1. Loading, setting up
A. Loading packages
B. Loading data
esm <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-esm.csv")
pre_survey_data_processed <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pre-survey.csv")
post_survey_data_partially_processed <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-post-survey.csv")
video <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-video.csv")
pqa <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pqa.csv")
attendance <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-attendance.csv")
class_data <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-class-video.csv")
demographics <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-demographics.csv")
# demographics$participant_ID <- ifelse(demographics$participant_ID == 7187, NA, demographics$participant_ID)
# write_csv(demographics, "/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-demographics.csv")
parent <- read_csv("/Users/joshuarosenberg/Google Drive/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-parent.csv")
pm <- read_csv("~/Google Drive/1_Research/STEM IE - JJP/STEM-IE/data/final/program_match.csv")
Joining
df <- left_join(esm, pre_survey_data_processed, by = "participant_ID") # esm & pre-survey
df <- left_join(df, post_survey_data_partially_processed, by = "participant_ID") # df & post-survey
df <- left_join(df, video, by = c("program_ID", "response_date", "sociedad_class", "signal_number")) # df & video
df <- left_join(df, demographics, by = c("participant_ID", "program_ID")) # df and demographics
Processing CLASS video data
class_data$sociedad_class <- ifelse(class_data$eighth_math == 1, "8th Math",
ifelse(class_data$seventh_math == 1, "7th Math",
ifelse(class_data$sixth_math == 1, "6th Math",
ifelse(class_data$robotics == 1, "Robotics",
ifelse(class_data$dance == 1, "Dance", NA)))))
class_data <- rename(class_data,
response_date = Responsedate,
signal_number = r_signal_number,
program_ID = SiteIDNumeric)
library(jmRtools)
class_data$CLASS_composite <- jmRtools::composite_mean_maker(class_data, ID, P, AI, ILF, QF, CU)
class_data <- select(class_data,
program_ID,
response_date,
signal_number,
sociedad_class,
CLASS_EmotionalSupportEncouragement = EmotionalSupportEncouragement,
CLASS_InstructionalSupport = InstructionalSupport,
CLASS_Autonomy = Autonomy,
CLASS_STEMConceptualDevelopment = STEMConceptualDevelopment,
CLASS_ActivityLeaderEnthusiasm = ActivityLeaderEnthusiasm)
class_data$response_date <- as.character(class_data$response_date)
df <- mutate(df, response_date = as.character(response_date))
df <- left_join(df, class_data, by = c("program_ID", "response_date", "signal_number", "sociedad_class"))
Further processing
df$participant_ID <- as.factor(df$participant_ID)
df$program_ID <- as.factor(df$program_ID)
df$beep_ID <- as.factor(df$beep_ID)
df$beep_ID_new <- as.factor(df$beep_ID_new)
# Recode problem solving, off task, student presentation, and showing video as other
df$youth_activity_rc <- ifelse(df$youth_activity == "Off Task", "Not Focused", df$youth_activity)
df$youth_activity_rc <- ifelse(df$youth_activity_rc == "Student Presentation" | df$youth_activity_rc == "Problem Solving", "Creating Product", df$youth_activity_rc)
df$youth_activity_rc <- ifelse(df$youth_activity_rc == "Showing Video", "Program Staff Led", df$youth_activity_rc)
df$youth_activity_rc <- as.factor(df$youth_activity_rc)
df$youth_activity_rc <- forcats::fct_relevel(df$youth_activity_rc, "Not Focused")
df$relevance <- jmRtools::composite_mean_maker(df, use_outside, future_goals, important)
# need to move up
video$youth_activity_rc <- ifelse(video$youth_activity == "Off Task", "Not Focused", video$youth_activity)
video$youth_activity_rc <- ifelse(video$youth_activity_rc == "Student Presentation" | video$youth_activity_rc == "Problem Solving", "Creating Product", video$youth_activity_rc)
video$youth_activity_rc <- ifelse(video$youth_activity_rc == "Showing Video", "Program Staff Led", video$youth_activity_rc)
2. Null models (ready)
Note that our normal display presently doesn’t work if there are no predictor variables: it should be fixed in a bit, but for now, we’re just using the standard display.
m0i <- lmer(challenge ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0i)
## Linear mixed model fit by REML ['lmerMod']
## Formula: challenge ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7917.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8994 -0.6294 -0.0368 0.5688 3.3809
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.08312 0.2883
## participant_ID (Intercept) 0.46839 0.6844
## program_ID (Intercept) 0.04290 0.2071
## Residual 0.66705 0.8167
## Number of obs: 2970, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.27652 0.08871 25.66
sjstats::icc(m0i)
## Linear mixed model
## Family: gaussian (identity)
## Formula: challenge ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.065888
## ICC (participant_ID): 0.371307
## ICC (program_ID): 0.034008
m0ii <- lmer(relevance ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0ii)
## Linear mixed model fit by REML ['lmerMod']
## Formula: relevance ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6537.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8676 -0.5160 0.0370 0.5953 3.6855
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.017619 0.13274
## participant_ID (Intercept) 0.477865 0.69128
## program_ID (Intercept) 0.008374 0.09151
## Residual 0.424016 0.65117
## Number of obs: 2970, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.57639 0.06003 42.92
sjstats::icc(m0ii)
## Linear mixed model
## Family: gaussian (identity)
## Formula: relevance ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.018988
## ICC (participant_ID): 0.515011
## ICC (program_ID): 0.009025
m0iii <- lmer(learning ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0iii)
## Linear mixed model fit by REML ['lmerMod']
## Formula: learning ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7917.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1195 -0.5609 0.1253 0.5859 2.6793
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02556 0.1599
## participant_ID (Intercept) 0.39406 0.6277
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70816 0.8415
## Number of obs: 2969, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.7680 0.0486 56.95
sjstats::icc(m0iii)
## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.022665
## ICC (participant_ID): 0.349411
## ICC (program_ID): 0.000000
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m0iv <- lmer(positive_affect ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0iv)
## Linear mixed model fit by REML ['lmerMod']
## Formula: positive_affect ~ 1 + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7258.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7217 -0.4324 0.0572 0.5455 3.4749
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02362 0.1537
## participant_ID (Intercept) 0.49933 0.7066
## program_ID (Intercept) 0.10697 0.3271
## Residual 0.54503 0.7383
## Number of obs: 2970, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.699 0.122 22.12
sjstats::icc(m0iv)
## Linear mixed model
## Family: gaussian (identity)
## Formula: positive_affect ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.020104
## ICC (participant_ID): 0.424976
## ICC (program_ID): 0.091043
3. Models for youth activity (ready)
video %>%
left_join(pm) %>%
count(program_name, youth_activity_rc) %>%
filter(!is.na(youth_activity_rc)) %>%
spread(youth_activity_rc, n, fill = 0) %>%
gather(youth_activity_rc, frequency, -program_name) %>%
group_by(program_name) %>%
mutate(frequency_prop = frequency / sum(frequency)) %>%
ggplot(aes(x = reorder(youth_activity_rc, frequency_prop), y = frequency_prop)) +
facet_wrap( ~ program_name) +
geom_col() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Frequency Proportion") +
xlab(NULL) +
ggtitle("Frequency of Youth Activity (Recoded) Codes by Program")
df$youth_activity_rc_fac <- as.factor(df$youth_activity_rc)
dc <- as.tibble(psych::dummy.code(df$youth_activity_rc_fac))
df_ss <- bind_cols(df, dc)
df_ss %>%
select(challenge, relevance, learning, positive_affect,
`Not Focused`, `Basic Skills Activity`, `Creating Product`,
`Field Trip Speaker`, `Lab Activity`, `Program Staff Led`) %>%
correlate() %>%
shave() %>%
fashion()
## rowname challenge relevance learning positive_affect
## 1 challenge
## 2 relevance .39
## 3 learning .30 .65
## 4 positive_affect .27 .52 .48
## 5 Not Focused -.02 -.06 -.05 .04
## 6 Basic Skills Activity -.01 -.01 .04 -.06
## 7 Creating Product .12 .08 .04 .05
## 8 Field Trip Speaker -.04 .01 -.02 .02
## 9 Lab Activity .00 -.03 .01 .02
## 10 Program Staff Led -.06 .01 -.00 -.07
## Not.Focused Basic.Skills.Activity Creating.Product Field.Trip.Speaker
## 1
## 2
## 3
## 4
## 5
## 6 -.35
## 7 -.33 -.25
## 8 -.15 -.11 -.11
## 9 -.14 -.11 -.10 -.05
## 10 -.27 -.21 -.20 -.09
## Lab.Activity Program.Staff.Led
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10 -.09
demographics %>% count(race)
## # A tibble: 6 x 2
## race n
## <chr> <int>
## 1 Asian 14
## 2 Black 72
## 3 Hispanic 97
## 4 Multiracial 5
## 5 White 13
## 6 <NA> 3
df$urm <- ifelse(df$race %in% c("White", "Asian"), 0, 1)
df$race <- as.factor(df$race)
df$race <- fct_lump(df$race, n = 2)
df$race_other <- fct_relevel(df$race, "Other")
df$gender_female <- as.factor(df$gender) # female is comparison_group
df$gender_female <- ifelse(df$gender_female == "F", 1,
ifelse(df$gender_female == "M", 0, NA))
m1i <- lmer(challenge ~ 1 +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1i, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
challenge
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.20
|
0.16
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.10
|
0.06
|
.136
|
|
youth_activity_rc (Creating Product)
|
|
0.36
|
0.06
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.09
|
0.13
|
.500
|
|
youth_activity_rc (Lab Activity)
|
|
0.21
|
0.13
|
.094
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.11
|
0.07
|
.147
|
|
gender_female
|
|
-0.26
|
0.11
|
.013
|
|
urm
|
|
0.16
|
0.15
|
.310
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.045
|
|
ICCparticipant_ID
|
|
0.382
|
|
ICCprogram_ID
|
|
0.031
|
|
Observations
|
|
2791
|
|
R2 / Ω02
|
|
.527 / .521
|
m1ii <- lmer(relevance ~ 1 +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1ii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
relevance
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.63
|
0.15
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.15
|
0.04
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.21
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.29
|
0.07
|
<.001
|
|
youth_activity_rc (Lab Activity)
|
|
0.14
|
0.07
|
.060
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
<.001
|
|
gender_female
|
|
-0.22
|
0.10
|
.036
|
|
urm
|
|
-0.06
|
0.15
|
.699
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.007
|
|
ICCparticipant_ID
|
|
0.520
|
|
ICCprogram_ID
|
|
0.016
|
|
Observations
|
|
2791
|
|
R2 / Ω02
|
|
.587 / .584
|
m1iii <- lmer(learning ~ 1 +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1iii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
learning
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.66
|
0.14
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.22
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.13
|
0.05
|
.016
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.10
|
0.10
|
.313
|
|
youth_activity_rc (Lab Activity)
|
|
0.16
|
0.10
|
.088
|
|
youth_activity_rc (Program Staff Led)
|
|
0.07
|
0.06
|
.213
|
|
gender_female
|
|
-0.05
|
0.10
|
.582
|
|
urm
|
|
0.06
|
0.14
|
.668
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.360
|
|
ICCprogram_ID
|
|
0.003
|
|
Observations
|
|
2790
|
|
R2 / Ω02
|
|
.427 / .420
|
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m1iv <- lmer(positive_affect ~ 1 +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
na.action="na.omit",
data = df)
summary(m1iv)
## Linear mixed model fit by REML ['lmerMod']
## Formula: positive_affect ~ 1 + youth_activity_rc + gender_female + urm +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6885.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4684 -0.4547 0.0540 0.5503 3.4496
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02719 0.1649
## participant_ID (Intercept) 0.49484 0.7034
## program_ID (Intercept) 0.09956 0.3155
## Residual 0.55039 0.7419
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 201; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.772173 0.185362 14.955
## youth_activity_rcBasic Skills Activity 0.030727 0.052411 0.586
## youth_activity_rcCreating Product 0.010813 0.053189 0.203
## youth_activity_rcField Trip Speaker 0.008905 0.103356 0.086
## youth_activity_rcLab Activity 0.076327 0.100887 0.757
## youth_activity_rcProgram Staff Led -0.052792 0.060497 -0.873
## gender_female -0.186570 0.108352 -1.722
## urm 0.024590 0.157946 0.156
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA y__PSL gndr_f
## yth_ctv_BSA -0.117
## yth_ctvt_CP -0.110 0.384
## yth_ctv_FTS -0.060 0.235 0.213
## yth_ctvt_LA -0.056 0.197 0.190 0.132
## yth_ctv_PSL -0.095 0.410 0.307 0.183 0.179
## gender_feml -0.233 0.001 0.010 0.003 -0.002 -0.008
## urm -0.695 0.002 -0.003 -0.004 -0.001 -0.002 -0.083
sjPlot::sjt.lmer(m1iv, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
positive_affect
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.77
|
0.19
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.03
|
0.05
|
.558
|
|
youth_activity_rc (Creating Product)
|
|
0.01
|
0.05
|
.839
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.01
|
0.10
|
.931
|
|
youth_activity_rc (Lab Activity)
|
|
0.08
|
0.10
|
.449
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.05
|
0.06
|
.383
|
|
gender_female
|
|
-0.19
|
0.11
|
.085
|
|
urm
|
|
0.02
|
0.16
|
.876
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.023
|
|
ICCparticipant_ID
|
|
0.422
|
|
ICCprogram_ID
|
|
0.085
|
|
Observations
|
|
2791
|
|
R2 / Ω02
|
|
.578 / .575
|
A multi-level model using brms, that accounts for auto-correlation of residuals: not run (nor ready).
#
# library(brms)
#
# m1 <- brm(positive_affect ~ 1 +
# youth_activity_rc +
# (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
# control = list(adapt_delta = 0.999),
# # autocor = cor_ar(TIME_POINT | PROGRAM, cov = TRUE),
# data = df)
#
# summary(m1)
# fit_brm <- brm(Y | se(sei) ~ X1 + X2 + (1|Area),
# data = spacetime,
# autocor = cor_ar(~ Time | Area, cov = TRUE),
# prior = c(set_prior("normal(0,1)", class = "ar"),
# set_prior("normal(0,5)", class = "b")))
4. CLASS models (not ready)
df %>%
select(CLASS_EmotionalSupportEncouragement, CLASS_InstructionalSupport, CLASS_STEMConceptualDevelopment, CLASS_ActivityLeaderEnthusiasm, CLASS_Autonomy,
challenge, relevance, learning, positive_affect) %>%
correlate() %>%
shave() %>%
fashion()
## rowname CLASS_EmotionalSupportEncouragement
## 1 CLASS_EmotionalSupportEncouragement
## 2 CLASS_InstructionalSupport .39
## 3 CLASS_STEMConceptualDevelopment .28
## 4 CLASS_ActivityLeaderEnthusiasm .63
## 5 CLASS_Autonomy .29
## 6 challenge .03
## 7 relevance -.01
## 8 learning .00
## 9 positive_affect .01
## CLASS_InstructionalSupport CLASS_STEMConceptualDevelopment
## 1
## 2
## 3 .89
## 4 .77 .63
## 5 .50 .51
## 6 .06 .04
## 7 .05 .04
## 8 .07 .07
## 9 .06 .05
## CLASS_ActivityLeaderEnthusiasm CLASS_Autonomy challenge relevance
## 1
## 2
## 3
## 4
## 5 .52
## 6 .07 .06
## 7 .04 .01 .39
## 8 .05 .02 .30 .65
## 9 .09 .02 .27 .52
## learning positive_affect
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9 .48
m1v <- lmer(relevance ~ 1 +
# CLASS_EmotionalSupportEncouragement +
# CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
# CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1v, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
relevance
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.64
|
0.15
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.013
|
|
gender_female
|
|
-0.22
|
0.10
|
.031
|
|
urm
|
|
-0.05
|
0.15
|
.753
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.517
|
|
ICCprogram_ID
|
|
0.011
|
|
Observations
|
|
2772
|
|
R2 / Ω02
|
|
.589 / .586
|
m1vi <- lmer(challenge ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1vi, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
challenge
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.00
|
0.18
|
<.001
|
|
CLASS_Autonomy
|
|
0.08
|
0.02
|
<.001
|
|
gender_female
|
|
-0.26
|
0.11
|
.013
|
|
urm
|
|
0.16
|
0.15
|
.287
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.052
|
|
ICCparticipant_ID
|
|
0.373
|
|
ICCprogram_ID
|
|
0.038
|
|
Observations
|
|
2772
|
|
R2 / Ω02
|
|
.529 / .522
|
m1viii <- lmer(learning ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1viii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
learning
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.65
|
0.15
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.040
|
|
gender_female
|
|
-0.06
|
0.10
|
.563
|
|
urm
|
|
0.06
|
0.14
|
.655
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.357
|
|
ICCprogram_ID
|
|
0.001
|
|
Observations
|
|
2771
|
|
R2 / Ω02
|
|
.427 / .420
|
m1viv <- lmer(positive_affect ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1viv, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
positive_affect
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.67
|
0.19
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.022
|
|
gender_female
|
|
-0.18
|
0.11
|
.096
|
|
urm
|
|
0.02
|
0.16
|
.901
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.022
|
|
ICCparticipant_ID
|
|
0.425
|
|
ICCprogram_ID
|
|
0.088
|
|
Observations
|
|
2772
|
|
R2 / Ω02
|
|
.582 / .579
|
5. Pre-survey measures (ready)
df %>%
select(overall_pre_competence_beliefs, overall_pre_interest, overall_pre_utility_value,
challenge, relevance, learning, positive_affect) %>%
correlate() %>%
shave() %>%
fashion()
## rowname overall_pre_competence_beliefs
## 1 overall_pre_competence_beliefs
## 2 overall_pre_interest .73
## 3 overall_pre_utility_value .60
## 4 challenge -.12
## 5 relevance .03
## 6 learning .09
## 7 positive_affect .08
## overall_pre_interest overall_pre_utility_value challenge relevance
## 1
## 2
## 3 .64
## 4 -.00 -.03
## 5 .09 .11 .39
## 6 .08 .09 .30 .65
## 7 .20 .04 .27 .52
## learning positive_affect
## 1
## 2
## 3
## 4
## 5
## 6
## 7 .48
m2i <- lmer(challenge ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
#youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2i, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
challenge
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.36
|
0.31
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.29
|
0.11
|
.008
|
|
overall_pre_interest
|
|
0.21
|
0.12
|
.070
|
|
overall_pre_utility_value
|
|
0.02
|
0.11
|
.882
|
|
classroom_versus_field_enrichment
|
|
0.15
|
0.06
|
.016
|
|
gender_female
|
|
-0.24
|
0.11
|
.036
|
|
urm
|
|
0.18
|
0.16
|
.248
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.061
|
|
ICCparticipant_ID
|
|
0.372
|
|
ICCprogram_ID
|
|
0.039
|
|
Observations
|
|
2603
|
|
R2 / Ω02
|
|
.533 / .526
|
m2ib <- lmer(challenge ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2ib, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
challenge
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.36
|
0.31
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.29
|
0.11
|
.008
|
|
overall_pre_interest
|
|
0.19
|
0.12
|
.098
|
|
overall_pre_utility_value
|
|
0.03
|
0.11
|
.785
|
|
classroom_versus_field_enrichment
|
|
0.05
|
0.06
|
.394
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.09
|
0.07
|
.159
|
|
youth_activity_rc (Creating Product)
|
|
0.32
|
0.07
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.14
|
.553
|
|
youth_activity_rc (Lab Activity)
|
|
0.17
|
0.13
|
.196
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.13
|
0.08
|
.087
|
|
gender_female
|
|
-0.23
|
0.11
|
.043
|
|
urm
|
|
0.18
|
0.16
|
.253
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.048
|
|
ICCparticipant_ID
|
|
0.382
|
|
ICCprogram_ID
|
|
0.031
|
|
Observations
|
|
2572
|
|
R2 / Ω02
|
|
.529 / .523
|
m2ii <- lmer(relevance ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
# youth_activity_rc +
classroom_versus_field_enrichment +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2ii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
relevance
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.26
|
0.29
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.08
|
0.11
|
.443
|
|
overall_pre_interest
|
|
0.06
|
0.11
|
.580
|
|
overall_pre_utility_value
|
|
0.17
|
0.11
|
.109
|
|
classroom_versus_field_enrichment
|
|
-0.04
|
0.04
|
.267
|
|
gender_female
|
|
-0.24
|
0.11
|
.028
|
|
urm
|
|
-0.01
|
0.16
|
.951
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.017
|
|
ICCparticipant_ID
|
|
0.514
|
|
ICCprogram_ID
|
|
0.018
|
|
Observations
|
|
2603
|
|
R2 / Ω02
|
|
.595 / .592
|
m2iib <- lmer(relevance ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
youth_activity_rc +
classroom_versus_field_enrichment +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2iib, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
relevance
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.16
|
0.30
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.08
|
0.11
|
.454
|
|
overall_pre_interest
|
|
0.06
|
0.11
|
.621
|
|
overall_pre_utility_value
|
|
0.18
|
0.11
|
.101
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.13
|
0.04
|
.002
|
|
youth_activity_rc (Creating Product)
|
|
0.22
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.23
|
0.08
|
.004
|
|
youth_activity_rc (Lab Activity)
|
|
0.09
|
0.08
|
.234
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
.002
|
|
classroom_versus_field_enrichment
|
|
-0.07
|
0.04
|
.061
|
|
gender_female
|
|
-0.24
|
0.11
|
.030
|
|
urm
|
|
-0.02
|
0.16
|
.920
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.009
|
|
ICCparticipant_ID
|
|
0.517
|
|
ICCprogram_ID
|
|
0.026
|
|
Observations
|
|
2572
|
|
R2 / Ω02
|
|
.593 / .591
|
m2iii <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
#youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2iii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
learning
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.33
|
0.27
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.01
|
0.10
|
.887
|
|
overall_pre_interest
|
|
0.03
|
0.10
|
.749
|
|
overall_pre_utility_value
|
|
0.07
|
0.10
|
.457
|
|
classroom_versus_field_enrichment
|
|
0.02
|
0.05
|
.716
|
|
gender_female
|
|
-0.07
|
0.10
|
.480
|
|
urm
|
|
0.11
|
0.15
|
.451
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.354
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2602
|
|
R2 / Ω02
|
|
.418 / .412
|
m2iiib <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2iiib, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
learning
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.25
|
0.28
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.01
|
0.10
|
.934
|
|
overall_pre_interest
|
|
0.04
|
0.10
|
.690
|
|
overall_pre_utility_value
|
|
0.07
|
0.10
|
.518
|
|
classroom_versus_field_enrichment
|
|
0.02
|
0.05
|
.723
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.21
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.11
|
0.06
|
.056
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.08
|
0.10
|
.399
|
|
youth_activity_rc (Lab Activity)
|
|
0.20
|
0.10
|
.041
|
|
youth_activity_rc (Program Staff Led)
|
|
0.06
|
0.06
|
.280
|
|
gender_female
|
|
-0.07
|
0.10
|
.475
|
|
urm
|
|
0.12
|
0.15
|
.429
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.009
|
|
ICCparticipant_ID
|
|
0.358
|
|
ICCprogram_ID
|
|
0.003
|
|
Observations
|
|
2571
|
|
R2 / Ω02
|
|
.419 / .413
|
df$positive_affect <- jmRtools::composite_mean_maker(df, happy, excited)
m2iv <- lmer(positive_affect ~
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
# youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2iv, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
positive_affect
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.35
|
0.32
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.04
|
0.11
|
.748
|
|
overall_pre_interest
|
|
0.28
|
0.12
|
.025
|
|
overall_pre_utility_value
|
|
-0.09
|
0.12
|
.457
|
|
classroom_versus_field_enrichment
|
|
-0.05
|
0.05
|
.282
|
|
gender_female
|
|
-0.16
|
0.12
|
.177
|
|
urm
|
|
0.04
|
0.17
|
.814
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.029
|
|
ICCparticipant_ID
|
|
0.450
|
|
ICCprogram_ID
|
|
0.050
|
|
Observations
|
|
2603
|
|
R2 / Ω02
|
|
.585 / .581
|
m2ivb <- lmer(positive_affect ~
overall_pre_competence_beliefs +
overall_pre_interest +
overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m2ivb, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
positive_affect
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
2.38
|
0.32
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.04
|
0.11
|
.718
|
|
overall_pre_interest
|
|
0.28
|
0.12
|
.024
|
|
overall_pre_utility_value
|
|
-0.09
|
0.12
|
.442
|
|
classroom_versus_field_enrichment
|
|
-0.06
|
0.05
|
.268
|
|
youth_activity_rc (Basic Skills Activity)
|
|
-0.00
|
0.06
|
.994
|
|
youth_activity_rc (Creating Product)
|
|
0.01
|
0.06
|
.869
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.04
|
0.11
|
.756
|
|
youth_activity_rc (Lab Activity)
|
|
0.07
|
0.11
|
.501
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.07
|
0.07
|
.252
|
|
gender_female
|
|
-0.15
|
0.12
|
.187
|
|
urm
|
|
0.04
|
0.17
|
.798
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.030
|
|
ICCparticipant_ID
|
|
0.448
|
|
ICCprogram_ID
|
|
0.047
|
|
Observations
|
|
2572
|
|
R2 / Ω02
|
|
.584 / .580
|
6. Situational experiences (ready)
df$overall_engagement <- jmRtools::composite_mean_maker(df, hard_working, concentrating, enjoy)
df %>%
select(overall_engagement, interest, challenge, relevance, learning, positive_affect) %>%
correlate() %>%
shave() %>%
fashion()
## rowname overall_engagement interest challenge relevance
## 1 overall_engagement
## 2 interest .69
## 3 challenge .31 .28
## 4 relevance .65 .61 .39
## 5 learning .68 .56 .30 .65
## 6 positive_affect .65 .56 .27 .52
## learning positive_affect
## 1
## 2
## 3
## 4
## 5
## 6 .48
m3i <- lmer(interest ~ 1 +
challenge + relevance + learning + positive_affect +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m3i, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
interest
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.63
|
0.09
|
<.001
|
|
challenge
|
|
0.02
|
0.01
|
.104
|
|
relevance
|
|
0.36
|
0.02
|
<.001
|
|
learning
|
|
0.17
|
0.02
|
<.001
|
|
positive_affect
|
|
0.28
|
0.02
|
<.001
|
|
gender_female
|
|
0.05
|
0.05
|
.285
|
|
urm
|
|
-0.02
|
0.06
|
.786
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.033
|
|
ICCparticipant_ID
|
|
0.099
|
|
ICCprogram_ID
|
|
0.014
|
|
Observations
|
|
2941
|
|
R2 / Ω02
|
|
.584 / .582
|
m3v <- lmer(overall_engagement ~ 1 +
challenge + relevance + learning + positive_affect +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m3v, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
overall_engagement
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.67
|
0.07
|
<.001
|
|
challenge
|
|
0.04
|
0.01
|
<.001
|
|
relevance
|
|
0.23
|
0.02
|
<.001
|
|
learning
|
|
0.27
|
0.01
|
<.001
|
|
positive_affect
|
|
0.28
|
0.01
|
<.001
|
|
gender_female
|
|
0.07
|
0.04
|
.072
|
|
urm
|
|
-0.03
|
0.06
|
.618
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
201
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.029
|
|
ICCparticipant_ID
|
|
0.201
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2941
|
|
R2 / Ω02
|
|
.737 / .736
|
m3i_update <- lmer(interest ~ 1 +
challenge + relevance + learning + positive_affect +
overall_pre_competence_beliefs +
overall_pre_interest +
classroom_versus_field_enrichment +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m3i_update, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
interest
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.51
|
0.14
|
<.001
|
|
challenge
|
|
0.03
|
0.02
|
.104
|
|
relevance
|
|
0.35
|
0.02
|
<.001
|
|
learning
|
|
0.17
|
0.02
|
<.001
|
|
positive_affect
|
|
0.28
|
0.02
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.02
|
0.05
|
.732
|
|
overall_pre_interest
|
|
0.02
|
0.05
|
.727
|
|
classroom_versus_field_enrichment
|
|
0.07
|
0.04
|
.097
|
|
gender_female
|
|
0.04
|
0.05
|
.402
|
|
urm
|
|
-0.00
|
0.07
|
.987
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
180
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.037
|
|
ICCparticipant_ID
|
|
0.101
|
|
ICCprogram_ID
|
|
0.014
|
|
Observations
|
|
2605
|
|
R2 / Ω02
|
|
.580 / .578
|
m3v_update <- lmer(overall_engagement ~ 1 +
challenge + relevance + learning + positive_affect +
overall_pre_competence_beliefs +
overall_pre_interest +
classroom_versus_field_enrichment +
gender_female +
urm +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m3v_update, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
overall_engagement
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.54
|
0.11
|
<.001
|
|
challenge
|
|
0.03
|
0.01
|
.005
|
|
relevance
|
|
0.24
|
0.02
|
<.001
|
|
learning
|
|
0.25
|
0.01
|
<.001
|
|
positive_affect
|
|
0.28
|
0.01
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.00
|
0.04
|
.950
|
|
overall_pre_interest
|
|
0.03
|
0.04
|
.507
|
|
classroom_versus_field_enrichment
|
|
0.09
|
0.03
|
<.001
|
|
gender_female
|
|
0.07
|
0.04
|
.099
|
|
urm
|
|
-0.02
|
0.06
|
.780
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
180
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.027
|
|
ICCparticipant_ID
|
|
0.213
|
|
ICCprogram_ID
|
|
0.002
|
|
Observations
|
|
2605
|
|
R2 / Ω02
|
|
.738 / .738
|
7. Outcomes (not ready)
participant_df <-df %>%
select(participant_ID, challenge, relevance, learning, positive_affect, good_at) %>%
group_by(participant_ID) %>%
mutate_at(vars(challenge, relevance, learning, positive_affect, good_at), funs(mean, sd)) %>%
select(participant_ID, contains("mean"), contains("sd")) %>%
distinct()
df_ss <- left_join(df, participant_df)
df_ss <- select(df_ss,
participant_ID, program_ID,
challenge_mean, relevance_mean, learning_mean, positive_affect_mean, good_at_mean,
challenge_sd, relevance_sd, learning_sd, positive_affect_sd, good_at_sd,
overall_post_interest, overall_pre_interest,
future_goals)
df_ss <- distinct(df_ss)
df_ss$program_ID <- as.integer(df_ss$program_ID)
df_ss <- left_join(df_ss, pm)
# df_ss <- left_join(df_ss, m)
m4ia <- lmer(overall_post_interest ~ 1 +
#challenge_mean + challenge_sd +
#learning_mean +
relevance_mean +
#positive_affect_mean +
overall_pre_interest +
(1|program_ID),
data = df_ss)
sjPlot::sjt.lmer(m4ia, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
overall_post_interest
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.94
|
0.22
|
<.001
|
|
relevance_mean
|
|
0.22
|
0.05
|
<.001
|
|
overall_pre_interest
|
|
0.51
|
0.05
|
<.001
|
|
Random Parts
|
|
Nprogram_ID
|
|
9
|
|
ICCprogram_ID
|
|
0.111
|
|
Observations
|
|
427
|
|
R2 / Ω02
|
|
.427 / .427
|
m4ib <- lmer(overall_post_interest ~ 1 +
challenge_mean + challenge_sd +
learning_mean + learning_sd +
relevance_mean + relevance_sd +
positive_affect_mean + positive_affect_sd +
overall_pre_interest +
(1|program_ID),
data = df_ss)
sjPlot::sjt.lmer(m4ib, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
overall_post_interest
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.69
|
0.27
|
.011
|
|
challenge_mean
|
|
-0.07
|
0.06
|
.275
|
|
challenge_sd
|
|
-0.50
|
0.13
|
<.001
|
|
learning_mean
|
|
0.51
|
0.11
|
<.001
|
|
learning_sd
|
|
0.05
|
0.13
|
.701
|
|
relevance_mean
|
|
-0.25
|
0.12
|
.037
|
|
relevance_sd
|
|
0.31
|
0.18
|
.087
|
|
positive_affect_mean
|
|
0.16
|
0.07
|
.022
|
|
positive_affect_sd
|
|
0.07
|
0.13
|
.570
|
|
overall_pre_interest
|
|
0.47
|
0.05
|
<.001
|
|
Random Parts
|
|
Nprogram_ID
|
|
9
|
|
ICCprogram_ID
|
|
0.110
|
|
Observations
|
|
425
|
|
R2 / Ω02
|
|
.492 / .492
|
m4iia <- lmer(future_goals ~ 1 +
challenge_mean +
learning_mean +
relevance_mean +
positive_affect_mean +
overall_pre_interest +
(1|program_ID),
data = df_ss)
sjPlot::sjt.lmer(m4iia, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
future_goals
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
1.27
|
0.27
|
<.001
|
|
challenge_mean
|
|
0.07
|
0.08
|
.351
|
|
learning_mean
|
|
-0.15
|
0.15
|
.302
|
|
relevance_mean
|
|
0.62
|
0.15
|
<.001
|
|
positive_affect_mean
|
|
-0.05
|
0.08
|
.556
|
|
overall_pre_interest
|
|
-0.00
|
0.05
|
.941
|
|
Random Parts
|
|
Nprogram_ID
|
|
9
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
529
|
|
R2 / Ω02
|
|
.092 / .092
|
m4iib <- lmer(future_goals ~ 1 +
challenge_mean + challenge_sd +
learning_mean + learning_sd +
relevance_mean + relevance_sd +
positive_affect_mean + positive_affect_sd +
overall_pre_interest +
(1|program_ID),
data = df_ss)
sjPlot::sjt.lmer(m4iib, p.kr = F, show.re.var = F, show.ci = F, show.se = T)
|
|
|
future_goals
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
1.29
|
0.33
|
<.001
|
|
challenge_mean
|
|
0.06
|
0.08
|
.443
|
|
challenge_sd
|
|
-0.09
|
0.16
|
.582
|
|
learning_mean
|
|
-0.13
|
0.15
|
.408
|
|
learning_sd
|
|
0.06
|
0.18
|
.753
|
|
relevance_mean
|
|
0.61
|
0.15
|
<.001
|
|
relevance_sd
|
|
-0.15
|
0.25
|
.559
|
|
positive_affect_mean
|
|
-0.04
|
0.08
|
.624
|
|
positive_affect_sd
|
|
0.08
|
0.17
|
.632
|
|
overall_pre_interest
|
|
-0.01
|
0.05
|
.818
|
|
Random Parts
|
|
Nprogram_ID
|
|
9
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
528
|
|
R2 / Ω02
|
|
.091 / .091
|