RQ #1: How are instructional practices in STEM summer programs related to perceptions of (a) challenge, (b) relevance, (c) learning, and (d) affect for participating youth?

Models with youth activity added

m1i <- lmer(challenge ~ 1 + 
                youth_activity_rc +
                (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
            data = df)

sjPlot::sjt.lmer(m1i, p.kr = F, show.re.var = F)

	challenge
	B	CI	p
Fixed Parts
(Intercept)	2.19	2.00 – 2.37	<.001
youth_activity_rc (Basic Skills Activity)	0.11	-0.02 – 0.23	.087
youth_activity_rc (Creating Product)	0.37	0.24 – 0.49	<.001
youth_activity_rc (Field Trip Speaker)	-0.08	-0.33 – 0.17	.552
youth_activity_rc (Lab Activity)	0.20	-0.04 – 0.44	.094
youth_activity_rc (Program Staff Led)	-0.10	-0.24 – 0.04	.175
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.042
ICC_{participant_ID}	0.392
ICC_{program_ID}	0.033
Observations	2818
R² / Ω₀²	.530 / .524

m1ii <- lmer(relevance ~ 1 + 
                 youth_activity_rc +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1ii, p.kr = F, show.re.var = F)

	relevance
	B	CI	p
Fixed Parts
(Intercept)	2.46	2.33 – 2.60	<.001
youth_activity_rc (Basic Skills Activity)	0.15	0.08 – 0.23	<.001
youth_activity_rc (Creating Product)	0.23	0.15 – 0.31	<.001
youth_activity_rc (Field Trip Speaker)	0.29	0.15 – 0.43	<.001
youth_activity_rc (Lab Activity)	0.11	-0.03 – 0.25	.139
youth_activity_rc (Program Staff Led)	0.15	0.07 – 0.24	<.001
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.006
ICC_{participant_ID}	0.523
ICC_{program_ID}	0.016
Observations	2818
R² / Ω₀²	.586 / .583

m1iii <- lmer(learning ~ 1 + 
                  youth_activity_rc +
                  (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
              data = df)

sjPlot::sjt.lmer(m1iii, p.kr = F, show.re.var = F)

	learning
	B	CI	p
Fixed Parts
(Intercept)	2.68	2.57 – 2.80	<.001
youth_activity_rc (Basic Skills Activity)	0.22	0.12 – 0.32	<.001
youth_activity_rc (Creating Product)	0.13	0.03 – 0.24	.010
youth_activity_rc (Field Trip Speaker)	0.10	-0.09 – 0.29	.310
youth_activity_rc (Lab Activity)	0.15	-0.03 – 0.34	.109
youth_activity_rc (Program Staff Led)	0.07	-0.04 – 0.19	.213
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.011
ICC_{participant_ID}	0.358
ICC_{program_ID}	0.002
Observations	2817
R² / Ω₀²	.429 / .422

df$positive_affect <- jmRtools::composite_mean_maker(df,
                                                     happy, excited)

m1iv <- lmer(positive_affect ~ 1 + 
                 youth_activity_rc +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1iv, p.kr = F, show.re.var = F)

	positive_affect
	B	CI	p
Fixed Parts
(Intercept)	2.69	2.45 – 2.94	<.001
youth_activity_rc (Basic Skills Activity)	0.04	-0.06 – 0.14	.455
youth_activity_rc (Creating Product)	0.01	-0.09 – 0.11	.866
youth_activity_rc (Field Trip Speaker)	0.01	-0.19 – 0.21	.917
youth_activity_rc (Lab Activity)	0.07	-0.13 – 0.26	.505
youth_activity_rc (Program Staff Led)	-0.05	-0.17 – 0.07	.392
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.024
ICC_{participant_ID}	0.427
ICC_{program_ID}	0.089
Observations	2818
R² / Ω₀²	.583 / .579

Models with CLASS codes

m1v <- lmer(relevance ~ 1 + 
                 CLASS_EmotionalSupportEncouragement +
                 CLASS_InstructionalSupport +
                 CLASS_STEMConceptualDevelopment +
                 CLASS_ActivityLeaderEnthusiasm +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1v, p.kr = F, show.re.var = F)

	relevance
	B	CI	p
Fixed Parts
(Intercept)	2.02	1.61 – 2.42	<.001
CLASS_EmotionalSupportEncouragement	0.05	-0.03 – 0.13	.255
CLASS_InstructionalSupport	-0.00	-0.07 – 0.07	.933
CLASS_STEMConceptualDevelopment	0.03	-0.03 – 0.08	.348
CLASS_ActivityLeaderEnthusiasm	0.04	-0.03 – 0.12	.284
Random Parts
N_{beep_ID_new}	236
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.010
ICC_{participant_ID}	0.517
ICC_{program_ID}	0.018
Observations	2800
R² / Ω₀²	.587 / .584

m1vi <- lmer(challenge ~ 1 + 
                 CLASS_EmotionalSupportEncouragement +
                 CLASS_InstructionalSupport +
                 CLASS_STEMConceptualDevelopment +
                 CLASS_ActivityLeaderEnthusiasm +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1vi, p.kr = F, show.re.var = F)

	challenge
	B	CI	p
Fixed Parts
(Intercept)	1.70	1.07 – 2.34	<.001
CLASS_EmotionalSupportEncouragement	0.04	-0.08 – 0.17	.505
CLASS_InstructionalSupport	0.05	-0.07 – 0.16	.451
CLASS_STEMConceptualDevelopment	0.03	-0.05 – 0.12	.435
CLASS_ActivityLeaderEnthusiasm	0.00	-0.12 – 0.13	.937
Random Parts
N_{beep_ID_new}	236
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.052
ICC_{participant_ID}	0.379
ICC_{program_ID}	0.046
Observations	2800
R² / Ω₀²	.533 / .526

m1vii <- lmer(learning ~ 1 + 
                 CLASS_EmotionalSupportEncouragement +
                 CLASS_InstructionalSupport +
                 CLASS_STEMConceptualDevelopment +
                 CLASS_ActivityLeaderEnthusiasm +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1vii, p.kr = F, show.re.var = F)

	learning
	B	CI	p
Fixed Parts
(Intercept)	2.35	1.86 – 2.83	<.001
CLASS_EmotionalSupportEncouragement	0.03	-0.07 – 0.13	.505
CLASS_InstructionalSupport	0.04	-0.05 – 0.13	.423
CLASS_STEMConceptualDevelopment	0.04	-0.03 – 0.10	.300
CLASS_ActivityLeaderEnthusiasm	-0.01	-0.10 – 0.09	.906
Random Parts
N_{beep_ID_new}	236
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.010
ICC_{participant_ID}	0.353
ICC_{program_ID}	0.004
Observations	2799
R² / Ω₀²	.426 / .419

m1viii <- lmer(positive_affect ~ 1 + 
                 CLASS_EmotionalSupportEncouragement +
                 CLASS_InstructionalSupport +
                 CLASS_STEMConceptualDevelopment +
                 CLASS_ActivityLeaderEnthusiasm +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m1viii, p.kr = F, show.re.var = F)

	positive_affect
	B	CI	p
Fixed Parts
(Intercept)	1.76	1.23 – 2.29	<.001
CLASS_EmotionalSupportEncouragement	0.13	0.03 – 0.23	.011
CLASS_InstructionalSupport	0.01	-0.08 – 0.10	.853
CLASS_STEMConceptualDevelopment	-0.04	-0.10 – 0.03	.295
CLASS_ActivityLeaderEnthusiasm	0.04	-0.05 – 0.13	.419
Random Parts
N_{beep_ID_new}	236
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.020
ICC_{participant_ID}	0.429
ICC_{program_ID}	0.096
Observations	2800
R² / Ω₀²	.587 / .584

RQ #2: How is the relationship between STEM practices and perceptions of challenge, relevance, learning, and affect moderated by: (a) youth characteristics at program entry (L2); (b) the type of activity, and (d) classroom versus field-based settings?

Models with youth characteristics, type of activity, and classroom versus field-based settings variables added

m2i <- lmer(challenge ~ 1 + 
                 overall_pre_competence_beliefs +
                 overall_pre_interest +
                 overall_pre_utility_value +
                 classroom_versus_field_enrichment +
                 youth_activity_rc +
                (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
            data = df)

sjPlot::sjt.lmer(m2i, p.kr = F, show.re.var = F)

	challenge
	B	CI	p
Fixed Parts
(Intercept)	2.40	1.85 – 2.96	<.001
overall_pre_competence_beliefs	-0.28	-0.50 – -0.07	.010
overall_pre_interest	0.21	-0.02 – 0.44	.071
overall_pre_utility_value	0.00	-0.22 – 0.22	.994
classroom_versus_field_enrichment	0.05	-0.07 – 0.17	.432
youth_activity_rc (Basic Skills Activity)	0.11	-0.02 – 0.24	.100
youth_activity_rc (Creating Product)	0.33	0.20 – 0.47	<.001
youth_activity_rc (Field Trip Speaker)	-0.07	-0.34 – 0.19	.586
youth_activity_rc (Lab Activity)	0.17	-0.08 – 0.41	.191
youth_activity_rc (Program Staff Led)	-0.12	-0.27 – 0.02	.101
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.045
ICC_{participant_ID}	0.397
ICC_{program_ID}	0.027
Observations	2579
R² / Ω₀²	.531 / .525

m2ib <- lmer(challenge ~ 1 + 
                 overall_pre_competence_beliefs*youth_activity_rc +
                 overall_pre_interest*youth_activity_rc +
                 overall_pre_utility_value*youth_activity_rc +
                 classroom_versus_field_enrichment*youth_activity_rc +
                (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
            data = df)

sjPlot::sjt.lmer(m2ib, p.kr = F, show.re.var = F)

	challenge
	B	CI	p
Fixed Parts
(Intercept)	2.34	1.75 – 2.94	<.001
overall_pre_competence_beliefs	-0.30	-0.53 – -0.07	.010
youth_activity_rcBasic Skills Activity	0.24	-0.27 – 0.76	.352
youth_activity_rcCreating Product	0.41	-0.16 – 0.97	.158
youth_activity_rcField Trip Speaker	0.07	-0.99 – 1.13	.899
youth_activity_rcLab Activity	0.06	-1.10 – 1.22	.919
youth_activity_rcProgram Staff Led	0.01	-0.59 – 0.61	.967
overall_pre_interest	0.22	-0.04 – 0.47	.094
overall_pre_utility_value	0.04	-0.20 – 0.28	.761
classroom_versus_field_enrichment	0.04	-0.14 – 0.21	.670
overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity	0.09	-0.08 – 0.26	.288
overall_pre_competence_beliefs:youth_activity_rcCreating Product	0.07	-0.12 – 0.25	.490
overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker	-0.16	-0.46 – 0.13	.279
overall_pre_competence_beliefs:youth_activity_rcLab Activity	-0.15	-0.56 – 0.26	.467
overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led	0.07	-0.13 – 0.26	.494
youth_activity_rcBasic Skills Activity:overall_pre_interest	-0.03	-0.21 – 0.15	.757
youth_activity_rcCreating Product:overall_pre_interest	-0.10	-0.32 – 0.11	.347
youth_activity_rcField Trip Speaker:overall_pre_interest	-0.13	-0.58 – 0.32	.572
youth_activity_rcLab Activity:overall_pre_interest	0.38	-0.07 – 0.84	.100
youth_activity_rcProgram Staff Led:overall_pre_interest	0.01	-0.19 – 0.21	.921
youth_activity_rcBasic Skills Activity:overall_pre_utility_value	-0.11	-0.29 – 0.08	.263
youth_activity_rcCreating Product:overall_pre_utility_value	-0.02	-0.21 – 0.17	.828
youth_activity_rcField Trip Speaker:overall_pre_utility_value	0.25	-0.10 – 0.60	.162
youth_activity_rcLab Activity:overall_pre_utility_value	-0.17	-0.60 – 0.26	.438
youth_activity_rcProgram Staff Led:overall_pre_utility_value	-0.12	-0.33 – 0.09	.265
youth_activity_rcBasic Skills Activity:classroom_versus_field_enrichment	0.01	-0.26 – 0.28	.926
youth_activity_rcCreating Product:classroom_versus_field_enrichment	0.13	-0.24 – 0.51	.484
youth_activity_rcLab Activity:classroom_versus_field_enrichment	-0.15	-0.66 – 0.36	.566
youth_activity_rcProgram Staff Led:classroom_versus_field_enrichment	0.02	-0.31 – 0.35	.897
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.048
ICC_{participant_ID}	0.395
ICC_{program_ID}	0.028
Observations	2579
R² / Ω₀²	.535 / .529

m2ii <- lmer(relevance ~ 1 + 
                 overall_pre_competence_beliefs +
                 overall_pre_interest +
                 overall_pre_utility_value +
                 classroom_versus_field_enrichment +
                 youth_activity_rc +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m2ii, p.kr = F, show.re.var = F)

	relevance
	B	CI	p
Fixed Parts
(Intercept)	2.06	1.54 – 2.58	<.001
overall_pre_competence_beliefs	-0.10	-0.31 – 0.11	.360
overall_pre_interest	0.09	-0.13 – 0.31	.418
overall_pre_utility_value	0.15	-0.06 – 0.36	.164
classroom_versus_field_enrichment	-0.08	-0.16 – -0.00	.037
youth_activity_rc (Basic Skills Activity)	0.14	0.06 – 0.22	<.001
youth_activity_rc (Creating Product)	0.24	0.15 – 0.32	<.001
youth_activity_rc (Field Trip Speaker)	0.23	0.08 – 0.38	.003
youth_activity_rc (Lab Activity)	0.09	-0.06 – 0.24	.245
youth_activity_rc (Program Staff Led)	0.15	0.06 – 0.25	.001
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.008
ICC_{participant_ID}	0.523
ICC_{program_ID}	0.022
Observations	2579
R² / Ω₀²	.593 / .591

m2iib <- lmer(relevance ~ 1 + 
                  overall_pre_competence_beliefs*youth_activity_rc +
                  overall_pre_interest*youth_activity_rc +
                  overall_pre_utility_value*youth_activity_rc +
                  classroom_versus_field_enrichment*youth_activity_rc +
              (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
              data = df)

sjPlot::sjt.lmer(m2iib, p.kr = F, show.re.var = F)

	relevance
	B	CI	p
Fixed Parts
(Intercept)	2.18	1.63 – 2.72	<.001
overall_pre_competence_beliefs	-0.04	-0.26 – 0.18	.718
youth_activity_rcBasic Skills Activity	0.19	-0.19 – 0.57	.339
youth_activity_rcCreating Product	-0.36	-0.77 – 0.04	.081
youth_activity_rcField Trip Speaker	0.21	-0.58 – 0.99	.606
youth_activity_rcLab Activity	-0.02	-0.89 – 0.86	.967
youth_activity_rcProgram Staff Led	0.21	-0.23 – 0.65	.349
overall_pre_interest	0.04	-0.19 – 0.27	.750
overall_pre_utility_value	0.11	-0.11 – 0.34	.326
classroom_versus_field_enrichment	-0.09	-0.19 – 0.02	.099
overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity	-0.16	-0.29 – -0.02	.022
overall_pre_competence_beliefs:youth_activity_rcCreating Product	-0.04	-0.19 – 0.10	.545
overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker	-0.05	-0.28 – 0.17	.639
overall_pre_competence_beliefs:youth_activity_rcLab Activity	0.03	-0.29 – 0.36	.833
overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led	-0.11	-0.27 – 0.04	.145
youth_activity_rcBasic Skills Activity:overall_pre_interest	0.10	-0.04 – 0.23	.153
youth_activity_rcCreating Product:overall_pre_interest	0.05	-0.11 – 0.21	.532
youth_activity_rcField Trip Speaker:overall_pre_interest	0.03	-0.33 – 0.38	.890
youth_activity_rcLab Activity:overall_pre_interest	-0.13	-0.49 – 0.24	.495
youth_activity_rcProgram Staff Led:overall_pre_interest	0.11	-0.03 – 0.26	.132
youth_activity_rcBasic Skills Activity:overall_pre_utility_value	0.05	-0.10 – 0.19	.523
youth_activity_rcCreating Product:overall_pre_utility_value	0.14	-0.01 – 0.29	.064
youth_activity_rcField Trip Speaker:overall_pre_utility_value	0.04	-0.24 – 0.31	.797
youth_activity_rcLab Activity:overall_pre_utility_value	0.09	-0.24 – 0.43	.582
youth_activity_rcProgram Staff Led:overall_pre_utility_value	0.01	-0.15 – 0.17	.875
youth_activity_rcBasic Skills Activity:classroom_versus_field_enrichment	-0.02	-0.18 – 0.15	.831
youth_activity_rcCreating Product:classroom_versus_field_enrichment	0.13	-0.10 – 0.37	.274
youth_activity_rcLab Activity:classroom_versus_field_enrichment	0.20	-0.11 – 0.50	.211
youth_activity_rcProgram Staff Led:classroom_versus_field_enrichment	-0.12	-0.32 – 0.08	.241
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.007
ICC_{participant_ID}	0.523
ICC_{program_ID}	0.020
Observations	2579
R² / Ω₀²	.594 / .592

m2iii <- lmer(learning ~ 1 + 
                 overall_pre_competence_beliefs +
                 overall_pre_interest +
                 overall_pre_utility_value +
                 classroom_versus_field_enrichment +
                 youth_activity_rc +
                  (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
              data = df)

sjPlot::sjt.lmer(m2iii, p.kr = F, show.re.var = F)

	learning
	B	CI	p
Fixed Parts
(Intercept)	2.33	1.85 – 2.80	<.001
overall_pre_competence_beliefs	0.02	-0.18 – 0.21	.879
overall_pre_interest	0.04	-0.15 – 0.23	.646
overall_pre_utility_value	0.05	-0.15 – 0.24	.628
classroom_versus_field_enrichment	0.01	-0.08 – 0.11	.768
youth_activity_rc (Basic Skills Activity)	0.22	0.12 – 0.32	<.001
youth_activity_rc (Creating Product)	0.12	0.01 – 0.23	.039
youth_activity_rc (Field Trip Speaker)	0.08	-0.11 – 0.28	.397
youth_activity_rc (Lab Activity)	0.19	0.00 – 0.39	.047
youth_activity_rc (Program Staff Led)	0.07	-0.05 – 0.19	.257
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.010
ICC_{participant_ID}	0.360
ICC_{program_ID}	0.000
Observations	2578
R² / Ω₀²	.421 / .415

m2iiib <- lmer(learning ~ 1 + 
                  overall_pre_competence_beliefs*youth_activity_rc +
                  overall_pre_interest*youth_activity_rc +
                  overall_pre_utility_value*youth_activity_rc +
                  classroom_versus_field_enrichment*youth_activity_rc +
              (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
              data = df)

sjPlot::sjt.lmer(m2iiib, p.kr = F, show.re.var = F)

	learning
	B	CI	p
Fixed Parts
(Intercept)	2.21	1.69 – 2.74	<.001
overall_pre_competence_beliefs	-0.03	-0.24 – 0.18	.792
youth_activity_rcBasic Skills Activity	0.57	0.07 – 1.07	.025
youth_activity_rcCreating Product	0.35	-0.18 – 0.88	.195
youth_activity_rcField Trip Speaker	0.14	-0.89 – 1.17	.788
youth_activity_rcLab Activity	0.47	-0.68 – 1.62	.422
youth_activity_rcProgram Staff Led	0.02	-0.56 – 0.60	.937
overall_pre_interest	0.09	-0.12 – 0.31	.399
overall_pre_utility_value	0.07	-0.14 – 0.29	.497
classroom_versus_field_enrichment	0.03	-0.11 – 0.17	.675
overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity	0.05	-0.12 – 0.23	.545
overall_pre_competence_beliefs:youth_activity_rcCreating Product	0.02	-0.17 – 0.21	.862
overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker	0.26	-0.04 – 0.56	.093
overall_pre_competence_beliefs:youth_activity_rcLab Activity	-0.28	-0.70 – 0.14	.188
overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led	0.15	-0.05 – 0.35	.131
youth_activity_rcBasic Skills Activity:overall_pre_interest	-0.06	-0.23 – 0.11	.498
youth_activity_rcCreating Product:overall_pre_interest	-0.01	-0.23 – 0.20	.904
youth_activity_rcField Trip Speaker:overall_pre_interest	-0.22	-0.68 – 0.25	.360
youth_activity_rcLab Activity:overall_pre_interest	0.25	-0.22 – 0.72	.303
youth_activity_rcProgram Staff Led:overall_pre_interest	-0.13	-0.33 – 0.06	.172
youth_activity_rcBasic Skills Activity:overall_pre_utility_value	-0.09	-0.28 – 0.10	.333
youth_activity_rcCreating Product:overall_pre_utility_value	-0.04	-0.24 – 0.15	.652
youth_activity_rcField Trip Speaker:overall_pre_utility_value	-0.05	-0.41 – 0.31	.786
youth_activity_rcLab Activity:overall_pre_utility_value	-0.05	-0.49 – 0.39	.834
youth_activity_rcProgram Staff Led:overall_pre_utility_value	-0.02	-0.23 – 0.19	.872
youth_activity_rcBasic Skills Activity:classroom_versus_field_enrichment	-0.04	-0.26 – 0.18	.719
youth_activity_rcCreating Product:classroom_versus_field_enrichment	-0.12	-0.43 – 0.19	.452
youth_activity_rcLab Activity:classroom_versus_field_enrichment	0.02	-0.39 – 0.42	.934
youth_activity_rcProgram Staff Led:classroom_versus_field_enrichment	0.02	-0.24 – 0.28	.873
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.011
ICC_{participant_ID}	0.358
ICC_{program_ID}	0.001
Observations	2578
R² / Ω₀²	.425 / .419

df$positive_affect <- jmRtools::composite_mean_maker(df, happy, excited)

m2iv <- lmer(positive_affect ~ 1 + 
                 overall_pre_competence_beliefs +
                 overall_pre_interest +
                 overall_pre_utility_value +
                 classroom_versus_field_enrichment +
                 youth_activity_rc +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m2iv, p.kr = F, show.re.var = F)

	positive_affect
	B	CI	p
Fixed Parts
(Intercept)	2.32	1.74 – 2.89	<.001
overall_pre_competence_beliefs	-0.03	-0.25 – 0.20	.808
overall_pre_interest	0.29	0.05 – 0.53	.019
overall_pre_utility_value	-0.11	-0.34 – 0.12	.351
classroom_versus_field_enrichment	-0.07	-0.17 – 0.04	.215
youth_activity_rc (Basic Skills Activity)	0.01	-0.10 – 0.12	.896
youth_activity_rc (Creating Product)	0.01	-0.11 – 0.12	.916
youth_activity_rc (Field Trip Speaker)	-0.04	-0.26 – 0.19	.747
youth_activity_rc (Lab Activity)	0.07	-0.14 – 0.29	.507
youth_activity_rc (Program Staff Led)	-0.07	-0.20 – 0.06	.265
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.032
ICC_{participant_ID}	0.457
ICC_{program_ID}	0.044
Observations	2579
R² / Ω₀²	.591 / .587

m2ivb <- lmer(positive_affect ~ 1 + 
                  overall_pre_competence_beliefs*youth_activity_rc +
                  overall_pre_interest*youth_activity_rc +
                  overall_pre_utility_value*youth_activity_rc +
                  classroom_versus_field_enrichment*youth_activity_rc +
              (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
              data = df)

sjPlot::sjt.lmer(m2ivb, p.kr = F, show.re.var = F)

	positive_affect
	B	CI	p
Fixed Parts
(Intercept)	2.15	1.55 – 2.75	<.001
overall_pre_competence_beliefs	0.05	-0.18 – 0.29	.656
youth_activity_rcBasic Skills Activity	0.48	0.03 – 0.94	.039
youth_activity_rcCreating Product	0.07	-0.43 – 0.57	.782
youth_activity_rcField Trip Speaker	-0.05	-1.00 – 0.89	.911
youth_activity_rcLab Activity	0.12	-0.92 – 1.16	.818
youth_activity_rcProgram Staff Led	0.61	0.08 – 1.14	.025
overall_pre_interest	0.27	0.02 – 0.53	.035
overall_pre_utility_value	-0.13	-0.38 – 0.11	.284
classroom_versus_field_enrichment	0.00	-0.14 – 0.15	.956
overall_pre_competence_beliefs:youth_activity_rcBasic Skills Activity	-0.10	-0.26 – 0.05	.198
overall_pre_competence_beliefs:youth_activity_rcCreating Product	-0.21	-0.38 – -0.04	.013
overall_pre_competence_beliefs:youth_activity_rcField Trip Speaker	0.14	-0.13 – 0.41	.313
overall_pre_competence_beliefs:youth_activity_rcLab Activity	-0.10	-0.47 – 0.27	.591
overall_pre_competence_beliefs:youth_activity_rcProgram Staff Led	-0.23	-0.40 – -0.05	.011
youth_activity_rcBasic Skills Activity:overall_pre_interest	0.06	-0.10 – 0.22	.488
youth_activity_rcCreating Product:overall_pre_interest	0.13	-0.06 – 0.33	.177
youth_activity_rcField Trip Speaker:overall_pre_interest	-0.27	-0.68 – 0.14	.191
youth_activity_rcLab Activity:overall_pre_interest	-0.15	-0.56 – 0.27	.485
youth_activity_rcProgram Staff Led:overall_pre_interest	0.02	-0.15 – 0.20	.803
youth_activity_rcBasic Skills Activity:overall_pre_utility_value	-0.07	-0.24 – 0.10	.402
youth_activity_rcCreating Product:overall_pre_utility_value	0.09	-0.09 – 0.26	.324
youth_activity_rcField Trip Speaker:overall_pre_utility_value	0.15	-0.17 – 0.46	.366
youth_activity_rcLab Activity:overall_pre_utility_value	0.19	-0.20 – 0.58	.335
youth_activity_rcProgram Staff Led:overall_pre_utility_value	0.03	-0.16 – 0.21	.780
youth_activity_rcBasic Skills Activity:classroom_versus_field_enrichment	-0.16	-0.39 – 0.06	.159
youth_activity_rcCreating Product:classroom_versus_field_enrichment	-0.14	-0.46 – 0.17	.377
youth_activity_rcLab Activity:classroom_versus_field_enrichment	0.23	-0.20 – 0.66	.288
youth_activity_rcProgram Staff Led:classroom_versus_field_enrichment	-0.23	-0.51 – 0.05	.106
Random Parts
N_{beep_ID_new}	235
N_{participant_ID}	180
N_{program_ID}	9
ICC_{beep_ID_new}	0.030
ICC_{participant_ID}	0.462
ICC_{program_ID}	0.038
Observations	2579
R² / Ω₀²	.594 / .590

RQ #3: How do in-the-moment experiences of youth (i.e., challenge, relevance, learning, and affect) cultivate situational interest and engagement in STEM activities?

m3i <- lmer(interest ~ 1 +
                 challenge + relevance + learning + positive_affect +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m3i, p.kr = F, show.re.var = F)

	interest
	B	CI	p
Fixed Parts
(Intercept)	0.65	0.52 – 0.78	<.001
challenge	0.02	-0.01 – 0.05	.153
relevance	0.36	0.32 – 0.40	<.001
learning	0.17	0.14 – 0.21	<.001
positive_affect	0.28	0.24 – 0.31	<.001
Random Parts
N_{beep_ID_new}	248
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.033
ICC_{participant_ID}	0.098
ICC_{program_ID}	0.016
Observations	2969
R² / Ω₀²	.583 / .582

m3v <- lmer(overall_engagement ~ 1 + 
                 challenge + relevance + learning + positive_affect +
                 (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
             data = df)

sjPlot::sjt.lmer(m3v, p.kr = F, show.re.var = F)

	overall_engagement
	B	CI	p
Fixed Parts
(Intercept)	0.70	0.61 – 0.79	<.001
challenge	0.03	0.01 – 0.05	.001
relevance	0.23	0.20 – 0.26	<.001
learning	0.27	0.24 – 0.29	<.001
positive_affect	0.28	0.25 – 0.30	<.001
Random Parts
N_{beep_ID_new}	248
N_{participant_ID}	204
N_{program_ID}	9
ICC_{beep_ID_new}	0.028
ICC_{participant_ID}	0.203
ICC_{program_ID}	0.000
Observations	2969
R² / Ω₀²	.734 / .733

RQ #4: Are situational (momentary) interest and engagement in STEM activities across several weeks associated with changes in: (a) individual (sustained) interest in STEM; (b) future goals and aspirations related to STEM?

Still in progress

Considering aggregating ESM meausures (i.e., challenge, relevance, learning, positive affect) to student-level with means to predict post-program outcomes

STEM-IE Models 5-9-2017

Joshua Rosenberg

5/9/2017

Models with CLASS codes

RQ #2: How is the relationship between STEM practices and perceptions of challenge, relevance, learning, and affect moderated by: (a) youth characteristics at program entry (L2); (b) the type of activity, and (d) classroom versus field-based settings?

Models with youth characteristics, type of activity, and classroom versus field-based settings variables added

RQ #3: How do in-the-moment experiences of youth (i.e., challenge, relevance, learning, and affect) cultivate situational interest and engagement in STEM activities?