1. Loading, setting up
2. Null models (ready)
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
m1i <- lmer(challenge ~ 1 +
youth_activity_rc +
(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)
|
|
2.19
|
0.09
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.10
|
0.06
|
.117
|
|
youth_activity_rc (Creating Product)
|
|
0.37
|
0.06
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.13
|
.550
|
|
youth_activity_rc (Lab Activity)
|
|
0.20
|
0.12
|
.102
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.10
|
0.07
|
.173
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.045
|
|
ICCparticipant_ID
|
|
0.385
|
|
ICCprogram_ID
|
|
0.034
|
|
Observations
|
|
2818
|
|
R2 / Ω02
|
|
.529 / .522
|
m1ii <- lmer(relevance ~ 1 +
youth_activity_rc +
(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)
|
|
2.46
|
0.07
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.15
|
0.04
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.23
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.29
|
0.07
|
<.001
|
|
youth_activity_rc (Lab Activity)
|
|
0.11
|
0.07
|
.144
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.04
|
<.001
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.007
|
|
ICCparticipant_ID
|
|
0.523
|
|
ICCprogram_ID
|
|
0.016
|
|
Observations
|
|
2818
|
|
R2 / Ω02
|
|
.586 / .583
|
m1iii <- lmer(learning ~ 1 +
youth_activity_rc +
(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)
|
|
2.68
|
0.06
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.22
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.14
|
0.05
|
.009
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.10
|
0.10
|
.312
|
|
youth_activity_rc (Lab Activity)
|
|
0.15
|
0.10
|
.111
|
|
youth_activity_rc (Program Staff Led)
|
|
0.07
|
0.06
|
.218
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.011
|
|
ICCparticipant_ID
|
|
0.357
|
|
ICCprogram_ID
|
|
0.002
|
|
Observations
|
|
2817
|
|
R2 / Ω02
|
|
.428 / .421
|
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m1iv <- lmer(positive_affect ~ 1 +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
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)
|
|
2.70
|
0.12
|
<.001
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.03
|
0.05
|
.532
|
|
youth_activity_rc (Creating Product)
|
|
0.01
|
0.05
|
.802
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.01
|
0.10
|
.926
|
|
youth_activity_rc (Lab Activity)
|
|
0.07
|
0.10
|
.510
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.05
|
0.06
|
.380
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.023
|
|
ICCparticipant_ID
|
|
0.421
|
|
ICCprogram_ID
|
|
0.091
|
|
Observations
|
|
2818
|
|
R2 / Ω02
|
|
.580 / .576
|
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 +
(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)
|
|
2.48
|
0.07
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.012
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.013
|
|
ICCparticipant_ID
|
|
0.518
|
|
ICCprogram_ID
|
|
0.012
|
|
Observations
|
|
2800
|
|
R2 / Ω02
|
|
.588 / .585
|
m1vi <- lmer(challenge ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
(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)
|
|
2.01
|
0.11
|
<.001
|
|
CLASS_Autonomy
|
|
0.08
|
0.02
|
<.001
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.053
|
|
ICCparticipant_ID
|
|
0.375
|
|
ICCprogram_ID
|
|
0.043
|
|
Observations
|
|
2800
|
|
R2 / Ω02
|
|
.531 / .523
|
m1viii <- lmer(learning ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
(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)
|
|
2.68
|
0.07
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.045
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.014
|
|
ICCparticipant_ID
|
|
0.354
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2799
|
|
R2 / Ω02
|
|
.428 / .421
|
m1viv <- lmer(positive_affect ~ 1 +
# CLASS_EmotionalSupportEncouragement +
#CLASS_InstructionalSupport +
# CLASS_STEMConceptualDevelopment +
#CLASS_ActivityLeaderEnthusiasm +
CLASS_Autonomy +
(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)
|
|
2.59
|
0.13
|
<.001
|
|
CLASS_Autonomy
|
|
0.03
|
0.01
|
.021
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
236
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.022
|
|
ICCparticipant_ID
|
|
0.424
|
|
ICCprogram_ID
|
|
0.093
|
|
Observations
|
|
2800
|
|
R2 / Ω02
|
|
.583 / .580
|
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 .68
## 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 .65
## 4 -.00 -.04
## 5 .09 .08 .39
## 6 .08 .07 .30 .65
## 7 .20 .07 .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 +
(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)
|
|
2.42
|
0.25
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.30
|
0.11
|
.005
|
|
overall_pre_interest
|
|
0.23
|
0.10
|
.024
|
|
classroom_versus_field_enrichment
|
|
0.15
|
0.06
|
.019
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.064
|
|
ICCparticipant_ID
|
|
0.376
|
|
ICCprogram_ID
|
|
0.034
|
|
Observations
|
|
2622
|
|
R2 / Ω02
|
|
.535 / .527
|
m2ib <- lmer(challenge ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
(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)
|
|
2.43
|
0.24
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.30
|
0.11
|
.005
|
|
overall_pre_interest
|
|
0.22
|
0.10
|
.033
|
|
classroom_versus_field_enrichment
|
|
0.05
|
0.06
|
.457
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.10
|
0.07
|
.142
|
|
youth_activity_rc (Creating Product)
|
|
0.34
|
0.07
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.14
|
.571
|
|
youth_activity_rc (Lab Activity)
|
|
0.17
|
0.13
|
.201
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.13
|
0.08
|
.099
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.049
|
|
ICCparticipant_ID
|
|
0.387
|
|
ICCprogram_ID
|
|
0.027
|
|
Observations
|
|
2590
|
|
R2 / Ω02
|
|
.531 / .524
|
m2ii <- lmer(relevance ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
# youth_activity_rc +
classroom_versus_field_enrichment +
(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)
|
|
2.37
|
0.22
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.08
|
0.10
|
.446
|
|
overall_pre_interest
|
|
0.16
|
0.10
|
.102
|
|
classroom_versus_field_enrichment
|
|
-0.04
|
0.04
|
.293
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.017
|
|
ICCparticipant_ID
|
|
0.526
|
|
ICCprogram_ID
|
|
0.008
|
|
Observations
|
|
2622
|
|
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 +
(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)
|
|
2.21
|
0.21
|
<.001
|
|
overall_pre_interest
|
|
0.11
|
0.06
|
.101
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.13
|
0.04
|
.001
|
|
youth_activity_rc (Creating Product)
|
|
0.23
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.23
|
0.08
|
.003
|
|
youth_activity_rc (Lab Activity)
|
|
0.09
|
0.08
|
.235
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
.001
|
|
classroom_versus_field_enrichment
|
|
-0.08
|
0.04
|
.051
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.009
|
|
ICCparticipant_ID
|
|
0.527
|
|
ICCprogram_ID
|
|
0.017
|
|
Observations
|
|
2590
|
|
R2 / Ω02
|
|
.593 / .590
|
m2iii <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
#youth_activity_rc +
(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)
|
|
2.47
|
0.20
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.03
|
0.09
|
.724
|
|
overall_pre_interest
|
|
0.06
|
0.09
|
.466
|
|
classroom_versus_field_enrichment
|
|
0.01
|
0.05
|
.751
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.014
|
|
ICCparticipant_ID
|
|
0.352
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2621
|
|
R2 / Ω02
|
|
.420 / .413
|
m2iiib <- lmer(learning ~ 1 +
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
(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)
|
|
2.41
|
0.20
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.02
|
0.09
|
.836
|
|
overall_pre_interest
|
|
0.07
|
0.09
|
.448
|
|
classroom_versus_field_enrichment
|
|
0.01
|
0.05
|
.799
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.22
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.12
|
0.06
|
.036
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.08
|
0.10
|
.417
|
|
youth_activity_rc (Lab Activity)
|
|
0.19
|
0.10
|
.053
|
|
youth_activity_rc (Program Staff Led)
|
|
0.07
|
0.06
|
.245
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.358
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2589
|
|
R2 / Ω02
|
|
.420 / .414
|
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 +
(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)
|
|
2.17
|
0.26
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.05
|
0.11
|
.683
|
|
overall_pre_interest
|
|
0.23
|
0.11
|
.031
|
|
classroom_versus_field_enrichment
|
|
-0.05
|
0.05
|
.273
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.029
|
|
ICCparticipant_ID
|
|
0.443
|
|
ICCprogram_ID
|
|
0.061
|
|
Observations
|
|
2622
|
|
R2 / Ω02
|
|
.586 / .582
|
m2ivb <- lmer(positive_affect ~
overall_pre_competence_beliefs +
overall_pre_interest +
#overall_pre_utility_value +
classroom_versus_field_enrichment +
youth_activity_rc +
(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)
|
|
2.20
|
0.26
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.05
|
0.11
|
.657
|
|
overall_pre_interest
|
|
0.23
|
0.11
|
.031
|
|
classroom_versus_field_enrichment
|
|
-0.06
|
0.05
|
.253
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.00
|
0.06
|
.977
|
|
youth_activity_rc (Creating Product)
|
|
0.01
|
0.06
|
.824
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.03
|
0.11
|
.768
|
|
youth_activity_rc (Lab Activity)
|
|
0.07
|
0.11
|
.530
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.07
|
0.07
|
.266
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
181
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.030
|
|
ICCparticipant_ID
|
|
0.441
|
|
ICCprogram_ID
|
|
0.058
|
|
Observations
|
|
2590
|
|
R2 / Ω02
|
|
.585 / .581
|
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 +
(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)
|
|
0.65
|
0.07
|
<.001
|
|
challenge
|
|
0.02
|
0.01
|
.141
|
|
relevance
|
|
0.36
|
0.02
|
<.001
|
|
learning
|
|
0.17
|
0.02
|
<.001
|
|
positive_affect
|
|
0.28
|
0.02
|
<.001
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.033
|
|
ICCparticipant_ID
|
|
0.098
|
|
ICCprogram_ID
|
|
0.016
|
|
Observations
|
|
2969
|
|
R2 / Ω02
|
|
.583 / .582
|
m3v <- lmer(overall_engagement ~ 1 +
challenge + relevance + learning + positive_affect +
(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)
|
|
0.70
|
0.05
|
<.001
|
|
challenge
|
|
0.03
|
0.01
|
<.001
|
|
relevance
|
|
0.23
|
0.02
|
<.001
|
|
learning
|
|
0.27
|
0.01
|
<.001
|
|
positive_affect
|
|
0.28
|
0.01
|
<.001
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
248
|
|
Nparticipant_ID
|
|
203
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.028
|
|
ICCparticipant_ID
|
|
0.203
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2969
|
|
R2 / Ω02
|
|
.734 / .733
|
7. Outcomes (not ready)
participant_df <-df %>%
select(participant_ID, challenge, relevance, learning, positive_affect) %>%
group_by(participant_ID) %>%
mutate_at(vars(challenge, relevance, learning, positive_affect), funs(mean, sd)) %>%
select(participant_ID, contains("mean"), contains("sd")) %>%
distinct()
df_ss <- left_join(df, participant_df)
## Joining, by = "participant_ID"
df_ss <- select(df_ss,
participant_ID, program_ID,
challenge_mean, relevance_mean, learning_mean, positive_affect_mean,
challenge_sd, relevance_sd, learning_sd, positive_affect_sd,
overall_post_interest, overall_pre_interest,
future_goals)
df_ss <- mutate(df_ss,
challenge_sd = abs(challenge_sd),
relevance_sd = abs(relevance_sd),
learning_sd = abs(learning_sd),
positive_affect_sd = abs(positive_affect_sd))
df_ss <- distinct(df_ss)
m4ia <- lmer(overall_post_interest ~ 1 +
challenge_mean +
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)
## Computing p-values via Wald-statistics approximation (treating t as Wald z).
|
|
|
overall_post_interest
|
|
|
|
B
|
std. Error
|
p
|
|
Fixed Parts
|
|
(Intercept)
|
|
0.73
|
0.24
|
.002
|
|
challenge_mean
|
|
-0.13
|
0.06
|
.025
|
|
learning_mean
|
|
0.46
|
0.11
|
<.001
|
|
relevance_mean
|
|
-0.22
|
0.12
|
.064
|
|
positive_affect_mean
|
|
0.17
|
0.07
|
.014
|
|
overall_pre_interest
|
|
0.47
|
0.05
|
<.001
|
|
Random Parts
|
|
Nprogram_ID
|
|
9
|
|
ICCprogram_ID
|
|
0.114
|
|
Observations
|
|
425
|
|
R2 / Ω02
|
|
.472 / .472
|
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)
## Computing p-values via Wald-statistics approximation (treating t as Wald z).
|
|
|
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)
## Computing p-values via Wald-statistics approximation (treating t as Wald z).
|
|
|
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)
## Computing p-values via Wald-statistics approximation (treating t as Wald z).
|
|
|
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
|