 9 9
## retry 7 9 11 7 7
##
## , , = Woman
##
##
## NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 36 32 28 34 26
## retry 28 28 29 25 25Helmerts
Prac v. Supp (sig)
m0CC <- glm(Q2noretry.d ~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP),
family = binomial("logit"), data = Quiz2.retry)
summary(m0CC)##
## Call:
## glm(formula = Q2noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP), family = binomial("logit"), data = Quiz2.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05462 0.15435 0.354 0.7235
## Gen.con -0.38781 0.30869 -1.256 0.2090
## NOvSupport 0.50783 0.32694 1.553 0.1204
## ALLvOther 0.43104 0.34306 1.256 0.2089
## NURTvPrSu -1.42883 0.57536 -2.483 0.0130 *
## PRACTvSUPP 0.13413 0.40491 0.331 0.7404
## Gen.con:NOvSupport -0.73561 0.65389 -1.125 0.2606
## Gen.con:ALLvOther -0.80277 0.68612 -1.170 0.2420
## Gen.con:NURTvPrSu 2.44077 1.15072 2.121 0.0339 *
## Gen.con:PRACTvSUPP 0.26826 0.80982 0.331 0.7404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 515.90 on 372 degrees of freedom
## Residual deviance: 503.05 on 363 degrees of freedom
## AIC: 523.05
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "P v S", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 1.06 | 0.16 | 0.723 |
| Gen con | 0.68 | 0.21 | 0.209 |
| NOvSupport | 1.66 | 0.54 | 0.120 |
| ALLvOther | 1.54 | 0.53 | 0.209 |
| NURTvPrSu | 0.24 | 0.14 | 0.013 |
| PRACTvSUPP | 1.14 | 0.46 | 0.740 |
| Gen con × NOvSupport | 0.48 | 0.31 | 0.261 |
| Gen con × ALLvOther | 0.45 | 0.31 | 0.242 |
| Gen con × NURTvPrSu | 11.48 | 13.21 | 0.034 |
| Gen con × PRACTvSUPP | 1.31 | 1.06 | 0.740 |
| Observations | 373 | ||
| R2 Tjur | 0.031 | ||
Prac v. Nurt (sig)
m0CC <- glm(Q2noretry.d ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT),
family = binomial("logit"), data = Quiz2.retry)
summary(m0CC)##
## Call:
## glm(formula = Q2noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## SUPvPrNu + PRACTvNURT), family = binomial("logit"), data = Quiz2.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05462 0.15435 0.354 0.7235
## Gen.con -0.38781 0.30869 -1.256 0.2090
## NOvSupport 0.50783 0.32694 1.553 0.1204
## ALLvOther 0.43104 0.34306 1.256 0.2089
## SUPvPrNu 0.61381 0.41932 1.464 0.1432
## PRACTvNURT 1.49589 0.60901 2.456 0.0140 *
## Gen.con:NOvSupport -0.73561 0.65389 -1.125 0.2606
## Gen.con:ALLvOther -0.80277 0.68612 -1.170 0.2420
## Gen.con:SUPvPrNu -1.42158 0.83864 -1.695 0.0901 .
## Gen.con:PRACTvNURT -2.30663 1.21802 -1.894 0.0583 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 515.90 on 372 degrees of freedom
## Residual deviance: 503.05 on 363 degrees of freedom
## AIC: 523.05
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "P v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 1.06 | 0.16 | 0.723 |
| Gen con | 0.68 | 0.21 | 0.209 |
| NOvSupport | 1.66 | 0.54 | 0.120 |
| ALLvOther | 1.54 | 0.53 | 0.209 |
| SUPvPrNu | 1.85 | 0.77 | 0.143 |
| PRACTvNURT | 4.46 | 2.72 | 0.014 |
| Gen con × NOvSupport | 0.48 | 0.31 | 0.261 |
| Gen con × ALLvOther | 0.45 | 0.31 | 0.242 |
| Gen con × SUPvPrNu | 0.24 | 0.20 | 0.090 |
| Gen con × PRACTvNURT | 0.10 | 0.12 | 0.058 |
| Observations | 373 | ||
| R2 Tjur | 0.031 | ||
Supp v. Nurt (sig)
m0CC <- glm(Q2noretry.d ~Gen.con*(NOvSupport + ALLvOther + PRAvSuNu + SUPPvNURT),
family = binomial("logit"), data = Quiz2.retry)
summary(m0CC)##
## Call:
## glm(formula = Q2noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## PRAvSuNu + SUPPvNURT), family = binomial("logit"), data = Quiz2.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05462 0.15435 0.354 0.7235
## Gen.con -0.38781 0.30869 -1.256 0.2090
## NOvSupport 0.50783 0.32694 1.553 0.1204
## ALLvOther 0.43104 0.34306 1.256 0.2089
## PRAvSuNu 0.81501 0.41729 1.953 0.0508 .
## SUPPvNURT 1.36176 0.61087 2.229 0.0258 *
## Gen.con:NOvSupport -0.73561 0.65389 -1.125 0.2606
## Gen.con:ALLvOther -0.80277 0.68612 -1.170 0.2420
## Gen.con:PRAvSuNu -1.01918 0.83459 -1.221 0.2220
## Gen.con:SUPPvNURT -2.57490 1.22173 -2.108 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 515.90 on 372 degrees of freedom
## Residual deviance: 503.05 on 363 degrees of freedom
## AIC: 523.05
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "S v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 1.06 | 0.16 | 0.723 |
| Gen con | 0.68 | 0.21 | 0.209 |
| NOvSupport | 1.66 | 0.54 | 0.120 |
| ALLvOther | 1.54 | 0.53 | 0.209 |
| PRAvSuNu | 2.26 | 0.94 | 0.051 |
| SUPPvNURT | 3.90 | 2.38 | 0.026 |
| Gen con × NOvSupport | 0.48 | 0.31 | 0.261 |
| Gen con × ALLvOther | 0.45 | 0.31 | 0.242 |
| Gen con × PRAvSuNu | 0.36 | 0.30 | 0.222 |
| Gen con × SUPPvNURT | 0.08 | 0.09 | 0.035 |
| Observations | 373 | ||
| R2 Tjur | 0.031 | ||
Graphs
ggplot(Quiz2.retry, aes(fill=Q2retry.CATS, y=nrow(Quiz2.retry), x=Condition)) +
geom_bar(position="stack", stat="identity")
library(viridis)
ggplot(Quiz2.retry, aes(fill=Q2retry.CATS, y=nrow(Quiz2.retry), x=Condition)) +
geom_bar(position="stack", stat="identity") +
facet_wrap("Gen.name") +
scale_fill_viridis(discrete = TRUE, alpha=0.6) 
library(viridis)
ggplot(Quiz2.retry, aes(fill=Q2retry.CATS, y=nrow(Quiz2.retry), x=Gen.name)) +
geom_bar(position="stack", stat="identity") +
facet_wrap("Condition") +
scale_fill_viridis(discrete = TRUE, alpha=0.6) 
#### Nurt v others
All conditions
Nurt v prac interaction
Nurt v sup interaction
table(Quiz2.retry$Q2noretry.d, Quiz2.retry$Gen.name, Quiz2.retry$Condition)## , , = ALLsupp
##
##
## Man Woman
## 0 11 32
## 1 9 28
##
## , , = NOsupp
##
##
## Man Woman
## 0 11 36
## 1 7 28
##
## , , = NURTURANT
##
##
## Man Woman
## 0 1 28
## 1 11 29
##
## , , = PRACTICAL
##
##
## Man Woman
## 0 9 34
## 1 7 25
##
## , , = SUPPLEMENTAL
##
##
## Man Woman
## 0 9 26
## 1 7 25
Dummies by condition
m0CC <- glm(Q2noretry.d ~Gen.con*NOsup_dummy,
family = binomial("logit"), data = Quiz2.retry)
tab_model(m0CC,title = "No Support.d")| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.70 | 0.40 – 1.19 | 0.197 |
| Gen con | 1.22 | 0.43 – 3.70 | 0.713 |
| NOsup dummy | 1.43 | 0.79 – 2.65 | 0.245 |
| Gen con × NOsup dummy | 0.64 | 0.19 – 2.12 | 0.473 |
| Observations | 373 | ||
| R2 Tjur | 0.005 | ||
m0CC <- glm(Q2noretry.d ~Gen.con*NURT_dummy,
family = binomial("logit"), data = Quiz2.retry)
summary(m0CC)##
## Call:
## glm(formula = Q2noretry.d ~ Gen.con * NURT_dummy, family = binomial("logit"),
## data = Quiz2.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.2165 0.5386 2.259 0.02390 *
## Gen.con -2.3628 1.0771 -2.194 0.02826 *
## NURT_dummy -1.4546 0.5558 -2.617 0.00887 **
## Gen.con:NURT_dummy 2.4619 1.1117 2.215 0.02679 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 515.90 on 372 degrees of freedom
## Residual deviance: 503.81 on 369 degrees of freedom
## AIC: 511.81
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC,title = "NURTURANT.d")| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 3.38 | 1.42 – 14.67 | 0.024 |
| Gen con | 0.09 | 0.00 – 0.53 | 0.028 |
| NURT dummy | 0.23 | 0.05 – 0.58 | 0.009 |
| Gen con × NURT dummy | 11.73 | 1.89 – 229.59 | 0.027 |
| Observations | 373 | ||
| R2 Tjur | 0.029 | ||
m0CC <- glm(Q2noretry.d ~Gen.con*PRAC_dummy,
family = binomial("logit"), data = Quiz2.retry)
tab_model(m0CC,title = "PRACTICAL.d")| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.76 | 0.43 – 1.32 | 0.326 |
| Gen con | 0.95 | 0.31 – 2.97 | 0.921 |
| PRAC dummy | 1.29 | 0.70 – 2.44 | 0.415 |
| Gen con × PRAC dummy | 0.90 | 0.25 – 3.10 | 0.865 |
| Observations | 373 | ||
| R2 Tjur | 0.003 | ||
m0CC <- glm(Q2noretry.d ~Gen.con*SUPP_dummy,
family = binomial("logit"), data = Quiz2.retry)
tab_model(m0CC, title = "SUPPLEMENTAL.d")| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.86 | 0.48 – 1.52 | 0.614 |
| Gen con | 1.24 | 0.40 – 3.94 | 0.713 |
| SUPP dummy | 1.10 | 0.59 – 2.08 | 0.774 |
| Gen con × SUPP dummy | 0.64 | 0.18 – 2.25 | 0.492 |
| Observations | 373 | ||
| R2 Tjur | 0.002 | ||
m0CC <- glm(Q2noretry.d ~Gen.con*ALL_dummy,
family = binomial("logit"), data = Quiz2.retry)
tab_model(m0CC,title = "All Support.d")| Q2noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.85 | 0.50 – 1.41 | 0.519 |
| Gen con | 1.07 | 0.39 – 3.01 | 0.897 |
| ALL dummy | 1.13 | 0.63 – 2.04 | 0.671 |
| Gen con × ALL dummy | 0.76 | 0.23 – 2.41 | 0.638 |
| Observations | 373 | ||
| R2 Tjur | 0.002 | ||
Quiz 3
Proportions within gender
table(dat$Q3retry.CATS)##
## ALLcorrect NOretry retry
## 153 230 109table(dat$Gen.name)##
## Man Woman
## 114 378table(dat$Q3retry.CATS, dat$Gen.name)##
## Man Woman
## ALLcorrect 46 107
## NOretry 41 189
## retry 27 82table(Quiz3.retry$Q3retry.CATS, Quiz3.retry$Condition.ordered)##
## NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
## NOretry 46 50 41 50 47
## retry 25 20 28 16 20table(Quiz3.retry$Q3retry.CATS, Quiz3.retry$Gen.name)##
## Man Woman
## NOretry 42 192
## retry 27 82Helmerts
Prac v. Supp (sig)
m0CC <- glm(Q3noretry.d ~Gen.con*(NOvSupport + ALLvOther + NURTvPrSu + PRACTvSUPP),
family = binomial("logit"), data = Quiz3.retry)
summary(m0CC)##
## Call:
## glm(formula = Q3noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## NURTvPrSu + PRACTvSUPP), family = binomial("logit"), data = Quiz3.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6453 0.1495 -4.315 1.6e-05 ***
## Gen.con -0.4936 0.2991 -1.650 0.0989 .
## NOvSupport 0.2537 0.3920 0.647 0.5175
## ALLvOther 0.3699 0.3650 1.014 0.3108
## NURTvPrSu -0.8630 0.4375 -1.973 0.0485 *
## PRACTvSUPP 0.2303 0.4402 0.523 0.6008
## Gen.con:NOvSupport -1.5063 0.7840 -1.921 0.0547 .
## Gen.con:ALLvOther -0.8371 0.7300 -1.147 0.2515
## Gen.con:NURTvPrSu 0.3534 0.8750 0.404 0.6863
## Gen.con:PRACTvSUPP -0.0331 0.8804 -0.038 0.9700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 428.88 on 342 degrees of freedom
## Residual deviance: 415.78 on 333 degrees of freedom
## AIC: 435.78
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "P v S", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 0.52 | 0.08 | <0.001 |
| Gen con | 0.61 | 0.18 | 0.099 |
| NOvSupport | 1.29 | 0.51 | 0.518 |
| ALLvOther | 1.45 | 0.53 | 0.311 |
| NURTvPrSu | 0.42 | 0.18 | 0.049 |
| PRACTvSUPP | 1.26 | 0.55 | 0.601 |
| Gen con × NOvSupport | 0.22 | 0.17 | 0.055 |
| Gen con × ALLvOther | 0.43 | 0.32 | 0.252 |
| Gen con × NURTvPrSu | 1.42 | 1.25 | 0.686 |
| Gen con × PRACTvSUPP | 0.97 | 0.85 | 0.970 |
| Observations | 343 | ||
| R2 Tjur | 0.039 | ||
Prac v. Nurt (sig)
m0CC <- glm(Q3noretry.d ~Gen.con*(NOvSupport + ALLvOther + SUPvPrNu + PRACTvNURT),
family = binomial("logit"), data = Quiz3.retry)
summary(m0CC)##
## Call:
## glm(formula = Q3noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## SUPvPrNu + PRACTvNURT), family = binomial("logit"), data = Quiz3.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6453 0.1496 -4.315 1.6e-05 ***
## Gen.con -0.4936 0.2991 -1.650 0.0989 .
## NOvSupport 0.2537 0.3920 0.647 0.5175
## ALLvOther 0.3699 0.3650 1.014 0.3108
## SUPvPrNu 0.2588 0.3835 0.675 0.4998
## PRACTvNURT 0.9782 0.5029 1.945 0.0517 .
## Gen.con:NOvSupport -1.5063 0.7840 -1.921 0.0547 .
## Gen.con:ALLvOther -0.8371 0.7300 -1.147 0.2515
## Gen.con:SUPvPrNu -0.1519 0.7670 -0.198 0.8430
## Gen.con:PRACTvNURT -0.3699 1.0057 -0.368 0.7130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 428.88 on 342 degrees of freedom
## Residual deviance: 415.78 on 333 degrees of freedom
## AIC: 435.78
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "P v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 0.52 | 0.08 | <0.001 |
| Gen con | 0.61 | 0.18 | 0.099 |
| NOvSupport | 1.29 | 0.51 | 0.518 |
| ALLvOther | 1.45 | 0.53 | 0.311 |
| SUPvPrNu | 1.30 | 0.50 | 0.500 |
| PRACTvNURT | 2.66 | 1.34 | 0.052 |
| Gen con × NOvSupport | 0.22 | 0.17 | 0.055 |
| Gen con × ALLvOther | 0.43 | 0.32 | 0.252 |
| Gen con × SUPvPrNu | 0.86 | 0.66 | 0.843 |
| Gen con × PRACTvNURT | 0.69 | 0.69 | 0.713 |
| Observations | 343 | ||
| R2 Tjur | 0.039 | ||
Supp v. Nurt (n.s.)
m0CC <- glm(Q3noretry.d ~Gen.con*(NOvSupport + ALLvOther + PRAvSuNu + SUPPvNURT),
family = binomial("logit"), data = Quiz3.retry)
summary(m0CC)##
## Call:
## glm(formula = Q3noretry.d ~ Gen.con * (NOvSupport + ALLvOther +
## PRAvSuNu + SUPPvNURT), family = binomial("logit"), data = Quiz3.retry)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6453 0.1496 -4.315 1.6e-05 ***
## Gen.con -0.4936 0.2991 -1.650 0.0989 .
## NOvSupport 0.2537 0.3920 0.647 0.5175
## ALLvOther 0.3699 0.3650 1.014 0.3108
## PRAvSuNu 0.6043 0.4082 1.480 0.1388
## SUPPvNURT 0.7479 0.4762 1.570 0.1163
## Gen.con:NOvSupport -1.5063 0.7840 -1.921 0.0547 .
## Gen.con:ALLvOther -0.8371 0.7300 -1.147 0.2515
## Gen.con:PRAvSuNu -0.2015 0.8164 -0.247 0.8050
## Gen.con:SUPPvNURT -0.3368 0.9524 -0.354 0.7236
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 428.88 on 342 degrees of freedom
## Residual deviance: 415.78 on 333 degrees of freedom
## AIC: 435.78
##
## Number of Fisher Scoring iterations: 4tab_model(m0CC, title = "S v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | std. Error | p |
| (Intercept) | 0.52 | 0.08 | <0.001 |
| Gen con | 0.61 | 0.18 | 0.099 |
| NOvSupport | 1.29 | 0.51 | 0.518 |
| ALLvOther | 1.45 | 0.53 | 0.311 |
| PRAvSuNu | 1.83 | 0.75 | 0.139 |
| SUPPvNURT | 2.11 | 1.01 | 0.116 |
| Gen con × NOvSupport | 0.22 | 0.17 | 0.055 |
| Gen con × ALLvOther | 0.43 | 0.32 | 0.252 |
| Gen con × PRAvSuNu | 0.82 | 0.67 | 0.805 |
| Gen con × SUPPvNURT | 0.71 | 0.68 | 0.724 |
| Observations | 343 | ||
| R2 Tjur | 0.039 | ||
Graphs
All conditions
ggplot(Quiz3.retry, aes(fill=Q3retry.CATS, y=nrow(Quiz3.retry), x=Condition)) +
geom_bar(position="stack", stat="identity")
library(viridis)
ggplot(Quiz3.retry, aes(fill=Q3retry.CATS, y=nrow(Quiz3.retry), x=Condition)) +
geom_bar(position="stack", stat="identity") +
facet_wrap("Gen.name") +
scale_fill_viridis(discrete = TRUE, alpha=0.6) 
library(viridis)
ggplot(Quiz3.retry, aes(fill=Q3retry.CATS, y=nrow(Quiz3.retry), x=Gen.name)) +
geom_bar(position="stack", stat="identity") +
facet_wrap("Condition") +
scale_fill_viridis(discrete = TRUE, alpha=0.6) 
table(Quiz3.retry$Q3noretry.d, Quiz3.retry$Gen.name, Quiz3.retry$Condition)## , , = ALLsupp
##
##
## Man Woman
## 0 11 39
## 1 5 15
##
## , , = NOsupp
##
##
## Man Woman
## 0 10 36
## 1 3 22
##
## , , = NURTURANT
##
##
## Man Woman
## 0 3 38
## 1 6 22
##
## , , = PRACTICAL
##
##
## Man Woman
## 0 8 42
## 1 5 11
##
## , , = SUPPLEMENTAL
##
##
## Man Woman
## 0 10 37
## 1 8 12
Dummies by condition
m0CC <- glm(Q3noretry.d ~Gen.con*NOsup_dummy,
family = binomial("logit"), data = Quiz3.retry)
tab_model(m0CC, title = "No Support.d")| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.43 | 0.19 – 0.82 | 0.017 |
| Gen con | 2.04 | 0.55 – 9.82 | 0.317 |
| NOsup dummy | 1.25 | 0.61 – 2.90 | 0.559 |
| Gen con × NOsup dummy | 0.25 | 0.05 – 1.07 | 0.076 |
| Observations | 343 | ||
| R2 Tjur | 0.018 | ||
m0CC <- glm(Q3noretry.d ~Gen.con*NURT_dummy,
family = binomial("logit"), data = Quiz3.retry)
tab_model(m0CC, title = "NURTURANT.d")| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.08 | 0.53 – 2.43 | 0.846 |
| Gen con | 0.29 | 0.06 – 1.21 | 0.101 |
| NURT dummy | 0.43 | 0.18 – 0.93 | 0.037 |
| Gen con × NURT dummy | 2.50 | 0.53 – 14.13 | 0.262 |
| Observations | 343 | ||
| R2 Tjur | 0.022 | ||
m0CC <- glm(Q3noretry.d ~Gen.con*PRAC_dummy,
family = binomial("logit"), data = Quiz3.retry)
tab_model(m0CC, title = "PRACTICAL.d")| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.40 | 0.20 – 0.76 | 0.006 |
| Gen con | 0.42 | 0.11 – 1.61 | 0.190 |
| PRAC dummy | 1.37 | 0.68 – 2.88 | 0.392 |
| Gen con × PRAC dummy | 1.75 | 0.40 – 7.30 | 0.446 |
| Observations | 343 | ||
| R2 Tjur | 0.014 | ||
m0CC <- glm(Q3noretry.d ~Gen.con*SUPP_dummy,
family = binomial("logit"), data = Quiz3.retry)
tab_model(m0CC, title = "SUPPLEMENTAL.d")| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.51 | 0.28 – 0.89 | 0.020 |
| Gen con | 0.41 | 0.13 – 1.28 | 0.119 |
| SUPP dummy | 1.02 | 0.53 – 1.97 | 0.960 |
| Gen con × SUPP dummy | 1.88 | 0.51 – 6.96 | 0.343 |
| Observations | 343 | ||
| R2 Tjur | 0.010 | ||
m0CC <- glm(Q3noretry.d ~Gen.con*ALL_dummy,
family = binomial("logit"), data = Quiz3.retry)
tab_model(m0CC, title = "All Support.d")| Q3noretry.d | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.42 | 0.22 – 0.75 | 0.005 |
| Gen con | 0.85 | 0.26 – 3.05 | 0.787 |
| ALL dummy | 1.33 | 0.69 – 2.72 | 0.408 |
| Gen con × ALL dummy | 0.73 | 0.18 – 2.78 | 0.649 |
| Observations | 343 | ||
| R2 Tjur | 0.008 | ||
Continuous support across conditions
Predictors
t.test(dat$TAsup.av~dat$Gen.con, var.equal = T)##
## Two Sample t-test
##
## data: dat$TAsup.av by dat$Gen.con
## t = -1.6631, df = 489, p-value = 0.09693
## alternative hypothesis: true difference in means between group -0.5 and group 0.5 is not equal to 0
## 95 percent confidence interval:
## -0.22618377 0.01881023
## sample estimates:
## mean in group -0.5 mean in group 0.5
## 4.918860 5.022546Predictors
t.test(dat$TAcomm.av~dat$Gen.con, var.equal = T)##
## Two Sample t-test
##
## data: dat$TAcomm.av by dat$Gen.con
## t = -1.7451, df = 490, p-value = 0.08159
## alternative hypothesis: true difference in means between group -0.5 and group 0.5 is not equal to 0
## 95 percent confidence interval:
## -0.25660489 0.01519582
## sample estimates:
## mean in group -0.5 mean in group 0.5
## 5.163158 5.283862SB
OG (sig support)
model<- lm(dat$SB.av ~ dat$TAsup.av_c*dat$Gen.con)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$SB.av ~ dat$TAsup.av_c * dat$Gen.con)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 36.209 3 12.070 0.117 21.364 0
## Error 274.565 486 0.565
## Corr Total 310.774 489 0.636
##
## RMSE AdjEtaSq
## 0.752 0.111
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 4.026 0.040 99.417 5583.797 0.953 NA 3.946
## dat$TAsup.av_c 0.470 0.075 6.253 22.093 0.074 0.631 0.322
## dat$Gen.con -0.315 0.081 -3.894 8.566 0.030 0.985 -0.474
## dat$TAsup.av_c:dat$Gen.con -0.112 0.150 -0.746 0.314 0.001 0.635 -0.408
## CI_97.5 p
## (Intercept) 4.105 0.000
## dat$TAsup.av_c 0.618 0.000
## dat$Gen.con -0.156 0.000
## dat$TAsup.av_c:dat$Gen.con 0.183 0.456+ Communality (sig support)
model<- lm(dat$SB.av ~ dat$TAsup.av_c*dat$Gen.con + dat$TTAcomm.av_c)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$SB.av ~ dat$TAsup.av_c * dat$Gen.con + dat$TTAcomm.av_c)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 37.565 4 9.391 0.121 16.671 0
## Error 273.209 485 0.563
## Corr Total 310.774 489 0.636
##
## RMSE AdjEtaSq
## 0.751 0.114
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 4.027 0.040 99.569 5584.676 0.953 NA 3.948
## dat$TAsup.av_c 0.393 0.090 4.375 10.780 0.038 0.440 0.217
## dat$Gen.con -0.319 0.081 -3.943 8.757 0.031 0.984 -0.478
## dat$TTAcomm.av_c 0.104 0.067 1.552 1.356 0.005 0.612 -0.028
## dat$TAsup.av_c:dat$Gen.con -0.100 0.150 -0.665 0.249 0.001 0.634 -0.396
## CI_97.5 p
## (Intercept) 4.107 0.000
## dat$TAsup.av_c 0.570 0.000
## dat$Gen.con -0.160 0.000
## dat$TTAcomm.av_c 0.235 0.121
## dat$TAsup.av_c:dat$Gen.con 0.195 0.506Graph
AB
OG (sig support)
model<- lm(dat$AB.av ~ dat$TAsup.av_c*dat$Gen.con)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$AB.av ~ dat$TAsup.av_c * dat$Gen.con)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 38.625 3 12.875 0.072 12.564 0
## Error 498.023 486 1.025
## Corr Total 536.648 489 1.097
##
## RMSE AdjEtaSq
## 1.012 0.066
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 3.952 0.055 72.487 5384.397 0.915 NA 3.845
## dat$TAsup.av_c 0.377 0.101 3.742 14.347 0.028 0.608 0.179
## dat$Gen.con -0.513 0.109 -4.704 22.675 0.044 0.985 -0.727
## dat$TAsup.av_c:dat$Gen.con -0.129 0.201 -0.643 0.424 0.001 0.611 -0.525
## CI_97.5 p
## (Intercept) 4.059 0.00
## dat$TAsup.av_c 0.574 0.00
## dat$Gen.con -0.299 0.00
## dat$TAsup.av_c:dat$Gen.con 0.266 0.52+ Communality (sig support)
model<- lm(dat$AB.av ~ dat$TAsup.av_c*dat$Gen.con + dat$TTAcomm.av_c)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$AB.av ~ dat$TAsup.av_c * dat$Gen.con + dat$TTAcomm.av_c)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 38.644 4 9.661 0.072 9.409 0
## Error 498.004 485 1.027
## Corr Total 536.648 489 1.097
##
## RMSE AdjEtaSq
## 1.013 0.064
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 3.952 0.055 72.397 5381.884 0.915 NA 3.845
## dat$TAsup.av_c 0.385 0.119 3.224 10.675 0.021 0.432 0.150
## dat$Gen.con -0.512 0.109 -4.692 22.606 0.043 0.984 -0.727
## dat$TTAcomm.av_c -0.012 0.089 -0.135 0.019 0.000 0.628 -0.187
## dat$TAsup.av_c:dat$Gen.con -0.131 0.202 -0.650 0.434 0.001 0.608 -0.528
## CI_97.5 p
## (Intercept) 4.059 0.000
## dat$TAsup.av_c 0.620 0.001
## dat$Gen.con -0.298 0.000
## dat$TTAcomm.av_c 0.163 0.893
## dat$TAsup.av_c:dat$Gen.con 0.266 0.516Graph
SE
OG (sig support)
model<- lm(dat$SE.av ~ dat$TAsup.av_c*dat$Gen.con)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$SE.av ~ dat$TAsup.av_c * dat$Gen.con)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 49.887 3 16.629 0.104 18.863 0
## Error 429.327 487 0.882
## Corr Total 479.214 490 0.978
##
## RMSE AdjEtaSq
## 0.939 0.099
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 4.608 0.051 91.150 7324.454 0.945 NA 4.509
## dat$TAsup.av_c 0.427 0.093 4.572 18.430 0.041 0.605 0.243
## dat$Gen.con -0.519 0.101 -5.128 23.182 0.051 0.985 -0.717
## dat$TAsup.av_c:dat$Gen.con -0.002 0.187 -0.009 0.000 0.000 0.608 -0.368
## CI_97.5 p
## (Intercept) 4.708 0.000
## dat$TAsup.av_c 0.610 0.000
## dat$Gen.con -0.320 0.000
## dat$TAsup.av_c:dat$Gen.con 0.365 0.993Graph
+ Communality (sig support)
model<- lm(dat$SE.av ~ dat$TAsup.av_c*dat$Gen.con + dat$TTAcomm.av_c)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$SE.av ~ dat$TAsup.av_c * dat$Gen.con + dat$TTAcomm.av_c)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 52.557 4 13.139 0.11 14.967 0
## Error 426.657 486 0.878
## Corr Total 479.214 490 0.978
##
## RMSE AdjEtaSq
## 0.937 0.102
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 4.610 0.050 91.358 7327.122 0.945 NA 4.511
## dat$TAsup.av_c 0.323 0.110 2.924 7.503 0.017 0.430 0.106
## dat$Gen.con -0.524 0.101 -5.194 23.685 0.053 0.984 -0.723
## dat$TTAcomm.av_c 0.144 0.082 1.744 2.670 0.006 0.627 -0.018
## dat$TAsup.av_c:dat$Gen.con 0.021 0.187 0.112 0.011 0.000 0.605 -0.346
## CI_97.5 p
## (Intercept) 4.709 0.000
## dat$TAsup.av_c 0.540 0.004
## dat$Gen.con -0.326 0.000
## dat$TTAcomm.av_c 0.306 0.082
## dat$TAsup.av_c:dat$Gen.con 0.388 0.911Anticipated Grade
OG (sig support)
model<- lm(dat$Ant.grade ~ dat$TAsup.av_c*dat$Gen.con)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$Ant.grade ~ dat$TAsup.av_c * dat$Gen.con)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 9.120 3 3.040 0.044 7.509 0
## Error 196.763 486 0.405
## Corr Total 205.883 489 0.421
##
## RMSE AdjEtaSq
## 0.636 0.038
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 3.309 0.034 96.562 3775.025 0.950 NA 3.242
## dat$TAsup.av_c 0.214 0.063 3.388 4.647 0.023 0.608 0.090
## dat$Gen.con -0.211 0.069 -3.085 3.853 0.019 0.985 -0.346
## dat$TAsup.av_c:dat$Gen.con -0.084 0.127 -0.667 0.180 0.001 0.611 -0.333
## CI_97.5 p
## (Intercept) 3.377 0.000
## dat$TAsup.av_c 0.339 0.001
## dat$Gen.con -0.077 0.002
## dat$TAsup.av_c:dat$Gen.con 0.164 0.505+ Communality (sig support)
model<- lm(dat$Ant.grade ~ dat$TAsup.av_c*dat$Gen.con + dat$TTAcomm.av_c)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$Ant.grade ~ dat$TAsup.av_c * dat$Gen.con + dat$TTAcomm.av_c)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 9.176 4 2.294 0.045 5.656 0
## Error 196.707 485 0.406
## Corr Total 205.883 489 0.421
##
## RMSE AdjEtaSq
## 0.637 0.037
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 3.309 0.034 96.465 3774.119 0.950 NA 3.242
## dat$TAsup.av_c 0.199 0.075 2.654 2.857 0.014 0.432 0.052
## dat$Gen.con -0.212 0.069 -3.093 3.880 0.019 0.984 -0.347
## dat$TTAcomm.av_c 0.021 0.056 0.372 0.056 0.000 0.628 -0.089
## dat$TAsup.av_c:dat$Gen.con -0.081 0.127 -0.639 0.166 0.001 0.608 -0.331
## CI_97.5 p
## (Intercept) 3.377 0.000
## dat$TAsup.av_c 0.347 0.008
## dat$Gen.con -0.077 0.002
## dat$TTAcomm.av_c 0.131 0.710
## dat$TAsup.av_c:dat$Gen.con 0.168 0.523Graph
Exam score
OG (sig support)
model<- lm(dat$Exam.total ~ dat$TAsup.av_c*dat$Gen.con)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$Exam.total ~ dat$TAsup.av_c * dat$Gen.con)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 26.109 3 8.703 0.015 2.502 0.059
## Error 1693.651 487 3.478
## Corr Total 1719.760 490 3.510
##
## RMSE AdjEtaSq
## 1.865 0.009
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 7.491 0.100 74.599 19353.595 0.920 NA 7.294
## dat$TAsup.av_c 0.394 0.185 2.124 15.696 0.009 0.605 0.030
## dat$Gen.con -0.337 0.201 -1.676 9.766 0.006 0.985 -0.731
## dat$TAsup.av_c:dat$Gen.con -0.224 0.371 -0.606 1.275 0.001 0.608 -0.953
## CI_97.5 p
## (Intercept) 7.688 0.000
## dat$TAsup.av_c 0.758 0.034
## dat$Gen.con 0.058 0.094
## dat$TAsup.av_c:dat$Gen.con 0.504 0.545+ Communality (n.s.)
model<- lm(dat$Exam.total ~ dat$TAsup.av_c*dat$Gen.con + dat$TTAcomm.av_c)
mcSummary(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## lm(formula = dat$Exam.total ~ dat$TAsup.av_c * dat$Gen.con +
## dat$TTAcomm.av_c)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 29.936 4 7.484 0.017 2.152 0.073
## Error 1689.824 486 3.477
## Corr Total 1719.760 490 3.510
##
## RMSE AdjEtaSq
## 1.865 0.009
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 7.493 0.100 74.613 19356.818 0.920 NA 7.296
## dat$TAsup.av_c 0.270 0.220 1.226 5.229 0.003 0.430 -0.162
## dat$Gen.con -0.344 0.201 -1.710 10.168 0.006 0.984 -0.738
## dat$TTAcomm.av_c 0.172 0.164 1.049 3.827 0.002 0.627 -0.150
## dat$TAsup.av_c:dat$Gen.con -0.197 0.371 -0.532 0.982 0.001 0.605 -0.927
## CI_97.5 p
## (Intercept) 7.690 0.000
## dat$TAsup.av_c 0.701 0.221
## dat$Gen.con 0.051 0.088
## dat$TTAcomm.av_c 0.495 0.295
## dat$TAsup.av_c:dat$Gen.con 0.532 0.595Graph
Manipulation fail followups
Support Subscales
dat$NURT.av<- rowMeans(subset(dat, select = c(TA2, TA3)), na.rm = T)
dat$PRAC.av<- rowMeans(subset(dat, select = c(TA4,TA6)), na.rm = T)
dat$SUP.av<- rowMeans(subset(dat, select = c(TA1, TA5)), na.rm = T)
dat$FAX.av<- rowMeans(subset(dat, select = c(TA_facil1, TA_facil3)), na.rm = T)sumtable(dat[,c("Condition.ordered", "NURT.av","PRAC.av","SUP.av","FAX.av")],group = c("Condition.ordered"), digits = 3)| Variable | N | Mean | SD | N | Mean | SD | N | Mean | SD | N | Mean | SD | N | Mean | SD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NURT.av | 106 | 4.89 | 0.754 | 101 | 4.89 | 0.777 | 91 | 4.84 | 0.727 | 96 | 4.78 | 0.836 | 95 | 4.92 | 0.81 |
| PRAC.av | 106 | 5.27 | 0.606 | 101 | 5.27 | 0.638 | 91 | 5.22 | 0.606 | 96 | 5.15 | 0.661 | 96 | 5.31 | 0.612 |
| SUP.av | 106 | 5 | 0.68 | 101 | 5.03 | 0.748 | 91 | 4.99 | 0.715 | 96 | 4.95 | 0.826 | 95 | 5.08 | 0.724 |
| FAX.av | 106 | 4.86 | 0.685 | 100 | 4.83 | 0.753 | 91 | 4.86 | 0.624 | 96 | 4.93 | 0.7 | 95 | 4.98 | 0.612 |
sup.only<-dat[dat$Condition == "NURTURANT"|dat$Condition == "PRACTICAL"|dat$Condition == "SUPPLEMENTAL",]Helmerts
sup.only$NvOther <- (-2/3)*(sup.only$Condition == "NURTURANT") +
(1/3)*(sup.only$Condition == "PRACTICAL") +
(1/3)*(sup.only$Condition == "SUPPLEMENTAL")
sup.only$PvS <- 0*(sup.only$Condition == "NURTURANT") +
(-1/5)*(sup.only$Condition == "PRACTICAL") +
(1/5)*(sup.only$Condition == "SUPPLEMENTAL")
sup.only$PvOther <- (1/3)*(sup.only$Condition == "NURTURANT") +
(-2/3)*(sup.only$Condition == "PRACTICAL") +
(1/3)*(sup.only$Condition == "SUPPLEMENTAL")
sup.only$NvS <- (-1/5)*(sup.only$Condition == "NURTURANT") +
0*(sup.only$Condition == "PRACTICAL") +
(1/5)*(sup.only$Condition == "SUPPLEMENTAL")
# Check your code
#sup.only[1:10,c("Condition", "NvOther", "PvS","PvOther","NvS" )]Nurturant subscale
summary(aov(sup.only$NURT.av~sup.only$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## sup.only$Condition 2 0.95 0.4741 0.753 0.472
## Residuals 279 175.59 0.6294
## 2 observations deleted due to missingnesspairwise.t.test(sup.only$NURT.av,sup.only$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: sup.only$NURT.av and sup.only$Condition
##
## NURTURANT PRACTICAL
## PRACTICAL 1.00 -
## SUPPLEMENTAL 1.00 0.67
##
## P value adjustment method: bonferronisummary(aov(dat$NURT.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 1.15 0.2866 0.469 0.758
## Residuals 484 295.67 0.6109
## 3 observations deleted due to missingnesspairwise.t.test(dat$NURT.av,dat$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$NURT.av and dat$Condition
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1 - - -
## NURTURANT 1 1 - -
## PRACTICAL 1 1 1 -
## SUPPLEMENTAL 1 1 1 1
##
## P value adjustment method: bonferroniPractical subscale
summary(aov(sup.only$PRAC.av~sup.only$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## sup.only$Condition 2 1.26 0.6301 1.6 0.204
## Residuals 280 110.29 0.3939
## 1 observation deleted due to missingnesspairwise.t.test(sup.only$PRAC.av,sup.only$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: sup.only$PRAC.av and sup.only$Condition
##
## NURTURANT PRACTICAL
## PRACTICAL 1.00 -
## SUPPLEMENTAL 0.94 0.23
##
## P value adjustment method: bonferronisummary(aov(dat$PRAC.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 1.48 0.3695 0.945 0.438
## Residuals 485 189.64 0.3910
## 2 observations deleted due to missingnesspairwise.t.test(dat$PRAC.av,dat$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$PRAC.av and dat$Condition
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1.00 - - -
## NURTURANT 1.00 1.00 - -
## PRACTICAL 1.00 1.00 1.00 -
## SUPPLEMENTAL 1.00 1.00 1.00 0.74
##
## P value adjustment method: bonferroniSupplemental subscale
summary(aov(sup.only$SUP.av~sup.only$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## sup.only$Condition 2 0.93 0.4655 0.811 0.445
## Residuals 279 160.05 0.5737
## 2 observations deleted due to missingnesspairwise.t.test(sup.only$SUP.av,sup.only$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: sup.only$SUP.av and sup.only$Condition
##
## NURTURANT PRACTICAL
## PRACTICAL 1.00 -
## SUPPLEMENTAL 1.00 0.64
##
## P value adjustment method: bonferronisummary(aov(dat$SUP.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 0.98 0.2458 0.45 0.773
## Residuals 484 264.47 0.5464
## 3 observations deleted due to missingnesspairwise.t.test(dat$SUP.av,dat$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$SUP.av and dat$Condition
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1 - - -
## NURTURANT 1 1 - -
## PRACTICAL 1 1 1 -
## SUPPLEMENTAL 1 1 1 1
##
## P value adjustment method: bonferroniFacilitative subscale
summary(aov(sup.only$FAX.av~sup.only$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## sup.only$Condition 2 0.69 0.3437 0.821 0.441
## Residuals 279 116.75 0.4185
## 2 observations deleted due to missingnesspairwise.t.test(sup.only$FAX.av,sup.only$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: sup.only$FAX.av and sup.only$Condition
##
## NURTURANT PRACTICAL
## PRACTICAL 1.0 -
## SUPPLEMENTAL 0.6 1.0
##
## P value adjustment method: bonferronisummary(aov(dat$FAX.av~dat$Condition))## Df Sum Sq Mean Sq F value Pr(>F)
## dat$Condition 4 1.48 0.3691 0.803 0.524
## Residuals 483 222.13 0.4599
## 4 observations deleted due to missingnesspairwise.t.test(dat$FAX.av,dat$Condition, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: dat$FAX.av and dat$Condition
##
## ALLsupp NOsupp NURTURANT PRACTICAL
## NOsupp 1 - - -
## NURTURANT 1 1 - -
## PRACTICAL 1 1 1 -
## SUPPLEMENTAL 1 1 1 1
##
## P value adjustment method: bonferroniEFAs by condition
NURT.dat<-dat[dat$Condition == "NURTURANT",] #n = 91
PRACT.dat<-dat[dat$Condition == "PRACTICAL",] #n = 97
SUPP.dat<-dat[dat$Condition == "SUPPLEMENTAL",] # n = 96
NO.dat<-dat[dat$Condition == "NOsupp",] #n = 106No sup
exog<-cor(NO.dat[,c("TA1", "TA2", "TA3","TA4","TA5","TA6","TA_facil1", "TA_facil3")],use="pairwise.complete.obs")
cormat<-round(exog,2) #rounding this to the hundredth place
#corrplot::corrplot(exog) #This function comes from the package by the same nameEigs & scree
#The number of objects in the last line of the output is a rough estimate of how many factors there might be. Look for number of values ≥ 1.00
ev<-eigen(exog) #Where the object "exog" is the correlation matrix we computed earlier
round(ev$values,2)## [1] 3.62 0.95 0.85 0.72 0.61 0.52 0.46 0.27ev$values[ev$values>1]## [1] 3.624344#Look for the largest "elbow", as well as amount of obvious elbows
plot(ev$values, type = "b", las = 1)
2 factor EFA
xx<-fa(exog,nfactors=2, fm = "pa", rotate="Promax", SMC=TRUE, scores="regression",use="pairwise",cor="poly")
print(xx$loadings,cut=.25)##
## Loadings:
## PA1 PA2
## TA1 0.532
## TA2 1.086 -0.295
## TA3 0.311
## TA4 -0.311 1.096
## TA5 0.429 0.275
## TA6 0.290
## TA_facil1 0.425
## TA_facil3 0.721
##
## PA1 PA2
## SS loadings 2.528 1.589
## Proportion Var 0.316 0.199
## Cumulative Var 0.316 0.515Factor Correlations
round(xx$Phi,2)## PA1 PA2
## PA1 1.00 0.76
## PA2 0.76 1.00NURT
exog<-cor(NURT.dat[,c("TA1", "TA2", "TA3","TA4","TA5","TA6","TA_facil1", "TA_facil3")],use="pairwise.complete.obs")
cormat<-round(exog,2) #rounding this to the hundredth place
#corrplot::corrplot(exog) #This function comes from the package by the same nameEigs & scree
#The number of objects in the last line of the output is a rough estimate of how many factors there might be. Look for number of values ≥ 1.00
ev<-eigen(exog) #Where the object "exog" is the correlation matrix we computed earlier
round(ev$values,2)## [1] 3.68 0.95 0.78 0.69 0.61 0.53 0.45 0.31ev$values[ev$values>1]## [1] 3.681367#Look for the largest "elbow", as well as amount of obvious elbows
plot(ev$values, type = "b", las = 1)
2 factor EFA
xx<-fa(exog,nfactors=2, fm = "pa", rotate="Promax", SMC=TRUE, scores="regression",use="pairwise",cor="poly") ## maximum iteration exceededprint(xx$loadings,cut=.25)##
## Loadings:
## PA1 PA2
## TA1 0.523
## TA2 0.342 0.373
## TA3 0.599
## TA4 0.900 -0.295
## TA5 1.042
## TA6 0.500
## TA_facil1 0.314
## TA_facil3 0.598
##
## PA1 PA2
## SS loadings 2.284 1.389
## Proportion Var 0.285 0.174
## Cumulative Var 0.285 0.459Factor Correlations
round(xx$Phi,2)## PA1 PA2
## PA1 1.00 0.76
## PA2 0.76 1.00