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            NOsupp

IV 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            0

Practical 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            0

Supplemental 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           0

Course 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         74
Quiz1.retry<-d[d$Q1allCORRECT.dummy == 1,]
table(Quiz1.retry$Q1retry.CATS)
## 
## NOretry   retry 
##     108      74
Quiz1.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        176
Quiz2.retry<-d[d$Q2allCORRECT.dummy == 1,]
table(Quiz2.retry$Q2retry.CATS)
## 
## NOretry   retry 
##     197     176
Quiz2.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        109
Quiz3.retry<-d[d$Q3allCORRECT.dummy == 1,]
table(Quiz3.retry$Q3retry.CATS)
## 
## NOretry   retry 
##     234     109
Quiz3.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                0

Analysis Strategy

  1. Look at Mahalanobis distance and univariate outliers

  2. Get descriptives & Correlations of ASPS, performance, and persistence

  3. Calcuate reliabilities & belonging EFA/CFA

  4. 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
  1. 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       1
Outliers[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    NA

Mahalanobis 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)
Summary Statistics
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)
Summary Statistics
Gen.name
Man
Woman
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            5.022546
t.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            5.283862
t.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            3.884350
t.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            3.702255
t.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            4.362698
t.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            3.208753

By Condition

sumtable(descripts,group = c("Condition"), digits = 3)
Summary Statistics
Condition
ALLsupp
NOsupp
NURTURANT
PRACTICAL
SUPPLEMENTAL
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      0.4753

Women 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      0.6522

Sup 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        1
Hmisc::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  
## TAcomm10

Eigs & 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$values,2)
##  [1] 8.93 1.80 0.94 0.83 0.68 0.63 0.58 0.53 0.49 0.43 0.36 0.33 0.30 0.27 0.26
## [16] 0.23 0.21 0.19
ev$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 0.530

Factor Correlations

round(xx$Phi,2)
##      PA1  PA2
## PA1 1.00 0.67
## PA2 0.67 1.00
cor.test(dat$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 
## 0.6077565

Social Belonging

Pracical v. Supplemental Support (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 0.729

Practical v. Nurturant (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 0.120

Supplemental v. Nurturant (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    0.225

Tukey’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$Condition   4   3.67   0.917   1.479 0.20722   
## Residuals                  481 298.08   0.620                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
TukeyHSD(model)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = dat$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 0.7824104

Differences 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 0.521

Graphs

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

Persistance

Quiz 2

Proportions within gender

table(dat$Q2retry.CATS)
## 
## ALLcorrect    NOretry      retry 
##        121        195        176
table(dat$Gen.name)
## 
##   Man Woman 
##   114   378
table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Gen.name)
##          
##           Man Woman
##   NOretry  41   156
##   retry    41   135
table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Condition.ordered)
##          
##           NOsupp ALLsupp NURTURANT PRACTICAL SUPPLEMENTAL
##   NOretry     47      43        29        43           35
##   retry       35      37        40        32           32
table(Quiz2.retry$Q2retry.CATS, Quiz2.retry$Condition,Quiz2.retry$Gen.name)
## , ,  = Man
## 
##          
##           ALLsupp NOsupp NURTURANT PRACTICAL SUPPLEMENTAL
##   NOretry      11     11         1         9            9
##   retry         9      7        11         7            7
## 
## , ,  = Woman
## 
##          
##           ALLsupp NOsupp NURTURANT PRACTICAL SUPPLEMENTAL
##   NOretry      32     36        28        34           26
##   retry        28     28        29        25           25
table(Quiz2.retry$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           25

Helmerts

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: 4
tab_model(m0CC, title = "P v S", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert
P v S
  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: 4
tab_model(m0CC, title = "P v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert
P v N
  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: 4
tab_model(m0CC, title = "S v N", show.ci = F, show.se = T) #output in Odds Ratios so you dont have to convert
S v N
  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")
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: 4
tab_model(m0CC,title = "NURTURANT.d")
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")
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")
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")
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