AERA STEMie analysis
Patrick N. Beymer
3/7/2018
library(tidyverse)## Warning: package 'tibble' was built under R version 3.4.3## Warning: package 'tidyr' was built under R version 3.4.3library(lmerTest)## Warning: package 'lmerTest' was built under R version 3.4.3## Warning: package 'lme4' was built under R version 3.4.3library(lme4)
library(corrr)
library(jmRtools)
library(gmodels)
library(psych)esm <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-esm.csv")
pre_survey_data_processed <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pre-survey.csv")
pre_survey_all_variables <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pre-survey-data-processed.csv")
post_survey_data_partially_processed <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-post-survey.csv")
video <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-video.csv")
pqa <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pqa.csv")
attendance <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-attendance.csv")
class_data <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-class-video.csv")
demographics <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-demographics.csv")
pm <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/STEM-IE/STEM-IE-program-match.csv")
value <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/STEM-IE/STEM-IE-value.csv")
community_space <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/STEM-IE/STEM-IE-community-space-content.csv")Creating motivation to attend variable
pre_survey_all_variables$motivation_to_attend <- ifelse(pre_survey_all_variables$how_much_looking_forward == 1, 0, 1)attendance <- rename(attendance, participant_ID = ParticipantID)
attendance <- mutate(attendance, prop_attend = DaysAttended / DaysScheduled,
participant_ID = as.integer(participant_ID))
attendance <- select(attendance, participant_ID, prop_attend)
demographics <- filter(demographics, participant_ID!= 7187)
demographics <- left_join(demographics, attendance)
esm$overall_engagement <- jmRtools::composite_mean_maker(esm, hard_working, concentrating, enjoy, interest)
pre_survey_all_variables <- rename(pre_survey_all_variables, program_ID_string = program_ID)
pre_survey_all_variables <- rename(pre_survey_all_variables, program_providence_useless = program_providence)df <- left_join(esm, pre_survey_data_processed, by = "participant_ID") # df = esm & pre-survey
df <- left_join(df, video, by = c("program_ID", "response_date", "sociedad_class", "signal_number")) # df & video
df <- left_join(df, demographics, by = c("participant_ID", "program_ID")) # df and demographics
df <- left_join(df, pre_survey_all_variables, by = "participant_ID") #df & pre-survey all variables Not sure what two program id's nowpqa <- mutate(pqa,
active = active_part_1 + active_part_2,
ho_thinking = ho_thinking_1 + ho_thinking_2 + ho_thinking_3,
belonging = belonging_1 + belonging_2,
agency = agency_1 + agency_2 + agency_3 + agency_4,
youth_development_overall = active_part_1 + active_part_2 + ho_thinking_1 + ho_thinking_2 + ho_thinking_3 + belonging_1 + belonging_2 + agency_1 + agency_2 + agency_3 + agency_4,
making_observations = stem_sb_8,
data_modeling = stem_sb_2 + stem_sb_3 + stem_sb_9,
interpreting_communicating = stem_sb_6,
generating_data = stem_sb_4,
asking_questions = stem_sb_1,
stem_sb = stem_sb_1 + stem_sb_2 + stem_sb_3 + stem_sb_4 + stem_sb_5 + stem_sb_6 + stem_sb_7 + stem_sb_8 + stem_sb_9)
pqa$stem_sb_dummy <- ifelse(pqa$stem_sb == 0, 0, 1)
# pqa <- rename(pqa, sixth_math_sociedad = sixth_math)
# pqa <- rename(pqa, seventh_math_sociedad = seventh_math)
# pqa <- rename(pqa, eighth_math_sociedad = eighth_math)
# pqa <- rename(pqa, dance_sociedad = dance)
# pqa <- rename(pqa, robotics_sociedad = robotics)
pqa$sociedad_class <- ifelse(pqa$eighth_math == 1, "8th Math",
ifelse(pqa$seventh_math == 1, "7th Math",
ifelse(pqa$sixth_math == 1, "6th Math",
ifelse(pqa$robotics == 1, "Robotics",
ifelse(pqa$dance == 1, "Dance", NA)))))
pqa <- rename(pqa,
program_ID = SiteIDNumeric,
response_date = resp_date,
signal_number = signal)
pqa$program_ID <- as.character(pqa$program_ID)
df <- left_join(df, pqa, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))## Warning: Column `program_ID` joining factor and character vector, coercing
## into character vector# !!! MERGE PQA
pqa$sociedad_class <- ifelse(pqa$eighth_math == 1, "8th Math",
ifelse(pqa$seventh_math == 1, "7th Math",
ifelse(pqa$sixth_math == 1, "6th Math",
ifelse(pqa$robotics == 1, "Robotics",
ifelse(pqa$dance == 1, "Dance", NA)))))
pqa$program_ID <- as.character(pqa$program_ID)
df <- left_join(df, pqa, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))
# !!!
# !!! MERGE CLASS
class_data$COMPOSIT <- jmRtools::composite_mean_maker(class_data, ID, P, AI, ILF, QF, CU)
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$program_ID <- as.character(class_data$SiteIDNumeric)
class_data <- rename(class_data,
response_date = Responsedate,
signal_number = r_signal_number)
df <- left_join(df, class_data, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))
df <-left_join(df, pqa)
# !!!df <- df %>%
mutate(youth_activity_three = case_when(
youth_activity_rc == "Creating Product" ~ "Creating Product",
youth_activity_rc == "Basic Skills Activity" ~ "Basic Skills Activity",
TRUE ~ "Other"
))
df$youth_activity_three <- fct_relevel(df$youth_activity_three,
"Other")df <- df %>%
mutate(ho_thinking_dummy = ifelse(ho_thinking > 0, 1, 0),
agency_dummy = ifelse(agency > 0, 1, 0),
active_dummy = ifelse(active > 0, 1, 0),
belonging_dummy = ifelse(belonging > 0, 1, 0),
stem_sb_dummy = ifelse(sum_stem_sb > 0, 1, 0))Creating subject variable
#pqa$subject <-ifelse(pqa$science == 1, "Science",
#ifelse(pqa$mathematics == 1, "Math",
#ifelse(pqa$building == 1, "Building", NA)))
df$subject <-ifelse(df$science == 1, "Science",
ifelse(df$mathematics == 1, "Math",
ifelse(df$building == 1, "Building", NA)))
df$subject_other <- ifelse(df$science == 0 &
df$mathematics == 0 &
df$building == 0, 1, 0)Creating variable that is >=3
df$agency_comp_three <- ifelse(df$agency >= 3 & df$COMPOSIT >=3, 1, 0)Joining Community Space data set
community_space <- rename(community_space,
response_date = resp_date,
signal_number = signal,
program_ID = SiteIDNumeric)
community_space$program_ID <- as.character(community_space$program_ID)
community_space$sociedad_class <- ifelse(community_space$eighth_math == 1, "8th Math",
ifelse(community_space$seventh_math == 1, "7th Math",
ifelse(community_space$sixth_math == 1, "6th Math",
ifelse(community_space$robotics == 1, "Robotics",
ifelse(community_space$dance == 1, "Dance", NA)))))
community_space$response_date <- format(as.Date(community_space$response_date, format = "%m/%d/%Y"), "%Y-%m-%d")## Warning in strptime(x, format, tz = "GMT"): unknown timezone 'zone/tz/
## 2018c.1.0/zoneinfo/America/Detroit'community_space <- mutate(community_space, response_date = as.character(response_date))
df <- mutate(df, response_date = as.character(response_date))
df <- left_join(df, community_space, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))Joining Value data set
value <- rename(value,
response_date = resp_date,
signal_number = signal,
program_ID = SiteIDNumeric)
value$program_ID <- as.character(value$program_ID)
value$sociedad_class <- ifelse(value$eighth_math == 1, "8th Math",
ifelse(value$seventh_math == 1, "7th Math",
ifelse(value$sixth_math == 1, "6th Math",
ifelse(value$robotics == 1, "Robotics",
ifelse(value$dance == 1, "Dance", NA)))))
value$response_date <- format(as.Date(value$response_date, format = "%m/%d/%Y"), "%Y-%m-%d")
value <- mutate(value, response_date = as.character(response_date))
df <- left_join(df, value, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))Creating variables for value
df$all_value_sum <- df$V01.01.HighUtility_sum + df$V01.03.HighIntrinsic_sum + df$V01.05.HighAttainment_sumEngagement Models
Predicting engagement
engagement_model <- lmer(overall_engagement ~
gender_female +
challenge +
relevance +
learning +
overall_pre_competence_beliefs +
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: overall_engagement ~ gender_female + challenge + relevance +
## learning + overall_pre_competence_beliefs + Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 3882.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0286 -0.5407 0.0273 0.5170 3.8923
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01656 0.1287
## participant_ID (Intercept) 0.07302 0.2702
## program_ID (Intercept) 0.01067 0.1033
## Residual 0.22235 0.4715
## Number of obs: 2558, groups:
## beep_ID_new, 237; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.88964 0.11163 132.00000 7.969
## gender_female 0.03517 0.04713 172.30000 0.746
## challenge 0.03602 0.01142 2518.00000 3.156
## relevance 0.36014 0.01633 2486.20000 22.049
## learning 0.28871 0.01295 2483.60000 22.302
## overall_pre_competence_beliefs 0.04457 0.02978 170.90000 1.497
## Community_Space_Content -0.06825 0.03592 216.60000 -1.900
## Pr(>|t|)
## (Intercept) 6.59e-13 ***
## gender_female 0.45661
## challenge 0.00162 **
## relevance < 2e-16 ***
## learning < 2e-16 ***
## overall_pre_competence_beliefs 0.13636
## Community_Space_Content 0.05875 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f chllng relvnc lernng ovr___
## gender_feml -0.204
## challenge -0.198 0.042
## relevance -0.175 0.068 -0.156
## learning -0.080 -0.023 -0.086 -0.496
## ovrll_pr_c_ -0.808 -0.052 0.051 -0.025 -0.034
## Cmmnty_Sp_C -0.045 0.001 -0.020 -0.037 -0.007 -0.001Predicting engagement with gender interactions
engagement_model_gender <- lmer(overall_engagement ~
gender_female +
challenge +
relevance +
learning +
overall_pre_competence_beliefs +
Community_Space_Content +
gender_female*challenge +
gender_female*relevance +
gender_female*learning +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model_gender)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: overall_engagement ~ gender_female + challenge + relevance +
## learning + overall_pre_competence_beliefs + Community_Space_Content +
## gender_female * challenge + gender_female * relevance + gender_female *
## learning + (1 | program_ID) + (1 | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0050 -0.5360 0.0197 0.5181 4.0385
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01681 0.1296
## participant_ID (Intercept) 0.07237 0.2690
## program_ID (Intercept) 0.01125 0.1060
## Residual 0.22134 0.4705
## Number of obs: 2558, groups:
## beep_ID_new, 237; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.75165 0.11957 161.00000 6.286
## gender_female 0.28988 0.09560 960.30000 3.032
## challenge 0.05620 0.01575 2506.30000 3.567
## relevance 0.40728 0.02396 2481.90000 16.997
## learning 0.27533 0.01982 2484.70000 13.888
## overall_pre_competence_beliefs 0.04354 0.02975 171.90000 1.463
## Community_Space_Content -0.06320 0.03604 216.80000 -1.754
## gender_female:challenge -0.04110 0.02237 2472.80000 -1.837
## gender_female:relevance -0.08581 0.03246 2449.90000 -2.643
## gender_female:learning 0.02294 0.02609 2467.20000 0.879
## Pr(>|t|)
## (Intercept) 2.92e-09 ***
## gender_female 0.002492 **
## challenge 0.000368 ***
## relevance < 2e-16 ***
## learning < 2e-16 ***
## overall_pre_competence_beliefs 0.145176
## Community_Space_Content 0.080913 .
## gender_female:challenge 0.066336 .
## gender_female:relevance 0.008264 **
## gender_female:learning 0.379470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f chllng relvnc lernng ovr___ Cm_S_C gndr_fml:c
## gender_feml -0.403
## challenge -0.228 0.234
## relevance -0.252 0.330 -0.132
## learning -0.109 0.194 -0.105 -0.516
## ovrll_pr_c_ -0.739 -0.058 0.038 -0.014 -0.057
## Cmmnty_Sp_C -0.057 0.033 0.007 -0.010 -0.007 -0.002
## gndr_fml:ch 0.138 -0.324 -0.690 0.091 0.073 0.000 -0.030
## gndr_fml:rl 0.196 -0.428 0.096 -0.732 0.379 -0.003 -0.021 -0.154
## gndr_fml:lr 0.081 -0.269 0.086 0.388 -0.759 0.046 0.003 -0.099
## gndr_fml:r
## gender_feml
## challenge
## relevance
## learning
## ovrll_pr_c_
## Cmmnty_Sp_C
## gndr_fml:ch
## gndr_fml:rl
## gndr_fml:lr -0.496Predicting engagement with competence interactions
engagement_model_competence <- lmer(overall_engagement ~
gender_female +
challenge +
relevance +
learning +
overall_pre_competence_beliefs +
Community_Space_Content +
overall_pre_competence_beliefs*challenge +
overall_pre_competence_beliefs*relevance +
overall_pre_competence_beliefs*learning +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model_competence)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: overall_engagement ~ gender_female + challenge + relevance +
## learning + overall_pre_competence_beliefs + Community_Space_Content +
## overall_pre_competence_beliefs * challenge + overall_pre_competence_beliefs *
## relevance + overall_pre_competence_beliefs * learning + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 3880.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9418 -0.5428 0.0256 0.5246 4.0097
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01682 0.1297
## participant_ID (Intercept) 0.07293 0.2701
## program_ID (Intercept) 0.01072 0.1036
## Residual 0.22041 0.4695
## Number of obs: 2558, groups:
## beep_ID_new, 237; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.91534 0.19373 608.90000
## gender_female 0.03889 0.04718 172.90000
## challenge 0.21781 0.04388 2501.80000
## relevance 0.25648 0.06574 2475.00000
## learning 0.21242 0.05076 2501.20000
## overall_pre_competence_beliefs 0.03537 0.05927 820.30000
## Community_Space_Content -0.06923 0.03599 216.60000
## challenge:overall_pre_competence_beliefs -0.05770 0.01353 2480.00000
## relevance:overall_pre_competence_beliefs 0.03252 0.02015 2434.50000
## learning:overall_pre_competence_beliefs 0.02383 0.01555 2505.50000
## t value Pr(>|t|)
## (Intercept) 4.725 2.86e-06 ***
## gender_female 0.824 0.4108
## challenge 4.964 7.37e-07 ***
## relevance 3.901 9.83e-05 ***
## learning 4.185 2.96e-05 ***
## overall_pre_competence_beliefs 0.597 0.5508
## Community_Space_Content -1.923 0.0557 .
## challenge:overall_pre_competence_beliefs -4.265 2.07e-05 ***
## relevance:overall_pre_competence_beliefs 1.614 0.1067
## learning:overall_pre_competence_beliefs 1.533 0.1255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f chllng relvnc lernng ovr___ Cm_S_C ch:___ rl:___
## gender_feml -0.075
## challenge -0.319 0.003
## relevance -0.438 0.016 -0.181
## learning -0.245 -0.059 -0.107 -0.453
## ovrll_pr_c_ -0.941 -0.070 0.310 0.429 0.250
## Cmmnty_Sp_C 0.002 0.001 -0.021 -0.041 0.009 -0.030
## chllng:v___ 0.309 0.009 -0.966 0.171 0.099 -0.325 0.017
## rlvnc:vr___ 0.429 0.001 0.173 -0.969 0.435 -0.448 0.033 -0.173
## lrnng:vr___ 0.243 0.055 0.101 0.437 -0.967 -0.265 -0.011 -0.098 -0.451Challenge models
Predicting challenge with gender interactions
challenge_model <- lmer(challenge ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(challenge_model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: challenge ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6561.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8804 -0.6343 -0.0339 0.5665 3.3306
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06010 0.2452
## participant_ID (Intercept) 0.46579 0.6825
## program_ID (Intercept) 0.01458 0.1207
## Residual 0.65855 0.8115
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.70923 0.23030 98.06000
## gender_female -0.24095 0.11062 174.27000
## overall_pre_competence_beliefs -0.12249 0.06811 133.55000
## Community_Space_Content 0.20156 0.07608 184.93000
## youth_activity_rcBasic Skills Activity 0.03632 0.07055 194.87000
## youth_activity_rcCreating Product 0.32834 0.06898 210.82000
## youth_activity_rcField Trip Speaker -0.27165 0.14565 133.87000
## youth_activity_rcLab Activity 0.07836 0.13397 152.93000
## youth_activity_rcProgram Staff Led -0.14343 0.07986 180.60000
## t value Pr(>|t|)
## (Intercept) 11.764 < 2e-16 ***
## gender_female -2.178 0.03075 *
## overall_pre_competence_beliefs -1.798 0.07439 .
## Community_Space_Content 2.649 0.00877 **
## youth_activity_rcBasic Skills Activity 0.515 0.60730
## youth_activity_rcCreating Product 4.760 3.6e-06 ***
## youth_activity_rcField Trip Speaker -1.865 0.06436 .
## youth_activity_rcLab Activity 0.585 0.55948
## youth_activity_rcProgram Staff Led -1.796 0.07418 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f ovr___ Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.213
## ovrll_pr_c_ -0.906 -0.036
## Cmmnty_Sp_C -0.042 0.011 0.009
## yth_ctv_BSA -0.088 -0.008 -0.023 -0.272
## yth_ctvt_CP -0.103 0.008 -0.016 0.033 0.375
## yth_ctv_FTS -0.023 0.008 -0.033 -0.391 0.320 0.189
## yth_ctvt_LA -0.034 -0.011 -0.022 -0.220 0.255 0.188 0.205
## yth_ctv_PSL -0.103 -0.020 0.004 -0.054 0.410 0.311 0.187 0.194Predicting challenge with gender interactions
challenge_model_female <- lmer(challenge ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
gender_female*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(challenge_model_female)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: challenge ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + gender_female *
## youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6570.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8251 -0.6385 -0.0339 0.5614 3.2442
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06034 0.2456
## participant_ID (Intercept) 0.46743 0.6837
## program_ID (Intercept) 0.01391 0.1179
## Residual 0.65884 0.8117
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.73668 0.23148
## gender_female -0.29683 0.12238
## overall_pre_competence_beliefs -0.12174 0.06816
## Community_Space_Content 0.19893 0.07620
## youth_activity_rcBasic Skills Activity -0.02198 0.08781
## youth_activity_rcCreating Product 0.27790 0.08728
## youth_activity_rcField Trip Speaker -0.35267 0.16260
## youth_activity_rcLab Activity 0.15492 0.17322
## youth_activity_rcProgram Staff Led -0.17431 0.10398
## gender_female:youth_activity_rcBasic Skills Activity 0.10958 0.09851
## gender_female:youth_activity_rcCreating Product 0.09366 0.10293
## gender_female:youth_activity_rcField Trip Speaker 0.18638 0.17330
## gender_female:youth_activity_rcLab Activity -0.12254 0.18513
## gender_female:youth_activity_rcProgram Staff Led 0.05859 0.11494
## df t value
## (Intercept) 99.00000 11.823
## gender_female 257.90000 -2.425
## overall_pre_competence_beliefs 132.20000 -1.786
## Community_Space_Content 185.10000 2.611
## youth_activity_rcBasic Skills Activity 428.50000 -0.250
## youth_activity_rcCreating Product 488.40000 3.184
## youth_activity_rcField Trip Speaker 204.40000 -2.169
## youth_activity_rcLab Activity 388.40000 0.894
## youth_activity_rcProgram Staff Led 471.40000 -1.676
## gender_female:youth_activity_rcBasic Skills Activity 2342.00000 1.112
## gender_female:youth_activity_rcCreating Product 2346.50000 0.910
## gender_female:youth_activity_rcField Trip Speaker 2253.20000 1.075
## gender_female:youth_activity_rcLab Activity 2348.40000 -0.662
## gender_female:youth_activity_rcProgram Staff Led 2320.60000 0.510
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.01598 *
## overall_pre_competence_beliefs 0.07639 .
## Community_Space_Content 0.00978 **
## youth_activity_rcBasic Skills Activity 0.80246
## youth_activity_rcCreating Product 0.00155 **
## youth_activity_rcField Trip Speaker 0.03124 *
## youth_activity_rcLab Activity 0.37166
## youth_activity_rcProgram Staff Led 0.09433 .
## gender_female:youth_activity_rcBasic Skills Activity 0.26608
## gender_female:youth_activity_rcCreating Product 0.36294
## gender_female:youth_activity_rcField Trip Speaker 0.28227
## gender_female:youth_activity_rcLab Activity 0.50808
## gender_female:youth_activity_rcProgram Staff Led 0.61029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Predicting challenge with competence interactions
challenge_model_competence <- lmer(challenge ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
overall_pre_competence_beliefs*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(challenge_model_competence)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: challenge ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + overall_pre_competence_beliefs *
## youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6573
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8421 -0.6336 -0.0343 0.5591 3.4796
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06049 0.2459
## participant_ID (Intercept) 0.46462 0.6816
## program_ID (Intercept) 0.01545 0.1243
## Residual 0.65862 0.8116
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.73062
## gender_female -0.23674
## overall_pre_competence_beliefs -0.12977
## Community_Space_Content 0.20635
## youth_activity_rcBasic Skills Activity -0.10980
## youth_activity_rcCreating Product 0.40251
## youth_activity_rcField Trip Speaker 0.31374
## youth_activity_rcLab Activity -0.09225
## youth_activity_rcProgram Staff Led -0.35243
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.04517
## overall_pre_competence_beliefs:youth_activity_rcCreating Product -0.02267
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker -0.17088
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.05089
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.06691
## Std. Error
## (Intercept) 0.24720
## gender_female 0.11060
## overall_pre_competence_beliefs 0.07434
## Community_Space_Content 0.07661
## youth_activity_rcBasic Skills Activity 0.20994
## youth_activity_rcCreating Product 0.20862
## youth_activity_rcField Trip Speaker 0.42647
## youth_activity_rcLab Activity 0.45104
## youth_activity_rcProgram Staff Led 0.23844
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.06250
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.06270
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 0.11931
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.13131
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.07343
## df
## (Intercept) 134.50000
## gender_female 174.40000
## overall_pre_competence_beliefs 199.10000
## Community_Space_Content 188.20000
## youth_activity_rcBasic Skills Activity 1687.30000
## youth_activity_rcCreating Product 2039.40000
## youth_activity_rcField Trip Speaker 1435.80000
## youth_activity_rcLab Activity 1862.70000
## youth_activity_rcProgram Staff Led 1731.00000
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 2271.90000
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 2348.80000
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 2153.80000
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 2212.90000
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 2231.20000
## t value
## (Intercept) 11.046
## gender_female -2.140
## overall_pre_competence_beliefs -1.746
## Community_Space_Content 2.693
## youth_activity_rcBasic Skills Activity -0.523
## youth_activity_rcCreating Product 1.929
## youth_activity_rcField Trip Speaker 0.736
## youth_activity_rcLab Activity -0.205
## youth_activity_rcProgram Staff Led -1.478
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.723
## overall_pre_competence_beliefs:youth_activity_rcCreating Product -0.362
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker -1.432
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.388
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.911
## Pr(>|t|)
## (Intercept) < 2e-16
## gender_female 0.03371
## overall_pre_competence_beliefs 0.08243
## Community_Space_Content 0.00771
## youth_activity_rcBasic Skills Activity 0.60103
## youth_activity_rcCreating Product 0.05382
## youth_activity_rcField Trip Speaker 0.46205
## youth_activity_rcLab Activity 0.83796
## youth_activity_rcProgram Staff Led 0.13957
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.46989
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.71775
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 0.15224
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.69837
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.36229
##
## (Intercept) ***
## gender_female *
## overall_pre_competence_beliefs .
## Community_Space_Content **
## youth_activity_rcBasic Skills Activity
## youth_activity_rcCreating Product .
## youth_activity_rcField Trip Speaker
## youth_activity_rcLab Activity
## youth_activity_rcProgram Staff Led
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity
## overall_pre_competence_beliefs:youth_activity_rcCreating Product
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker
## overall_pre_competence_beliefs:youth_activity_rcLab Activity
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Relevance Models
Predicting relevance
relevance_model <- lmer(relevance ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(relevance_model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: relevance ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5375.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8799 -0.5198 0.0240 0.5733 4.0732
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008112 0.09006
## participant_ID (Intercept) 0.478767 0.69193
## program_ID (Intercept) 0.010293 0.10145
## Residual 0.410223 0.64049
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.41826 0.22230 97.81000
## gender_female -0.24514 0.10890 176.89000
## overall_pre_competence_beliefs 0.05636 0.06641 124.14000
## Community_Space_Content 0.14332 0.04670 181.27000
## youth_activity_rcBasic Skills Activity 0.09519 0.04348 189.58000
## youth_activity_rcCreating Product 0.20051 0.04315 230.25000
## youth_activity_rcField Trip Speaker 0.15841 0.08455 115.88000
## youth_activity_rcLab Activity 0.04009 0.07951 140.29000
## youth_activity_rcProgram Staff Led 0.11707 0.04878 180.82000
## t value Pr(>|t|)
## (Intercept) 10.878 < 2e-16 ***
## gender_female -2.251 0.02561 *
## overall_pre_competence_beliefs 0.849 0.39769
## Community_Space_Content 3.069 0.00248 **
## youth_activity_rcBasic Skills Activity 2.189 0.02980 *
## youth_activity_rcCreating Product 4.647 5.67e-06 ***
## youth_activity_rcField Trip Speaker 1.874 0.06351 .
## youth_activity_rcLab Activity 0.504 0.61492
## youth_activity_rcProgram Staff Led 2.400 0.01741 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f ovr___ Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.223
## ovrll_pr_c_ -0.918 -0.030
## Cmmnty_Sp_C -0.030 0.011 0.006
## yth_ctv_BSA -0.052 -0.006 -0.017 -0.267
## yth_ctvt_CP -0.062 0.005 -0.014 0.049 0.361
## yth_ctv_FTS -0.012 0.003 -0.025 -0.406 0.337 0.189
## yth_ctvt_LA -0.017 -0.009 -0.018 -0.226 0.262 0.188 0.225
## yth_ctv_PSL -0.064 -0.015 0.002 -0.052 0.419 0.306 0.196 0.200Predicting relevance with gender interactions
relevance_model_female <- lmer(relevance ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
gender_female*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(relevance_model_female)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: relevance ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + gender_female *
## youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5384.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8874 -0.5205 0.0297 0.5783 4.1480
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007551 0.08690
## participant_ID (Intercept) 0.480271 0.69302
## program_ID (Intercept) 0.009871 0.09935
## Residual 0.410514 0.64071
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.45382 0.22301
## gender_female -0.31901 0.11618
## overall_pre_competence_beliefs 0.05796 0.06645
## Community_Space_Content 0.13989 0.04646
## youth_activity_rcBasic Skills Activity 0.03152 0.05924
## youth_activity_rcCreating Product 0.11812 0.05976
## youth_activity_rcField Trip Speaker 0.15043 0.10103
## youth_activity_rcLab Activity -0.05262 0.11568
## youth_activity_rcProgram Staff Led 0.07868 0.07092
## gender_female:youth_activity_rcBasic Skills Activity 0.11685 0.07643
## gender_female:youth_activity_rcCreating Product 0.15473 0.07937
## gender_female:youth_activity_rcField Trip Speaker -0.00497 0.13651
## gender_female:youth_activity_rcLab Activity 0.16103 0.14164
## gender_female:youth_activity_rcProgram Staff Led 0.07140 0.08941
## df t value
## (Intercept) 98.40000 11.003
## gender_female 227.00000 -2.746
## overall_pre_competence_beliefs 123.10000 0.872
## Community_Space_Content 181.80000 3.011
## youth_activity_rcBasic Skills Activity 543.30000 0.532
## youth_activity_rcCreating Product 684.50000 1.977
## youth_activity_rcField Trip Speaker 231.90000 1.489
## youth_activity_rcLab Activity 480.60000 -0.455
## youth_activity_rcProgram Staff Led 648.00000 1.109
## gender_female:youth_activity_rcBasic Skills Activity 2323.70000 1.529
## gender_female:youth_activity_rcCreating Product 2282.70000 1.950
## gender_female:youth_activity_rcField Trip Speaker 2295.10000 -0.036
## gender_female:youth_activity_rcLab Activity 2092.20000 1.137
## gender_female:youth_activity_rcProgram Staff Led 2329.60000 0.799
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.00652 **
## overall_pre_competence_beliefs 0.38475
## Community_Space_Content 0.00297 **
## youth_activity_rcBasic Skills Activity 0.59485
## youth_activity_rcCreating Product 0.04848 *
## youth_activity_rcField Trip Speaker 0.13784
## youth_activity_rcLab Activity 0.64937
## youth_activity_rcProgram Staff Led 0.26767
## gender_female:youth_activity_rcBasic Skills Activity 0.12642
## gender_female:youth_activity_rcCreating Product 0.05135 .
## gender_female:youth_activity_rcField Trip Speaker 0.97096
## gender_female:youth_activity_rcLab Activity 0.25573
## gender_female:youth_activity_rcProgram Staff Led 0.42459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Predicting relevance with competence interactions
relevance_model_competence <- lmer(relevance ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
overall_pre_competence_beliefs*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(relevance_model_competence)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: relevance ~ gender_female + overall_pre_competence_beliefs +
## Community_Space_Content + youth_activity_rc + overall_pre_competence_beliefs *
## youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5384.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0007 -0.5180 0.0261 0.5655 4.0816
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007749 0.08803
## participant_ID (Intercept) 0.475006 0.68921
## program_ID (Intercept) 0.010388 0.10192
## Residual 0.409952 0.64028
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.39506
## gender_female -0.24746
## overall_pre_competence_beliefs 0.06434
## Community_Space_Content 0.14624
## youth_activity_rcBasic Skills Activity 0.30623
## youth_activity_rcCreating Product -0.07031
## youth_activity_rcField Trip Speaker 0.31808
## youth_activity_rcLab Activity 0.37203
## youth_activity_rcProgram Staff Led 0.23394
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity -0.06628
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.08395
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker -0.04934
## overall_pre_competence_beliefs:youth_activity_rcLab Activity -0.10098
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led -0.03735
## Std. Error
## (Intercept) 0.23177
## gender_female 0.10854
## overall_pre_competence_beliefs 0.07000
## Community_Space_Content 0.04687
## youth_activity_rcBasic Skills Activity 0.15519
## youth_activity_rcCreating Product 0.15729
## youth_activity_rcField Trip Speaker 0.30865
## youth_activity_rcLab Activity 0.33469
## youth_activity_rcProgram Staff Led 0.17669
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.04747
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.04821
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 0.08891
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.09862
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.05557
## df
## (Intercept) 119.50000
## gender_female 177.10000
## overall_pre_competence_beliefs 160.30000
## Community_Space_Content 186.30000
## youth_activity_rcBasic Skills Activity 1549.40000
## youth_activity_rcCreating Product 2032.00000
## youth_activity_rcField Trip Speaker 937.50000
## youth_activity_rcLab Activity 1503.30000
## youth_activity_rcProgram Staff Led 1613.10000
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 2045.80000
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 2267.80000
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 1343.80000
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 1633.80000
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 1972.90000
## t value
## (Intercept) 10.334
## gender_female -2.280
## overall_pre_competence_beliefs 0.919
## Community_Space_Content 3.120
## youth_activity_rcBasic Skills Activity 1.973
## youth_activity_rcCreating Product -0.447
## youth_activity_rcField Trip Speaker 1.031
## youth_activity_rcLab Activity 1.112
## youth_activity_rcProgram Staff Led 1.324
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity -1.396
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 1.741
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker -0.555
## overall_pre_competence_beliefs:youth_activity_rcLab Activity -1.024
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led -0.672
## Pr(>|t|)
## (Intercept) <2e-16
## gender_female 0.0238
## overall_pre_competence_beliefs 0.3594
## Community_Space_Content 0.0021
## youth_activity_rcBasic Skills Activity 0.0486
## youth_activity_rcCreating Product 0.6549
## youth_activity_rcField Trip Speaker 0.3030
## youth_activity_rcLab Activity 0.2665
## youth_activity_rcProgram Staff Led 0.1857
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.1628
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.0818
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 0.5791
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.3060
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.5016
##
## (Intercept) ***
## gender_female *
## overall_pre_competence_beliefs
## Community_Space_Content **
## youth_activity_rcBasic Skills Activity *
## youth_activity_rcCreating Product
## youth_activity_rcField Trip Speaker
## youth_activity_rcLab Activity
## youth_activity_rcProgram Staff Led
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity
## overall_pre_competence_beliefs:youth_activity_rcCreating Product .
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker
## overall_pre_competence_beliefs:youth_activity_rcLab Activity
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Learning models
Predicting learning
learning_model <- lmer(learning ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(learning_model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ gender_female + overall_pre_competence_beliefs + Community_Space_Content +
## youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6604.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1852 -0.5625 0.1224 0.5818 2.7914
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.084e-02 1.041e-01
## participant_ID (Intercept) 3.993e-01 6.319e-01
## program_ID (Intercept) 1.133e-14 1.064e-07
## Residual 7.115e-01 8.435e-01
## Number of obs: 2473, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.48735 0.20357 185.09000
## gender_female -0.07685 0.10225 177.61000
## overall_pre_competence_beliefs 0.07299 0.06109 182.55000
## Community_Space_Content 0.08146 0.05930 160.88000
## youth_activity_rcBasic Skills Activity 0.18533 0.05551 167.20000
## youth_activity_rcCreating Product 0.10821 0.05544 203.74000
## youth_activity_rcField Trip Speaker 0.01733 0.10710 100.16000
## youth_activity_rcLab Activity 0.16330 0.10143 121.53000
## youth_activity_rcProgram Staff Led 0.06118 0.06235 159.90000
## t value Pr(>|t|)
## (Intercept) 12.218 < 2e-16 ***
## gender_female -0.752 0.45327
## overall_pre_competence_beliefs 1.195 0.23371
## Community_Space_Content 1.374 0.17144
## youth_activity_rcBasic Skills Activity 3.339 0.00104 **
## youth_activity_rcCreating Product 1.952 0.05231 .
## youth_activity_rcField Trip Speaker 0.162 0.87178
## youth_activity_rcLab Activity 1.610 0.11001
## youth_activity_rcProgram Staff Led 0.981 0.32796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f ovr___ Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.228
## ovrll_pr_c_ -0.922 -0.030
## Cmmnty_Sp_C -0.041 0.016 0.008
## yth_ctv_BSA -0.070 -0.010 -0.028 -0.257
## yth_ctvt_CP -0.082 0.008 -0.025 0.049 0.362
## yth_ctv_FTS -0.008 0.010 -0.046 -0.406 0.333 0.189
## yth_ctvt_LA -0.017 -0.016 -0.034 -0.228 0.263 0.190 0.226
## yth_ctv_PSL -0.089 -0.024 0.002 -0.044 0.413 0.309 0.194 0.199Predicting learning with gender interactions
learning_model_female <- lmer(learning ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
gender_female*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(learning_model_female)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ gender_female + overall_pre_competence_beliefs + Community_Space_Content +
## youth_activity_rc + gender_female * youth_activity_rc + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6610.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1461 -0.5641 0.1169 0.5737 2.8443
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009958 0.09979
## participant_ID (Intercept) 0.402090 0.63411
## program_ID (Intercept) 0.000000 0.00000
## Residual 0.711799 0.84368
## Number of obs: 2473, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.530e+00 2.054e-01
## gender_female -1.784e-01 1.152e-01
## overall_pre_competence_beliefs 7.694e-02 6.129e-02
## Community_Space_Content 7.430e-02 5.901e-02
## youth_activity_rcBasic Skills Activity 7.178e-02 7.652e-02
## youth_activity_rcCreating Product 3.822e-02 7.738e-02
## youth_activity_rcField Trip Speaker -3.035e-02 1.292e-01
## youth_activity_rcLab Activity 1.724e-01 1.495e-01
## youth_activity_rcProgram Staff Led -2.973e-02 9.182e-02
## gender_female:youth_activity_rcBasic Skills Activity 2.154e-01 9.991e-02
## gender_female:youth_activity_rcCreating Product 1.260e-01 1.038e-01
## gender_female:youth_activity_rcField Trip Speaker 9.710e-02 1.783e-01
## gender_female:youth_activity_rcLab Activity -4.078e-03 1.849e-01
## gender_female:youth_activity_rcProgram Staff Led 1.664e-01 1.169e-01
## df t value
## (Intercept) 1.892e+02 12.315
## gender_female 2.799e+02 -1.548
## overall_pre_competence_beliefs 1.824e+02 1.255
## Community_Space_Content 1.607e+02 1.259
## youth_activity_rcBasic Skills Activity 5.005e+02 0.938
## youth_activity_rcCreating Product 6.342e+02 0.494
## youth_activity_rcField Trip Speaker 2.072e+02 -0.235
## youth_activity_rcLab Activity 4.326e+02 1.153
## youth_activity_rcProgram Staff Led 6.057e+02 -0.324
## gender_female:youth_activity_rcBasic Skills Activity 2.354e+03 2.156
## gender_female:youth_activity_rcCreating Product 2.295e+03 1.214
## gender_female:youth_activity_rcField Trip Speaker 2.337e+03 0.545
## gender_female:youth_activity_rcLab Activity 2.043e+03 -0.022
## gender_female:youth_activity_rcProgram Staff Led 2.364e+03 1.423
## Pr(>|t|)
## (Intercept) <2e-16 ***
## gender_female 0.1227
## overall_pre_competence_beliefs 0.2110
## Community_Space_Content 0.2099
## youth_activity_rcBasic Skills Activity 0.3487
## youth_activity_rcCreating Product 0.6215
## youth_activity_rcField Trip Speaker 0.8145
## youth_activity_rcLab Activity 0.2496
## youth_activity_rcProgram Staff Led 0.7462
## gender_female:youth_activity_rcBasic Skills Activity 0.0312 *
## gender_female:youth_activity_rcCreating Product 0.2248
## gender_female:youth_activity_rcField Trip Speaker 0.5860
## gender_female:youth_activity_rcLab Activity 0.9824
## gender_female:youth_activity_rcProgram Staff Led 0.1547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Predicting learning with competence interactions
learning_model_competence <- lmer(learning ~
gender_female +
overall_pre_competence_beliefs +
Community_Space_Content +
youth_activity_rc +
overall_pre_competence_beliefs*youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(learning_model_competence)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## learning ~ gender_female + overall_pre_competence_beliefs + Community_Space_Content +
## youth_activity_rc + overall_pre_competence_beliefs * youth_activity_rc +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6617.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1585 -0.5640 0.1210 0.5813 2.7626
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01195 0.1093
## participant_ID (Intercept) 0.39893 0.6316
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.71129 0.8434
## Number of obs: 2473, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.490e+00
## gender_female -7.711e-02
## overall_pre_competence_beliefs 7.206e-02
## Community_Space_Content 8.873e-02
## youth_activity_rcBasic Skills Activity 2.541e-01
## youth_activity_rcCreating Product 7.938e-02
## youth_activity_rcField Trip Speaker -3.983e-01
## youth_activity_rcLab Activity 6.318e-01
## youth_activity_rcProgram Staff Led -1.318e-02
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity -2.233e-02
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 9.215e-03
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 1.199e-01
## overall_pre_competence_beliefs:youth_activity_rcLab Activity -1.400e-01
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 2.470e-02
## Std. Error
## (Intercept) 2.226e-01
## gender_female 1.023e-01
## overall_pre_competence_beliefs 6.815e-02
## Community_Space_Content 6.026e-02
## youth_activity_rcBasic Skills Activity 2.026e-01
## youth_activity_rcCreating Product 2.057e-01
## youth_activity_rcField Trip Speaker 4.017e-01
## youth_activity_rcLab Activity 4.371e-01
## youth_activity_rcProgram Staff Led 2.308e-01
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 6.209e-02
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 6.313e-02
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 1.159e-01
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 1.288e-01
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 7.271e-02
## df
## (Intercept) 2.619e+02
## gender_female 1.779e+02
## overall_pre_competence_beliefs 2.790e+02
## Community_Space_Content 1.674e+02
## youth_activity_rcBasic Skills Activity 1.501e+03
## youth_activity_rcCreating Product 2.023e+03
## youth_activity_rcField Trip Speaker 8.477e+02
## youth_activity_rcLab Activity 1.425e+03
## youth_activity_rcProgram Staff Led 1.567e+03
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 2.032e+03
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 2.289e+03
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 1.235e+03
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 1.551e+03
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 1.945e+03
## t value
## (Intercept) 11.185
## gender_female -0.754
## overall_pre_competence_beliefs 1.057
## Community_Space_Content 1.472
## youth_activity_rcBasic Skills Activity 1.254
## youth_activity_rcCreating Product 0.386
## youth_activity_rcField Trip Speaker -0.992
## youth_activity_rcLab Activity 1.445
## youth_activity_rcProgram Staff Led -0.057
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity -0.360
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.146
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 1.035
## overall_pre_competence_beliefs:youth_activity_rcLab Activity -1.087
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.340
## Pr(>|t|)
## (Intercept) <2e-16
## gender_female 0.452
## overall_pre_competence_beliefs 0.291
## Community_Space_Content 0.143
## youth_activity_rcBasic Skills Activity 0.210
## youth_activity_rcCreating Product 0.700
## youth_activity_rcField Trip Speaker 0.322
## youth_activity_rcLab Activity 0.149
## youth_activity_rcProgram Staff Led 0.954
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity 0.719
## overall_pre_competence_beliefs:youth_activity_rcCreating Product 0.884
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker 0.301
## overall_pre_competence_beliefs:youth_activity_rcLab Activity 0.277
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led 0.734
##
## (Intercept) ***
## gender_female
## overall_pre_competence_beliefs
## Community_Space_Content
## youth_activity_rcBasic Skills Activity
## youth_activity_rcCreating Product
## youth_activity_rcField Trip Speaker
## youth_activity_rcLab Activity
## youth_activity_rcProgram Staff Led
## overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity
## overall_pre_competence_beliefs:youth_activity_rcCreating Product
## overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker
## overall_pre_competence_beliefs:youth_activity_rcLab Activity
## overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1March 22 Analysis - Final Models and descriptives
Correlations
Correlation Function
#correlation table function
corstarsl <- function(x) {
require(Hmisc)
x <- as.matrix(x)
R <- rcorr(x)$r
p <- rcorr(x)$P
## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
## build a new matrix that includes the correlations with their apropriate stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)
## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew)
}Correlations
Stemie_Corr <- select(df, overall_engagement, challenge, relevance, learning, overall_pre_competence_beliefs)
corstarsl(Stemie_Corr)## Warning: package 'Hmisc' was built under R version 3.4.3## overall_engagement challenge relevance
## overall_engagement
## challenge 0.32***
## relevance 0.68*** 0.39***
## learning 0.69*** 0.30*** 0.65***
## overall_pre_competence_beliefs 0.08*** -0.12*** 0.03
## learning
## overall_engagement
## challenge
## relevance
## learning
## overall_pre_competence_beliefs 0.09***Descriptives
psych::describe(df$overall_engagement)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2970 2.87 0.87 3 2.94 1.11 1 4 3 -0.42 -0.65
## se
## X1 0.02psych::describe(df$challenge)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2970 2.27 1.12 2 2.21 1.48 1 4 3 0.27 -1.31
## se
## X1 0.02psych::describe(df$relevance)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2970 2.58 0.96 2.67 2.6 0.99 1 4 3 -0.09 -1.02
## se
## X1 0.02psych::describe(df$learning)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2969 2.77 1.06 3 2.84 1.48 1 4 3 -0.35 -1.12
## se
## X1 0.02psych::describe(df$overall_pre_competence_beliefs)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2730 3.13 0.81 3.33 3.23 0.99 1 4 3 -0.85 0.04
## se
## X1 0.02Group mean centering
library(dplyr)
df <- df %>%
group_by(participant_ID) %>%
mutate(challenge_group = challenge - mean(challenge),
relevance_group = relevance - mean(relevance),
learning_group = learning - mean(learning))Predicting engagement with gender interactions
engagement_model_gender_final <- lmer(overall_engagement ~
gender_female +
challenge_group +
relevance_group +
learning_group +
scale(overall_pre_competence_beliefs, scale=FALSE) +
Community_Space_Content +
gender_female*challenge_group +
gender_female*relevance_group +
gender_female*learning_group +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model_gender_final)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## overall_engagement ~ gender_female + challenge_group + relevance_group +
## learning_group + scale(overall_pre_competence_beliefs, scale = FALSE) +
## Community_Space_Content + gender_female * challenge_group +
## gender_female * relevance_group + gender_female * learning_group +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 4114
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8978 -0.5366 0.0339 0.5229 3.9133
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.017166 0.1310
## participant_ID (Intercept) 0.347724 0.5897
## program_ID (Intercept) 0.008427 0.0918
## Residual 0.220988 0.4701
## Number of obs: 2555, groups:
## beep_ID_new, 237; participant_ID, 179; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.90276 0.07356
## gender_female -0.08075 0.09223
## challenge_group 0.05978 0.01656
## relevance_group 0.39133 0.02528
## learning_group 0.27142 0.02028
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.08389 0.05623
## Community_Space_Content -0.06722 0.03655
## gender_female:challenge_group -0.04551 0.02360
## gender_female:relevance_group -0.08697 0.03443
## gender_female:learning_group 0.01622 0.02661
## df t value
## (Intercept) 19.00000 39.462
## gender_female 175.40000 -0.876
## challenge_group 2354.20000 3.611
## relevance_group 2338.50000 15.481
## learning_group 2338.60000 13.386
## scale(overall_pre_competence_beliefs, scale = FALSE) 126.60000 1.492
## Community_Space_Content 216.10000 -1.839
## gender_female:challenge_group 2339.70000 -1.928
## gender_female:relevance_group 2334.60000 -2.526
## gender_female:learning_group 2336.40000 0.610
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.382468
## challenge_group 0.000311 ***
## relevance_group < 2e-16 ***
## learning_group < 2e-16 ***
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.138185
## Community_Space_Content 0.067296 .
## gender_female:challenge_group 0.053929 .
## gender_female:relevance_group 0.011597 *
## gender_female:learning_group 0.542219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f chlln_ rlvnc_ lrnng_ s(_s=F Cm_S_C gndr_fml:c_
## gender_feml -0.641
## challng_grp -0.005 0.002
## relevnc_grp 0.001 0.000 -0.085
## learnng_grp 0.000 0.001 -0.102 -0.462
## s(___,s=FAL 0.037 -0.028 0.000 -0.002 0.001
## Cmmnty_Sp_C -0.110 0.007 0.012 -0.009 -0.012 -0.010
## gndr_fml:c_ 0.007 -0.005 -0.689 0.059 0.075 0.002 -0.035
## gndr_fml:r_ 0.003 0.000 0.060 -0.730 0.339 0.000 -0.028 -0.110
## gndr_fml:l_ 0.001 -0.003 0.084 0.348 -0.760 0.000 0.006 -0.102
## gndr_fml:r_
## gender_feml
## challng_grp
## relevnc_grp
## learnng_grp
## s(___,s=FAL
## Cmmnty_Sp_C
## gndr_fml:c_
## gndr_fml:r_
## gndr_fml:l_ -0.450rand(engagement_model_gender_final)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.511 1 0.5
## participant_ID 1715.421 1 <2e-16 ***
## beep_ID_new 52.301 1 5e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rengagement<-r2glmm::r2beta(engagement_model_gender_final, method = "nsj")
Rengagement## Effect Rsq upper.CL
## 1 Model 0.212 0.241
## 4 relevance_group 0.035 0.051
## 5 learning_group 0.027 0.040
## 6 scale(overall_pre_competence_beliefs, scale = FALSE) 0.008 0.016
## 2 gender_female 0.003 0.008
## 3 challenge_group 0.002 0.007
## 7 Community_Space_Content 0.001 0.005
## 9 gender_female:relevance_group 0.001 0.005
## 8 gender_female:challenge_group 0.001 0.004
## 10 gender_female:learning_group 0.000 0.002
## lower.CL
## 1 0.187
## 4 0.023
## 5 0.016
## 6 0.002
## 2 0.000
## 3 0.000
## 7 0.000
## 9 0.000
## 8 0.000
## 10 0.000sjPlot::sjp.int(engagement_model_gender_final, type = "eff")
Predicting challenge with activities
challenge_model_final <- lmer(challenge ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(challenge_model_final)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## challenge ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + Community_Space_Content + youth_activity_rc +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6561.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8804 -0.6343 -0.0339 0.5665 3.3306
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06010 0.2452
## participant_ID (Intercept) 0.46579 0.6825
## program_ID (Intercept) 0.01458 0.1207
## Residual 0.65855 0.8115
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.32619 0.09745
## gender_female -0.24095 0.11062
## scale(overall_pre_competence_beliefs, scale = FALSE) -0.12249 0.06811
## Community_Space_Content 0.20156 0.07608
## youth_activity_rcBasic Skills Activity 0.03632 0.07055
## youth_activity_rcCreating Product 0.32834 0.06898
## youth_activity_rcField Trip Speaker -0.27165 0.14565
## youth_activity_rcLab Activity 0.07836 0.13397
## youth_activity_rcProgram Staff Led -0.14343 0.07986
## df t value
## (Intercept) 22.19000 23.871
## gender_female 174.27000 -2.178
## scale(overall_pre_competence_beliefs, scale = FALSE) 133.55000 -1.798
## Community_Space_Content 184.93000 2.649
## youth_activity_rcBasic Skills Activity 194.87000 0.515
## youth_activity_rcCreating Product 210.82000 4.760
## youth_activity_rcField Trip Speaker 133.87000 -1.865
## youth_activity_rcLab Activity 152.93000 0.585
## youth_activity_rcProgram Staff Led 180.60000 -1.796
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.03075 *
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.07439 .
## Community_Space_Content 0.00877 **
## youth_activity_rcBasic Skills Activity 0.60730
## youth_activity_rcCreating Product 3.6e-06 ***
## youth_activity_rcField Trip Speaker 0.06436 .
## youth_activity_rcLab Activity 0.55948
## youth_activity_rcProgram Staff Led 0.07418 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.583
## s(___,s=FAL 0.044 -0.036
## Cmmnty_Sp_C -0.078 0.011 0.009
## yth_ctv_BSA -0.257 -0.008 -0.023 -0.272
## yth_ctvt_CP -0.280 0.008 -0.016 0.033 0.375
## yth_ctv_FTS -0.127 0.008 -0.033 -0.391 0.320 0.189
## yth_ctvt_LA -0.128 -0.011 -0.022 -0.220 0.255 0.188 0.205
## yth_ctv_PSL -0.235 -0.020 0.004 -0.054 0.410 0.311 0.187 0.194rand(challenge_model_final)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.572 1 0.4
## participant_ID 810.059 1 <2e-16 ***
## beep_ID_new 50.220 1 1e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rchallenge<-r2glmm::r2beta(challenge_model_final, method = "nsj")
Rchallenge## Effect Rsq upper.CL
## 1 Model 0.043 0.063
## 2 gender_female 0.012 0.022
## 6 youth_activity_rcCreating Product 0.011 0.020
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.008 0.016
## 4 Community_Space_Content 0.004 0.011
## 7 youth_activity_rcField Trip Speaker 0.002 0.008
## 9 youth_activity_rcProgram Staff Led 0.002 0.007
## 8 youth_activity_rcLab Activity 0.000 0.003
## 5 youth_activity_rcBasic Skills Activity 0.000 0.003
## lower.CL
## 1 0.031
## 2 0.005
## 6 0.004
## 3 0.002
## 4 0.001
## 7 0.000
## 9 0.000
## 8 0.000
## 5 0.000Predicting relevance with activities
relevance_model_final <- lmer(relevance ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(relevance_model_final)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## relevance ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + Community_Space_Content + youth_activity_rc +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5375.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8799 -0.5198 0.0240 0.5733 4.0732
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008112 0.09006
## participant_ID (Intercept) 0.478767 0.69193
## program_ID (Intercept) 0.010293 0.10145
## Residual 0.410223 0.64049
## Number of obs: 2474, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.59451 0.08803
## gender_female -0.24514 0.10890
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.05636 0.06641
## Community_Space_Content 0.14332 0.04670
## youth_activity_rcBasic Skills Activity 0.09519 0.04348
## youth_activity_rcCreating Product 0.20051 0.04315
## youth_activity_rcField Trip Speaker 0.15841 0.08455
## youth_activity_rcLab Activity 0.04009 0.07951
## youth_activity_rcProgram Staff Led 0.11707 0.04878
## df t value
## (Intercept) 22.97000 29.474
## gender_female 176.89000 -2.251
## scale(overall_pre_competence_beliefs, scale = FALSE) 124.14000 0.849
## Community_Space_Content 181.27000 3.069
## youth_activity_rcBasic Skills Activity 189.58000 2.189
## youth_activity_rcCreating Product 230.25000 4.647
## youth_activity_rcField Trip Speaker 115.88000 1.874
## youth_activity_rcLab Activity 140.29000 0.504
## youth_activity_rcProgram Staff Led 180.82000 2.400
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.02561 *
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.39769
## Community_Space_Content 0.00248 **
## youth_activity_rcBasic Skills Activity 0.02980 *
## youth_activity_rcCreating Product 5.67e-06 ***
## youth_activity_rcField Trip Speaker 0.06351 .
## youth_activity_rcLab Activity 0.61492
## youth_activity_rcProgram Staff Led 0.01741 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.634
## s(___,s=FAL 0.040 -0.030
## Cmmnty_Sp_C -0.060 0.011 0.006
## yth_ctv_BSA -0.172 -0.006 -0.017 -0.267
## yth_ctvt_CP -0.188 0.005 -0.014 0.049 0.361
## yth_ctv_FTS -0.088 0.003 -0.025 -0.406 0.337 0.189
## yth_ctvt_LA -0.088 -0.009 -0.018 -0.226 0.262 0.188 0.225
## yth_ctv_PSL -0.157 -0.015 0.002 -0.052 0.419 0.306 0.196 0.200rand(relevance_model_final)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.448 1 0.50
## participant_ID 1359.529 1 <2e-16 ***
## beep_ID_new 4.263 1 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rrelevance<-r2glmm::r2beta(relevance_model_final, method = "nsj")
Rrelevance## Effect Rsq upper.CL
## 1 Model 0.029 0.046
## 2 gender_female 0.016 0.027
## 6 youth_activity_rcCreating Product 0.005 0.013
## 4 Community_Space_Content 0.003 0.008
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.002 0.007
## 9 youth_activity_rcProgram Staff Led 0.002 0.006
## 5 youth_activity_rcBasic Skills Activity 0.001 0.006
## 7 youth_activity_rcField Trip Speaker 0.001 0.005
## 8 youth_activity_rcLab Activity 0.000 0.002
## lower.CL
## 1 0.020
## 2 0.008
## 6 0.001
## 4 0.000
## 3 0.000
## 9 0.000
## 5 0.000
## 7 0.000
## 8 0.000Predicting learning with activities
learning_model_final <- lmer(learning ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
Community_Space_Content +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(learning_model_final)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: learning ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + Community_Space_Content + youth_activity_rc +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6604.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1852 -0.5625 0.1224 0.5818 2.7914
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01084 0.1041
## participant_ID (Intercept) 0.39930 0.6319
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.71153 0.8435
## Number of obs: 2473, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.71559 0.07885
## gender_female -0.07685 0.10225
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.07299 0.06109
## Community_Space_Content 0.08146 0.05930
## youth_activity_rcBasic Skills Activity 0.18533 0.05551
## youth_activity_rcCreating Product 0.10821 0.05544
## youth_activity_rcField Trip Speaker 0.01733 0.10710
## youth_activity_rcLab Activity 0.16330 0.10143
## youth_activity_rcProgram Staff Led 0.06118 0.06235
## df t value
## (Intercept) 228.02000 34.439
## gender_female 177.61000 -0.752
## scale(overall_pre_competence_beliefs, scale = FALSE) 182.55000 1.195
## Community_Space_Content 160.88000 1.374
## youth_activity_rcBasic Skills Activity 167.20000 3.339
## youth_activity_rcCreating Product 203.74000 1.952
## youth_activity_rcField Trip Speaker 100.16000 0.162
## youth_activity_rcLab Activity 121.53000 1.610
## youth_activity_rcProgram Staff Led 159.90000 0.981
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.45327
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.23371
## Community_Space_Content 0.17144
## youth_activity_rcBasic Skills Activity 0.00104 **
## youth_activity_rcCreating Product 0.05231 .
## youth_activity_rcField Trip Speaker 0.87178
## youth_activity_rcLab Activity 0.11001
## youth_activity_rcProgram Staff Led 0.32796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F Cm_S_C y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.662
## s(___,s=FAL 0.042 -0.030
## Cmmnty_Sp_C -0.085 0.016 0.008
## yth_ctv_BSA -0.249 -0.010 -0.028 -0.257
## yth_ctvt_CP -0.271 0.008 -0.025 0.049 0.362
## yth_ctv_FTS -0.133 0.010 -0.046 -0.406 0.333 0.189
## yth_ctvt_LA -0.126 -0.016 -0.034 -0.228 0.263 0.190 0.226
## yth_ctv_PSL -0.225 -0.024 0.002 -0.044 0.413 0.309 0.194 0.199rand(learning_model_final)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.00 1 1.0
## participant_ID 681.42 1 <2e-16 ***
## beep_ID_new 2.27 1 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rlearning<-r2glmm::r2beta(learning_model_final, method = "nsj")
Rlearning## Effect Rsq upper.CL
## 1 Model 0.011 0.024
## 5 youth_activity_rcBasic Skills Activity 0.004 0.010
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.003 0.009
## 2 gender_female 0.001 0.006
## 6 youth_activity_rcCreating Product 0.001 0.006
## 8 youth_activity_rcLab Activity 0.001 0.005
## 4 Community_Space_Content 0.001 0.004
## 9 youth_activity_rcProgram Staff Led 0.000 0.003
## 7 youth_activity_rcField Trip Speaker 0.000 0.002
## lower.CL
## 1 0.007
## 5 0.001
## 3 0.000
## 2 0.000
## 6 0.000
## 8 0.000
## 4 0.000
## 9 0.000
## 7 0.000Reliability for engagement
#engagement_reliability <- select(df, hard_working, concentrating, enjoy, interest)
#cronbach(engagement_reliability)Reliability for relevance
#relevance_reliability <- select(df, use_outside, future_goals, important)
#cronbach(relevance_reliability)Manova of challenge, relevance, and learning by activity. Post hocs included.
fit<-manova(cbind(df$challenge, df$relevance, df$learning) ~ df$youth_activity_rc, data = df)
summary(fit, test="Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## df$youth_activity_rc 5 0.030931 5.8568 15 8433 2.94e-12 ***
## Residuals 2811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.aov(fit)## Response 1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$youth_activity_rc 5 65.2 13.0424 10.648 3.747e-10 ***
## Residuals 2811 3443.0 1.2248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$youth_activity_rc 5 23.12 4.6233 5.0325 0.0001354 ***
## Residuals 2811 2582.44 0.9187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$youth_activity_rc 5 17.3 3.4600 3.077 0.008963 **
## Residuals 2811 3160.9 1.1245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 153 observations deleted due to missingnessTukeyHSD(aov(df$challenge ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$challenge ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr
## Basic Skills Activity-Other 0.02110436 -0.1415982
## Creating Product-Other 0.31258581 0.1443279
## Field Trip Speaker-Other -0.19033816 -0.4812618
## Lab Activity-Other 0.04214047 -0.2536218
## Program Staff Led-Other -0.13619106 -0.3211765
## Creating Product-Basic Skills Activity 0.29148145 0.1094862
## Field Trip Speaker-Basic Skills Activity -0.21144253 -0.5105214
## Lab Activity-Basic Skills Activity 0.02103611 -0.2827514
## Program Staff Led-Basic Skills Activity -0.15729542 -0.3548585
## Field Trip Speaker-Creating Product -0.50292398 -0.8050610
## Lab Activity-Creating Product -0.27044534 -0.5772441
## Program Staff Led-Creating Product -0.44877687 -0.6509396
## Lab Activity-Field Trip Speaker 0.23247863 -0.1554019
## Program Staff Led-Field Trip Speaker 0.05414710 -0.2576150
## Program Staff Led-Lab Activity -0.17833153 -0.4946136
## upr p adj
## Basic Skills Activity-Other 0.18380690 0.9991000
## Creating Product-Other 0.48084370 0.0000019
## Field Trip Speaker-Other 0.10058552 0.4236949
## Lab Activity-Other 0.33790269 0.9985829
## Program Staff Led-Other 0.04879434 0.2877274
## Creating Product-Basic Skills Activity 0.47347674 0.0000755
## Field Trip Speaker-Basic Skills Activity 0.08763632 0.3332668
## Lab Activity-Basic Skills Activity 0.32482363 0.9999588
## Program Staff Led-Basic Skills Activity 0.04026765 0.2065564
## Field Trip Speaker-Creating Product -0.20078699 0.0000320
## Lab Activity-Creating Product 0.03635339 0.1203417
## Program Staff Led-Creating Product -0.24661415 0.0000000
## Lab Activity-Field Trip Speaker 0.62035920 0.5257331
## Program Staff Led-Field Trip Speaker 0.36590926 0.9963503
## Program Staff Led-Lab Activity 0.13795054 0.5933632pairwise.t.test(df$challenge, df$youth_activity_rc, p.adj = "bonf")##
## Pairwise comparisons using t tests with pooled SD
##
## data: df$challenge and df$youth_activity_rc
##
## Other Basic Skills Activity Creating Product
## Basic Skills Activity 1.00 - -
## Creating Product 1.9e-06 7.7e-05 -
## Field Trip Speaker 0.93 0.66 3.3e-05
## Lab Activity 1.00 1.00 0.18
## Program Staff Led 0.54 0.35 4.3e-09
## Field Trip Speaker Lab Activity
## Basic Skills Activity - -
## Creating Product - -
## Field Trip Speaker - -
## Lab Activity 1.00 -
## Program Staff Led 1.00 1.00
##
## P value adjustment method: bonferroniTukeyHSD(aov(df$relevance ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$relevance ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr
## Basic Skills Activity-Other 0.06784634 -0.07301535
## Creating Product-Other 0.24106280 0.09539150
## Field Trip Speaker-Other 0.12686527 -0.12500543
## Lab Activity-Other -0.03885173 -0.29491145
## Program Staff Led-Other 0.10193917 -0.05821418
## Creating Product-Basic Skills Activity 0.17321647 0.01565185
## Field Trip Speaker-Basic Skills Activity 0.05901894 -0.19991219
## Lab Activity-Basic Skills Activity -0.10669806 -0.36970579
## Program Staff Led-Basic Skills Activity 0.03409284 -0.13694978
## Field Trip Speaker-Creating Product -0.11419753 -0.37577628
## Lab Activity-Creating Product -0.27991453 -0.54552924
## Program Staff Led-Creating Product -0.13912363 -0.31414846
## Lab Activity-Field Trip Speaker -0.16571700 -0.50152929
## Program Staff Led-Field Trip Speaker -0.02492610 -0.29483795
## Program Staff Led-Lab Activity 0.14079090 -0.13303412
## upr p adj
## Basic Skills Activity-Other 0.20870802 0.7430174
## Creating Product-Other 0.38673411 0.0000366
## Field Trip Speaker-Other 0.37873597 0.7047328
## Lab Activity-Other 0.21720800 0.9980828
## Program Staff Led-Other 0.26209252 0.4560414
## Creating Product-Basic Skills Activity 0.33078109 0.0214667
## Field Trip Speaker-Basic Skills Activity 0.31795006 0.9870965
## Lab Activity-Basic Skills Activity 0.15630966 0.8571654
## Program Staff Led-Basic Skills Activity 0.20513545 0.9930359
## Field Trip Speaker-Creating Product 0.14738122 0.8145751
## Lab Activity-Creating Product -0.01429982 0.0320020
## Program Staff Led-Creating Product 0.03590120 0.2081337
## Lab Activity-Field Trip Speaker 0.17009529 0.7226974
## Program Staff Led-Field Trip Speaker 0.24498576 0.9998291
## Program Staff Led-Lab Activity 0.41461592 0.6859489pairwise.t.test(df$relevance, df$youth_activity_rc, p.adj = "bonf")##
## Pairwise comparisons using t tests with pooled SD
##
## data: df$relevance and df$youth_activity_rc
##
## Other Basic Skills Activity Creating Product
## Basic Skills Activity 1.000 - -
## Creating Product 3.7e-05 0.026 -
## Field Trip Speaker 1.000 1.000 1.000
## Lab Activity 1.000 1.000 0.040
## Program Staff Led 1.000 1.000 0.352
## Field Trip Speaker Lab Activity
## Basic Skills Activity - -
## Creating Product - -
## Field Trip Speaker - -
## Lab Activity 1.000 -
## Program Staff Led 1.000 1.000
##
## P value adjustment method: bonferroniTukeyHSD(aov(df$learning ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$learning ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr
## Basic Skills Activity-Other 0.169703433 0.01383560
## Creating Product-Other 0.171775426 0.01049817
## Field Trip Speaker-Other -0.026610306 -0.30531306
## Lab Activity-Other 0.121822742 -0.16151530
## Program Staff Led-Other 0.081736069 -0.09547859
## Creating Product-Basic Skills Activity 0.002071993 -0.17235899
## Field Trip Speaker-Basic Skills Activity -0.196313739 -0.48282907
## Lab Activity-Basic Skills Activity -0.047880691 -0.33890691
## Program Staff Led-Basic Skills Activity -0.087967365 -0.27723133
## Field Trip Speaker-Creating Product -0.198385732 -0.48787944
## Lab Activity-Creating Product -0.049952684 -0.34391158
## Program Staff Led-Creating Product -0.090039357 -0.28378254
## Lab Activity-Field Trip Speaker 0.148433048 -0.22315368
## Program Staff Led-Field Trip Speaker 0.108346375 -0.19031948
## Program Staff Led-Lab Activity -0.040086674 -0.34308257
## upr p adj
## Basic Skills Activity-Other 0.32557127 0.0235955
## Creating Product-Other 0.33305268 0.0290459
## Field Trip Speaker-Other 0.25209244 0.9997986
## Lab Activity-Other 0.40516078 0.8241730
## Program Staff Led-Other 0.25895073 0.7766871
## Creating Product-Basic Skills Activity 0.17650298 1.0000000
## Field Trip Speaker-Basic Skills Activity 0.09020160 0.3694812
## Lab Activity-Basic Skills Activity 0.24314553 0.9971775
## Program Staff Led-Basic Skills Activity 0.10129660 0.7709579
## Field Trip Speaker-Creating Product 0.09110798 0.3692974
## Lab Activity-Creating Product 0.24400621 0.9967088
## Program Staff Led-Creating Product 0.10370383 0.7710404
## Lab Activity-Field Trip Speaker 0.52001978 0.8650428
## Program Staff Led-Field Trip Speaker 0.40701223 0.9063684
## Program Staff Led-Lab Activity 0.26290922 0.9990095pairwise.t.test(df$learning, df$youth_activity_rc, p.adj = "bonf")##
## Pairwise comparisons using t tests with pooled SD
##
## data: df$learning and df$youth_activity_rc
##
## Other Basic Skills Activity Creating Product
## Basic Skills Activity 0.029 - -
## Creating Product 0.036 1.000 -
## Field Trip Speaker 1.000 0.762 0.762
## Lab Activity 1.000 1.000 1.000
## Program Staff Led 1.000 1.000 1.000
## Field Trip Speaker Lab Activity
## Basic Skills Activity - -
## Creating Product - -
## Field Trip Speaker - -
## Lab Activity 1.000 -
## Program Staff Led 1.000 1.000
##
## P value adjustment method: bonferroniMarch 30 Analysis - No Class Content Variable
Predicting engagement with gender interactions
engagement_model_gender_final_2 <- lmer(overall_engagement ~
gender_female +
challenge_group +
relevance_group +
learning_group +
scale(overall_pre_competence_beliefs, scale=FALSE) +
gender_female*challenge_group +
gender_female*relevance_group +
gender_female*learning_group +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model_gender_final_2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## overall_engagement ~ gender_female + challenge_group + relevance_group +
## learning_group + scale(overall_pre_competence_beliefs, scale = FALSE) +
## gender_female * challenge_group + gender_female * relevance_group +
## gender_female * learning_group + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 4330.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9057 -0.5411 0.0379 0.5222 4.0440
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.017397 0.13190
## participant_ID (Intercept) 0.348175 0.59006
## program_ID (Intercept) 0.005726 0.07567
## Residual 0.220266 0.46932
## Number of obs: 2710, groups:
## beep_ID_new, 248; participant_ID, 179; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.88661 0.07070
## gender_female -0.07706 0.09169
## challenge_group 0.06575 0.01595
## relevance_group 0.39061 0.02391
## learning_group 0.27450 0.01950
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.07917 0.05558
## gender_female:challenge_group -0.04597 0.02275
## gender_female:relevance_group -0.08155 0.03296
## gender_female:learning_group 0.01058 0.02559
## df t value
## (Intercept) 19.80000 40.828
## gender_female 175.30000 -0.840
## challenge_group 2507.40000 4.122
## relevance_group 2496.60000 16.338
## learning_group 2487.20000 14.079
## scale(overall_pre_competence_beliefs, scale = FALSE) 118.90000 1.424
## gender_female:challenge_group 2486.10000 -2.021
## gender_female:relevance_group 2490.20000 -2.474
## gender_female:learning_group 2481.50000 0.413
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.4018
## challenge_group 3.88e-05 ***
## relevance_group < 2e-16 ***
## learning_group < 2e-16 ***
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.1570
## gender_female:challenge_group 0.0434 *
## gender_female:relevance_group 0.0134 *
## gender_female:learning_group 0.6793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f chlln_ rlvnc_ lrnng_ s(_s=F gndr_fml:c_
## gender_feml -0.663
## challng_grp -0.001 0.000
## relevnc_grp 0.000 0.000 -0.091
## learnng_grp 0.000 -0.001 -0.113 -0.460
## s(___,s=FAL 0.035 -0.026 0.001 0.000 0.000
## gndr_fml:c_ 0.000 0.000 -0.687 0.064 0.083 0.000
## gndr_fml:r_ 0.000 0.000 0.064 -0.722 0.332 0.000 -0.120
## gndr_fml:l_ 0.000 0.000 0.092 0.347 -0.760 0.000 -0.111
## gndr_fml:r_
## gender_feml
## challng_grp
## relevnc_grp
## learnng_grp
## s(___,s=FAL
## gndr_fml:c_
## gndr_fml:r_
## gndr_fml:l_ -0.443rand(engagement_model_gender_final_2)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.268 1 0.6
## participant_ID 1850.109 1 <2e-16 ***
## beep_ID_new 56.392 1 6e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rengagement_2<-r2glmm::r2beta(engagement_model_gender_final_2, method = "nsj")
Rengagement_2## Effect Rsq upper.CL
## 1 Model 0.217 0.245
## 4 relevance_group 0.037 0.052
## 5 learning_group 0.028 0.041
## 6 scale(overall_pre_competence_beliefs, scale = FALSE) 0.007 0.014
## 2 gender_female 0.002 0.008
## 3 challenge_group 0.002 0.008
## 8 gender_female:relevance_group 0.001 0.005
## 7 gender_female:challenge_group 0.001 0.004
## 9 gender_female:learning_group 0.000 0.002
## lower.CL
## 1 0.193
## 4 0.025
## 5 0.017
## 6 0.002
## 2 0.000
## 3 0.000
## 8 0.000
## 7 0.000
## 9 0.000x<-sjPlot::sjp.int(engagement_model_gender_final_2, type = "eff")
x[[1]][[1]] +
xlab("Challenge") +
ylab("Engagement") +
ggtitle("Interaction Between Gender and Challenge on Engagement") +
scale_color_discrete(name="",
labels=c("Male", "Female"))
x[[1]][[2]] +
xlab("Relevance") +
ylab("Engagement") +
ggtitle("Interaction Between Gender and Relevance on Engagement") +
scale_color_discrete(name="",
labels=c("Male", "Female"))
Predicting challenge with activities
challenge_model_final_2 <- lmer(challenge ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(challenge_model_final_2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## challenge ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + youth_activity_rc + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6807.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9164 -0.6406 -0.0343 0.5573 3.4151
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05880 0.2425
## participant_ID (Intercept) 0.47418 0.6886
## program_ID (Intercept) 0.02675 0.1635
## Residual 0.65529 0.8095
## Number of obs: 2575, groups:
## beep_ID_new, 235; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.34167 0.10399
## gender_female -0.24392 0.11162
## scale(overall_pre_competence_beliefs, scale = FALSE) -0.12057 0.06949
## youth_activity_rcBasic Skills Activity 0.08786 0.06622
## youth_activity_rcCreating Product 0.33751 0.06716
## youth_activity_rcField Trip Speaker -0.10755 0.13215
## youth_activity_rcLab Activity 0.16549 0.12924
## youth_activity_rcProgram Staff Led -0.13468 0.07716
## df t value
## (Intercept) 19.50000 22.519
## gender_female 174.07000 -2.185
## scale(overall_pre_competence_beliefs, scale = FALSE) 153.12000 -1.735
## youth_activity_rcBasic Skills Activity 203.20000 1.327
## youth_activity_rcCreating Product 214.46000 5.026
## youth_activity_rcField Trip Speaker 131.48000 -0.814
## youth_activity_rcLab Activity 156.00000 1.280
## youth_activity_rcProgram Staff Led 182.64000 -1.745
## Pr(>|t|)
## (Intercept) 2.00e-15 ***
## gender_female 0.0302 *
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.0847 .
## youth_activity_rcBasic Skills Activity 0.1861
## youth_activity_rcCreating Product 1.06e-06 ***
## youth_activity_rcField Trip Speaker 0.4172
## youth_activity_rcLab Activity 0.2023
## youth_activity_rcProgram Staff Led 0.0826 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.554
## s(___,s=FAL 0.043 -0.038
## yth_ctv_BSA -0.258 -0.003 -0.019
## yth_ctvt_CP -0.249 0.009 -0.014 0.389
## yth_ctv_FTS -0.146 0.009 -0.025 0.230 0.213
## yth_ctvt_LA -0.131 -0.005 -0.016 0.197 0.191 0.127
## yth_ctv_PSL -0.218 -0.013 0.004 0.405 0.309 0.181 0.177rand(challenge_model_final_2)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 1.69 1 0.2
## participant_ID 865.08 1 <2e-16 ***
## beep_ID_new 51.29 1 8e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rchallenge_2<-r2glmm::r2beta(challenge_model_final_2, method = "nsj")
Rchallenge_2## Effect Rsq upper.CL
## 1 Model 0.039 0.057
## 2 gender_female 0.012 0.022
## 5 youth_activity_rcCreating Product 0.011 0.021
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.008 0.016
## 8 youth_activity_rcProgram Staff Led 0.002 0.006
## 7 youth_activity_rcLab Activity 0.001 0.005
## 4 youth_activity_rcBasic Skills Activity 0.001 0.005
## 6 youth_activity_rcField Trip Speaker 0.000 0.003
## lower.CL
## 1 0.028
## 2 0.005
## 5 0.005
## 3 0.002
## 8 0.000
## 7 0.000
## 4 0.000
## 6 0.000Predicting relevance with activities
relevance_model_final_2 <- lmer(relevance ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(relevance_model_final_2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## relevance ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + youth_activity_rc + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5584.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9174 -0.5193 0.0221 0.5771 4.0731
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008748 0.09353
## participant_ID (Intercept) 0.480982 0.69353
## program_ID (Intercept) 0.013358 0.11558
## Residual 0.410634 0.64081
## Number of obs: 2575, groups:
## beep_ID_new, 235; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.61442 0.08988
## gender_female -0.25061 0.10908
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.05558 0.06686
## youth_activity_rcBasic Skills Activity 0.13273 0.04141
## youth_activity_rcCreating Product 0.19636 0.04242
## youth_activity_rcField Trip Speaker 0.26210 0.07719
## youth_activity_rcLab Activity 0.09932 0.07770
## youth_activity_rcProgram Staff Led 0.14665 0.04758
## df t value
## (Intercept) 21.72000 29.087
## gender_female 176.36000 -2.297
## scale(overall_pre_competence_beliefs, scale = FALSE) 131.84000 0.831
## youth_activity_rcBasic Skills Activity 203.27000 3.206
## youth_activity_rcCreating Product 230.10000 4.630
## youth_activity_rcField Trip Speaker 113.42000 3.395
## youth_activity_rcLab Activity 143.86000 1.278
## youth_activity_rcProgram Staff Led 181.13000 3.082
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.022767 *
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.407330
## youth_activity_rcBasic Skills Activity 0.001566 **
## youth_activity_rcCreating Product 6.13e-06 ***
## youth_activity_rcField Trip Speaker 0.000945 ***
## youth_activity_rcLab Activity 0.203201
## youth_activity_rcProgram Staff Led 0.002378 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.624
## s(___,s=FAL 0.040 -0.031
## yth_ctv_BSA -0.184 -0.002 -0.015
## yth_ctvt_CP -0.177 0.006 -0.013 0.379
## yth_ctv_FTS -0.111 0.007 -0.023 0.246 0.223
## yth_ctvt_LA -0.097 -0.004 -0.016 0.203 0.195 0.143
## yth_ctv_PSL -0.155 -0.011 0.002 0.414 0.306 0.193 0.184rand(relevance_model_final_2)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.71 1 0.40
## participant_ID 1411.39 1 <2e-16 ***
## beep_ID_new 5.09 1 0.02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rrelevance_2<-r2glmm::r2beta(relevance_model_final_2, method = "nsj")
Rrelevance_2## Effect Rsq upper.CL
## 1 Model 0.027 0.043
## 2 gender_female 0.017 0.028
## 5 youth_activity_rcCreating Product 0.005 0.012
## 6 youth_activity_rcField Trip Speaker 0.003 0.009
## 4 youth_activity_rcBasic Skills Activity 0.003 0.008
## 8 youth_activity_rcProgram Staff Led 0.002 0.008
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.002 0.007
## 7 youth_activity_rcLab Activity 0.000 0.004
## lower.CL
## 1 0.018
## 2 0.008
## 5 0.001
## 6 0.000
## 4 0.000
## 8 0.000
## 3 0.000
## 7 0.000Predicting learning with activities
learning_model_final_2 <- lmer(learning ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(learning_model_final_2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: learning ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + youth_activity_rc + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6851.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2150 -0.5656 0.1266 0.5831 2.7867
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 9.800e-03 9.899e-02
## participant_ID (Intercept) 4.012e-01 6.334e-01
## program_ID (Intercept) 3.547e-13 5.955e-07
## Residual 7.089e-01 8.420e-01
## Number of obs: 2574, groups:
## beep_ID_new, 235; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.72558 0.07801
## gender_female -0.07634 0.10199
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.07506 0.06097
## youth_activity_rcBasic Skills Activity 0.21054 0.05215
## youth_activity_rcCreating Product 0.11284 0.05363
## youth_activity_rcField Trip Speaker 0.07216 0.09576
## youth_activity_rcLab Activity 0.19405 0.09719
## youth_activity_rcProgram Staff Led 0.06627 0.05978
## df t value
## (Intercept) 221.07000 34.937
## gender_female 176.50000 -0.748
## scale(overall_pre_competence_beliefs, scale = FALSE) 181.46000 1.231
## youth_activity_rcBasic Skills Activity 180.48000 4.037
## youth_activity_rcCreating Product 206.00000 2.104
## youth_activity_rcField Trip Speaker 97.63000 0.754
## youth_activity_rcLab Activity 125.18000 1.997
## youth_activity_rcProgram Staff Led 160.93000 1.109
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.4552
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.2199
## youth_activity_rcBasic Skills Activity 7.98e-05 ***
## youth_activity_rcCreating Product 0.0366 *
## youth_activity_rcField Trip Speaker 0.4529
## youth_activity_rcLab Activity 0.0480 *
## youth_activity_rcProgram Staff Led 0.2693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.669
## s(___,s=FAL 0.042 -0.030
## yth_ctv_BSA -0.268 -0.005 -0.027
## yth_ctvt_CP -0.259 0.011 -0.025 0.378
## yth_ctv_FTS -0.172 0.017 -0.047 0.246 0.223
## yth_ctvt_LA -0.141 -0.010 -0.032 0.206 0.197 0.144
## yth_ctv_PSL -0.224 -0.018 0.003 0.410 0.309 0.194 0.185rand(learning_model_final_2)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 2.73e-12 1 1.0
## participant_ID 7.19e+02 1 <2e-16 ***
## beep_ID_new 1.99e+00 1 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rlearning_2<-r2glmm::r2beta(learning_model_final_2, method = "nsj")
Rlearning_2## Effect Rsq upper.CL
## 1 Model 0.011 0.023
## 4 youth_activity_rcBasic Skills Activity 0.005 0.012
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.003 0.009
## 5 youth_activity_rcCreating Product 0.001 0.006
## 7 youth_activity_rcLab Activity 0.001 0.006
## 2 gender_female 0.001 0.006
## 8 youth_activity_rcProgram Staff Led 0.000 0.003
## 6 youth_activity_rcField Trip Speaker 0.000 0.003
## lower.CL
## 1 0.006
## 4 0.001
## 3 0.000
## 5 0.000
## 7 0.000
## 2 0.000
## 8 0.000
## 6 0.000Predicting engagement with activities
engagement_model_final_21 <- lmer(overall_engagement ~
gender_female +
scale(overall_pre_competence_beliefs, scale=FALSE) +
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(engagement_model_final_21)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## overall_engagement ~ gender_female + scale(overall_pre_competence_beliefs,
## scale = FALSE) + youth_activity_rc + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5399.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1618 -0.5064 0.0642 0.5746 3.7718
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02451 0.1566
## participant_ID (Intercept) 0.33229 0.5764
## program_ID (Intercept) 0.01135 0.1066
## Residual 0.37894 0.6156
## Number of obs: 2575, groups:
## beep_ID_new, 235; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.839440 0.079411
## gender_female -0.073587 0.092054
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.087106 0.056715
## youth_activity_rcBasic Skills Activity 0.076927 0.046851
## youth_activity_rcCreating Product 0.137974 0.047626
## youth_activity_rcField Trip Speaker 0.133098 0.091759
## youth_activity_rcLab Activity 0.120945 0.090443
## youth_activity_rcProgram Staff Led 0.008682 0.054364
## df t value
## (Intercept) 21.130000 35.756
## gender_female 177.580000 -0.799
## scale(overall_pre_competence_beliefs, scale = FALSE) 138.070000 1.536
## youth_activity_rcBasic Skills Activity 218.860000 1.642
## youth_activity_rcCreating Product 235.010000 2.897
## youth_activity_rcField Trip Speaker 136.310000 1.451
## youth_activity_rcLab Activity 164.440000 1.337
## youth_activity_rcProgram Staff Led 196.230000 0.160
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender_female 0.42514
## scale(overall_pre_competence_beliefs, scale = FALSE) 0.12686
## youth_activity_rcBasic Skills Activity 0.10203
## youth_activity_rcCreating Product 0.00412 **
## youth_activity_rcField Trip Speaker 0.14921
## youth_activity_rcLab Activity 0.18299
## youth_activity_rcProgram Staff Led 0.87328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f s(_s=F y__BSA yt__CP y__FTS yt__LA
## gender_feml -0.597
## s(___,s=FAL 0.043 -0.034
## yth_ctv_BSA -0.238 -0.003 -0.019
## yth_ctvt_CP -0.230 0.008 -0.015 0.386
## yth_ctv_FTS -0.139 0.009 -0.027 0.234 0.216
## yth_ctvt_LA -0.122 -0.005 -0.017 0.199 0.192 0.131
## yth_ctv_PSL -0.200 -0.013 0.004 0.408 0.308 0.184 0.179rand(engagement_model_final_21)## Analysis of Random effects Table:
## Chi.sq Chi.DF p.value
## program_ID 0.824 1 0.4
## participant_ID 1080.598 1 <2e-16 ***
## beep_ID_new 35.740 1 2e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rengagement_21<-r2glmm::r2beta(engagement_model_final_21, method = "nsj")
Rengagement_21## Effect Rsq upper.CL
## 1 Model 0.014 0.027
## 3 scale(overall_pre_competence_beliefs, scale = FALSE) 0.006 0.014
## 5 youth_activity_rcCreating Product 0.003 0.009
## 2 gender_female 0.002 0.007
## 4 youth_activity_rcBasic Skills Activity 0.001 0.005
## 6 youth_activity_rcField Trip Speaker 0.001 0.005
## 7 youth_activity_rcLab Activity 0.001 0.004
## 8 youth_activity_rcProgram Staff Led 0.000 0.002
## lower.CL
## 1 0.008
## 3 0.002
## 5 0.000
## 2 0.000
## 4 0.000
## 6 0.000
## 7 0.000
## 8 0.000vars_by_activity<-df%>%
group_by(youth_activity_rc) %>%
summarise_at(vars(challenge, relevance, learning), funs (mean(., na.rm=TRUE)))
vars_by_activity## # A tibble: 7 x 4
## youth_activity_rc challenge relevance learning
## <fct> <dbl> <dbl> <dbl>
## 1 Other 2.23 2.50 2.69
## 2 Basic Skills Activity 2.26 2.57 2.86
## 3 Creating Product 2.55 2.74 2.86
## 4 Field Trip Speaker 2.04 2.62 2.66
## 5 Lab Activity 2.28 2.46 2.81
## 6 Program Staff Led 2.10 2.60 2.77
## 7 <NA> 2.18 2.59 2.63# d <- df %>%
# ungroup() %>%
# select(youth_activity_rc, challenge) %>%
# group_by(youth_activity_rc) %>%
# summarize(mean_challenge = mean(challenge, na.rm = T)) %>%
# filter(!is.na(youth_activity_rc)) %>%
# ggplot(aes(x = youth_activity_rc, y = mean_challenge)) +
# geom_col()
df %>%
ungroup() %>%
select(youth_activity_rc, challenge, relevance, learning) %>%
gather(key, val, -youth_activity_rc) %>%
filter(!is.na(youth_activity_rc)) %>%
ggplot(aes(x = reorder(youth_activity_rc, val), y = val, fill=youth_activity_rc)) +
scale_fill_hue(l=40) +
theme(legend.position = "none") +
stat_summary(fun.y = mean, geom = "bar") +
stat_summary(fun.data = mean_se, geom="errorbar") +
facet_wrap("key") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Youth Activity") +
ylab("Mean") +
ggtitle("Means of Outcomes by Youth Activity")## Warning: Removed 1 rows containing non-finite values (stat_summary).
## Warning: Removed 1 rows containing non-finite values (stat_summary).
#vars_by_activity<-df%>%
#group_by(youth_activity_rc) %>%
#summarise_at(vars(challenge, relevance, learning), funs (mean(., na.rm=TRUE)))
# vars_by_activity %>%
# gather(key, val, -youth_activity_rc) %>%
# filter(!is.na(youth_activity_rc)) %>%
# ggplot(ggplot2::aes(x=youth_activity_rc, y=val, fill=key)) +
# geom_col(position="dodge")