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)
|
|
2.37
|
0.27
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.25
|
0.12
|
.046
|
|
overall_pre_interest
|
|
0.19
|
0.12
|
.120
|
|
overall_pre_utility_value
|
|
0.00
|
0.11
|
.972
|
|
classroom_versus_field_enrichment
|
|
0.16
|
0.06
|
.012
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.064
|
|
ICCparticipant_ID
|
|
0.375
|
|
ICCprogram_ID
|
|
0.039
|
|
Observations
|
|
2602
|
|
R2 / Ω02
|
|
.532 / .524
|
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)
|
|
2.37
|
0.27
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.25
|
0.12
|
.047
|
|
overall_pre_interest
|
|
0.17
|
0.12
|
.158
|
|
overall_pre_utility_value
|
|
0.02
|
0.11
|
.891
|
|
classroom_versus_field_enrichment
|
|
0.06
|
0.06
|
.360
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.11
|
0.07
|
.109
|
|
youth_activity_rc (Creating Product)
|
|
0.34
|
0.07
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.08
|
0.14
|
.562
|
|
youth_activity_rc (Lab Activity)
|
|
0.17
|
0.13
|
.199
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.13
|
0.08
|
.101
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.050
|
|
ICCparticipant_ID
|
|
0.385
|
|
ICCprogram_ID
|
|
0.032
|
|
Observations
|
|
2570
|
|
R2 / Ω02
|
|
.528 / .521
|
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)
|
|
2.26
|
0.25
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.11
|
0.12
|
.381
|
|
overall_pre_interest
|
|
0.12
|
0.11
|
.271
|
|
overall_pre_utility_value
|
|
0.09
|
0.11
|
.378
|
|
classroom_versus_field_enrichment
|
|
-0.04
|
0.04
|
.274
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.017
|
|
ICCparticipant_ID
|
|
0.523
|
|
ICCprogram_ID
|
|
0.013
|
|
Observations
|
|
2602
|
|
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)
|
|
2.16
|
0.26
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.10
|
0.12
|
.397
|
|
overall_pre_interest
|
|
0.12
|
0.11
|
.309
|
|
overall_pre_utility_value
|
|
0.10
|
0.11
|
.348
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.13
|
0.04
|
.001
|
|
youth_activity_rc (Creating Product)
|
|
0.24
|
0.04
|
<.001
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.24
|
0.08
|
.003
|
|
youth_activity_rc (Lab Activity)
|
|
0.09
|
0.08
|
.243
|
|
youth_activity_rc (Program Staff Led)
|
|
0.15
|
0.05
|
.001
|
|
classroom_versus_field_enrichment
|
|
-0.08
|
0.04
|
.046
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.008
|
|
ICCparticipant_ID
|
|
0.526
|
|
ICCprogram_ID
|
|
0.020
|
|
Observations
|
|
2570
|
|
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 +
(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)
|
|
2.44
|
0.23
|
<.001
|
|
overall_pre_competence_beliefs
|
|
0.00
|
0.11
|
.972
|
|
overall_pre_interest
|
|
0.07
|
0.10
|
.513
|
|
overall_pre_utility_value
|
|
0.03
|
0.10
|
.731
|
|
classroom_versus_field_enrichment
|
|
0.02
|
0.05
|
.671
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.014
|
|
ICCparticipant_ID
|
|
0.355
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2601
|
|
R2 / Ω02
|
|
.422 / .415
|
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)
|
|
2.38
|
0.23
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.01
|
0.11
|
.934
|
|
overall_pre_interest
|
|
0.07
|
0.10
|
.475
|
|
overall_pre_utility_value
|
|
0.03
|
0.10
|
.784
|
|
classroom_versus_field_enrichment
|
|
0.02
|
0.05
|
.658
|
|
youth_activity_rc (Basic Skills Activity)
|
|
0.22
|
0.05
|
<.001
|
|
youth_activity_rc (Creating Product)
|
|
0.12
|
0.06
|
.038
|
|
youth_activity_rc (Field Trip Speaker)
|
|
0.10
|
0.10
|
.300
|
|
youth_activity_rc (Lab Activity)
|
|
0.20
|
0.10
|
.044
|
|
youth_activity_rc (Program Staff Led)
|
|
0.07
|
0.06
|
.217
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.010
|
|
ICCparticipant_ID
|
|
0.362
|
|
ICCprogram_ID
|
|
0.000
|
|
Observations
|
|
2569
|
|
R2 / Ω02
|
|
.422 / .416
|
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)
|
|
2.25
|
0.28
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.05
|
0.13
|
.682
|
|
overall_pre_interest
|
|
0.28
|
0.13
|
.024
|
|
overall_pre_utility_value
|
|
-0.06
|
0.11
|
.592
|
|
classroom_versus_field_enrichment
|
|
-0.06
|
0.05
|
.241
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
238
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.028
|
|
ICCparticipant_ID
|
|
0.450
|
|
ICCprogram_ID
|
|
0.055
|
|
Observations
|
|
2602
|
|
R2 / Ω02
|
|
.587 / .583
|
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)
|
|
2.28
|
0.28
|
<.001
|
|
overall_pre_competence_beliefs
|
|
-0.06
|
0.13
|
.660
|
|
overall_pre_interest
|
|
0.28
|
0.12
|
.024
|
|
overall_pre_utility_value
|
|
-0.06
|
0.11
|
.584
|
|
classroom_versus_field_enrichment
|
|
-0.06
|
0.05
|
.229
|
|
youth_activity_rc (Basic Skills Activity)
|
|
-0.00
|
0.06
|
.997
|
|
youth_activity_rc (Creating Product)
|
|
0.01
|
0.06
|
.818
|
|
youth_activity_rc (Field Trip Speaker)
|
|
-0.03
|
0.11
|
.800
|
|
youth_activity_rc (Lab Activity)
|
|
0.07
|
0.11
|
.501
|
|
youth_activity_rc (Program Staff Led)
|
|
-0.07
|
0.06
|
.250
|
|
Random Parts
|
|
Nbeep_ID_new
|
|
235
|
|
Nparticipant_ID
|
|
179
|
|
Nprogram_ID
|
|
9
|
|
ICCbeep_ID_new
|
|
0.029
|
|
ICCparticipant_ID
|
|
0.448
|
|
ICCprogram_ID
|
|
0.052
|
|
Observations
|
|
2570
|
|
R2 / Ω02
|
|
.586 / .582
|
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)