“For linear mixed models, an r-squared approximation by computing the correlation between the fitted and observed values, as suggested by Byrnes (2008), is returned as well as a simplified version of the Omega-squared value (1 - (residual variance / response variance), Xu (2003), Nakagawa, Schielzeth 2013), unless n is specified.”
To make it easier to interpret the results, only output is printed and displayed, though there are still processing steps going on (and the headers for the processing steps are still included). Some descriptive statistics and correlation tables are also not included to simplify interpretation, but these (and the processing steps) can be quickly added back in.
Note that parent and future_goals_plans had to be processed first for merging later on; can change.
df$participant_ID <- as.factor(df$participant_ID)
df$program_ID <- as.factor(df$program_ID)
df$beep_ID <- as.factor(df$beep_ID)
df$beep_ID_new <- as.factor(df$beep_ID_new)
# Recode problem solving, off task, student presentation, and showing video as other
df$youth_activity_rc <- ifelse(df$youth_activity == "Off Task", "Not Focused", df$youth_activity)
df$youth_activity_rc <- ifelse(df$youth_activity_rc == "Student Presentation" | df$youth_activity_rc == "Problem Solving", "Creating Product", df$youth_activity_rc)
df$youth_activity_rc <- ifelse(df$youth_activity_rc == "Showing Video", "Program Staff Led", df$youth_activity_rc)
df$youth_activity_rc <- as.factor(df$youth_activity_rc)
df$youth_activity_rc <- forcats::fct_relevel(df$youth_activity_rc, "Not Focused")
df$relevance <- jmRtools::composite_mean_maker(df, use_outside, future_goals, important)
# need to move up
video$youth_activity_rc <- ifelse(video$youth_activity == "Off Task", "Not Focused", video$youth_activity)
video$youth_activity_rc <- ifelse(video$youth_activity_rc == "Student Presentation" | video$youth_activity_rc == "Problem Solving", "Creating Product", video$youth_activity_rc)
video$youth_activity_rc <- ifelse(video$youth_activity_rc == "Showing Video", "Program Staff Led", video$youth_activity_rc)
ncol(df)## [1] 161df %>%
select(CLASS_comp, CLASS_EmotionalSupportEncouragement, CLASS_InstructionalSupport, CLASS_STEMConceptualDevelopment, CLASS_ActivityLeaderEnthusiasm, CLASS_Autonomy,
challenge, relevance, learning, positive_affect) %>%
correlate() %>%
shave() %>%
fashion() %>%
knitr::kable()## # A tibble: 6 x 2
## race n
## <chr> <int>
## 1 Asian 14
## 2 Black 72
## 3 Hispanic 97
## 4 Multiracial 6
## 5 White 13
## 6 <NA> 3How are instructional practices in STEM summer programs related to perceptions of challenge, relevance, learning, and affect for participating youth?
Findings seem to suggest specific youth activities impacts upon challenge, relevance, and learning (we are considering not including affect for now). Females, generally, experience lower levels of these outcomes, and the overall quality composite, measured through the CLASS variable, seems to be an important control variable to include.
Note that our normal display presently doesn’t work if there are no predictor variables: it should be fixed in a bit, but for now, we’re just printing the ICCs.
m0i <- lmer(challenge ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjstats::icc(m0i)## Linear mixed model
## Family: gaussian (identity)
## Formula: challenge ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.066428
## ICC (participant_ID): 0.371089
## ICC (program_ID): 0.034016m0ii <- lmer(relevance ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjstats::icc(m0ii)## Linear mixed model
## Family: gaussian (identity)
## Formula: relevance ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.019134
## ICC (participant_ID): 0.515087
## ICC (program_ID): 0.009082m0iii <- lmer(learning ~ 1 +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjstats::icc(m0iii)## Linear mixed model
## Family: gaussian (identity)
## Formula: learning ~ 1 + (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
##
## ICC (beep_ID_new): 0.023511
## ICC (participant_ID): 0.349134
## ICC (program_ID): 0.000000# df$positive_affect <- jmRtools::composite_mean_maker(df,
# happy, excited)m1i <- lmer(challenge ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1i, p.kr = F, show.re.var = F, show.ci = F, show.se = T)| challenge | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.21 | 0.14 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.06 | 0.07 | .399 | |
| youth_activity_rc (Creating Product) | 0.29 | 0.08 | <.001 | |
| youth_activity_rc (Field Trip Speaker) | -0.12 | 0.13 | .359 | |
| youth_activity_rc (Lab Activity) | 0.15 | 0.13 | .245 | |
| youth_activity_rc (Program Staff Led) | -0.13 | 0.08 | .112 | |
| gender_female | -0.25 | 0.11 | .016 | |
| CLASS_comp | 0.04 | 0.03 | .155 | |
| Random Parts | ||||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 201 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.046 | |||
| ICCparticipant_ID | 0.376 | |||
| ICCprogram_ID | 0.032 | |||
| Observations | 2693 | |||
| R2 / Ω02 | .527 / .520 | |||
m1ii <- lmer(relevance ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1ii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)| relevance | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.49 | 0.10 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.12 | 0.04 | .004 | |
| youth_activity_rc (Creating Product) | 0.18 | 0.05 | <.001 | |
| youth_activity_rc (Field Trip Speaker) | 0.27 | 0.07 | <.001 | |
| youth_activity_rc (Lab Activity) | 0.10 | 0.08 | .183 | |
| youth_activity_rc (Program Staff Led) | 0.11 | 0.05 | .015 | |
| gender_female | -0.22 | 0.10 | .032 | |
| CLASS_comp | 0.03 | 0.02 | .081 | |
| Random Parts | ||||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 201 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.006 | |||
| ICCparticipant_ID | 0.518 | |||
| ICCprogram_ID | 0.017 | |||
| Observations | 2693 | |||
| R2 / Ω02 | .589 / .586 | |||
m1iii <- lmer(learning ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df)
sjPlot::sjt.lmer(m1iii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)| learning | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.53 | 0.10 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.17 | 0.05 | .002 | |
| youth_activity_rc (Creating Product) | 0.04 | 0.06 | .499 | |
| youth_activity_rc (Field Trip Speaker) | 0.06 | 0.10 | .502 | |
| youth_activity_rc (Lab Activity) | 0.09 | 0.10 | .347 | |
| youth_activity_rc (Program Staff Led) | 0.04 | 0.06 | .490 | |
| gender_female | -0.06 | 0.10 | .549 | |
| CLASS_comp | 0.06 | 0.02 | .005 | |
| Random Parts | ||||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 201 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.008 | |||
| ICCparticipant_ID | 0.355 | |||
| ICCprogram_ID | 0.006 | |||
| Observations | 2692 | |||
| R2 / Ω02 | .427 / .421 | |||
df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
# m1iv <- lmer(positive_affect ~ 1 +
# youth_activity_rc +
# gender_female +
# CLASS_comp +
# (1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
# na.action = "na.omit",
# data = df)
#
# sjPlot::sjt.lmer(m1iv, p.kr = F, show.re.var = F, show.ci = F, show.se = T)How is the relationship between STEM practices and perceptions of challenge, relevance, learning, and affect moderated by: (a) youth characteristics at program entry (L2); (b) activity leader perceptions of effective instructional practice (L3 – limited power); (c) the type of activity(L1 – how different from STEM practices?); and (d) classroom versus field-based settings? STEM practices = CLASS items, value statements, etc. stuff from video
In order to examine how youth characteristics at program entry, particularly their pre-program interest, we examined the fixed effect impact of interest and whether there is pre-interest variability at the youth level. Broadly, interest does not seem to be an important predictor overall as a fixed effect, and, predictably, there is not substantial variability at the youth level. Thus there does not seem to be the potential for significant pre-interest by youth activity interactions. We can consider interactions for classroom versus field settings, which we have not yet considered, and activity leader perceptions of effective instructional practice, which are not in these models.
df$youth_activity_rc_fac <- as.factor(df$youth_activity_rc)
dc <- as.tibble(psych::dummy.code(df$youth_activity_rc_fac))
df_ss <- bind_cols(df, dc)
df_ss %>%
select(challenge, relevance, learning, positive_affect, overall_pre_interest,
`Not Focused`, `Basic Skills Activity`, `Creating Product`,
`Field Trip Speaker`, `Lab Activity`, `Program Staff Led`) %>%
correlate() %>%
shave() %>%
fashion() %>%
knitr::kable()df_d <- df[!is.na(df$youth_activity_rc), ]
m1i <- lmer(challenge ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df_d)
sjPlot::sjt.lmer(m1i, p.kr = F, show.re.var = T, show.ci = F, show.se = T)| challenge | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.20 | 0.26 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.06 | 0.07 | .440 | |
| youth_activity_rc (Creating Product) | 0.26 | 0.08 | .001 | |
| youth_activity_rc (Field Trip Speaker) | -0.12 | 0.14 | .399 | |
| youth_activity_rc (Lab Activity) | 0.11 | 0.14 | .415 | |
| youth_activity_rc (Program Staff Led) | -0.15 | 0.08 | .065 | |
| gender_female | -0.25 | 0.11 | .023 | |
| CLASS_comp | 0.04 | 0.03 | .193 | |
| overall_pre_interest | 0.01 | 0.07 | .936 | |
| classroom_versus_field_enrichment | 0.04 | 0.07 | .512 | |
| Random Parts | ||||
| σ2 | 0.658 | |||
| τ00, beep_ID_new | 0.061 | |||
| τ00, participant_ID | 0.469 | |||
| τ00, program_ID | 0.049 | |||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 180 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.050 | |||
| ICCparticipant_ID | 0.379 | |||
| ICCprogram_ID | 0.039 | |||
| Observations | 2483 | |||
| R2 / Ω02 | .530 / .522 | |||
m1i_f <- lmer(challenge ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1 + overall_pre_interest|participant_ID) + (1|beep_ID_new),
data = df_d)
summary(m1i_f)## Linear mixed model fit by REML ['lmerMod']
## Formula: challenge ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## overall_pre_interest + classroom_versus_field_enrichment +
## (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## (1 | beep_ID_new)
## Data: df_d
##
## REML criterion at convergence: 6593.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8162 -0.6446 -0.0319 0.5593 3.3399
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.06129 0.2476
## participant_ID (Intercept) 0.87545 0.9357
## overall_pre_interest 0.08377 0.2894 -0.76
## program_ID (Intercept) 0.04968 0.2229
## Residual 0.65846 0.8115
## Number of obs: 2483, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.217e+00 2.570e-01 8.628
## youth_activity_rcBasic Skills Activity 5.694e-02 7.286e-02 0.782
## youth_activity_rcCreating Product 2.599e-01 8.057e-02 3.226
## youth_activity_rcField Trip Speaker -1.171e-01 1.411e-01 -0.830
## youth_activity_rcLab Activity 1.117e-01 1.370e-01 0.816
## youth_activity_rcProgram Staff Led -1.513e-01 8.228e-02 -1.839
## gender_female -2.477e-01 1.105e-01 -2.241
## CLASS_comp 3.528e-02 2.773e-02 1.272
## overall_pre_interest 6.355e-05 6.997e-02 0.001
## classroom_versus_field_enrichment 4.396e-02 6.562e-02 0.670
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA y__PSL gndr_f CLASS_ ovrl__
## yth_ctv_BSA 0.007
## yth_ctvt_CP 0.100 0.466
## yth_ctv_FTS -0.041 0.283 0.187
## yth_ctvt_LA 0.032 0.284 0.277 0.170
## yth_ctv_PSL -0.030 0.451 0.364 0.193 0.232
## gender_feml -0.254 -0.008 0.004 0.001 -0.007 -0.013
## CLASS_comp -0.315 -0.356 -0.452 -0.136 -0.270 -0.214 0.005
## ovrll_pr_nt -0.817 -0.005 -0.008 -0.015 -0.006 0.016 0.043 -0.012
## clssrm_vr__ -0.146 0.090 -0.201 0.255 0.059 -0.006 -0.011 -0.058 0.009anova(m1i, m1i_f) # the model with the random slope does not seem to fit better## Data: df_d
## Models:
## m1i: challenge ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1i: overall_pre_interest + classroom_versus_field_enrichment +
## m1i: (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## m1i_f: challenge ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1i_f: overall_pre_interest + classroom_versus_field_enrichment +
## m1i_f: (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## m1i_f: (1 | beep_ID_new)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m1i 14 6590.6 6672.1 -3281.3 6562.6
## m1i_f 16 6592.5 6685.5 -3280.2 6560.5 2.1626 2 0.3392m1ii <- lmer(relevance ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df_d)
sjPlot::sjt.lmer(m1ii, p.kr = F, show.re.var = T, show.ci = F, show.se = T)| relevance | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.23 | 0.23 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.10 | 0.04 | .026 | |
| youth_activity_rc (Creating Product) | 0.17 | 0.05 | <.001 | |
| youth_activity_rc (Field Trip Speaker) | 0.21 | 0.08 | .010 | |
| youth_activity_rc (Lab Activity) | 0.05 | 0.08 | .556 | |
| youth_activity_rc (Program Staff Led) | 0.11 | 0.05 | .029 | |
| gender_female | -0.24 | 0.11 | .029 | |
| CLASS_comp | 0.03 | 0.02 | .051 | |
| overall_pre_interest | 0.10 | 0.06 | .111 | |
| classroom_versus_field_enrichment | -0.06 | 0.04 | .108 | |
| Random Parts | ||||
| σ2 | 0.412 | |||
| τ00, beep_ID_new | 0.008 | |||
| τ00, participant_ID | 0.473 | |||
| τ00, program_ID | 0.020 | |||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 180 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.008 | |||
| ICCparticipant_ID | 0.519 | |||
| ICCprogram_ID | 0.022 | |||
| Observations | 2483 | |||
| R2 / Ω02 | .596 / .594 | |||
m1ii_f <- lmer(relevance ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1 + overall_pre_interest|participant_ID) + (1|beep_ID_new),
data = df_d)
summary(m1ii_f)## Linear mixed model fit by REML ['lmerMod']
## Formula: relevance ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## overall_pre_interest + classroom_versus_field_enrichment +
## (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## (1 | beep_ID_new)
## Data: df_d
##
## REML criterion at convergence: 5403.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9108 -0.5360 0.0289 0.5731 4.0679
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.007588 0.08711
## participant_ID (Intercept) 0.799636 0.89422
## overall_pre_interest 0.074581 0.27309 -0.72
## program_ID (Intercept) 0.016179 0.12720
## Residual 0.411662 0.64161
## Number of obs: 2483, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.28356 0.22280 10.249
## youth_activity_rcBasic Skills Activity 0.09918 0.04447 2.230
## youth_activity_rcCreating Product 0.16685 0.04996 3.340
## youth_activity_rcField Trip Speaker 0.20544 0.08000 2.568
## youth_activity_rcLab Activity 0.04773 0.08039 0.594
## youth_activity_rcProgram Staff Led 0.10890 0.04966 2.193
## gender_female -0.23917 0.10782 -2.218
## CLASS_comp 0.03264 0.01679 1.944
## overall_pre_interest 0.08493 0.06508 1.305
## classroom_versus_field_enrichment -0.06284 0.03959 -1.587
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA y__PSL gndr_f CLASS_ ovrl__
## yth_ctv_BSA 0.005
## yth_ctvt_CP 0.073 0.459
## yth_ctv_FTS -0.021 0.301 0.197
## yth_ctvt_LA 0.030 0.296 0.287 0.190
## yth_ctv_PSL -0.026 0.458 0.363 0.205 0.241
## gender_feml -0.298 -0.007 0.003 0.001 -0.008 -0.012
## CLASS_comp -0.214 -0.359 -0.449 -0.152 -0.284 -0.216 0.004
## ovrll_pr_nt -0.879 0.000 -0.008 -0.016 -0.008 0.018 0.060 -0.015
## clssrm_vr__ -0.097 0.085 -0.207 0.258 0.059 -0.009 -0.012 -0.079 0.013anova(m1ii, m1ii_f) # the model with the random slope does not seem to fit better## Data: df_d
## Models:
## m1ii: relevance ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1ii: overall_pre_interest + classroom_versus_field_enrichment +
## m1ii: (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## m1ii_f: relevance ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1ii_f: overall_pre_interest + classroom_versus_field_enrichment +
## m1ii_f: (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## m1ii_f: (1 | beep_ID_new)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m1ii 14 5391.8 5473.2 -2681.9 5363.8
## m1ii_f 16 5393.8 5486.9 -2680.9 5361.8 1.9577 2 0.3757m1iii <- lmer(learning ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
data = df_d)
sjPlot::sjt.lmer(m1iii, p.kr = F, show.re.var = F, show.ci = F, show.se = T)| learning | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.28 | 0.21 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.16 | 0.06 | .005 | |
| youth_activity_rc (Creating Product) | 0.02 | 0.06 | .747 | |
| youth_activity_rc (Field Trip Speaker) | 0.04 | 0.10 | .681 | |
| youth_activity_rc (Lab Activity) | 0.12 | 0.10 | .242 | |
| youth_activity_rc (Program Staff Led) | 0.03 | 0.06 | .596 | |
| gender_female | -0.07 | 0.10 | .510 | |
| CLASS_comp | 0.06 | 0.02 | .006 | |
| overall_pre_interest | 0.08 | 0.06 | .164 | |
| classroom_versus_field_enrichment | 0.01 | 0.05 | .879 | |
| Random Parts | ||||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 180 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.008 | |||
| ICCparticipant_ID | 0.354 | |||
| ICCprogram_ID | 0.004 | |||
| Observations | 2482 | |||
| R2 / Ω02 | .420 / .414 | |||
m1iii_f <- lmer(learning ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
overall_pre_interest +
classroom_versus_field_enrichment +
(1|program_ID) + (1 + overall_pre_interest|participant_ID) + (1|beep_ID_new),
data = df_d)
summary(m1iii_f)## Linear mixed model fit by REML ['lmerMod']
## Formula: learning ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## overall_pre_interest + classroom_versus_field_enrichment +
## (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## (1 | beep_ID_new)
## Data: df_d
##
## REML criterion at convergence: 6623.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1271 -0.5639 0.1119 0.5813 2.8205
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beep_ID_new (Intercept) 0.008630 0.09290
## participant_ID (Intercept) 0.907311 0.95253
## overall_pre_interest 0.083892 0.28964 -0.81
## program_ID (Intercept) 0.002774 0.05266
## Residual 0.710832 0.84311
## Number of obs: 2482, groups:
## beep_ID_new, 228; participant_ID, 180; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.339625 0.214340 10.915
## youth_activity_rcBasic Skills Activity 0.157320 0.056209 2.799
## youth_activity_rcCreating Product 0.019408 0.063401 0.306
## youth_activity_rcField Trip Speaker 0.041391 0.099993 0.414
## youth_activity_rcLab Activity 0.118621 0.101310 1.171
## youth_activity_rcProgram Staff Led 0.033598 0.062770 0.535
## gender_female -0.062370 0.101470 -0.615
## CLASS_comp 0.057521 0.021117 2.724
## overall_pre_interest 0.060915 0.061264 0.994
## classroom_versus_field_enrichment 0.009493 0.049412 0.192
##
## Correlation of Fixed Effects:
## (Intr) y__BSA yt__CP y__FTS yt__LA y__PSL gndr_f CLASS_ ovrl__
## yth_ctv_BSA 0.005
## yth_ctvt_CP 0.095 0.460
## yth_ctv_FTS -0.020 0.300 0.200
## yth_ctvt_LA 0.045 0.298 0.290 0.194
## yth_ctv_PSL -0.040 0.454 0.365 0.204 0.242
## gender_feml -0.291 -0.010 0.005 0.007 -0.016 -0.019
## CLASS_comp -0.273 -0.357 -0.447 -0.157 -0.289 -0.214 0.006
## ovrll_pr_nt -0.869 0.004 -0.013 -0.031 -0.016 0.033 0.062 -0.030
## clssrm_vr__ -0.132 0.071 -0.208 0.257 0.059 -0.020 -0.016 -0.073 0.022anova(m1iii, m1iii_f) # the model with the random slope does not seem to fit better## Data: df_d
## Models:
## m1iii: learning ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1iii: overall_pre_interest + classroom_versus_field_enrichment +
## m1iii: (1 | program_ID) + (1 | participant_ID) + (1 | beep_ID_new)
## m1iii_f: learning ~ 1 + youth_activity_rc + gender_female + CLASS_comp +
## m1iii_f: overall_pre_interest + classroom_versus_field_enrichment +
## m1iii_f: (1 | program_ID) + (1 + overall_pre_interest | participant_ID) +
## m1iii_f: (1 | beep_ID_new)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m1iii 14 6614.1 6695.6 -3293.1 6586.1
## m1iii_f 16 6616.5 6709.5 -3292.2 6584.5 1.6794 2 0.4318df$positive_affect <- jmRtools::composite_mean_maker(df,
happy, excited)
m1iv <- lmer(positive_affect ~ 1 +
youth_activity_rc +
gender_female +
CLASS_comp +
(1|program_ID) + (1|participant_ID) + (1|beep_ID_new),
na.action = "na.omit",
data = df)
sjPlot::sjt.lmer(m1iv, p.kr = F, show.re.var = F, show.ci = F, show.se = T)| positive_affect | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.75 | 0.15 | <.001 | |
| youth_activity_rc (Basic Skills Activity) | 0.01 | 0.06 | .880 | |
| youth_activity_rc (Creating Product) | -0.02 | 0.06 | .718 | |
| youth_activity_rc (Field Trip Speaker) | -0.02 | 0.11 | .875 | |
| youth_activity_rc (Lab Activity) | 0.05 | 0.11 | .657 | |
| youth_activity_rc (Program Staff Led) | -0.07 | 0.06 | .269 | |
| gender_female | -0.18 | 0.11 | .099 | |
| CLASS_comp | 0.01 | 0.02 | .543 | |
| Random Parts | ||||
| Nbeep_ID_new | 228 | |||
| Nparticipant_ID | 201 | |||
| Nprogram_ID | 9 | |||
| ICCbeep_ID_new | 0.025 | |||
| ICCparticipant_ID | 0.419 | |||
| ICCprogram_ID | 0.084 | |||
| Observations | 2693 | |||
| R2 / Ω02 | .582 / .578 | |||