Choice Vs. Interest
Patrick N. Beymer
2/6/2018
Loading packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## intersect, setdiff, setequal, unionlibrary(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 2.2.1 ✔ readr 1.1.1
## ✔ tibble 1.4.2 ✔ purrr 0.2.4
## ✔ tidyr 0.8.0 ✔ stringr 1.2.0
## ✔ ggplot2 2.2.1 ✔ forcats 0.2.0## Warning: package 'tibble' was built under R version 3.4.3## Warning: package 'tidyr' was built under R version 3.4.3## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(lme4)## Warning: package 'lme4' was built under R version 3.4.3## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
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## expandlibrary(lmerTest)## Warning: package 'lmerTest' was built under R version 3.4.3##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
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## lmer## The following object is masked from 'package:stats':
##
## steplibrary(sjstats)## Warning: package 'sjstats' was built under R version 3.4.3library(jmRtools)
library(MuMIn)## Warning: package 'MuMIn' was built under R version 3.4.3Loading data
SciMo_esm <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-Mo-esm.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## stud_ID = col_character(),
## teacher_ID = col_integer(),
## subject = col_integer(),
## month = col_integer(),
## day = col_integer(),
## year = col_integer(),
## pager = col_integer(),
## Wave = col_integer(),
## part_stat = col_integer(),
## gender = col_integer(),
## race = col_integer(),
## grade = col_integer(),
## age = col_integer(),
## beep = col_integer(),
## lecture = col_integer(),
## lab = col_integer(),
## manage = col_integer(),
## present = col_integer(),
## seat = col_integer(),
## test = col_integer()
## # ... with 3 more columns
## )## See spec(...) for full column specifications.## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)## Warning: 38 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 2165 race an integer NaN '/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-M… file 2 2166 race an integer NaN '/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-M… row 3 2167 race an integer NaN '/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-M… col 4 2168 race an integer NaN '/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-M… expected 5 2169 race an integer NaN '/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-M…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.SciMo_student_survey <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/SciMo/Sci-Mo-student-survey.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## stud_ID = col_character(),
## FirstQuar_2010 = col_integer(),
## SecondQuar_2010 = col_integer(),
## ThirdQuar_2010 = col_character(),
## FourthQuar_2010 = col_character(),
## ExploreComp_2010 = col_integer(),
## PlanComp_2010 = col_integer(),
## ACTComp_2010 = col_integer(),
## Grad_2010 = col_integer(),
## FirstQuar_2011 = col_integer(),
## SecondQuar_2011 = col_integer(),
## ThirdQuar_2011 = col_character(),
## FourthQuar_2011 = col_integer(),
## ExloreComp2011 = col_integer(),
## ExploreSciComp_2011 = col_integer(),
## PlanComp_2011 = col_integer(),
## PlanSciComp_2011 = col_integer(),
## ACTComp_2011 = col_integer(),
## ACTSciComp_2011 = col_integer(),
## Grad_2011 = col_integer()
## )
## See spec(...) for full column specifications.Creating variables
SciMo_student_survey$female <- ifelse(SciMo_student_survey$gender == 2, 1, 0)
SciMo_student_survey$minority <- ifelse(SciMo_student_survey$race == 4, 0, 1)
SciMo_esm$engagement_three <- composite_mean_maker(SciMo_esm, enjoy, conc, hardwk)fix_missing <- function(x) {
x[x == NA] <- 0
x
}
SciMo_esm$ch_who[is.na(SciMo_esm$ch_who)] <- 0
SciMo_esm$ch_howdo[is.na(SciMo_esm$ch_howdo)] <- 0
SciMo_esm$ch_mat[is.na(SciMo_esm$ch_mat)] <- 0
SciMo_esm$ch_time[is.na(SciMo_esm$ch_time)] <- 0
SciMo_esm$ch_doing[is.na(SciMo_esm$ch_doing)] <- 0
SciMo_esm$ch_topic[is.na(SciMo_esm$ch_topic)] <- 0
SciMo_esm$ch_defin[is.na(SciMo_esm$ch_defin)] <- 0
SciMo_esm$ch_other[is.na(SciMo_esm$ch_other)] <- 0
SciMo_esm$ch_none[is.na(SciMo_esm$ch_none)] <- 0
SciMo_esm$ch_none <- ifelse(((SciMo_esm$ch_who == 0 & SciMo_esm$ch_howdo == 0 & SciMo_esm$ch_mat == 0 & SciMo_esm$ch_time == 0 & SciMo_esm$ch_doing == 0 &
SciMo_esm$ch_defin == 0 & SciMo_esm$ch_topic == 0 & SciMo_esm$ch_other == 0) & SciMo_esm$ch_none == 0), 1, SciMo_esm$ch_none)
SciMo_esm$ch_none <- ifelse(((SciMo_esm$ch_who == 1 | SciMo_esm$ch_howdo == 1 | SciMo_esm$ch_mat == 1 | SciMo_esm$ch_time == 1 | SciMo_esm$ch_doing |
SciMo_esm$ch_defin == 1 | SciMo_esm$ch_topic == 1 | SciMo_esm$ch_other == 1) & SciMo_esm$ch_none == 1), 0, SciMo_esm$ch_none)
SciMo_esm$ch_framing <- ifelse(SciMo_esm$ch_defin == 1 | SciMo_esm$ch_topic == 1 | SciMo_esm$ch_doing, 1, 0)
SciMo_esm$anychoice <- ifelse(SciMo_esm$ch_mat == 1 | SciMo_esm$ch_howdo == 1 | SciMo_esm$ch_topic == 1 | SciMo_esm$ch_doing == 1 |SciMo_esm$ch_time == 1 |
SciMo_esm$ch_who == 1 | SciMo_esm$ch_defin == 1 | SciMo_esm$ch_other == 1, 1, 0)Join data
SciMo_All <- left_join(SciMo_student_survey, SciMo_esm, by = "stud_ID")Creating new beep_id variable for cross classification
SciMo_All<- SciMo_All %>%
dplyr::mutate(beep_id = stringr::str_c(signal, teacher_ID1, month, day, year))Creating scaled interest variable
SciMo_All$interest_c <- scale(SciMo_All$interest)Null Models
Null engagement model
M0 <- lmer(engagement_three ~
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M0)## summary from lme4 is returned
## some computational error has occurred in lmerTest## Linear mixed model fit by REML ['lmerMod']
## Formula:
## engagement_three ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 8115.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9111 -0.6117 0.0361 0.6341 2.9416
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.04092 0.2023
## stud_ID (Intercept) 0.22590 0.4753
## teacher_ID1 (Intercept) 0.01347 0.1160
## Residual 0.36309 0.6026
## Number of obs: 4001, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.63997 0.04887 33.55sjstats::icc(M0)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: engagement_three ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.063595
## ICC (stud_ID): 0.351116
## ICC (teacher_ID1): 0.020930Null positive affect model
M00 <- lmer(posaffect ~
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M00)## summary from lme4 is returned
## some computational error has occurred in lmerTest## Linear mixed model fit by REML ['lmerMod']
## Formula: posaffect ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 8242.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5316 -0.6240 -0.1028 0.5386 3.8416
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.0148 0.1217
## stud_ID (Intercept) 0.3326 0.5767
## teacher_ID1 (Intercept) 0.0000 0.0000
## Residual 0.3845 0.6200
## Number of obs: 3982, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.05434 0.04006 26.32sjstats::icc(M00)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: posaffect ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.020229
## ICC (stud_ID): 0.454454
## ICC (teacher_ID1): 0.000000Null negative affect model
M000 <- lmer(negaffect ~
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M000)## summary from lme4 is returned
## some computational error has occurred in lmerTest## Linear mixed model fit by REML ['lmerMod']
## Formula: negaffect ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7882.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5612 -0.5353 -0.1660 0.3599 4.5191
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01679 0.1296
## stud_ID (Intercept) 0.19937 0.4465
## teacher_ID1 (Intercept) 0.01915 0.1384
## Residual 0.35720 0.5977
## Number of obs: 3980, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.57815 0.05145 11.24sjstats::icc(M000)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: negaffect ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.028338
## ICC (stud_ID): 0.336484
## ICC (teacher_ID1): 0.032312Null learning model
M0000 <- lmer(learning ~
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M0000)## summary from lme4 is returned
## some computational error has occurred in lmerTest## Linear mixed model fit by REML ['lmerMod']
## Formula: learning ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 10203.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1964 -0.6184 0.0670 0.6803 2.7711
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.06631 0.2575
## stud_ID (Intercept) 0.24948 0.4995
## teacher_ID1 (Intercept) 0.02262 0.1504
## Residual 0.63575 0.7973
## Number of obs: 3981, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.68251 0.05866 28.68sjstats::icc(M0000)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.068066
## ICC (stud_ID): 0.256098
## ICC (teacher_ID1): 0.023218Models with just any choice and interest as predictors
Engagement
M1 <- lmer(engagement_three ~
interest_c +
anychoice +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: engagement_three ~ interest_c + anychoice + (1 | stud_ID) + (1 |
## teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 6884.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4677 -0.6179 0.0269 0.6459 3.9276
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01584 0.1259
## stud_ID (Intercept) 0.10336 0.3215
## teacher_ID1 (Intercept) 0.01337 0.1156
## Residual 0.29452 0.5427
## Number of obs: 3884, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.585e+00 4.340e-02 1.100e+01 36.525 1.09e-12 ***
## interest_c 3.754e-01 1.089e-02 3.747e+03 34.486 < 2e-16 ***
## anychoice 8.930e-02 2.248e-02 3.836e+03 3.973 7.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_
## interest_c 0.017
## anychoice -0.292 -0.059sjstats::icc(M1)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: engagement_three ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.037095
## ICC (stud_ID): 0.242000
## ICC (teacher_ID1): 0.031316Positive Affect
M2 <- lmer(posaffect ~
interest_c +
anychoice +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## posaffect ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7543
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0082 -0.6226 -0.0950 0.5438 4.3392
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.0032709 0.05719
## stud_ID (Intercept) 0.2712346 0.52080
## teacher_ID1 (Intercept) 0.0004154 0.02038
## Residual 0.3469680 0.58904
## Number of obs: 3873, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.019e+00 3.870e-02 1.300e+01 26.341 2.28e-12 ***
## interest_c 2.674e-01 1.169e-02 3.302e+03 22.871 < 2e-16 ***
## anychoice 6.539e-02 2.437e-02 3.650e+03 2.683 0.00733 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_
## interest_c 0.018
## anychoice -0.346 -0.048sjstats::icc(M2)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: posaffect ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.005260
## ICC (stud_ID): 0.436146
## ICC (teacher_ID1): 0.000668Negative Affect
M3 <- lmer(negaffect ~
interest_c +
anychoice +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M3)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## negaffect ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7657.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6525 -0.5391 -0.1693 0.3570 4.6170
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01513 0.1230
## stud_ID (Intercept) 0.20000 0.4472
## teacher_ID1 (Intercept) 0.01904 0.1380
## Residual 0.35490 0.5957
## Number of obs: 3873, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.818e-01 5.322e-02 1.200e+01 10.933 1.73e-07 ***
## interest_c -7.660e-02 1.207e-02 3.679e+03 -6.346 2.48e-10 ***
## anychoice -8.287e-03 2.494e-02 3.851e+03 -0.332 0.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_
## interest_c 0.014
## anychoice -0.263 -0.053sjstats::icc(M3)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: negaffect ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.025678
## ICC (stud_ID): 0.339520
## ICC (teacher_ID1): 0.032315Learning
M4 <- lmer(learning ~
interest_c +
anychoice +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M4)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 9298.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6915 -0.6149 0.0334 0.6511 3.0104
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.03259 0.1805
## stud_ID (Intercept) 0.12625 0.3553
## teacher_ID1 (Intercept) 0.02394 0.1547
## Residual 0.56424 0.7512
## Number of obs: 3869, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.634e+00 5.615e-02 1.200e+01 29.100 1.55e-12 ***
## interest_c 3.947e-01 1.489e-02 3.632e+03 26.511 < 2e-16 ***
## anychoice 8.185e-02 3.070e-02 3.662e+03 2.666 0.00771 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_
## interest_c 0.020
## anychoice -0.309 -0.064sjstats::icc(M4)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ interest_c + anychoice + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.043628
## ICC (stud_ID): 0.169009
## ICC (teacher_ID1): 0.032042Models with just all choices and interest as predictors
Engagement
M1a <- lmer(engagement_three ~
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M1a)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: engagement_three ~ interest_c + ch_who + ch_howdo + ch_mat +
## ch_time + ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 6887.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4265 -0.6107 0.0271 0.6403 3.9307
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01475 0.1215
## stud_ID (Intercept) 0.10413 0.3227
## teacher_ID1 (Intercept) 0.01298 0.1139
## Residual 0.29359 0.5418
## Number of obs: 3884, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.59546 0.04240 10.00000 37.633 2.35e-12 ***
## interest_c 0.37382 0.01088 3742.00000 34.356 < 2e-16 ***
## ch_who -0.01970 0.03424 3348.00000 -0.575 0.565090
## ch_howdo 0.07240 0.02805 3821.00000 2.581 0.009891 **
## ch_mat 0.06899 0.03296 3861.00000 2.093 0.036378 *
## ch_time 0.10604 0.03052 3857.00000 3.475 0.000517 ***
## ch_other -0.02295 0.02957 3798.00000 -0.776 0.437728
## ch_framing 0.02676 0.02846 3836.00000 0.940 0.347155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr
## interest_c 0.018
## ch_who -0.046 -0.002
## ch_howdo -0.082 -0.027 -0.110
## ch_mat -0.052 -0.060 -0.136 -0.228
## ch_time -0.065 -0.010 -0.136 -0.152 -0.107
## ch_other -0.143 -0.039 0.010 0.029 0.041 0.024
## ch_framing -0.111 -0.016 -0.059 -0.063 -0.057 -0.017 0.058sjstats::icc(M1a)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: engagement_three ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.034671
## ICC (stud_ID): 0.244756
## ICC (teacher_ID1): 0.030498Positive Affect
M2a <- lmer(posaffect ~
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M2a)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: posaffect ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time +
## ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7560.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0218 -0.6184 -0.0989 0.5427 4.2937
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.0031145 0.05581
## stud_ID (Intercept) 0.2714202 0.52098
## teacher_ID1 (Intercept) 0.0002111 0.01453
## Residual 0.3468789 0.58896
## Number of obs: 3873, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.024e+00 3.771e-02 1.200e+01 27.150 7.34e-12 ***
## interest_c 2.667e-01 1.172e-02 3.327e+03 22.756 < 2e-16 ***
## ch_who 9.445e-02 3.603e-02 2.629e+03 2.621 0.00881 **
## ch_howdo 6.700e-03 3.055e-02 3.771e+03 0.219 0.82642
## ch_mat 3.299e-02 3.560e-02 3.651e+03 0.927 0.35417
## ch_time 3.143e-02 3.308e-02 3.744e+03 0.950 0.34208
## ch_other 5.146e-02 3.246e-02 3.833e+03 1.585 0.11295
## ch_framing 6.211e-03 3.124e-02 3.834e+03 0.199 0.84244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr
## interest_c 0.019
## ch_who -0.045 0.001
## ch_howdo -0.098 -0.023 -0.113
## ch_mat -0.062 -0.070 -0.136 -0.235
## ch_time -0.079 0.001 -0.142 -0.149 -0.104
## ch_other -0.173 -0.029 0.002 0.030 0.045 0.023
## ch_framing -0.135 -0.010 -0.063 -0.057 -0.053 -0.012 0.062sjstats::icc(M2a)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: posaffect ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.005010
## ICC (stud_ID): 0.436630
## ICC (teacher_ID1): 0.000340Engagement
M3a <- lmer(negaffect ~
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M3a)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: negaffect ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time +
## ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7672.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5981 -0.5473 -0.1670 0.3578 4.7040
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01371 0.1171
## stud_ID (Intercept) 0.20156 0.4490
## teacher_ID1 (Intercept) 0.01846 0.1359
## Residual 0.35520 0.5960
## Number of obs: 3873, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.769e-01 5.219e-02 1.100e+01 11.054 2.36e-07 ***
## interest_c -7.739e-02 1.208e-02 3.663e+03 -6.407 1.67e-10 ***
## ch_who -3.956e-02 3.762e-02 3.135e+03 -1.052 0.2931
## ch_howdo 1.308e-02 3.109e-02 3.780e+03 0.421 0.6740
## ch_mat 2.833e-02 3.647e-02 3.798e+03 0.777 0.4374
## ch_time 6.592e-02 3.378e-02 3.805e+03 1.951 0.0511 .
## ch_other -5.799e-02 3.298e-02 3.860e+03 -1.758 0.0788 .
## ch_framing -9.226e-03 3.171e-02 3.844e+03 -0.291 0.7711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr
## interest_c 0.016
## ch_who -0.039 -0.001
## ch_howdo -0.075 -0.023 -0.108
## ch_mat -0.048 -0.062 -0.136 -0.229
## ch_time -0.059 -0.008 -0.138 -0.148 -0.106
## ch_other -0.129 -0.033 0.007 0.030 0.042 0.023
## ch_framing -0.101 -0.013 -0.063 -0.058 -0.055 -0.012 0.061sjstats::icc(M3a)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: negaffect ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.023286
## ICC (stud_ID): 0.342241
## ICC (teacher_ID1): 0.031351Learning
M4a <- lmer(learning ~
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
(1|stud_ID) +
(1|teacher_ID1) +
(1|beep_id),
data = SciMo_All)
summary(M4a)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: learning ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time +
## ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) +
## (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 9321.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6850 -0.6131 0.0352 0.6562 2.9832
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.03251 0.1803
## stud_ID (Intercept) 0.12626 0.3553
## teacher_ID1 (Intercept) 0.02256 0.1502
## Residual 0.56537 0.7519
## Number of obs: 3869, groups: beep_id, 246; stud_ID, 232; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.65997 0.05423 11.00000 30.609 3.73e-12 ***
## interest_c 0.39517 0.01494 3643.00000 26.456 < 2e-16 ***
## ch_who -0.04334 0.04739 3445.00000 -0.914 0.3606
## ch_howdo 0.03303 0.03862 3818.00000 0.855 0.3924
## ch_mat 0.03239 0.04540 3835.00000 0.713 0.4756
## ch_time -0.01684 0.04201 3855.00000 -0.401 0.6886
## ch_other 0.07535 0.04037 3553.00000 1.866 0.0621 .
## ch_framing 0.01709 0.03897 3699.00000 0.439 0.6610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr
## interest_c 0.021
## ch_who -0.051 -0.001
## ch_howdo -0.086 -0.028 -0.111
## ch_mat -0.055 -0.060 -0.136 -0.231
## ch_time -0.068 -0.013 -0.137 -0.158 -0.107
## ch_other -0.153 -0.042 0.012 0.029 0.041 0.023
## ch_framing -0.117 -0.021 -0.057 -0.071 -0.063 -0.021 0.056sjstats::icc(M4a)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.043534
## ICC (stud_ID): 0.169088
## ICC (teacher_ID1): 0.030210Any Choice & Interest Models
Any choice predicting engagement
M1_1 <- lmer(engagement_three ~ scale(interesfun1, scale=FALSE) +
interest_c +
anychoice +
interest_c*anychoice +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M1_1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## engagement_three ~ scale(interesfun1, scale = FALSE) + interest_c +
## anychoice + interest_c * anychoice + female + minority +
## (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 6844.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3567 -0.6159 0.0292 0.6424 4.0472
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01605 0.1267
## stud_ID (Intercept) 0.09072 0.3012
## teacher_ID1 (Intercept) 0.01010 0.1005
## Residual 0.29431 0.5425
## Number of obs: 3868, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.66578 0.05308 33.00000 31.384
## scale(interesfun1, scale = FALSE) 0.15274 0.02859 226.00000 5.343
## interest_c 0.39715 0.01549 3840.00000 25.642
## anychoice 0.08355 0.02247 3789.00000 3.719
## female -0.03003 0.04459 216.00000 -0.674
## minority -0.09188 0.04761 219.00000 -1.930
## interest_c:anychoice -0.04864 0.01964 3805.00000 -2.477
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 2.23e-07 ***
## interest_c < 2e-16 ***
## anychoice 0.000203 ***
## female 0.501304
## minority 0.054896 .
## interest_c:anychoice 0.013292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ anychc female minrty
## s(1,s=FALSE 0.007
## interest_c 0.039 -0.087
## anychoice -0.216 -0.040 -0.069
## female -0.365 0.103 -0.008 0.000
## minority -0.542 -0.054 -0.016 -0.042 -0.047
## intrst_c:ny -0.033 0.001 -0.707 0.046 0.013 0.001rand(M1_1)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 531.48 1 <2e-16 ***
## teacher_ID1 4.47 1 0.03 *
## beep_id 48.05 1 4e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M1_1)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: engagement_three ~ scale(interesfun1, scale = FALSE) + interest_c + anychoice + interest_c * anychoice + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.039030
## ICC (stud_ID): 0.220640
## ICC (teacher_ID1): 0.024558sjPlot::sjp.int(M1_1, type = "eff", swap.pred = TRUE)## `sjp.int()` will become deprecated in the future. Please use `plot_model()` instead.
#konfound::konfound(M1_1, `interest*choice`)
M1_1r<-r2glmm::r2beta(M1_1, method = "nsj")
M1_1r## Effect Rsq upper.CL lower.CL
## 1 Model 0.306 0.329 0.285
## 3 interest_c 0.141 0.161 0.122
## 2 scale(interesfun1, scale = FALSE) 0.032 0.044 0.022
## 6 minority 0.005 0.010 0.001
## 4 anychoice 0.004 0.009 0.001
## 7 interest_c:anychoice 0.001 0.005 0.000
## 5 female 0.001 0.003 0.000MuMIn::r.squaredGLMM(M1_1)## R2m R2c
## 0.3062082 0.5034031plot(M1_1r)
Any choice predicting positive affect
M2_2 <- lmer(posaffect ~ scale(interesfun1, scale=FALSE) +
interest_c +
anychoice +
interest_c*anychoice +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M2_2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: posaffect ~ scale(interesfun1, scale = FALSE) + interest_c +
## anychoice + interest_c * anychoice + female + minority +
## (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7526.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9681 -0.6127 -0.0911 0.5483 4.3593
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.003049 0.05521
## stud_ID (Intercept) 0.265018 0.51480
## teacher_ID1 (Intercept) 0.000000 0.00000
## Residual 0.347860 0.58980
## Number of obs: 3857, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.00751 0.06745 240.00000 14.937
## scale(interesfun1, scale = FALSE) 0.11122 0.04467 231.00000 2.490
## interest_c 0.24921 0.01666 3503.00000 14.958
## anychoice 0.06440 0.02447 3693.00000 2.631
## female -0.07465 0.07109 226.00000 -1.050
## minority 0.07734 0.07367 226.00000 1.050
## interest_c:anychoice 0.02717 0.02131 3737.00000 1.275
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 0.01349 *
## interest_c < 2e-16 ***
## anychoice 0.00854 **
## female 0.29482
## minority 0.29489
## interest_c:anychoice 0.20244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ anychc female minrty
## s(1,s=FALSE 0.028
## interest_c 0.029 -0.056
## anychoice -0.176 -0.029 -0.064
## female -0.467 0.089 -0.008 0.005
## minority -0.660 -0.087 -0.006 -0.040 -0.045
## intrst_c:ny -0.025 -0.002 -0.708 0.047 0.007 0.000rand(M2_2)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 1485.56 1 <2e-16 ***
## teacher_ID1 0.00 1 1.0
## beep_id 1.77 1 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M2_2)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: posaffect ~ scale(interesfun1, scale = FALSE) + interest_c + anychoice + interest_c * anychoice + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.004950
## ICC (stud_ID): 0.430275
## ICC (teacher_ID1): 0.000000#sjPlot::sjp.int(M2_2, type = "eff")
#konfound::konfound(M2_2, `interest*choice`)
M2_2r<-r2glmm::r2beta(M2_2, method = "nsj")
M2_2r## Effect Rsq upper.CL lower.CL
## 1 Model 0.140 0.161 0.122
## 3 interest_c 0.042 0.055 0.030
## 2 scale(interesfun1, scale = FALSE) 0.012 0.019 0.006
## 6 minority 0.002 0.006 0.000
## 5 female 0.002 0.006 0.000
## 4 anychoice 0.002 0.005 0.000
## 7 interest_c:anychoice 0.000 0.002 0.000MuMIn::r.squaredGLMM(M2_2)## R2m R2c
## 0.1396755 0.5141098#plot(M2_2r)Any choice predicting negative affect
M3_3 <- lmer(negaffect ~ scale(interesfun1, scale=FALSE) +
interest_c +
anychoice +
interest_c*anychoice +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M3_3)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: negaffect ~ scale(interesfun1, scale = FALSE) + interest_c +
## anychoice + interest_c * anychoice + female + minority +
## (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7639.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6106 -0.5468 -0.1664 0.3723 4.6167
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01506 0.1227
## stud_ID (Intercept) 0.18930 0.4351
## teacher_ID1 (Intercept) 0.02346 0.1532
## Residual 0.35584 0.5965
## Number of obs: 3857, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.253e-01 7.509e-02 3.300e+01 6.995
## scale(interesfun1, scale = FALSE) -6.493e-02 3.971e-02 2.250e+02 -1.635
## interest_c -7.728e-02 1.715e-02 3.761e+03 -4.506
## anychoice -5.840e-03 2.505e-02 3.835e+03 -0.233
## female 1.858e-01 6.205e-02 2.170e+02 2.994
## minority -4.905e-02 6.642e-02 2.210e+02 -0.739
## interest_c:anychoice 5.923e-03 2.174e-02 3.754e+03 0.272
## Pr(>|t|)
## (Intercept) 5.22e-08 ***
## scale(interesfun1, scale = FALSE) 0.10340
## interest_c 6.81e-06 ***
## anychoice 0.81568
## female 0.00308 **
## minority 0.46098
## interest_c:anychoice 0.78531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ anychc female minrty
## s(1,s=FALSE 0.006
## interest_c 0.030 -0.069
## anychoice -0.170 -0.034 -0.069
## female -0.359 0.100 -0.006 -0.001
## minority -0.539 -0.054 -0.012 -0.033 -0.047
## intrst_c:ny -0.026 -0.001 -0.705 0.049 0.009 0.001rand(M3_3)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 971.50 1 <2e-16 ***
## teacher_ID1 7.49 1 0.006 **
## beep_id 30.22 1 4e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M3_3)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: negaffect ~ scale(interesfun1, scale = FALSE) + interest_c + anychoice + interest_c * anychoice + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.025802
## ICC (stud_ID): 0.324334
## ICC (teacher_ID1): 0.040188#sjPlot::sjp.int(M3_3, type = "eff")
#konfound::konfound(M3_3, `interest*choice`)
M3_3r<-r2glmm::r2beta(M3_3, method = "nsj")
M3_3r## Effect Rsq upper.CL lower.CL
## 1 Model 0.035 0.049 0.026
## 5 female 0.014 0.023 0.008
## 3 interest_c 0.004 0.009 0.001
## 2 scale(interesfun1, scale = FALSE) 0.004 0.009 0.001
## 6 minority 0.001 0.004 0.000
## 7 interest_c:anychoice 0.000 0.001 0.000
## 4 anychoice 0.000 0.001 0.000MuMIn::r.squaredGLMM(M3_3)## R2m R2c
## 0.03521135 0.41179243#plot(M3_3r)Any choice predicting learning
M4_4 <- lmer(learning ~ scale(interesfun1, scale=FALSE) +
interest_c +
anychoice +
interest_c*anychoice +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M4_4)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ scale(interesfun1, scale = FALSE) + interest_c + anychoice +
## interest_c * anychoice + female + minority + (1 | stud_ID) +
## (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 9256.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6416 -0.6180 0.0337 0.6554 3.1005
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.03262 0.1806
## stud_ID (Intercept) 0.11200 0.3347
## teacher_ID1 (Intercept) 0.01751 0.1323
## Residual 0.56498 0.7516
## Number of obs: 3853, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.72011 0.06523 30.00000 26.370
## scale(interesfun1, scale = FALSE) 0.16186 0.03343 225.00000 4.842
## interest_c 0.39990 0.02133 3818.00000 18.749
## anychoice 0.07701 0.03069 3582.00000 2.509
## female 0.01395 0.05195 214.00000 0.268
## minority -0.13537 0.05559 218.00000 -2.435
## interest_c:anychoice -0.01875 0.02704 3811.00000 -0.693
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 2.39e-06 ***
## interest_c < 2e-16 ***
## anychoice 0.0121 *
## female 0.7886
## minority 0.0157 *
## interest_c:anychoice 0.4882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ anychc female minrty
## s(1,s=FALSE 0.006
## interest_c 0.045 -0.103
## anychoice -0.241 -0.046 -0.069
## female -0.345 0.108 -0.009 -0.001
## minority -0.511 -0.050 -0.022 -0.048 -0.048
## intrst_c:ny -0.037 0.002 -0.709 0.044 0.015 0.003rand(M4_4)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 315.3 1 <2e-16 ***
## teacher_ID1 5.7 1 0.02 *
## beep_id 53.3 1 3e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M4_4)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ scale(interesfun1, scale = FALSE) + interest_c + anychoice + interest_c * anychoice + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.044860
## ICC (stud_ID): 0.154034
## ICC (teacher_ID1): 0.024079#sjPlot::sjp.int(M4_4, type = "eff")
#konfound::konfound(M4_4, `interest*choice`)
M4_4r<-r2glmm::r2beta(M4_4, method = "nsj")
M4_4r## Effect Rsq upper.CL lower.CL
## 1 Model 0.215 0.238 0.194
## 3 interest_c 0.086 0.103 0.070
## 2 scale(interesfun1, scale = FALSE) 0.021 0.031 0.013
## 6 minority 0.006 0.011 0.002
## 4 anychoice 0.002 0.006 0.000
## 7 interest_c:anychoice 0.000 0.002 0.000
## 5 female 0.000 0.002 0.000MuMIn::r.squaredGLMM(M4_4)## R2m R2c
## 0.2150382 0.3900638#plot(M4_4r)All Choices and Interest Models
All choices predicting engagement
M11<-lmer(engagement_three ~ scale(interesfun1, scale=FALSE) +
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M11)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## engagement_three ~ scale(interesfun1, scale = FALSE) + interest_c +
## ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing +
## female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 |
## beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 6848.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3660 -0.6093 0.0310 0.6405 3.9732
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01493 0.1222
## stud_ID (Intercept) 0.09117 0.3019
## teacher_ID1 (Intercept) 0.01038 0.1019
## Residual 0.29392 0.5421
## Number of obs: 3868, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.67195 0.05290 32.00000 31.608
## scale(interesfun1, scale = FALSE) 0.15011 0.02866 227.00000 5.237
## interest_c 0.36863 0.01096 3735.00000 33.636
## ch_who -0.01846 0.03425 3345.00000 -0.539
## ch_howdo 0.07135 0.02801 3812.00000 2.548
## ch_mat 0.06895 0.03298 3844.00000 2.091
## ch_time 0.10060 0.03048 3847.00000 3.300
## ch_other -0.02361 0.02945 3737.00000 -0.802
## ch_framing 0.02286 0.02842 3800.00000 0.804
## female -0.02879 0.04471 217.00000 -0.644
## minority -0.09190 0.04780 221.00000 -1.923
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 3.72e-07 ***
## interest_c < 2e-16 ***
## ch_who 0.589821
## ch_howdo 0.010882 *
## ch_mat 0.036629 *
## ch_time 0.000975 ***
## ch_other 0.422734
## ch_framing 0.421374
## female 0.520306
## minority 0.055815 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr ch_frm
## s(1,s=FALSE 0.006
## interest_c 0.022 -0.120
## ch_who -0.042 0.002 -0.002
## ch_howdo -0.063 -0.001 -0.027 -0.111
## ch_mat -0.010 -0.024 -0.056 -0.133 -0.229
## ch_time -0.060 -0.027 -0.008 -0.137 -0.154 -0.108
## ch_other -0.103 -0.015 -0.037 0.010 0.029 0.041 0.025
## ch_framing -0.074 -0.029 -0.011 -0.059 -0.065 -0.056 -0.018 0.058
## female -0.364 0.104 0.003 0.007 -0.001 0.001 -0.017 -0.030 0.015
## minority -0.549 -0.053 -0.019 0.004 -0.003 -0.056 0.025 0.000 -0.037
## female
## s(1,s=FALSE
## interest_c
## ch_who
## ch_howdo
## ch_mat
## ch_time
## ch_other
## ch_framing
## female
## minority -0.048rand(M11)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 535.38 1 <2e-16 ***
## teacher_ID1 4.71 1 0.03 *
## beep_id 42.33 1 8e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M11)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: engagement_three ~ scale(interesfun1, scale = FALSE) + interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.036370
## ICC (stud_ID): 0.222159
## ICC (teacher_ID1): 0.025289#sjPlot::sjp.int(M11, type = "eff")
M11r<-r2glmm::r2beta(M11, method = "nsj")
M11r## Effect Rsq upper.CL lower.CL
## 1 Model 0.310 0.333 0.289
## 3 interest_c 0.236 0.258 0.214
## 2 scale(interesfun1, scale = FALSE) 0.031 0.043 0.021
## 11 minority 0.005 0.010 0.001
## 7 ch_time 0.003 0.007 0.000
## 5 ch_howdo 0.002 0.005 0.000
## 6 ch_mat 0.001 0.004 0.000
## 10 female 0.000 0.003 0.000
## 8 ch_other 0.000 0.002 0.000
## 9 ch_framing 0.000 0.002 0.000
## 4 ch_who 0.000 0.002 0.000MuMIn::r.squaredGLMM(M11)## R2m R2c
## 0.3097582 0.5056612#plot(M11r)All choices predicting positive affect
M22<-lmer(posaffect ~ scale(interesfun1, scale=FALSE) +
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M22)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: posaffect ~ scale(interesfun1, scale = FALSE) + interest_c +
## ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing +
## female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 |
## beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7539.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9918 -0.6170 -0.0985 0.5450 4.3094
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 2.903e-03 5.388e-02
## stud_ID (Intercept) 2.649e-01 5.147e-01
## teacher_ID1 (Intercept) 3.000e-13 5.477e-07
## Residual 3.478e-01 5.898e-01
## Number of obs: 3857, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.015e+00 6.703e-02 2.340e+02 15.144
## scale(interesfun1, scale = FALSE) 1.103e-01 4.468e-02 2.300e+02 2.469
## interest_c 2.635e-01 1.180e-02 3.302e+03 22.332
## ch_who 9.528e-02 3.609e-02 2.601e+03 2.640
## ch_howdo 5.891e-03 3.058e-02 3.761e+03 0.193
## ch_mat 3.144e-02 3.571e-02 3.626e+03 0.881
## ch_time 2.928e-02 3.312e-02 3.728e+03 0.884
## ch_other 5.122e-02 3.248e-02 3.830e+03 1.577
## ch_framing 2.708e-03 3.133e-02 3.818e+03 0.086
## female -7.664e-02 7.109e-02 2.260e+02 -1.078
## minority 7.673e-02 7.372e-02 2.260e+02 1.041
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 0.01429 *
## interest_c < 2e-16 ***
## ch_who 0.00834 **
## ch_howdo 0.84726
## ch_mat 0.37863
## ch_time 0.37669
## ch_other 0.11493
## ch_framing 0.93111
## female 0.28221
## minority 0.29907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr ch_frm
## s(1,s=FALSE 0.028
## interest_c 0.015 -0.080
## ch_who -0.023 -0.003 0.001
## ch_howdo -0.056 0.000 -0.023 -0.114
## ch_mat -0.006 -0.013 -0.068 -0.132 -0.236
## ch_time -0.047 -0.024 0.003 -0.143 -0.149 -0.104
## ch_other -0.084 -0.011 -0.028 0.002 0.030 0.045 0.024
## ch_framing -0.061 -0.020 -0.006 -0.063 -0.057 -0.052 -0.012 0.063
## female -0.468 0.089 -0.004 0.007 0.002 0.002 -0.010 -0.016 0.009
## minority -0.666 -0.087 -0.006 -0.009 0.000 -0.043 0.010 -0.008 -0.028
## female
## s(1,s=FALSE
## interest_c
## ch_who
## ch_howdo
## ch_mat
## ch_time
## ch_other
## ch_framing
## female
## minority -0.046rand(M22)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 1.46e+03 1 <2e-16 ***
## teacher_ID1 6.37e-12 1 1.0
## beep_id 1.61e+00 1 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M22)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: posaffect ~ scale(interesfun1, scale = FALSE) + interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.004716
## ICC (stud_ID): 0.430321
## ICC (teacher_ID1): 0.000000#sjPlot::sjp.int(M22, type = "eff")
M22r<-r2glmm::r2beta(M22, method = "nsj")
M22r## Effect Rsq upper.CL lower.CL
## 1 Model 0.140 0.162 0.123
## 3 interest_c 0.095 0.113 0.078
## 2 scale(interesfun1, scale = FALSE) 0.011 0.019 0.006
## 10 female 0.002 0.006 0.000
## 11 minority 0.002 0.006 0.000
## 4 ch_who 0.001 0.005 0.000
## 8 ch_other 0.001 0.003 0.000
## 6 ch_mat 0.000 0.002 0.000
## 7 ch_time 0.000 0.002 0.000
## 5 ch_howdo 0.000 0.001 0.000
## 9 ch_framing 0.000 0.001 0.000MuMIn::r.squaredGLMM(M22)## R2m R2c
## 0.1403063 0.5143046#plot(M22r)All choices predicting negative affect
M33<-lmer(negaffect ~ scale(interesfun1, scale=FALSE) +
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M33)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: negaffect ~ scale(interesfun1, scale = FALSE) + interest_c +
## ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing +
## female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 |
## beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 7648.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5645 -0.5472 -0.1640 0.3684 4.7130
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.01367 0.1169
## stud_ID (Intercept) 0.19050 0.4365
## teacher_ID1 (Intercept) 0.02291 0.1514
## Residual 0.35603 0.5967
## Number of obs: 3857, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.221e-01 7.450e-02 3.300e+01 7.008
## scale(interesfun1, scale = FALSE) -6.732e-02 3.982e-02 2.250e+02 -1.691
## interest_c -7.473e-02 1.218e-02 3.637e+03 -6.137
## ch_who -3.938e-02 3.770e-02 3.120e+03 -1.044
## ch_howdo 1.388e-02 3.111e-02 3.768e+03 0.446
## ch_mat 3.008e-02 3.657e-02 3.779e+03 0.823
## ch_time 6.585e-02 3.382e-02 3.790e+03 1.947
## ch_other -5.934e-02 3.299e-02 3.840e+03 -1.799
## ch_framing -5.923e-03 3.178e-02 3.826e+03 -0.186
## female 1.865e-01 6.225e-02 2.170e+02 2.996
## minority -5.109e-02 6.665e-02 2.220e+02 -0.767
## Pr(>|t|)
## (Intercept) 5.24e-08 ***
## scale(interesfun1, scale = FALSE) 0.09231 .
## interest_c 9.29e-10 ***
## ch_who 0.29640
## ch_howdo 0.65549
## ch_mat 0.41074
## ch_time 0.05158 .
## ch_other 0.07213 .
## ch_framing 0.85213
## female 0.00305 **
## minority 0.44418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr ch_frm
## s(1,s=FALSE 0.005
## interest_c 0.016 -0.096
## ch_who -0.032 0.002 -0.001
## ch_howdo -0.051 -0.003 -0.022 -0.109
## ch_mat -0.009 -0.018 -0.060 -0.133 -0.229
## ch_time -0.047 -0.023 -0.006 -0.138 -0.148 -0.106
## ch_other -0.082 -0.013 -0.032 0.007 0.030 0.042 0.024
## ch_framing -0.060 -0.025 -0.008 -0.063 -0.058 -0.053 -0.013 0.061
## female -0.361 0.100 0.002 0.006 0.000 0.001 -0.014 -0.025 0.011
## minority -0.547 -0.053 -0.014 0.004 -0.002 -0.046 0.020 0.000 -0.029
## female
## s(1,s=FALSE
## interest_c
## ch_who
## ch_howdo
## ch_mat
## ch_time
## ch_other
## ch_framing
## female
## minority -0.048rand(M33)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 972.88 1 <2e-16 ***
## teacher_ID1 7.24 1 0.007 **
## beep_id 25.30 1 5e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M33)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: negaffect ~ scale(interesfun1, scale = FALSE) + interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.023439
## ICC (stud_ID): 0.326697
## ICC (teacher_ID1): 0.039295#sjPlot::sjp.int(M33, type = "eff")
M33r<-r2glmm::r2beta(M33, method = "nsj")
M33r## Effect Rsq upper.CL lower.CL
## 1 Model 0.037 0.052 0.029
## 10 female 0.014 0.023 0.008
## 3 interest_c 0.009 0.016 0.004
## 2 scale(interesfun1, scale = FALSE) 0.005 0.010 0.001
## 11 minority 0.001 0.004 0.000
## 8 ch_other 0.001 0.004 0.000
## 7 ch_time 0.001 0.004 0.000
## 4 ch_who 0.000 0.002 0.000
## 6 ch_mat 0.000 0.002 0.000
## 5 ch_howdo 0.000 0.001 0.000
## 9 ch_framing 0.000 0.001 0.000MuMIn::r.squaredGLMM(M33)## R2m R2c
## 0.03741364 0.41227476#plot(M33r)All choices predicting learning
M44<-lmer(learning ~ scale(interesfun1, scale=FALSE) +
interest_c +
ch_who +
ch_howdo +
ch_mat +
ch_time +
ch_other +
ch_framing +
female +
minority +
(1|stud_ID) + (1|teacher_ID1) + (1|beep_id), data = SciMo_All)
summary(M44)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ scale(interesfun1, scale = FALSE) + interest_c + ch_who +
## ch_howdo + ch_mat + ch_time + ch_other + ch_framing + female +
## minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
## Data: SciMo_All
##
## REML criterion at convergence: 9274.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6547 -0.6142 0.0361 0.6537 3.0528
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_id (Intercept) 0.03263 0.1806
## stud_ID (Intercept) 0.11155 0.3340
## teacher_ID1 (Intercept) 0.01627 0.1275
## Residual 0.56604 0.7524
## Number of obs: 3853, groups: beep_id, 246; stud_ID, 231; teacher_ID1, 12
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.74583 0.06368 29.00000 27.417
## scale(interesfun1, scale = FALSE) 0.16421 0.03339 226.00000 4.917
## interest_c 0.38976 0.01507 3674.00000 25.856
## ch_who -0.04324 0.04737 3430.00000 -0.913
## ch_howdo 0.03288 0.03853 3796.00000 0.853
## ch_mat 0.03229 0.04542 3805.00000 0.711
## ch_time -0.02581 0.04195 3833.00000 -0.615
## ch_other 0.07041 0.04018 3450.00000 1.753
## ch_framing 0.01364 0.03890 3637.00000 0.351
## female 0.01201 0.05191 215.00000 0.231
## minority -0.13444 0.05560 219.00000 -2.418
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(interesfun1, scale = FALSE) 1.69e-06 ***
## interest_c < 2e-16 ***
## ch_who 0.3614
## ch_howdo 0.3936
## ch_mat 0.4773
## ch_time 0.5384
## ch_other 0.0798 .
## ch_framing 0.7259
## female 0.8173
## minority 0.0164 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(1s=F intrs_ ch_who ch_hwd ch_mat ch_tim ch_thr ch_frm
## s(1,s=FALSE 0.005
## interest_c 0.027 -0.142
## ch_who -0.049 0.003 -0.001
## ch_howdo -0.070 -0.001 -0.027 -0.113
## ch_mat -0.012 -0.028 -0.054 -0.134 -0.232
## ch_time -0.068 -0.031 -0.010 -0.139 -0.160 -0.108
## ch_other -0.118 -0.018 -0.040 0.012 0.029 0.040 0.024
## ch_framing -0.082 -0.034 -0.014 -0.057 -0.073 -0.061 -0.023 0.055
## female -0.349 0.108 0.004 0.008 -0.002 0.001 -0.020 -0.035 0.018
## minority -0.528 -0.050 -0.025 0.007 -0.004 -0.065 0.032 0.001 -0.044
## female
## s(1,s=FALSE
## interest_c
## ch_who
## ch_howdo
## ch_mat
## ch_time
## ch_other
## ch_framing
## female
## minority -0.050rand(M44)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## stud_ID 311.47 1 <2e-16 ***
## teacher_ID1 4.99 1 0.03 *
## beep_id 52.71 1 4e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(M44)##
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ scale(interesfun1, scale = FALSE) + interest_c + ch_who + ch_howdo + ch_mat + ch_time + ch_other + ch_framing + female + minority + (1 | stud_ID) + (1 | teacher_ID1) + (1 | beep_id)
##
## ICC (beep_id): 0.044914
## ICC (stud_ID): 0.153544
## ICC (teacher_ID1): 0.022393#sjPlot::sjp.int(M44, type = "eff")
M44r<-r2glmm::r2beta(M44, method = "nsj")
M44r## Effect Rsq upper.CL lower.CL
## 1 Model 0.216 0.239 0.196
## 3 interest_c 0.163 0.184 0.143
## 2 scale(interesfun1, scale = FALSE) 0.021 0.031 0.013
## 11 minority 0.006 0.011 0.002
## 8 ch_other 0.001 0.004 0.000
## 4 ch_who 0.000 0.002 0.000
## 5 ch_howdo 0.000 0.002 0.000
## 6 ch_mat 0.000 0.002 0.000
## 7 ch_time 0.000 0.002 0.000
## 10 female 0.000 0.002 0.000
## 9 ch_framing 0.000 0.001 0.000MuMIn::r.squaredGLMM(M44)## R2m R2c
## 0.2156818 0.3888997#plot(M44r)Correlation Function
#correlation table function
corstarsl <- function(x) {
require(Hmisc)
x <- as.matrix(x)
R <- rcorr(x)$r
p <- rcorr(x)$P
## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
## build a new matrix that includes the correlations with their apropriate stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)
## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew)
}Correlations
Scimo_All_Corr <- select(SciMo_All, engagement_three, learning, posaffect, negaffect, interesfun1, interest, anychoice, ch_framing, ch_mat, ch_time, ch_who, ch_howdo, ch_other)
corstarsl(Scimo_All_Corr)## Loading required package: Hmisc## Warning: package 'Hmisc' was built under R version 3.4.3## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following object is masked from 'package:sjstats':
##
## deff## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## engagement_three learning posaffect negaffect interesfun1
## engagement_three
## learning 0.64***
## posaffect 0.44*** 0.34***
## negaffect -0.13*** -0.12*** -0.07***
## interesfun1 0.28*** 0.23*** 0.20*** -0.11***
## interest 0.59*** 0.49*** 0.39*** -0.12*** 0.25***
## anychoice 0.09*** 0.05** 0.08*** -0.01 0.10***
## ch_framing 0.07*** 0.02 0.03 0.00 0.09***
## ch_mat 0.10*** 0.04** 0.05** 0.00 0.10***
## ch_time 0.08*** 0.01 -0.01 0.03 0.09***
## ch_who 0.02 -0.03 0.05** 0.02 0.05**
## ch_howdo 0.08*** 0.04** 0.04* 0.02 0.06***
## ch_other 0.01 0.03 0.09*** -0.01 0.03
## interest anychoice ch_framing ch_mat ch_time ch_who
## engagement_three
## learning
## posaffect
## negaffect
## interesfun1
## interest
## anychoice 0.09***
## ch_framing 0.07*** 0.43***
## ch_mat 0.11*** 0.37*** 0.22***
## ch_time 0.04* 0.38*** 0.16*** 0.23***
## ch_who 0.02 0.33*** 0.15*** 0.26*** 0.27***
## ch_howdo 0.07*** 0.46*** 0.23*** 0.38*** 0.32*** 0.27***
## ch_other 0.05** 0.40*** -0.05** -0.04** -0.04** -0.03
## ch_howdo
## engagement_three
## learning
## posaffect
## negaffect
## interesfun1
## interest
## anychoice
## ch_framing
## ch_mat
## ch_time
## ch_who
## ch_howdo
## ch_other -0.06***interest_corr <- select(SciMo_student_survey, interesfun1, notinterest1r)
corstarsl(interest_corr)## interesfun1
## interesfun1
## notinterest1r 0.48***reliability for interest
library(psy)##
## Attaching package: 'psy'## The following object is masked from 'package:sjstats':
##
## iccinterest_reliability <- select(SciMo_student_survey, interesfun1, notinterest1r)
cronbach(interest_reliability)## $sample.size
## [1] 231
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.635647Descriptives
psych::describe(SciMo_esm$engagement_three)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 4106 1.64 0.8 1.67 1.67 0.99 0 3 3 -0.24 -0.58
## se
## X1 0.01psych::describe(SciMo_esm$posaffect)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 4087 1.06 0.86 1 0.98 0.99 0 3 3 0.62 -0.58
## se
## X1 0.01psych::describe(SciMo_esm$negaffect)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 4081 0.57 0.76 0.33 0.41 0.49 0 3 3 1.44 1.35
## se
## X1 0.01psych::describe(SciMo_esm$learning)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 4086 1.69 0.98 2 1.74 1.48 0 3 3 -0.27 -0.93
## se
## X1 0.02psych::describe(SciMo_esm$interest)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3986 1.23 0.97 1 1.16 1.48 0 3 3 0.27 -0.95
## se
## X1 0.02psych::describe(SciMo_esm$anychoice)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.55 0.5 1 0.56 0 0 1 1 -0.19 -1.96 0.01psych::describe(SciMo_esm$ch_framing)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.18 0.39 0 0.11 0 0 1 1 1.62 0.64 0.01psych::describe(SciMo_esm$ch_mat)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.14 0.35 0 0.05 0 0 1 1 2.07 2.29 0.01psych::describe(SciMo_esm$ch_time)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.15 0.36 0 0.06 0 0 1 1 1.96 1.83 0.01psych::describe(SciMo_esm$ch_who)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.11 0.32 0 0.02 0 0 1 1 2.42 3.87 0psych::describe(SciMo_esm$ch_howdo)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.2 0.4 0 0.13 0 0 1 1 1.48 0.2 0.01psych::describe(SciMo_esm$ch_other)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4136 0.17 0.37 0 0.08 0 0 1 1 1.8 1.24 0.01psych::describe(SciMo_student_survey$interesfun1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 232 2.67 0.81 3 2.7 0 1 4 3 -0.37 -0.29 0.05psych::describe(scale(SciMo_student_survey$interesfun1, center=TRUE, scale=FALSE))## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 232 0 0.81 0.33 0.03 0 -1.67 1.33 3 -0.37 -0.29
## se
## X1 0.05table(SciMo_esm$ch_who)##
## 0 1
## 3663 473table(SciMo_esm$ch_mat)##
## 0 1
## 3556 580table(SciMo_esm$ch_time)##
## 0 1
## 3515 621table(SciMo_esm$ch_howdo)##
## 0 1
## 3301 835table(SciMo_esm$ch_other)##
## 0 1
## 3452 684table(SciMo_esm$ch_framing)##
## 0 1
## 3372 764table(SciMo_esm$ch_doing)##
## 0 1
## 3779 357table(SciMo_esm$ch_defin)##
## 0 1
## 3774 362table(SciMo_esm$ch_topic)##
## 0 1
## 3903 233table(SciMo_esm$ch_none)##
## 0 1
## 2267 1869table(SciMo_esm$anychoice)##
## 0 1
## 1869 2267