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-pqa.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$dance == 1, "Dance", NA)))))
class_data <- rename(class_data,
response_date = Responsedate,
signal_number = r_signal_number,
program_ID = SiteIDNumeric)
class_data <- select(class_data,
program_ID,
response_date,
signal_number,
sociedad_class,
CLASS_EmotionalSupportEncouragement = EmotionalSupportEncouragement,
CLASS_InstructionalSupport = InstructionalSupport,
CLASS_Autonomy = Autonomy,
CLASS_STEMConceptualDevelopment = STEMConceptualDevelopment,
CLASS_ActivityLeaderEnthusiasm = ActivityLeaderEnthusiasm)
class_data$response_date <- as.character(class_data$response_date)
class_data
## # A tibble: 236 x 9
## program_ID response_date signal_number sociedad_class
## <int> <chr> <int> <chr>
## 1 1 2015-07-14 1 <NA>
## 2 1 2015-07-14 2 <NA>
## 3 1 2015-07-14 4 <NA>
## 4 1 2015-07-15 1 <NA>
## 5 1 2015-07-15 2 <NA>
## 6 1 2015-07-15 3 <NA>
## 7 1 2015-07-15 4 <NA>
## 8 1 2015-07-21 1 <NA>
## 9 1 2015-07-21 2 <NA>
## 10 1 2015-07-21 3 <NA>
## # ... with 226 more rows, and 5 more variables:
## # CLASS_EmotionalSupportEncouragement <dbl>,
## # CLASS_InstructionalSupport <dbl>, CLASS_Autonomy <int>,
## # CLASS_STEMConceptualDevelopment <dbl>,
## # CLASS_ActivityLeaderEnthusiasm <dbl>
df <- mutate(df, response_date = as.character(response_date))
df <- left_join(df, class_data, by = c("program_ID", "response_date", "signal_number", "sociedad_class"))
Further processing
df$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 + learning +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0i)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## challenge ~ 1 + learning + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7817.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9899 -0.6311 -0.0309 0.5566 3.3387
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07003 0.2646
## participant_ID (Intercept) 0.40103 0.6333
## program_ID (Intercept) 0.03732 0.1932
## Residual 0.65414 0.8088
## Number of obs: 2969, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.76093 0.09650 18.25
## learning 0.18618 0.01796 10.37
##
## Correlation of Fixed Effects:
## (Intr)
## learning -0.516
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
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
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m0iv <- lmer(positive_affect ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m0iv)
## Linear mixed model fit by REML ['lmerMod']
## Formula: positive_affect ~ 1 + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7258.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7217 -0.4324 0.0572 0.5455 3.4749
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02362 0.1537
## participant_ID (Intercept) 0.49933 0.7066
## program_ID (Intercept) 0.10697 0.3271
## Residual 0.54503 0.7383
## Number of obs: 2970, groups:
## beep_ID_new, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.699 0.122 22.12
3. Models for youth activity (ready)
video %>%
left_join(pm) %>%
count(program_name, youth_activity_rc) %>%
filter(!is.na(youth_activity_rc)) %>%
spread(youth_activity_rc, n, fill = 0) %>%
gather(youth_activity_rc, frequency, -program_name) %>%
group_by(program_name) %>%
mutate(frequency_prop = frequency / sum(frequency)) %>%
ggplot(aes(x = reorder(youth_activity_rc, frequency_prop), y = frequency_prop)) +
facet_wrap( ~ program_name) +
geom_col() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Frequency Proportion") +
xlab(NULL) +
ggtitle("Frequency of Youth Activity (Recoded) Codes by Program")
df$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$race <- as.factor(df$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
m1i <- lmer(challenge ~ 1 +
youth_activity_rc +
gender_female +
race_other +
(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)
|
|
2.02
|
0.16
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.10
|
0.06
|
.115
|
|
youth_activity_rc (Creating Product)
|
|
0.36
|
0.07
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.13
|
.513
|
|
youth_activity_rc (Lab Activity)
|
|
0.21
|
0.13
|
.095
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.11
|
0.07
|
.136
|
|
gender_female (M)
|
|
0.25
|
0.11
|
.019
|
|
race_other (Black)
|
|
0.04
|
0.16
|
.826
|
|
race_other (Hispanic)
|
|
0.09
|
0.15
|
.578
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.045
|
|
ICCparticipant_ID
|
|
0.384
|
|
ICCprogram_ID
|
|
0.030
|
|
Observations
|
|
2767
|
|
R2 / Ω02
|
|
.527 / .520
|
m1ii <- lmer(relevance ~ 1 +
youth_activity_rc +
gender_female +
race_other +
(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)
|
|
2.43
|
0.15
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.14
|
0.04
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.22
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.29
|
0.08
|
<.001
|
|
youth_activity_rc (Lab Activity)
|
|
0.14
|
0.07
|
.065
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
<.001
|
|
gender_female (M)
|
|
0.20
|
0.10
|
.048
|
|
race_other (Black)
|
|
-0.16
|
0.16
|
.303
|
|
race_other (Hispanic)
|
|
-0.01
|
0.15
|
.961
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.008
|
|
ICCparticipant_ID
|
|
0.520
|
|
ICCprogram_ID
|
|
0.012
|
|
Observations
|
|
2767
|
|
R2 / Ω02
|
|
.584 / .582
|
m1iii <- lmer(learning ~ 1 +
youth_activity_rc +
gender_female +
race_other +
(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)
|
|
0.21
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.12
|
0.05
|
.027
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.09
|
0.10
|
.363
|
|
youth_activity_rc (Lab Activity)
|
|
0.15
|
0.10
|
.107
|
|
youth_activity_rc (Program Staff Led)
|
|
0.06
|
0.06
|
.336
|
|
gender_female (M)
|
|
0.05
|
0.10
|
.646
|
|
race_other (Black)
|
|
-0.08
|
0.15
|
.605
|
|
race_other (Hispanic)
|
|
0.08
|
0.14
|
.560
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.365
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2766
|
|
R2 / Ω02
|
|
.431 / .425
|
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m1iv <- lmer(positive_affect ~ 1 +
youth_activity_rc +
gender_female +
race_other +
(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 + race_other +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6798.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4868 -0.4516 0.0529 0.5520 3.4745
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02813 0.1677
## participant_ID (Intercept) 0.50178 0.7084
## program_ID (Intercept) 0.09505 0.3083
## Residual 0.54258 0.7366
## Number of obs: 2767, groups:
## beep_ID_new, 235; participant_ID, 200; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.622025 0.183953 14.254
## youth_activity_rcBasic Skills Activity 0.027367 0.052663 0.520
## youth_activity_rcCreating Product 0.022836 0.053397 0.428
## youth_activity_rcField Trip Speaker 0.011171 0.103798 0.108
## youth_activity_rcLab Activity 0.077942 0.101236 0.770
## youth_activity_rcProgram Staff Led -0.054424 0.060824 -0.895
## gender_femaleM 0.184540 0.109204 1.690
## race_otherBlack -0.051718 0.167531 -0.309
## race_otherHispanic 0.001509 0.157997 0.010
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA y__PSL gndr_M rc_thB
## yth_ctv_BSA -0.118
## yth_ctvt_CP -0.103 0.384
## yth_ctv_FTS -0.059 0.235 0.213
## yth_ctvt_LA -0.061 0.197 0.190 0.132
## yth_ctv_PSL -0.102 0.410 0.307 0.183 0.179
## gender_fmlM -0.358 -0.001 -0.010 -0.003 0.002 0.008
## rac_thrBlck -0.634 0.000 -0.005 -0.006 0.004 -0.003 0.086
## rc_thrHspnc -0.659 0.003 -0.005 -0.003 0.000 -0.001 0.082 0.720
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)
|
|
2.62
|
0.18
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.03
|
0.05
|
.603
|
|
youth_activity_rc (Creating Product)
|
|
0.02
|
0.05
|
.669
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.01
|
0.10
|
.914
|
|
youth_activity_rc (Lab Activity)
|
|
0.08
|
0.10
|
.441
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.05
|
0.06
|
.371
|
|
gender_female (M)
|
|
0.18
|
0.11
|
.091
|
|
race_other (Black)
|
|
-0.05
|
0.17
|
.758
|
|
race_other (Hispanic)
|
|
0.00
|
0.16
|
.992
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.024
|
|
ICCparticipant_ID
|
|
0.430
|
|
ICCprogram_ID
|
|
0.081
|
|
Observations
|
|
2767
|
|
R2 / Ω02
|
|
.585 / .581
|
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 +
race_other +
(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)
|
|
2.44
|
0.15
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.016
|
|
gender_female (M)
|
|
0.21
|
0.10
|
.043
|
|
race_other (Black)
|
|
-0.14
|
0.15
|
.372
|
|
race_other (Hispanic)
|
|
-0.01
|
0.15
|
.963
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.014
|
|
ICCparticipant_ID
|
|
0.516
|
|
ICCprogram_ID
|
|
0.009
|
|
Observations
|
|
2748
|
|
R2 / Ω02
|
|
.587 / .584
|
m1vi <- lmer(challenge ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
race_other +
(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)
|
|
1.84
|
0.18
|
<.001
|
|
CLASS_Autonomy
|
|
0.08
|
0.02
|
<.001
|
|
gender_female (M)
|
|
0.25
|
0.11
|
.019
|
|
race_other (Black)
|
|
0.04
|
0.16
|
.790
|
|
race_other (Hispanic)
|
|
0.08
|
0.15
|
.614
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.053
|
|
ICCparticipant_ID
|
|
0.374
|
|
ICCprogram_ID
|
|
0.038
|
|
Observations
|
|
2748
|
|
R2 / Ω02
|
|
.529 / .522
|
m1viii <- lmer(learning ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
race_other +
(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)
|
|
2.66
|
0.14
|
<.001
|
|
CLASS_Autonomy
|
|
0.02
|
0.01
|
.069
|
|
gender_female (M)
|
|
0.05
|
0.10
|
.624
|
|
race_other (Black)
|
|
-0.08
|
0.15
|
.603
|
|
race_other (Hispanic)
|
|
0.07
|
0.14
|
.625
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.361
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2747
|
|
R2 / Ω02
|
|
.431 / .424
|
m1viv <- lmer(positive_affect ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
gender_female +
race_other +
(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)
|
|
2.52
|
0.19
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.013
|
|
gender_female (M)
|
|
0.18
|
0.11
|
.104
|
|
race_other (Black)
|
|
-0.07
|
0.17
|
.679
|
|
race_other (Hispanic)
|
|
-0.01
|
0.16
|
.928
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.022
|
|
ICCparticipant_ID
|
|
0.432
|
|
ICCprogram_ID
|
|
0.085
|
|
Observations
|
|
2748
|
|
R2 / Ω02
|
|
.588 / .585
|
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 +
race_other +
(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)
|
|
2.21
|
0.28
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.28
|
0.11
|
.011
|
|
overall_pre_interest
|
|
0.21
|
0.10
|
.043
|
|
classroom_versus_field_enrichment
|
|
0.14
|
0.06
|
.022
|
|
gender_female (M)
|
|
0.23
|
0.11
|
.039
|
|
race_other (Black)
|
|
0.09
|
0.17
|
.577
|
|
race_other (Hispanic)
|
|
0.12
|
0.16
|
.446
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.063
|
|
ICCparticipant_ID
|
|
0.372
|
|
ICCprogram_ID
|
|
0.038
|
|
Observations
|
|
2582
|
|
R2 / Ω02
|
|
.534 / .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 +
race_other +
(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)
|
|
2.24
|
0.28
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.27
|
0.11
|
.011
|
|
overall_pre_interest
|
|
0.20
|
0.10
|
.058
|
|
classroom_versus_field_enrichment
|
|
0.05
|
0.06
|
.459
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.10
|
0.07
|
.136
|
|
youth_activity_rc (Creating Product)
|
|
0.32
|
0.07
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.14
|
.546
|
|
youth_activity_rc (Lab Activity)
|
|
0.17
|
0.13
|
.193
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.13
|
0.08
|
.083
|
|
gender_female (M)
|
|
0.22
|
0.11
|
.048
|
|
race_other (Black)
|
|
0.09
|
0.17
|
.608
|
|
race_other (Hispanic)
|
|
0.13
|
0.16
|
.418
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.049
|
|
ICCparticipant_ID
|
|
0.383
|
|
ICCprogram_ID
|
|
0.030
|
|
Observations
|
|
2551
|
|
R2 / Ω02
|
|
.530 / .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 +
race_other +
(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)
|
|
2.27
|
0.26
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.04
|
0.10
|
.735
|
|
overall_pre_interest
|
|
0.12
|
0.10
|
.215
|
|
classroom_versus_field_enrichment
|
|
-0.04
|
0.04
|
.322
|
|
gender_female (M)
|
|
0.22
|
0.11
|
.046
|
|
race_other (Black)
|
|
-0.08
|
0.16
|
.623
|
|
race_other (Hispanic)
|
|
0.02
|
0.16
|
.881
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.018
|
|
ICCparticipant_ID
|
|
0.518
|
|
ICCprogram_ID
|
|
0.012
|
|
Observations
|
|
2582
|
|
R2 / Ω02
|
|
.593 / .590
|
m2iib <- lmer(relevance ~ 1 +
#overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
youth_activity_rc +
classroom_versus_field_enrichment +
gender_female +
race_other +
(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
|
0.25
|
<.001
|
|
overall_pre_interest
|
|
0.10
|
0.06
|
.129
|
|
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)
|
|
0.10
|
0.08
|
.223
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
.002
|
|
classroom_versus_field_enrichment
|
|
-0.07
|
0.04
|
.076
|
|
gender_female (M)
|
|
0.22
|
0.11
|
.043
|
|
race_other (Black)
|
|
-0.09
|
0.16
|
.568
|
|
race_other (Hispanic)
|
|
0.02
|
0.16
|
.922
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.520
|
|
ICCprogram_ID
|
|
0.019
|
|
Observations
|
|
2551
|
|
R2 / Ω02
|
|
.591 / .589
|
m2iii <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
#youth_activity_rc +
gender_female +
race_other +
(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)
|
|
2.41
|
0.23
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.04
|
0.10
|
.648
|
|
overall_pre_interest
|
|
0.05
|
0.09
|
.586
|
|
classroom_versus_field_enrichment
|
|
0.01
|
0.05
|
.781
|
|
gender_female (M)
|
|
0.07
|
0.10
|
.529
|
|
race_other (Black)
|
|
-0.02
|
0.15
|
.897
|
|
race_other (Hispanic)
|
|
0.10
|
0.15
|
.501
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.358
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2581
|
|
R2 / Ω02
|
|
.423 / .416
|
m2iiib <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
gender_female +
race_other +
(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)
|
|
2.36
|
0.24
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.03
|
0.10
|
.750
|
|
overall_pre_interest
|
|
0.05
|
0.09
|
.569
|
|
classroom_versus_field_enrichment
|
|
0.01
|
0.05
|
.787
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.20
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.10
|
0.06
|
.083
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.07
|
0.10
|
.477
|
|
youth_activity_rc (Lab Activity)
|
|
0.19
|
0.10
|
.057
|
|
youth_activity_rc (Program Staff Led)
|
|
0.05
|
0.06
|
.400
|
|
gender_female (M)
|
|
0.06
|
0.10
|
.543
|
|
race_other (Black)
|
|
-0.02
|
0.15
|
.888
|
|
race_other (Hispanic)
|
|
0.11
|
0.15
|
.468
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.364
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2550
|
|
R2 / Ω02
|
|
.425 / .419
|
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 +
race_other +
(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)
|
|
2.15
|
0.30
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.05
|
0.11
|
.683
|
|
overall_pre_interest
|
|
0.23
|
0.11
|
.039
|
|
classroom_versus_field_enrichment
|
|
-0.05
|
0.05
|
.348
|
|
gender_female (M)
|
|
0.16
|
0.12
|
.179
|
|
race_other (Black)
|
|
-0.07
|
0.18
|
.700
|
|
race_other (Hispanic)
|
|
-0.01
|
0.17
|
.936
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.030
|
|
ICCparticipant_ID
|
|
0.452
|
|
ICCprogram_ID
|
|
0.056
|
|
Observations
|
|
2582
|
|
R2 / Ω02
|
|
.592 / .588
|
m2ivb <- lmer(positive_affect ~
overall_pre_competence_beliefs +
overall_pre_interest +
classroom_versus_field_enrichment +
youth_activity_rc +
gender_female +
race_other +
(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)
|
|
2.18
|
0.30
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.05
|
0.11
|
.649
|
|
overall_pre_interest
|
|
0.23
|
0.11
|
.039
|
|
classroom_versus_field_enrichment
|
|
-0.06
|
0.05
|
.288
|
|
youth_activity_rc (Basic Skills Activity)
|
|
-0.00
|
0.06
|
.948
|
|
youth_activity_rc (Creating Product)
|
|
0.02
|
0.06
|
.707
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.03
|
0.11
|
.786
|
|
youth_activity_rc (Lab Activity)
|
|
0.07
|
0.11
|
.521
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.08
|
0.07
|
.251
|
|
gender_female (M)
|
|
0.15
|
0.12
|
.190
|
|
race_other (Black)
|
|
-0.07
|
0.18
|
.706
|
|
race_other (Hispanic)
|
|
-0.01
|
0.17
|
.956
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.032
|
|
ICCparticipant_ID
|
|
0.450
|
|
ICCprogram_ID
|
|
0.051
|
|
Observations
|
|
2551
|
|
R2 / Ω02
|
|
.591 / .587
|
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 +
race_other +
(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)
|
|
0.66
|
0.09
|
<.001
|
|
challenge
|
|
0.02
|
0.01
|
.099
|
|
relevance
|
|
0.37
|
0.02
|
<.001
|
|
learning
|
|
0.18
|
0.02
|
<.001
|
|
positive_affect
|
|
0.27
|
0.02
|
<.001
|
|
gender_female (M)
|
|
-0.04
|
0.05
|
.364
|
|
race_other (Black)
|
|
-0.03
|
0.07
|
.678
|
|
race_other (Hispanic)
|
|
0.00
|
0.06
|
.980
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.034
|
|
ICCparticipant_ID
|
|
0.093
|
|
ICCprogram_ID
|
|
0.014
|
|
Observations
|
|
2917
|
|
R2 / Ω02
|
|
.585 / .583
|
m3v <- lmer(overall_engagement ~ 1 +
challenge + relevance + learning + positive_affect +
gender_female +
race_other +
(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)
|
|
0.71
|
0.07
|
<.001
|
|
challenge
|
|
0.04
|
0.01
|
<.001
|
|
relevance
|
|
0.24
|
0.02
|
<.001
|
|
learning
|
|
0.27
|
0.01
|
<.001
|
|
positive_affect
|
|
0.27
|
0.01
|
<.001
|
|
gender_female (M)
|
|
-0.06
|
0.04
|
.103
|
|
race_other (Black)
|
|
-0.02
|
0.06
|
.729
|
|
race_other (Hispanic)
|
|
-0.01
|
0.06
|
.908
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
200
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.032
|
|
ICCparticipant_ID
|
|
0.196
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2917
|
|
R2 / Ω02
|
|
.740 / .739
|
m3i_update <- lmer(interest ~ 1 +
challenge + relevance + learning + positive_affect +
overall_pre_competence_beliefs +
overall_pre_interest +
classroom_versus_field_enrichment +
gender_female +
race_other +
(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)
|
|
0.56
|
0.13
|
<.001
|
|
challenge
|
|
0.03
|
0.02
|
.098
|
|
relevance
|
|
0.36
|
0.02
|
<.001
|
|
learning
|
|
0.17
|
0.02
|
<.001
|
|
positive_affect
|
|
0.27
|
0.02
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.00
|
0.05
|
.936
|
|
overall_pre_interest
|
|
0.02
|
0.04
|
.642
|
|
classroom_versus_field_enrichment
|
|
0.08
|
0.04
|
.073
|
|
gender_female (M)
|
|
-0.04
|
0.05
|
.465
|
|
race_other (Black)
|
|
-0.03
|
0.07
|
.688
|
|
race_other (Hispanic)
|
|
0.03
|
0.07
|
.664
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.038
|
|
ICCparticipant_ID
|
|
0.093
|
|
ICCprogram_ID
|
|
0.013
|
|
Observations
|
|
2581
|
|
R2 / Ω02
|
|
.581 / .579
|
m3v_update <- lmer(overall_engagement ~ 1 +
challenge + relevance + learning + positive_affect +
overall_pre_competence_beliefs +
overall_pre_interest +
classroom_versus_field_enrichment +
gender_female +
race_other +
(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)
|
|
0.59
|
0.11
|
<.001
|
|
challenge
|
|
0.03
|
0.01
|
.007
|
|
relevance
|
|
0.25
|
0.02
|
<.001
|
|
learning
|
|
0.25
|
0.01
|
<.001
|
|
positive_affect
|
|
0.28
|
0.01
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.01
|
0.04
|
.807
|
|
overall_pre_interest
|
|
0.03
|
0.04
|
.450
|
|
classroom_versus_field_enrichment
|
|
0.10
|
0.03
|
<.001
|
|
gender_female (M)
|
|
-0.07
|
0.04
|
.126
|
|
race_other (Black)
|
|
-0.01
|
0.06
|
.817
|
|
race_other (Hispanic)
|
|
-0.01
|
0.06
|
.919
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.029
|
|
ICCparticipant_ID
|
|
0.205
|
|
ICCprogram_ID
|
|
0.004
|
|
Observations
|
|
2581
|
|
R2 / Ω02
|
|
.741 / .741
|
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
|