## # A tibble: 161 x 5
## kids_friendly kids_treat_with_respect kids_listen_to_teachers
## <int> <int> <int>
## 1 3 2 3
## 2 3 3 3
## 3 3 3 3
## 4 4 3 3
## 5 3 3 4
## 6 4 4 3
## 7 3 3 2
## 8 4 4 4
## 9 4 4 4
## 10 3 3 3
## # ... with 151 more rows, and 2 more variables: kids_tease_or_bully <int>,
## # kids_support_help <int>RQ: Are challenge, relevance, possibly learning and positive affect during STEM activities across several weeks associated with changes in: (a) individual (sustained) interest in STEM and future goals and aspirations related to STEM? (We have been answering this question.)
OR
RQ: Are situational (momentary) interest and engagement in STEM activities across several weeks associated with changes in: (a) individual (sustained) interest in STEM and future goals and aspirations related to STEM?
A bit more processing.
mod_df <- mod_df %>%
left_join(act) %>%
left_join(loc) %>%
rename(lab = `ACT_Lab Activity`,
create = `ACT_Creating Product`,
not_focused = `ACT_Not Focused`,
basic = `ACT_Basic Skills Activity`,
psl = `ACT_Program Staff Led`,
fts = `ACT_Field Trip Speaker`,
class_space = `LOC_Classroom Space`,
in_comm = `LOC_Outdoors - Community`) %>%
mutate(doing = lab + create)
mod_df %>%
select(contains("pred"), overall_pre_interest, overall_post_interest, overall_post_interest, pre_future_goals_plans,
post_future_goals_plans) %>%
corrr::correlate() %>%
corrr::rearrange() %>%
fashion() %>%
knitr::kable()| rowname | pred_relevance | pred_interest | pred_learning | pred_engagement | pred_positive_affect | pred_challenge | post_future_goals_plans | pre_future_goals_plans | overall_post_interest | overall_pre_interest |
|---|---|---|---|---|---|---|---|---|---|---|
| pred_relevance | 1.00 | .86 | .80 | .67 | .58 | .30 | .28 | .17 | .11 | |
| pred_interest | 1.00 | .86 | .80 | .67 | .58 | .30 | .28 | .17 | .11 | |
| pred_learning | .86 | .86 | .85 | .66 | .46 | .28 | .23 | .21 | .10 | |
| pred_engagement | .80 | .80 | .85 | .75 | .42 | .28 | .24 | .32 | .15 | |
| pred_positive_affect | .67 | .67 | .66 | .75 | .42 | .25 | .25 | .24 | .17 | |
| pred_challenge | .58 | .58 | .46 | .42 | .42 | .08 | .05 | -.06 | .01 | |
| post_future_goals_plans | .30 | .30 | .28 | .28 | .25 | .08 | .47 | .47 | .20 | |
| pre_future_goals_plans | .28 | .28 | .23 | .24 | .25 | .05 | .47 | .33 | .39 | |
| overall_post_interest | .17 | .17 | .21 | .32 | .24 | -.06 | .47 | .33 | .59 | |
| overall_pre_interest | .11 | .11 | .10 | .15 | .17 | .01 | .20 | .39 | .59 |
mod_df %>%
select(lab, create, not_focused, basic, psl, fts) %>%
corrr::correlate() %>%
corrr::rearrange() %>%
fashion() %>%
knitr::kable()| rowname | fts | lab | create | not_focused | basic | psl |
|---|---|---|---|---|---|---|
| fts | -.14 | .15 | -.16 | -.28 | -.15 | |
| lab | -.14 | -.47 | .39 | -.27 | .07 | |
| create | .15 | -.47 | -.06 | -.45 | -.65 | |
| not_focused | -.16 | .39 | -.06 | -.72 | -.65 | |
| basic | -.28 | -.27 | -.45 | -.72 | .79 | |
| psl | -.15 | .07 | -.65 | -.65 | .79 |
attendance_ss <- mutate(attendance, prop_attend = DaysAttended / DaysScheduled, participant_ID = as.character(participant_ID))
attendance_ss <- select(attendance_ss, participant_ID, prop_attend)
mod_df <- left_join(mod_df, attendance_ss, by = "participant_ID")These correlations suggest that (at the program level):
Perhaps we could use:
mod_df <- mutate(mod_df,
lab_or_create = lab + create)Specific variables:
mod_df %>%
select(participant_ID, program_name, gender, pred_challenge, pred_relevance, overall_pre_interest, overall_post_interest, pre_future_goals_plans, post_future_goals_plans, lab, create, basic) %>%
skimr::skim()## Numeric Variables
## # A tibble: 9 x 13
## var type missing complete n mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 basic numeric 0 203 203 0.21187398
## 2 create numeric 0 203 203 0.17663357
## 3 lab numeric 0 203 203 0.02509145
## 4 overall_post_interest numeric 44 159 203 3.09433962
## 5 overall_pre_interest numeric 22 181 203 3.04419890
## 6 post_future_goals_plans numeric 50 153 203 2.80718954
## 7 pre_future_goals_plans numeric 25 178 203 2.73033708
## 8 pred_challenge numeric 0 203 203 2.27636813
## 9 pred_relevance numeric 0 203 203 2.57620061
## # ... with 7 more variables: sd <dbl>, min <dbl>, `25% quantile` <dbl>,
## # median <dbl>, `75% quantile` <dbl>, max <dbl>, hist <chr>
##
## Character Variables
## # A tibble: 3 x 9
## var type complete missing empty n min max
## * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 gender character 201 2 0 203 1 1
## 2 participant_ID character 203 0 0 203 4 5
## 3 program_name character 203 0 0 203 18 29
## # ... with 1 more variables: n_unique <dbl>Overall:
mod_df %>%
select(participant_ID, program_name, gender, pred_challenge, pred_relevance, overall_pre_interest, overall_post_interest, pre_future_goals_plans, post_future_goals_plans, lab, create, basic) %>%
complete.cases() %>%
table() # this suggests that 70 obs are not complete cases## .
## FALSE TRUE
## 70 133Examining raw data (not run):
mod_df %>%
select(participant_ID, program_name, gender, pred_challenge, pred_relevance, overall_pre_interest, overall_post_interest, pre_future_goals_plans, post_future_goals_plans, lab, create, basic) %>%
mutate_if(is.numeric, round) %>%
knitr::kable()mod_df$prop_attend## [1] 0.9200000 1.0000000 0.6400000 0.9600000 0.9600000 0.2000000 0.8400000
## [8] 0.7600000 NaN 0.5833333 0.5600000 0.9200000 0.7200000 0.8000000
## [15] 0.9600000 1.0000000 0.9200000 0.3600000 0.9200000 0.9200000 0.6000000
## [22] 0.8400000 0.8400000 0.9200000 0.7600000 1.0000000 0.9200000 0.9600000
## [29] 0.6400000 0.6400000 0.7200000 0.9583333 1.0000000 1.0000000 0.9583333
## [36] 1.0000000 1.0000000 0.8333333 1.0000000 0.7083333 0.9166667 0.7500000
## [43] NaN 1.0000000 0.9166667 0.9583333 NaN 0.8750000 0.9166667
## [50] 0.9583333 0.9583333 0.6250000 NaN 0.7500000 NaN 0.8750000
## [57] 0.8750000 0.9166667 0.9583333 0.9583333 0.4583333 0.9166667 1.0000000
## [64] 0.8750000 0.4400000 0.9200000 1.0000000 0.9600000 0.5200000 0.8800000
## [71] 0.9600000 1.0000000 0.5600000 0.4000000 0.7200000 0.9200000 0.9200000
## [78] 0.8400000 1.0000000 0.8400000 0.6800000 0.9600000 0.9600000 1.0000000
## [85] 0.9200000 1.0000000 0.9200000 0.5200000 0.6400000 0.7600000 0.9600000
## [92] 0.6363636 0.6666667 0.8000000 0.7333333 0.7333333 0.6000000 0.8666667
## [99] 1.0000000 0.7500000 0.9333333 0.8000000 0.8181818 0.9333333 0.7333333
## [106] 1.0000000 0.8000000 1.0000000 0.8666667 0.7333333 0.6666667 0.8666667
## [113] 0.8666667 1.0000000 0.7333333 1.0000000 1.0000000 0.8000000 0.9333333
## [120] 0.8000000 0.7333333 0.8666667 0.6666667 0.7333333 0.9333333 1.0000000
## [127] 0.6000000 0.9333333 0.7142857 0.6153846 0.9333333 1.0000000 0.8666667
## [134] 0.8666667 0.8666667 1.0000000 1.0000000 1.0000000 0.6250000 0.8666667
## [141] 0.5833333 0.9333333 0.6666667 1.0000000 0.7272727 0.9333333 1.0000000
## [148] 0.9333333 0.7333333 0.8000000 1.0000000 0.9333333 0.9333333 0.8666667
## [155] 1.0000000 0.8000000 0.6666667 0.9333333 0.8666667 0.8666667 0.9333333
## [162] 1.0000000 0.9333333 0.9333333 1.0000000 0.6363636 0.6666667 1.0000000
## [169] 0.9333333 0.9333333 0.8000000 0.8000000 1.0000000 0.9333333 1.0000000
## [176] 0.8000000 0.8666667 0.7333333 0.6666667 0.7333333 0.5000000 0.8000000
## [183] 0.8666667 0.7142857 1.0000000 0.8666667 0.5000000 0.8666667 0.6666667
## [190] 0.8000000 0.6666667 0.6000000 0.7142857 0.8666667 0.7333333 1.0000000
## [197] 1.0000000 0.5333333 0.5000000 0.5833333 0.7333333 0.8666667 0.8666667mod_df## # A tibble: 203 x 136
## participant_ID pred_challenge pred_learning pred_positive_affect
## <chr> <dbl> <dbl> <dbl>
## 1 1001 2.008037 3.020906 3.517998
## 2 1002 1.662917 3.358161 2.562492
## 3 1003 2.685600 2.638687 3.020306
## 4 1004 3.828588 3.844531 2.196869
## 5 1005 2.045985 2.673338 2.782299
## 6 1006 1.490989 2.391861 2.332055
## 7 1007 2.127746 1.934174 2.220552
## 8 1008 2.142987 2.190048 1.671972
## 9 1009 1.630411 2.883714 2.326783
## 10 1010 2.701656 3.042243 3.050524
## # ... with 193 more rows, and 132 more variables: pred_relevance <dbl>,
## # pred_interest <dbl>, pred_engagement <dbl>,
## # pre_science_interest <dbl>, pre_science_competence_beliefs <dbl>,
## # pre_science_utility_value <dbl>, pre_mathematics_interest <dbl>,
## # pre_mathematics_competence_beliefs <dbl>,
## # pre_mathematics_utility_value <dbl>, pre_engineering_interest <dbl>,
## # pre_engineering_competence_beliefs <dbl>,
## # pre_engineering_utility_value <dbl>,
## # pre_student_and_other_chose <int>, pre_student_alone_chose <int>,
## # pre_anybody_but_student_chose <int>,
## # pre_future_job_work_science_computer <int>,
## # pre_future_job_use_science_math <int>,
## # pre_future_job_become_STEM <int>, overall_pre_interest <dbl>,
## # overall_pre_competence_beliefs <dbl>, overall_pre_utility_value <dbl>,
## # pre_future_goals_plans <dbl>, post_science_interest <dbl>,
## # post_science_competence_beliefs <dbl>,
## # post_science_utility_value <dbl>, post_mathematics_interest <dbl>,
## # post_mathematics_competence_beliefs <dbl>,
## # post_mathematics_utility_value <dbl>, post_engineering_interest <dbl>,
## # post_engineering_competence_beliefs <dbl>,
## # post_engineering_utility_value <dbl>,
## # post_future_job_work_science_computer <int>,
## # post_future_job_use_science_math <int>,
## # post_future_job_become_STEM <int>, overall_post_interest <dbl>,
## # overall_post_competence_beliefs <dbl>,
## # overall_post_utility_value <dbl>, post_future_goals_plans <dbl>,
## # program_ID <int>, program_providence <int>, have_no_demo_data <int>,
## # have_pre_survey_data <int>, have_post_survey_data <int>,
## # has_grade <int>, race <chr>, gender <chr>, English <chr>,
## # Mathematics <chr>, Science <chr>, STAR_BOY_Math_ScaledScore <int>,
## # STAR_MOY_Math_ScaledScore <int>, STAR_EOY_Math_ScaledScore <int>,
## # standardized_bos <chr>, has_standardized_score <int>,
## # standardized_but_no_grade <int>, grade_but_no_standardized <int>,
## # LEP <int>, SP <int>, `DaysEnrolled14/15` <int>, `English15/16` <chr>,
## # `Math15/16` <chr>, `Science15/16` <chr>, `Standardized_Bos
## # 15/16` <int>, ParticipantFirstName <chr>, ParticipantLastName <chr>,
## # Site <chr>, DaysAttended <dbl>, DaysScheduled <dbl>, SurveyID <int>,
## # q01_MusicDanceArt <dbl>, q01_SportsTeam <dbl>,
## # q01_ReligiousSchoolGroup <dbl>, q01_ScoutingClubs <dbl>,
## # q01_Tutoring <dbl>, q01_MathScience <dbl>, q01_AnotherCamp <dbl>,
## # q01_AfterschoolProgram <dbl>, q01_NoneOfThese <dbl>,
## # q02_HaveFun <dbl>, q02_HaveSomething <dbl>, q02_HaveSafePlace <dbl>,
## # q02_LearnScienceMathComp <dbl>, q02_LearnSpecificSkills <dbl>,
## # q02_BeWithFriendsFamily <dbl>, q02_DoBetterInSchool <dbl>,
## # q02_NotSure <dbl>, q03a <dbl>, q03b <dbl>, q03c <dbl>, q03d <dbl>,
## # q03e <dbl>, q03f <dbl>, q03g <dbl>, q03h <dbl>, q03i <dbl>,
## # q03j <dbl>, q03k <dbl>, q03l <dbl>, q03m <dbl>, q03n <dbl>, ...sjstats::icc(lmer(overall_post_interest ~
(1|program_ID),
data = mod_df))## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.2.10
## Current Matrix version is 1.2.11
## Please re-install 'TMB' from source or restore original 'Matrix' package## Linear mixed model
## Family: gaussian (identity)
## Formula: overall_post_interest ~ (1 | program_ID)
##
## ICC (program_ID): 0.249150m4a <- lmer(overall_post_interest ~
pred_challenge + pred_relevance +
gender +
scale(overall_pre_interest, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`| overall_post_interest | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.14 | 0.48 | <.001 | |
| pred_challenge | -0.19 | 0.11 | .083 | |
| pred_relevance | 0.31 | 0.11 | .004 | |
| gender (M) | 0.17 | 0.12 | .172 | |
| scale(overall_pre_interest, scale = F) | 0.57 | 0.07 | <.001 | |
| prop_attend | 0.60 | 0.46 | .198 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.041 | |||
| Observations | 138 | |||
| R2 / Ω02 | .469 / .469 | |||
Relevance is a significant positive predictor.
With video codes added in:
m4ai <- lmer(overall_post_interest ~
pred_challenge + pred_relevance +
gender +
scale(lab) + scale(psl) + scale(basic) +
scale(overall_pre_interest, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4ai, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(overall_pre_interest, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(overall_pre_interest, scale = F)`| overall_post_interest | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 2.00 | 0.47 | <.001 | |
| pred_challenge | -0.21 | 0.11 | .052 | |
| pred_relevance | 0.31 | 0.11 | .003 | |
| gender (M) | 0.17 | 0.12 | .159 | |
| scale(lab) | 0.15 | 0.07 | .026 | |
| scale(psl) | -0.07 | 0.10 | .474 | |
| scale(basic) | -0.06 | 0.11 | .598 | |
| scale(overall_pre_interest, scale = F) | 0.52 | 0.07 | <.001 | |
| prop_attend | 0.80 | 0.45 | .077 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 138 | |||
| R2 / Ω02 | .484 / .484 | |||
Relevance is a significant positive predictor; challenge is (nearly) a negative significant predictor. Basic is approaching a negative, statistically significant relationship.
sjstats::icc(lmer(post_future_goals_plans ~
(1|program_ID),
data = mod_df))## Linear mixed model
## Family: gaussian (identity)
## Formula: post_future_goals_plans ~ (1 | program_ID)
##
## ICC (program_ID): 0.000000m4b <- lmer(post_future_goals_plans ~
pred_challenge + pred_relevance +
gender +
prop_attend +
scale(pre_future_goals_plans, scale = F) + (1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4b, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(pre_future_goals_plans, scale = F)`| post_future_goals_plans | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.56 | 0.55 | .004 | |
| pred_challenge | -0.11 | 0.13 | .384 | |
| pred_relevance | 0.37 | 0.13 | .005 | |
| gender (M) | 0.37 | 0.14 | .011 | |
| prop_attend | 0.43 | 0.52 | .411 | |
| scale(pre_future_goals_plans, scale = F) | 0.33 | 0.09 | <.001 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 130 | |||
| R2 / Ω02 | .313 / .313 | |||
Relevance is a significant posive predictor of changes in future goals and plans; gender is, as well.
With video codes added in:
m4bi <- lmer(post_future_goals_plans ~
pred_challenge + pred_relevance +
gender +
scale(lab) + scale(psl) + scale(basic) +
prop_attend +
scale(pre_future_goals_plans, scale = F) + (1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4bi, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`| post_future_goals_plans | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.50 | 0.56 | .008 | |
| pred_challenge | -0.11 | 0.13 | .394 | |
| pred_relevance | 0.37 | 0.13 | .005 | |
| gender (M) | 0.39 | 0.15 | .009 | |
| scale(lab) | 0.02 | 0.08 | .781 | |
| scale(psl) | 0.05 | 0.13 | .704 | |
| scale(basic) | 0.00 | 0.13 | .992 | |
| prop_attend | 0.48 | 0.54 | .374 | |
| scale(pre_future_goals_plans, scale = F) | 0.32 | 0.09 | <.001 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 130 | |||
| R2 / Ω02 | .317 / .317 | |||
Relevance is a significant posive predictor of changes in future goals and plans; gender is, as well.
Just interest:
m4a <- lmer(overall_post_interest ~
pred_interest +
gender +
scale(overall_pre_interest, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`
## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`| overall_post_interest | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.89 | 0.48 | <.001 | |
| pred_interest | 0.21 | 0.09 | .023 | |
| gender (M) | 0.15 | 0.12 | .214 | |
| scale(overall_pre_interest, scale = F) | 0.58 | 0.07 | <.001 | |
| prop_attend | 0.62 | 0.47 | .184 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.035 | |||
| Observations | 138 | |||
| R2 / Ω02 | .454 / .454 | |||
Interest is approaching statistical signficance as a positive predictor.
Just engagement:
m4b <- lmer(overall_post_interest ~
pred_engagement +
gender +
scale(overall_pre_interest, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4b, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`
## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`| overall_post_interest | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.15 | 0.46 | .012 | |
| pred_engagement | 0.52 | 0.10 | <.001 | |
| gender (M) | 0.14 | 0.11 | .223 | |
| scale(overall_pre_interest, scale = F) | 0.54 | 0.07 | <.001 | |
| prop_attend | 0.47 | 0.44 | .287 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.053 | |||
| Observations | 138 | |||
| R2 / Ω02 | .534 / .533 | |||
Engagement predicts changes in post interest.
Both interest and engagement:
m4c <- lmer(overall_post_interest ~
pred_interest + pred_engagement +
gender +
scale(overall_pre_interest, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m4c, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`
## Warning: Unknown variables: `scale(overall_pre_interest, scale = F)`| overall_post_interest | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.25 | 0.45 | .005 | |
| pred_interest | -0.44 | 0.14 | .002 | |
| pred_engagement | 0.95 | 0.17 | <.001 | |
| gender (M) | 0.21 | 0.11 | .057 | |
| scale(overall_pre_interest, scale = F) | 0.53 | 0.07 | <.001 | |
| prop_attend | 0.38 | 0.43 | .366 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.056 | |||
| Observations | 138 | |||
| R2 / Ω02 | .566 / .566 | |||
Interest is a negative significant predicotr of changes in interest; engagement is a positive predictor of changes.
Just interest:
m5a <- lmer(post_future_goals_plans ~
pred_interest +
gender +
scale(lab) + scale(psl) + scale(basic) +
scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m5a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`| post_future_goals_plans | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.31 | 0.57 | .021 | |
| pred_interest | 0.31 | 0.11 | .005 | |
| gender (M) | 0.38 | 0.15 | .011 | |
| scale(lab) | 0.02 | 0.08 | .780 | |
| scale(psl) | 0.05 | 0.13 | .719 | |
| scale(basic) | 0.01 | 0.13 | .964 | |
| scale(pre_future_goals_plans, scale = F) | 0.34 | 0.09 | <.001 | |
| prop_attend | 0.49 | 0.54 | .356 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 130 | |||
| R2 / Ω02 | .313 / .313 | |||
Interest is approaching statistical signficance as a positive predictor of future goals and plans. Males are associated with changes in future goals and plans.
Just engagement:
m5b <- lmer(post_future_goals_plans ~
pred_engagement +
gender +
scale(lab) + scale(psl) + scale(basic) +
scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m5b, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`| post_future_goals_plans | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.19 | 0.59 | .043 | |
| pred_engagement | 0.37 | 0.13 | .005 | |
| gender (M) | 0.41 | 0.15 | .005 | |
| scale(lab) | 0.01 | 0.08 | .850 | |
| scale(psl) | 0.03 | 0.12 | .801 | |
| scale(basic) | 0.03 | 0.13 | .826 | |
| scale(pre_future_goals_plans, scale = F) | 0.34 | 0.09 | <.001 | |
| prop_attend | 0.41 | 0.54 | .444 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 130 | |||
| R2 / Ω02 | .313 / .313 | |||
Engagement predicts changes in future goals and plans, as does gender.
Both interest and engagement:
m5c <- lmer(post_future_goals_plans ~
pred_interest + pred_engagement +
gender +
scale(lab) + scale(psl) + scale(basic) +
scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m5c, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`| post_future_goals_plans | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.15 | 0.59 | .051 | |
| pred_interest | 0.16 | 0.19 | .396 | |
| pred_engagement | 0.21 | 0.23 | .352 | |
| gender (M) | 0.39 | 0.15 | .008 | |
| scale(lab) | 0.02 | 0.08 | .812 | |
| scale(psl) | 0.04 | 0.13 | .764 | |
| scale(basic) | 0.02 | 0.13 | .884 | |
| scale(pre_future_goals_plans, scale = F) | 0.33 | 0.09 | <.001 | |
| prop_attend | 0.45 | 0.54 | .407 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.000 | |||
| Observations | 130 | |||
| R2 / Ω02 | .318 / .318 | |||
Males are associated with changes in future goals and plans.
post_ss <- select(post, participant_ID, contains("post_"))
post_ss <- mutate(post_ss, participant_ID = as.character(participant_ID))
mod_df <- left_join(mod_df, post_ss)Reliabilities for new post-survey measures (need to think about pre-measures)
psych::alpha(jmRtools::composite_matrix_maker(post, contains("at_program")))##
## Reliability analysis
## Call: psych::alpha(x = jmRtools::composite_matrix_maker(post, contains("at_program")))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.83 0.83 0.82 0.4 4.7 0.021 2.5 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.83 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N
## at_program_choose_time 0.81 0.81 0.80 0.42 4.4
## at_program_suggest_ideas 0.80 0.80 0.78 0.40 3.9
## at_program_choose_activities 0.80 0.80 0.79 0.41 4.1
## at_program_plan_activities 0.80 0.81 0.80 0.41 4.1
## at_program_lead_activities 0.80 0.80 0.79 0.40 4.0
## at_program_in_charge 0.79 0.79 0.77 0.39 3.8
## at_program_decicision_rules 0.80 0.80 0.80 0.41 4.1
## alpha se
## at_program_choose_time 0.023
## at_program_suggest_ideas 0.024
## at_program_choose_activities 0.024
## at_program_plan_activities 0.024
## at_program_lead_activities 0.025
## at_program_in_charge 0.026
## at_program_decicision_rules 0.024
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## at_program_choose_time 159 0.63 0.64 0.55 0.49 2.7 0.85
## at_program_suggest_ideas 160 0.71 0.72 0.67 0.59 2.8 0.90
## at_program_choose_activities 158 0.69 0.69 0.62 0.56 2.8 0.94
## at_program_plan_activities 156 0.69 0.68 0.61 0.55 2.4 1.01
## at_program_lead_activities 160 0.72 0.71 0.65 0.59 2.4 1.00
## at_program_in_charge 158 0.77 0.75 0.71 0.64 2.3 1.05
## at_program_decicision_rules 160 0.71 0.69 0.61 0.56 2.4 1.08
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## at_program_choose_time 0.11 0.25 0.50 0.14 0.01
## at_program_suggest_ideas 0.10 0.21 0.46 0.22 0.01
## at_program_choose_activities 0.11 0.23 0.41 0.24 0.02
## at_program_plan_activities 0.24 0.29 0.32 0.15 0.03
## at_program_lead_activities 0.24 0.23 0.39 0.14 0.01
## at_program_in_charge 0.31 0.23 0.33 0.13 0.02
## at_program_decicision_rules 0.27 0.26 0.28 0.19 0.01psych::alpha(jmRtools::composite_matrix_maker(post, contains("teacher_staff")))##
## Reliability analysis
## Call: psych::alpha(x = jmRtools::composite_matrix_maker(post, contains("teacher_staff")))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.87 0.87 0.87 0.5 6.9 0.016 2.9 0.78
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.87 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N
## teacher_staff_interested_in_you 0.85 0.85 0.84 0.48 5.6
## teacher_staff_can_talk_to 0.86 0.86 0.85 0.50 6.1
## teacher_staff_helps_you 0.85 0.85 0.84 0.48 5.6
## teacher_staff_enjoy 0.85 0.85 0.85 0.49 5.7
## teacher_staff_interests_talents 0.85 0.85 0.84 0.49 5.7
## teacher_staff_life_goals 0.85 0.85 0.84 0.50 5.9
## teacher_staff_will_miss 0.88 0.88 0.87 0.55 7.2
## alpha se
## teacher_staff_interested_in_you 0.019
## teacher_staff_can_talk_to 0.018
## teacher_staff_helps_you 0.019
## teacher_staff_enjoy 0.019
## teacher_staff_interests_talents 0.019
## teacher_staff_life_goals 0.018
## teacher_staff_will_miss 0.015
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## teacher_staff_interested_in_you 160 0.79 0.80 0.76 0.71 2.9 0.89
## teacher_staff_can_talk_to 159 0.74 0.74 0.69 0.63 2.9 1.09
## teacher_staff_helps_you 158 0.79 0.79 0.77 0.70 3.0 1.01
## teacher_staff_enjoy 159 0.78 0.79 0.74 0.69 3.1 0.99
## teacher_staff_interests_talents 157 0.78 0.78 0.74 0.69 2.6 1.06
## teacher_staff_life_goals 157 0.77 0.76 0.71 0.66 2.6 1.07
## teacher_staff_will_miss 158 0.62 0.62 0.51 0.48 3.0 1.07
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## teacher_staff_interested_in_you 0.07 0.26 0.41 0.27 0.01
## teacher_staff_can_talk_to 0.16 0.19 0.27 0.38 0.01
## teacher_staff_helps_you 0.11 0.18 0.31 0.41 0.02
## teacher_staff_enjoy 0.09 0.16 0.29 0.46 0.01
## teacher_staff_interests_talents 0.18 0.29 0.27 0.26 0.02
## teacher_staff_life_goals 0.18 0.27 0.28 0.27 0.02
## teacher_staff_will_miss 0.14 0.15 0.27 0.44 0.02psych::alpha(jmRtools::composite_matrix_maker(post, contains("kids_")))## Warning in psych::alpha(jmRtools::composite_matrix_maker(post, contains("kids_"))): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( kids_tease_or_bully ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = jmRtools::composite_matrix_maker(post, contains("kids_")))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.5 0.54 0.62 0.19 1.2 0.062 2.5 0.52
##
## lower alpha upper 95% confidence boundaries
## 0.38 0.5 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N
## kids_friendly 0.35 0.40 0.46 0.141 0.66
## kids_treat_with_respect 0.26 0.30 0.40 0.097 0.43
## kids_listen_to_teachers 0.29 0.35 0.49 0.121 0.55
## kids_tease_or_bully 0.77 0.77 0.74 0.457 3.36
## kids_support_help 0.32 0.40 0.53 0.142 0.66
## alpha se
## kids_friendly 0.085
## kids_treat_with_respect 0.096
## kids_listen_to_teachers 0.092
## kids_tease_or_bully 0.030
## kids_support_help 0.089
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## kids_friendly 160 0.67 0.696 0.66 0.41 2.7 0.83
## kids_treat_with_respect 159 0.76 0.784 0.78 0.56 2.5 0.80
## kids_listen_to_teachers 159 0.72 0.738 0.65 0.50 2.6 0.84
## kids_tease_or_bully 160 0.12 0.059 -0.30 -0.27 2.2 0.99
## kids_support_help 159 0.71 0.695 0.56 0.43 2.6 0.94
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## kids_friendly 0.10 0.26 0.51 0.13 0.01
## kids_treat_with_respect 0.11 0.35 0.45 0.09 0.01
## kids_listen_to_teachers 0.10 0.36 0.42 0.13 0.01
## kids_tease_or_bully 0.28 0.39 0.20 0.13 0.01
## kids_support_help 0.13 0.30 0.38 0.19 0.01m6a <- lmer(post_agency ~
pred_interest + pred_engagement +
gender +
scale(lab) + scale(psl) + scale(basic) +
scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m6a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`,
## `scale(pre_future_goals_plans, scale = F)`| post_agency | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.78 | 0.47 | <.001 | |
| pred_interest | 0.11 | 0.15 | .490 | |
| pred_engagement | 0.30 | 0.18 | .098 | |
| gender (M) | 0.01 | 0.12 | .901 | |
| scale(lab) | -0.09 | 0.07 | .208 | |
| scale(psl) | -0.04 | 0.11 | .751 | |
| scale(basic) | -0.00 | 0.11 | .990 | |
| scale(pre_future_goals_plans, scale = F) | -0.05 | 0.07 | .452 | |
| prop_attend | -0.46 | 0.43 | .290 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.015 | |||
| Observations | 136 | |||
| R2 / Ω02 | .158 / .158 | |||
m6a <- lmer(post_peer_relations ~
pred_interest + pred_engagement +
gender +
scale(lab) + scale(psl) + scale(basic) +
#scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m6a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`| post_peer_relations | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.82 | 0.32 | <.001 | |
| pred_interest | -0.03 | 0.10 | .790 | |
| pred_engagement | 0.18 | 0.12 | .145 | |
| gender (M) | 0.20 | 0.08 | .013 | |
| scale(lab) | 0.14 | 0.06 | .025 | |
| scale(psl) | -0.20 | 0.10 | .048 | |
| scale(basic) | 0.19 | 0.10 | .068 | |
| prop_attend | 0.19 | 0.30 | .530 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.059 | |||
| Observations | 154 | |||
| R2 / Ω02 | .178 / .177 | |||
m6a <- lmer(post_feelings_of_staff ~
pred_interest + pred_engagement +
gender +
scale(lab) + scale(psl) + scale(basic) +
# scale(pre_future_goals_plans, scale = F) +
prop_attend +
(1|program_ID),
data = mod_df)
sjPlot::sjt.lmer(m6a, p.kr = F, show.re.var = F, show.ci = F, show.se = T)## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`
## Warning: Unknown variables: `scale(lab)`, `scale(psl)`, `scale(basic)`| post_feelings_of_staff | ||||
| B | std. Error | p | ||
| Fixed Parts | ||||
| (Intercept) | 1.80 | 0.49 | <.001 | |
| pred_interest | 0.17 | 0.15 | .277 | |
| pred_engagement | 0.22 | 0.18 | .236 | |
| gender (M) | -0.25 | 0.12 | .046 | |
| scale(lab) | 0.05 | 0.11 | .626 | |
| scale(psl) | -0.13 | 0.18 | .475 | |
| scale(basic) | 0.17 | 0.18 | .352 | |
| prop_attend | 0.12 | 0.46 | .792 | |
| Random Parts | ||||
| Nprogram_ID | 9 | |||
| ICCprogram_ID | 0.096 | |||
| Observations | 153 | |||
| R2 / Ω02 | .188 / .186 | |||