RQ_1 & RQ_2 & RQ_3
Patrick N. Beymer
2/28/2018
This is analysis associated with the first RQ for the STEM-IE project.
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")
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_sum1. Challenge
1A: Just youth activity
m <- lmer(challenge ~
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7481
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0550 -0.6292 -0.0205 0.5705 3.3727
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05620 0.2371
## participant_ID (Intercept) 0.47819 0.6915
## program_ID (Intercept) 0.04243 0.2060
## Residual 0.66391 0.8148
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.19137 0.09430 10.43185
## youth_activity_rcBasic Skills Activity 0.10066 0.06422 212.71251
## youth_activity_rcCreating Product 0.37479 0.06479 219.23383
## youth_activity_rcField Trip Speaker -0.07778 0.13021 142.99330
## youth_activity_rcLab Activity 0.20410 0.12490 158.92999
## youth_activity_rcProgram Staff Led -0.10191 0.07475 188.19071
## t value Pr(>|t|)
## (Intercept) 23.237 2.53e-10 ***
## youth_activity_rcBasic Skills Activity 1.567 0.119
## youth_activity_rcCreating Product 5.785 2.49e-08 ***
## youth_activity_rcField Trip Speaker -0.597 0.551
## youth_activity_rcLab Activity 1.634 0.104
## youth_activity_rcProgram Staff Led -1.363 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA
## yth_ctv_BSA -0.279
## yth_ctvt_CP -0.264 0.388
## yth_ctv_FTS -0.148 0.227 0.209
## yth_ctvt_LA -0.142 0.196 0.191 0.125
## yth_ctv_PSL -0.240 0.405 0.308 0.178 0.1771B: Just STEM-SB as counts
m <- lmer(challenge ~
active +
ho_thinking +
belonging +
agency +
sum_stem_sb +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ active + ho_thinking + belonging + agency + sum_stem_sb +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7435.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0283 -0.6295 -0.0361 0.5656 3.3976
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06419 0.2533
## participant_ID (Intercept) 0.47154 0.6867
## program_ID (Intercept) 0.05074 0.2253
## Residual 0.66270 0.8141
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.17033 0.11809 19.85696 18.379 6.21e-14 ***
## active 0.03449 0.05555 194.34404 0.621 0.535485
## ho_thinking -0.09565 0.02834 212.41407 -3.375 0.000878 ***
## belonging 0.04900 0.03968 191.98032 1.235 0.218352
## agency 0.06775 0.02151 194.18601 3.150 0.001890 **
## sum_stem_sb 0.01720 0.01337 209.04682 1.287 0.199672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) active h_thnk blngng agency
## active -0.448
## ho_thinking -0.091 -0.237
## belonging -0.081 -0.042 -0.100
## agency -0.146 0.122 -0.198 -0.423
## sum_stem_sb 0.153 -0.423 -0.253 -0.151 -0.1671C: Just STEM-SB as dummy codes (if count >= 1, then dummy code = 1)
m <- lmer(challenge ~
active_dummy +
ho_thinking_dummy +
belonging_dummy +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ active_dummy + ho_thinking_dummy + belonging_dummy +
## agency_dummy + stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7433.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0383 -0.6399 -0.0410 0.5700 3.4131
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07136 0.2671
## participant_ID (Intercept) 0.47012 0.6857
## program_ID (Intercept) 0.04906 0.2215
## Residual 0.66228 0.8138
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.98108 0.21032 125.88537 9.419 2.86e-16 ***
## active_dummy 0.12492 0.19411 206.36013 0.644 0.5206
## ho_thinking_dummy -0.16206 0.07696 248.01322 -2.106 0.0362 *
## belonging_dummy 0.11297 0.06528 199.80399 1.730 0.0851 .
## agency_dummy 0.14195 0.07285 199.49786 1.949 0.0528 .
## stem_sb_dummy 0.13613 0.07085 223.45257 1.922 0.0559 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actv_d h_thn_ blngn_ agncy_
## active_dmmy -0.812
## h_thnkng_dm -0.113 -0.018
## blngng_dmmy -0.009 -0.126 -0.074
## agency_dmmy -0.104 -0.023 -0.215 -0.394
## stm_sb_dmmy -0.012 -0.193 -0.348 0.067 -0.0041D: Only select activities and STEM-SB dummy codes
m <- lmer(challenge ~
youth_activity_three +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ youth_activity_three + agency_dummy + stem_sb_dummy +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7410.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9558 -0.6374 -0.0259 0.5668 3.3248
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05824 0.2413
## participant_ID (Intercept) 0.47183 0.6869
## program_ID (Intercept) 0.04751 0.2180
## Residual 0.66217 0.8137
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.08444 0.11401 19.36181
## youth_activity_threeBasic Skills Activity 0.12565 0.05911 204.54670
## youth_activity_threeCreating Product 0.35262 0.06339 220.32058
## agency_dummy 0.08439 0.06248 194.33134
## stem_sb_dummy 0.03685 0.06287 213.11308
## t value Pr(>|t|)
## (Intercept) 18.283 1.12e-13 ***
## youth_activity_threeBasic Skills Activity 2.126 0.0347 *
## youth_activity_threeCreating Product 5.563 7.66e-08 ***
## agency_dummy 1.351 0.1783
## stem_sb_dummy 0.586 0.5585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP agncy_
## yth_ctv_BSA -0.057
## yth_ctvt_CP 0.016 0.282
## agency_dmmy -0.392 -0.024 -0.221
## stm_sb_dmmy -0.388 -0.175 -0.141 -0.0692. Relevance
2A: Just youth activity
m <- lmer(relevance ~
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6154.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9073 -0.5252 0.0208 0.5821 4.0864
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.006284 0.07927
## participant_ID (Intercept) 0.481129 0.69363
## program_ID (Intercept) 0.014479 0.12033
## Residual 0.418746 0.64711
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.46261 0.06849 9.30629
## youth_activity_rcBasic Skills Activity 0.14917 0.03922 211.85422
## youth_activity_rcCreating Product 0.22967 0.03988 232.82941
## youth_activity_rcField Trip Speaker 0.28977 0.07411 122.97587
## youth_activity_rcLab Activity 0.10583 0.07249 141.05255
## youth_activity_rcProgram Staff Led 0.15383 0.04480 183.95916
## t value Pr(>|t|)
## (Intercept) 35.954 2.64e-11 ***
## youth_activity_rcBasic Skills Activity 3.804 0.000186 ***
## youth_activity_rcCreating Product 5.759 2.66e-08 ***
## youth_activity_rcField Trip Speaker 3.910 0.000152 ***
## youth_activity_rcLab Activity 1.460 0.146546
## youth_activity_rcProgram Staff Led 3.434 0.000735 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA
## yth_ctv_BSA -0.231
## yth_ctvt_CP -0.218 0.376
## yth_ctv_FTS -0.131 0.242 0.216
## yth_ctvt_LA -0.122 0.202 0.197 0.140
## yth_ctv_PSL -0.200 0.411 0.306 0.188 0.1842B: Just STEM-SB as counts
m <- lmer(relevance ~
active +
ho_thinking +
belonging +
agency +
sum_stem_sb +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ active + ho_thinking + belonging + agency + sum_stem_sb +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6148
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8836 -0.5322 0.0417 0.5815 3.7321
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01077 0.1038
## participant_ID (Intercept) 0.47439 0.6888
## program_ID (Intercept) 0.01329 0.1153
## Residual 0.42152 0.6492
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.502094 0.078810 17.010005 31.749 <2e-16 ***
## active 0.020638 0.034393 196.450276 0.600 0.5492
## ho_thinking 0.012931 0.017738 212.543615 0.729 0.4668
## belonging -0.037413 0.024490 189.232173 -1.528 0.1283
## agency 0.006237 0.013302 192.973707 0.469 0.6397
## sum_stem_sb 0.015976 0.008350 211.198562 1.913 0.0571 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) active h_thnk blngng agency
## active -0.424
## ho_thinking -0.104 -0.218
## belonging -0.079 -0.024 -0.101
## agency -0.137 0.126 -0.185 -0.424
## sum_stem_sb 0.158 -0.438 -0.266 -0.159 -0.1652C: Just STEM-SB as dummy codes (if count >= 1, then dummy code = 1)
m <- lmer(relevance ~
active_dummy +
ho_thinking_dummy +
belonging_dummy +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ active_dummy + ho_thinking_dummy + belonging_dummy +
## agency_dummy + stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6132.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9025 -0.5202 0.0467 0.5861 3.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011035 0.10505
## participant_ID (Intercept) 0.476388 0.69021
## program_ID (Intercept) 0.008036 0.08964
## Residual 0.420800 0.64869
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.32209 0.12952 115.65614 17.928 < 2e-16 ***
## active_dummy 0.20731 0.11804 227.17709 1.756 0.08038 .
## ho_thinking_dummy -0.07754 0.04790 266.72439 -1.619 0.10671
## belonging_dummy 0.01465 0.03934 202.40742 0.372 0.71004
## agency_dummy -0.00245 0.04386 198.33669 -0.056 0.95552
## stem_sb_dummy 0.13278 0.04340 228.46293 3.059 0.00248 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actv_d h_thn_ blngn_ agncy_
## active_dmmy -0.804
## h_thnkng_dm -0.117 -0.019
## blngng_dmmy -0.017 -0.119 -0.072
## agency_dmmy -0.092 -0.022 -0.227 -0.392
## stm_sb_dmmy -0.002 -0.196 -0.355 0.076 -0.0172D: Only select activities and STEM-SB dummy codes
m <- lmer(relevance ~
youth_activity_three +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ youth_activity_three + agency_dummy + stem_sb_dummy +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6121.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9648 -0.5272 0.0381 0.5776 3.8099
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008987 0.09480
## participant_ID (Intercept) 0.475537 0.68959
## program_ID (Intercept) 0.007308 0.08549
## Residual 0.420807 0.64870
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.48803 0.07223 17.02293
## youth_activity_threeBasic Skills Activity 0.05470 0.03684 206.16519
## youth_activity_threeCreating Product 0.15468 0.04001 238.48878
## agency_dummy -0.04141 0.03863 190.14562
## stem_sb_dummy 0.09993 0.03943 218.14556
## t value Pr(>|t|)
## (Intercept) 34.446 < 2e-16 ***
## youth_activity_threeBasic Skills Activity 1.485 0.139203
## youth_activity_threeCreating Product 3.866 0.000143 ***
## agency_dummy -1.072 0.285157
## stem_sb_dummy 2.535 0.011959 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP agncy_
## yth_ctv_BSA -0.054
## yth_ctvt_CP 0.017 0.268
## agency_dmmy -0.373 -0.030 -0.217
## stm_sb_dmmy -0.379 -0.164 -0.139 -0.0853. Learning
3A: Just youth activity
m <- lmer(learning ~
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ youth_activity_rc + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7501.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1939 -0.5605 0.1330 0.5800 2.7928
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.012317 0.11098
## participant_ID (Intercept) 0.401844 0.63391
## program_ID (Intercept) 0.002588 0.05087
## Residual 0.707322 0.84102
## Number of obs: 2817, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.68252 0.05808 11.52849
## youth_activity_rcBasic Skills Activity 0.22074 0.05128 196.87122
## youth_activity_rcCreating Product 0.13631 0.05218 214.26151
## youth_activity_rcField Trip Speaker 0.09801 0.09704 114.32790
## youth_activity_rcLab Activity 0.15170 0.09513 131.15180
## youth_activity_rcProgram Staff Led 0.07220 0.05867 170.95870
## t value Pr(>|t|)
## (Intercept) 46.184 1.91e-14 ***
## youth_activity_rcBasic Skills Activity 4.304 2.64e-05 ***
## youth_activity_rcCreating Product 2.612 0.00964 **
## youth_activity_rcField Trip Speaker 1.010 0.31460
## youth_activity_rcLab Activity 1.595 0.11318
## youth_activity_rcProgram Staff Led 1.231 0.22013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA
## yth_ctv_BSA -0.358
## yth_ctvt_CP -0.337 0.378
## yth_ctv_FTS -0.203 0.238 0.215
## yth_ctvt_LA -0.192 0.203 0.198 0.137
## yth_ctv_PSL -0.310 0.407 0.309 0.188 0.1843B: Just STEM-SB as counts
m <- lmer(learning ~
active +
ho_thinking +
belonging +
agency +
sum_stem_sb +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ active + ho_thinking + belonging + agency + sum_stem_sb +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7453.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2245 -0.5664 0.1133 0.5885 2.7252
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.406e-02 1.186e-01
## participant_ID (Intercept) 3.975e-01 6.305e-01
## program_ID (Intercept) 1.924e-13 4.386e-07
## Residual 7.088e-01 8.419e-01
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.63283 0.07507 352.02765 35.070 <2e-16 ***
## active 0.07971 0.04325 182.26279 1.843 0.0669 .
## ho_thinking -0.02628 0.02221 200.02024 -1.183 0.2380
## belonging 0.03364 0.03078 175.51645 1.093 0.2758
## agency 0.01007 0.01669 180.04797 0.603 0.5471
## sum_stem_sb 0.00731 0.01048 197.61309 0.697 0.4864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) active h_thnk blngng agency
## active -0.561
## ho_thinking -0.142 -0.217
## belonging -0.107 -0.020 -0.100
## agency -0.178 0.124 -0.183 -0.425
## sum_stem_sb 0.212 -0.439 -0.268 -0.162 -0.1653C: Just STEM-SB as dummy codes (if count >= 1, then dummy code = 1)
m <- lmer(learning ~
active_dummy +
ho_thinking_dummy +
belonging_dummy +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ active_dummy + ho_thinking_dummy + belonging_dummy +
## agency_dummy + stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7434.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1973 -0.5592 0.1158 0.5969 2.6502
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.289e-02 1.135e-01
## participant_ID (Intercept) 3.976e-01 6.305e-01
## program_ID (Intercept) 1.355e-14 1.164e-07
## Residual 7.077e-01 8.413e-01
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.33353 0.15137 256.59473 15.416 <2e-16 ***
## active_dummy 0.34000 0.14712 214.74780 2.311 0.0218 *
## ho_thinking_dummy -0.09172 0.05952 252.35864 -1.541 0.1246
## belonging_dummy 0.07660 0.04887 186.70720 1.567 0.1187
## agency_dummy 0.02351 0.05445 182.57246 0.432 0.6664
## stem_sb_dummy 0.13268 0.05390 212.02199 2.462 0.0146 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actv_d h_thn_ blngn_ agncy_
## active_dmmy -0.858
## h_thnkng_dm -0.126 -0.021
## blngng_dmmy -0.019 -0.117 -0.070
## agency_dmmy -0.095 -0.022 -0.227 -0.395
## stm_sb_dmmy -0.001 -0.196 -0.354 0.077 -0.0193D: Only select activities and STEM-SB dummy codes
m <- lmer(learning ~
youth_activity_three +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ youth_activity_three + agency_dummy + stem_sb_dummy +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7432.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2279 -0.5623 0.1253 0.5847 2.7105
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011747 0.10838
## participant_ID (Intercept) 0.397673 0.63061
## program_ID (Intercept) 0.001111 0.03334
## Residual 0.708050 0.84146
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.62012 0.07270 31.22221
## youth_activity_threeBasic Skills Activity 0.15592 0.04643 190.07081
## youth_activity_threeCreating Product 0.06521 0.05047 221.50073
## agency_dummy 0.03817 0.04867 173.59360
## stem_sb_dummy 0.09673 0.04970 200.58196
## t value Pr(>|t|)
## (Intercept) 36.038 < 2e-16 ***
## youth_activity_threeBasic Skills Activity 3.358 0.000948 ***
## youth_activity_threeCreating Product 1.292 0.197753
## agency_dummy 0.784 0.434012
## stem_sb_dummy 1.946 0.053040 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP agncy_
## yth_ctv_BSA -0.070
## yth_ctvt_CP 0.021 0.268
## agency_dmmy -0.466 -0.030 -0.217
## stm_sb_dmmy -0.475 -0.162 -0.137 -0.0854. Affect
4A: Just youth activity
m <- lmer(positive_affect ~
youth_activity_rc +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ youth_activity_rc + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6932
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4855 -0.4501 0.0545 0.5451 3.4573
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02746 0.1657
## participant_ID (Intercept) 0.49524 0.7037
## program_ID (Intercept) 0.10688 0.3269
## Residual 0.54637 0.7392
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.697317 0.124977 8.270227
## youth_activity_rcBasic Skills Activity 0.032646 0.052281 208.154491
## youth_activity_rcCreating Product 0.013239 0.052845 220.285317
## youth_activity_rcField Trip Speaker 0.009619 0.103361 131.744677
## youth_activity_rcLab Activity 0.065791 0.099763 148.613621
## youth_activity_rcProgram Staff Led -0.053066 0.060406 182.362047
## t value Pr(>|t|)
## (Intercept) 21.583 1.45e-08 ***
## youth_activity_rcBasic Skills Activity 0.624 0.533
## youth_activity_rcCreating Product 0.251 0.802
## youth_activity_rcField Trip Speaker 0.093 0.926
## youth_activity_rcLab Activity 0.659 0.511
## youth_activity_rcProgram Staff Led -0.878 0.381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA
## yth_ctv_BSA -0.170
## yth_ctvt_CP -0.161 0.383
## yth_ctv_FTS -0.091 0.234 0.212
## yth_ctvt_LA -0.086 0.198 0.193 0.131
## yth_ctv_PSL -0.146 0.409 0.306 0.182 0.1804B: Just STEM-SB as counts
m <- lmer(positive_affect ~
active +
ho_thinking +
belonging +
agency +
sum_stem_sb +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ active + ho_thinking + belonging + agency +
## sum_stem_sb + (1 | program_ID) + (1 | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5758 -0.4436 0.0519 0.5440 3.4977
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02694 0.1641
## participant_ID (Intercept) 0.49574 0.7041
## program_ID (Intercept) 0.10467 0.3235
## Residual 0.53906 0.7342
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.633080 0.133626 11.193384 19.705 4.83e-10 ***
## active 0.051183 0.043145 192.264022 1.186 0.237
## ho_thinking -0.005364 0.022196 208.113317 -0.242 0.809
## belonging 0.032648 0.030772 187.298545 1.061 0.290
## agency 0.016242 0.016709 189.717523 0.972 0.332
## sum_stem_sb -0.018822 0.010444 206.329824 -1.802 0.073 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) active h_thnk blngng agency
## active -0.311
## ho_thinking -0.067 -0.227
## belonging -0.057 -0.033 -0.101
## agency -0.103 0.124 -0.192 -0.424
## sum_stem_sb 0.111 -0.432 -0.260 -0.155 -0.1664C: Just STEM-SB as dummy codes (if count >= 1, then dummy code = 1)
m <- lmer(positive_affect ~
active_dummy +
ho_thinking +
belonging_dummy +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ active_dummy + ho_thinking + belonging_dummy +
## agency_dummy + stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6833.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6013 -0.4511 0.0584 0.5387 3.4621
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02628 0.1621
## participant_ID (Intercept) 0.49444 0.7032
## program_ID (Intercept) 0.10796 0.3286
## Residual 0.53862 0.7339
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.61760 0.18713 38.67080 13.988 <2e-16 ***
## active_dummy -0.00270 0.14656 209.30542 -0.018 0.9853
## ho_thinking -0.01545 0.02097 234.15215 -0.737 0.4620
## belonging_dummy 0.04301 0.05030 196.73034 0.855 0.3936
## agency_dummy 0.13097 0.05408 186.28140 2.422 0.0164 *
## stem_sb_dummy -0.02708 0.05516 225.39066 -0.491 0.6240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actv_d h_thnk blngn_ agncy_
## active_dmmy -0.691
## ho_thinking 0.009 -0.042
## blngng_dmmy -0.021 -0.110 -0.235
## agency_dmmy -0.107 -0.019 -0.167 -0.362
## stm_sb_dmmy -0.043 -0.180 -0.409 0.141 -0.0214D: Only select activities and STEM-SB dummy codes
m <- lmer(positive_affect ~
youth_activity_three +
agency_dummy +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ youth_activity_three + agency_dummy + stem_sb_dummy +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6829.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6010 -0.4506 0.0562 0.5362 3.4743
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02566 0.1602
## participant_ID (Intercept) 0.49480 0.7034
## program_ID (Intercept) 0.10966 0.3312
## Residual 0.53867 0.7339
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.62008 0.13448 10.73512
## youth_activity_threeBasic Skills Activity 0.03820 0.04678 197.82163
## youth_activity_threeCreating Product -0.03089 0.05048 220.72075
## agency_dummy 0.14815 0.04923 185.70187
## stem_sb_dummy -0.04785 0.04990 208.27850
## t value Pr(>|t|)
## (Intercept) 19.483 1.01e-09 ***
## youth_activity_threeBasic Skills Activity 0.817 0.41513
## youth_activity_threeCreating Product -0.612 0.54116
## agency_dummy 3.009 0.00298 **
## stem_sb_dummy -0.959 0.33874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP agncy_
## yth_ctv_BSA -0.037
## yth_ctvt_CP 0.011 0.275
## agency_dmmy -0.259 -0.027 -0.219
## stm_sb_dmmy -0.260 -0.170 -0.141 -0.076Descriptives on class composite, stem sb dummy, and agency.
psych::describe(class_data$COMPOSIT)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 236 3.77 1.11 3.83 3.78 1.24 1.17 6 4.83 -0.08 -0.75
## se
## X1 0.07psych::describe(pqa$stem_sb_dummy)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 236 0.82 0.39 1 0.89 0 0 1 1 -1.64 0.68 0.03psych::describe(pqa$agency)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 236 2 1.4 2 2 1.48 0 4 4 0.11 -1.23 0.09RQ1 challenge with quality predictors only
RQ1_challenge_quality3 <- lmer(challenge ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7427.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9426 -0.6398 -0.0370 0.5791 3.3700
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06431 0.2536
## participant_ID (Intercept) 0.46802 0.6841
## program_ID (Intercept) 0.05857 0.2420
## Residual 0.66351 0.8146
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.92375 0.13325 26.82127 14.437 3.64e-14 ***
## COMPOSIT 0.07123 0.02644 198.42857 2.694 0.00766 **
## agency 0.05108 0.01838 207.30441 2.778 0.00597 **
## stem_sb_dummy -0.01117 0.06902 212.06624 -0.162 0.87163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.525
## agency -0.040 -0.253
## stm_sb_dmmy -0.132 -0.351 -0.120With interaction term.
RQ1_challenge_quality31 <- lmer(challenge ~
COMPOSIT +
agency +
stem_sb_dummy +
COMPOSIT*agency +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + agency + stem_sb_dummy + COMPOSIT * agency +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7432.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9437 -0.6423 -0.0365 0.5773 3.3783
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06387 0.2527
## participant_ID (Intercept) 0.46887 0.6847
## program_ID (Intercept) 0.05700 0.2388
## Residual 0.66356 0.8146
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06151 0.17573 71.31070 11.731 <2e-16 ***
## COMPOSIT 0.02728 0.04529 203.19303 0.602 0.548
## agency -0.01848 0.06116 204.06778 -0.302 0.763
## stem_sb_dummy 0.01429 0.07214 217.03192 0.198 0.843
## COMPOSIT:agency 0.01836 0.01540 196.04120 1.192 0.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOSIT agency stm_s_
## COMPOSIT -0.766
## agency -0.636 0.731
## stm_sb_dmmy 0.099 -0.436 -0.317
## COMPOSIT:gn 0.657 -0.813 -0.954 0.296With variable where agency>=3 and class_comp is >=3
RQ1_challenge_agency_comp_three <- lmer(challenge ~
agency_comp_three +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_agency_comp_three)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ agency_comp_three + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7428.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9467 -0.6376 -0.0396 0.5692 3.3943
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06955 0.2637
## participant_ID (Intercept) 0.46902 0.6849
## program_ID (Intercept) 0.04708 0.2170
## Residual 0.66332 0.8144
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.17065 0.10586 14.35524 20.505 4.9e-12 ***
## agency_comp_three 0.17987 0.05487 201.73095 3.278 0.00123 **
## stem_sb_dummy 0.08031 0.06468 211.15985 1.242 0.21575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agnc__
## agncy_cmp_t -0.078
## stm_sb_dmmy -0.487 -0.123Next three models only two quality measures predicting challenge
RQ1_challenge_quality21 <- lmer(challenge ~
#COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality21)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ agency + stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7429.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9545 -0.6413 -0.0387 0.5754 3.4034
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06895 0.2626
## participant_ID (Intercept) 0.47080 0.6862
## program_ID (Intercept) 0.05065 0.2251
## Residual 0.66292 0.8142
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.11283 0.11009 15.17618 19.192 4.66e-12 ***
## agency 0.06346 0.01811 206.92073 3.504 0.000561 ***
## stem_sb_dummy 0.05372 0.06579 215.75678 0.817 0.415114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agency
## agency -0.220
## stm_sb_dmmy -0.417 -0.230RQ1_challenge_quality22 <- lmer(challenge ~
COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality22)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ COMPOSIT + stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7429.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9436 -0.6343 -0.0393 0.5713 3.3457
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06850 0.2617
## participant_ID (Intercept) 0.46592 0.6826
## program_ID (Intercept) 0.06023 0.2454
## Residual 0.66350 0.8146
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.93929 0.13491 26.90993 14.374 3.81e-14 ***
## COMPOSIT 0.08966 0.02602 198.07777 3.446 0.000695 ***
## stem_sb_dummy 0.01190 0.06966 214.03462 0.171 0.864521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.556
## stm_sb_dmmy -0.139 -0.397RQ1_challenge_quality23 <- lmer(challenge ~
COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7424.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9394 -0.6407 -0.0365 0.5796 3.3693
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06375 0.2525
## participant_ID (Intercept) 0.46805 0.6841
## program_ID (Intercept) 0.05855 0.2420
## Residual 0.66351 0.8146
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.92078 0.13191 25.87703 14.561 5.6e-14 ***
## COMPOSIT 0.06976 0.02470 199.45153 2.824 0.00523 **
## agency 0.05072 0.01821 207.30166 2.785 0.00584 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.615
## agency -0.056 -0.317Next three models only one quality measures predicting challenge
RQ1_challenge_quality11 <- lmer(challenge ~
COMPOSIT +
#agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7445.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9491 -0.6344 -0.0330 0.5702 3.3482
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06810 0.2610
## participant_ID (Intercept) 0.46759 0.6838
## program_ID (Intercept) 0.05996 0.2449
## Residual 0.66298 0.8142
## Number of obs: 2800, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.94720 0.13334 25.86714 14.604 5.27e-14 ***
## COMPOSIT 0.08940 0.02379 197.82789 3.758 0.000225 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.672RQ1_challenge_quality12 <- lmer(challenge ~
#COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7426.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9715 -0.6428 -0.0411 0.5701 3.4092
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06877 0.2622
## participant_ID (Intercept) 0.47081 0.6862
## program_ID (Intercept) 0.04987 0.2233
## Residual 0.66294 0.8142
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.15028 0.09962 10.43990 21.586 5.31e-10 ***
## agency 0.06686 0.01761 204.51948 3.796 0.000194 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## agency -0.358RQ1_challenge_quality13 <- lmer(challenge ~
#COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality13)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7435.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9593 -0.6417 -0.0410 0.5669 3.3822
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07664 0.2768
## participant_ID (Intercept) 0.46901 0.6848
## program_ID (Intercept) 0.04902 0.2214
## Residual 0.66266 0.8140
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.19845 0.10742 14.22781 20.466 5.9e-12 ***
## stem_sb_dummy 0.10614 0.06588 217.05626 1.611 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## stm_sb_dmmy -0.506Next models include pqa subject variable only predicting challenge.
RQ1_challenge_subject3 <- lmer(challenge ~
science +
mathematics +
building +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_subject3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ science + mathematics + building + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7428.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0069 -0.6454 -0.0321 0.5691 3.4028
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07210 0.2685
## participant_ID (Intercept) 0.47105 0.6863
## program_ID (Intercept) 0.03779 0.1944
## Residual 0.66207 0.8137
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.22597 0.10331 17.42503 21.547 5.28e-14 ***
## science 0.13101 0.08297 202.78573 1.579 0.1159
## mathematics -0.17602 0.09572 282.63969 -1.839 0.0670 .
## building 0.24339 0.11050 176.25263 2.203 0.0289 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc mthmtc
## science -0.463
## mathematics -0.400 0.374
## building -0.366 0.279 0.278RQ1 relevance with quality predictors only
RQ1_relevance_quality3 <- lmer(relevance ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ COMPOSIT + agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6129.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8807 -0.5239 0.0483 0.5834 3.7251
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009414 0.09702
## participant_ID (Intercept) 0.475027 0.68922
## program_ID (Intercept) 0.013513 0.11625
## Residual 0.421480 0.64921
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.36534 0.08490 22.42204 27.861 <2e-16 ***
## COMPOSIT 0.04151 0.01609 198.61358 2.579 0.0106 *
## agency -0.00452 0.01128 211.39833 -0.401 0.6892
## stem_sb_dummy 0.08145 0.04254 219.18053 1.915 0.0568 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.504
## agency -0.026 -0.261
## stm_sb_dmmy -0.128 -0.351 -0.125With interaction
RQ1_relevance_quality31 <- lmer(relevance ~
COMPOSIT +
agency +
stem_sb_dummy +
COMPOSIT*agency +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ COMPOSIT + agency + stem_sb_dummy + COMPOSIT * agency +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6135.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9056 -0.5270 0.0433 0.5829 3.7304
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008737 0.09347
## participant_ID (Intercept) 0.475267 0.68940
## program_ID (Intercept) 0.013238 0.11506
## Residual 0.421852 0.64950
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.46292 0.10984 56.82064 22.422 <2e-16 ***
## COMPOSIT 0.01032 0.02755 198.83536 0.375 0.708
## agency -0.05441 0.03736 210.24428 -1.456 0.147
## stem_sb_dummy 0.10099 0.04445 223.49085 2.272 0.024 *
## COMPOSIT:agency 0.01304 0.00934 196.60355 1.396 0.164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOSIT agency stm_s_
## COMPOSIT -0.745
## agency -0.616 0.732
## stm_sb_dmmy 0.106 -0.448 -0.333
## COMPOSIT:gn 0.640 -0.815 -0.954 0.312With variable where agency>=3 and class_comp is >=3
RQ1_relevance_agency_comp_three <- lmer(relevance ~
agency_comp_three +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_agency_comp_three)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ agency_comp_three + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6127.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9087 -0.5257 0.0426 0.5809 3.7093
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01122 0.1059
## participant_ID (Intercept) 0.47536 0.6895
## program_ID (Intercept) 0.01018 0.1009
## Residual 0.42106 0.6489
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.47718 0.06982 12.51907 35.482 6.1e-14 ***
## agency_comp_three 0.01824 0.03353 214.72708 0.544 0.58699
## stem_sb_dummy 0.11900 0.03974 219.73327 2.995 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agnc__
## agncy_cmp_t -0.068
## stm_sb_dmmy -0.454 -0.126Next three models only two quality measures predicting relevance
RQ1_relevance_quality21 <- lmer(relevance ~
#COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality21)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ agency + stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6129.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9089 -0.5240 0.0417 0.5836 3.7031
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01133 0.1064
## participant_ID (Intercept) 0.47554 0.6896
## program_ID (Intercept) 0.01041 0.1020
## Residual 0.42099 0.6488
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.476e+00 7.129e-02 1.335e+01 34.726 1.75e-14 ***
## agency 3.331e-03 1.112e-02 2.133e+02 0.299 0.76488
## stem_sb_dummy 1.188e-01 4.065e-02 2.233e+02 2.922 0.00384 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agency
## agency -0.199
## stm_sb_dmmy -0.396 -0.240RQ1_relevance_quality22 <- lmer(relevance ~
COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality22)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ COMPOSIT + stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6122.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8720 -0.5230 0.0461 0.5804 3.7280
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009376 0.09683
## participant_ID (Intercept) 0.474861 0.68910
## program_ID (Intercept) 0.013395 0.11574
## Residual 0.421386 0.64914
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.36444 0.08476 22.54710 27.894 <2e-16 ***
## COMPOSIT 0.03982 0.01552 197.70681 2.565 0.0111 *
## stem_sb_dummy 0.07932 0.04218 221.37472 1.880 0.0614 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.530
## stm_sb_dmmy -0.132 -0.401RQ1_relevance_quality23 <- lmer(relevance ~
COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ COMPOSIT + agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6128.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8657 -0.5225 0.0489 0.5790 3.7416
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01019 0.1010
## participant_ID (Intercept) 0.47496 0.6892
## program_ID (Intercept) 0.01341 0.1158
## Residual 0.42132 0.6491
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.386596 0.084476 21.878426 28.252 < 2e-16 ***
## COMPOSIT 0.052111 0.015200 199.410662 3.428 0.000738 ***
## agency -0.001667 0.011293 213.132278 -0.148 0.882779
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.595
## agency -0.043 -0.328Next three models only one quality measures predicting relevance
RQ1_relevance_quality11 <- lmer(relevance ~
#COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_quality11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7445.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9491 -0.6344 -0.0330 0.5702 3.3482
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06810 0.2610
## participant_ID (Intercept) 0.46759 0.6838
## program_ID (Intercept) 0.05996 0.2449
## Residual 0.66298 0.8142
## Number of obs: 2800, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.94720 0.13334 25.86714 14.604 5.27e-14 ***
## COMPOSIT 0.08940 0.02379 197.82789 3.758 0.000225 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.672RQ1_relevance_quality12 <- lmer(relevance ~
COMPOSIT +
#agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6135.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8687 -0.5276 0.0476 0.5796 3.7463
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009899 0.09949
## participant_ID (Intercept) 0.476599 0.69036
## program_ID (Intercept) 0.013064 0.11430
## Residual 0.420840 0.64872
## Number of obs: 2800, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.38498 0.08403 21.92795 28.384 < 2e-16 ***
## COMPOSIT 0.05144 0.01427 196.75516 3.603 0.000398 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.645RQ1_relevance_quality13 <- lmer(relevance ~
#COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_quality13)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6122.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9187 -0.5231 0.0415 0.5878 3.7017
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01106 0.1052
## participant_ID (Intercept) 0.47568 0.6897
## program_ID (Intercept) 0.01035 0.1017
## Residual 0.42104 0.6489
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.47974 0.06977 12.34932 35.542 8.19e-14 ***
## stem_sb_dummy 0.12179 0.03935 220.57310 3.095 0.00222 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## stm_sb_dmmy -0.465Next models include pqa subject variable only predicting challenge.
RQ1_relevance_subject3 <- lmer(relevance ~
science +
mathematics +
building +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_subject3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ science + mathematics + building + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6113.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8501 -0.5243 0.0305 0.5819 3.8367
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007758 0.08808
## participant_ID (Intercept) 0.475069 0.68925
## program_ID (Intercept) 0.014910 0.12211
## Residual 0.421105 0.64893
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.451e+00 7.408e-02 1.291e+01 33.091 7.26e-14 ***
## science 2.115e-01 4.821e-02 1.924e+02 4.388 1.88e-05 ***
## mathematics 6.098e-03 6.035e-02 3.554e+02 0.101 0.91957
## building 2.093e-01 6.486e-02 1.824e+02 3.228 0.00148 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc mthmtc
## science -0.377
## mathematics -0.327 0.347
## building -0.290 0.254 0.259RQ1 learning with quality predictors only
RQ1_learning_quality3 <- lmer(learning ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ COMPOSIT + agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7434.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2308 -0.5628 0.1146 0.5837 2.7613
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011101 0.10536
## participant_ID (Intercept) 0.394830 0.62835
## program_ID (Intercept) 0.003373 0.05808
## Residual 0.709024 0.84204
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.489e+00 8.635e-02 4.946e+01 28.823 < 2e-16 ***
## COMPOSIT 6.098e-02 1.994e-02 1.866e+02 3.058 0.00256 **
## agency 3.327e-03 1.408e-02 1.957e+02 0.236 0.81339
## stem_sb_dummy 6.190e-02 5.314e-02 2.028e+02 1.165 0.24547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.618
## agency -0.032 -0.259
## stm_sb_dmmy -0.157 -0.351 -0.127With interaction
RQ1_learning_quality31 <- lmer(learning ~
COMPOSIT +
agency +
stem_sb_dummy +
COMPOSIT*agency +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ COMPOSIT + agency + stem_sb_dummy + COMPOSIT * agency +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7441.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2283 -0.5624 0.1145 0.5839 2.7587
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011469 0.1071
## participant_ID (Intercept) 0.394888 0.6284
## program_ID (Intercept) 0.003411 0.0584
## Residual 0.708983 0.8420
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.480899 0.123948 123.936889 20.016 <2e-16 ***
## COMPOSIT 0.063486 0.034641 190.125804 1.833 0.0684 .
## agency 0.007313 0.047195 200.344647 0.155 0.8770
## stem_sb_dummy 0.060428 0.056051 213.188089 1.078 0.2822
## COMPOSIT:agency -0.001050 0.011789 186.786129 -0.089 0.9291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOSIT agency stm_s_
## COMPOSIT -0.834
## agency -0.690 0.735
## stm_sb_dmmy 0.118 -0.446 -0.332
## COMPOSIT:gn 0.716 -0.817 -0.954 0.310With variable where agency>=3 and class_comp is >=3
RQ1_learning_agency_comp_three <- lmer(learning ~
agency_comp_three +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_agency_comp_three)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ agency_comp_three + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7435.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2227 -0.5644 0.1104 0.5926 2.6851
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01425 0.1194
## participant_ID (Intercept) 0.39528 0.6287
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70883 0.8419
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.66302 0.06357 341.92189 41.893 <2e-16 ***
## agency_comp_three 0.05006 0.04204 196.27695 1.191 0.2353
## stem_sb_dummy 0.12375 0.04974 200.72841 2.488 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agnc__
## agncy_cmp_t -0.093
## stm_sb_dmmy -0.623 -0.126Next three models only two quality measures predicting learning
RQ1_learning_quality21 <- lmer(learning ~
#COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality21)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ agency + stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7437.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2217 -0.5649 0.1147 0.5879 2.6524
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01442 0.1201
## participant_ID (Intercept) 0.39641 0.6296
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70866 0.8418
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.65221 0.06575 339.74045 40.340 <2e-16 ***
## agency 0.01417 0.01392 194.73666 1.018 0.3098
## stem_sb_dummy 0.11874 0.05089 204.21560 2.333 0.0206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agency
## agency -0.267
## stm_sb_dmmy -0.537 -0.240RQ1_learning_quality22 <- lmer(learning ~
COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality22)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ COMPOSIT + stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7428.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2354 -0.5631 0.1162 0.5844 2.7679
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010826 0.10405
## participant_ID (Intercept) 0.394902 0.62841
## program_ID (Intercept) 0.003447 0.05871
## Residual 0.708978 0.84201
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.48935 0.08623 49.10935 28.869 < 2e-16 ***
## COMPOSIT 0.06223 0.01922 184.26484 3.237 0.00143 **
## stem_sb_dummy 0.06345 0.05261 203.82552 1.206 0.22923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.648
## stm_sb_dmmy -0.162 -0.401RQ1_learning_quality23 <- lmer(learning ~
COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ COMPOSIT + agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7432
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2132 -0.5641 0.1186 0.5829 2.7844
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010990 0.10483
## participant_ID (Intercept) 0.394537 0.62812
## program_ID (Intercept) 0.003433 0.05859
## Residual 0.709244 0.84217
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.505e+00 8.526e-02 4.699e+01 29.375 < 2e-16 ***
## COMPOSIT 6.915e-02 1.866e-02 1.837e+02 3.707 0.000278 ***
## agency 5.413e-03 1.395e-02 1.943e+02 0.388 0.698493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.728
## agency -0.053 -0.327Next three models only one quality measures predicting learning
RQ1_learning_quality11 <- lmer(learning ~
#COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7439
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1806 -0.5629 0.1146 0.5898 2.6693
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.578e-02 1.256e-01
## participant_ID (Intercept) 3.957e-01 6.291e-01
## program_ID (Intercept) 1.113e-14 1.055e-07
## Residual 7.089e-01 8.420e-01
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.73480 0.05562 291.63044 49.173 <2e-16 ***
## agency 0.02188 0.01364 190.30435 1.604 0.11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## agency -0.487RQ1_learning_quality12 <- lmer(learning ~
COMPOSIT +
#agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7442.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2210 -0.5679 0.1165 0.5838 2.7990
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010747 0.10367
## participant_ID (Intercept) 0.394590 0.62816
## program_ID (Intercept) 0.003546 0.05955
## Residual 0.708125 0.84150
## Number of obs: 2799, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.50307 0.08503 46.43376 29.438 < 2e-16 ***
## COMPOSIT 0.07224 0.01756 180.03083 4.114 5.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.788RQ1_learning_quality13 <- lmer(learning ~
#COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_quality13)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7432
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2334 -0.5611 0.1160 0.5866 2.6685
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.475e-02 1.214e-01
## participant_ID (Intercept) 3.970e-01 6.301e-01
## program_ID (Intercept) 5.435e-13 7.372e-07
## Residual 7.083e-01 8.416e-01
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67005 0.06346 346.52162 42.08 < 2e-16 ***
## stem_sb_dummy 0.13121 0.04950 204.30906 2.65 0.00867 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## stm_sb_dmmy -0.643Next models include pqa subject variable only predicting challenge.
RQ1_learning_subject3 <- lmer(learning ~
science +
mathematics +
building +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_subject3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ science + mathematics + building + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7438.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2323 -0.5644 0.1150 0.5951 2.6924
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.015204 0.12330
## participant_ID (Intercept) 0.397740 0.63067
## program_ID (Intercept) 0.002407 0.04906
## Residual 0.708212 0.84155
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.698e+00 6.829e-02 2.337e+01 39.509 <2e-16 ***
## science 1.495e-01 6.178e-02 1.791e+02 2.420 0.0165 *
## mathematics 5.199e-03 7.547e-02 2.349e+02 0.069 0.9451
## building 8.573e-02 8.089e-02 1.481e+02 1.060 0.2909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc mthmtc
## science -0.556
## mathematics -0.479 0.402
## building -0.428 0.323 0.308RQ1 affect with quality predictors only
RQ1_affect_quality3 <- lmer(positive_affect ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ COMPOSIT + agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6837.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5622 -0.4515 0.0535 0.5405 3.4848
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02709 0.1646
## participant_ID (Intercept) 0.49440 0.7031
## program_ID (Intercept) 0.10445 0.3232
## Residual 0.53932 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67155 0.13975 13.55752 19.116 3.39e-11 ***
## COMPOSIT 0.01313 0.02063 191.37756 0.636 0.525
## agency 0.01522 0.01436 204.06107 1.060 0.290
## stem_sb_dummy -0.05688 0.05401 210.72039 -1.053 0.293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.387
## agency -0.026 -0.260
## stm_sb_dmmy -0.100 -0.351 -0.122With interaction
RQ1_affect_quality31 <- lmer(positive_affect ~
COMPOSIT +
agency +
stem_sb_dummy +
COMPOSIT*agency +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ COMPOSIT + agency + stem_sb_dummy + COMPOSIT *
## agency + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6844.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5644 -0.4526 0.0527 0.5395 3.4828
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02747 0.1657
## participant_ID (Intercept) 0.49441 0.7031
## program_ID (Intercept) 0.10455 0.3233
## Residual 0.53930 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.656933 0.166550 26.580310 15.953 3.92e-15 ***
## COMPOSIT 0.017764 0.035519 198.997868 0.500 0.618
## agency 0.022605 0.047946 205.398962 0.471 0.638
## stem_sb_dummy -0.059611 0.056837 218.511573 -1.049 0.295
## COMPOSIT:agency -0.001935 0.012032 194.873436 -0.161 0.872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOSIT agency stm_s_
## COMPOSIT -0.631
## agency -0.524 0.730
## stm_sb_dmmy 0.085 -0.442 -0.325
## COMPOSIT:gn 0.543 -0.813 -0.954 0.304With variable where agency>=3 and class_comp is >=3
RQ1_affect_agency_comp_three <- lmer(positive_affect ~
agency_comp_three +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_agency_comp_three)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ agency_comp_three + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6831.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5607 -0.4525 0.0476 0.5388 3.4698
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02715 0.1648
## participant_ID (Intercept) 0.49542 0.7039
## program_ID (Intercept) 0.10675 0.3267
## Residual 0.53939 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.730045 0.128741 9.415637 21.206 2.89e-09 ***
## agency_comp_three -0.007088 0.041877 201.132329 -0.169 0.866
## stem_sb_dummy -0.028647 0.049555 208.039268 -0.578 0.564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agnc__
## agncy_cmp_t -0.048
## stm_sb_dmmy -0.307 -0.125Next three models only two quality measures predicting affect
RQ1_affect_quality21 <- lmer(positive_affect ~
#COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality21)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6832.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5797 -0.4510 0.0518 0.5411 3.4818
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02691 0.1640
## participant_ID (Intercept) 0.49458 0.7033
## program_ID (Intercept) 0.10562 0.3250
## Residual 0.53928 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.70597 0.12935 9.75215 20.920 1.99e-09 ***
## agency 0.01758 0.01385 201.81254 1.269 0.206
## stem_sb_dummy -0.04483 0.05051 211.32091 -0.888 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agency
## agency -0.141
## stm_sb_dmmy -0.271 -0.236RQ1_affect_quality22 <- lmer(positive_affect ~
COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality22)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ COMPOSIT + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6832.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5553 -0.4516 0.0504 0.5349 3.4806
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02686 0.1639
## participant_ID (Intercept) 0.49486 0.7035
## program_ID (Intercept) 0.10464 0.3235
## Residual 0.53946 0.7345
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67539 0.13973 13.53202 19.146 3.42e-11 ***
## COMPOSIT 0.01880 0.01989 188.86149 0.945 0.346
## stem_sb_dummy -0.04995 0.05352 210.61363 -0.933 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.407
## stm_sb_dmmy -0.103 -0.399RQ1_affect_quality23 <- lmer(positive_affect ~
COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ COMPOSIT + agency + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6834.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5705 -0.4503 0.0504 0.5394 3.4751
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02728 0.1652
## participant_ID (Intercept) 0.49463 0.7033
## program_ID (Intercept) 0.10556 0.3249
## Residual 0.53920 0.7343
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.657e+00 1.396e-01 1.326e+01 19.038 5.14e-11 ***
## COMPOSIT 5.491e-03 1.934e-02 1.935e+02 0.284 0.777
## agency 1.340e-02 1.427e-02 2.061e+02 0.938 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.452
## agency -0.038 -0.325Next three models only one quality measures predicting affect
RQ1_affect_quality11 <- lmer(positive_affect ~
#COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6828.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5785 -0.4483 0.0492 0.5413 3.4756
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02695 0.1642
## participant_ID (Intercept) 0.49467 0.7033
## program_ID (Intercept) 0.10597 0.3255
## Residual 0.53920 0.7343
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67479 0.12465 8.37537 21.458 1.29e-08 ***
## agency 0.01469 0.01347 200.78955 1.091 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## agency -0.219RQ1_affect_quality12 <- lmer(positive_affect ~
COMPOSIT +
#agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6843.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5524 -0.4518 0.0476 0.5384 3.4755
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02687 0.1639
## participant_ID (Intercept) 0.49499 0.7036
## program_ID (Intercept) 0.10738 0.3277
## Residual 0.53854 0.7339
## Number of obs: 2800, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.66202 0.14003 13.08053 19.011 6.52e-11 ***
## COMPOSIT 0.01076 0.01819 189.60388 0.592 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.488RQ1_affect_quality13 <- lmer(positive_affect ~
#COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_quality13)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ stem_sb_dummy + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6827
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5595 -0.4521 0.0466 0.5384 3.4736
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02682 0.1638
## participant_ID (Intercept) 0.49528 0.7038
## program_ID (Intercept) 0.10649 0.3263
## Residual 0.53940 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.72907 0.12844 9.37438 21.248 3.01e-09 ***
## stem_sb_dummy -0.02974 0.04906 208.65830 -0.606 0.545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## stm_sb_dmmy -0.315Next models include pqa subject variable only predicting challenge.
RQ1_affect_subject3 <- lmer(positive_affect ~
science +
mathematics +
building +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_subject3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ science + mathematics + building + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6823.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5804 -0.4661 0.0467 0.5313 3.5760
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02527 0.1590
## participant_ID (Intercept) 0.49651 0.7046
## program_ID (Intercept) 0.06542 0.2558
## Residual 0.53930 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.76465 0.11028 9.59210 25.069 4.56e-10 ***
## science 0.01600 0.06252 185.58293 0.256 0.79829
## mathematics -0.22451 0.07580 327.16423 -2.962 0.00328 **
## building -0.05021 0.08479 180.32152 -0.592 0.55452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc mthmtc
## science -0.317
## mathematics -0.279 0.339
## building -0.251 0.237 0.249RQ1 challenge with activity predictors only
RQ1_challenge_activity2 <- lmer(challenge ~
creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_activity2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ creating_product + basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7470.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0384 -0.6353 -0.0181 0.5593 3.4064
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0526 0.2293
## participant_ID (Intercept) 0.4782 0.6915
## program_ID (Intercept) 0.0420 0.2049
## Residual 0.6644 0.8151
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.16943 0.09003 8.87980 24.097 2.13e-09 ***
## creating_product 0.44116 0.06172 224.41365 7.148 1.23e-11 ***
## basic_skills 0.12553 0.05616 204.38662 2.235 0.0265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.172
## basic_sklls -0.173 0.248RQ1_challenge_activity11 <- lmer(challenge ~
creating_product +
#basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_activity11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7471.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0498 -0.6383 -0.0248 0.5596 3.3819
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05535 0.2353
## participant_ID (Intercept) 0.47939 0.6924
## program_ID (Intercept) 0.03814 0.1953
## Residual 0.66407 0.8149
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.20412 0.08630 8.36945 25.539 3.09e-09 ***
## creating_product 0.40721 0.06047 234.94902 6.734 1.25e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## crtng_prdct -0.140RQ1_challenge_activity12 <- lmer(challenge ~
#creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_activity12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7513.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0868 -0.6256 -0.0324 0.5617 3.3903
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07845 0.2801
## participant_ID (Intercept) 0.47281 0.6876
## program_ID (Intercept) 0.04938 0.2222
## Residual 0.66462 0.8152
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.28210 0.09394 8.09602 24.294 7.46e-09 ***
## basic_skills 0.02149 0.06029 219.25777 0.356 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## basic_sklls -0.144RQ1 relevance with activity predictors only
RQ1_relevance_activity2 <- lmer(relevance ~
creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_activity2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ creating_product + basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6161.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9333 -0.5138 0.0372 0.5719 4.0151
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010758 0.10372
## participant_ID (Intercept) 0.480631 0.69328
## program_ID (Intercept) 0.004805 0.06932
## Residual 0.418026 0.64655
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.53000 0.05782 8.43945 43.753 3.01e-11 ***
## creating_product 0.18722 0.04061 258.05973 4.610 6.34e-06 ***
## basic_skills 0.06771 0.03653 221.86789 1.853 0.0651 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.170
## basic_sklls -0.171 0.234RQ1_relevance_activity11 <- lmer(relevance ~
creating_product +
#basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_activity11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6159.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9413 -0.5163 0.0426 0.5696 3.9954
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011403 0.10679
## participant_ID (Intercept) 0.479939 0.69278
## program_ID (Intercept) 0.004221 0.06497
## Residual 0.418000 0.64653
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.54813 0.05638 8.01971 45.199 6.05e-11 ***
## creating_product 0.16984 0.03975 270.07024 4.273 2.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## crtng_prdct -0.138RQ1_relevance_activity12 <- lmer(relevance ~
#creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_activity12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6177.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8501 -0.5151 0.0342 0.5886 3.8870
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.014199 0.11916
## participant_ID (Intercept) 0.481040 0.69357
## program_ID (Intercept) 0.008234 0.09074
## Residual 0.418517 0.64693
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.57602 0.06058 7.84151 42.522 1.48e-10 ***
## basic_skills 0.02742 0.03686 227.96139 0.744 0.458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## basic_sklls -0.134RQ1 learning with activity predictors only
RQ1_learning_activity2 <- lmer(learning ~
creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_activity2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ creating_product + basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7494.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1815 -0.5629 0.1353 0.5832 2.8175
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 1.261e-02 1.123e-01
## participant_ID (Intercept) 4.018e-01 6.339e-01
## program_ID (Intercept) 2.198e-17 4.689e-09
## Residual 7.074e-01 8.411e-01
## Number of obs: 2817, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.72024 0.05109 239.76638 53.247 < 2e-16 ***
## creating_product 0.11122 0.05059 228.22925 2.198 0.028921 *
## basic_skills 0.17895 0.04552 192.62252 3.931 0.000118 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.237
## basic_sklls -0.241 0.233RQ1_learning_activity11 <- lmer(learning ~
creating_product +
#basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_activity11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7505.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1551 -0.5703 0.1203 0.5800 2.7977
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01758 0.1326
## participant_ID (Intercept) 0.39894 0.6316
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70764 0.8412
## Number of obs: 2817, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.76861 0.04975 224.26550 55.653 <2e-16 ***
## creating_product 0.06518 0.05077 240.00017 1.284 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## crtng_prdct -0.198RQ1_learning_activity12 <- lmer(learning ~
#creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_activity12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7495.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2169 -0.5711 0.1363 0.5704 2.8080
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0135757 0.11651
## participant_ID (Intercept) 0.4037132 0.63538
## program_ID (Intercept) 0.0005978 0.02445
## Residual 0.7073974 0.84107
## Number of obs: 2817, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74694 0.05049 7.85306 54.403 2.09e-11 ***
## basic_skills 0.15582 0.04458 202.05260 3.495 0.000582 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## basic_sklls -0.195RQ1 affect with activity predictors only
RQ1_affect_activity2 <- lmer(positive_affect ~
creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_activity2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ creating_product + basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6923.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4640 -0.4518 0.0506 0.5499 3.4745
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02638 0.1624
## participant_ID (Intercept) 0.49457 0.7033
## program_ID (Intercept) 0.10906 0.3302
## Residual 0.54655 0.7393
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.68139 0.12405 7.81711 21.616 2.97e-08 ***
## creating_product 0.05184 0.05088 229.50599 1.019 0.309
## basic_skills 0.05093 0.04597 203.89652 1.108 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.101
## basic_sklls -0.102 0.240RQ1_affect_activity11 <- lmer(positive_affect ~
creating_product +
#basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_activity11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ creating_product + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6920.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4777 -0.4494 0.0513 0.5510 3.4649
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02633 0.1623
## participant_ID (Intercept) 0.49431 0.7031
## program_ID (Intercept) 0.10641 0.3262
## Residual 0.54668 0.7394
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69550 0.12219 7.65005 22.059 3.34e-08 ***
## creating_product 0.03838 0.04937 237.71186 0.777 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## crtng_prdct -0.080RQ1_affect_activity12 <- lmer(positive_affect ~
#creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_activity12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6920.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4933 -0.4485 0.0494 0.5471 3.4532
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02595 0.1611
## participant_ID (Intercept) 0.49497 0.7035
## program_ID (Intercept) 0.11168 0.3342
## Residual 0.54676 0.7394
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69408 0.12459 7.66649 21.624 3.78e-08 ***
## basic_skills 0.03976 0.04450 210.84960 0.893 0.373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## basic_sklls -0.079Manova of class composite, agency, stem_sb by activity. Post hocs included.
fit<-manova(cbind(df$COMPOSIT, df$agency, df$stem_sb_dummy) ~ 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 0.51688 112.4 15 8100 < 2.2e-16 ***
## Residuals 2700
## ---
## 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$youth_activity_rc 5 808.46 161.693 169.63 < 2.2e-16 ***
## Residuals 2700 2573.73 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$youth_activity_rc 5 1249.4 249.890 166.14 < 2.2e-16 ***
## Residuals 2700 4060.9 1.504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$youth_activity_rc 5 68.31 13.6625 111.86 < 2.2e-16 ***
## Residuals 2700 329.76 0.1221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 264 observations deleted due to missingnessTukeyHSD(aov(df$COMPOSIT ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$COMPOSIT ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr upr
## Basic Skills Activity-Not Focused 0.9393988 0.7941020 1.0846955
## Creating Product-Not Focused 1.3643686 1.2121864 1.5165509
## Field Trip Speaker-Not Focused 0.9957199 0.7383650 1.2530748
## Lab Activity-Not Focused 1.5348462 1.2732479 1.7964444
## Program Staff Led-Not Focused 0.5656280 0.3982513 0.7330047
## Creating Product-Basic Skills Activity 0.4249699 0.2627470 0.5871927
## Field Trip Speaker-Basic Skills Activity 0.0563211 -0.2070956 0.3197378
## Lab Activity-Basic Skills Activity 0.5954474 0.3278835 0.8630113
## Program Staff Led-Basic Skills Activity -0.3737707 -0.5503260 -0.1972155
## Field Trip Speaker-Creating Product -0.3686488 -0.6359252 -0.1013724
## Lab Activity-Creating Product 0.1704775 -0.1008871 0.4418422
## Program Staff Led-Creating Product -0.7987406 -0.9810043 -0.6164769
## Lab Activity-Field Trip Speaker 0.5391263 0.1974966 0.8807560
## Program Staff Led-Field Trip Speaker -0.4300918 -0.7063022 -0.1538814
## Program Staff Led-Lab Activity -0.9692181 -1.2493864 -0.6890498
## p adj
## Basic Skills Activity-Not Focused 0.0000000
## Creating Product-Not Focused 0.0000000
## Field Trip Speaker-Not Focused 0.0000000
## Lab Activity-Not Focused 0.0000000
## Program Staff Led-Not Focused 0.0000000
## Creating Product-Basic Skills Activity 0.0000000
## Field Trip Speaker-Basic Skills Activity 0.9903661
## Lab Activity-Basic Skills Activity 0.0000000
## Program Staff Led-Basic Skills Activity 0.0000000
## Field Trip Speaker-Creating Product 0.0012062
## Lab Activity-Creating Product 0.4713671
## Program Staff Led-Creating Product 0.0000000
## Lab Activity-Field Trip Speaker 0.0001032
## Program Staff Led-Field Trip Speaker 0.0001358
## Program Staff Led-Lab Activity 0.0000000TukeyHSD(aov(df$agency ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$agency ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr
## Basic Skills Activity-Not Focused -0.117682142 -0.3011287
## Creating Product-Not Focused 1.588144786 1.3967032
## Field Trip Speaker-Not Focused -0.292657117 -0.6164033
## Lab Activity-Not Focused 0.837827213 0.5087431
## Program Staff Led-Not Focused -0.123673355 -0.3342291
## Creating Product-Basic Skills Activity 1.705826928 1.5011571
## Field Trip Speaker-Basic Skills Activity -0.174974975 -0.5067150
## Lab Activity-Basic Skills Activity 0.955509356 0.6185579
## Program Staff Led-Basic Skills Activity -0.005991213 -0.2286425
## Field Trip Speaker-Creating Product -1.880801903 -2.2170290
## Lab Activity-Creating Product -0.750317572 -1.0916876
## Program Staff Led-Creating Product -1.711818141 -1.9411014
## Lab Activity-Field Trip Speaker 1.130484330 0.7007226
## Program Staff Led-Field Trip Speaker 0.168983762 -0.1784821
## Program Staff Led-Lab Activity -0.961500568 -1.3139454
## upr p adj
## Basic Skills Activity-Not Focused 0.06576444 0.4468305
## Creating Product-Not Focused 1.77958635 0.0000000
## Field Trip Speaker-Not Focused 0.03108902 0.1030306
## Lab Activity-Not Focused 1.16691136 0.0000000
## Program Staff Led-Not Focused 0.08688239 0.5485189
## Creating Product-Basic Skills Activity 1.91049678 0.0000000
## Field Trip Speaker-Basic Skills Activity 0.15676504 0.6616461
## Lab Activity-Basic Skills Activity 1.29246077 0.0000000
## Program Staff Led-Basic Skills Activity 0.21666008 0.9999996
## Field Trip Speaker-Creating Product -1.54457481 0.0000000
## Lab Activity-Creating Product -0.40894757 0.0000000
## Program Staff Led-Creating Product -1.48253486 0.0000000
## Lab Activity-Field Trip Speaker 1.56024609 0.0000000
## Program Staff Led-Field Trip Speaker 0.51644961 0.7350207
## Program Staff Led-Lab Activity -0.60905578 0.0000000TukeyHSD(aov(df$stem_sb_dummy ~ df$youth_activity_rc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$stem_sb_dummy ~ df$youth_activity_rc)
##
## $`df$youth_activity_rc`
## diff lwr
## Basic Skills Activity-Not Focused 3.166171e-01 0.264341825
## Creating Product-Not Focused 3.225884e-01 0.268034887
## Field Trip Speaker-Not Focused 4.088269e-01 0.316571671
## Lab Activity-Not Focused 4.088269e-01 0.315050542
## Program Staff Led-Not Focused 3.398614e-01 0.279861088
## Creating Product-Basic Skills Activity 5.971325e-03 -0.052351758
## Field Trip Speaker-Basic Skills Activity 9.220986e-02 -0.002323367
## Lab Activity-Basic Skills Activity 9.220986e-02 -0.003808415
## Program Staff Led-Basic Skills Activity 2.324434e-02 -0.040202768
## Field Trip Speaker-Creating Product 8.623853e-02 -0.009573338
## Lab Activity-Creating Product 8.623853e-02 -0.011038868
## Program Staff Led-Creating Product 1.727301e-02 -0.048063955
## Lab Activity-Field Trip Speaker -1.820766e-14 -0.122465673
## Program Staff Led-Field Trip Speaker -6.896552e-02 -0.167980001
## Program Staff Led-Lab Activity -6.896552e-02 -0.169398809
## upr p adj
## Basic Skills Activity-Not Focused 0.36889235 0.0000000
## Creating Product-Not Focused 0.37714194 0.0000000
## Field Trip Speaker-Not Focused 0.50108222 0.0000000
## Lab Activity-Not Focused 0.50260335 0.0000000
## Program Staff Led-Not Focused 0.39986177 0.0000000
## Creating Product-Basic Skills Activity 0.06429441 0.9997162
## Field Trip Speaker-Basic Skills Activity 0.18674308 0.0607078
## Lab Activity-Basic Skills Activity 0.18822813 0.0681796
## Program Staff Led-Basic Skills Activity 0.08669145 0.9027019
## Field Trip Speaker-Creating Product 0.18205040 0.1058781
## Lab Activity-Creating Product 0.18351593 0.1162908
## Program Staff Led-Creating Product 0.08260998 0.9749532
## Lab Activity-Field Trip Speaker 0.12246567 1.0000000
## Program Staff Led-Field Trip Speaker 0.03004897 0.3503920
## Program Staff Led-Lab Activity 0.03146777 0.3669051Manova of quality measures by subject.
fit<-manova(cbind(df$COMPOSIT, df$agency, df$stem_sb_dummy) ~ df$subject, data = df)
summary(fit, test="Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## df$subject 2 0.15201 64.791 6 4726 < 2.2e-16 ***
## Residuals 2364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.aov(fit)## Response 1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$subject 2 314.11 157.054 151.53 < 2.2e-16 ***
## Residuals 2364 2450.23 1.036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$subject 2 215.5 107.746 58.487 < 2.2e-16 ***
## Residuals 2364 4355.0 1.842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## df$subject 2 6.265 3.13257 39.139 < 2.2e-16 ***
## Residuals 2364 189.206 0.08004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 603 observations deleted due to missingnessTukeyHSD(aov(df$COMPOSIT ~ df$subject))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$COMPOSIT ~ df$subject)
##
## $`df$subject`
## diff lwr upr p adj
## Math-Building -0.3298073 -0.4692741 -0.1903405 1e-07
## Science-Building 0.5011466 0.3744265 0.6278667 0e+00
## Science-Math 0.8309539 0.7162625 0.9456453 0e+00TukeyHSD(aov(df$agency ~ df$subject))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$agency ~ df$subject)
##
## $`df$subject`
## diff lwr upr p adj
## Math-Building -0.7894505 -0.9753854 -0.60351556 0.0000000
## Science-Building -0.2180533 -0.3869945 -0.04911214 0.0070475
## Science-Math 0.5713972 0.4184924 0.72430190 0.0000000#TukeyHSD(aov(df$stem_sb_dummy ~ df$subject))
chisq.test(df$stem_sb_dummy, df$subject)##
## Pearson's Chi-squared test
##
## data: df$stem_sb_dummy and df$subject
## X-squared = 75.866, df = 2, p-value < 2.2e-16Crosstabs of subject by quality measures
table1 <- table(df$stem_sb_dummy, df$subject)
prop.table(table1)##
## Building Math Science
## 0 0.02746092 0.04393747 0.01943388
## 1 0.18800169 0.24714829 0.47401774table2 <-table(df$agency, df$subject)
prop.table(table2)##
## Building Math Science
## 0 0.03126320 0.07435572 0.08027038
## 1 0.04351500 0.07435572 0.04520490
## 2 0.04267005 0.07520068 0.20532319
## 3 0.01182932 0.03548796 0.04816223
## 4 0.08618504 0.03168568 0.11449092table3 <-table(df$COMPOSIT, df$subject)
prop.table(table3)##
## Building Math Science
## 1.16666666666667 0.000000000 0.000000000 0.000000000
## 1.33333333333333 0.000000000 0.005492184 0.000000000
## 1.5 0.005914660 0.003802281 0.000000000
## 1.66666666666667 0.002957330 0.000000000 0.000000000
## 1.83333333333333 0.000000000 0.000000000 0.005492184
## 2 0.003802281 0.008871990 0.000000000
## 2.16666666666667 0.002957330 0.019433883 0.000000000
## 2.33333333333333 0.003379806 0.017743980 0.013941698
## 2.5 0.015631601 0.001689903 0.015209125
## 2.66666666666667 0.013096747 0.002957330 0.000000000
## 2.83333333333333 0.000000000 0.016899028 0.021123785
## 3 0.024926067 0.012251796 0.008449514
## 3.16666666666667 0.010984368 0.026193494 0.006759611
## 3.33333333333333 0.008871990 0.010139417 0.020701310
## 3.5 0.013941698 0.020278834 0.019011407
## 3.66666666666667 0.004647233 0.005914660 0.024081115
## 3.83333333333333 0.011406844 0.028305872 0.008027038
## 4 0.005069708 0.021968737 0.034643008
## 4.16666666666667 0.000000000 0.024503591 0.026193494
## 4.33333333333333 0.020278834 0.013519223 0.034220532
## 4.5 0.008449514 0.012251796 0.032530629
## 4.66666666666667 0.003379806 0.016054077 0.027038445
## 4.83333333333333 0.000000000 0.009716941 0.029573300
## 5 0.000000000 0.005492184 0.042247571
## 5.16666666666667 0.021968737 0.005069708 0.042670046
## 5.33333333333333 0.004647233 0.000000000 0.016899028
## 5.5 0.000000000 0.002534854 0.019856358
## 5.66666666666667 0.011406844 0.000000000 0.028728348
## 5.83333333333333 0.012674271 0.000000000 0.004647233
## 6 0.005069708 0.000000000 0.011406844RQ3 engagement with quality predictors only
RQ3_engagement_quality3 <- lmer(overall_engagement ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ COMPOSIT + agency + stem_sb_dummy + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5851.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2157 -0.5058 0.0743 0.5729 3.8408
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02141 0.1463
## participant_ID (Intercept) 0.32527 0.5703
## program_ID (Intercept) 0.01461 0.1209
## Residual 0.38014 0.6166
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69069 0.08498 30.94751 31.661 <2e-16 ***
## COMPOSIT 0.03917 0.01759 203.90317 2.226 0.0271 *
## agency 0.02330 0.01230 213.74863 1.895 0.0595 .
## stem_sb_dummy -0.03017 0.04626 219.91354 -0.652 0.5150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.551
## agency -0.035 -0.256
## stm_sb_dmmy -0.139 -0.350 -0.123With interaction
RQ1_engagement_quality31 <- lmer(overall_engagement ~
COMPOSIT +
agency +
stem_sb_dummy +
COMPOSIT*agency +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_engagement_quality31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ COMPOSIT + agency + stem_sb_dummy + COMPOSIT *
## agency + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5858.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2134 -0.5058 0.0715 0.5761 3.8464
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02137 0.1462
## participant_ID (Intercept) 0.32534 0.5704
## program_ID (Intercept) 0.01424 0.1194
## Residual 0.38024 0.6166
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.749343 0.114689 84.880990 23.972 <2e-16 ***
## COMPOSIT 0.020464 0.030283 208.393836 0.676 0.500
## agency -0.006346 0.040978 212.300989 -0.155 0.877
## stem_sb_dummy -0.019059 0.048508 226.457922 -0.393 0.695
## COMPOSIT:agency 0.007803 0.010288 201.800055 0.758 0.449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOSIT agency stm_s_
## COMPOSIT -0.786
## agency -0.651 0.732
## stm_sb_dmmy 0.105 -0.440 -0.323
## COMPOSIT:gn 0.674 -0.814 -0.954 0.302With variable where agency>=3 and class_comp is >=3
RQ1_engagement_agency_comp_three <- lmer(overall_engagement ~
agency_comp_three +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_engagement_agency_comp_three)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ agency_comp_three + stem_sb_dummy + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5849.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1616 -0.5094 0.0769 0.5772 3.8680
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02261 0.1504
## participant_ID (Intercept) 0.32584 0.5708
## program_ID (Intercept) 0.01322 0.1150
## Residual 0.38022 0.6166
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.82260 0.06848 14.11372 41.215 4.11e-16 ***
## agency_comp_three 0.08251 0.03629 209.55611 2.274 0.024 *
## stem_sb_dummy 0.01954 0.04290 217.90904 0.455 0.649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agnc__
## agncy_cmp_t -0.078
## stm_sb_dmmy -0.499 -0.125Next three models only two quality measures predicting affect
RQ3_engagement_quality21 <- lmer(overall_engagement ~
#COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality21)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5850
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1735 -0.5113 0.0736 0.5814 3.8779
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0227 0.1507
## participant_ID (Intercept) 0.3262 0.5712
## program_ID (Intercept) 0.0139 0.1179
## Residual 0.3799 0.6164
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.795e+00 7.064e-02 1.519e+01 39.567 <2e-16 ***
## agency 3.041e-02 1.203e-02 2.133e+02 2.529 0.0122 *
## stem_sb_dummy 5.688e-03 4.382e-02 2.232e+02 0.130 0.8968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agency
## agency -0.222
## stm_sb_dmmy -0.432 -0.235RQ3_engagement_quality22 <- lmer(overall_engagement ~
COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality22)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ COMPOSIT + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5848
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2238 -0.5107 0.0714 0.5734 3.8184
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02161 0.1470
## participant_ID (Intercept) 0.32536 0.5704
## program_ID (Intercept) 0.01525 0.1235
## Residual 0.38038 0.6168
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69633 0.08545 30.43575 31.553 < 2e-16 ***
## COMPOSIT 0.04771 0.01704 200.14305 2.799 0.00563 **
## stem_sb_dummy -0.01938 0.04600 219.19099 -0.421 0.67400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.578
## stm_sb_dmmy -0.144 -0.398RQ3_engagement_quality23 <- lmer(overall_engagement ~
COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ COMPOSIT + agency + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2139 -0.5109 0.0731 0.5747 3.8374
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02124 0.1457
## participant_ID (Intercept) 0.32523 0.5703
## program_ID (Intercept) 0.01467 0.1211
## Residual 0.38016 0.6166
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.68298 0.08412 29.77935 31.894 <2e-16 ***
## COMPOSIT 0.03516 0.01645 204.53841 2.138 0.0337 *
## agency 0.02229 0.01218 214.30044 1.830 0.0687 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS
## COMPOSIT -0.646
## agency -0.053 -0.322Next three models only one quality measures predicting affect
RQ3_engagement_quality11 <- lmer(overall_engagement ~
#COMPOSIT +
#agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ stem_sb_dummy + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5849.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1723 -0.5088 0.0743 0.5809 3.8595
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02361 0.1537
## participant_ID (Intercept) 0.32661 0.5715
## program_ID (Intercept) 0.01437 0.1199
## Residual 0.38015 0.6166
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.83468 0.06945 13.81022 40.815 8.48e-16 ***
## stem_sb_dummy 0.03159 0.04295 219.96488 0.736 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## stm_sb_dmmy -0.510RQ3_engagement_quality12 <- lmer(overall_engagement ~
COMPOSIT +
#agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ COMPOSIT + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5857.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2260 -0.5119 0.0689 0.5737 3.8176
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02143 0.1464
## participant_ID (Intercept) 0.32508 0.5702
## program_ID (Intercept) 0.01528 0.1236
## Residual 0.38001 0.6164
## Number of obs: 2800, groups:
## beep_ID_new, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.68665 0.08443 29.19140 31.820 < 2e-16 ***
## COMPOSIT 0.04599 0.01557 198.95203 2.954 0.00352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## COMPOSIT -0.699RQ3_engagement_quality13 <- lmer(overall_engagement ~
#COMPOSIT +
agency +
#stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_quality13)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ agency + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5845.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1734 -0.5106 0.0740 0.5812 3.8781
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02247 0.1499
## participant_ID (Intercept) 0.32623 0.5712
## program_ID (Intercept) 0.01388 0.1178
## Residual 0.37991 0.6164
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.79896 0.06368 10.11194 43.956 6.99e-13 ***
## agency 0.03076 0.01166 210.78200 2.637 0.00898 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## agency -0.368Next models include pqa subject variable only predicting challenge.
RQ1_engagement_subject3 <- lmer(overall_engagement ~
science +
mathematics +
building +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_engagement_subject3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ science + mathematics + building + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5842.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1620 -0.5085 0.0738 0.5787 3.8696
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.022258 0.14919
## participant_ID (Intercept) 0.325427 0.57046
## program_ID (Intercept) 0.003754 0.06127
## Residual 0.379765 0.61625
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.86463 0.06158 20.72098 46.516 < 2e-16 ***
## science 0.07439 0.05347 201.47087 1.391 0.16571
## mathematics -0.17227 0.06334 276.25314 -2.720 0.00695 **
## building 0.03196 0.07057 170.48382 0.453 0.65120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc mthmtc
## science -0.522
## mathematics -0.446 0.390
## building -0.401 0.304 0.294RQ3 engagement with activity predictors only
RQ3_engagement_activity2 <- lmer(overall_engagement ~
creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_activity2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ creating_product + basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5912.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1384 -0.5088 0.0752 0.5829 3.8766
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02320 0.1523
## participant_ID (Intercept) 0.32431 0.5695
## program_ID (Intercept) 0.01282 0.1132
## Residual 0.38185 0.6179
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.82111 0.05974 8.38313 47.224 1.81e-11 ***
## creating_product 0.14574 0.04422 240.59138 3.296 0.00113 **
## basic_skills 0.07300 0.04014 215.35996 1.819 0.07035 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.183
## basic_sklls -0.185 0.244RQ3_engagement_activity11 <- lmer(overall_engagement ~
#creating_product +
basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_activity11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5919.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1453 -0.5157 0.0714 0.5826 3.8717
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02509 0.1584
## participant_ID (Intercept) 0.32471 0.5698
## program_ID (Intercept) 0.01437 0.1199
## Residual 0.38214 0.6182
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.85698 0.06032 7.73147 47.366 8.29e-11 ***
## basic_skills 0.04060 0.03958 221.01633 1.026 0.306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## basic_sklls -0.146RQ3_engagement_activity12 <- lmer(overall_engagement ~
creating_product +
#basic_skills +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_activity12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ creating_product + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5911.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1599 -0.5125 0.0728 0.5848 3.8554
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02395 0.1548
## participant_ID (Intercept) 0.32405 0.5693
## program_ID (Intercept) 0.01072 0.1035
## Residual 0.38186 0.6180
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.84130 0.05665 7.77654 50.160 4.77e-11 ***
## creating_product 0.12658 0.04314 250.20545 2.934 0.00365 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## crtng_prdct -0.151Feb 14, 2018
Predicting interest
RQ1_important_quality <- lmer(important ~
COMPOSIT +
agency +
stem_sb_dummy +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_important_quality)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## important ~ COMPOSIT + agency + stem_sb_dummy + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7372.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2171 -0.5720 0.0631 0.6205 3.1347
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007692 0.08770
## participant_ID (Intercept) 0.473832 0.68835
## program_ID (Intercept) 0.007806 0.08835
## Residual 0.686290 0.82843
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.466108 0.089334 32.822607 27.605 <2e-16 ***
## COMPOSIT 0.031903 0.019237 191.512734 1.658 0.0989 .
## agency -0.007449 0.013555 202.627748 -0.550 0.5832
## stem_sb_dummy 0.096265 0.051172 210.209953 1.881 0.0613 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.574
## agency -0.028 -0.261
## stm_sb_dmmy -0.146 -0.352 -0.126Models with science, building, and other predicting all outcomes (comparing to math)
RQ1_challenge_subject <- lmer(challenge ~
science +
building +
subject_other +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_challenge_subject)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ science + building + subject_other + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7428.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0069 -0.6454 -0.0321 0.5691 3.4028
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07210 0.2685
## participant_ID (Intercept) 0.47105 0.6863
## program_ID (Intercept) 0.03779 0.1944
## Residual 0.66207 0.8137
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.04995 0.10917 18.56068 18.777 1.58e-13 ***
## science 0.30702 0.10055 182.61582 3.054 0.002600 **
## building 0.41941 0.12444 156.61451 3.370 0.000946 ***
## subject_other 0.17602 0.09572 282.63969 1.839 0.066995 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc buldng
## science -0.565
## building -0.474 0.464
## subject_thr -0.498 0.644 0.522RQ1_relevance_subject <- lmer(relevance ~
science +
building +
subject_other +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_relevance_subject)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ science + building + subject_other + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6113.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8501 -0.5243 0.0305 0.5819 3.8367
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007758 0.08808
## participant_ID (Intercept) 0.475069 0.68925
## program_ID (Intercept) 0.014910 0.12211
## Residual 0.421105 0.64893
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.457475 0.078785 15.453598 31.192 2.24e-15 ***
## science 0.205451 0.062841 257.851136 3.269 0.00122 **
## building 0.203250 0.076275 213.954161 2.665 0.00829 **
## subject_other -0.006098 0.060348 355.378147 -0.101 0.91957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc buldng
## science -0.509
## building -0.426 0.503
## subject_thr -0.459 0.694 0.571RQ1_learning_subject <- lmer(learning ~
science +
building +
subject_other +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_learning_subject)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ science + building + subject_other + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7438.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2323 -0.5644 0.1150 0.5951 2.6924
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.015204 0.12330
## participant_ID (Intercept) 0.397740 0.63067
## program_ID (Intercept) 0.002407 0.04906
## Residual 0.708212 0.84155
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.703488 0.073601 21.385091 36.732 <2e-16 ***
## science 0.144330 0.075932 112.713586 1.901 0.0599 .
## building 0.080534 0.092095 110.590658 0.874 0.3838
## subject_other -0.005199 0.075470 234.870489 -0.069 0.9451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc buldng
## science -0.662
## building -0.547 0.509
## subject_thr -0.580 0.667 0.549RQ1_affect_subject <- lmer(positive_affect ~
science +
building +
subject_other +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_affect_subject)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## positive_affect ~ science + building + subject_other + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6823.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5804 -0.4661 0.0467 0.5313 3.5760
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02527 0.1590
## participant_ID (Intercept) 0.49651 0.7046
## program_ID (Intercept) 0.06542 0.2558
## Residual 0.53930 0.7344
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.54014 0.11510 11.04919 22.068 1.73e-10 ***
## science 0.24051 0.08027 258.05012 2.996 0.00300 **
## building 0.17430 0.09867 213.00995 1.766 0.07875 .
## subject_other 0.22451 0.07580 327.16424 2.962 0.00328 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc buldng
## science -0.432
## building -0.367 0.479
## subject_thr -0.392 0.680 0.554RQ3_engagement_subject <- lmer(overall_engagement ~
science +
building +
subject_other +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_engagement_subject)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ science + building + subject_other + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5842.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1620 -0.5085 0.0738 0.5787 3.8696
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.022258 0.14919
## participant_ID (Intercept) 0.325427 0.57046
## program_ID (Intercept) 0.003754 0.06127
## Residual 0.379765 0.61625
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69235 0.06579 20.47920 40.926 < 2e-16 ***
## science 0.24666 0.06504 146.57355 3.792 0.000218 ***
## building 0.20424 0.07979 137.08846 2.560 0.011564 *
## subject_other 0.17227 0.06334 276.25315 2.720 0.006948 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scienc buldng
## science -0.624
## building -0.515 0.487
## subject_thr -0.546 0.653 0.534Community space variable predicting outcomes
RQ1_space_challenge <- lmer(challenge ~
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_space_challenge)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ Community_Space_Content + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7489
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9657 -0.6298 -0.0402 0.5636 3.3832
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.07725 0.2779
## participant_ID (Intercept) 0.46548 0.6823
## program_ID (Intercept) 0.04392 0.2096
## Residual 0.66562 0.8159
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.26276 0.09053 8.29651 24.995 4.19e-09
## Community_Space_Content 0.12801 0.06819 198.79065 1.877 0.0619
##
## (Intercept) ***
## Community_Space_Content .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cmmnty_Sp_C -0.159RQ1_space_relevance <- lmer(relevance ~
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_space_relevance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ Community_Space_Content + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6156.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9125 -0.5148 0.0342 0.5924 3.7226
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011998 0.10953
## participant_ID (Intercept) 0.475229 0.68937
## program_ID (Intercept) 0.009382 0.09686
## Residual 0.420234 0.64825
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.55551 0.06147 8.07539 41.575 1.04e-10
## Community_Space_Content 0.11174 0.04030 202.35486 2.773 0.00607
##
## (Intercept) ***
## Community_Space_Content **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cmmnty_Sp_C -0.142RQ1_space_learning <- lmer(learning ~
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_space_learning)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ Community_Space_Content + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7480.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2521 -0.5567 0.1164 0.5875 2.7170
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01633 0.1278
## participant_ID (Intercept) 0.39879 0.6315
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70828 0.8416
## Number of obs: 2808, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.75465 0.04994 226.74998 55.155 <2e-16
## Community_Space_Content 0.10127 0.05030 187.02698 2.014 0.0455
##
## (Intercept) ***
## Community_Space_Content *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cmmnty_Sp_C -0.218RQ1_space_affect <- lmer(positive_affect ~
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_space_affect)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive_affect ~ Community_Space_Content + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6860.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5350 -0.4428 0.0523 0.5432 3.4767
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02573 0.1604
## participant_ID (Intercept) 0.49594 0.7042
## program_ID (Intercept) 0.09889 0.3145
## Residual 0.53896 0.7341
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6829 0.1188 7.6933 22.583 2.61e-08 ***
## Community_Space_Content 0.1116 0.0498 189.1983 2.241 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cmmnty_Sp_C -0.088RQ3_space_engagement <- lmer(overall_engagement ~
Community_Space_Content +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_space_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ Community_Space_Content + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5886.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1669 -0.5091 0.0729 0.5731 3.8627
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02370 0.1539
## participant_ID (Intercept) 0.32702 0.5719
## program_ID (Intercept) 0.01394 0.1181
## Residual 0.38060 0.6169
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.85207 0.06006 8.01637 47.487 4.11e-11
## Community_Space_Content 0.03386 0.04394 198.45322 0.771 0.442
##
## (Intercept) ***
## Community_Space_Content
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cmmnty_Sp_C -0.156Value on engagement models
RQ3_value_engagement <- lmer(overall_engagement ~
all_value_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_value_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ all_value_sum + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1652 -0.5103 0.0709 0.5772 3.8586
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02276 0.1509
## participant_ID (Intercept) 0.32715 0.5720
## program_ID (Intercept) 0.01261 0.1123
## Residual 0.38082 0.6171
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.841e+00 5.896e-02 8.083e+00 48.191 3.12e-11 ***
## all_value_sum 7.121e-03 4.094e-03 1.729e+02 1.739 0.0838 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## all_valu_sm -0.179RQ3_high_utility_engagement <- lmer(overall_engagement ~
V01.01.HighUtility_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_high_utility_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ V01.01.HighUtility_sum + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5890.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1658 -0.5104 0.0714 0.5732 3.8611
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02333 0.1528
## participant_ID (Intercept) 0.32713 0.5720
## program_ID (Intercept) 0.01412 0.1188
## Residual 0.38073 0.6170
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.851e+00 6.012e-02 7.977e+00 47.412 4.57e-11 ***
## V01.01.HighUtility_sum 5.529e-03 5.533e-03 1.641e+02 0.999 0.319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.01.HgU_ -0.145RQ3_high_intrinsic_engagement <- lmer(overall_engagement ~
V01.03.HighIntrinsic_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_high_intrinsic_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ V01.03.HighIntrinsic_sum + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5885.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1662 -0.5069 0.0758 0.5718 3.8574
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02248 0.1499
## participant_ID (Intercept) 0.32718 0.5720
## program_ID (Intercept) 0.01176 0.1084
## Residual 0.38076 0.6171
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.844e+00 5.760e-02 7.720e+00 49.371 6.19e-11
## V01.03.HighIntrinsic_sum 1.653e-02 7.577e-03 1.922e+02 2.182 0.0303
##
## (Intercept) ***
## V01.03.HighIntrinsic_sum *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.03.HgI_ -0.125Predicting relevance and importance using high utility
RQ1_high_utility_relevance <- lmer(relevance ~
V01.01.HighUtility_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_high_utility_relevance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ V01.01.HighUtility_sum + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6158
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0044 -0.5268 0.0400 0.5843 3.6052
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010284 0.10141
## participant_ID (Intercept) 0.475507 0.68957
## program_ID (Intercept) 0.008634 0.09292
## Residual 0.421068 0.64890
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.553e+00 6.060e-02 7.827e+00 42.130 1.64e-10 ***
## V01.01.HighUtility_sum 1.626e-02 4.911e-03 1.533e+02 3.311 0.00116 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.01.HgU_ -0.131RQ1_high_utility_importance <- lmer(important ~
V01.01.HighUtility_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_high_utility_importance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: important ~ V01.01.HighUtility_sum + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7411.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2541 -0.5658 0.0710 0.6250 3.1078
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008372 0.09150
## participant_ID (Intercept) 0.473830 0.68835
## program_ID (Intercept) 0.003870 0.06221
## Residual 0.686520 0.82857
## Number of obs: 2808, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.626e+00 5.699e-02 7.346e+00 46.08 2.57e-10 ***
## V01.01.HighUtility_sum 1.364e-02 5.805e-03 1.409e+02 2.35 0.0201 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.01.HgU_ -0.167Predicting relevance and importance using high intrinsic
RQ1_high_intrinsic_relevance <- lmer(relevance ~
V01.03.HighIntrinsic_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_high_intrinsic_relevance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ V01.03.HighIntrinsic_sum + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6164.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9089 -0.5115 0.0415 0.5878 3.7188
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.012427 0.11148
## participant_ID (Intercept) 0.475927 0.68987
## program_ID (Intercept) 0.008639 0.09295
## Residual 0.420595 0.64853
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.567e+00 6.057e-02 7.744e+00 42.375 1.9e-10
## V01.03.HighIntrinsic_sum 1.301e-02 7.058e-03 1.998e+02 1.844 0.0667
##
## (Intercept) ***
## V01.03.HighIntrinsic_sum .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.03.HgI_ -0.113RQ3_high_intrinsic_importance <- lmer(important ~
V01.03.HighIntrinsic_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_high_intrinsic_importance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: important ~ V01.03.HighIntrinsic_sum + (1 | program_ID) + (1 |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7413.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2432 -0.5723 0.0769 0.6226 3.1189
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.010104 0.10052
## participant_ID (Intercept) 0.473529 0.68813
## program_ID (Intercept) 0.002993 0.05471
## Residual 0.685920 0.82820
## Number of obs: 2808, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.636e+00 5.592e-02 7.089e+00 47.137 4.07e-10
## V01.03.HighIntrinsic_sum 1.292e-02 8.313e-03 1.923e+02 1.555 0.122
##
## (Intercept) ***
## V01.03.HighIntrinsic_sum
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V01.03.HgI_ -0.144Feb 21, 2018
Frequencies of Utility Value Variables
count(value, V06.01.RealLifeNoGoal_sum)## # A tibble: 11 x 2
## V06.01.RealLifeNoGoal_sum n
## <int> <int>
## 1 0 195
## 2 1 17
## 3 2 6
## 4 3 8
## 5 4 2
## 6 5 2
## 7 7 2
## 8 8 1
## 9 9 3
## 10 13 1
## 11 NA 21count(value, V06.02.ImpactsForPassiveOutcome_sum)## # A tibble: 7 x 2
## V06.02.ImpactsForPassiveOutcome_sum n
## <int> <int>
## 1 0 219
## 2 1 8
## 3 2 4
## 4 4 1
## 5 5 3
## 6 8 2
## 7 NA 21count(value, V06.03.UsefulForSpecificGoal_sum)## # A tibble: 10 x 2
## V06.03.UsefulForSpecificGoal_sum n
## <int> <int>
## 1 0 183
## 2 1 19
## 3 2 15
## 4 3 6
## 5 4 8
## 6 5 1
## 7 6 2
## 8 7 2
## 9 16 1
## 10 NA 21Frequencies of Utility Value Variables by program
table(value$Site, value$V06.01.RealLifeNoGoal_sum)##
## 0 1 2 3 4 5 7 8 9 13
## 001. BASB Dorchester House - ESM Signaling 21 1 0 0 0 0 0 0 0 0
## 002. BASB MathPOWER - ESM Signaling 23 1 0 0 0 0 0 0 0 0
## 004. BASB Sociedad Latina - ESM Signaling 50 2 0 0 0 0 1 0 0 0
## 005. BASB Thompson Island - ESM Signaling 14 3 3 3 2 1 0 0 0 0
## 006. PASA Biomes - ESM Signaling 19 1 0 0 0 0 0 0 0 1
## 007. PASA DownCity Design - ESM Signaling 21 3 0 0 0 0 0 0 0 0
## 008. PASA Crazy Machines - ESM Signaling 14 3 0 1 0 0 0 1 1 0
## 009. PASA Explore the Bay - ESM Signaling 15 1 2 2 0 1 1 0 2 0
## 010. PASA RWP Zoo - ESM Signaling 18 2 1 2 0 0 0 0 0 0table(value$Site, value$V06.02.ImpactsForPassiveOutcome_sum)##
## 0 1 2 4 5 8
## 001. BASB Dorchester House - ESM Signaling 21 0 0 1 0 0
## 002. BASB MathPOWER - ESM Signaling 23 0 1 0 0 0
## 004. BASB Sociedad Latina - ESM Signaling 42 4 2 0 3 2
## 005. BASB Thompson Island - ESM Signaling 25 1 0 0 0 0
## 006. PASA Biomes - ESM Signaling 21 0 0 0 0 0
## 007. PASA DownCity Design - ESM Signaling 24 0 0 0 0 0
## 008. PASA Crazy Machines - ESM Signaling 18 1 1 0 0 0
## 009. PASA Explore the Bay - ESM Signaling 24 0 0 0 0 0
## 010. PASA RWP Zoo - ESM Signaling 21 2 0 0 0 0table(value$Site, value$V06.03.UsefulForSpecificGoal_sum)##
## 0 1 2 3 4 5 6 7 16
## 001. BASB Dorchester House - ESM Signaling 18 2 1 0 0 0 1 0 0
## 002. BASB MathPOWER - ESM Signaling 24 0 0 0 0 0 0 0 0
## 004. BASB Sociedad Latina - ESM Signaling 52 1 0 0 0 0 0 0 0
## 005. BASB Thompson Island - ESM Signaling 19 4 1 0 1 0 1 0 0
## 006. PASA Biomes - ESM Signaling 17 3 1 0 0 0 0 0 0
## 007. PASA DownCity Design - ESM Signaling 15 1 0 5 3 0 0 0 0
## 008. PASA Crazy Machines - ESM Signaling 13 3 3 0 0 0 0 1 0
## 009. PASA Explore the Bay - ESM Signaling 11 2 5 1 2 1 0 1 1
## 010. PASA RWP Zoo - ESM Signaling 14 3 4 0 2 0 0 0 0Predicting engagement, relevance, and importance with Useful for specific goal
RQ3_goals_engagement <- lmer(overall_engagement ~
V06.03.UsefulForSpecificGoal_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ3_goals_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: overall_engagement ~ V06.03.UsefulForSpecificGoal_sum + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5889
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1668 -0.5104 0.0728 0.5771 3.8613
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02346 0.1532
## participant_ID (Intercept) 0.32698 0.5718
## program_ID (Intercept) 0.01377 0.1173
## Residual 0.38066 0.6170
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.852e+00 5.954e-02 7.804e+00 47.903
## V06.03.UsefulForSpecificGoal_sum 1.018e-02 9.664e-03 1.601e+02 1.053
## Pr(>|t|)
## (Intercept) 6.39e-11 ***
## V06.03.UsefulForSpecificGoal_sum 0.294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V06.03.UFSG -0.115RQ1_goals_relevance <- lmer(relevance ~
V06.03.UsefulForSpecificGoal_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_goals_relevance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ V06.03.UsefulForSpecificGoal_sum + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6159.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9138 -0.5200 0.0465 0.5793 3.7232
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.011327 0.10643
## participant_ID (Intercept) 0.475740 0.68974
## program_ID (Intercept) 0.007194 0.08482
## Residual 0.420811 0.64870
## Number of obs: 2809, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.562e+00 5.904e-02 7.762e+00 43.394
## V06.03.UsefulForSpecificGoal_sum 2.399e-02 8.653e-03 1.504e+02 2.772
## Pr(>|t|)
## (Intercept) 1.52e-10 ***
## V06.03.UsefulForSpecificGoal_sum 0.00627 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V06.03.UFSG -0.103RQ1_goals_importance <- lmer(important ~
V06.03.UsefulForSpecificGoal_sum +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ1_goals_importance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## important ~ V06.03.UsefulForSpecificGoal_sum + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7411.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1921 -0.5673 0.0777 0.6252 3.1096
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009099 0.09539
## participant_ID (Intercept) 0.473954 0.68844
## program_ID (Intercept) 0.002383 0.04882
## Residual 0.686402 0.82849
## Number of obs: 2808, groups:
## beep_ID_new, 237; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.63398 0.05512 7.06892 47.783
## V06.03.UsefulForSpecificGoal_sum 0.02007 0.01014 136.48260 1.979
## Pr(>|t|)
## (Intercept) 3.88e-10 ***
## V06.03.UsefulForSpecificGoal_sum 0.0498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## V06.03.UFSG -0.129Feb 26 Analysis RQ2
Correlations
df %>%
#select(COMPOSIT, agency, stem_sb_dummy, challenge, relevance, learning, positive_affect, Community_Space_Content, overall_pre_interest, overall_pre_competence_beliefs, basic_skills, creating_product, motivation_to_attend) %>%
select(overall_pre_interest, overall_pre_competence_beliefs, motivation_to_attend) %>%
correlate() %>%
shave() %>%
fashion() %>%
knitr::kable()| rowname | overall_pre_interest | overall_pre_competence_beliefs | motivation_to_attend |
|---|---|---|---|
| overall_pre_interest | |||
| overall_pre_competence_beliefs | .73 | ||
| motivation_to_attend | .25 | .16 |
RQ 2 Models
Chose to retain perceived competence as pre-interest was never significant alone
RQ2_challenge_interest <- lmer(challenge ~
overall_pre_interest +
#overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_challenge_interest)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ overall_pre_interest + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7240.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9286 -0.6439 -0.0477 0.5572 3.4005
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.08578 0.2929
## participant_ID (Intercept) 0.47899 0.6921
## program_ID (Intercept) 0.04229 0.2056
## Residual 0.65499 0.8093
## Number of obs: 2730, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.25442 0.22592 73.52639 9.979 2.59e-15 ***
## overall_pre_interest 0.01061 0.06834 140.34540 0.155 0.877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_nt -0.917RQ2_challenge_competence <- lmer(challenge ~
#overall_pre_interest +
overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_challenge_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ overall_pre_competence_beliefs + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7237.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9434 -0.6418 -0.0480 0.5566 3.4071
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.08594 0.2931
## participant_ID (Intercept) 0.47370 0.6883
## program_ID (Intercept) 0.02841 0.1686
## Residual 0.65502 0.8093
## Number of obs: 2730, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.66831 0.22889 96.33921 11.658
## overall_pre_competence_beliefs -0.12386 0.06906 157.19221 -1.793
## Pr(>|t|)
## (Intercept) <2e-16 ***
## overall_pre_competence_beliefs 0.0748 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_c_ -0.935RQ2_relevance_interest <- lmer(relevance ~
overall_pre_interest +
#overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_relevance_interest)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ overall_pre_interest + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5964.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9044 -0.5192 0.0364 0.5917 3.7074
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.019010 0.13788
## participant_ID (Intercept) 0.481323 0.69377
## program_ID (Intercept) 0.007357 0.08577
## Residual 0.416193 0.64513
## Number of obs: 2730, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.28158 0.19905 61.29508 11.463 <2e-16 ***
## overall_pre_interest 0.09915 0.06228 80.14418 1.592 0.115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_nt -0.951RQ2_relevance_competence <- lmer(relevance ~
#overall_pre_interest +
overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_relevance_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ overall_pre_competence_beliefs + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5966.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9004 -0.5166 0.0376 0.5912 3.7136
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.018978 0.13776
## participant_ID (Intercept) 0.489415 0.69958
## program_ID (Intercept) 0.003635 0.06029
## Residual 0.416230 0.64516
## Number of obs: 2730, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.44870 0.21112 83.94647 11.599
## overall_pre_competence_beliefs 0.04273 0.06533 107.32604 0.654
## Pr(>|t|)
## (Intercept) <2e-16 ***
## overall_pre_competence_beliefs 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_c_ -0.961RQ2_learning_interest <- lmer(learning ~
overall_pre_interest +
#overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_learning_interest)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ overall_pre_interest + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7260.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1357 -0.5689 0.1208 0.5835 2.6668
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02319 0.1523
## participant_ID (Intercept) 0.38829 0.6231
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.70641 0.8405
## Number of obs: 2729, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.51912 0.17835 187.62834 14.125 <2e-16 ***
## overall_pre_interest 0.07885 0.05610 186.81955 1.406 0.162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_nt -0.959RQ2_learning_competence <- lmer(learning ~
#overall_pre_interest +
overall_pre_competence_beliefs +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_learning_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ overall_pre_competence_beliefs + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7260.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1355 -0.5668 0.1209 0.5843 2.6671
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 2.319e-02 1.523e-01
## participant_ID (Intercept) 3.882e-01 6.231e-01
## program_ID (Intercept) 2.419e-16 1.555e-08
## Residual 7.064e-01 8.405e-01
## Number of obs: 2729, groups:
## beep_ID_new, 248; participant_ID, 181; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.50483 0.19228 185.77514 13.027
## overall_pre_competence_beliefs 0.08187 0.05964 184.70780 1.373
## Pr(>|t|)
## (Intercept) <2e-16 ***
## overall_pre_competence_beliefs 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ovrll_pr_c_ -0.964Model building looking at challenge
RQ2_challenge_all <- lmer(challenge ~ COMPOSIT +
agency +
V06.03.UsefulForSpecificGoal_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
basic_skills +
creating_product +
motivation_to_attend +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_challenge_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + agency + V06.03.UsefulForSpecificGoal_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## basic_skills + creating_product + motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6487.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8501 -0.6319 -0.0441 0.5631 3.3967
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05169 0.2274
## participant_ID (Intercept) 0.46709 0.6834
## program_ID (Intercept) 0.03366 0.1835
## Residual 0.66342 0.8145
## Number of obs: 2441, groups:
## beep_ID_new, 227; participant_ID, 176; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.35161 0.28457 132.30947 8.264
## COMPOSIT 0.02405 0.02670 182.89579 0.901
## agency 0.02271 0.02041 184.18951 1.113
## V06.03.UsefulForSpecificGoal_sum -0.03032 0.01464 136.24553 -2.072
## Community_Space_Content 0.18913 0.07030 174.08305 2.690
## overall_pre_competence_beliefs -0.16107 0.07150 158.58445 -2.253
## basic_skills 0.11897 0.06042 177.40070 1.969
## creating_product 0.37814 0.07735 213.45932 4.888
## motivation_to_attend 0.21995 0.18566 177.18835 1.185
## Pr(>|t|)
## (Intercept) 1.29e-13 ***
## COMPOSIT 0.36895
## agency 0.26731
## V06.03.UsefulForSpecificGoal_sum 0.04018 *
## Community_Space_Content 0.00784 **
## overall_pre_competence_beliefs 0.02565 *
## basic_skills 0.05050 .
## creating_product 2.00e-06 ***
## motivation_to_attend 0.23772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency V06.03 Cm_S_C ovr___ bsc_sk crtng_
## COMPOSIT -0.273
## agency -0.053 -0.229
## V06.03.UFSG 0.033 -0.207 0.040
## Cmmnty_Sp_C -0.005 -0.153 0.190 -0.118
## ovrll_pr_c_ -0.694 -0.012 0.001 0.013 0.004
## basic_sklls 0.012 -0.195 0.043 -0.051 -0.120 -0.015
## crtng_prdct 0.090 -0.293 -0.364 0.092 0.093 -0.001 0.234
## mtvtn_t_ttn -0.470 0.001 -0.009 -0.015 -0.026 -0.140 0.018 -0.008Removing agency from challenge model (agency is not significant in any model)
RQ2_challenge_minus_agency<- lmer(challenge ~ COMPOSIT +
V06.03.UsefulForSpecificGoal_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
basic_skills +
creating_product +
motivation_to_attend +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_challenge_minus_agency)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + V06.03.UsefulForSpecificGoal_sum + Community_Space_Content +
## overall_pre_competence_beliefs + basic_skills + creating_product +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6482.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8602 -0.6330 -0.0359 0.5705 3.3843
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05219 0.2285
## participant_ID (Intercept) 0.46681 0.6832
## program_ID (Intercept) 0.03331 0.1825
## Residual 0.66326 0.8144
## Number of obs: 2441, groups:
## beep_ID_new, 227; participant_ID, 176; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.36879 0.28404 131.98123 8.340
## COMPOSIT 0.03080 0.02604 181.13106 1.183
## V06.03.UsefulForSpecificGoal_sum -0.03097 0.01466 138.25982 -2.113
## Community_Space_Content 0.17452 0.06918 173.30637 2.523
## overall_pre_competence_beliefs -0.16137 0.07146 158.36144 -2.258
## basic_skills 0.11597 0.06049 180.15636 1.917
## creating_product 0.40950 0.07220 223.30311 5.672
## motivation_to_attend 0.22222 0.18557 177.11776 1.198
## Pr(>|t|)
## (Intercept) 8.61e-14 ***
## COMPOSIT 0.2385
## V06.03.UsefulForSpecificGoal_sum 0.0364 *
## Community_Space_Content 0.0125 *
## overall_pre_competence_beliefs 0.0253 *
## basic_skills 0.0568 .
## creating_product 4.34e-08 ***
## motivation_to_attend 0.2327
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS V06.03 Cm_S_C ovr___ bsc_sk crtng_
## COMPOSIT -0.293
## V06.03.UFSG 0.035 -0.203
## Cmmnty_Sp_C 0.005 -0.114 -0.128
## ovrll_pr_c_ -0.695 -0.012 0.013 0.004
## basic_sklls 0.014 -0.190 -0.053 -0.131 -0.015
## crtng_prdct 0.077 -0.415 0.115 0.176 0.000 0.269
## mtvtn_t_ttn -0.472 -0.002 -0.015 -0.025 -0.140 0.018 -0.012Examining interactions for challenge (without agency)
RQ2_challenge_product_value <- lmer(challenge ~ COMPOSIT +
V06.03.UsefulForSpecificGoal_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
basic_skills +
creating_product +
motivation_to_attend +
V06.03.UsefulForSpecificGoal_sum*creating_product +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_challenge_product_value)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ COMPOSIT + V06.03.UsefulForSpecificGoal_sum + Community_Space_Content +
## overall_pre_competence_beliefs + basic_skills + creating_product +
## motivation_to_attend + V06.03.UsefulForSpecificGoal_sum *
## creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6483.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8544 -0.6288 -0.0336 0.5571 3.3822
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05026 0.2242
## participant_ID (Intercept) 0.46662 0.6831
## program_ID (Intercept) 0.03936 0.1984
## Residual 0.66338 0.8145
## Number of obs: 2441, groups:
## beep_ID_new, 227; participant_ID, 176; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.36332 0.28597
## COMPOSIT 0.03336 0.02587
## V06.03.UsefulForSpecificGoal_sum -0.02359 0.01509
## Community_Space_Content 0.15707 0.06916
## overall_pre_competence_beliefs -0.16009 0.07177
## basic_skills 0.11600 0.05999
## creating_product 0.44447 0.07426
## motivation_to_attend 0.21648 0.18602
## V06.03.UsefulForSpecificGoal_sum:creating_product -0.09788 0.05298
## df t value
## (Intercept) 131.82635 8.264
## COMPOSIT 179.38604 1.290
## V06.03.UsefulForSpecificGoal_sum 131.34669 -1.563
## Community_Space_Content 169.72172 2.271
## overall_pre_competence_beliefs 161.95719 -2.231
## basic_skills 178.51587 1.934
## creating_product 217.70491 5.985
## motivation_to_attend 177.81623 1.164
## V06.03.UsefulForSpecificGoal_sum:creating_product 241.67888 -1.847
## Pr(>|t|)
## (Intercept) 1.31e-13 ***
## COMPOSIT 0.1989
## V06.03.UsefulForSpecificGoal_sum 0.1205
## Community_Space_Content 0.0244 *
## overall_pre_competence_beliefs 0.0271 *
## basic_skills 0.0547 .
## creating_product 8.81e-09 ***
## motivation_to_attend 0.2461
## V06.03.UsefulForSpecificGoal_sum:creating_product 0.0659 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS V06.03.UsFSG_ Cm_S_C ovr___ bsc_sk crtng_
## COMPOSIT -0.290
## V06.03.UsFSG_ 0.033 -0.184
## Cmmnty_Sp_C 0.003 -0.119 -0.156
## ovrll_pr_c_ -0.694 -0.010 0.008 0.006
## basic_sklls 0.015 -0.190 -0.053 -0.129 -0.014
## crtng_prdct 0.073 -0.390 0.178 0.137 -0.005 0.257
## mtvtn_t_ttn -0.470 -0.001 -0.012 -0.023 -0.139 0.017 -0.011
## V06.03.UFSG_: 0.000 -0.041 -0.272 0.124 0.015 0.006 -0.261
## mtvt__
## COMPOSIT
## V06.03.UsFSG_
## Cmmnty_Sp_C
## ovrll_pr_c_
## basic_sklls
## crtng_prdct
## mtvtn_t_ttn
## V06.03.UFSG_: -0.001Model building looking at learning
RQ2_learning_all <- lmer(learning ~ COMPOSIT +
agency +
V06.03.UsefulForSpecificGoal_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
basic_skills +
creating_product +
motivation_to_attend +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_learning_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ COMPOSIT + agency + V06.03.UsefulForSpecificGoal_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## basic_skills + creating_product + motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6507.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0814 -0.5692 0.1197 0.5802 2.8384
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008055 0.08975
## participant_ID (Intercept) 0.396414 0.62961
## program_ID (Intercept) 0.000000 0.00000
## Residual 0.709076 0.84207
## Number of obs: 2440, groups:
## beep_ID_new, 227; participant_ID, 176; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.013705 0.242193 198.759258 8.314
## COMPOSIT 0.057876 0.021107 167.314337 2.742
## agency 0.013285 0.016279 167.377223 0.816
## V06.03.UsefulForSpecificGoal_sum -0.003747 0.011083 109.748579 -0.338
## Community_Space_Content 0.087988 0.055240 158.785279 1.593
## overall_pre_competence_beliefs 0.057748 0.062684 178.009446 0.921
## basic_skills 0.133851 0.047934 156.773150 2.792
## creating_product -0.004875 0.063013 215.646445 -0.077
## motivation_to_attend 0.322400 0.167287 183.157142 1.927
## Pr(>|t|)
## (Intercept) 1.44e-14 ***
## COMPOSIT 0.00677 **
## agency 0.41560
## V06.03.UsefulForSpecificGoal_sum 0.73594
## Community_Space_Content 0.11319
## overall_pre_competence_beliefs 0.35817
## basic_skills 0.00588 **
## creating_product 0.93840
## motivation_to_attend 0.05550 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency V06.03 Cm_S_C ovr___ bsc_sk crtng_
## COMPOSIT -0.244
## agency -0.035 -0.241
## V06.03.UFSG 0.033 -0.198 0.034
## Cmmnty_Sp_C 0.019 -0.172 0.202 -0.121
## ovrll_pr_c_ -0.707 -0.026 -0.005 0.020 -0.006
## basic_sklls 0.001 -0.191 0.037 -0.060 -0.095 -0.013
## crtng_prdct 0.084 -0.291 -0.354 0.097 0.105 0.001 0.224
## mtvtn_t_ttn -0.495 0.000 -0.016 -0.027 -0.046 -0.144 0.033 -0.009Model building looking at relevance
RQ2_relevance_all <- lmer(relevance ~ COMPOSIT +
agency +
V06.03.UsefulForSpecificGoal_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
basic_skills +
creating_product +
motivation_to_attend +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
summary(RQ2_relevance_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ COMPOSIT + agency + V06.03.UsefulForSpecificGoal_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## basic_skills + creating_product + motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5305.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9528 -0.5372 0.0213 0.5683 4.1141
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007623 0.08731
## participant_ID (Intercept) 0.483705 0.69549
## program_ID (Intercept) 0.005162 0.07185
## Residual 0.409592 0.63999
## Number of obs: 2441, groups:
## beep_ID_new, 227; participant_ID, 176; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.00146 0.25825 107.26453 7.750
## COMPOSIT 0.02659 0.01684 189.04059 1.579
## agency -0.01562 0.01289 194.58606 -1.211
## V06.03.UsefulForSpecificGoal_sum 0.01647 0.00886 129.55579 1.859
## Community_Space_Content 0.15384 0.04412 182.45907 3.487
## overall_pre_competence_beliefs 0.01597 0.06764 114.82646 0.236
## basic_skills 0.02465 0.03798 183.04212 0.649
## creating_product 0.20406 0.04985 243.35153 4.094
## motivation_to_attend 0.41995 0.17825 160.55630 2.356
## Pr(>|t|)
## (Intercept) 5.55e-12 ***
## COMPOSIT 0.116062
## agency 0.227258
## V06.03.UsefulForSpecificGoal_sum 0.065339 .
## Community_Space_Content 0.000612 ***
## overall_pre_competence_beliefs 0.813786
## basic_skills 0.517161
## creating_product 5.78e-05 ***
## motivation_to_attend 0.019686 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency V06.03 Cm_S_C ovr___ bsc_sk crtng_
## COMPOSIT -0.185
## agency -0.027 -0.241
## V06.03.UFSG 0.024 -0.200 0.036
## Cmmnty_Sp_C 0.010 -0.170 0.201 -0.117
## ovrll_pr_c_ -0.727 -0.015 -0.002 0.013 -0.002
## basic_sklls 0.004 -0.191 0.036 -0.059 -0.104 -0.010
## crtng_prdct 0.064 -0.295 -0.354 0.099 0.106 0.000 0.224
## mtvtn_t_ttn -0.503 0.000 -0.012 -0.017 -0.031 -0.135 0.021 -0.007March 13 Analysis
Checking Variance components of agency, class, and value on challenge, relevance, learning
RQ2_challenge_variance <- lmer(challenge ~
COMPOSIT +
agency +
V01.01.HighUtility_sum +
(1|program_ID) +
(COMPOSIT + agency + V01.01.HighUtility_sum|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ COMPOSIT + agency + V01.01.HighUtility_sum + (1 |
## program_ID) + (COMPOSIT + agency + V01.01.HighUtility_sum |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7398.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9702 -0.6003 -0.0220 0.5385 3.4017
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.0633582 0.25171
## participant_ID (Intercept) 0.7418361 0.86130
## COMPOSIT 0.0049723 0.07051 -0.76
## agency 0.0082975 0.09109 -0.28 0.35
## V01.01.HighUtility_sum 0.0005789 0.02406 0.47 -0.14
## program_ID (Intercept) 0.0639964 0.25298
## Residual 0.6323820 0.79522
##
##
##
##
##
## -0.83
##
##
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.925123 0.139353 27.885364 13.815 5.38e-14
## COMPOSIT 0.082224 0.025857 179.616278 3.180 0.00174
## agency 0.038414 0.019962 182.711614 1.924 0.05586
## V01.01.HighUtility_sum -0.019803 0.008991 150.737414 -2.203 0.02915
##
## (Intercept) ***
## COMPOSIT **
## agency .
## V01.01.HighUtility_sum *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.620
## agency -0.095 -0.299
## V01.01.HgU_ 0.047 -0.234 0.141RQ2_relevance_variance <- lmer(relevance ~
COMPOSIT +
agency +
V01.01.HighUtility_sum +
(1|program_ID) +
(COMPOSIT + agency + V01.01.HighUtility_sum|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ COMPOSIT + agency + V01.01.HighUtility_sum + (1 |
## program_ID) + (COMPOSIT + agency + V01.01.HighUtility_sum |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6111.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9564 -0.5084 0.0474 0.5605 3.2914
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.0084302 0.09182
## participant_ID (Intercept) 0.6048486 0.77772
## COMPOSIT 0.0106961 0.10342 -0.46
## agency 0.0036364 0.06030 0.33 -0.66
## V01.01.HighUtility_sum 0.0007402 0.02721 -0.03 -0.14
## program_ID (Intercept) 0.0097882 0.09894
## Residual 0.4009903 0.63324
##
##
##
##
##
## -0.56
##
##
## Number of obs: 2792, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.381e+00 8.541e-02 2.718e+01 27.880 <2e-16 ***
## COMPOSIT 4.357e-02 1.724e-02 1.159e+02 2.526 0.0129 *
## agency 5.149e-03 1.232e-02 1.216e+02 0.418 0.6769
## V01.01.HighUtility_sum 1.296e-02 5.732e-03 8.387e+01 2.260 0.0264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.640
## agency 0.055 -0.424
## V01.01.HgU_ 0.010 -0.226 0.105RQ2_learning_variance <- lmer(learning ~
COMPOSIT +
agency +
V01.01.HighUtility_sum +
(1|program_ID) +
(COMPOSIT + agency + V01.01.HighUtility_sum|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ COMPOSIT + agency + V01.01.HighUtility_sum + (1 |
## program_ID) + (COMPOSIT + agency + V01.01.HighUtility_sum |
## participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7427.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2262 -0.5588 0.1131 0.5910 2.7598
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.0119405 0.10927
## participant_ID (Intercept) 0.4694002 0.68513
## COMPOSIT 0.0021906 0.04680 -0.47
## agency 0.0033244 0.05766 0.21 -0.55
## V01.01.HighUtility_sum 0.0009079 0.03013 -0.18 0.37
## program_ID (Intercept) 0.0037415 0.06117
## Residual 0.6905731 0.83101
##
##
##
##
##
## -0.98
##
##
## Number of obs: 2791, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.505139 0.088003 43.823449 28.467 < 2e-16
## COMPOSIT 0.069383 0.019708 98.210474 3.521 0.000655
## agency 0.006224 0.015158 152.262621 0.411 0.681932
## V01.01.HighUtility_sum -0.001491 0.007071 89.501399 -0.211 0.833469
##
## (Intercept) ***
## COMPOSIT ***
## agency
## V01.01.HighUtility_sum
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) COMPOS agency
## COMPOSIT -0.725
## agency -0.005 -0.379
## V01.01.HgU_ -0.009 -0.210 0.098Full models below, no moderators
RQ2_challenge_full <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_full)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6640.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8650 -0.6428 -0.0526 0.5635 3.3815
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06037 0.2457
## participant_ID (Intercept) 0.45936 0.6778
## program_ID (Intercept) 0.04392 0.2096
## Residual 0.66099 0.8130
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.368904 0.293140 138.015695 8.081
## gender_female -0.228032 0.113935 168.755303 -2.001
## COMPOSIT 0.071639 0.026009 196.731171 2.754
## agency 0.045995 0.019447 208.515260 2.365
## V01.01.HighUtility_sum -0.028798 0.009112 160.537555 -3.160
## Community_Space_Content 0.196679 0.073106 190.679289 2.690
## overall_pre_competence_beliefs -0.148285 0.071825 162.518285 -2.065
## motivation_to_attend 0.168243 0.187253 177.252256 0.898
## Pr(>|t|)
## (Intercept) 2.89e-13 ***
## gender_female 0.04695 *
## COMPOSIT 0.00643 **
## agency 0.01894 *
## V01.01.HighUtility_sum 0.00188 **
## Community_Space_Content 0.00777 **
## overall_pre_competence_beliefs 0.04056 *
## motivation_to_attend 0.37015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___
## gender_feml -0.223
## COMPOSIT -0.259 0.002
## agency -0.022 0.000 -0.373
## V01.01.HgU_ 0.007 0.002 -0.191 0.151
## Cmmnty_Sp_C -0.011 0.000 -0.130 0.218 -0.312
## ovrll_pr_c_ -0.653 -0.081 -0.015 0.004 0.015 -0.002
## mtvtn_t_ttn -0.479 0.145 0.003 -0.016 -0.014 -0.017 -0.153RQ2_relevance_full <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_full)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5441.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9830 -0.5461 0.0375 0.5849 3.7036
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01037 0.1018
## participant_ID (Intercept) 0.47080 0.6862
## program_ID (Intercept) 0.01422 0.1193
## Residual 0.41183 0.6417
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.059236 0.269647 125.048284 7.637
## gender_female -0.215550 0.111126 169.333988 -1.940
## COMPOSIT 0.039353 0.016403 194.118332 2.399
## agency 0.002512 0.012361 211.534281 0.203
## V01.01.HighUtility_sum 0.011171 0.005591 150.740781 1.998
## Community_Space_Content 0.094878 0.046004 193.866820 2.062
## overall_pre_competence_beliefs 0.035509 0.068555 133.062118 0.518
## motivation_to_attend 0.375999 0.180341 167.572154 2.085
## Pr(>|t|)
## (Intercept) 5.01e-12 ***
## gender_female 0.0541 .
## COMPOSIT 0.0174 *
## agency 0.8391
## V01.01.HighUtility_sum 0.0475 *
## Community_Space_Content 0.0405 *
## overall_pre_competence_beliefs 0.6053
## motivation_to_attend 0.0386 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___
## gender_feml -0.244
## COMPOSIT -0.174 0.001
## agency -0.005 -0.003 -0.384
## V01.01.HgU_ 0.005 0.002 -0.192 0.157
## Cmmnty_Sp_C 0.001 0.002 -0.137 0.223 -0.320
## ovrll_pr_c_ -0.686 -0.068 -0.015 0.000 0.013 -0.005
## mtvtn_t_ttn -0.509 0.142 0.002 -0.017 -0.013 -0.019 -0.145RQ2_learning_full <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_full)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6658
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1326 -0.5728 0.1019 0.5893 2.8558
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.00815 0.09028
## participant_ID (Intercept) 0.39417 0.62783
## program_ID (Intercept) 0.00274 0.05234
## Residual 0.70995 0.84258
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.054806 0.251000 106.675024 8.186
## gender_female -0.055606 0.104235 161.712110 -0.533
## COMPOSIT 0.069506 0.019970 179.996768 3.480
## agency 0.001904 0.015142 195.067132 0.126
## V01.01.HighUtility_sum -0.006243 0.006770 134.632442 -0.922
## Community_Space_Content 0.116676 0.055902 177.833597 2.087
## overall_pre_competence_beliefs 0.064883 0.063350 100.896933 1.024
## motivation_to_attend 0.295305 0.169546 150.689740 1.742
## Pr(>|t|)
## (Intercept) 6.24e-13 ***
## gender_female 0.594443
## COMPOSIT 0.000628 ***
## agency 0.900052
## V01.01.HighUtility_sum 0.358076
## Community_Space_Content 0.038299 *
## overall_pre_competence_beliefs 0.308192
## motivation_to_attend 0.083594 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___
## gender_feml -0.246
## COMPOSIT -0.223 0.003
## agency -0.002 -0.006 -0.386
## V01.01.HgU_ 0.009 0.003 -0.198 0.160
## Cmmnty_Sp_C 0.008 0.003 -0.138 0.223 -0.320
## ovrll_pr_c_ -0.671 -0.066 -0.027 -0.003 0.017 -0.010
## mtvtn_t_ttn -0.507 0.140 0.003 -0.024 -0.020 -0.032 -0.153Moderation models for challenge and gender
RQ2_challenge_composite_gender <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6642.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8449 -0.6367 -0.0565 0.5596 3.3953
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06143 0.2479
## participant_ID (Intercept) 0.46132 0.6792
## program_ID (Intercept) 0.04235 0.2058
## Residual 0.65985 0.8123
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.475e+00 3.003e-01 1.521e+02 8.243
## gender_female -4.367e-01 1.723e-01 7.437e+02 -2.534
## COMPOSIT 4.259e-02 3.164e-02 3.986e+02 1.346
## agency 4.569e-02 1.952e-02 2.086e+02 2.341
## V01.01.HighUtility_sum -2.915e-02 9.153e-03 1.609e+02 -3.185
## Community_Space_Content 1.977e-01 7.338e-02 1.908e+02 2.694
## overall_pre_competence_beliefs -1.476e-01 7.188e-02 1.618e+02 -2.053
## motivation_to_attend 1.712e-01 1.875e-01 1.772e+02 0.913
## gender_female:COMPOSIT 5.488e-02 3.393e-02 2.409e+03 1.617
## Pr(>|t|)
## (Intercept) 7.26e-14 ***
## gender_female 0.01148 *
## COMPOSIT 0.17913
## agency 0.02018 *
## V01.01.HighUtility_sum 0.00174 **
## Community_Space_Content 0.00769 **
## overall_pre_competence_beliefs 0.04164 *
## motivation_to_attend 0.36245
## gender_female:COMPOSIT 0.10595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.307
## COMPOSIT -0.331 0.425
## agency -0.023 0.007 -0.301
## V01.01.HgU_ 0.002 0.018 -0.145 0.151
## Cmmnty_Sp_C -0.009 -0.003 -0.110 0.217 -0.312
## ovrll_pr_c_ -0.635 -0.064 -0.021 0.004 0.015 -0.002
## mtvtn_t_ttn -0.467 0.093 0.000 -0.016 -0.015 -0.018 -0.153
## g_:COMPOSIT 0.216 -0.749 -0.565 -0.010 -0.022 0.005 0.014 0.004RQ2_challenge_agency_gender <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_agency_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6645.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8627 -0.6436 -0.0533 0.5617 3.3852
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06036 0.2457
## participant_ID (Intercept) 0.45938 0.6778
## program_ID (Intercept) 0.04382 0.2093
## Residual 0.66129 0.8132
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.372e+00 2.944e-01 1.406e+02 8.055
## gender_female -2.328e-01 1.241e-01 2.360e+02 -1.876
## COMPOSIT 7.162e-02 2.601e-02 1.967e+02 2.753
## agency 4.470e-02 2.360e-02 4.186e+02 1.894
## V01.01.HighUtility_sum -2.882e-02 9.117e-03 1.608e+02 -3.161
## Community_Space_Content 1.969e-01 7.315e-02 1.909e+02 2.692
## overall_pre_competence_beliefs -1.484e-01 7.183e-02 1.624e+02 -2.066
## motivation_to_attend 1.683e-01 1.873e-01 1.772e+02 0.899
## gender_female:agency 2.469e-03 2.553e-02 2.374e+03 0.097
## Pr(>|t|)
## (Intercept) 3.07e-13 ***
## gender_female 0.06189 .
## COMPOSIT 0.00645 **
## agency 0.05894 .
## V01.01.HighUtility_sum 0.00188 **
## Community_Space_Content 0.00774 **
## overall_pre_competence_beliefs 0.04041 *
## motivation_to_attend 0.36998
## gender_female:agency 0.92299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.241
## COMPOSIT -0.258 0.004
## agency -0.072 0.224 -0.304
## V01.01.HgU_ 0.004 0.014 -0.191 0.141
## Cmmnty_Sp_C -0.008 -0.012 -0.131 0.162 -0.312
## ovrll_pr_c_ -0.651 -0.071 -0.015 0.008 0.016 -0.002
## mtvtn_t_ttn -0.477 0.133 0.003 -0.012 -0.014 -0.017 -0.153
## gndr_fml:gn 0.095 -0.396 -0.006 -0.567 -0.029 0.031 -0.008 -0.002RQ2_challenge_value_gender <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * gender_female +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6647.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8662 -0.6425 -0.0533 0.5627 3.3905
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0604 0.2458
## participant_ID (Intercept) 0.4592 0.6776
## program_ID (Intercept) 0.0440 0.2098
## Residual 0.6613 0.8132
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.367e+00 2.935e-01 1.388e+02
## gender_female -2.262e-01 1.156e-01 1.785e+02
## COMPOSIT 7.171e-02 2.602e-02 1.968e+02
## agency 4.599e-02 1.945e-02 2.084e+02
## V01.01.HighUtility_sum -2.826e-02 1.079e-02 3.080e+02
## Community_Space_Content 1.964e-01 7.317e-02 1.912e+02
## overall_pre_competence_beliefs -1.483e-01 7.182e-02 1.624e+02
## motivation_to_attend 1.684e-01 1.873e-01 1.771e+02
## gender_female:V01.01.HighUtility_sum -1.058e-03 1.127e-02 2.360e+03
## t value Pr(>|t|)
## (Intercept) 8.067 3.06e-13 ***
## gender_female -1.957 0.05192 .
## COMPOSIT 2.755 0.00641 **
## agency 2.364 0.01897 *
## V01.01.HighUtility_sum -2.619 0.00925 **
## Community_Space_Content 2.685 0.00790 **
## overall_pre_competence_beliefs -2.064 0.04057 *
## motivation_to_attend 0.899 0.36963
## gender_female:V01.01.HighUtility_sum -0.094 0.92525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.228
## COMPOSIT -0.260 0.006
## agency -0.022 0.000 -0.373
## V01.01.HgU_ -0.021 0.093 -0.147 0.128
## Cmmnty_Sp_C -0.009 -0.006 -0.131 0.217 -0.282
## ovrll_pr_c_ -0.652 -0.080 -0.015 0.004 0.011 -0.002
## mtvtn_t_ttn -0.479 0.145 0.003 -0.016 -0.004 -0.018 -0.153
## g_:V01.01.H 0.050 -0.169 -0.027 0.000 -0.535 0.035 0.003 -0.016Moderation models for challenge and competence
RQ2_challenge_composite_competence <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6645.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8623 -0.6423 -0.0531 0.5599 3.3823
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06046 0.2459
## participant_ID (Intercept) 0.45950 0.6779
## program_ID (Intercept) 0.04390 0.2095
## Residual 0.66123 0.8132
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.342e+00 3.864e-01 3.971e+02
## gender_female -2.282e-01 1.140e-01 1.688e+02
## COMPOSIT 7.917e-02 7.411e-02 1.852e+03
## agency 4.596e-02 1.946e-02 2.084e+02
## V01.01.HighUtility_sum -2.883e-02 9.122e-03 1.607e+02
## Community_Space_Content 1.967e-01 7.314e-02 1.906e+02
## overall_pre_competence_beliefs -1.394e-01 1.087e-01 7.341e+02
## motivation_to_attend 1.679e-01 1.873e-01 1.773e+02
## COMPOSIT:overall_pre_competence_beliefs -2.380e-03 2.191e-02 2.370e+03
## t value Pr(>|t|)
## (Intercept) 6.061 3.16e-09 ***
## gender_female -2.003 0.04682 *
## COMPOSIT 1.068 0.28551
## agency 2.362 0.01912 *
## V01.01.HighUtility_sum -3.161 0.00188 **
## Community_Space_Content 2.689 0.00779 **
## overall_pre_competence_beliefs -1.283 0.20000
## motivation_to_attend 0.896 0.37129
## COMPOSIT:overall_pre_competence_beliefs -0.109 0.91351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.158
## COMPOSIT -0.679 -0.015
## agency -0.004 0.000 -0.148
## V01.01.HgU_ 0.027 0.003 -0.099 0.151
## Cmmnty_Sp_C -0.008 0.000 -0.046 0.217 -0.311
## ovrll_pr_c_ -0.816 -0.066 0.699 -0.011 -0.015 -0.001
## mtvtn_t_ttn -0.352 0.145 -0.016 -0.016 -0.014 -0.017 -0.115
## COMPOSIT:__ 0.651 0.016 -0.936 0.019 0.034 0.000 -0.751 0.018RQ2_challenge_agency_competence <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_agency_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6646.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8509 -0.6471 -0.0508 0.5686 3.3571
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06053 0.2460
## participant_ID (Intercept) 0.45947 0.6778
## program_ID (Intercept) 0.04398 0.2097
## Residual 0.66111 0.8131
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.319e+00 3.085e-01 1.668e+02
## gender_female -2.275e-01 1.140e-01 1.687e+02
## COMPOSIT 7.146e-02 2.603e-02 1.967e+02
## agency 7.167e-02 5.321e-02 1.882e+03
## V01.01.HighUtility_sum -2.876e-02 9.120e-03 1.606e+02
## Community_Space_Content 1.981e-01 7.321e-02 1.910e+02
## overall_pre_competence_beliefs -1.327e-01 7.788e-02 2.226e+02
## motivation_to_attend 1.695e-01 1.873e-01 1.772e+02
## agency:overall_pre_competence_beliefs -8.167e-03 1.575e-02 2.379e+03
## t value Pr(>|t|)
## (Intercept) 7.519 3.23e-12 ***
## gender_female -1.996 0.04750 *
## COMPOSIT 2.745 0.00661 **
## agency 1.347 0.17815
## V01.01.HighUtility_sum -3.154 0.00193 **
## Community_Space_Content 2.706 0.00743 **
## overall_pre_competence_beliefs -1.703 0.08990 .
## motivation_to_attend 0.905 0.36682
## agency:overall_pre_competence_beliefs -0.519 0.60415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.215
## COMPOSIT -0.242 0.002
## agency -0.297 0.008 -0.149
## V01.01.HgU_ 0.004 0.002 -0.191 0.063
## Cmmnty_Sp_C -0.022 0.001 -0.131 0.114 -0.311
## ovrll_pr_c_ -0.693 -0.071 -0.019 0.361 0.018 0.013
## mtvtn_t_ttn -0.459 0.145 0.002 0.006 -0.014 -0.017 -0.136
## agncy:vr___ 0.310 -0.009 0.013 -0.931 -0.009 -0.037 -0.386 -0.013RQ2_challenge_value_motivation <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6645.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8183 -0.6410 -0.0520 0.5684 3.5657
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06008 0.2451
## participant_ID (Intercept) 0.45904 0.6775
## program_ID (Intercept) 0.04397 0.2097
## Residual 0.66065 0.8128
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.313e+00
## gender_female -2.289e-01
## COMPOSIT 7.033e-02
## agency 4.682e-02
## V01.01.HighUtility_sum 8.022e-03
## Community_Space_Content 1.994e-01
## overall_pre_competence_beliefs -1.273e-01
## motivation_to_attend 1.610e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs -1.189e-02
## Std. Error
## (Intercept) 2.950e-01
## gender_female 1.139e-01
## COMPOSIT 2.599e-02
## agency 1.943e-02
## V01.01.HighUtility_sum 2.395e-02
## Community_Space_Content 7.303e-02
## overall_pre_competence_beliefs 7.290e-02
## motivation_to_attend 1.872e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 7.156e-03
## df t value
## (Intercept) 1.413e+02 7.841
## gender_female 1.687e+02 -2.009
## COMPOSIT 1.963e+02 2.706
## agency 2.081e+02 2.410
## V01.01.HighUtility_sum 1.872e+03 0.335
## Community_Space_Content 1.903e+02 2.730
## overall_pre_competence_beliefs 1.722e+02 -1.746
## motivation_to_attend 1.774e+02 0.860
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 2.377e+03 -1.662
## Pr(>|t|)
## (Intercept) 9.86e-13 ***
## gender_female 0.04609 *
## COMPOSIT 0.00740 **
## agency 0.01683 *
## V01.01.HighUtility_sum 0.73770
## Community_Space_Content 0.00693 **
## overall_pre_competence_beliefs 0.08251 .
## motivation_to_attend 0.39089
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 0.09670 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.221
## COMPOSIT -0.253 0.002
## agency -0.025 0.000 -0.373
## V01.01.HgU_ -0.103 -0.003 -0.101 0.081
## Cmmnty_Sp_C -0.013 0.000 -0.131 0.218 -0.097
## ovrll_pr_c_ -0.659 -0.080 -0.020 0.008 0.166 0.002
## mtvtn_t_ttn -0.473 0.145 0.003 -0.016 -0.027 -0.018 -0.155
## V01.01.HU_: 0.114 0.004 0.031 -0.026 -0.925 -0.023 -0.173 0.023Moderation models for challenge and motivation to attend
RQ2_challenge_composite_motivation <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6637.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8786 -0.6403 -0.0544 0.5635 3.4114
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06287 0.2507
## participant_ID (Intercept) 0.46132 0.6792
## program_ID (Intercept) 0.04154 0.2038
## Residual 0.65799 0.8112
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.893e+00 3.467e-01 2.626e+02 5.460
## gender_female -2.302e-01 1.141e-01 1.691e+02 -2.018
## COMPOSIT 2.023e-01 5.713e-02 1.657e+03 3.541
## agency 4.900e-02 1.965e-02 2.106e+02 2.494
## V01.01.HighUtility_sum -2.791e-02 9.211e-03 1.622e+02 -3.031
## Community_Space_Content 2.008e-01 7.376e-02 1.919e+02 2.722
## overall_pre_competence_beliefs -1.521e-01 7.184e-02 1.616e+02 -2.117
## motivation_to_attend 7.146e-01 2.822e-01 7.578e+02 2.533
## COMPOSIT:motivation_to_attend -1.478e-01 5.733e-02 2.439e+03 -2.578
## Pr(>|t|)
## (Intercept) 1.1e-07 ***
## gender_female 0.04516 *
## COMPOSIT 0.00041 ***
## agency 0.01339 *
## V01.01.HighUtility_sum 0.00284 **
## Community_Space_Content 0.00708 **
## overall_pre_competence_beliefs 0.03576 *
## motivation_to_attend 0.01152 *
## COMPOSIT:motivation_to_attend 0.00999 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.184
## COMPOSIT -0.576 -0.007
## agency -0.050 -0.001 -0.118
## V01.01.HgU_ -0.015 0.002 -0.052 0.153
## Cmmnty_Sp_C -0.018 0.000 -0.045 0.218 -0.310
## ovrll_pr_c_ -0.545 -0.080 -0.019 0.003 0.015 -0.002
## mtvtn_t_ttn -0.669 0.089 0.665 0.034 0.021 0.000 -0.111
## COMPOSIT:__ 0.535 0.009 -0.888 -0.059 -0.040 -0.017 0.013 -0.748sjPlot::sjp.int(RQ2_challenge_composite_motivation, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
RQ2_challenge_agency_motivation <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_agency_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * motivation_to_attend + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6644
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8679 -0.6472 -0.0528 0.5626 3.3745
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06071 0.2464
## participant_ID (Intercept) 0.45957 0.6779
## program_ID (Intercept) 0.04385 0.2094
## Residual 0.66096 0.8130
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.315e+00 3.040e-01 1.588e+02 7.615
## gender_female -2.280e-01 1.140e-01 1.688e+02 -2.000
## COMPOSIT 7.254e-02 2.608e-02 1.973e+02 2.781
## agency 7.321e-02 4.488e-02 1.768e+03 1.631
## V01.01.HighUtility_sum -2.890e-02 9.127e-03 1.606e+02 -3.167
## Community_Space_Content 1.950e-01 7.325e-02 1.911e+02 2.663
## overall_pre_competence_beliefs -1.475e-01 7.185e-02 1.625e+02 -2.053
## motivation_to_attend 2.232e-01 2.043e-01 2.518e+02 1.093
## agency:motivation_to_attend -3.037e-02 4.512e-02 2.390e+03 -0.673
## Pr(>|t|)
## (Intercept) 2.22e-12 ***
## gender_female 0.04706 *
## COMPOSIT 0.00594 **
## agency 0.10298
## V01.01.HighUtility_sum 0.00185 **
## Community_Space_Content 0.00841 **
## overall_pre_competence_beliefs 0.04166 *
## motivation_to_attend 0.27554
## agency:motivation_to_attend 0.50088
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.215
## COMPOSIT -0.263 0.002
## agency -0.247 0.000 -0.115
## V01.01.HgU_ 0.011 0.002 -0.192 0.052
## Cmmnty_Sp_C -0.001 0.000 -0.132 0.063 -0.311
## ovrll_pr_c_ -0.634 -0.081 -0.014 0.017 0.015 -0.002
## mtvtn_t_ttn -0.529 0.133 0.023 0.353 -0.019 -0.030 -0.133
## agncy:mtv__ 0.264 0.000 -0.052 -0.901 0.015 0.035 -0.017 -0.399RQ2_challenge_value_motivation <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6645.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8628 -0.6432 -0.0523 0.5654 3.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06028 0.2455
## participant_ID (Intercept) 0.45895 0.6775
## program_ID (Intercept) 0.04404 0.2099
## Residual 0.66133 0.8132
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.377e+00 2.941e-01
## gender_female -2.285e-01 1.139e-01
## COMPOSIT 7.152e-02 2.601e-02
## agency 4.601e-02 1.944e-02
## V01.01.HighUtility_sum -3.783e-02 2.847e-02
## Community_Space_Content 1.970e-01 7.310e-02
## overall_pre_competence_beliefs -1.479e-01 7.181e-02
## motivation_to_attend 1.588e-01 1.892e-01
## V01.01.HighUtility_sum:motivation_to_attend 9.429e-03 2.816e-02
## df t value Pr(>|t|)
## (Intercept) 1.393e+02 8.083 2.74e-13
## gender_female 1.687e+02 -2.006 0.04646
## COMPOSIT 1.967e+02 2.750 0.00651
## agency 2.084e+02 2.366 0.01889
## V01.01.HighUtility_sum 2.330e+03 -1.329 0.18412
## Community_Space_Content 1.907e+02 2.695 0.00767
## overall_pre_competence_beliefs 1.626e+02 -2.059 0.04107
## motivation_to_attend 1.844e+02 0.839 0.40244
## V01.01.HighUtility_sum:motivation_to_attend 2.333e+03 0.335 0.73776
##
## (Intercept) ***
## gender_female *
## COMPOSIT **
## agency *
## V01.01.HighUtility_sum
## Community_Space_Content **
## overall_pre_competence_beliefs *
## motivation_to_attend
## V01.01.HighUtility_sum:motivation_to_attend
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.223
## COMPOSIT -0.259 0.002
## agency -0.022 0.000 -0.373
## V01.01.HgU_ -0.075 0.012 -0.047 0.046
## Cmmnty_Sp_C -0.010 0.000 -0.131 0.218 -0.113
## ovrll_pr_c_ -0.650 -0.081 -0.015 0.004 -0.008 -0.001
## mtvtn_t_ttn -0.484 0.145 0.005 -0.016 0.134 -0.019 -0.153
## V01.01.HU_: 0.081 -0.011 -0.015 0.002 -0.947 0.014 0.014 -0.146Moderation models for relevance and gender
RQ2_relevance_composite_gender <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5446.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9968 -0.5384 0.0357 0.5856 3.7073
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01038 0.1019
## participant_ID (Intercept) 0.47118 0.6864
## program_ID (Intercept) 0.01403 0.1185
## Residual 0.41191 0.6418
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.094e+00 2.744e-01 1.345e+02 7.629
## gender_female -2.820e-01 1.500e-01 5.246e+02 -1.880
## COMPOSIT 3.012e-02 2.155e-02 4.930e+02 1.398
## agency 2.388e-03 1.236e-02 2.115e+02 0.193
## V01.01.HighUtility_sum 1.107e-02 5.594e-03 1.507e+02 1.980
## Community_Space_Content 9.508e-02 4.602e-02 1.937e+02 2.066
## overall_pre_competence_beliefs 3.585e-02 6.856e-02 1.328e+02 0.523
## motivation_to_attend 3.760e-01 1.804e-01 1.674e+02 2.084
## gender_female:COMPOSIT 1.744e-02 2.641e-02 2.379e+03 0.660
## Pr(>|t|)
## (Intercept) 3.88e-12 ***
## gender_female 0.0607 .
## COMPOSIT 0.1628
## agency 0.8470
## V01.01.HighUtility_sum 0.0496 *
## Community_Space_Content 0.0401 *
## overall_pre_competence_beliefs 0.6019
## motivation_to_attend 0.0386 *
## gender_female:COMPOSIT 0.5091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.303
## COMPOSIT -0.251 0.436
## agency -0.008 0.008 -0.283
## V01.01.HgU_ 0.000 0.019 -0.129 0.158
## Cmmnty_Sp_C 0.002 -0.003 -0.108 0.223 -0.320
## ovrll_pr_c_ -0.672 -0.058 -0.020 0.000 0.012 -0.005
## mtvtn_t_ttn -0.500 0.105 0.002 -0.017 -0.013 -0.019 -0.145
## g_:COMPOSIT 0.187 -0.672 -0.648 -0.015 -0.026 0.007 0.012 0.000RQ2_relevance_agency_gender <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_agency_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5447.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0031 -0.5368 0.0338 0.5863 3.7104
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01034 0.1017
## participant_ID (Intercept) 0.47118 0.6864
## program_ID (Intercept) 0.01413 0.1189
## Residual 0.41193 0.6418
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.075e+00 2.706e-01 1.269e+02 7.669
## gender_female -2.426e-01 1.176e-01 2.120e+02 -2.063
## COMPOSIT 3.923e-02 1.640e-02 1.945e+02 2.392
## agency -4.825e-03 1.615e-02 5.210e+02 -0.299
## V01.01.HighUtility_sum 1.102e-02 5.593e-03 1.513e+02 1.971
## Community_Space_Content 9.604e-02 4.602e-02 1.945e+02 2.087
## overall_pre_competence_beliefs 3.519e-02 6.857e-02 1.329e+02 0.513
## motivation_to_attend 3.754e-01 1.804e-01 1.675e+02 2.081
## gender_female:agency 1.394e-02 1.976e-02 2.332e+03 0.705
## Pr(>|t|)
## (Intercept) 3.97e-12 ***
## gender_female 0.0403 *
## COMPOSIT 0.0177 *
## agency 0.7653
## V01.01.HighUtility_sum 0.0506 .
## Community_Space_Content 0.0382 *
## overall_pre_competence_beliefs 0.6086
## motivation_to_attend 0.0389 *
## gender_female:agency 0.4807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.256
## COMPOSIT -0.174 0.005
## agency -0.057 0.208 -0.287
## V01.01.HgU_ 0.002 0.014 -0.192 0.145
## Cmmnty_Sp_C 0.004 -0.010 -0.137 0.147 -0.321
## ovrll_pr_c_ -0.684 -0.063 -0.015 0.003 0.013 -0.005
## mtvtn_t_ttn -0.507 0.135 0.002 -0.010 -0.013 -0.019 -0.145
## gndr_fml:gn 0.081 -0.326 -0.011 -0.644 -0.038 0.037 -0.004 -0.004RQ2_relevance_value_gender <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * gender_female +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5447.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9476 -0.5314 0.0452 0.5888 3.6470
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01043 0.1021
## participant_ID (Intercept) 0.47174 0.6868
## program_ID (Intercept) 0.01397 0.1182
## Residual 0.41162 0.6416
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.075e+00 2.700e-01 1.254e+02
## gender_female -2.347e-01 1.122e-01 1.754e+02
## COMPOSIT 3.869e-02 1.642e-02 1.946e+02
## agency 2.491e-03 1.237e-02 2.118e+02
## V01.01.HighUtility_sum 5.391e-03 7.194e-03 3.797e+02
## Community_Space_Content 9.772e-02 4.608e-02 1.954e+02
## overall_pre_competence_beliefs 3.537e-02 6.858e-02 1.325e+02
## motivation_to_attend 3.728e-01 1.805e-01 1.674e+02
## gender_female:V01.01.HighUtility_sum 1.121e-02 8.774e-03 2.364e+03
## t value Pr(>|t|)
## (Intercept) 7.685 3.83e-12 ***
## gender_female -2.092 0.0379 *
## COMPOSIT 2.356 0.0195 *
## agency 0.201 0.8405
## V01.01.HighUtility_sum 0.749 0.4541
## Community_Space_Content 2.121 0.0352 *
## overall_pre_competence_beliefs 0.516 0.6069
## motivation_to_attend 2.066 0.0404 *
## gender_female:V01.01.HighUtility_sum 1.277 0.2016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.247
## COMPOSIT -0.175 0.005
## agency -0.006 -0.003 -0.384
## V01.01.HgU_ -0.023 0.086 -0.131 0.123
## Cmmnty_Sp_C 0.003 -0.005 -0.138 0.223 -0.278
## ovrll_pr_c_ -0.685 -0.067 -0.015 0.000 0.009 -0.005
## mtvtn_t_ttn -0.509 0.142 0.002 -0.017 -0.002 -0.020 -0.145
## g_:V01.01.H 0.043 -0.135 -0.030 -0.002 -0.629 0.047 0.002 -0.014Moderation models for relevance and competence
RQ2_relevance_composite_competence <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5447.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9954 -0.5416 0.0353 0.5868 3.7039
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01037 0.1018
## participant_ID (Intercept) 0.47112 0.6864
## program_ID (Intercept) 0.01380 0.1175
## Residual 0.41192 0.6418
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.933e+00 3.313e-01 2.821e+02
## gender_female -2.165e-01 1.111e-01 1.693e+02
## COMPOSIT 7.447e-02 5.554e-02 1.706e+03
## agency 2.366e-03 1.236e-02 2.113e+02
## V01.01.HighUtility_sum 1.103e-02 5.596e-03 1.509e+02
## Community_Space_Content 9.499e-02 4.601e-02 1.938e+02
## overall_pre_competence_beliefs 7.643e-02 9.274e-02 4.282e+02
## motivation_to_attend 3.743e-01 1.803e-01 1.672e+02
## COMPOSIT:overall_pre_competence_beliefs -1.111e-02 1.677e-02 2.109e+03
## t value Pr(>|t|)
## (Intercept) 5.834 1.48e-08 ***
## gender_female -1.948 0.0531 .
## COMPOSIT 1.341 0.1801
## agency 0.191 0.8484
## V01.01.HighUtility_sum 1.971 0.0506 .
## Community_Space_Content 2.065 0.0403 *
## overall_pre_competence_beliefs 0.824 0.4104
## motivation_to_attend 2.076 0.0394 *
## COMPOSIT:overall_pre_competence_beliefs -0.662 0.5078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.190
## COMPOSIT -0.598 -0.013
## agency 0.006 -0.003 -0.130
## V01.01.HgU_ 0.027 0.003 -0.094 0.158
## Cmmnty_Sp_C -0.002 0.002 -0.036 0.223 -0.320
## ovrll_pr_c_ -0.804 -0.059 0.641 -0.012 -0.017 0.000
## mtvtn_t_ttn -0.406 0.142 -0.012 -0.016 -0.012 -0.019 -0.116
## COMPOSIT:__ 0.582 0.014 -0.955 0.018 0.039 -0.005 -0.674 0.014RQ2_relevance_agency_competence <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_agency_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5446.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9438 -0.5449 0.0367 0.5803 3.7072
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009969 0.09985
## participant_ID (Intercept) 0.471091 0.68636
## program_ID (Intercept) 0.014227 0.11928
## Residual 0.411923 0.64181
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.165e+00 2.794e-01 1.425e+02
## gender_female -2.166e-01 1.112e-01 1.694e+02
## COMPOSIT 3.970e-02 1.633e-02 1.937e+02
## agency -5.271e-02 3.996e-02 1.853e+03
## V01.01.HighUtility_sum 1.112e-02 5.564e-03 1.503e+02
## Community_Space_Content 9.164e-02 4.585e-02 1.942e+02
## overall_pre_competence_beliefs 2.320e-03 7.227e-02 1.621e+02
## motivation_to_attend 3.730e-01 1.804e-01 1.676e+02
## agency:overall_pre_competence_beliefs 1.758e-02 1.212e-02 2.262e+03
## t value Pr(>|t|)
## (Intercept) 7.750 1.58e-12 ***
## gender_female -1.948 0.0530 .
## COMPOSIT 2.430 0.0160 *
## agency -1.319 0.1874
## V01.01.HighUtility_sum 1.998 0.0475 *
## Community_Space_Content 1.999 0.0470 *
## overall_pre_competence_beliefs 0.032 0.9744
## motivation_to_attend 2.068 0.0402 *
## agency:overall_pre_competence_beliefs 1.451 0.1469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.237
## COMPOSIT -0.164 0.001
## agency -0.250 0.005 -0.128
## V01.01.HgU_ 0.003 0.002 -0.193 0.057
## Cmmnty_Sp_C -0.011 0.002 -0.137 0.110 -0.319
## ovrll_pr_c_ -0.711 -0.062 -0.017 0.301 0.015 0.009
## mtvtn_t_ttn -0.494 0.142 0.002 0.006 -0.013 -0.019 -0.134
## agncy:vr___ 0.261 -0.006 0.010 -0.951 -0.009 -0.043 -0.316 -0.011RQ2_relevance_value_competence <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5447.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9395 -0.5424 0.0329 0.5819 3.7332
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01033 0.1017
## participant_ID (Intercept) 0.47120 0.6864
## program_ID (Intercept) 0.01395 0.1181
## Residual 0.41155 0.6415
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.018e+00
## gender_female -2.159e-01
## COMPOSIT 3.836e-02
## agency 3.207e-03
## V01.01.HighUtility_sum 3.901e-02
## Community_Space_Content 9.718e-02
## overall_pre_competence_beliefs 5.098e-02
## motivation_to_attend 3.703e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs -8.989e-03
## Std. Error
## (Intercept) 2.708e-01
## gender_female 1.111e-01
## COMPOSIT 1.640e-02
## agency 1.236e-02
## V01.01.HighUtility_sum 1.801e-02
## Community_Space_Content 4.600e-02
## overall_pre_competence_beliefs 6.923e-02
## motivation_to_attend 1.804e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 5.531e-03
## df t value
## (Intercept) 1.268e+02 7.452
## gender_female 1.693e+02 -1.943
## COMPOSIT 1.960e+02 2.338
## agency 2.139e+02 0.259
## V01.01.HighUtility_sum 1.897e+03 2.165
## Community_Space_Content 1.958e+02 2.113
## overall_pre_competence_beliefs 1.374e+02 0.736
## motivation_to_attend 1.675e+02 2.053
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 2.308e+03 -1.625
## Pr(>|t|)
## (Intercept) 1.26e-11 ***
## gender_female 0.0537 .
## COMPOSIT 0.0204 *
## agency 0.7955
## V01.01.HighUtility_sum 0.0305 *
## Community_Space_Content 0.0359 *
## overall_pre_competence_beliefs 0.4627
## motivation_to_attend 0.0417 *
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 0.1042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.242
## COMPOSIT -0.169 0.001
## agency -0.009 -0.003 -0.385
## V01.01.HgU_ -0.089 -0.002 -0.095 0.082
## Cmmnty_Sp_C -0.002 0.002 -0.138 0.224 -0.069
## ovrll_pr_c_ -0.690 -0.067 -0.020 0.005 0.137 0.000
## mtvtn_t_ttn -0.505 0.141 0.003 -0.017 -0.022 -0.020 -0.146
## V01.01.HU_: 0.096 0.003 0.037 -0.035 -0.951 -0.031 -0.140 0.019Moderation models for relevance and motivation to attend
RQ2_relevance_composite_motivation <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5443
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9960 -0.5403 0.0452 0.5856 3.7283
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01055 0.1027
## participant_ID (Intercept) 0.47179 0.6869
## program_ID (Intercept) 0.01390 0.1179
## Residual 0.41124 0.6413
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.803e+00 3.056e-01 2.027e+02 5.899
## gender_female -2.163e-01 1.112e-01 1.692e+02 -1.945
## COMPOSIT 1.100e-01 4.268e-02 1.710e+03 2.576
## agency 4.102e-03 1.241e-02 2.138e+02 0.331
## V01.01.HighUtility_sum 1.164e-02 5.607e-03 1.518e+02 2.076
## Community_Space_Content 9.685e-02 4.608e-02 1.947e+02 2.102
## overall_pre_competence_beliefs 3.392e-02 6.858e-02 1.324e+02 0.495
## motivation_to_attend 6.682e-01 2.431e-01 5.045e+02 2.748
## COMPOSIT:motivation_to_attend -7.965e-02 4.440e-02 2.319e+03 -1.794
## Pr(>|t|)
## (Intercept) 1.51e-08 ***
## gender_female 0.0535 .
## COMPOSIT 0.0101 *
## agency 0.7413
## V01.01.HighUtility_sum 0.0396 *
## Community_Space_Content 0.0369 *
## overall_pre_competence_beliefs 0.6217
## motivation_to_attend 0.0062 **
## COMPOSIT:motivation_to_attend 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.213
## COMPOSIT -0.493 -0.003
## agency -0.038 -0.003 -0.083
## V01.01.HgU_ -0.018 0.002 -0.030 0.160
## Cmmnty_Sp_C -0.010 0.002 -0.032 0.224 -0.318
## ovrll_pr_c_ -0.601 -0.067 -0.015 0.000 0.012 -0.005
## mtvtn_t_ttn -0.648 0.102 0.619 0.035 0.022 0.000 -0.114
## COMPOSIT:__ 0.470 0.004 -0.923 -0.070 -0.047 -0.022 0.010 -0.670sjPlot::sjp.int(RQ2_relevance_composite_motivation, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
RQ2_relevance_agency_motivation <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_agency_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * motivation_to_attend + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5445.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9898 -0.5434 0.0399 0.5869 3.7236
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01048 0.1024
## participant_ID (Intercept) 0.47112 0.6864
## program_ID (Intercept) 0.01434 0.1198
## Residual 0.41176 0.6417
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.004e+00 2.767e-01 1.381e+02 7.242
## gender_female -2.154e-01 1.112e-01 1.693e+02 -1.938
## COMPOSIT 4.021e-02 1.645e-02 1.951e+02 2.444
## agency 3.043e-02 3.364e-02 1.812e+03 0.904
## V01.01.HighUtility_sum 1.107e-02 5.599e-03 1.510e+02 1.978
## Community_Space_Content 9.316e-02 4.610e-02 1.948e+02 2.021
## overall_pre_competence_beliefs 3.655e-02 6.860e-02 1.333e+02 0.533
## motivation_to_attend 4.314e-01 1.908e-01 2.098e+02 2.261
## agency:motivation_to_attend -3.106e-02 3.483e-02 2.298e+03 -0.892
## Pr(>|t|)
## (Intercept) 2.81e-11 ***
## gender_female 0.0543 .
## COMPOSIT 0.0154 *
## agency 0.3659
## V01.01.HighUtility_sum 0.0498 *
## Community_Space_Content 0.0447 *
## overall_pre_competence_beliefs 0.5950
## motivation_to_attend 0.0248 *
## agency:motivation_to_attend 0.3726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.238
## COMPOSIT -0.183 0.001
## agency -0.209 0.001 -0.085
## V01.01.HgU_ 0.009 0.002 -0.193 0.040
## Cmmnty_Sp_C 0.010 0.002 -0.139 0.041 -0.318
## ovrll_pr_c_ -0.672 -0.068 -0.014 0.014 0.012 -0.005
## mtvtn_t_ttn -0.541 0.134 0.022 0.297 -0.018 -0.033 -0.132
## agncy:mtv__ 0.222 -0.002 -0.060 -0.930 0.019 0.045 -0.015 -0.326RQ2_relevance_value_motivation <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5443.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9766 -0.5443 0.0371 0.5860 3.3731
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01018 0.1009
## participant_ID (Intercept) 0.47298 0.6877
## program_ID (Intercept) 0.01472 0.1213
## Residual 0.41130 0.6413
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.021e+00 2.710e-01
## gender_female -2.133e-01 1.114e-01
## COMPOSIT 3.998e-02 1.637e-02
## agency 2.417e-03 1.233e-02
## V01.01.HighUtility_sum 5.344e-02 2.191e-02
## Community_Space_Content 9.305e-02 4.590e-02
## overall_pre_competence_beliefs 3.428e-02 6.876e-02
## motivation_to_attend 4.184e-01 1.820e-01
## V01.01.HighUtility_sum:motivation_to_attend -4.404e-02 2.208e-02
## df t value Pr(>|t|)
## (Intercept) 1.261e+02 7.458 1.24e-11
## gender_female 1.693e+02 -1.915 0.0572
## COMPOSIT 1.931e+02 2.443 0.0155
## agency 2.105e+02 0.196 0.8448
## V01.01.HighUtility_sum 2.350e+03 2.439 0.0148
## Community_Space_Content 1.931e+02 2.027 0.0440
## overall_pre_competence_beliefs 1.339e+02 0.499 0.6189
## motivation_to_attend 1.719e+02 2.298 0.0227
## V01.01.HighUtility_sum:motivation_to_attend 2.364e+03 -1.995 0.0462
##
## (Intercept) ***
## gender_female .
## COMPOSIT *
## agency
## V01.01.HighUtility_sum *
## Community_Space_Content *
## overall_pre_competence_beliefs
## motivation_to_attend *
## V01.01.HighUtility_sum:motivation_to_attend *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.244
## COMPOSIT -0.174 0.001
## agency -0.005 -0.003 -0.385
## V01.01.HgU_ -0.064 0.011 -0.033 0.037
## Cmmnty_Sp_C 0.002 0.001 -0.137 0.223 -0.100
## ovrll_pr_c_ -0.684 -0.068 -0.015 0.000 -0.009 -0.004
## mtvtn_t_ttn -0.512 0.142 0.004 -0.017 0.110 -0.021 -0.145
## V01.01.HU_: 0.067 -0.011 -0.017 0.003 -0.967 0.019 0.013 -0.117sjPlot::sjp.int(RQ2_relevance_value_motivation, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
Moderation models for learning and gender
RQ2_learning_composite_gender <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6662.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1569 -0.5726 0.1084 0.5837 2.8772
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008359 0.09143
## participant_ID (Intercept) 0.394786 0.62832
## program_ID (Intercept) 0.002574 0.05073
## Residual 0.709805 0.84250
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.111e+00 2.597e-01 1.229e+02 8.131
## gender_female -1.645e-01 1.671e-01 8.654e+02 -0.984
## COMPOSIT 5.442e-02 2.693e-02 4.935e+02 2.021
## agency 1.652e-03 1.516e-02 1.957e+02 0.109
## V01.01.HighUtility_sum -6.397e-03 6.783e-03 1.351e+02 -0.943
## Community_Space_Content 1.169e-01 5.598e-02 1.782e+02 2.089
## overall_pre_competence_beliefs 6.552e-02 6.336e-02 1.005e+02 1.034
## motivation_to_attend 2.948e-01 1.696e-01 1.503e+02 1.738
## gender_female:COMPOSIT 2.855e-02 3.416e-02 2.401e+03 0.836
## Pr(>|t|)
## (Intercept) 3.91e-13 ***
## gender_female 0.3252
## COMPOSIT 0.0438 *
## agency 0.9134
## V01.01.HighUtility_sum 0.3473
## Community_Space_Content 0.0381 *
## overall_pre_competence_beliefs 0.3036
## motivation_to_attend 0.0842 .
## gender_female:COMPOSIT 0.4035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.349
## COMPOSIT -0.332 0.525
## agency -0.006 0.010 -0.274
## V01.01.HgU_ 0.002 0.023 -0.129 0.161
## Cmmnty_Sp_C 0.010 -0.002 -0.106 0.223 -0.320
## ovrll_pr_c_ -0.644 -0.055 -0.033 -0.003 0.016 -0.010
## mtvtn_t_ttn -0.491 0.089 0.004 -0.024 -0.020 -0.032 -0.153
## g_:COMPOSIT 0.256 -0.781 -0.670 -0.018 -0.026 0.006 0.018 -0.002RQ2_learning_agency_gender <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_agency_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * gender_female + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6663.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1316 -0.5714 0.1002 0.5871 2.8441
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008121 0.09012
## participant_ID (Intercept) 0.394776 0.62831
## program_ID (Intercept) 0.002710 0.05206
## Residual 0.710139 0.84270
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.069e+00 2.528e-01 1.100e+02 8.187
## gender_female -8.049e-02 1.157e-01 2.468e+02 -0.696
## COMPOSIT 6.939e-02 1.997e-02 1.802e+02 3.475
## agency -4.816e-03 2.030e-02 5.146e+02 -0.237
## V01.01.HighUtility_sum -6.376e-03 6.774e-03 1.351e+02 -0.941
## Community_Space_Content 1.176e-01 5.593e-02 1.782e+02 2.103
## overall_pre_competence_beliefs 6.470e-02 6.338e-02 1.009e+02 1.021
## motivation_to_attend 2.945e-01 1.697e-01 1.508e+02 1.736
## gender_female:agency 1.275e-02 2.564e-02 2.323e+03 0.497
## Pr(>|t|)
## (Intercept) 5.25e-13 ***
## gender_female 0.487373
## COMPOSIT 0.000641 ***
## agency 0.812545
## V01.01.HighUtility_sum 0.348276
## Community_Space_Content 0.036842 *
## overall_pre_competence_beliefs 0.309788
## motivation_to_attend 0.084635 .
## gender_female:agency 0.619028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.270
## COMPOSIT -0.223 0.008
## agency -0.077 0.284 -0.280
## V01.01.HgU_ 0.005 0.020 -0.197 0.146
## Cmmnty_Sp_C 0.012 -0.012 -0.138 0.143 -0.321
## ovrll_pr_c_ -0.667 -0.058 -0.027 0.000 0.017 -0.010
## mtvtn_t_ttn -0.505 0.130 0.003 -0.012 -0.020 -0.032 -0.153
## gndr_fml:gn 0.114 -0.433 -0.012 -0.666 -0.040 0.035 -0.003 -0.009RQ2_learning_value_gender <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*gender_female +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_gender)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * gender_female +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6661
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1458 -0.5713 0.1023 0.5927 2.8890
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007938 0.08910
## participant_ID (Intercept) 0.395305 0.62873
## program_ID (Intercept) 0.002810 0.05301
## Residual 0.709057 0.84206
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.085e+00 2.518e-01 1.081e+02
## gender_female -9.508e-02 1.062e-01 1.725e+02
## COMPOSIT 6.831e-02 1.994e-02 1.793e+02
## agency 1.829e-03 1.511e-02 1.943e+02
## V01.01.HighUtility_sum -1.811e-02 8.952e-03 3.697e+02
## Community_Space_Content 1.225e-01 5.586e-02 1.785e+02
## overall_pre_competence_beliefs 6.506e-02 6.344e-02 1.013e+02
## motivation_to_attend 2.887e-01 1.698e-01 1.511e+02
## gender_female:V01.01.HighUtility_sum 2.302e-02 1.138e-02 2.383e+03
## t value Pr(>|t|)
## (Intercept) 8.283 3.53e-13 ***
## gender_female -0.896 0.371745
## COMPOSIT 3.426 0.000759 ***
## agency 0.121 0.903808
## V01.01.HighUtility_sum -2.024 0.043735 *
## Community_Space_Content 2.193 0.029629 *
## overall_pre_competence_beliefs 1.026 0.307496
## motivation_to_attend 1.701 0.091080 .
## gender_female:V01.01.HighUtility_sum 2.022 0.043332 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.253
## COMPOSIT -0.224 0.008
## agency -0.002 -0.006 -0.385
## V01.01.HgU_ -0.032 0.123 -0.129 0.123
## Cmmnty_Sp_C 0.011 -0.006 -0.139 0.222 -0.275
## ovrll_pr_c_ -0.670 -0.065 -0.027 -0.003 0.012 -0.010
## mtvtn_t_ttn -0.507 0.141 0.004 -0.024 -0.002 -0.033 -0.153
## g_:V01.01.H 0.060 -0.184 -0.030 -0.003 -0.656 0.051 0.001 -0.019sjPlot::sjp.int(RQ2_learning_value_gender, type = "eff")## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
Moderation models for learning and competence
RQ2_learning_composite_competence <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6663.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1428 -0.5695 0.1058 0.6010 2.8330
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008018 0.08954
## participant_ID (Intercept) 0.393877 0.62760
## program_ID (Intercept) 0.003274 0.05722
## Residual 0.710240 0.84276
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.206e+00 3.536e-01 4.047e+02
## gender_female -5.486e-02 1.043e-01 1.625e+02
## COMPOSIT 2.698e-02 7.122e-02 1.601e+03
## agency 2.093e-03 1.513e-02 1.946e+02
## V01.01.HighUtility_sum -6.056e-03 6.771e-03 1.346e+02
## Community_Space_Content 1.164e-01 5.588e-02 1.779e+02
## overall_pre_competence_beliefs 1.571e-02 1.025e-01 6.292e+02
## motivation_to_attend 2.978e-01 1.697e-01 1.520e+02
## COMPOSIT:overall_pre_competence_beliefs 1.346e-02 2.159e-02 1.988e+03
## t value Pr(>|t|)
## (Intercept) 6.240 1.11e-09 ***
## gender_female -0.526 0.5996
## COMPOSIT 0.379 0.7049
## agency 0.138 0.8902
## V01.01.HighUtility_sum -0.894 0.3727
## Community_Space_Content 2.083 0.0387 *
## overall_pre_competence_beliefs 0.153 0.8783
## motivation_to_attend 1.754 0.0814 .
## COMPOSIT:overall_pre_competence_beliefs 0.623 0.5331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.161
## COMPOSIT -0.719 -0.018
## agency 0.011 -0.006 -0.125
## V01.01.HgU_ 0.037 0.004 -0.097 0.161
## Cmmnty_Sp_C -0.003 0.003 -0.027 0.223 -0.321
## ovrll_pr_c_ -0.848 -0.056 0.749 -0.016 -0.024 0.004
## mtvtn_t_ttn -0.347 0.140 -0.017 -0.024 -0.019 -0.032 -0.109
## COMPOSIT:__ 0.703 0.019 -0.960 0.018 0.044 -0.012 -0.786 0.019RQ2_learning_agency_competence <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_agency_competence)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6664.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1309 -0.5710 0.1014 0.5911 2.8462
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008289 0.09104
## participant_ID (Intercept) 0.394006 0.62770
## program_ID (Intercept) 0.002754 0.05248
## Residual 0.710126 0.84269
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.027e+00 2.680e-01 1.341e+02
## gender_female -5.538e-02 1.042e-01 1.617e+02
## COMPOSIT 6.945e-02 1.999e-02 1.795e+02
## agency 1.636e-02 5.147e-02 1.804e+03
## V01.01.HighUtility_sum -6.230e-03 6.779e-03 1.344e+02
## Community_Space_Content 1.175e-01 5.602e-02 1.780e+02
## overall_pre_competence_beliefs 7.360e-02 6.986e-02 1.425e+02
## motivation_to_attend 2.960e-01 1.695e-01 1.507e+02
## agency:overall_pre_competence_beliefs -4.615e-03 1.569e-02 2.225e+03
## t value Pr(>|t|)
## (Intercept) 7.565 5.55e-12 ***
## gender_female -0.531 0.595939
## COMPOSIT 3.474 0.000644 ***
## agency 0.318 0.750656
## V01.01.HighUtility_sum -0.919 0.359669
## Community_Space_Content 2.097 0.037422 *
## overall_pre_competence_beliefs 1.054 0.293883
## motivation_to_attend 1.746 0.082833 .
## agency:overall_pre_competence_beliefs -0.294 0.768681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.233
## COMPOSIT -0.206 0.003
## agency -0.335 0.005 -0.123
## V01.01.HgU_ 0.006 0.003 -0.198 0.055
## Cmmnty_Sp_C -0.008 0.003 -0.138 0.109 -0.320
## ovrll_pr_c_ -0.718 -0.056 -0.029 0.402 0.019 0.011
## mtvtn_t_ttn -0.480 0.140 0.003 0.006 -0.020 -0.031 -0.133
## agncy:vr___ 0.350 -0.008 0.010 -0.956 -0.008 -0.046 -0.422 -0.014RQ2_learning_value_motivation <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*overall_pre_competence_beliefs +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * overall_pre_competence_beliefs +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6665.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1301 -0.5714 0.1004 0.5942 2.8468
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008272 0.09095
## participant_ID (Intercept) 0.394026 0.62772
## program_ID (Intercept) 0.002847 0.05336
## Residual 0.710093 0.84267
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate
## (Intercept) 2.069e+00
## gender_female -5.561e-02
## COMPOSIT 6.987e-02
## agency 1.632e-03
## V01.01.HighUtility_sum -1.637e-02
## Community_Space_Content 1.158e-01
## overall_pre_competence_beliefs 5.939e-02
## motivation_to_attend 2.976e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 3.270e-03
## Std. Error
## (Intercept) 2.533e-01
## gender_female 1.042e-01
## COMPOSIT 2.000e-02
## agency 1.517e-02
## V01.01.HighUtility_sum 2.322e-02
## Community_Space_Content 5.600e-02
## overall_pre_competence_beliefs 6.460e-02
## motivation_to_attend 1.696e-01
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 7.174e-03
## df t value
## (Intercept) 1.103e+02 8.169
## gender_female 1.618e+02 -0.533
## COMPOSIT 1.802e+02 3.493
## agency 1.957e+02 0.108
## V01.01.HighUtility_sum 1.848e+03 -0.705
## Community_Space_Content 1.782e+02 2.069
## overall_pre_competence_beliefs 1.087e+02 0.919
## motivation_to_attend 1.513e+02 1.754
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 2.280e+03 0.456
## Pr(>|t|)
## (Intercept) 5.66e-13 ***
## gender_female 0.594454
## COMPOSIT 0.000601 ***
## agency 0.914451
## V01.01.HighUtility_sum 0.480915
## Community_Space_Content 0.040007 *
## overall_pre_competence_beliefs 0.359936
## motivation_to_attend 0.081437 .
## V01.01.HighUtility_sum:overall_pre_competence_beliefs 0.648540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.244
## COMPOSIT -0.216 0.003
## agency -0.007 -0.006 -0.386
## V01.01.HgU_ -0.124 -0.001 -0.094 0.083
## Cmmnty_Sp_C 0.003 0.003 -0.139 0.224 -0.060
## ovrll_pr_c_ -0.678 -0.065 -0.034 0.005 0.191 -0.003
## mtvtn_t_ttn -0.499 0.140 0.004 -0.025 -0.032 -0.033 -0.155
## V01.01.HU_: 0.132 0.002 0.038 -0.038 -0.956 -0.036 -0.194 0.027Moderation models for learning and motivation to attend
RQ2_learning_composite_motivation <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6661.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1329 -0.5725 0.1021 0.5898 2.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008171 0.09040
## participant_ID (Intercept) 0.394173 0.62783
## program_ID (Intercept) 0.002754 0.05248
## Residual 0.710233 0.84275
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.069e+00 3.120e-01 2.431e+02 6.630
## gender_female -5.559e-02 1.042e-01 1.617e+02 -0.533
## COMPOSIT 6.564e-02 5.460e-02 1.671e+03 1.202
## agency 1.819e-03 1.519e-02 1.966e+02 0.120
## V01.01.HighUtility_sum -6.269e-03 6.780e-03 1.350e+02 -0.925
## Community_Space_Content 1.166e-01 5.593e-02 1.779e+02 2.084
## overall_pre_competence_beliefs 6.498e-02 6.336e-02 1.010e+02 1.026
## motivation_to_attend 2.794e-01 2.699e-01 7.358e+02 1.035
## COMPOSIT:motivation_to_attend 4.353e-03 5.725e-02 2.289e+03 0.076
## Pr(>|t|)
## (Intercept) 2.15e-10 ***
## gender_female 0.5946
## COMPOSIT 0.2294
## agency 0.9048
## V01.01.HighUtility_sum 0.3568
## Community_Space_Content 0.0385 *
## overall_pre_competence_beliefs 0.3075
## motivation_to_attend 0.3010
## COMPOSIT:motivation_to_attend 0.9394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.196
## COMPOSIT -0.618 -0.003
## agency -0.045 -0.007 -0.073
## V01.01.HgU_ -0.022 0.003 -0.026 0.163
## Cmmnty_Sp_C -0.006 0.003 -0.031 0.224 -0.319
## ovrll_pr_c_ -0.532 -0.066 -0.023 -0.004 0.016 -0.010
## mtvtn_t_ttn -0.718 0.084 0.725 0.041 0.026 -0.004 -0.107
## COMPOSIT:__ 0.594 0.005 -0.931 -0.073 -0.050 -0.021 0.014 -0.778sjPlot::sjp.int(RQ2_learning_composite_motivation, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
RQ2_learning_agency_motivation <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_agency_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * motivation_to_attend + (1 |
## program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6662.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1313 -0.5709 0.1031 0.5900 2.8774
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008069 0.08983
## participant_ID (Intercept) 0.394290 0.62793
## program_ID (Intercept) 0.002678 0.05175
## Residual 0.710289 0.84279
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.079e+00 2.633e-01 1.283e+02 7.897
## gender_female -5.570e-02 1.042e-01 1.616e+02 -0.534
## COMPOSIT 6.912e-02 2.000e-02 1.804e+02 3.456
## agency -1.038e-02 4.325e-02 1.761e+03 -0.240
## V01.01.HighUtility_sum -6.198e-03 6.768e-03 1.343e+02 -0.916
## Community_Space_Content 1.174e-01 5.594e-02 1.782e+02 2.099
## overall_pre_competence_beliefs 6.441e-02 6.335e-02 1.007e+02 1.017
## motivation_to_attend 2.706e-01 1.880e-01 2.251e+02 1.439
## agency:motivation_to_attend 1.368e-02 4.509e-02 2.254e+03 0.303
## Pr(>|t|)
## (Intercept) 1.11e-12 ***
## gender_female 0.593834
## COMPOSIT 0.000684 ***
## agency 0.810322
## V01.01.HighUtility_sum 0.361443
## Community_Space_Content 0.037207 *
## overall_pre_competence_beliefs 0.311752
## motivation_to_attend 0.151447
## agency:motivation_to_attend 0.761564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.236
## COMPOSIT -0.231 0.003
## agency -0.284 0.002 -0.077
## V01.01.HgU_ 0.015 0.003 -0.199 0.036
## Cmmnty_Sp_C 0.022 0.003 -0.140 0.033 -0.319
## ovrll_pr_c_ -0.645 -0.066 -0.026 0.016 0.017 -0.011
## mtvtn_t_ttn -0.567 0.128 0.029 0.397 -0.027 -0.050 -0.130
## agncy:mtv__ 0.303 -0.004 -0.062 -0.937 0.021 0.048 -0.018 -0.432RQ2_learning_value_motivation <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_motivation)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * motivation_to_attend +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6662.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1370 -0.5719 0.1027 0.5926 2.9308
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.008142 0.09023
## participant_ID (Intercept) 0.395724 0.62907
## program_ID (Intercept) 0.003094 0.05562
## Residual 0.709732 0.84246
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.029e+00 2.527e-01
## gender_female -5.424e-02 1.045e-01
## COMPOSIT 6.990e-02 1.997e-02
## agency 1.825e-03 1.514e-02
## V01.01.HighUtility_sum 2.214e-02 2.835e-02
## Community_Space_Content 1.155e-01 5.592e-02
## overall_pre_competence_beliefs 6.433e-02 6.355e-02
## motivation_to_attend 3.234e-01 1.721e-01
## V01.01.HighUtility_sum:motivation_to_attend -2.957e-02 2.868e-02
## df t value Pr(>|t|)
## (Intercept) 1.081e+02 8.028 1.3e-12
## gender_female 1.621e+02 -0.519 0.604373
## COMPOSIT 1.802e+02 3.500 0.000586
## agency 1.953e+02 0.121 0.904186
## V01.01.HighUtility_sum 2.390e+03 0.781 0.434872
## Community_Space_Content 1.784e+02 2.066 0.040292
## overall_pre_competence_beliefs 1.023e+02 1.012 0.313827
## motivation_to_attend 1.570e+02 1.880 0.062028
## V01.01.HighUtility_sum:motivation_to_attend 2.421e+03 -1.031 0.302624
##
## (Intercept) ***
## gender_female
## COMPOSIT ***
## agency
## V01.01.HighUtility_sum
## Community_Space_Content *
## overall_pre_competence_beliefs
## motivation_to_attend .
## V01.01.HighUtility_sum:motivation_to_attend
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.246
## COMPOSIT -0.223 0.003
## agency -0.001 -0.006 -0.386
## V01.01.HgU_ -0.085 0.015 -0.032 0.033
## Cmmnty_Sp_C 0.010 0.003 -0.138 0.223 -0.099
## ovrll_pr_c_ -0.667 -0.066 -0.027 -0.002 -0.014 -0.009
## mtvtn_t_ttn -0.513 0.140 0.006 -0.025 0.148 -0.035 -0.154
## V01.01.HU_: 0.090 -0.015 -0.016 0.005 -0.971 0.023 0.018 -0.157March 28 Analysis
Checking Variance components of creating product and basic skills
RQ2_challenge_activity_variance <- lmer(challenge ~
creating_product +
basic_skills +
(1|program_ID) +
(creating_product + basic_skills|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_activity_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ creating_product + basic_skills + (1 | program_ID) +
## (creating_product + basic_skills | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7431.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1361 -0.5938 -0.0292 0.5283 3.4415
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.05428 0.2330
## participant_ID (Intercept) 0.53447 0.7311
## creating_product 0.25938 0.5093 -0.37
## basic_skills 0.07209 0.2685 -0.33 0.62
## program_ID (Intercept) 0.04197 0.2049
## Residual 0.62384 0.7898
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.16739 0.09166 9.51901 23.646 8.92e-10 ***
## creating_product 0.44064 0.07458 187.08296 5.908 1.61e-08 ***
## basic_skills 0.12650 0.06010 168.42057 2.105 0.0368 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.244
## basic_sklls -0.224 0.286RQ2_relevance_activity_variance <- lmer(relevance ~
creating_product +
basic_skills +
(1|program_ID) +
(creating_product + basic_skills|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_activity_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ creating_product + basic_skills + (1 | program_ID) +
## (creating_product + basic_skills | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6135.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9279 -0.4967 0.0385 0.5588 3.8526
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.010845 0.10414
## participant_ID (Intercept) 0.486848 0.69774
## creating_product 0.129218 0.35947 -0.04
## basic_skills 0.053486 0.23127 -0.18 0.45
## program_ID (Intercept) 0.003689 0.06074
## Residual 0.395569 0.62894
## Number of obs: 2818, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.52765 0.05697 8.41623 44.366 2.82e-11 ***
## creating_product 0.18062 0.05040 137.97580 3.584 0.000469 ***
## basic_skills 0.07331 0.04072 112.83995 1.801 0.074443 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.157
## basic_sklls -0.222 0.258RQ2_learning_activity_variance <- lmer(learning ~
creating_product +
basic_skills +
(1|program_ID) +
(creating_product + basic_skills|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_activity_variance)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ creating_product + basic_skills + (1 | program_ID) +
## (creating_product + basic_skills | participant_ID) + (1 |
## beep_ID_new)
## Data: df
##
## REML criterion at convergence: 7490.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1828 -0.5598 0.1225 0.5712 2.8678
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.01269 0.1127
## participant_ID (Intercept) 0.41201 0.6419
## creating_product 0.03387 0.1840 0.06
## basic_skills 0.02007 0.1417 -0.34 -0.96
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.69790 0.8354
## Number of obs: 2817, groups:
## beep_ID_new, 235; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.71912 0.05157 207.73229 52.726 < 2e-16 ***
## creating_product 0.10874 0.05346 136.24624 2.034 0.043907 *
## basic_skills 0.17688 0.04685 124.07328 3.775 0.000246 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) crtng_
## crtng_prdct -0.208
## basic_sklls -0.295 0.166Full models below, no moderators
RQ2_challenge_full_2 <- lmer(challenge ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_full_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6450.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8449 -0.6310 -0.0363 0.5660 3.3610
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05125 0.2264
## participant_ID (Intercept) 0.46098 0.6790
## program_ID (Intercept) 0.03128 0.1769
## Residual 0.66603 0.8161
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.495388 0.289880 138.316007 8.608
## gender_female -0.220211 0.113950 168.772773 -1.933
## COMPOSIT 0.033914 0.026211 179.836087 1.294
## V01.01.HighUtility_sum -0.020720 0.009387 141.053204 -2.207
## Community_Space_Content 0.205876 0.072544 175.376797 2.838
## overall_pre_competence_beliefs -0.152067 0.071460 154.626397 -2.128
## motivation_to_attend 0.175089 0.186685 174.614922 0.938
## creating_product 0.380883 0.073173 223.488327 5.205
## basic_skills 0.105381 0.060274 178.255515 1.748
## Pr(>|t|)
## (Intercept) 1.47e-14 ***
## gender_female 0.05497 .
## COMPOSIT 0.19737
## V01.01.HighUtility_sum 0.02891 *
## Community_Space_Content 0.00508 **
## overall_pre_competence_beliefs 0.03492 *
## motivation_to_attend 0.34960
## creating_product 4.38e-07 ***
## basic_skills 0.08212 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.228
## COMPOSIT -0.286 0.000
## V01.01.HgU_ 0.039 0.000 -0.240
## Cmmnty_Sp_C -0.006 0.008 -0.053 -0.334
## ovrll_pr_c_ -0.657 -0.078 -0.013 0.013 0.000
## mtvtn_t_ttn -0.484 0.144 0.000 -0.015 -0.020 -0.153
## crtng_prdct 0.075 0.011 -0.428 0.188 0.118 -0.001 -0.011
## basic_sklls 0.015 0.001 -0.201 0.004 -0.132 -0.013 0.018 0.273RQ2_relevance_full_2 <- lmer(relevance ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_full_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5267
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9202 -0.5451 0.0227 0.5605 3.9652
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007092 0.08421
## participant_ID (Intercept) 0.477533 0.69104
## program_ID (Intercept) 0.008936 0.09453
## Residual 0.409733 0.64010
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.099516 0.268330 120.898339 7.824
## gender_female -0.202913 0.111442 167.458836 -1.821
## COMPOSIT 0.019848 0.016417 181.077146 1.209
## V01.01.HighUtility_sum 0.014016 0.005686 135.247435 2.465
## Community_Space_Content 0.133357 0.045371 181.777507 2.939
## overall_pre_competence_beliefs 0.029642 0.068290 121.768925 0.434
## motivation_to_attend 0.373654 0.180244 161.749573 2.073
## creating_product 0.176932 0.047185 252.837860 3.750
## basic_skills 0.026077 0.037697 180.496272 0.692
## Pr(>|t|)
## (Intercept) 2.16e-12 ***
## gender_female 0.070423 .
## COMPOSIT 0.228226
## V01.01.HighUtility_sum 0.014954 *
## Community_Space_Content 0.003717 **
## overall_pre_competence_beliefs 0.665011
## motivation_to_attend 0.039754 *
## creating_product 0.000219 ***
## basic_skills 0.489971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.248
## COMPOSIT -0.190 -0.001
## V01.01.HgU_ 0.027 0.000 -0.235
## Cmmnty_Sp_C 0.003 0.010 -0.064 -0.342
## ovrll_pr_c_ -0.688 -0.064 -0.014 0.011 -0.003
## mtvtn_t_ttn -0.512 0.141 -0.002 -0.013 -0.021 -0.145
## crtng_prdct 0.055 0.010 -0.431 0.188 0.133 -0.002 -0.010
## basic_sklls 0.007 0.002 -0.198 -0.003 -0.114 -0.009 0.018 0.258RQ2_learning_full_2 <- lmer(learning ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_full_2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6471.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1042 -0.5618 0.1120 0.5919 2.8577
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0068193 0.08258
## participant_ID (Intercept) 0.3995373 0.63209
## program_ID (Intercept) 0.0008293 0.02880
## Residual 0.7117943 0.84368
## Number of obs: 2425, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.036367 0.251923 101.692090 8.083
## gender_female -0.047599 0.104762 158.409650 -0.454
## COMPOSIT 0.068350 0.020567 160.655313 3.323
## V01.01.HighUtility_sum -0.008720 0.007109 116.415183 -1.227
## Community_Space_Content 0.097464 0.056731 158.512092 1.718
## overall_pre_competence_beliefs 0.058989 0.063436 92.212708 0.930
## motivation_to_attend 0.319933 0.169950 144.606189 1.883
## creating_product -0.010031 0.059612 228.860625 -0.168
## basic_skills 0.127778 0.047507 157.652893 2.690
## Pr(>|t|)
## (Intercept) 1.37e-12 ***
## gender_female 0.65020
## COMPOSIT 0.00110 **
## V01.01.HighUtility_sum 0.22246
## Community_Space_Content 0.08775 .
## overall_pre_competence_beliefs 0.35485
## motivation_to_attend 0.06178 .
## creating_product 0.86652
## basic_skills 0.00792 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.249
## COMPOSIT -0.248 -0.002
## V01.01.HgU_ 0.037 0.001 -0.237
## Cmmnty_Sp_C 0.010 0.015 -0.065 -0.344
## ovrll_pr_c_ -0.670 -0.063 -0.027 0.016 -0.007
## mtvtn_t_ttn -0.507 0.139 -0.003 -0.021 -0.034 -0.154
## crtng_prdct 0.073 0.016 -0.428 0.188 0.132 -0.003 -0.014
## basic_sklls 0.004 0.003 -0.198 -0.004 -0.104 -0.012 0.031 0.257Moderation models for challenge and creating product
RQ2_challenge_composite_product <- lmer(challenge ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6453.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8461 -0.6345 -0.0386 0.5647 3.3675
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05100 0.2258
## participant_ID (Intercept) 0.46095 0.6789
## program_ID (Intercept) 0.03089 0.1758
## Residual 0.66623 0.8162
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.530082 0.292020 141.534792 8.664
## gender_female -0.219340 0.113936 168.767411 -1.925
## COMPOSIT 0.022811 0.028739 167.861472 0.794
## V01.01.HighUtility_sum -0.019871 0.009419 139.875960 -2.110
## Community_Space_Content 0.217173 0.073465 169.383208 2.956
## overall_pre_competence_beliefs -0.151919 0.071435 154.295166 -2.127
## motivation_to_attend 0.175343 0.186642 174.501511 0.939
## creating_product 0.143493 0.264248 251.143052 0.543
## basic_skills 0.108944 0.060329 176.186229 1.806
## COMPOSIT:creating_product 0.055396 0.059245 222.368844 0.935
## Pr(>|t|)
## (Intercept) 9.39e-15 ***
## gender_female 0.05590 .
## COMPOSIT 0.42847
## V01.01.HighUtility_sum 0.03666 *
## Community_Space_Content 0.00356 **
## overall_pre_competence_beliefs 0.03504 *
## motivation_to_attend 0.34879
## creating_product 0.58759
## basic_skills 0.07265 .
## COMPOSIT:creating_product 0.35079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.225
## COMPOSIT -0.310 -0.003
## V01.01.HgU_ 0.051 0.001 -0.257
## Cmmnty_Sp_C 0.015 0.009 -0.115 -0.312
## ovrll_pr_c_ -0.651 -0.078 -0.015 0.013 0.001
## mtvtn_t_ttn -0.481 0.144 0.000 -0.015 -0.020 -0.153
## crtng_prdct -0.100 -0.004 0.288 -0.040 -0.126 -0.006 -0.002
## basic_sklls 0.023 0.002 -0.209 0.010 -0.119 -0.013 0.018 0.015
## COMPOSIT:c_ 0.126 0.008 -0.412 0.095 0.165 0.006 -0.001 -0.961
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:c_ 0.063RQ2_challenge_value_product <- lmer(challenge ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * creating_product + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6452
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8383 -0.6292 -0.0359 0.5609 3.3592
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.04968 0.2229
## participant_ID (Intercept) 0.46090 0.6789
## program_ID (Intercept) 0.03714 0.1927
## Residual 0.66605 0.8161
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.48553 0.29180 137.89741
## gender_female -0.21885 0.11416 168.72956
## COMPOSIT 0.03730 0.02610 178.35907
## V01.01.HighUtility_sum -0.01675 0.00959 137.07939
## Community_Space_Content 0.18257 0.07316 171.53008
## overall_pre_competence_beliefs -0.15076 0.07180 158.63171
## motivation_to_attend 0.16969 0.18720 175.50973
## creating_product 0.42012 0.07622 218.18666
## basic_skills 0.10612 0.05986 177.02445
## V01.01.HighUtility_sum:creating_product -0.08019 0.04558 230.09843
## t value Pr(>|t|)
## (Intercept) 8.518 2.50e-14 ***
## gender_female -1.917 0.0569 .
## COMPOSIT 1.429 0.1548
## V01.01.HighUtility_sum -1.747 0.0829 .
## Community_Space_Content 2.495 0.0135 *
## overall_pre_competence_beliefs -2.100 0.0373 *
## motivation_to_attend 0.906 0.3659
## creating_product 5.512 9.98e-08 ***
## basic_skills 1.773 0.0780 .
## V01.01.HighUtility_sum:creating_product -1.759 0.0798 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.226
## COMPOSIT -0.284 0.001
## V01.01.HgU_ 0.035 0.003 -0.217
## Cmmnty_Sp_C -0.005 0.006 -0.063 -0.360
## ovrll_pr_c_ -0.656 -0.079 -0.013 0.008 0.004
## mtvtn_t_ttn -0.482 0.144 0.000 -0.013 -0.019 -0.153
## crtng_prdct 0.069 0.014 -0.390 0.245 0.061 -0.007 -0.009
## basic_sklls 0.016 0.001 -0.201 0.004 -0.130 -0.013 0.016 0.261
## V01.01.HU_: 0.009 -0.011 -0.062 -0.237 0.170 0.018 -0.004 -0.298
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: -0.001sjPlot::sjp.int(RQ2_challenge_value_product, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
Moderation models for challenge and basic skills
RQ2_challenge_composite_basic <- lmer(challenge ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6454.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8451 -0.6316 -0.0360 0.5662 3.3613
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05186 0.2277
## participant_ID (Intercept) 0.46097 0.6790
## program_ID (Intercept) 0.03134 0.1770
## Residual 0.66601 0.8161
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.493170 0.293393 146.235294 8.498
## gender_female -0.220182 0.113954 168.771572 -1.932
## COMPOSIT 0.034520 0.029051 178.313026 1.188
## V01.01.HighUtility_sum -0.020742 0.009419 140.437976 -2.202
## Community_Space_Content 0.206185 0.072786 173.999399 2.833
## overall_pre_competence_beliefs -0.152013 0.071466 154.636160 -2.127
## motivation_to_attend 0.175066 0.186705 174.714526 0.938
## creating_product 0.380104 0.074934 222.794529 5.073
## basic_skills 0.116079 0.220613 189.914131 0.526
## COMPOSIT:basic_skills -0.002750 0.054046 182.161443 -0.051
## Pr(>|t|)
## (Intercept) 2.04e-14 ***
## gender_female 0.05501 .
## COMPOSIT 0.23631
## V01.01.HighUtility_sum 0.02929 *
## Community_Space_Content 0.00516 **
## overall_pre_competence_beliefs 0.03500 *
## motivation_to_attend 0.34971
## creating_product 8.26e-07 ***
## basic_skills 0.59939
## COMPOSIT:basic_skills 0.95947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.225
## COMPOSIT -0.321 0.000
## V01.01.HgU_ 0.043 0.000 -0.228
## Cmmnty_Sp_C -0.011 0.008 -0.033 -0.335
## ovrll_pr_c_ -0.650 -0.078 -0.011 0.013 0.000
## mtvtn_t_ttn -0.480 0.144 0.004 -0.015 -0.020 -0.153
## crtng_prdct 0.104 0.011 -0.466 0.189 0.108 -0.002 -0.013
## basic_sklls -0.142 0.000 0.360 -0.025 -0.003 -0.002 0.015 -0.123
## COMPOSIT:b_ 0.152 0.000 -0.426 0.027 -0.035 -0.002 -0.010 0.205
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:b_ -0.962RQ2_challenge_value_basic <- lmer(challenge ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: challenge ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6456.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8424 -0.6329 -0.0338 0.5641 3.3650
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.05171 0.2274
## participant_ID (Intercept) 0.46074 0.6788
## program_ID (Intercept) 0.03257 0.1805
## Residual 0.66601 0.8161
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.491918 0.290422 138.595123
## gender_female -0.220281 0.113977 168.754549
## COMPOSIT 0.033781 0.026274 178.684970
## V01.01.HighUtility_sum -0.018384 0.010800 141.191764
## Community_Space_Content 0.203858 0.072838 175.623431
## overall_pre_competence_beliefs -0.151581 0.071532 155.677457
## motivation_to_attend 0.174202 0.186784 174.931172
## creating_product 0.382975 0.073492 221.733997
## basic_skills 0.121760 0.070746 187.687225
## V01.01.HighUtility_sum:basic_skills -0.007804 0.017568 138.876783
## t value Pr(>|t|)
## (Intercept) 8.580 1.71e-14 ***
## gender_female -1.933 0.0549 .
## COMPOSIT 1.286 0.2002
## V01.01.HighUtility_sum -1.702 0.0909 .
## Community_Space_Content 2.799 0.0057 **
## overall_pre_competence_beliefs -2.119 0.0357 *
## motivation_to_attend 0.933 0.3523
## creating_product 5.211 4.29e-07 ***
## basic_skills 1.721 0.0869 .
## V01.01.HighUtility_sum:basic_skills -0.444 0.6576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.227
## COMPOSIT -0.286 0.000
## V01.01.HgU_ 0.025 0.001 -0.218
## Cmmnty_Sp_C -0.005 0.008 -0.051 -0.320
## ovrll_pr_c_ -0.656 -0.078 -0.013 0.005 0.001
## mtvtn_t_ttn -0.484 0.144 0.000 -0.008 -0.020 -0.153
## crtng_prdct 0.074 0.011 -0.428 0.197 0.113 -0.002 -0.011
## basic_sklls 0.003 0.002 -0.181 0.259 -0.144 -0.018 0.020 0.269
## V01.01.HU_: 0.019 -0.002 0.019 -0.491 0.060 0.012 -0.009 -0.069
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: -0.521Moderation models for relevance and creating product
RQ2_relevance_composite_product <- lmer(relevance ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5269.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9131 -0.5435 0.0140 0.5590 3.9623
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007367 0.08583
## participant_ID (Intercept) 0.477372 0.69092
## program_ID (Intercept) 0.009022 0.09498
## Residual 0.409283 0.63975
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.066684 0.269245 122.170868 7.676
## gender_female -0.203851 0.111434 167.550634 -1.829
## COMPOSIT 0.030471 0.017922 169.358400 1.700
## V01.01.HighUtility_sum 0.013156 0.005733 136.921645 2.295
## Community_Space_Content 0.123638 0.045987 178.502632 2.689
## overall_pre_competence_beliefs 0.029082 0.068296 122.083464 0.426
## motivation_to_attend 0.374054 0.180237 161.886938 2.075
## creating_product 0.428812 0.173671 319.136464 2.469
## basic_skills 0.022466 0.037878 182.257062 0.593
## COMPOSIT:creating_product -0.057884 0.038406 270.022126 -1.507
## Pr(>|t|)
## (Intercept) 4.5e-12 ***
## gender_female 0.06913 .
## COMPOSIT 0.09092 .
## V01.01.HighUtility_sum 0.02327 *
## Community_Space_Content 0.00786 **
## overall_pre_competence_beliefs 0.67099
## motivation_to_attend 0.03954 *
## creating_product 0.01407 *
## basic_skills 0.55384
## COMPOSIT:creating_product 0.13293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.246
## COMPOSIT -0.206 -0.003
## V01.01.HgU_ 0.034 0.001 -0.253
## Cmmnty_Sp_C 0.015 0.010 -0.115 -0.322
## ovrll_pr_c_ -0.685 -0.064 -0.016 0.011 -0.002
## mtvtn_t_ttn -0.510 0.141 -0.001 -0.013 -0.021 -0.145
## crtng_prdct -0.062 -0.003 0.272 -0.044 -0.103 -0.007 -0.001
## basic_sklls 0.012 0.002 -0.206 0.003 -0.104 -0.008 0.018 0.011
## COMPOSIT:c_ 0.080 0.005 -0.395 0.098 0.145 0.007 -0.001 -0.962
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:c_ 0.061RQ2_relevance_value_product <- lmer(relevance ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * creating_product + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5270.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9440 -0.5433 0.0205 0.5608 3.9574
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.006939 0.08330
## participant_ID (Intercept) 0.477313 0.69088
## program_ID (Intercept) 0.009873 0.09936
## Residual 0.409784 0.64014
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.095142 0.268892 121.469534
## gender_female -0.201888 0.111525 168.002830
## COMPOSIT 0.021127 0.016422 180.096170
## V01.01.HighUtility_sum 0.015505 0.005823 130.983171
## Community_Space_Content 0.125006 0.045880 177.522204
## overall_pre_competence_beliefs 0.029285 0.068443 123.888497
## motivation_to_attend 0.374993 0.180452 162.786313
## creating_product 0.193125 0.049257 246.243665
## basic_skills 0.025961 0.037629 179.796375
## V01.01.HighUtility_sum:creating_product -0.033723 0.029732 291.708512
## t value Pr(>|t|)
## (Intercept) 7.792 2.51e-12 ***
## gender_female -1.810 0.072044 .
## COMPOSIT 1.287 0.199918
## V01.01.HighUtility_sum 2.663 0.008728 **
## Community_Space_Content 2.725 0.007081 **
## overall_pre_competence_beliefs 0.428 0.669484
## motivation_to_attend 2.078 0.039272 *
## creating_product 3.921 0.000114 ***
## basic_skills 0.690 0.491139
## V01.01.HighUtility_sum:creating_product -1.134 0.257639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.247
## COMPOSIT -0.189 -0.001
## V01.01.HgU_ 0.025 0.003 -0.214
## Cmmnty_Sp_C 0.003 0.007 -0.074 -0.365
## ovrll_pr_c_ -0.687 -0.065 -0.015 0.006 0.001
## mtvtn_t_ttn -0.511 0.141 -0.001 -0.011 -0.021 -0.145
## crtng_prdct 0.052 0.013 -0.393 0.240 0.079 -0.008 -0.008
## basic_sklls 0.007 0.002 -0.198 -0.004 -0.112 -0.009 0.017 0.245
## V01.01.HU_: 0.002 -0.012 -0.063 -0.225 0.159 0.021 -0.005 -0.292
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: 0.003Moderation models for relevance and basic skills
RQ2_relevance_composite_basic <- lmer(relevance ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5270.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9194 -0.5449 0.0223 0.5603 3.9688
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.006841 0.08271
## participant_ID (Intercept) 0.477744 0.69119
## program_ID (Intercept) 0.009206 0.09595
## Residual 0.409832 0.64018
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.133283 0.270079 125.054909 7.899
## gender_female -0.202808 0.111493 167.601648 -1.819
## COMPOSIT 0.010600 0.018098 180.362988 0.586
## V01.01.HighUtility_sum 0.014174 0.005668 132.808037 2.501
## Community_Space_Content 0.132117 0.045246 178.393076 2.920
## overall_pre_competence_beliefs 0.029780 0.068346 122.493575 0.436
## motivation_to_attend 0.371154 0.180360 162.305779 2.058
## creating_product 0.188868 0.048104 250.219589 3.926
## basic_skills -0.134702 0.138490 197.766231 -0.973
## COMPOSIT:basic_skills 0.040717 0.033737 186.644015 1.207
## Pr(>|t|)
## (Intercept) 1.24e-12 ***
## gender_female 0.070693 .
## COMPOSIT 0.558806
## V01.01.HighUtility_sum 0.013609 *
## Community_Space_Content 0.003952 **
## overall_pre_competence_beliefs 0.663806
## motivation_to_attend 0.041203 *
## creating_product 0.000112 ***
## basic_skills 0.331917
## COMPOSIT:basic_skills 0.229000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.246
## COMPOSIT -0.216 -0.002
## V01.01.HgU_ 0.029 0.000 -0.221
## Cmmnty_Sp_C 0.001 0.009 -0.050 -0.342
## ovrll_pr_c_ -0.684 -0.064 -0.012 0.010 -0.003
## mtvtn_t_ttn -0.510 0.141 0.004 -0.013 -0.021 -0.145
## crtng_prdct 0.075 0.010 -0.469 0.188 0.126 -0.003 -0.013
## basic_sklls -0.101 -0.001 0.362 -0.022 -0.013 0.000 0.016 -0.130
## COMPOSIT:b_ 0.107 0.002 -0.427 0.022 -0.018 -0.002 -0.012 0.206
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:b_ -0.962RQ2_relevance_value_basic <- lmer(relevance ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: relevance ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5274.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9184 -0.5445 0.0221 0.5599 3.9638
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007360 0.08579
## participant_ID (Intercept) 0.477544 0.69105
## program_ID (Intercept) 0.008934 0.09452
## Residual 0.409698 0.64008
## Number of obs: 2426, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.100e+00 2.684e-01 1.209e+02
## gender_female -2.029e-01 1.114e-01 1.674e+02
## COMPOSIT 1.980e-02 1.647e-02 1.802e+02
## V01.01.HighUtility_sum 1.396e-02 6.560e-03 1.367e+02
## Community_Space_Content 1.337e-01 4.565e-02 1.832e+02
## overall_pre_competence_beliefs 2.969e-02 6.830e-02 1.218e+02
## motivation_to_attend 3.736e-01 1.803e-01 1.619e+02
## creating_product 1.769e-01 4.742e-02 2.507e+02
## basic_skills 2.565e-02 4.460e-02 1.904e+02
## V01.01.HighUtility_sum:basic_skills 1.453e-04 1.064e-02 1.323e+02
## t value Pr(>|t|)
## (Intercept) 7.823 2.17e-12 ***
## gender_female -1.821 0.070463 .
## COMPOSIT 1.202 0.230920
## V01.01.HighUtility_sum 2.128 0.035123 *
## Community_Space_Content 2.928 0.003839 **
## overall_pre_competence_beliefs 0.435 0.664489
## motivation_to_attend 2.073 0.039779 *
## creating_product 3.731 0.000236 ***
## basic_skills 0.575 0.565903
## V01.01.HighUtility_sum:basic_skills 0.014 0.989119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.248
## COMPOSIT -0.190 -0.001
## V01.01.HgU_ 0.016 0.002 -0.212
## Cmmnty_Sp_C 0.004 0.009 -0.063 -0.334
## ovrll_pr_c_ -0.687 -0.064 -0.014 0.004 -0.002
## mtvtn_t_ttn -0.512 0.141 -0.002 -0.007 -0.022 -0.145
## crtng_prdct 0.054 0.010 -0.431 0.196 0.127 -0.003 -0.010
## basic_sklls -0.002 0.003 -0.177 0.259 -0.137 -0.013 0.020 0.254
## V01.01.HU_: 0.014 -0.002 0.016 -0.493 0.076 0.010 -0.010 -0.067
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: -0.530Moderation models for learning and creating product
RQ2_learning_composite_product <- lmer(learning ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * creating_product + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6475.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1167 -0.5618 0.1131 0.5932 2.8534
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0071855 0.08477
## participant_ID (Intercept) 0.3996243 0.63216
## program_ID (Intercept) 0.0006976 0.02641
## Residual 0.7116647 0.84360
## Number of obs: 2425, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.019985 0.253309 103.000887 7.974
## gender_female -0.047899 0.104753 158.251307 -0.457
## COMPOSIT 0.073961 0.022471 147.139059 3.291
## V01.01.HighUtility_sum -0.009172 0.007166 116.547055 -1.280
## Community_Space_Content 0.092252 0.057493 153.872518 1.605
## overall_pre_competence_beliefs 0.058267 0.063414 92.111605 0.919
## motivation_to_attend 0.320303 0.169916 144.412496 1.885
## creating_product 0.125215 0.220859 292.208924 0.567
## basic_skills 0.125898 0.047732 157.468067 2.638
## COMPOSIT:creating_product -0.030993 0.048787 242.660085 -0.635
## Pr(>|t|)
## (Intercept) 2.2e-12 ***
## gender_female 0.64811
## COMPOSIT 0.00125 **
## V01.01.HighUtility_sum 0.20314
## Community_Space_Content 0.11064
## overall_pre_competence_beliefs 0.36058
## motivation_to_attend 0.06143 .
## creating_product 0.57118
## basic_skills 0.00919 **
## COMPOSIT:creating_product 0.52585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.247
## COMPOSIT -0.270 -0.004
## V01.01.HgU_ 0.048 0.002 -0.255
## Cmmnty_Sp_C 0.026 0.016 -0.117 -0.324
## ovrll_pr_c_ -0.665 -0.062 -0.029 0.017 -0.005
## mtvtn_t_ttn -0.505 0.139 -0.001 -0.021 -0.034 -0.154
## crtng_prdct -0.083 -0.003 0.276 -0.044 -0.105 -0.012 0.000
## basic_sklls 0.010 0.003 -0.206 0.002 -0.094 -0.011 0.031 0.009
## COMPOSIT:c_ 0.107 0.008 -0.397 0.098 0.145 0.012 -0.004 -0.963
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:c_ 0.063RQ2_learning_value_product <- lmer(learning ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*creating_product +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_product)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * creating_product + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6475.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1049 -0.5633 0.1138 0.5891 2.8615
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.006955 0.08340
## participant_ID (Intercept) 0.399815 0.63231
## program_ID (Intercept) 0.001800 0.04243
## Residual 0.711727 0.84364
## Number of obs: 2425, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.029850 0.253013 102.352212
## gender_female -0.047333 0.104991 159.368720
## COMPOSIT 0.069292 0.020624 159.937490
## V01.01.HighUtility_sum -0.007637 0.007299 112.916899
## Community_Space_Content 0.092027 0.057465 154.991118
## overall_pre_competence_beliefs 0.059797 0.063746 94.774345
## motivation_to_attend 0.321304 0.170427 145.965011
## creating_product 0.002371 0.062567 220.974821
## basic_skills 0.127520 0.047562 157.243253
## V01.01.HighUtility_sum:creating_product -0.025553 0.037695 269.241667
## t value Pr(>|t|)
## (Intercept) 8.023 1.79e-12 ***
## gender_female -0.451 0.652724
## COMPOSIT 3.360 0.000976 ***
## V01.01.HighUtility_sum -1.046 0.297631
## Community_Space_Content 1.601 0.111318
## overall_pre_competence_beliefs 0.938 0.350608
## motivation_to_attend 1.885 0.061378 .
## creating_product 0.038 0.969799
## basic_skills 2.681 0.008120 **
## V01.01.HighUtility_sum:creating_product -0.678 0.498426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.248
## COMPOSIT -0.248 -0.001
## V01.01.HgU_ 0.036 0.004 -0.219
## Cmmnty_Sp_C 0.009 0.012 -0.072 -0.364
## ovrll_pr_c_ -0.670 -0.064 -0.027 0.009 -0.002
## mtvtn_t_ttn -0.506 0.139 -0.002 -0.019 -0.033 -0.154
## crtng_prdct 0.069 0.020 -0.392 0.241 0.080 -0.012 -0.011
## basic_sklls 0.004 0.002 -0.197 -0.005 -0.103 -0.012 0.030 0.244
## V01.01.HU_: -0.001 -0.016 -0.051 -0.220 0.149 0.029 -0.007 -0.300
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: 0.004Moderation models for learning and basic skills
RQ2_learning_composite_basic <- lmer(learning ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
COMPOSIT*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## COMPOSIT * basic_skills + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6474.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0742 -0.5747 0.1161 0.5871 2.8427
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.006714 0.08194
## participant_ID (Intercept) 0.400143 0.63257
## program_ID (Intercept) 0.001059 0.03255
## Residual 0.711637 0.84359
## Number of obs: 2425, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.080696 0.255053 109.266600 8.158
## gender_female -0.047347 0.104872 158.955789 -0.451
## COMPOSIT 0.056470 0.022745 162.294995 2.483
## V01.01.HighUtility_sum -0.008568 0.007103 115.907010 -1.206
## Community_Space_Content 0.096198 0.056700 157.490175 1.697
## overall_pre_competence_beliefs 0.059276 0.063538 93.489925 0.933
## motivation_to_attend 0.315484 0.170195 146.285236 1.854
## creating_product 0.004985 0.060840 229.290323 0.082
## basic_skills -0.079085 0.175253 176.272099 -0.451
## COMPOSIT:basic_skills 0.052314 0.042672 165.361890 1.226
## Pr(>|t|)
## (Intercept) 6.32e-13 ***
## gender_female 0.6523
## COMPOSIT 0.0141 *
## V01.01.HighUtility_sum 0.2302
## Community_Space_Content 0.0917 .
## overall_pre_competence_beliefs 0.3533
## motivation_to_attend 0.0658 .
## creating_product 0.9348
## basic_skills 0.6524
## COMPOSIT:basic_skills 0.2220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT V01.01 Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.245
## COMPOSIT -0.285 -0.003
## V01.01.HgU_ 0.039 0.001 -0.221
## Cmmnty_Sp_C 0.007 0.015 -0.051 -0.344
## ovrll_pr_c_ -0.664 -0.063 -0.023 0.016 -0.007
## mtvtn_t_ttn -0.505 0.139 0.007 -0.021 -0.033 -0.154
## crtng_prdct 0.100 0.016 -0.466 0.187 0.125 -0.003 -0.018
## basic_sklls -0.140 -0.003 0.364 -0.018 -0.010 -0.001 0.029 -0.127
## COMPOSIT:b_ 0.147 0.004 -0.429 0.017 -0.019 -0.002 -0.022 0.203
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## COMPOSIT:b_ -0.963RQ2_learning_value_basic <- lmer(learning ~
gender_female +
COMPOSIT +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
creating_product +
basic_skills +
V01.01.HighUtility_sum*basic_skills +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_basic)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: learning ~ gender_female + COMPOSIT + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + creating_product + basic_skills +
## V01.01.HighUtility_sum * basic_skills + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6478.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1037 -0.5632 0.1137 0.5894 2.8530
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.0072675 0.08525
## participant_ID (Intercept) 0.3995978 0.63214
## program_ID (Intercept) 0.0006693 0.02587
## Residual 0.7116966 0.84362
## Number of obs: 2425, groups:
## beep_ID_new, 227; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.038990 0.251905 101.345185
## gender_female -0.047540 0.104744 158.135877
## COMPOSIT 0.068372 0.020637 160.005217
## V01.01.HighUtility_sum -0.009991 0.008225 116.848900
## Community_Space_Content 0.098993 0.057099 159.994790
## overall_pre_competence_beliefs 0.059024 0.063411 91.720803
## motivation_to_attend 0.318876 0.169933 144.721937
## creating_product -0.011061 0.059912 226.930925
## basic_skills 0.118616 0.056198 167.258747
## V01.01.HighUtility_sum:basic_skills 0.004117 0.013316 114.174722
## t value Pr(>|t|)
## (Intercept) 8.094 1.32e-12 ***
## gender_female -0.454 0.65055
## COMPOSIT 3.313 0.00114 **
## V01.01.HighUtility_sum -1.215 0.22691
## Community_Space_Content 1.734 0.08489 .
## overall_pre_competence_beliefs 0.931 0.35438
## motivation_to_attend 1.876 0.06260 .
## creating_product -0.185 0.85369
## basic_skills 2.111 0.03629 *
## V01.01.HighUtility_sum:basic_skills 0.309 0.75773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS V01.01.HgU_ Cm_S_C ovr___ mtvt__ crtng_
## gender_feml -0.249
## COMPOSIT -0.249 -0.002
## V01.01.HgU_ 0.022 0.004 -0.213
## Cmmnty_Sp_C 0.012 0.014 -0.064 -0.337
## ovrll_pr_c_ -0.669 -0.063 -0.027 0.005 -0.006
## mtvtn_t_ttn -0.507 0.139 -0.003 -0.009 -0.035 -0.154
## crtng_prdct 0.071 0.016 -0.428 0.196 0.126 -0.004 -0.012
## basic_sklls -0.008 0.005 -0.175 0.260 -0.131 -0.019 0.037 0.254
## V01.01.HU_: 0.022 -0.006 0.015 -0.497 0.080 0.017 -0.020 -0.067
## bsc_sk
## gender_feml
## COMPOSIT
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## crtng_prdct
## basic_sklls
## V01.01.HU_: -0.530April 11 Analysis
Interaction between community space and composite
RQ2_challenge_composite_community <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6637.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8628 -0.6432 -0.0557 0.5590 3.3885
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06014 0.2452
## participant_ID (Intercept) 0.46197 0.6797
## program_ID (Intercept) 0.03743 0.1935
## Residual 0.65964 0.8122
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.290846 0.293521 143.182557 7.805
## gender_female -0.225800 0.114010 168.974712 -1.981
## COMPOSIT 0.091754 0.027305 200.540433 3.360
## agency 0.045439 0.019415 212.810355 2.340
## V01.01.HighUtility_sum -0.026972 0.009126 163.231624 -2.955
## Community_Space_Content 0.813755 0.264769 209.815065 3.073
## overall_pre_competence_beliefs -0.149170 0.071681 159.424832 -2.081
## motivation_to_attend 0.170434 0.187183 176.648641 0.911
## COMPOSIT:Community_Space_Content -0.151772 0.062678 200.047102 -2.421
## Pr(>|t|)
## (Intercept) 1.14e-12 ***
## gender_female 0.049268 *
## COMPOSIT 0.000932 ***
## agency 0.020188 *
## V01.01.HighUtility_sum 0.003585 **
## Community_Space_Content 0.002397 **
## overall_pre_competence_beliefs 0.039029 *
## motivation_to_attend 0.363789
## COMPOSIT:Community_Space_Content 0.016351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.224
## COMPOSIT -0.281 0.004
## agency -0.019 -0.001 -0.358
## V01.01.HgU_ -0.002 0.003 -0.156 0.149
## Cmmnty_Sp_C -0.115 0.006 0.265 0.047 -0.007
## ovrll_pr_c_ -0.653 -0.079 -0.011 0.004 0.017 0.015
## mtvtn_t_ttn -0.477 0.144 -0.001 -0.016 -0.016 -0.015 -0.153
## COMPOSIT:C_ 0.117 -0.006 -0.311 0.014 -0.082 -0.961 -0.016 0.010sjPlot::sjp.int(RQ2_challenge_composite_community, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
RQ2_relevance_composite_community <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5439.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9768 -0.5369 0.0296 0.5813 3.7064
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.009826 0.09913
## participant_ID (Intercept) 0.473806 0.68834
## program_ID (Intercept) 0.011520 0.10733
## Residual 0.411085 0.64116
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.008238 0.269593 125.037828 7.449
## gender_female -0.213224 0.111198 168.514173 -1.918
## COMPOSIT 0.053421 0.017134 196.454971 3.118
## agency 0.002097 0.012281 213.482089 0.171
## V01.01.HighUtility_sum 0.012278 0.005563 151.118397 2.207
## Community_Space_Content 0.521324 0.167301 210.821012 3.116
## overall_pre_competence_beliefs 0.035480 0.068375 127.765543 0.519
## motivation_to_attend 0.370569 0.180236 165.031528 2.056
## COMPOSIT:Community_Space_Content -0.104499 0.039372 200.929094 -2.654
## Pr(>|t|)
## (Intercept) 1.35e-11 ***
## gender_female 0.05686 .
## COMPOSIT 0.00210 **
## agency 0.86461
## V01.01.HighUtility_sum 0.02883 *
## Community_Space_Content 0.00209 **
## overall_pre_competence_beliefs 0.60473
## motivation_to_attend 0.04136 *
## COMPOSIT:Community_Space_Content 0.00859 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.245
## COMPOSIT -0.190 0.003
## agency -0.004 -0.003 -0.369
## V01.01.HgU_ -0.001 0.003 -0.160 0.156
## Cmmnty_Sp_C -0.081 0.005 0.263 0.051 -0.017
## ovrll_pr_c_ -0.686 -0.066 -0.010 0.000 0.014 0.016
## mtvtn_t_ttn -0.508 0.141 -0.001 -0.017 -0.014 -0.015 -0.145
## COMPOSIT:C_ 0.084 -0.005 -0.310 0.010 -0.073 -0.962 -0.018 0.010sjPlot::sjp.int(RQ2_relevance_composite_community, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
RQ2_learning_composite_community <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6661.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1130 -0.5697 0.1049 0.5901 2.8494
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007933 0.08907
## participant_ID (Intercept) 0.393518 0.62731
## program_ID (Intercept) 0.003708 0.06090
## Residual 0.710277 0.84278
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.069572 0.253375 113.262827 8.168
## gender_female -0.056978 0.104323 162.838836 -0.546
## COMPOSIT 0.064880 0.021012 177.648725 3.088
## agency 0.002052 0.015126 192.157608 0.136
## V01.01.HighUtility_sum -0.006587 0.006777 131.930528 -0.972
## Community_Space_Content -0.026702 0.205915 188.726326 -0.130
## overall_pre_competence_beliefs 0.065381 0.063564 104.997685 1.029
## motivation_to_attend 0.298100 0.169822 153.000142 1.755
## COMPOSIT:Community_Space_Content 0.035115 0.048441 180.289037 0.725
## Pr(>|t|)
## (Intercept) 4.93e-13 ***
## gender_female 0.58570
## COMPOSIT 0.00234 **
## agency 0.89225
## V01.01.HighUtility_sum 0.33288
## Community_Space_Content 0.89696
## overall_pre_competence_beliefs 0.30604
## motivation_to_attend 0.08120 .
## COMPOSIT:Community_Space_Content 0.46945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.245
## COMPOSIT -0.246 0.005
## agency -0.001 -0.006 -0.370
## V01.01.HgU_ 0.001 0.004 -0.165 0.159
## Cmmnty_Sp_C -0.108 0.007 0.267 0.050 -0.018
## ovrll_pr_c_ -0.670 -0.067 -0.015 -0.003 0.019 0.026
## mtvtn_t_ttn -0.501 0.140 -0.002 -0.024 -0.021 -0.024 -0.153
## COMPOSIT:C_ 0.114 -0.006 -0.314 0.011 -0.071 -0.963 -0.030 0.016Interaction between community space and agency
RQ2_challenge_agency_community <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_agency_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6644
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8651 -0.6440 -0.0523 0.5622 3.3802
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06106 0.2471
## participant_ID (Intercept) 0.45937 0.6778
## program_ID (Intercept) 0.04394 0.2096
## Residual 0.66094 0.8130
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.369e+00 2.935e-01 1.391e+02 8.070
## gender_female -2.280e-01 1.139e-01 1.688e+02 -2.001
## COMPOSIT 7.162e-02 2.609e-02 1.958e+02 2.745
## agency 4.602e-02 2.068e-02 2.116e+02 2.225
## V01.01.HighUtility_sum -2.882e-02 9.231e-03 1.588e+02 -3.122
## Community_Space_Content 1.972e-01 1.145e-01 1.586e+02 1.723
## overall_pre_competence_beliefs -1.483e-01 7.184e-02 1.627e+02 -2.064
## motivation_to_attend 1.682e-01 1.873e-01 1.772e+02 0.898
## agency:Community_Space_Content -2.312e-04 5.546e-02 1.660e+02 -0.004
## Pr(>|t|)
## (Intercept) 2.96e-13 ***
## gender_female 0.04701 *
## COMPOSIT 0.00661 **
## agency 0.02715 *
## V01.01.HighUtility_sum 0.00213 **
## Community_Space_Content 0.08684 .
## overall_pre_competence_beliefs 0.04058 *
## motivation_to_attend 0.37026
## agency:Community_Space_Content 0.99668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.223
## COMPOSIT -0.258 0.002
## agency -0.036 0.001 -0.359
## V01.01.HgU_ 0.014 0.002 -0.186 0.094
## Cmmnty_Sp_C -0.042 0.002 -0.102 0.387 -0.305
## ovrll_pr_c_ -0.653 -0.081 -0.016 0.009 0.013 0.010
## mtvtn_t_ttn -0.478 0.145 0.003 -0.016 -0.014 -0.014 -0.153
## agncy:C_S_C 0.046 -0.003 0.025 -0.333 0.140 -0.768 -0.014 0.004RQ2_relevance_agency_community <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_agency_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5445.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0012 -0.5471 0.0392 0.5878 3.6944
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01040 0.1020
## participant_ID (Intercept) 0.47067 0.6861
## program_ID (Intercept) 0.01519 0.1232
## Residual 0.41171 0.6416
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.067591 0.270339 126.466760 7.648
## gender_female -0.216088 0.111188 169.548125 -1.943
## COMPOSIT 0.039823 0.016414 193.536403 2.426
## agency -0.002741 0.013151 217.751393 -0.208
## V01.01.HighUtility_sum 0.012054 0.005644 149.365621 2.136
## Community_Space_Content 0.033036 0.069962 148.827411 0.472
## overall_pre_competence_beliefs 0.035253 0.068676 135.189573 0.513
## motivation_to_attend 0.377352 0.180512 168.347548 2.090
## agency:Community_Space_Content 0.040123 0.034119 158.104998 1.176
## Pr(>|t|)
## (Intercept) 4.5e-12 ***
## gender_female 0.0536 .
## COMPOSIT 0.0162 *
## agency 0.8351
## V01.01.HighUtility_sum 0.0343 *
## Community_Space_Content 0.6375
## overall_pre_competence_beliefs 0.6086
## motivation_to_attend 0.0381 *
## agency:Community_Space_Content 0.2414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.243
## COMPOSIT -0.173 0.001
## agency -0.018 -0.002 -0.369
## V01.01.HgU_ 0.010 0.002 -0.188 0.101
## Cmmnty_Sp_C -0.027 0.003 -0.106 0.395 -0.309
## ovrll_pr_c_ -0.686 -0.068 -0.015 0.006 0.010 0.009
## mtvtn_t_ttn -0.508 0.142 0.002 -0.017 -0.012 -0.017 -0.145
## agncy:C_S_C 0.036 -0.002 0.022 -0.340 0.134 -0.753 -0.016 0.006RQ2_learning_agency_community <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
agency*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_agency_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + agency * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6660
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1106 -0.5641 0.1091 0.5895 2.8622
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007595 0.08715
## participant_ID (Intercept) 0.394146 0.62781
## program_ID (Intercept) 0.004532 0.06732
## Residual 0.709827 0.84251
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.064845 0.252890 112.236967 8.165
## gender_female -0.057451 0.104510 163.638044 -0.550
## COMPOSIT 0.070385 0.019904 179.147489 3.536
## agency -0.006795 0.016063 202.334348 -0.423
## V01.01.HighUtility_sum -0.004781 0.006801 132.913234 -0.703
## Community_Space_Content 0.016773 0.084234 131.815496 0.199
## overall_pre_competence_beliefs 0.065177 0.063783 108.255339 1.022
## motivation_to_attend 0.299525 0.170203 154.711701 1.760
## agency:Community_Space_Content 0.065505 0.041206 141.194481 1.590
## Pr(>|t|)
## (Intercept) 5.26e-13 ***
## gender_female 0.583260
## COMPOSIT 0.000517 ***
## agency 0.672744
## V01.01.HighUtility_sum 0.483276
## Community_Space_Content 0.842473
## overall_pre_competence_beliefs 0.309128
## motivation_to_attend 0.080417 .
## agency:Community_Space_Content 0.114138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.244
## COMPOSIT -0.221 0.003
## agency -0.020 -0.005 -0.369
## V01.01.HgU_ 0.016 0.003 -0.193 0.103
## Cmmnty_Sp_C -0.035 0.003 -0.105 0.396 -0.311
## ovrll_pr_c_ -0.672 -0.068 -0.026 0.008 0.013 0.017
## mtvtn_t_ttn -0.505 0.140 0.003 -0.025 -0.018 -0.027 -0.153
## agncy:C_S_C 0.052 -0.002 0.019 -0.343 0.134 -0.750 -0.030 0.010Interaction between community space and value
RQ2_challenge_value_community <- lmer(challenge ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_challenge_value_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## challenge ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6645.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8673 -0.6422 -0.0527 0.5670 3.4119
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.06121 0.2474
## participant_ID (Intercept) 0.45997 0.6782
## program_ID (Intercept) 0.04239 0.2059
## Residual 0.66074 0.8129
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.36886 0.29288
## gender_female -0.22818 0.11396
## COMPOSIT 0.07100 0.02610
## agency 0.04535 0.01953
## V01.01.HighUtility_sum -0.02466 0.01119
## Community_Space_Content 0.23163 0.09084
## overall_pre_competence_beliefs -0.14863 0.07180
## motivation_to_attend 0.16805 0.18727
## V01.01.HighUtility_sum:Community_Space_Content -0.01165 0.01807
## df t value Pr(>|t|)
## (Intercept) 137.95226 8.088 2.79e-13
## gender_female 168.79896 -2.002 0.04686
## COMPOSIT 197.04320 2.720 0.00711
## agency 207.57299 2.321 0.02123
## V01.01.HighUtility_sum 170.47362 -2.204 0.02888
## Community_Space_Content 191.05661 2.550 0.01156
## overall_pre_competence_beliefs 161.83471 -2.070 0.04004
## motivation_to_attend 177.21459 0.897 0.37072
## V01.01.HighUtility_sum:Community_Space_Content 150.35951 -0.644 0.52026
##
## (Intercept) ***
## gender_female *
## COMPOSIT **
## agency *
## V01.01.HighUtility_sum *
## Community_Space_Content *
## overall_pre_competence_beliefs *
## motivation_to_attend
## V01.01.HighUtility_sum:Community_Space_Content
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.223
## COMPOSIT -0.259 0.002
## agency -0.021 0.000 -0.370
## V01.01.HgU_ 0.002 -0.001 -0.174 0.093
## Cmmnty_Sp_C -0.013 -0.002 -0.124 0.144 0.135
## ovrll_pr_c_ -0.653 -0.081 -0.016 0.003 0.019 0.005
## mtvtn_t_ttn -0.479 0.145 0.003 -0.015 -0.020 -0.022 -0.153
## V01.01.HU_: 0.007 0.005 0.031 0.052 -0.576 -0.590 -0.011 0.014RQ2_relevance_value_community <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_value_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5446.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0192 -0.5421 0.0362 0.5862 3.6587
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01038 0.1019
## participant_ID (Intercept) 0.47194 0.6870
## program_ID (Intercept) 0.01288 0.1135
## Residual 0.41160 0.6416
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.062397 0.269117
## gender_female -0.215941 0.111132
## COMPOSIT 0.038282 0.016413
## agency 0.001684 0.012372
## V01.01.HighUtility_sum 0.017084 0.006909
## Community_Space_Content 0.143765 0.056989
## overall_pre_competence_beliefs 0.035007 0.068450
## motivation_to_attend 0.372066 0.180259
## V01.01.HighUtility_sum:Community_Space_Content -0.015929 0.010943
## df t value Pr(>|t|)
## (Intercept) 123.540374 7.664 4.58e-12
## gender_female 168.966381 -1.943 0.0537
## COMPOSIT 195.154805 2.332 0.0207
## agency 211.148172 0.136 0.8919
## V01.01.HighUtility_sum 167.187780 2.473 0.0144
## Community_Space_Content 193.603650 2.523 0.0125
## overall_pre_competence_beliefs 130.193548 0.511 0.6099
## motivation_to_attend 166.442926 2.064 0.0406
## V01.01.HighUtility_sum:Community_Space_Content 139.129365 -1.456 0.1477
##
## (Intercept) ***
## gender_female .
## COMPOSIT *
## agency
## V01.01.HighUtility_sum *
## Community_Space_Content *
## overall_pre_competence_beliefs
## motivation_to_attend *
## V01.01.HighUtility_sum:Community_Space_Content
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.244
## COMPOSIT -0.174 0.001
## agency -0.005 -0.003 -0.382
## V01.01.HgU_ 0.002 -0.001 -0.180 0.100
## Cmmnty_Sp_C -0.001 -0.002 -0.135 0.153 0.139
## ovrll_pr_c_ -0.686 -0.067 -0.016 0.000 0.016 0.002
## mtvtn_t_ttn -0.509 0.141 0.003 -0.016 -0.019 -0.024 -0.145
## V01.01.HU_: 0.004 0.005 0.041 0.046 -0.588 -0.591 -0.011 0.014RQ2_learning_value_community <- lmer(learning ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
V01.01.HighUtility_sum*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_learning_value_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## learning ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + V01.01.HighUtility_sum * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6661.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1336 -0.5681 0.1010 0.5992 2.8471
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.007616 0.08727
## participant_ID (Intercept) 0.395313 0.62874
## program_ID (Intercept) 0.001553 0.03941
## Residual 0.709779 0.84248
## Number of obs: 2499, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.063e+00 2.500e-01
## gender_female -5.641e-02 1.042e-01
## COMPOSIT 6.782e-02 1.990e-02
## agency 7.677e-04 1.510e-02
## V01.01.HighUtility_sum 2.405e-03 8.377e-03
## Community_Space_Content 1.865e-01 6.892e-02
## overall_pre_competence_beliefs 6.409e-02 6.312e-02
## motivation_to_attend 2.870e-01 1.693e-01
## V01.01.HighUtility_sum:Community_Space_Content -2.281e-02 1.315e-02
## df t value Pr(>|t|)
## (Intercept) 1.032e+02 8.249 5.49e-13
## gender_female 1.600e+02 -0.542 0.588821
## COMPOSIT 1.792e+02 3.408 0.000808
## agency 1.926e+02 0.051 0.959495
## V01.01.HighUtility_sum 1.502e+02 0.287 0.774396
## Community_Space_Content 1.750e+02 2.707 0.007464
## overall_pre_competence_beliefs 9.602e+01 1.015 0.312500
## motivation_to_attend 1.479e+02 1.695 0.092150
## V01.01.HighUtility_sum:Community_Space_Content 1.216e+02 -1.734 0.085463
##
## (Intercept) ***
## gender_female
## COMPOSIT ***
## agency
## V01.01.HighUtility_sum
## Community_Space_Content **
## overall_pre_competence_beliefs
## motivation_to_attend .
## V01.01.HighUtility_sum:Community_Space_Content .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.248
## COMPOSIT -0.222 0.003
## agency -0.001 -0.006 -0.383
## V01.01.HgU_ 0.007 -0.003 -0.185 0.102
## Cmmnty_Sp_C 0.007 -0.003 -0.136 0.153 0.143
## ovrll_pr_c_ -0.671 -0.064 -0.029 -0.004 0.023 0.001
## mtvtn_t_ttn -0.508 0.139 0.004 -0.024 -0.030 -0.041 -0.153
## V01.01.HU_: 0.002 0.010 0.043 0.045 -0.594 -0.590 -0.016 0.024sjPlot::sjp.int(RQ2_learning_value_community, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
Models with all interactions that were significant for challenge, relevance, and learning
RQ2_relevance_composite_motivation_all <- lmer(relevance ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*motivation_to_attend +
V01.01.HighUtility_sum*motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_relevance_composite_motivation_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## relevance ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * motivation_to_attend +
## V01.01.HighUtility_sum * motivation_to_attend + (1 | program_ID) +
## (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5445.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9889 -0.5385 0.0496 0.5867 3.3578
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.01037 0.1018
## participant_ID (Intercept) 0.47392 0.6884
## program_ID (Intercept) 0.01441 0.1200
## Residual 0.41080 0.6409
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.788e+00 3.063e-01
## gender_female -2.141e-01 1.115e-01
## COMPOSIT 1.049e-01 4.273e-02
## agency 3.888e-03 1.238e-02
## V01.01.HighUtility_sum 5.120e-02 2.194e-02
## Community_Space_Content 9.499e-02 4.599e-02
## overall_pre_competence_beliefs 3.290e-02 6.878e-02
## motivation_to_attend 6.847e-01 2.435e-01
## COMPOSIT:motivation_to_attend -7.331e-02 4.449e-02
## V01.01.HighUtility_sum:motivation_to_attend -4.126e-02 2.213e-02
## df t value Pr(>|t|)
## (Intercept) 2.025e+02 5.836 2.09e-08
## gender_female 1.693e+02 -1.921 0.05645
## COMPOSIT 1.711e+03 2.456 0.01416
## agency 2.128e+02 0.314 0.75382
## V01.01.HighUtility_sum 2.348e+03 2.333 0.01972
## Community_Space_Content 1.940e+02 2.066 0.04019
## overall_pre_competence_beliefs 1.333e+02 0.478 0.63315
## motivation_to_attend 5.029e+02 2.811 0.00513
## COMPOSIT:motivation_to_attend 2.314e+03 -1.648 0.09954
## V01.01.HighUtility_sum:motivation_to_attend 2.356e+03 -1.864 0.06243
##
## (Intercept) ***
## gender_female .
## COMPOSIT *
## agency
## V01.01.HighUtility_sum *
## Community_Space_Content *
## overall_pre_competence_beliefs
## motivation_to_attend **
## COMPOSIT:motivation_to_attend .
## V01.01.HighUtility_sum:motivation_to_attend .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01.HgU_ Cm_S_C ovr___ mtvt__
## gender_feml -0.213
## COMPOSIT -0.489 -0.004
## agency -0.038 -0.003 -0.082
## V01.01.HgU_ -0.028 0.011 -0.069 0.033
## Cmmnty_Sp_C -0.009 0.001 -0.031 0.224 -0.101
## ovrll_pr_c_ -0.601 -0.068 -0.014 0.000 -0.008 -0.005
## mtvtn_t_ttn -0.648 0.103 0.614 0.035 0.041 0.000 -0.115
## COMPOSIT:__ 0.466 0.005 -0.923 -0.071 0.061 -0.023 0.009 -0.664
## V01.01.HU_: 0.024 -0.011 0.064 0.008 -0.967 0.021 0.012 -0.037
## COMPOSIT:
## gender_feml
## COMPOSIT
## agency
## V01.01.HgU_
## Cmmnty_Sp_C
## ovrll_pr_c_
## mtvtn_t_ttn
## COMPOSIT:__
## V01.01.HU_: -0.076sjPlot::sjp.int(RQ2_relevance_composite_motivation_all, type = "eff", swap.pred = TRUE)## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
## Warning in Effect.merMod(predictors, mod, vcov. = vcov., ...): pbkrtest is
## not available, KR set to FALSE
May 2 Analysis
Predicting Control
RQ2_control_composite_community <- lmer(in_control ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_control_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## in_control ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6498.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3531 -0.5394 0.0355 0.5616 3.2228
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02106 0.1451
## participant_ID (Intercept) 0.47933 0.6923
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.64437 0.8027
## Number of obs: 2498, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.164478 0.270992 193.421500 7.987
## gender_female -0.232547 0.112557 168.928355 -2.066
## COMPOSIT 0.056455 0.022170 221.847152 2.547
## agency 0.032201 0.015904 238.308935 2.025
## V01.01.HighUtility_sum 0.001540 0.007245 170.791082 0.213
## Community_Space_Content 0.086545 0.216439 233.957101 0.400
## overall_pre_competence_beliefs 0.031413 0.067997 173.962836 0.462
## motivation_to_attend 0.297333 0.182162 178.836013 1.632
## COMPOSIT:Community_Space_Content -0.015724 0.051013 222.822350 -0.308
## Pr(>|t|)
## (Intercept) 1.21e-13 ***
## gender_female 0.0404 *
## COMPOSIT 0.0116 *
## agency 0.0440 *
## V01.01.HighUtility_sum 0.8319
## Community_Space_Content 0.6896
## overall_pre_competence_beliefs 0.6447
## motivation_to_attend 0.1044
## COMPOSIT:Community_Space_Content 0.7582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.250
## COMPOSIT -0.241 0.005
## agency -0.001 -0.006 -0.366
## V01.01.HgU_ 0.001 0.004 -0.163 0.157
## Cmmnty_Sp_C -0.107 0.006 0.267 0.047 -0.015
## ovrll_pr_c_ -0.674 -0.060 -0.019 -0.003 0.017 0.028
## mtvtn_t_ttn -0.505 0.138 -0.003 -0.024 -0.020 -0.024 -0.149
## COMPOSIT:C_ 0.114 -0.005 -0.314 0.013 -0.074 -0.962 -0.032 0.015Predicting interest
RQ2_interest_composite_community <- lmer(interest ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_interest_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## interest ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6616.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2639 -0.5394 0.1170 0.6016 2.7449
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.03806 0.1951
## participant_ID (Intercept) 0.37476 0.6122
## program_ID (Intercept) 0.01858 0.1363
## Residual 0.67524 0.8217
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.309290 0.262234 140.465736 8.806
## gender_female -0.126259 0.103511 168.465566 -1.220
## COMPOSIT 0.011008 0.024793 198.154618 0.444
## agency 0.036364 0.017709 211.273161 2.053
## V01.01.HighUtility_sum 0.005373 0.008202 156.356747 0.655
## Community_Space_Content 0.080902 0.241354 208.407887 0.335
## overall_pre_competence_beliefs 0.096112 0.064536 145.473082 1.489
## motivation_to_attend 0.204457 0.169829 171.967409 1.204
## COMPOSIT:Community_Space_Content 0.006494 0.057019 198.310190 0.114
## Pr(>|t|)
## (Intercept) 4.34e-15 ***
## gender_female 0.2243
## COMPOSIT 0.6575
## agency 0.0413 *
## V01.01.HighUtility_sum 0.5134
## Community_Space_Content 0.7378
## overall_pre_competence_beliefs 0.1386
## motivation_to_attend 0.2303
## COMPOSIT:Community_Space_Content 0.9094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.229
## COMPOSIT -0.284 0.004
## agency -0.015 -0.002 -0.361
## V01.01.HgU_ -0.001 0.003 -0.159 0.153
## Cmmnty_Sp_C -0.119 0.007 0.266 0.047 -0.011
## ovrll_pr_c_ -0.655 -0.077 -0.013 0.002 0.019 0.020
## mtvtn_t_ttn -0.483 0.143 -0.001 -0.020 -0.019 -0.018 -0.155
## COMPOSIT:C_ 0.122 -0.007 -0.313 0.013 -0.078 -0.962 -0.021 0.012Reliability for engagement = .67 (also creating control + interest composite)
#control_interest_reliability <- select(df, in_control, interest)
#cronbach(control_interest_reliability)
df$control_interest <- jmRtools::composite_mean_maker(df, in_control, interest)Predicting interest-control composite
RQ2_interest_control_composite_community <- lmer(control_interest ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
COMPOSIT*Community_Space_Content +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_interest_control_composite_community)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## control_interest ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + COMPOSIT * Community_Space_Content +
## (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5611.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7878 -0.5074 0.0622 0.5907 3.5378
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02375 0.1541
## participant_ID (Intercept) 0.37314 0.6109
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.44122 0.6642
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.255096 0.238336 196.153876 9.462
## gender_female -0.169543 0.098769 170.103229 -1.717
## COMPOSIT 0.032781 0.019863 210.848642 1.650
## agency 0.034748 0.014198 224.383149 2.447
## V01.01.HighUtility_sum 0.003497 0.006562 165.699215 0.533
## Community_Space_Content 0.105035 0.193619 221.119139 0.542
## overall_pre_competence_beliefs 0.049696 0.059679 175.296059 0.833
## motivation_to_attend 0.270914 0.159670 179.977820 1.697
## COMPOSIT:Community_Space_Content -0.009206 0.045715 210.452175 -0.201
## Pr(>|t|)
## (Intercept) <2e-16 ***
## gender_female 0.0879 .
## COMPOSIT 0.1004
## agency 0.0152 *
## V01.01.HighUtility_sum 0.5948
## Community_Space_Content 0.5880
## overall_pre_competence_beliefs 0.4061
## motivation_to_attend 0.0915 .
## COMPOSIT:Community_Space_Content 0.8406
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOSIT agency V01.01 Cm_S_C ovr___ mtvt__
## gender_feml -0.250
## COMPOSIT -0.245 0.005
## agency -0.004 -0.005 -0.362
## V01.01.HgU_ 0.001 0.004 -0.161 0.154
## Cmmnty_Sp_C -0.108 0.005 0.267 0.047 -0.011
## ovrll_pr_c_ -0.673 -0.059 -0.020 -0.001 0.017 0.027
## mtvtn_t_ttn -0.504 0.138 -0.003 -0.024 -0.020 -0.023 -0.148
## COMPOSIT:C_ 0.115 -0.005 -0.313 0.013 -0.077 -0.962 -0.031 0.014Predicting engagement
RQ3_overall_engagement <- lmer(overall_engagement ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ3_overall_engagement)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## overall_engagement ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 5237.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2397 -0.5166 0.0652 0.5695 3.8282
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.022709 0.15070
## participant_ID (Intercept) 0.333568 0.57755
## program_ID (Intercept) 0.004963 0.07045
## Residual 0.377561 0.61446
## Number of obs: 2500, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.235185 0.228580 111.829219 9.779
## gender_female -0.065908 0.094064 165.515657 -0.701
## COMPOSIT 0.032503 0.017870 212.955541 1.819
## agency 0.019262 0.013425 226.562881 1.435
## V01.01.HighUtility_sum 0.003013 0.006207 169.369786 0.485
## Community_Space_Content 0.028287 0.050133 207.302105 0.564
## overall_pre_competence_beliefs 0.080777 0.057572 111.661263 1.403
## motivation_to_attend 0.248645 0.152642 157.178884 1.629
## Pr(>|t|)
## (Intercept) <2e-16 ***
## gender_female 0.4845
## COMPOSIT 0.0703 .
## agency 0.1527
## V01.01.HighUtility_sum 0.6280
## Community_Space_Content 0.5732
## overall_pre_competence_beliefs 0.1634
## motivation_to_attend 0.1053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___
## gender_feml -0.245
## COMPOSIT -0.221 0.003
## agency -0.009 -0.003 -0.376
## V01.01.HgU_ 0.008 0.003 -0.194 0.154
## Cmmnty_Sp_C 0.004 0.001 -0.133 0.218 -0.313
## ovrll_pr_c_ -0.675 -0.066 -0.025 0.001 0.014 -0.007
## mtvtn_t_ttn -0.504 0.140 0.001 -0.021 -0.017 -0.028 -0.147June 20 Analysis
Predicting Control w/ no interaction
RQ2_control_no_interaction <- lmer(in_control ~
gender_female +
COMPOSIT +
agency +
V01.01.HighUtility_sum +
Community_Space_Content +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = df)
summary(RQ2_control_no_interaction)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## in_control ~ gender_female + COMPOSIT + agency + V01.01.HighUtility_sum +
## Community_Space_Content + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | participant_ID) +
## (1 | beep_ID_new)
## Data: df
##
## REML criterion at convergence: 6494.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3479 -0.5379 0.0367 0.5616 3.2223
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.02062 0.1436
## participant_ID (Intercept) 0.47895 0.6921
## program_ID (Intercept) 0.00000 0.0000
## Residual 0.64447 0.8028
## Number of obs: 2498, groups:
## beep_ID_new, 235; participant_ID, 175; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.174174 0.269076 188.849430 8.080
## gender_female -0.232765 0.112511 168.971096 -2.069
## COMPOSIT 0.054329 0.020992 223.735303 2.588
## agency 0.032244 0.015860 239.519239 2.033
## V01.01.HighUtility_sum 0.001381 0.007203 172.184427 0.192
## Community_Space_Content 0.022180 0.058737 221.085060 0.378
## overall_pre_competence_beliefs 0.030710 0.067932 173.672618 0.452
## motivation_to_attend 0.298137 0.182068 178.801844 1.638
## Pr(>|t|)
## (Intercept) 7.51e-14 ***
## gender_female 0.0401 *
## COMPOSIT 0.0103 *
## agency 0.0431 *
## V01.01.HighUtility_sum 0.8482
## Community_Space_Content 0.7061
## overall_pre_competence_beliefs 0.6518
## motivation_to_attend 0.1033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr_f COMPOS agency V01.01 Cm_S_C ovr___
## gender_feml -0.251
## COMPOSIT -0.217 0.003
## agency -0.002 -0.006 -0.382
## V01.01.HgU_ 0.010 0.004 -0.196 0.158
## Cmmnty_Sp_C 0.010 0.003 -0.135 0.220 -0.316
## ovrll_pr_c_ -0.675 -0.060 -0.030 -0.002 0.015 -0.010
## mtvtn_t_ttn -0.510 0.138 0.002 -0.024 -0.019 -0.034 -0.149Reliability for agency = .823 (7 items) and making composite
#agency_reliability <- select(post_survey_all, at_program_choose_time, at_program_suggest_ideas, at_program_choose_activities, at_program_plan_activities, at_program_lead_activities, at_program_in_charge, at_program_decicision_rules)
#cronbach(agency_reliability)July 3 Analysis
Preparing for BLUPS
d_red <- df %>%
group_by(participant_ID, program_ID) %>%
mutate(rownum = row_number()) %>%
mutate(post_agency = ifelse(rownum == 1, post_agency, NA))
d_red <- filter(d_red, !is.na(overall_pre_competence_beliefs) & !is.na(gender_female), !is.na(motivation_to_attend))Predicting Post_Agency using control
RQ2_control_post_agency <- lmer(in_control ~
gender_female +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
(1|participant_ID) +
(1|beep_ID_new),
data = d_red)
#summary(RQ2_control_post_agency)
control_agency_BLUP <-ranef(RQ2_control_post_agency) %>%
pluck(2) %>%
rownames_to_column("participant_ID") %>%
mutate(participant_ID = as.integer(participant_ID)) %>%
rename(in_control_BLUP = `(Intercept)`)
d_ind_level_2 <- distinct(df, participant_ID, post_agency, program_ID, .keep_all = TRUE)
d_ind_level_2$participant_ID <- as.integer(as.character(d_ind_level_2$participant_ID))
d_for_m2 <- left_join(d_ind_level_2, control_agency_BLUP, by = "participant_ID")
d_for_m2 <- filter(d_for_m2, !is.na(post_agency) & !is.na(gender_female) & !is.na(overall_pre_competence_beliefs) & !is.na(in_control_BLUP) & !is.na(motivation_to_attend))
BLUP_model <- lm(post_agency ~ 1 + in_control_BLUP + gender_female + overall_pre_competence_beliefs + motivation_to_attend, data = d_for_m2)
summary(BLUP_model)##
## Call:
## lm(formula = post_agency ~ 1 + in_control_BLUP + gender_female +
## overall_pre_competence_beliefs + motivation_to_attend, data = d_for_m2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.63094 -0.46498 -0.03061 0.46625 1.59712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.60290 0.27562 9.444 < 2e-16 ***
## in_control_BLUP 0.25459 0.08556 2.975 0.00347 **
## gender_female -0.01279 0.11405 -0.112 0.91088
## overall_pre_competence_beliefs -0.05139 0.07136 -0.720 0.47272
## motivation_to_attend 0.11107 0.19114 0.581 0.56215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6642 on 134 degrees of freedom
## Multiple R-squared: 0.07046, Adjusted R-squared: 0.04271
## F-statistic: 2.539 on 4 and 134 DF, p-value: 0.0428Predicting post_agency using pqa agency
temp_df <- df %>%
group_by(program_ID) %>%
summarise(new_agency = mean(agency, na.rm = TRUE))
df_new <- left_join(df, temp_df, by = "program_ID")
RQ2_pqa_agency_post_agency <- lmer(post_agency ~
new_agency +
gender_female +
overall_pre_competence_beliefs +
motivation_to_attend +
(1|program_ID) +
#(1|participant_ID),
(1|beep_ID_new),
data = df_new)
summary(RQ2_pqa_agency_post_agency)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## post_agency ~ new_agency + gender_female + overall_pre_competence_beliefs +
## motivation_to_attend + (1 | program_ID) + (1 | beep_ID_new)
## Data: df_new
##
## REML criterion at convergence: 4680.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4270 -0.5811 0.0028 0.7249 1.9840
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID_new (Intercept) 0.00000 0.0000
## program_ID (Intercept) 0.05669 0.2381
## Residual 0.43832 0.6621
## Number of obs: 2302, groups: beep_ID_new, 248; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.21488 0.49057 7.17158 6.553
## new_agency -0.38763 0.23570 7.00602 -1.645
## gender_female -0.07172 0.02973 2296.93187 -2.412
## overall_pre_competence_beliefs 0.06674 0.01966 2280.47850 3.394
## motivation_to_attend -0.03699 0.05045 2295.19987 -0.733
## Pr(>|t|)
## (Intercept) 0.000287 ***
## new_agency 0.144021
## gender_female 0.015942 *
## overall_pre_competence_beliefs 0.000699 ***
## motivation_to_attend 0.463446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nw_gnc gndr_f ovr___
## new_agency -0.976
## gender_feml -0.030 -0.002
## ovrll_pr_c_ -0.088 -0.016 -0.135
## mtvtn_t_ttn -0.056 -0.019 0.163 -0.188