ESM_vs_Not_Descriptives
Patrick N. Beymer
6/27/2020
Loading packages
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## extractLoading data
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## 509 57 41 20 20 12 15 19 16 22 25 26URM variable
EOC average variable creation
Creating interactions
Joining pre post
Response Rate
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## %+%, alpha## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 273 0.47 0.33 0.36 0.46 0.4 0.09 1 0.91 0.28 -1.45 0.02## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 273 5.22 3.6 4 5.03 4.45 1 11 10 0.28 -1.45 0.22Female by condition
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## 0 1
## 0 278 148
## 1 235 121Ethnicity by condition
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## Asian (non-Hispanic) Black or African American (non-Hispanic)
## 0 37 12
## 1 26 21
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## Hispanic Ethnicity International Not Reported
## 0 17 63 6
## 1 16 53 2
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## Two or more races (non-Hispanic) White (non-Hispanic)
## 0 14 277
## 1 6 232Pre Career type by condition (1 = STEM; 2 = Education; 3 = Other field; 4 = Undecided)
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## 1 2 3 4
## 0 302 13 81 30
## 1 248 12 62 34Post Career type by condition (1 = STEM; 2 = Education; 3 = Other field; 4 = Undecided)
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## 1 2 3 4
## 0 233 9 39 28
## 1 198 8 29 19Proportions and means by group for Pre Survey eoc_participate
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc | prop_female | mean_pre_interest | mean_pre_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 4.840 | 4.922 | 5.563 | 5.874 | 3.328 | 2.892 | 3.152 | 3.671 |
| 1 | 0.339 | 5.067 | 4.969 | 5.763 | 5.910 | 3.286 | 2.716 | 3.160 | 3.650 |
Proportions and means by group for Post Survey eoc_participate
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc | prop_female | mean_Grade | mean_post_interest | mean_post_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 2.813 | 4.592 | 5.059 | 5.179 | 5.632 | 3.484 | 3.158 | 3.268 | 3.875 |
| 1 | 0.339 | 2.868 | 4.768 | 5.230 | 5.382 | 5.755 | 3.306 | 3.100 | 3.129 | 3.617 |
Pre Survey Graphs
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Post Survey Graphs eoc_participate
## Warning: Removed 44 rows containing non-finite values (stat_summary).
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Proportions and means by group for Pre Survey eoc_participate_2
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_2 | prop_female | mean_pre_interest | mean_pre_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.350 | 4.861 | 4.921 | 5.566 | 5.845 | 3.309 | 2.875 | 3.148 | 3.665 |
| 1 | 0.333 | 5.097 | 4.985 | 5.820 | 5.977 | 3.308 | 2.693 | 3.170 | 3.655 |
Proportions and means by group for Post Survey eoc_participate_2
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_2 | prop_female | mean_Grade | mean_post_interest | mean_post_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.350 | 2.792 | 4.622 | 5.055 | 5.177 | 5.637 | 3.495 | 3.197 | 3.281 | 3.888 |
| 1 | 0.333 | 2.922 | 4.752 | 5.268 | 5.423 | 5.770 | 3.254 | 3.026 | 3.081 | 3.548 |
Post Survey Graphs eoc_participate
## Warning: Removed 44 rows containing non-finite values (stat_summary).
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## Warning: Removed 226 rows containing non-finite values (stat_summary).## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Don't know how to automatically pick scale for object of type haven_labelled/vctrs_vctr/double. Defaulting to continuous.## Warning: Removed 234 rows containing non-finite values (stat_summary).## Warning: Removed 234 rows containing non-finite values (stat_summary).
## Warning: Removed 227 rows containing non-finite values (stat_summary).## Warning: Removed 227 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Warning: Removed 227 rows containing non-finite values (stat_summary).## Warning: Removed 227 rows containing non-finite values (stat_summary).
Proportions and means by group for Pre Survey eoc_participate_3
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_3 | prop_female | mean_pre_interest | mean_pre_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 4.840 | 4.922 | 5.563 | 5.874 | 3.328 | 2.892 | 3.152 | 3.671 |
| 1 | 0.333 | 5.097 | 4.985 | 5.820 | 5.977 | 3.308 | 2.693 | 3.170 | 3.655 |
| 2 | 0.357 | 4.968 | 4.916 | 5.581 | 5.695 | 3.216 | 2.789 | 3.128 | 3.632 |
Proportions and means by group for Post Survey eoc_participate_3
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_3 | prop_female | mean_Grade | mean_post_interest | mean_post_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 2.813 | 4.592 | 5.059 | 5.179 | 5.632 | 3.484 | 3.158 | 3.268 | 3.875 |
| 1 | 0.333 | 2.922 | 4.752 | 5.268 | 5.423 | 5.770 | 3.254 | 3.026 | 3.081 | 3.548 |
| 2 | 0.357 | 2.676 | 4.855 | 5.026 | 5.167 | 5.675 | 3.580 | 3.487 | 3.381 | 3.979 |
Post Survey Graphs eoc_participate_3
## Warning: Removed 44 rows containing non-finite values (stat_summary).
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## Don't know how to automatically pick scale for object of type haven_labelled/vctrs_vctr/double. Defaulting to continuous.## Warning: Removed 225 rows containing non-finite values (stat_summary).## Warning: Removed 225 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Don't know how to automatically pick scale for object of type haven_labelled/vctrs_vctr/double. Defaulting to continuous.## Warning: Removed 234 rows containing non-finite values (stat_summary).## Warning: Removed 234 rows containing non-finite values (stat_summary).
## Warning: Removed 227 rows containing non-finite values (stat_summary).## Warning: Removed 227 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).## Warning: Removed 226 rows containing non-finite values (stat_summary).
## Warning: Removed 226 rows containing non-finite values (stat_summary).
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## Warning: Removed 227 rows containing non-finite values (stat_summary).## Warning: Removed 227 rows containing non-finite values (stat_summary).
##
## Call:
## lm(formula = post_int_overall ~ pre_int_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4943 -0.5174 0.1213 0.6979 3.2060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.647520 0.175980 3.680 0.000257 ***
## pre_int_overall 0.807800 0.033875 23.846 < 2e-16 ***
## signals_responded_to -0.007573 0.012500 -0.606 0.544888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.061 on 546 degrees of freedom
## (233 observations deleted due to missingness)
## Multiple R-squared: 0.5104, Adjusted R-squared: 0.5086
## F-statistic: 284.6 on 2 and 546 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_val_overall ~ pre_val_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5430 -0.5172 0.1511 0.6507 2.6482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.35664 0.27665 8.518 <2e-16 ***
## pre_val_overall 0.49878 0.04778 10.440 <2e-16 ***
## signals_responded_to 0.02822 0.01304 2.165 0.0309 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 546 degrees of freedom
## (233 observations deleted due to missingness)
## Multiple R-squared: 0.1768, Adjusted R-squared: 0.1738
## F-statistic: 58.62 on 2 and 546 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_stem_int ~ pre_stem_int + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4323 -0.4323 0.3379 0.5677 3.0895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85899 0.25475 3.372 0.000801 ***
## pre_stem_int 0.79619 0.04160 19.137 < 2e-16 ***
## signals_responded_to 0.02553 0.01372 1.861 0.063294 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.153 on 532 degrees of freedom
## (247 observations deleted due to missingness)
## Multiple R-squared: 0.4118, Adjusted R-squared: 0.4096
## F-statistic: 186.2 on 2 and 532 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_cost_te_overall ~ pre_cost_te_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3232 -0.7873 -0.0765 0.7561 3.9827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.60225 0.15454 10.368 <2e-16 ***
## pre_cost_te_overall 0.56857 0.04290 13.255 <2e-16 ***
## signals_responded_to -0.03633 0.01423 -2.553 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.21 on 545 degrees of freedom
## (234 observations deleted due to missingness)
## Multiple R-squared: 0.2512, Adjusted R-squared: 0.2485
## F-statistic: 91.43 on 2 and 545 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_cost_oe_overall ~ pre_cost_oe_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2663 -0.7731 -0.0570 0.8265 3.8152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.70511 0.14348 11.884 <2e-16 ***
## pre_cost_oe_overall 0.53397 0.04652 11.479 <2e-16 ***
## signals_responded_to -0.02717 0.01371 -1.981 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.163 on 546 degrees of freedom
## (233 observations deleted due to missingness)
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.2002
## F-statistic: 69.6 on 2 and 546 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_cost_lv_overall ~ pre_cost_lv_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4986 -0.8242 -0.0813 0.7909 4.4349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.66956 0.15226 10.965 <2e-16 ***
## pre_cost_lv_overall 0.51317 0.04454 11.522 <2e-16 ***
## signals_responded_to -0.03239 0.01401 -2.311 0.0212 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.191 on 546 degrees of freedom
## (233 observations deleted due to missingness)
## Multiple R-squared: 0.2036, Adjusted R-squared: 0.2007
## F-statistic: 69.78 on 2 and 546 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_cost_em_overall ~ pre_cost_em_overall + signals_responded_to,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2369 -0.8745 -0.0672 0.8808 4.0394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.61547 0.17188 9.399 < 2e-16 ***
## pre_cost_em_overall 0.62082 0.04388 14.149 < 2e-16 ***
## signals_responded_to -0.05082 0.01526 -3.329 0.000929 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.298 on 545 degrees of freedom
## (234 observations deleted due to missingness)
## Multiple R-squared: 0.2792, Adjusted R-squared: 0.2765
## F-statistic: 105.5 on 2 and 545 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_int_overall ~ pre_int_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4112 -0.5352 0.1269 0.6329 2.6960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.451916 0.290135 1.558 0.121
## pre_int_overall 0.845032 0.052019 16.245 <2e-16 ***
## signals_responded_to_eoc_only -0.006988 0.016276 -0.429 0.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9984 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.5225, Adjusted R-squared: 0.5185
## F-statistic: 132.4 on 2 and 242 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_val_overall ~ pre_val_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3744 -0.5503 0.1164 0.5939 2.1216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.03712 0.37160 8.173 1.68e-14 ***
## pre_val_overall 0.37698 0.06179 6.101 4.16e-09 ***
## signals_responded_to_eoc_only 0.03175 0.01579 2.010 0.0455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9691 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.1469, Adjusted R-squared: 0.1398
## F-statistic: 20.83 on 2 and 242 DF, p-value: 4.485e-09##
## Call:
## lm(formula = post_stem_int ~ pre_stem_int + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1457 -0.3896 0.2660 0.6364 2.9508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.87467 0.39711 2.203 0.0286 *
## pre_stem_int 0.77413 0.06294 12.298 <2e-16 ***
## signals_responded_to_eoc_only 0.04004 0.01854 2.160 0.0318 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 237 degrees of freedom
## (542 observations deleted due to missingness)
## Multiple R-squared: 0.3977, Adjusted R-squared: 0.3926
## F-statistic: 78.25 on 2 and 237 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = post_cost_te_overall ~ pre_cost_te_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2073 -0.7652 -0.0457 0.7543 3.9721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.74145 0.25553 6.815 7.44e-11 ***
## pre_cost_te_overall 0.53257 0.06561 8.117 2.42e-14 ***
## signals_responded_to_eoc_only -0.03891 0.02057 -1.892 0.0597 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.262 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.2247, Adjusted R-squared: 0.2183
## F-statistic: 35.07 on 2 and 242 DF, p-value: 4.213e-14##
## Call:
## lm(formula = post_cost_oe_overall ~ pre_cost_oe_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5007 -0.8568 -0.0843 0.7946 3.6807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.86486 0.23324 7.995 5.31e-14 ***
## pre_cost_oe_overall 0.57566 0.07344 7.839 1.44e-13 ***
## signals_responded_to_eoc_only -0.06061 0.01957 -3.097 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.201 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.2279, Adjusted R-squared: 0.2215
## F-statistic: 35.71 on 2 and 242 DF, p-value: 2.567e-14##
## Call:
## lm(formula = post_cost_lv_overall ~ pre_cost_lv_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7563 -0.8771 -0.1067 0.7997 4.4493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.64324 0.25848 6.357 1.01e-09 ***
## pre_cost_lv_overall 0.53101 0.06958 7.631 5.34e-13 ***
## signals_responded_to_eoc_only -0.03592 0.02043 -1.758 0.08 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.252 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.2065, Adjusted R-squared: 0.1999
## F-statistic: 31.48 on 2 and 242 DF, p-value: 7.024e-13##
## Call:
## lm(formula = post_cost_em_overall ~ pre_cost_em_overall + signals_responded_to_eoc_only,
## data = MTH_132_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4234 -0.9524 -0.0450 0.8364 3.5486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.64969 0.26742 6.169 2.87e-09 ***
## pre_cost_em_overall 0.61859 0.06524 9.482 < 2e-16 ***
## signals_responded_to_eoc_only -0.05405 0.02093 -2.583 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.284 on 242 degrees of freedom
## (537 observations deleted due to missingness)
## Multiple R-squared: 0.2822, Adjusted R-squared: 0.2763
## F-statistic: 47.58 on 2 and 242 DF, p-value: < 2.2e-16## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 425 rows containing non-finite values (stat_bin).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 509 rows containing non-finite values (stat_bin).
| post_int_overall | post_confident_math | post_val_overall | post_stem_int | post_exp_overall | post_cost_te_overall | post_cost_oe_overall | post_cost_lv_overall | post_cost_em_overall | |
|---|---|---|---|---|---|---|---|---|---|
| post_int_overall | |||||||||
| post_confident_math | 0.54*** | ||||||||
| post_val_overall | 0.66*** | 0.38*** | |||||||
| post_stem_int | 0.21*** | 0.22*** | 0.27*** | ||||||
| post_exp_overall | 0.55*** | 0.55*** | 0.63*** | 0.29*** | |||||
| post_cost_te_overall | -0.32*** | -0.45*** | -0.30*** | -0.20*** | -0.45*** | ||||
| post_cost_oe_overall | -0.21*** | -0.29*** | -0.29*** | -0.21*** | -0.42*** | 0.73*** | |||
| post_cost_lv_overall | -0.27*** | -0.38*** | -0.30*** | -0.17*** | -0.43*** | 0.86*** | 0.76*** | ||
| post_cost_em_overall | -0.40*** | -0.57*** | -0.32*** | -0.16*** | -0.48*** | 0.83*** | 0.62*** | 0.74*** |
| pre_int_overall | pre_confident | pre_val_overall | pre_exp_overall | pre_cost_te_overall | pre_cost_oe_overall | pre_cost_lv_overall | pre_cost_em_overall | eoc_int_avg | eoc_comp_avg | eoc_val_avg | eoc_cost_te_avg | eoc_cost_oe_avg | eoc_cost_lv_avg | eoc_cost_em_avg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pre_int_overall | |||||||||||||||
| pre_confident | 0.45*** | ||||||||||||||
| pre_val_overall | 0.65*** | 0.26*** | |||||||||||||
| pre_exp_overall | 0.53*** | 0.48*** | 0.60*** | ||||||||||||
| pre_cost_te_overall | -0.26*** | -0.39*** | -0.23*** | -0.40*** | |||||||||||
| pre_cost_oe_overall | -0.23*** | -0.32*** | -0.28*** | -0.42*** | 0.71*** | ||||||||||
| pre_cost_lv_overall | -0.22*** | -0.34*** | -0.21*** | -0.36*** | 0.78*** | 0.71*** | |||||||||
| pre_cost_em_overall | -0.30*** | -0.50*** | -0.19*** | -0.41*** | 0.79*** | 0.60*** | 0.76*** | ||||||||
| eoc_int_avg | 0.45*** | 0.24*** | 0.28*** | 0.17** | -0.10 | -0.11 | -0.09 | -0.23*** | |||||||
| eoc_comp_avg | 0.31*** | 0.12 | 0.22*** | 0.18** | -0.09 | -0.14* | -0.10 | -0.10 | 0.69*** | ||||||
| eoc_val_avg | 0.23*** | 0.03 | 0.30*** | 0.14* | -0.08 | -0.12 | -0.11 | -0.13* | 0.64*** | 0.59*** | |||||
| eoc_cost_te_avg | -0.33*** | -0.31*** | -0.31*** | -0.37*** | 0.56*** | 0.49*** | 0.45*** | 0.51*** | -0.23*** | -0.19** | -0.09 | ||||
| eoc_cost_oe_avg | -0.27*** | -0.27*** | -0.31*** | -0.36*** | 0.46*** | 0.53*** | 0.48*** | 0.49*** | -0.21*** | -0.15* | -0.15* | 0.76*** | |||
| eoc_cost_lv_avg | -0.31*** | -0.32*** | -0.35*** | -0.39*** | 0.48*** | 0.47*** | 0.47*** | 0.48*** | -0.18** | -0.19** | -0.07 | 0.80*** | 0.80*** | ||
| eoc_cost_em_avg | -0.41*** | -0.35*** | -0.28*** | -0.36*** | 0.43*** | 0.35*** | 0.37*** | 0.54*** | -0.35*** | -0.24*** | -0.14* | 0.74*** | 0.64*** | 0.66*** |
Sep 9 Analysis
Path Analysis with eoc_2 (1 = invited and responded; 0 = not invited OR did not participate)
## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.210 0.092 -2.277 0.023 -0.210 -0.066
## urm -0.013 0.170 -0.078 0.938 -0.013 -0.002
## Best_MPS 0.032 0.012 2.722 0.006 0.032 0.105
## pre_int_overll 0.778 0.035 22.296 0.000 0.778 0.694
## participat_c_2 -0.068 0.092 -0.739 0.460 -0.068 -0.021
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.245 0.258 0.951 0.342 0.245 0.161
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.085 0.066 16.449 0.000 1.085 0.469## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.449 0.122 -3.668 0.000 -0.449 -0.130
## urm 0.048 0.222 0.219 0.827 0.048 0.008
## Best_MPS 0.090 0.014 6.314 0.000 0.090 0.271
## pre_confident 0.764 0.067 11.319 0.000 0.764 0.423
## participat_c_2 0.197 0.121 1.628 0.104 0.197 0.057
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.381 0.427 -0.891 0.373 -0.381 -0.231
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.855 0.115 16.109 0.000 1.855 0.684## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.088 0.097 -0.910 0.363 -0.088 -0.034
## urm -0.040 0.179 -0.223 0.823 -0.040 -0.009
## Best_MPS 0.035 0.012 2.971 0.003 0.035 0.143
## pre_val_overll 0.478 0.048 9.984 0.000 0.478 0.405
## participat_c_2 0.148 0.097 1.529 0.126 0.148 0.058
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.821 0.340 5.350 0.000 1.821 1.489
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.188 0.072 16.484 0.000 1.188 0.794## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.175 0.090 -1.952 0.051 -0.175 -0.072
## urm 0.316 0.166 1.897 0.058 0.316 0.076
## Best_MPS 0.038 0.011 3.324 0.001 0.038 0.162
## pre_exp_overll 0.509 0.046 11.139 0.000 0.509 0.436
## participat_c_2 0.037 0.090 0.407 0.684 0.037 0.015
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.716 0.335 5.131 0.000 1.716 1.483
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.021 0.062 16.334 0.000 1.021 0.762## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 13
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_stem_int ~
## female -0.282 0.102 -2.763 0.006 -0.282 -0.088
## urm -0.104 0.196 -0.531 0.596 -0.104 -0.019
## Best_MPS 0.010 0.012 0.858 0.391 0.010 0.034
## pre_stem_int 0.801 0.041 19.441 0.000 0.801 0.654
## participat_c_2 0.056 0.102 0.549 0.583 0.056 0.017
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 0.779 0.349 2.228 0.026 0.779 0.511
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 1.308 0.080 16.399 0.000 1.308 0.564## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.054 0.105 0.510 0.610 0.054 0.018
## urm -0.162 0.195 -0.830 0.407 -0.162 -0.032
## Best_MPS -0.050 0.013 -3.760 0.000 -0.050 -0.177
## pre_cst_t_vrll 0.557 0.042 13.100 0.000 0.557 0.486
## participat_c_2 -0.264 0.105 -2.520 0.012 -0.264 -0.090
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.653 0.324 8.183 0.000 2.653 1.888
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.406 0.087 16.234 0.000 1.406 0.712## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.044 0.102 0.436 0.663 0.044 0.016
## urm -0.216 0.190 -1.140 0.254 -0.216 -0.046
## Best_MPS -0.044 0.012 -3.533 0.000 -0.044 -0.166
## pre_cost__vrll 0.535 0.046 11.566 0.000 0.535 0.452
## participat_c_2 -0.073 0.102 -0.720 0.472 -0.073 -0.027
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.550 0.297 8.589 0.000 2.550 1.938
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.319 0.081 16.341 0.000 1.319 0.762## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.027 0.104 -0.258 0.797 -0.027 -0.009
## urm -0.360 0.192 -1.874 0.061 -0.360 -0.075
## Best_MPS -0.046 0.013 -3.573 0.000 -0.046 -0.171
## pr_cst_lv_vrll 0.504 0.044 11.466 0.000 0.504 0.441
## participat_c_2 -0.193 0.103 -1.870 0.062 -0.193 -0.069
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.656 0.312 8.509 0.000 2.656 1.986
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.363 0.083 16.336 0.000 1.363 0.762## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.105 0.114 0.923 0.356 0.105 0.033
## urm -0.266 0.209 -1.270 0.204 -0.266 -0.048
## Best_MPS -0.055 0.014 -3.854 0.000 -0.055 -0.180
## pre_cst_m_vrll 0.576 0.045 12.924 0.000 0.576 0.486
## participat_c_2 -0.336 0.112 -2.991 0.003 -0.336 -0.104
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.886 0.368 7.837 0.000 2.886 1.884
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.609 0.099 16.231 0.000 1.609 0.686## cfi tli rmsea srmr
## 1 1 0 0Path Analysis with eoc_3 (1 = invited and responded; 0 = not invited OR did not participate) Filtering out 2’s
## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.204 0.097 -2.106 0.035 -0.204 -0.063
## urm -0.067 0.182 -0.368 0.713 -0.067 -0.012
## Best_MPS 0.031 0.013 2.479 0.013 0.031 0.100
## pre_int_overll 0.782 0.036 21.666 0.000 0.782 0.695
## participat_c_3 -0.048 0.096 -0.505 0.614 -0.048 -0.015
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.228 0.278 0.819 0.413 0.228 0.149
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.101 0.069 15.850 0.000 1.101 0.469## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.483 0.124 -3.882 0.000 -0.483 -0.140
## urm -0.111 0.229 -0.487 0.626 -0.111 -0.019
## Best_MPS 0.092 0.015 6.237 0.000 0.092 0.277
## pre_confident 0.786 0.069 11.465 0.000 0.786 0.437
## participat_c_3 0.233 0.121 1.915 0.055 0.233 0.069
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.550 0.441 -1.247 0.213 -0.550 -0.335
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.766 0.114 15.471 0.000 1.766 0.655## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.095 0.101 -0.937 0.349 -0.095 -0.036
## urm -0.063 0.191 -0.332 0.740 -0.063 -0.014
## Best_MPS 0.039 0.013 3.113 0.002 0.039 0.155
## pre_val_overll 0.497 0.051 9.803 0.000 0.497 0.414
## participat_c_3 0.162 0.100 1.613 0.107 0.162 0.064
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.618 0.371 4.357 0.000 1.618 1.303
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.206 0.076 15.839 0.000 1.206 0.782## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.177 0.094 -1.884 0.060 -0.177 -0.072
## urm 0.244 0.177 1.377 0.168 0.244 0.057
## Best_MPS 0.041 0.012 3.385 0.001 0.041 0.173
## pre_exp_overll 0.517 0.048 10.738 0.000 0.517 0.439
## participat_c_3 0.062 0.093 0.668 0.504 0.062 0.026
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.582 0.359 4.410 0.000 1.582 1.353
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.033 0.066 15.677 0.000 1.033 0.755## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 13
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_stem_int ~
## female -0.303 0.107 -2.839 0.005 -0.303 -0.094
## urm -0.123 0.209 -0.588 0.557 -0.123 -0.022
## Best_MPS 0.002 0.013 0.121 0.904 0.002 0.005
## pre_stem_int 0.799 0.043 18.367 0.000 0.799 0.648
## participat_c_3 0.052 0.105 0.493 0.622 0.052 0.017
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 0.978 0.373 2.624 0.009 0.978 0.642
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 1.319 0.084 15.791 0.000 1.319 0.569## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.102 0.109 0.939 0.348 0.102 0.034
## urm -0.111 0.205 -0.542 0.588 -0.111 -0.022
## Best_MPS -0.054 0.014 -3.869 0.000 -0.054 -0.190
## pre_cst_t_vrll 0.568 0.044 13.003 0.000 0.568 0.494
## participat_c_3 -0.264 0.107 -2.475 0.013 -0.264 -0.092
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.678 0.342 7.830 0.000 2.678 1.904
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.380 0.089 15.556 0.000 1.380 0.697## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.087 0.105 0.827 0.408 0.087 0.032
## urm -0.314 0.199 -1.579 0.114 -0.314 -0.066
## Best_MPS -0.040 0.013 -3.075 0.002 -0.040 -0.152
## pre_cost__vrll 0.533 0.047 11.410 0.000 0.533 0.460
## participat_c_3 -0.036 0.103 -0.345 0.730 -0.036 -0.013
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.441 0.313 7.806 0.000 2.441 1.868
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.295 0.082 15.759 0.000 1.295 0.758## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female 0.030 0.108 0.281 0.779 0.030 0.011
## urm -0.389 0.204 -1.911 0.056 -0.389 -0.080
## Best_MPS -0.045 0.014 -3.312 0.001 -0.045 -0.168
## pr_cst_lv_vrll 0.496 0.045 10.937 0.000 0.496 0.440
## participat_c_3 -0.196 0.106 -1.847 0.065 -0.196 -0.072
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.656 0.331 8.035 0.000 2.656 1.991
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.366 0.087 15.733 0.000 1.366 0.768## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.178 0.119 1.495 0.135 0.178 0.055
## urm -0.215 0.222 -0.966 0.334 -0.215 -0.038
## Best_MPS -0.059 0.015 -3.863 0.000 -0.059 -0.189
## pre_cst_m_vrll 0.567 0.046 12.242 0.000 0.567 0.478
## participat_c_3 -0.335 0.116 -2.891 0.004 -0.335 -0.106
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.960 0.393 7.531 0.000 2.960 1.924
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.623 0.104 15.591 0.000 1.623 0.686## cfi tli rmsea srmr
## 1 1 0 0With signals responded to EOC only
## lavaan 0.6-7 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.198 0.094 -2.110 0.035 -0.198 -0.062
## urm -0.026 0.172 -0.149 0.882 -0.026 -0.005
## Best_MPS 0.032 0.012 2.766 0.006 0.032 0.106
## pre_int_overll 0.776 0.035 22.341 0.000 0.776 0.692
## sgnls_rspnd___ -0.009 0.018 -0.508 0.612 -0.009 -0.023
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.259 0.266 0.973 0.331 0.259 0.170
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.084 0.066 16.421 0.000 1.084 0.469## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 14
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.472 0.124 -3.789 0.000 -0.472 -0.136
## urm 0.070 0.223 0.315 0.753 0.070 0.012
## Best_MPS 0.089 0.014 6.233 0.000 0.089 0.268
## pre_confident 0.764 0.068 11.293 0.000 0.764 0.424
## sgnls_rspnd___ 0.015 0.023 0.640 0.522 0.015 0.035
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.346 0.433 -0.799 0.424 -0.346 -0.210
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.862 0.116 16.089 0.000 1.862 0.687## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.135 0.097 -1.391 0.164 -0.135 -0.053
## urm 0.018 0.179 0.101 0.920 0.018 0.004
## Best_MPS 0.035 0.012 2.998 0.003 0.035 0.143
## pre_val_overll 0.474 0.048 9.951 0.000 0.474 0.401
## sgnls_rspnd___ 0.046 0.019 2.449 0.014 0.046 0.146
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.703 0.342 4.975 0.000 1.703 1.393
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.161 0.073 15.815 0.000 1.161 0.776## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.199 0.091 -2.182 0.029 -0.199 -0.082
## urm 0.344 0.167 2.056 0.040 0.344 0.083
## Best_MPS 0.037 0.011 3.331 0.001 0.037 0.162
## pre_exp_overll 0.504 0.046 11.079 0.000 0.504 0.432
## sgnls_rspnd___ 0.024 0.018 1.342 0.180 0.024 0.079
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.661 0.336 4.943 0.000 1.661 1.435
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.013 0.063 16.142 0.000 1.013 0.756## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 21
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_stem_int ~
## female -0.321 0.103 -3.120 0.002 -0.321 -0.100
## urm -0.052 0.196 -0.267 0.790 -0.052 -0.010
## Best_MPS 0.011 0.012 0.884 0.376 0.011 0.035
## pre_stem_int 0.795 0.041 19.336 0.000 0.795 0.649
## sgnls_rspnd___ 0.041 0.018 2.245 0.025 0.041 0.104
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 0.656 0.352 1.865 0.062 0.656 0.430
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_stem_int 1.283 0.080 16.031 0.000 1.283 0.552## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.102 0.107 0.957 0.339 0.102 0.035
## urm -0.211 0.196 -1.074 0.283 -0.211 -0.042
## Best_MPS -0.048 0.013 -3.649 0.000 -0.048 -0.172
## pre_cst_t_vrll 0.554 0.043 13.001 0.000 0.554 0.484
## sgnls_rspnd___ -0.039 0.018 -2.118 0.034 -0.039 -0.106
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.696 0.330 8.161 0.000 2.696 1.921
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.401 0.087 16.009 0.000 1.401 0.711## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.095 0.103 0.927 0.354 0.095 0.034
## urm -0.268 0.189 -1.420 0.156 -0.268 -0.057
## Best_MPS -0.043 0.012 -3.530 0.000 -0.043 -0.163
## pre_cost__vrll 0.532 0.046 11.617 0.000 0.532 0.450
## sgnls_rspnd___ -0.051 0.018 -2.934 0.003 -0.051 -0.150
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.734 0.298 9.179 0.000 2.734 2.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.281 0.081 15.900 0.000 1.281 0.741## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female 0.017 0.105 0.160 0.873 0.017 0.006
## urm -0.404 0.193 -2.096 0.036 -0.404 -0.084
## Best_MPS -0.045 0.013 -3.493 0.000 -0.045 -0.167
## pr_cst_lv_vrll 0.500 0.044 11.346 0.000 0.500 0.437
## sgnls_rspnd___ -0.036 0.018 -2.035 0.042 -0.036 -0.105
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.728 0.319 8.558 0.000 2.728 2.039
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.353 0.084 16.136 0.000 1.353 0.757## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 7
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.172 0.115 1.488 0.137 0.172 0.053
## urm -0.339 0.210 -1.612 0.107 -0.339 -0.062
## Best_MPS -0.054 0.014 -3.743 0.000 -0.054 -0.175
## pre_cst_m_vrll 0.576 0.045 12.919 0.000 0.576 0.486
## sgnls_rspnd___ -0.060 0.020 -2.960 0.003 -0.060 -0.149
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.975 0.375 7.923 0.000 2.975 1.942
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.584 0.101 15.670 0.000 1.584 0.675## cfi tli rmsea srmr
## 1 1 0 0Sep 15 Analysis
Path Analysis with eoc_2 (1 = invited and responded; 0 = not invited OR did not participate) + average eoc
## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.147 0.094 -1.562 0.118 -0.147 -0.046
## urm -0.068 0.170 -0.403 0.687 -0.068 -0.013
## Best_MPS 0.030 0.012 2.576 0.010 0.030 0.097
## pre_int_overll 0.685 0.045 15.217 0.000 0.685 0.610
## eoc_int_avg 0.183 0.057 3.186 0.001 0.183 0.184
## participat_c_2 -0.060 0.091 -0.662 0.508 -0.060 -0.019
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.013 0.264 0.050 0.960 0.013 0.009
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.026 0.069 14.927 0.000 1.026 0.442## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 13
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.447 0.124 -3.612 0.000 -0.447 -0.129
## urm 0.042 0.226 0.185 0.853 0.042 0.007
## Best_MPS 0.089 0.015 6.053 0.000 0.089 0.269
## pre_confident 0.763 0.069 11.112 0.000 0.763 0.423
## eoc_comp_avg 0.018 0.098 0.184 0.854 0.018 0.014
## participat_c_2 0.198 0.121 1.635 0.102 0.198 0.057
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.451 0.532 -0.847 0.397 -0.451 -0.274
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.854 0.115 16.105 0.000 1.854 0.683## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female 0.015 0.097 0.155 0.876 0.015 0.006
## urm -0.051 0.174 -0.293 0.770 -0.051 -0.012
## Best_MPS 0.033 0.011 2.890 0.004 0.033 0.135
## pre_val_overll 0.342 0.055 6.258 0.000 0.342 0.289
## eoc_val_avg 0.293 0.058 5.046 0.000 0.293 0.336
## participat_c_2 0.170 0.093 1.825 0.068 0.170 0.066
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.176 0.354 3.321 0.001 1.176 0.961
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.047 0.079 13.292 0.000 1.047 0.699## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.170 0.091 -1.869 0.062 -0.170 -0.070
## urm 0.304 0.169 1.797 0.072 0.304 0.073
## Best_MPS 0.037 0.012 3.116 0.002 0.037 0.158
## pre_exp_overll 0.504 0.048 10.508 0.000 0.504 0.431
## eoc_comp_avg 0.031 0.071 0.438 0.661 0.031 0.034
## participat_c_2 0.037 0.090 0.415 0.678 0.037 0.015
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.619 0.383 4.230 0.000 1.619 1.399
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.019 0.063 16.293 0.000 1.019 0.760## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.029 0.101 0.288 0.773 0.029 0.010
## urm -0.180 0.186 -0.968 0.333 -0.180 -0.036
## Best_MPS -0.027 0.013 -2.122 0.034 -0.027 -0.097
## pre_cst_t_vrll 0.247 0.055 4.468 0.000 0.247 0.216
## eoc_cost_te_vg 0.480 0.058 8.228 0.000 0.480 0.485
## participat_c_2 -0.227 0.098 -2.322 0.020 -0.227 -0.077
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.780 0.329 5.414 0.000 1.780 1.269
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.111 0.085 13.016 0.000 1.111 0.565## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.036 0.098 -0.370 0.712 -0.036 -0.013
## urm -0.443 0.183 -2.419 0.016 -0.443 -0.094
## Best_MPS -0.035 0.012 -2.967 0.003 -0.035 -0.134
## pre_cost__vrll 0.227 0.058 3.929 0.000 0.227 0.191
## eoc_cost_oe_vg 0.506 0.062 8.216 0.000 0.506 0.490
## participat_c_2 -0.093 0.095 -0.982 0.326 -0.093 -0.034
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.945 0.296 6.578 0.000 1.945 1.473
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.038 0.081 12.764 0.000 1.038 0.595## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.066 0.099 -0.661 0.508 -0.066 -0.023
## urm -0.319 0.184 -1.736 0.083 -0.319 -0.066
## Best_MPS -0.029 0.012 -2.370 0.018 -0.029 -0.108
## pr_cst_lv_vrll 0.257 0.052 4.952 0.000 0.257 0.223
## eoc_cost_lv_vg 0.454 0.055 8.178 0.000 0.454 0.462
## participat_c_2 -0.185 0.096 -1.912 0.056 -0.185 -0.065
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.870 0.314 5.964 0.000 1.870 1.391
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.087 0.082 13.273 0.000 1.087 0.602## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.027 0.107 0.248 0.804 0.027 0.008
## urm -0.336 0.194 -1.732 0.083 -0.336 -0.061
## Best_MPS -0.019 0.014 -1.371 0.170 -0.019 -0.063
## pre_cst_m_vrll 0.238 0.052 4.599 0.000 0.238 0.200
## eoc_cost_em_vg 0.552 0.051 10.730 0.000 0.552 0.566
## participat_c_2 -0.305 0.102 -3.007 0.003 -0.305 -0.095
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.707 0.370 4.617 0.000 1.707 1.112
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.132 0.093 12.189 0.000 1.132 0.480## cfi tli rmsea srmr
## 1 1 0 0Path Analysis with eoc_3 (1 = invited and responded; 0 = not invited OR did not participate) Filtering out 2’s + average eoc
## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.139 0.098 -1.422 0.155 -0.139 -0.043
## urm -0.121 0.181 -0.669 0.503 -0.121 -0.022
## Best_MPS 0.029 0.012 2.360 0.018 0.029 0.093
## pre_int_overll 0.686 0.046 14.855 0.000 0.686 0.609
## eoc_int_avg 0.186 0.058 3.197 0.001 0.186 0.186
## participat_c_3 -0.038 0.094 -0.407 0.684 -0.038 -0.012
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll -0.013 0.283 -0.046 0.963 -0.013 -0.009
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.040 0.072 14.474 0.000 1.040 0.442## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.480 0.125 -3.830 0.000 -0.480 -0.139
## urm -0.117 0.233 -0.505 0.613 -0.117 -0.020
## Best_MPS 0.092 0.015 6.024 0.000 0.092 0.275
## pre_confident 0.786 0.070 11.281 0.000 0.786 0.437
## eoc_comp_avg 0.018 0.093 0.191 0.848 0.018 0.014
## participat_c_3 0.234 0.122 1.920 0.055 0.234 0.069
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.621 0.534 -1.162 0.245 -0.621 -0.378
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.765 0.114 15.470 0.000 1.765 0.655## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female 0.010 0.101 0.095 0.925 0.010 0.004
## urm -0.074 0.186 -0.398 0.690 -0.074 -0.016
## Best_MPS 0.036 0.012 3.002 0.003 0.036 0.145
## pre_val_overll 0.357 0.058 6.207 0.000 0.357 0.297
## eoc_val_avg 0.298 0.059 5.054 0.000 0.298 0.336
## participat_c_3 0.184 0.097 1.901 0.057 0.184 0.072
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 0.991 0.379 2.614 0.009 0.991 0.797
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.061 0.082 12.966 0.000 1.061 0.687## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.172 0.095 -1.806 0.071 -0.172 -0.070
## urm 0.232 0.180 1.293 0.196 0.232 0.055
## Best_MPS 0.040 0.013 3.194 0.001 0.040 0.169
## pre_exp_overll 0.512 0.050 10.166 0.000 0.512 0.435
## eoc_comp_avg 0.032 0.072 0.439 0.660 0.032 0.035
## participat_c_3 0.063 0.093 0.675 0.500 0.063 0.026
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.481 0.405 3.657 0.000 1.481 1.266
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.031 0.066 15.644 0.000 1.031 0.753## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.068 0.104 0.658 0.510 0.068 0.023
## urm -0.139 0.194 -0.717 0.474 -0.139 -0.027
## Best_MPS -0.031 0.014 -2.264 0.024 -0.031 -0.108
## pre_cst_t_vrll 0.261 0.057 4.618 0.000 0.261 0.228
## eoc_cost_te_vg 0.472 0.059 8.047 0.000 0.472 0.478
## participat_c_3 -0.226 0.100 -2.249 0.024 -0.226 -0.078
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.805 0.345 5.234 0.000 1.805 1.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.097 0.085 12.871 0.000 1.097 0.557## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.001 0.101 -0.014 0.989 -0.001 -0.000
## urm -0.520 0.190 -2.731 0.006 -0.520 -0.109
## Best_MPS -0.033 0.013 -2.594 0.009 -0.033 -0.123
## pre_cost__vrll 0.231 0.058 3.967 0.000 0.231 0.198
## eoc_cost_oe_vg 0.497 0.062 8.079 0.000 0.497 0.486
## participat_c_3 -0.057 0.097 -0.591 0.555 -0.057 -0.021
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.860 0.309 6.015 0.000 1.860 1.417
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.026 0.081 12.699 0.000 1.026 0.595## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.021 0.103 -0.199 0.842 -0.021 -0.007
## urm -0.350 0.194 -1.807 0.071 -0.350 -0.072
## Best_MPS -0.029 0.013 -2.205 0.027 -0.029 -0.106
## pr_cst_lv_vrll 0.249 0.053 4.708 0.000 0.249 0.220
## eoc_cost_lv_vg 0.455 0.056 8.124 0.000 0.455 0.465
## participat_c_3 -0.183 0.100 -1.833 0.067 -0.183 -0.066
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.864 0.329 5.669 0.000 1.864 1.390
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.089 0.083 13.056 0.000 1.089 0.606## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.077 0.111 0.693 0.488 0.077 0.024
## urm -0.300 0.204 -1.469 0.142 -0.300 -0.053
## Best_MPS -0.021 0.015 -1.419 0.156 -0.021 -0.067
## pre_cst_m_vrll 0.230 0.053 4.336 0.000 0.230 0.193
## eoc_cost_em_vg 0.556 0.052 10.628 0.000 0.556 0.568
## participat_c_3 -0.294 0.106 -2.789 0.005 -0.294 -0.093
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.730 0.392 4.412 0.000 1.730 1.122
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.140 0.095 12.011 0.000 1.140 0.479## cfi tli rmsea srmr
## 1 1 0 0With signals responded to EOC only + average eoc
## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.136 0.095 -1.434 0.152 -0.136 -0.043
## urm -0.078 0.171 -0.456 0.648 -0.078 -0.014
## Best_MPS 0.030 0.012 2.632 0.008 0.030 0.099
## pre_int_overll 0.684 0.045 15.171 0.000 0.684 0.610
## eoc_int_avg 0.179 0.058 3.111 0.002 0.179 0.180
## sgnls_rspnd___ -0.008 0.017 -0.453 0.650 -0.008 -0.020
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.044 0.270 0.161 0.872 0.044 0.029
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.028 0.069 14.978 0.000 1.028 0.444## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.468 0.126 -3.720 0.000 -0.468 -0.135
## urm 0.059 0.228 0.258 0.796 0.059 0.010
## Best_MPS 0.088 0.015 5.947 0.000 0.088 0.265
## pre_confident 0.762 0.069 11.058 0.000 0.762 0.422
## eoc_comp_avg 0.030 0.097 0.311 0.756 0.030 0.023
## sgnls_rspnd___ 0.016 0.023 0.690 0.490 0.016 0.037
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.457 0.534 -0.856 0.392 -0.457 -0.278
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.860 0.116 16.058 0.000 1.860 0.686## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.031 0.097 -0.316 0.752 -0.031 -0.012
## urm 0.004 0.174 0.023 0.982 0.004 0.001
## Best_MPS 0.033 0.011 2.931 0.003 0.033 0.135
## pre_val_overll 0.339 0.054 6.289 0.000 0.339 0.286
## eoc_val_avg 0.295 0.057 5.155 0.000 0.295 0.337
## sgnls_rspnd___ 0.045 0.018 2.546 0.011 0.045 0.142
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.085 0.352 3.082 0.002 1.085 0.886
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.018 0.078 12.972 0.000 1.018 0.679## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.194 0.092 -2.098 0.036 -0.194 -0.079
## urm 0.332 0.170 1.950 0.051 0.332 0.080
## Best_MPS 0.036 0.012 3.114 0.002 0.036 0.157
## pre_exp_overll 0.498 0.048 10.405 0.000 0.498 0.426
## eoc_comp_avg 0.035 0.071 0.492 0.622 0.035 0.038
## sgnls_rspnd___ 0.025 0.018 1.396 0.163 0.025 0.082
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.552 0.382 4.064 0.000 1.552 1.340
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.010 0.063 16.064 0.000 1.010 0.753## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.068 0.102 0.671 0.502 0.068 0.023
## urm -0.218 0.187 -1.170 0.242 -0.218 -0.043
## Best_MPS -0.026 0.013 -2.038 0.042 -0.026 -0.093
## pre_cst_t_vrll 0.242 0.055 4.391 0.000 0.242 0.212
## eoc_cost_te_vg 0.483 0.058 8.353 0.000 0.483 0.489
## sgnls_rspnd___ -0.034 0.017 -2.014 0.044 -0.034 -0.092
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.792 0.335 5.353 0.000 1.792 1.277
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.100 0.085 12.928 0.000 1.100 0.558## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.007 0.099 0.076 0.940 0.007 0.003
## urm -0.484 0.182 -2.662 0.008 -0.484 -0.102
## Best_MPS -0.035 0.012 -2.971 0.003 -0.035 -0.132
## pre_cost__vrll 0.229 0.057 4.007 0.000 0.229 0.193
## eoc_cost_oe_vg 0.498 0.061 8.157 0.000 0.498 0.482
## sgnls_rspnd___ -0.045 0.016 -2.777 0.005 -0.045 -0.129
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.074 0.298 6.959 0.000 2.074 1.570
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.009 0.080 12.650 0.000 1.009 0.578## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.016 0.100 -0.163 0.871 -0.016 -0.006
## urm -0.366 0.184 -1.990 0.047 -0.366 -0.076
## Best_MPS -0.028 0.012 -2.329 0.020 -0.028 -0.106
## pr_cst_lv_vrll 0.248 0.052 4.803 0.000 0.248 0.216
## eoc_cost_lv_vg 0.460 0.055 8.367 0.000 0.460 0.468
## sgnls_rspnd___ -0.041 0.016 -2.487 0.013 -0.041 -0.116
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.961 0.317 6.186 0.000 1.961 1.459
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.069 0.081 13.146 0.000 1.069 0.592## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.073 0.108 0.673 0.501 0.073 0.022
## urm -0.384 0.195 -1.976 0.048 -0.384 -0.070
## Best_MPS -0.018 0.014 -1.297 0.195 -0.018 -0.059
## pre_cst_m_vrll 0.241 0.052 4.658 0.000 0.241 0.203
## eoc_cost_em_vg 0.548 0.052 10.632 0.000 0.548 0.561
## sgnls_rspnd___ -0.044 0.018 -2.477 0.013 -0.044 -0.109
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.706 0.380 4.490 0.000 1.706 1.110
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.116 0.092 12.141 0.000 1.116 0.472## cfi tli rmsea srmr
## 1 1 0 0Sep 15 Analysis
Path Analysis with eoc_2 (1 = invited and responded; 0 = not invited OR did not participate) + interaction
## lavaan 0.6-7 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.207 0.092 -2.239 0.025 -0.207 -0.065
## urm -0.008 0.170 -0.050 0.960 -0.008 -0.002
## Best_MPS 0.032 0.012 2.704 0.007 0.032 0.104
## pre_int_overll 0.761 0.042 18.334 0.000 0.761 0.679
## participat_c_2 -0.340 0.377 -0.903 0.367 -0.340 -0.107
## pre_int_eoc_2 0.053 0.072 0.744 0.457 0.053 0.089
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.331 0.284 1.166 0.243 0.331 0.218
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.083 0.066 16.451 0.000 1.083 0.469## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.460 0.122 -3.756 0.000 -0.460 -0.132
## urm 0.049 0.221 0.221 0.825 0.049 0.008
## Best_MPS 0.091 0.014 6.372 0.000 0.091 0.273
## pre_confident 0.838 0.085 9.864 0.000 0.838 0.463
## participat_c_2 1.169 0.694 1.685 0.092 1.169 0.337
## pre_con_eoc_2 -0.195 0.137 -1.425 0.154 -0.195 -0.288
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.762 0.503 -1.514 0.130 -0.762 -0.461
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.847 0.115 16.097 0.000 1.847 0.677## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.100 0.096 -1.037 0.300 -0.100 -0.039
## urm -0.050 0.178 -0.278 0.781 -0.050 -0.011
## Best_MPS 0.035 0.012 2.999 0.003 0.035 0.143
## pre_val_overll 0.547 0.059 9.245 0.000 0.547 0.462
## participat_c_2 1.279 0.579 2.208 0.027 1.279 0.497
## pre_val_eoc_2 -0.196 0.099 -1.980 0.048 -0.196 -0.452
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.432 0.392 3.652 0.000 1.432 1.168
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.179 0.072 16.478 0.000 1.179 0.784## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.176 0.090 -1.969 0.049 -0.176 -0.072
## urm 0.313 0.166 1.884 0.060 0.313 0.075
## Best_MPS 0.038 0.011 3.353 0.001 0.038 0.163
## pre_exp_overll 0.557 0.058 9.546 0.000 0.557 0.475
## participat_c_2 0.739 0.544 1.360 0.174 0.739 0.304
## pre_exp_eoc_2 -0.122 0.093 -1.311 0.190 -0.122 -0.297
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.447 0.393 3.681 0.000 1.447 1.246
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.018 0.062 16.332 0.000 1.018 0.755## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.054 0.106 0.516 0.606 0.054 0.018
## urm -0.159 0.195 -0.815 0.415 -0.159 -0.032
## Best_MPS -0.050 0.013 -3.746 0.000 -0.050 -0.176
## pre_cst_t_vrll 0.565 0.055 10.340 0.000 0.565 0.493
## participat_c_2 -0.195 0.306 -0.635 0.525 -0.195 -0.066
## pre_cost_t_c_2 -0.021 0.087 -0.240 0.811 -0.021 -0.026
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.621 0.346 7.582 0.000 2.621 1.865
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.407 0.087 16.236 0.000 1.407 0.712## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.043 0.102 0.419 0.675 0.043 0.015
## urm -0.214 0.190 -1.124 0.261 -0.214 -0.045
## Best_MPS -0.044 0.012 -3.543 0.000 -0.044 -0.166
## pre_cost__vrll 0.521 0.059 8.808 0.000 0.521 0.441
## participat_c_2 -0.168 0.279 -0.602 0.547 -0.168 -0.061
## pre_cost_o_c_2 0.035 0.095 0.367 0.714 0.035 0.038
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.591 0.314 8.251 0.000 2.591 1.970
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.318 0.081 16.340 0.000 1.318 0.762## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.027 0.104 -0.259 0.795 -0.027 -0.010
## urm -0.361 0.192 -1.877 0.060 -0.361 -0.075
## Best_MPS -0.046 0.013 -3.572 0.000 -0.046 -0.171
## pr_cst_lv_vrll 0.502 0.057 8.883 0.000 0.502 0.439
## participat_c_2 -0.208 0.300 -0.692 0.489 -0.208 -0.074
## pre_cst_lv_c_2 0.005 0.090 0.053 0.958 0.005 0.006
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.664 0.338 7.887 0.000 2.664 1.992
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.363 0.083 16.336 0.000 1.363 0.762## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.107 0.114 0.940 0.347 0.107 0.033
## urm -0.261 0.209 -1.249 0.212 -0.261 -0.048
## Best_MPS -0.055 0.014 -3.856 0.000 -0.055 -0.180
## pre_cst_m_vrll 0.588 0.056 10.545 0.000 0.588 0.496
## participat_c_2 -0.220 0.342 -0.642 0.521 -0.220 -0.068
## pre_cost_m_c_2 -0.032 0.089 -0.356 0.722 -0.032 -0.039
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.841 0.388 7.328 0.000 2.841 1.854
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.609 0.099 16.231 0.000 1.609 0.685## cfi tli rmsea srmr
## 1 1 0 0Path Analysis with eoc_3 (1 = invited and responded; 0 = not invited OR did not participate) Filtering out 2’s + Interaction
## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.200 0.097 -2.073 0.038 -0.200 -0.062
## urm -0.062 0.182 -0.342 0.733 -0.062 -0.011
## Best_MPS 0.031 0.013 2.441 0.015 0.031 0.098
## pre_int_overll 0.765 0.043 17.588 0.000 0.765 0.680
## participat_c_3 -0.302 0.386 -0.784 0.433 -0.302 -0.096
## pre_int_eoc_3 0.050 0.074 0.679 0.497 0.050 0.085
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.316 0.309 1.024 0.306 0.316 0.206
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.100 0.069 15.854 0.000 1.100 0.469## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.496 0.124 -3.996 0.000 -0.496 -0.143
## urm -0.106 0.228 -0.466 0.642 -0.106 -0.018
## Best_MPS 0.094 0.015 6.334 0.000 0.094 0.279
## pre_confident 0.890 0.088 10.083 0.000 0.890 0.493
## participat_c_3 1.496 0.694 2.157 0.031 1.496 0.443
## pre_con_eoc_3 -0.253 0.137 -1.851 0.064 -0.253 -0.384
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -1.090 0.528 -2.065 0.039 -1.090 -0.661
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.752 0.113 15.446 0.000 1.752 0.645## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.109 0.101 -1.078 0.281 -0.109 -0.041
## urm -0.075 0.190 -0.394 0.694 -0.075 -0.016
## Best_MPS 0.040 0.013 3.216 0.001 0.040 0.159
## pre_val_overll 0.593 0.065 9.185 0.000 0.593 0.491
## participat_c_3 1.586 0.602 2.633 0.008 1.586 0.620
## pre_val_eoc_3 -0.247 0.103 -2.398 0.016 -0.247 -0.575
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.058 0.438 2.416 0.016 1.058 0.848
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.192 0.075 15.822 0.000 1.192 0.766## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.176 0.094 -1.873 0.061 -0.176 -0.071
## urm 0.241 0.176 1.366 0.172 0.241 0.057
## Best_MPS 0.041 0.012 3.427 0.001 0.041 0.174
## pre_exp_overll 0.580 0.063 9.145 0.000 0.580 0.491
## participat_c_3 0.903 0.563 1.604 0.109 0.903 0.376
## pre_exp_eoc_3 -0.147 0.097 -1.515 0.130 -0.147 -0.361
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.224 0.429 2.851 0.004 1.224 1.044
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.028 0.066 15.673 0.000 1.028 0.747## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.104 0.109 0.951 0.341 0.104 0.035
## urm -0.106 0.205 -0.515 0.607 -0.106 -0.021
## Best_MPS -0.054 0.014 -3.852 0.000 -0.054 -0.189
## pre_cst_t_vrll 0.588 0.058 10.199 0.000 0.588 0.511
## participat_c_3 -0.111 0.312 -0.358 0.721 -0.111 -0.039
## pre_cost_t_c_3 -0.046 0.088 -0.519 0.604 -0.046 -0.058
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.607 0.365 7.141 0.000 2.607 1.853
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.379 0.089 15.561 0.000 1.379 0.697## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.085 0.105 0.810 0.418 0.085 0.031
## urm -0.311 0.199 -1.562 0.118 -0.311 -0.065
## Best_MPS -0.040 0.013 -3.084 0.002 -0.040 -0.153
## pre_cost__vrll 0.520 0.061 8.563 0.000 0.520 0.448
## participat_c_3 -0.130 0.282 -0.460 0.645 -0.130 -0.048
## pre_cost_o_c_3 0.035 0.096 0.361 0.718 0.035 0.039
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.483 0.329 7.535 0.000 2.483 1.900
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.294 0.082 15.758 0.000 1.294 0.758## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female 0.029 0.108 0.269 0.788 0.029 0.010
## urm -0.389 0.204 -1.908 0.056 -0.389 -0.080
## Best_MPS -0.045 0.014 -3.314 0.001 -0.045 -0.168
## pr_cst_lv_vrll 0.490 0.060 8.222 0.000 0.490 0.434
## participat_c_3 -0.242 0.307 -0.788 0.431 -0.242 -0.089
## pre_cst_lv_c_3 0.015 0.092 0.161 0.872 0.015 0.019
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.677 0.356 7.513 0.000 2.677 2.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.366 0.087 15.732 0.000 1.366 0.768## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.180 0.119 1.511 0.131 0.180 0.056
## urm -0.210 0.222 -0.943 0.346 -0.210 -0.037
## Best_MPS -0.059 0.015 -3.863 0.000 -0.059 -0.189
## pre_cst_m_vrll 0.581 0.059 9.818 0.000 0.581 0.489
## participat_c_3 -0.213 0.352 -0.604 0.546 -0.213 -0.067
## pre_cost_m_c_3 -0.033 0.092 -0.364 0.715 -0.033 -0.042
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.909 0.414 7.024 0.000 2.909 1.890
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.622 0.104 15.591 0.000 1.622 0.685## cfi tli rmsea srmr
## 1 1 0 0With signals responded to EOC only + Interaction
## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.194 0.094 -2.062 0.039 -0.194 -0.061
## urm -0.024 0.171 -0.140 0.888 -0.024 -0.004
## Best_MPS 0.034 0.012 2.896 0.004 0.034 0.113
## pre_int_overll 0.734 0.071 10.308 0.000 0.734 0.656
## sgnls_rspnd___ -0.058 0.072 -0.806 0.420 -0.058 -0.146
## pre_int_signls 0.010 0.014 0.699 0.484 0.010 0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.434 0.379 1.145 0.252 0.434 0.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.080 0.066 16.346 0.000 1.080 0.470## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 14
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.468 0.125 -3.754 0.000 -0.468 -0.135
## urm 0.067 0.223 0.303 0.762 0.067 0.011
## Best_MPS 0.088 0.014 6.188 0.000 0.088 0.267
## pre_confident 0.721 0.135 5.351 0.000 0.721 0.401
## sgnls_rspnd___ -0.029 0.121 -0.236 0.814 -0.029 -0.067
## pre_con_signls 0.009 0.024 0.361 0.718 0.009 0.104
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.125 0.725 -0.172 0.863 -0.125 -0.076
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.862 0.116 16.078 0.000 1.862 0.689## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.138 0.097 -1.419 0.156 -0.138 -0.053
## urm 0.010 0.179 0.058 0.954 0.010 0.002
## Best_MPS 0.034 0.012 2.852 0.004 0.034 0.138
## pre_val_overll 0.544 0.094 5.792 0.000 0.544 0.459
## sgnls_rspnd___ 0.127 0.099 1.281 0.200 0.127 0.400
## pre_val_signls -0.014 0.017 -0.835 0.404 -0.014 -0.271
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.337 0.529 2.528 0.011 1.337 1.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.157 0.073 15.791 0.000 1.157 0.769## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.204 0.091 -2.234 0.026 -0.204 -0.084
## urm 0.341 0.167 2.041 0.041 0.341 0.082
## Best_MPS 0.038 0.011 3.355 0.001 0.038 0.162
## pre_exp_overll 0.562 0.089 6.320 0.000 0.562 0.480
## sgnls_rspnd___ 0.094 0.096 0.974 0.330 0.094 0.310
## pre_exp_signls -0.012 0.016 -0.739 0.460 -0.012 -0.242
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.334 0.531 2.511 0.012 1.334 1.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.009 0.063 16.096 0.000 1.009 0.750## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.089 0.107 0.831 0.406 0.089 0.030
## urm -0.217 0.196 -1.107 0.268 -0.217 -0.043
## Best_MPS -0.050 0.013 -3.742 0.000 -0.050 -0.177
## pre_cst_t_vrll 0.441 0.082 5.400 0.000 0.441 0.388
## sgnls_rspnd___ -0.123 0.055 -2.247 0.025 -0.123 -0.339
## pr_cst_t_sgnls 0.026 0.016 1.620 0.105 0.026 0.259
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 3.097 0.413 7.498 0.000 3.097 2.217
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.386 0.088 15.822 0.000 1.386 0.711## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.083 0.103 0.806 0.420 0.083 0.030
## urm -0.269 0.189 -1.426 0.154 -0.269 -0.057
## Best_MPS -0.043 0.012 -3.559 0.000 -0.043 -0.165
## pre_cost__vrll 0.437 0.097 4.486 0.000 0.437 0.370
## sgnls_rspnd___ -0.104 0.051 -2.060 0.039 -0.104 -0.306
## pre_cst__sgnls 0.019 0.018 1.110 0.267 0.019 0.180
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 3.008 0.387 7.779 0.000 3.008 2.296
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.275 0.081 15.821 0.000 1.275 0.742## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female 0.010 0.105 0.091 0.927 0.010 0.003
## urm -0.412 0.193 -2.135 0.033 -0.412 -0.086
## Best_MPS -0.045 0.013 -3.537 0.000 -0.045 -0.170
## pr_cst_lv_vrll 0.447 0.085 5.264 0.000 0.447 0.392
## sgnls_rspnd___ -0.075 0.055 -1.347 0.178 -0.075 -0.215
## pr_cst_lv_sgnl 0.012 0.017 0.724 0.469 0.012 0.121
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.912 0.409 7.114 0.000 2.912 2.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.350 0.084 16.091 0.000 1.350 0.758## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.176 0.115 1.525 0.127 0.176 0.054
## urm -0.333 0.210 -1.586 0.113 -0.333 -0.061
## Best_MPS -0.053 0.014 -3.729 0.000 -0.053 -0.174
## pre_cst_m_vrll 0.631 0.087 7.241 0.000 0.631 0.530
## sgnls_rspnd___ -0.016 0.063 -0.254 0.800 -0.016 -0.040
## pr_cst_m_sgnls -0.012 0.016 -0.727 0.467 -0.012 -0.123
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 2.770 0.471 5.875 0.000 2.770 1.803
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.580 0.101 15.631 0.000 1.580 0.670## cfi tli rmsea srmr
## 1 1 0 0Sep 15 Analysis
Path Analysis with eoc_2 (1 = invited and responded; 0 = not invited OR did not participate) + interaction + eoc_avg
## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.142 0.094 -1.514 0.130 -0.142 -0.044
## urm -0.062 0.170 -0.368 0.713 -0.062 -0.011
## Best_MPS 0.030 0.012 2.586 0.010 0.030 0.097
## eoc_int_avg 0.185 0.057 3.211 0.001 0.185 0.213
## pre_int_overll 0.590 0.068 8.732 0.000 0.590 0.525
## participat_c_2 -0.717 0.387 -1.850 0.064 -0.717 -0.224
## pre_int_eoc_2 0.132 0.075 1.771 0.077 0.132 0.221
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.468 0.285 1.641 0.101 0.468 0.307
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.023 0.069 14.913 0.000 1.023 0.442## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female 0.004 0.097 0.037 0.970 0.004 0.001
## urm -0.062 0.174 -0.355 0.723 -0.062 -0.014
## Best_MPS 0.033 0.011 2.921 0.003 0.033 0.135
## eoc_val_avg 0.293 0.058 5.052 0.000 0.293 0.323
## pre_val_overll 0.490 0.060 8.132 0.000 0.490 0.413
## participat_c_2 1.678 0.562 2.984 0.003 1.678 0.651
## pre_val_eoc_2 -0.264 0.096 -2.749 0.006 -0.264 -0.609
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 0.347 0.446 0.779 0.436 0.347 0.283
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.038 0.078 13.250 0.000 1.038 0.688## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.170 0.091 -1.876 0.061 -0.170 -0.070
## urm 0.301 0.169 1.783 0.075 0.301 0.072
## Best_MPS 0.037 0.012 3.131 0.002 0.037 0.158
## eoc_comp_avg 0.030 0.071 0.426 0.670 0.030 0.033
## pre_exp_overll 0.560 0.059 9.504 0.000 0.560 0.478
## participat_c_2 0.774 0.556 1.392 0.164 0.774 0.318
## pre_exp_eoc_2 -0.128 0.096 -1.344 0.179 -0.128 -0.312
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.306 0.503 2.595 0.009 1.306 1.125
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.016 0.062 16.294 0.000 1.016 0.754## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.030 0.101 0.298 0.766 0.030 0.010
## urm -0.176 0.186 -0.948 0.343 -0.176 -0.035
## Best_MPS -0.027 0.013 -2.089 0.037 -0.027 -0.096
## eoc_cost_te_vg 0.481 0.058 8.258 0.000 0.481 0.448
## pre_cst_t_vrll 0.417 0.058 7.247 0.000 0.417 0.364
## participat_c_2 0.396 0.295 1.344 0.179 0.396 0.134
## pre_cost_t_c_2 -0.189 0.084 -2.266 0.023 -0.189 -0.234
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.211 0.380 3.185 0.001 1.211 0.862
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.110 0.085 13.002 0.000 1.110 0.563## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.039 0.098 -0.395 0.693 -0.039 -0.014
## urm -0.440 0.183 -2.405 0.016 -0.440 -0.093
## Best_MPS -0.035 0.012 -2.955 0.003 -0.035 -0.134
## eoc_cost_oe_vg 0.507 0.061 8.249 0.000 0.507 0.448
## pre_cost__vrll 0.476 0.060 7.967 0.000 0.476 0.401
## participat_c_2 0.588 0.277 2.121 0.034 0.588 0.212
## pre_cost_o_c_2 -0.237 0.095 -2.501 0.012 -0.237 -0.259
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.227 0.349 3.513 0.000 1.227 0.930
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.035 0.081 12.719 0.000 1.035 0.594## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.069 0.100 -0.696 0.486 -0.069 -0.024
## urm -0.317 0.183 -1.731 0.083 -0.317 -0.066
## Best_MPS -0.028 0.012 -2.292 0.022 -0.028 -0.105
## eoc_cost_lv_vg 0.461 0.055 8.318 0.000 0.461 0.452
## pr_cst_lv_vrll 0.681 0.061 11.203 0.000 0.681 0.593
## participat_c_2 1.167 0.326 3.575 0.000 1.167 0.414
## pre_cst_lv_c_2 -0.429 0.099 -4.321 0.000 -0.429 -0.532
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 0.498 0.424 1.175 0.240 0.498 0.370
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.076 0.082 13.134 0.000 1.076 0.595## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.028 0.107 0.259 0.796 0.028 0.009
## urm -0.333 0.194 -1.717 0.086 -0.333 -0.061
## Best_MPS -0.019 0.014 -1.353 0.176 -0.019 -0.062
## eoc_cost_em_vg 0.552 0.051 10.724 0.000 0.552 0.551
## pre_cst_m_vrll 0.297 0.062 4.795 0.000 0.297 0.250
## participat_c_2 -0.052 0.309 -0.167 0.867 -0.052 -0.016
## pre_cost_m_c_2 -0.071 0.081 -0.876 0.381 -0.071 -0.087
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.492 0.399 3.738 0.000 1.492 0.971
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.132 0.093 12.188 0.000 1.132 0.480## cfi tli rmsea srmr
## 1 1 0 0Path Analysis with eoc_3 (1 = invited and responded; 0 = not invited OR did not participate) Filtering out 2’s + Interaction
## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.135 0.098 -1.378 0.168 -0.135 -0.042
## urm -0.115 0.180 -0.639 0.523 -0.115 -0.021
## Best_MPS 0.029 0.012 2.352 0.019 0.029 0.093
## eoc_int_avg 0.188 0.058 3.222 0.001 0.188 0.218
## pre_int_overll 0.582 0.071 8.154 0.000 0.582 0.517
## participat_c_3 -0.719 0.400 -1.797 0.072 -0.719 -0.229
## pre_int_eoc_3 0.138 0.077 1.782 0.075 0.138 0.234
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.487 0.311 1.564 0.118 0.487 0.317
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.038 0.072 14.464 0.000 1.038 0.441## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.493 0.125 -3.937 0.000 -0.493 -0.142
## urm -0.113 0.232 -0.488 0.626 -0.113 -0.019
## Best_MPS 0.093 0.015 6.110 0.000 0.093 0.277
## eoc_comp_avg 0.020 0.093 0.214 0.831 0.020 0.015
## pre_confident 0.891 0.088 10.083 0.000 0.891 0.493
## participat_c_3 1.488 0.697 2.135 0.033 1.488 0.441
## pre_con_eoc_3 -0.252 0.138 -1.827 0.068 -0.252 -0.381
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -1.171 0.650 -1.802 0.072 -1.171 -0.710
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.751 0.113 15.443 0.000 1.751 0.645## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.004 0.101 -0.036 0.971 -0.004 -0.001
## urm -0.086 0.185 -0.466 0.641 -0.086 -0.019
## Best_MPS 0.038 0.012 3.110 0.002 0.038 0.149
## eoc_val_avg 0.297 0.059 5.054 0.000 0.297 0.322
## pre_val_overll 0.535 0.066 8.129 0.000 0.535 0.442
## participat_c_3 1.976 0.585 3.377 0.001 1.976 0.772
## pre_val_eoc_3 -0.314 0.100 -3.135 0.002 -0.314 -0.730
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll -0.017 0.484 -0.035 0.972 -0.017 -0.014
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.048 0.081 12.917 0.000 1.048 0.671## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.170 0.095 -1.786 0.074 -0.170 -0.069
## urm 0.230 0.179 1.282 0.200 0.230 0.054
## Best_MPS 0.040 0.013 3.221 0.001 0.040 0.170
## eoc_comp_avg 0.031 0.072 0.424 0.672 0.031 0.033
## pre_exp_overll 0.584 0.064 9.102 0.000 0.584 0.494
## participat_c_3 0.940 0.577 1.629 0.103 0.940 0.391
## pre_exp_eoc_3 -0.153 0.099 -1.541 0.123 -0.153 -0.377
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.080 0.537 2.011 0.044 1.080 0.921
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.026 0.066 15.643 0.000 1.026 0.745## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.070 0.104 0.675 0.499 0.070 0.024
## urm -0.133 0.194 -0.686 0.493 -0.133 -0.026
## Best_MPS -0.030 0.014 -2.232 0.026 -0.030 -0.106
## eoc_cost_te_vg 0.473 0.058 8.083 0.000 0.473 0.444
## pre_cst_t_vrll 0.431 0.061 7.085 0.000 0.431 0.376
## participat_c_3 0.437 0.300 1.454 0.146 0.437 0.152
## pre_cost_t_c_3 -0.200 0.085 -2.352 0.019 -0.200 -0.255
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.230 0.396 3.104 0.002 1.230 0.876
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.096 0.085 12.858 0.000 1.096 0.555## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.004 0.101 -0.039 0.969 -0.004 -0.001
## urm -0.517 0.190 -2.715 0.007 -0.517 -0.108
## Best_MPS -0.033 0.013 -2.584 0.010 -0.033 -0.122
## eoc_cost_oe_vg 0.499 0.061 8.110 0.000 0.499 0.446
## pre_cost__vrll 0.477 0.061 7.782 0.000 0.477 0.410
## participat_c_3 0.621 0.281 2.211 0.027 0.621 0.231
## pre_cost_o_c_3 -0.235 0.096 -2.452 0.014 -0.235 -0.266
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.146 0.361 3.172 0.002 1.146 0.873
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.023 0.081 12.655 0.000 1.023 0.594## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.025 0.103 -0.247 0.805 -0.025 -0.009
## urm -0.347 0.193 -1.799 0.072 -0.347 -0.071
## Best_MPS -0.028 0.013 -2.133 0.033 -0.028 -0.103
## eoc_cost_lv_vg 0.462 0.056 8.256 0.000 0.462 0.459
## pr_cst_lv_vrll 0.683 0.064 10.602 0.000 0.683 0.602
## participat_c_3 1.181 0.336 3.513 0.000 1.181 0.430
## pre_cst_lv_c_3 -0.433 0.102 -4.228 0.000 -0.433 -0.553
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 0.464 0.442 1.051 0.293 0.464 0.346
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.079 0.083 12.927 0.000 1.079 0.600## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.078 0.111 0.702 0.482 0.078 0.024
## urm -0.296 0.204 -1.451 0.147 -0.296 -0.053
## Best_MPS -0.021 0.015 -1.393 0.163 -0.021 -0.066
## eoc_cost_em_vg 0.556 0.052 10.622 0.000 0.556 0.556
## pre_cst_m_vrll 0.286 0.065 4.375 0.000 0.286 0.240
## participat_c_3 -0.052 0.320 -0.161 0.872 -0.052 -0.016
## pre_cost_m_c_3 -0.067 0.083 -0.810 0.418 -0.067 -0.085
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.522 0.423 3.597 0.000 1.522 0.986
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.141 0.095 12.010 0.000 1.141 0.479## cfi tli rmsea srmr
## 1 1 0 0With signals responded to EOC only + Interaction
## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.131 0.095 -1.372 0.170 -0.131 -0.041
## urm -0.080 0.171 -0.469 0.639 -0.080 -0.015
## Best_MPS 0.032 0.012 2.763 0.006 0.032 0.106
## eoc_int_avg 0.180 0.058 3.138 0.002 0.180 0.183
## pre_int_overll 0.627 0.077 8.154 0.000 0.627 0.561
## sgnls_rspnd___ -0.069 0.070 -0.995 0.320 -0.069 -0.175
## pre_int_signls 0.012 0.013 0.908 0.364 0.012 0.166
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.285 0.376 0.760 0.447 0.285 0.188
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.023 0.069 14.888 0.000 1.023 0.445## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 18
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.465 0.126 -3.689 0.000 -0.465 -0.134
## urm 0.057 0.227 0.252 0.801 0.057 0.010
## Best_MPS 0.087 0.015 5.921 0.000 0.087 0.264
## eoc_comp_avg 0.029 0.097 0.299 0.765 0.029 0.023
## pre_confident 0.721 0.135 5.353 0.000 0.721 0.400
## sgnls_rspnd___ -0.026 0.121 -0.213 0.831 -0.026 -0.060
## pre_con_signls 0.008 0.024 0.348 0.728 0.008 0.101
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.245 0.811 -0.302 0.763 -0.245 -0.149
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.860 0.116 16.049 0.000 1.860 0.688## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.034 0.098 -0.351 0.725 -0.034 -0.013
## urm -0.002 0.175 -0.010 0.992 -0.002 -0.000
## Best_MPS 0.032 0.012 2.805 0.005 0.032 0.131
## eoc_val_avg 0.291 0.058 5.041 0.000 0.291 0.333
## pre_val_overll 0.382 0.097 3.944 0.000 0.382 0.322
## sgnls_rspnd___ 0.092 0.094 0.975 0.329 0.092 0.288
## pre_val_signls -0.008 0.016 -0.510 0.610 -0.008 -0.157
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 0.883 0.521 1.695 0.090 0.883 0.720
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.019 0.078 12.995 0.000 1.019 0.677## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.200 0.093 -2.161 0.031 -0.200 -0.082
## urm 0.332 0.170 1.953 0.051 0.332 0.080
## Best_MPS 0.037 0.012 3.154 0.002 0.037 0.158
## eoc_comp_avg 0.027 0.071 0.380 0.704 0.027 0.030
## pre_exp_overll 0.550 0.092 5.947 0.000 0.550 0.470
## sgnls_rspnd___ 0.087 0.097 0.897 0.370 0.087 0.287
## pre_exp_signls -0.011 0.017 -0.652 0.514 -0.011 -0.215
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.287 0.544 2.367 0.018 1.287 1.109
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.007 0.063 16.053 0.000 1.007 0.749## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.061 0.102 0.595 0.552 0.061 0.021
## urm -0.223 0.186 -1.198 0.231 -0.223 -0.045
## Best_MPS -0.027 0.013 -2.109 0.035 -0.027 -0.097
## eoc_cost_te_vg 0.477 0.058 8.202 0.000 0.477 0.482
## pre_cst_t_vrll 0.180 0.083 2.182 0.029 0.180 0.158
## sgnls_rspnd___ -0.086 0.050 -1.696 0.090 -0.086 -0.235
## pr_cst_t_sgnls 0.016 0.014 1.087 0.277 0.016 0.159
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 2.038 0.417 4.889 0.000 2.038 1.458
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.095 0.085 12.920 0.000 1.095 0.561## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.010 0.099 -0.106 0.916 -0.010 -0.004
## urm -0.487 0.181 -2.682 0.007 -0.487 -0.103
## Best_MPS -0.035 0.012 -2.978 0.003 -0.035 -0.133
## eoc_cost_oe_vg 0.505 0.061 8.309 0.000 0.505 0.498
## pre_cost__vrll 0.074 0.101 0.740 0.459 0.074 0.063
## sgnls_rspnd___ -0.121 0.046 -2.628 0.009 -0.121 -0.352
## pre_cst__sgnls 0.028 0.016 1.763 0.078 0.028 0.258
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.487 0.373 6.674 0.000 2.487 1.891
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 0.997 0.079 12.566 0.000 0.997 0.576## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.020 0.101 -0.202 0.840 -0.020 -0.007
## urm -0.372 0.184 -2.024 0.043 -0.372 -0.078
## Best_MPS -0.029 0.012 -2.371 0.018 -0.029 -0.108
## eoc_cost_lv_vg 0.457 0.055 8.287 0.000 0.457 0.463
## pr_cst_lv_vrll 0.235 0.084 2.789 0.005 0.235 0.205
## sgnls_rspnd___ -0.056 0.051 -1.094 0.274 -0.056 -0.159
## pr_cst_lv_sgnl 0.005 0.015 0.311 0.755 0.005 0.047
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 2.023 0.404 5.007 0.000 2.023 1.507
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.070 0.081 13.138 0.000 1.070 0.594## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 23
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.076 0.108 0.707 0.480 0.076 0.024
## urm -0.378 0.194 -1.944 0.052 -0.378 -0.068
## Best_MPS -0.018 0.014 -1.289 0.197 -0.018 -0.059
## eoc_cost_em_vg 0.547 0.051 10.635 0.000 0.547 0.559
## pre_cst_m_vrll 0.294 0.085 3.444 0.001 0.294 0.246
## sgnls_rspnd___ -0.003 0.054 -0.056 0.955 -0.003 -0.008
## pr_cst_m_sgnls -0.011 0.014 -0.791 0.429 -0.011 -0.114
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.512 0.453 3.336 0.001 1.512 0.980
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.112 0.092 12.127 0.000 1.112 0.467## cfi tli rmsea srmr
## 1 1 0 0Sep 29 Analysis Final Models
Path Analysis with eoc_3 (1 = invited and responded; 0 = not invited OR did not participate) Filtering out 2’s + average eoc
## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.139 0.098 -1.422 0.155 -0.139 -0.043
## urm -0.121 0.181 -0.669 0.503 -0.121 -0.022
## Best_MPS 0.029 0.012 2.360 0.018 0.029 0.093
## pre_int_overll 0.686 0.046 14.855 0.000 0.686 0.609
## eoc_int_avg 0.186 0.058 3.197 0.001 0.186 0.186
## participat_c_3 -0.038 0.094 -0.407 0.684 -0.038 -0.012
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll -0.013 0.283 -0.046 0.963 -0.013 -0.009
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.040 0.072 14.474 0.000 1.040 0.442## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.480 0.125 -3.830 0.000 -0.480 -0.139
## urm -0.117 0.233 -0.505 0.613 -0.117 -0.020
## Best_MPS 0.092 0.015 6.024 0.000 0.092 0.275
## pre_confident 0.786 0.070 11.281 0.000 0.786 0.437
## eoc_comp_avg 0.018 0.093 0.191 0.848 0.018 0.014
## participat_c_3 0.234 0.122 1.920 0.055 0.234 0.069
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.621 0.534 -1.162 0.245 -0.621 -0.378
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.765 0.114 15.470 0.000 1.765 0.655## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.172 0.095 -1.806 0.071 -0.172 -0.070
## urm 0.232 0.180 1.293 0.196 0.232 0.055
## Best_MPS 0.040 0.013 3.194 0.001 0.040 0.169
## pre_exp_overll 0.512 0.050 10.166 0.000 0.512 0.435
## eoc_comp_avg 0.032 0.072 0.439 0.660 0.032 0.035
## participat_c_3 0.063 0.093 0.675 0.500 0.063 0.026
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.481 0.405 3.657 0.000 1.481 1.266
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.031 0.066 15.644 0.000 1.031 0.753## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female 0.010 0.101 0.095 0.925 0.010 0.004
## urm -0.074 0.186 -0.398 0.690 -0.074 -0.016
## Best_MPS 0.036 0.012 3.002 0.003 0.036 0.145
## pre_val_overll 0.357 0.058 6.207 0.000 0.357 0.297
## eoc_val_avg 0.298 0.059 5.054 0.000 0.298 0.336
## participat_c_3 0.184 0.097 1.901 0.057 0.184 0.072
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 0.991 0.379 2.614 0.009 0.991 0.797
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.061 0.082 12.966 0.000 1.061 0.687## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.068 0.104 0.658 0.510 0.068 0.023
## urm -0.139 0.194 -0.717 0.474 -0.139 -0.027
## Best_MPS -0.031 0.014 -2.264 0.024 -0.031 -0.108
## pre_cst_t_vrll 0.261 0.057 4.618 0.000 0.261 0.228
## eoc_cost_te_vg 0.472 0.059 8.047 0.000 0.472 0.478
## participat_c_3 -0.226 0.100 -2.249 0.024 -0.226 -0.078
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.805 0.345 5.234 0.000 1.805 1.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.097 0.085 12.871 0.000 1.097 0.557## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female -0.001 0.101 -0.014 0.989 -0.001 -0.000
## urm -0.520 0.190 -2.731 0.006 -0.520 -0.109
## Best_MPS -0.033 0.013 -2.594 0.009 -0.033 -0.123
## pre_cost__vrll 0.231 0.058 3.967 0.000 0.231 0.198
## eoc_cost_oe_vg 0.497 0.062 8.079 0.000 0.497 0.486
## participat_c_3 -0.057 0.097 -0.591 0.555 -0.057 -0.021
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.860 0.309 6.015 0.000 1.860 1.417
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.026 0.081 12.699 0.000 1.026 0.595## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female -0.021 0.103 -0.199 0.842 -0.021 -0.007
## urm -0.350 0.194 -1.807 0.071 -0.350 -0.072
## Best_MPS -0.029 0.013 -2.205 0.027 -0.029 -0.106
## pr_cst_lv_vrll 0.249 0.053 4.708 0.000 0.249 0.220
## eoc_cost_lv_vg 0.455 0.056 8.124 0.000 0.455 0.465
## participat_c_3 -0.183 0.100 -1.833 0.067 -0.183 -0.066
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.864 0.329 5.669 0.000 1.864 1.390
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.089 0.083 13.056 0.000 1.089 0.606## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 698
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.077 0.111 0.693 0.488 0.077 0.024
## urm -0.300 0.204 -1.469 0.142 -0.300 -0.053
## Best_MPS -0.021 0.015 -1.419 0.156 -0.021 -0.067
## pre_cst_m_vrll 0.230 0.053 4.336 0.000 0.230 0.193
## eoc_cost_em_vg 0.556 0.052 10.628 0.000 0.556 0.568
## participat_c_3 -0.294 0.106 -2.789 0.005 -0.294 -0.093
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.730 0.392 4.412 0.000 1.730 1.122
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.140 0.095 12.011 0.000 1.140 0.479## cfi tli rmsea srmr
## 1 1 0 0With signals responded as tertiles
## lavaan 0.6-7 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_int_overall ~
## female -0.142 0.099 -1.440 0.150 -0.142 -0.044
## urm -0.056 0.172 -0.328 0.743 -0.056 -0.010
## Best_MPS 0.031 0.012 2.669 0.008 0.031 0.101
## pre_int_overll 0.691 0.047 14.777 0.000 0.691 0.616
## eoc_int_avg 0.177 0.058 3.044 0.002 0.177 0.178
## second_tertile -0.134 0.196 -0.686 0.493 -0.134 -0.041
## third_tertile -0.010 0.188 -0.055 0.956 -0.010 -0.003
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 0.019 0.276 0.068 0.946 0.019 0.012
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_int_ovrll 1.025 0.069 14.925 0.000 1.025 0.443## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 16
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_confident_math ~
## female -0.494 0.133 -3.717 0.000 -0.494 -0.143
## urm 0.075 0.230 0.328 0.743 0.075 0.013
## Best_MPS 0.088 0.015 5.899 0.000 0.088 0.267
## pre_confident 0.761 0.071 10.678 0.000 0.761 0.422
## eoc_comp_avg 0.031 0.098 0.315 0.753 0.031 0.024
## second_tertile 0.060 0.290 0.206 0.837 0.060 0.017
## third_tertile 0.231 0.273 0.845 0.398 0.231 0.066
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth -0.492 0.543 -0.906 0.365 -0.492 -0.299
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cnfdnt_mth 1.856 0.117 15.928 0.000 1.856 0.684## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_exp_overall ~
## female -0.250 0.096 -2.602 0.009 -0.250 -0.103
## urm 0.363 0.170 2.128 0.033 0.363 0.087
## Best_MPS 0.037 0.012 3.135 0.002 0.037 0.159
## pre_exp_overll 0.489 0.050 9.797 0.000 0.489 0.418
## eoc_comp_avg 0.046 0.070 0.661 0.508 0.046 0.051
## second_tertile 0.157 0.204 0.772 0.440 0.157 0.063
## third_tertile 0.454 0.188 2.418 0.016 0.454 0.184
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 1.456 0.380 3.834 0.000 1.456 1.256
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_exp_ovrll 0.984 0.065 15.216 0.000 0.984 0.733## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_val_overall ~
## female -0.076 0.101 -0.748 0.455 -0.076 -0.029
## urm -0.002 0.176 -0.009 0.993 -0.002 -0.000
## Best_MPS 0.033 0.011 2.847 0.004 0.033 0.132
## pre_val_overll 0.326 0.055 5.969 0.000 0.326 0.276
## eoc_val_avg 0.301 0.056 5.359 0.000 0.301 0.344
## second_tertile 0.449 0.197 2.286 0.022 0.449 0.170
## third_tertile 0.525 0.185 2.838 0.005 0.525 0.200
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 1.032 0.356 2.900 0.004 1.032 0.841
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_val_ovrll 0.999 0.079 12.693 0.000 0.999 0.664## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## female 0.097 0.106 0.915 0.360 0.097 0.033
## urm -0.247 0.188 -1.318 0.187 -0.247 -0.049
## Best_MPS -0.028 0.013 -2.173 0.030 -0.028 -0.101
## pre_cst_t_vrll 0.241 0.055 4.361 0.000 0.241 0.210
## eoc_cost_te_vg 0.480 0.058 8.299 0.000 0.480 0.486
## second_tertile 0.042 0.186 0.227 0.820 0.042 0.014
## third_tertile -0.318 0.178 -1.788 0.074 -0.318 -0.106
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.790 0.344 5.208 0.000 1.790 1.275
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.091 0.085 12.850 0.000 1.091 0.554## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## female 0.063 0.102 0.617 0.537 0.063 0.023
## urm -0.509 0.183 -2.789 0.005 -0.509 -0.108
## Best_MPS -0.035 0.012 -2.977 0.003 -0.035 -0.133
## pre_cost__vrll 0.238 0.057 4.152 0.000 0.238 0.200
## eoc_cost_oe_vg 0.492 0.061 8.046 0.000 0.492 0.476
## second_tertile -0.208 0.177 -1.179 0.238 -0.208 -0.073
## third_tertile -0.535 0.169 -3.173 0.002 -0.535 -0.190
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.114 0.304 6.956 0.000 2.114 1.600
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 0.995 0.079 12.547 0.000 0.995 0.570## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## female 0.011 0.104 0.103 0.918 0.011 0.004
## urm -0.381 0.185 -2.054 0.040 -0.381 -0.079
## Best_MPS -0.029 0.012 -2.321 0.020 -0.029 -0.107
## pr_cst_lv_vrll 0.250 0.052 4.817 0.000 0.250 0.218
## eoc_cost_lv_vg 0.453 0.055 8.195 0.000 0.453 0.461
## second_tertile -0.088 0.179 -0.493 0.622 -0.088 -0.031
## third_tertile -0.377 0.172 -2.196 0.028 -0.377 -0.131
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.940 0.329 5.895 0.000 1.940 1.443
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.070 0.081 13.159 0.000 1.070 0.592## cfi tli rmsea srmr
## 1 1 0 0## lavaan 0.6-7 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 9
##
## Number of observations 782
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## female 0.113 0.112 1.007 0.314 0.113 0.035
## urm -0.356 0.196 -1.814 0.070 -0.356 -0.065
## Best_MPS -0.017 0.014 -1.177 0.239 -0.017 -0.054
## pre_cst_m_vrll 0.228 0.051 4.419 0.000 0.228 0.191
## eoc_cost_em_vg 0.554 0.051 10.811 0.000 0.554 0.567
## second_tertile -0.446 0.187 -2.380 0.017 -0.446 -0.135
## third_tertile -0.435 0.181 -2.405 0.016 -0.435 -0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.794 0.389 4.611 0.000 1.794 1.166
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.105 0.092 12.069 0.000 1.105 0.467## cfi tli rmsea srmr
## 1 1 0 0Demographics we need
##
## 0 1
## 425 357##
## 0 1
## 509 273##
## 0 1 2
## 425 273 84| prop_female |
|---|
| 0.342 |
##
## Asian (non-Hispanic)
## 56
## Black or African American (non-Hispanic)
## 27
## Hispanic Ethnicity
## 30
## International
## 110
## Not Reported
## 7
## Two or more races (non-Hispanic)
## 18
## White (non-Hispanic)
## 450## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_3 | prop_female |
|---|---|
| 0 | 0.348 |
| 1 | 0.333 |
##
## Asian (non-Hispanic) Black or African American (non-Hispanic)
## 0 37 12
## 1 19 15
##
## Hispanic Ethnicity International Not Reported
## 0 17 62 6
## 1 13 48 1
##
## Two or more races (non-Hispanic) White (non-Hispanic)
## 0 14 277
## 1 4 173## Warning in cor(x, use = use, method = method): the standard deviation is zero| pre_int_overall | pre_val_overall | pre_confident | pre_cost_te_overall | pre_cost_oe_overall | pre_cost_lv_overall | pre_cost_em_overall | eoc_int_avg | eoc_val_avg | eoc_comp_avg | eoc_cost_te_avg | eoc_cost_oe_avg | eoc_cost_lv_avg | eoc_cost_em_avg | post_int_overall | post_val_overall | post_confident_math | post_cost_te_overall | post_cost_oe_overall | post_cost_lv_overall | post_cost_em_overall | female | urm | Best_MPS | participate_eoc_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pre_int_overall | |||||||||||||||||||||||||
| pre_val_overall | 0.65*** | ||||||||||||||||||||||||
| pre_confident | 0.45*** | 0.25*** | |||||||||||||||||||||||
| pre_cost_te_overall | -0.25*** | -0.22*** | -0.39*** | ||||||||||||||||||||||
| pre_cost_oe_overall | -0.22*** | -0.28*** | -0.32*** | 0.70*** | |||||||||||||||||||||
| pre_cost_lv_overall | -0.22*** | -0.21*** | -0.33*** | 0.78*** | 0.72*** | ||||||||||||||||||||
| pre_cost_em_overall | -0.31*** | -0.19*** | -0.51*** | 0.79*** | 0.60*** | 0.75*** | |||||||||||||||||||
| eoc_int_avg | 0.45*** | 0.28*** | 0.24*** | -0.10 | -0.11 | -0.09 | -0.23*** | ||||||||||||||||||
| eoc_val_avg | 0.23*** | 0.30*** | 0.03 | -0.08 | -0.12 | -0.11 | -0.13* | 0.64*** | |||||||||||||||||
| eoc_comp_avg | 0.31*** | 0.22*** | 0.12 | -0.09 | -0.14* | -0.10 | -0.10 | 0.69*** | 0.59*** | ||||||||||||||||
| eoc_cost_te_avg | -0.33*** | -0.31*** | -0.31*** | 0.56*** | 0.49*** | 0.45*** | 0.51*** | -0.23*** | -0.09 | -0.19** | |||||||||||||||
| eoc_cost_oe_avg | -0.27*** | -0.31*** | -0.27*** | 0.46*** | 0.53*** | 0.48*** | 0.49*** | -0.21*** | -0.15* | -0.15* | 0.76*** | ||||||||||||||
| eoc_cost_lv_avg | -0.31*** | -0.35*** | -0.32*** | 0.48*** | 0.47*** | 0.47*** | 0.48*** | -0.18** | -0.07 | -0.19** | 0.80*** | 0.80*** | |||||||||||||
| eoc_cost_em_avg | -0.41*** | -0.28*** | -0.35*** | 0.43*** | 0.35*** | 0.37*** | 0.54*** | -0.35*** | -0.14* | -0.24*** | 0.74*** | 0.64*** | 0.66*** | ||||||||||||
| post_int_overall | 0.71*** | 0.42*** | 0.36*** | -0.20*** | -0.13** | -0.17*** | -0.29*** | 0.46*** | 0.29*** | 0.28*** | -0.30*** | -0.34*** | -0.29*** | -0.44*** | |||||||||||
| post_val_overall | 0.44*** | 0.41*** | 0.20*** | -0.17*** | -0.14** | -0.14** | -0.17*** | 0.28*** | 0.37*** | 0.30*** | -0.18** | -0.21** | -0.15* | -0.25*** | 0.67*** | ||||||||||
| post_confident_math | 0.42*** | 0.24*** | 0.48*** | -0.25*** | -0.24*** | -0.23*** | -0.39*** | 0.22** | 0.05 | 0.11 | -0.40*** | -0.33*** | -0.36*** | -0.48*** | 0.53*** | 0.38*** | |||||||||
| post_cost_te_overall | -0.26*** | -0.22*** | -0.31*** | 0.50*** | 0.40*** | 0.42*** | 0.48*** | -0.17* | -0.03 | -0.11 | 0.63*** | 0.54*** | 0.62*** | 0.61*** | -0.33*** | -0.32*** | -0.47*** | ||||||||
| post_cost_oe_overall | -0.14** | -0.20*** | -0.18*** | 0.40*** | 0.45*** | 0.40*** | 0.37*** | -0.10 | -0.03 | -0.11 | 0.55*** | 0.60*** | 0.56*** | 0.49*** | -0.21*** | -0.29*** | -0.31*** | 0.76*** | |||||||
| post_cost_lv_overall | -0.21*** | -0.21*** | -0.25*** | 0.44*** | 0.39*** | 0.43*** | 0.44*** | -0.15* | -0.05 | -0.16* | 0.57*** | 0.52*** | 0.59*** | 0.57*** | -0.28*** | -0.32*** | -0.39*** | 0.87*** | 0.77*** | ||||||
| post_cost_em_overall | -0.30*** | -0.20*** | -0.34*** | 0.39*** | 0.31*** | 0.35*** | 0.51*** | -0.18* | 0.03 | -0.09 | 0.57*** | 0.49*** | 0.57*** | 0.69*** | -0.41*** | -0.33*** | -0.60*** | 0.83*** | 0.63*** | 0.75*** | |||||
| female | -0.02 | 0.04 | -0.14*** | 0.02 | 0.00 | -0.02 | 0.13*** | -0.13* | -0.12* | -0.09 | 0.06 | 0.05 | 0.05 | 0.13* | -0.08 | -0.02 | -0.19*** | 0.05 | 0.02 | 0.00 | 0.12** | ||||
| urm | -0.02 | 0.00 | 0.00 | -0.03 | -0.07 | 0.02 | 0.00 | -0.01 | -0.02 | 0.05 | 0.08 | 0.10 | 0.02 | 0.16* | -0.03 | -0.04 | -0.05 | -0.02 | -0.07 | -0.05 | -0.04 | 0.05 | |||
| Best_MPS | 0.24*** | 0.10* | 0.15** | -0.06 | -0.01 | -0.03 | -0.17*** | 0.17* | 0.07 | 0.16* | -0.23** | -0.14 | -0.18* | -0.32*** | 0.25*** | 0.18*** | 0.32*** | -0.20*** | -0.14* | -0.15** | -0.24*** | 0.01 | -0.18*** | ||
| participate_eoc_3 | 0.09* | 0.12** | 0.03 | -0.01 | -0.09* | 0.01 | -0.01 | NANA | NANA | NANA | NANA | NANA | NANA | NANA | 0.05 | 0.10* | 0.06 | -0.08 | -0.05 | -0.07 | -0.11* | -0.02 | 0.06 | -0.05 |
## Warning in cor(x, use = use, method = method): the standard deviation is zero| pre_int_overall | pre_val_overall | pre_exp_overall | pre_cost_te_overall | pre_cost_oe_overall | pre_cost_lv_overall | pre_cost_em_overall | eoc_int_avg | eoc_val_avg | eoc_comp_avg | eoc_cost_te_avg | eoc_cost_oe_avg | eoc_cost_lv_avg | eoc_cost_em_avg | post_int_overall | post_val_overall | post_exp_overall | post_cost_te_overall | post_cost_oe_overall | post_cost_lv_overall | post_cost_em_overall | female | urm | Best_MPS | participate_eoc_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pre_int_overall | |||||||||||||||||||||||||
| pre_val_overall | 0.65*** | ||||||||||||||||||||||||
| pre_exp_overall | 0.53*** | 0.61*** | |||||||||||||||||||||||
| pre_cost_te_overall | -0.25*** | -0.22*** | -0.41*** | ||||||||||||||||||||||
| pre_cost_oe_overall | -0.22*** | -0.28*** | -0.42*** | 0.70*** | |||||||||||||||||||||
| pre_cost_lv_overall | -0.22*** | -0.21*** | -0.36*** | 0.78*** | 0.72*** | ||||||||||||||||||||
| pre_cost_em_overall | -0.31*** | -0.19*** | -0.42*** | 0.79*** | 0.60*** | 0.75*** | |||||||||||||||||||
| eoc_int_avg | 0.45*** | 0.28*** | 0.17** | -0.10 | -0.11 | -0.09 | -0.23*** | ||||||||||||||||||
| eoc_val_avg | 0.23*** | 0.30*** | 0.14* | -0.08 | -0.12 | -0.11 | -0.13* | 0.64*** | |||||||||||||||||
| eoc_comp_avg | 0.31*** | 0.22*** | 0.18** | -0.09 | -0.14* | -0.10 | -0.10 | 0.69*** | 0.59*** | ||||||||||||||||
| eoc_cost_te_avg | -0.33*** | -0.31*** | -0.37*** | 0.56*** | 0.49*** | 0.45*** | 0.51*** | -0.23*** | -0.09 | -0.19** | |||||||||||||||
| eoc_cost_oe_avg | -0.27*** | -0.31*** | -0.36*** | 0.46*** | 0.53*** | 0.48*** | 0.49*** | -0.21*** | -0.15* | -0.15* | 0.76*** | ||||||||||||||
| eoc_cost_lv_avg | -0.31*** | -0.35*** | -0.39*** | 0.48*** | 0.47*** | 0.47*** | 0.48*** | -0.18** | -0.07 | -0.19** | 0.80*** | 0.80*** | |||||||||||||
| eoc_cost_em_avg | -0.41*** | -0.28*** | -0.36*** | 0.43*** | 0.35*** | 0.37*** | 0.54*** | -0.35*** | -0.14* | -0.24*** | 0.74*** | 0.64*** | 0.66*** | ||||||||||||
| post_int_overall | 0.71*** | 0.42*** | 0.29*** | -0.20*** | -0.13** | -0.17*** | -0.29*** | 0.46*** | 0.29*** | 0.28*** | -0.30*** | -0.34*** | -0.29*** | -0.44*** | |||||||||||
| post_val_overall | 0.44*** | 0.41*** | 0.27*** | -0.17*** | -0.14** | -0.14** | -0.17*** | 0.28*** | 0.37*** | 0.30*** | -0.18** | -0.21** | -0.15* | -0.25*** | 0.67*** | ||||||||||
| post_exp_overall | 0.34*** | 0.31*** | 0.45*** | -0.25*** | -0.28*** | -0.24*** | -0.28*** | 0.14* | 0.06 | 0.13 | -0.33*** | -0.37*** | -0.32*** | -0.40*** | 0.54*** | 0.63*** | |||||||||
| post_cost_te_overall | -0.26*** | -0.22*** | -0.33*** | 0.50*** | 0.40*** | 0.42*** | 0.48*** | -0.17* | -0.03 | -0.11 | 0.63*** | 0.54*** | 0.62*** | 0.61*** | -0.33*** | -0.32*** | -0.46*** | ||||||||
| post_cost_oe_overall | -0.14** | -0.20*** | -0.28*** | 0.40*** | 0.45*** | 0.40*** | 0.37*** | -0.10 | -0.03 | -0.11 | 0.55*** | 0.60*** | 0.56*** | 0.49*** | -0.21*** | -0.29*** | -0.43*** | 0.76*** | |||||||
| post_cost_lv_overall | -0.21*** | -0.21*** | -0.30*** | 0.44*** | 0.39*** | 0.43*** | 0.44*** | -0.15* | -0.05 | -0.16* | 0.57*** | 0.52*** | 0.59*** | 0.57*** | -0.28*** | -0.32*** | -0.44*** | 0.87*** | 0.77*** | ||||||
| post_cost_em_overall | -0.30*** | -0.20*** | -0.34*** | 0.39*** | 0.31*** | 0.35*** | 0.51*** | -0.18* | 0.03 | -0.09 | 0.57*** | 0.49*** | 0.57*** | 0.69*** | -0.41*** | -0.33*** | -0.49*** | 0.83*** | 0.63*** | 0.75*** | |||||
| female | -0.02 | 0.04 | -0.05 | 0.02 | 0.00 | -0.02 | 0.13*** | -0.13* | -0.12* | -0.09 | 0.06 | 0.05 | 0.05 | 0.13* | -0.08 | -0.02 | -0.09* | 0.05 | 0.02 | 0.00 | 0.12** | ||||
| urm | -0.02 | 0.00 | 0.07 | -0.03 | -0.07 | 0.02 | 0.00 | -0.01 | -0.02 | 0.05 | 0.08 | 0.10 | 0.02 | 0.16* | -0.03 | -0.04 | 0.06 | -0.02 | -0.07 | -0.05 | -0.04 | 0.05 | |||
| Best_MPS | 0.24*** | 0.10* | 0.04 | -0.06 | -0.01 | -0.03 | -0.17*** | 0.17* | 0.07 | 0.16* | -0.23** | -0.14 | -0.18* | -0.32*** | 0.25*** | 0.18*** | 0.18** | -0.20*** | -0.14* | -0.15** | -0.24*** | 0.01 | -0.18*** | ||
| participate_eoc_3 | 0.09* | 0.12** | 0.13*** | -0.01 | -0.09* | 0.01 | -0.01 | NANA | NANA | NANA | NANA | NANA | NANA | NANA | 0.05 | 0.10* | 0.08 | -0.08 | -0.05 | -0.07 | -0.11* | -0.02 | 0.06 | -0.05 |
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 4.94 1.36 5.2 5.04 1.19 1 7 6 -0.65 -0.09 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 5.66 1.03 6 5.77 0.99 1 7 6 -1.16 1.99 0.04## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 698 4.95 0.91 5 4.95 1.48 1 6 5 -0.79 0.84 0.03## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 685 5.64 0.99 6 5.73 0.99 1 7 6 -0.99 1.56 0.04## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 3.32 1.22 3.2 3.29 1.48 1 7 6 0.19 -0.54 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 2.81 1.13 2.62 2.74 0.93 1 7 6 0.66 0.21 0.04## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 3.16 1.18 3 3.11 1.48 1 7 6 0.37 -0.26 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 686 3.67 1.3 3.67 3.66 1.48 1 7 6 -0.01 -0.6 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 3.89 1.52 4 3.89 1.48 1 7 6 0 -0.8 0.09## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 4.79 1.37 5 4.86 1.48 1 7 6 -0.47 -0.24 0.08## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 4.65 1.28 4.95 4.69 1.3 1 7 6 -0.39 0.03 0.08## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 262 3.04 1.44 3 2.98 1.48 1 6.71 5.71 0.31 -0.93 0.09## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 262 2.7 1.3 2.33 2.59 1.25 1 7 6 0.73 -0.1 0.08## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 262 2.74 1.39 2.33 2.62 1.61 1 7 6 0.62 -0.53 0.09## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 262 3.13 1.59 3 3.03 1.68 1 7 6 0.41 -0.85 0.1## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 516 4.66 1.52 4.9 4.75 1.33 1 7 6 -0.51 -0.46 0.07## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 516 5.28 1.23 5.33 5.39 0.99 1 7 6 -0.94 0.98 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 518 5.14 1.62 5 5.14 1.48 1 7 6 -0.79 -0.17 0.07## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 515 5.33 1.16 5.5 5.41 1.24 1 7 6 -0.68 0.34 0.05## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 515 3.39 1.4 3.4 3.36 1.78 1 7 6 0.17 -0.58 0.06## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 516 3.1 1.3 3 3.07 1.48 1 7 6 0.29 -0.54 0.06## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 516 3.19 1.33 3.25 3.17 1.48 1 7 6 0.18 -0.5 0.06## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 515 3.74 1.53 3.83 3.73 1.73 1 7 6 0.08 -0.75 0.07## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 698 0.34 0.47 0 0.3 0 0 1 1 0.66 -1.56 0.02## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 691 0.08 0.28 0 0 0 0 1 1 3.03 7.18 0.01## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 449 19.92 4.96 21 20.4 2.97 3 28 25 -0.93 0.6 0.23## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 698 0.39 0.49 0 0.36 0 0 1 1 0.45 -1.8 0.02## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc_3 | prop_female | prop_urm | prop_participate | mean_pre_interest | mean_pre_confidence | mean_pre_val | mean_pre_cost_te | mean_pre_cost_oe | mean_pre_cost_lv | mean_pre_cost_em | mean_post_interest | mean_post_confidence | mean_post_val | mean_post_cost_te | mean_post_cost_oe | mean_post_cost_lv | mean_post_cost_em | mean_MPS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 0.068 | 0 | 4.840 | 4.922 | 5.563 | 3.328 | 2.892 | 3.152 | 3.671 | 4.592 | 5.059 | 5.179 | 3.484 | 3.158 | 3.268 | 3.875 | 20.136 |
| 1 | 0.333 | 0.103 | 1 | 5.097 | 4.985 | 5.820 | 3.308 | 2.693 | 3.170 | 3.655 | 4.752 | 5.268 | 5.423 | 3.254 | 3.026 | 3.081 | 3.548 | 19.639 |
| prop_female | prop_urm | prop_participate | mean_pre_interest | mean_pre_confidence | mean_pre_val | mean_pre_cost_te | mean_pre_cost_oe | mean_pre_cost_lv | mean_pre_cost_em | mean_post_interest | mean_post_confidence | mean_post_val | mean_post_cost_te | mean_post_cost_oe | mean_post_cost_lv | mean_post_cost_em | mean_MPS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.342 | 0.082 | 0.391 | 4.94 | 4.947 | 5.662 | 3.32 | 2.815 | 3.159 | 3.665 | 4.657 | 5.145 | 5.278 | 3.39 | 3.104 | 3.192 | 3.741 | 19.924 |
## Warning in cor(x, use = use, method = method): the standard deviation is zero| participate_eoc_3 | pre_int_overall | pre_confident | pre_val_overall | pre_cost_te_overall | pre_cost_oe_overall | pre_cost_lv_overall | pre_cost_em_overall | eoc_int_avg | eoc_comp_avg | eoc_val_avg | eoc_cost_te_avg | eoc_cost_oe_avg | eoc_cost_lv_avg | eoc_cost_em_avg | pre_int_eoc_3 | pre_val_eoc_3 | pre_con_eoc_3 | pre_cost_te_eoc_3 | pre_cost_oe_eoc_3 | pre_cost_lv_eoc_3 | pre_cost_em_eoc_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| participate_eoc_3 | ||||||||||||||||||||||
| pre_int_overall | 0.09* | |||||||||||||||||||||
| pre_confident | 0.03 | 0.45*** | ||||||||||||||||||||
| pre_val_overall | 0.12** | 0.65*** | 0.25*** | |||||||||||||||||||
| pre_cost_te_overall | -0.01 | -0.25*** | -0.39*** | -0.22*** | ||||||||||||||||||
| pre_cost_oe_overall | -0.09* | -0.22*** | -0.32*** | -0.28*** | 0.70*** | |||||||||||||||||
| pre_cost_lv_overall | 0.01 | -0.22*** | -0.33*** | -0.21*** | 0.78*** | 0.72*** | ||||||||||||||||
| pre_cost_em_overall | -0.01 | -0.31*** | -0.51*** | -0.19*** | 0.79*** | 0.60*** | 0.75*** | |||||||||||||||
| eoc_int_avg | NANA | 0.45*** | 0.24*** | 0.28*** | -0.10 | -0.11 | -0.09 | -0.23*** | ||||||||||||||
| eoc_comp_avg | NANA | 0.31*** | 0.12 | 0.22*** | -0.09 | -0.14* | -0.10 | -0.10 | 0.69*** | |||||||||||||
| eoc_val_avg | NANA | 0.23*** | 0.03 | 0.30*** | -0.08 | -0.12 | -0.11 | -0.13* | 0.64*** | 0.59*** | ||||||||||||
| eoc_cost_te_avg | NANA | -0.33*** | -0.31*** | -0.31*** | 0.56*** | 0.49*** | 0.45*** | 0.51*** | -0.23*** | -0.19** | -0.09 | |||||||||||
| eoc_cost_oe_avg | NANA | -0.27*** | -0.27*** | -0.31*** | 0.46*** | 0.53*** | 0.48*** | 0.49*** | -0.21*** | -0.15* | -0.15* | 0.76*** | ||||||||||
| eoc_cost_lv_avg | NANA | -0.31*** | -0.32*** | -0.35*** | 0.48*** | 0.47*** | 0.47*** | 0.48*** | -0.18** | -0.19** | -0.07 | 0.80*** | 0.80*** | |||||||||
| eoc_cost_em_avg | NANA | -0.41*** | -0.35*** | -0.28*** | 0.43*** | 0.35*** | 0.37*** | 0.54*** | -0.35*** | -0.24*** | -0.14* | 0.74*** | 0.64*** | 0.66*** | ||||||||
| pre_int_eoc_3 | 0.95*** | 0.26*** | 0.12** | 0.24*** | -0.05 | -0.12** | -0.03 | -0.06 | 0.45*** | 0.31*** | 0.23*** | -0.33*** | -0.27*** | -0.31*** | -0.41*** | |||||||
| pre_val_eoc_3 | 0.98*** | 0.17*** | 0.07 | 0.24*** | -0.04 | -0.12** | -0.03 | -0.03 | 0.28*** | 0.22*** | 0.30*** | -0.31*** | -0.31*** | -0.35*** | -0.28*** | 0.97*** | ||||||
| pre_con_eoc_3 | 0.98*** | 0.15*** | 0.17*** | 0.15*** | -0.05 | -0.12** | -0.03 | -0.07 | 0.24*** | 0.12 | 0.03 | -0.31*** | -0.27*** | -0.32*** | -0.35*** | 0.96*** | 0.96*** | |||||
| pre_cost_te_eoc_3 | 0.90*** | 0.02 | -0.05 | 0.05 | 0.27*** | 0.11** | 0.22*** | 0.20*** | -0.10 | -0.09 | -0.08 | 0.56*** | 0.46*** | 0.48*** | 0.43*** | 0.83*** | 0.86*** | 0.85*** | ||||
| pre_cost_oe_eoc_3 | 0.88*** | 0.03 | -0.04 | 0.03 | 0.20*** | 0.21*** | 0.21*** | 0.16*** | -0.11 | -0.14* | -0.12 | 0.49*** | 0.53*** | 0.47*** | 0.35*** | 0.82*** | 0.84*** | 0.83*** | 0.94*** | |||
| pre_cost_lv_eoc_3 | 0.90*** | 0.04 | -0.04 | 0.05 | 0.21*** | 0.11** | 0.28*** | 0.20*** | -0.09 | -0.10 | -0.11 | 0.45*** | 0.48*** | 0.47*** | 0.37*** | 0.84*** | 0.86*** | 0.85*** | 0.96*** | 0.94*** | ||
| pre_cost_em_eoc_3 | 0.91*** | 0.01 | -0.07 | 0.06 | 0.19*** | 0.07 | 0.21*** | 0.25*** | -0.23*** | -0.10 | -0.13* | 0.51*** | 0.49*** | 0.48*** | 0.54*** | 0.83*** | 0.87*** | 0.85*** | 0.96*** | 0.92*** | 0.96*** |
Alphas
##
## Attaching package: 'psy'## The following object is masked from 'package:psych':
##
## wkappa## $sample.size
## [1] 671
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9484241## $sample.size
## [1] 508
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9558871## $sample.size
## [1] 676
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8401684## $sample.size
## [1] 507
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8519153## $sample.size
## [1] 674
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.839374## $sample.size
## [1] 508
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8155961## $sample.size
## [1] 670
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9166209## $sample.size
## [1] 503
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9302039## $sample.size
## [1] 677
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.8857804## $sample.size
## [1] 508
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.9099416## $sample.size
## [1] 678
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.8550658## $sample.size
## [1] 506
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.8954087## $sample.size
## [1] 675
##
## $number.of.items
## [1] 6
##
## $alpha
## [1] 0.9089861## $sample.size
## [1] 504
##
## $number.of.items
## [1] 6
##
## $alpha
## [1] 0.9362468October 6 Final Final Models sem and cfas
## Warning in lavaan::lavaan(model = Interest_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 53
##
## Number of observations 698
## Number of missing patterns 37
##
## Model Test User Model:
##
## Test statistic 270.012
## Degrees of freedom 82
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_interest =~
## pre_int_1 1.000 1.369 0.920
## pre_int_2 0.992 0.025 39.992 0.000 1.359 0.910
## pre_int_3 0.968 0.028 34.724 0.000 1.326 0.864
## pre_int_4 0.880 0.029 30.482 0.000 1.204 0.815
## pre_int_5 1.002 0.023 44.020 0.000 1.371 0.934
## post_interest =~
## post_int_1 1.000 1.497 0.912
## post_int_2 1.009 0.028 35.536 0.000 1.511 0.922
## post_int_3 1.005 0.030 33.417 0.000 1.505 0.901
## post_int_4 0.922 0.033 28.229 0.000 1.380 0.843
## post_int_5 1.014 0.028 35.848 0.000 1.517 0.924
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_interest ~
## female -0.164 0.098 -1.672 0.095 -0.109 -0.052
## urm -0.121 0.183 -0.662 0.508 -0.081 -0.022
## Best_MPS 0.033 0.012 2.649 0.008 0.022 0.109
## pre_interest 0.713 0.051 13.898 0.000 0.652 0.652
## eoc_int_avg 0.171 0.059 2.878 0.004 0.114 0.175
## participat_c_3 -0.038 0.097 -0.387 0.699 -0.025 -0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_interest ~~
## eoc_int_avg 1.013 0.136 7.460 0.000 0.740 0.482
## female ~~
## urm 0.006 0.005 1.277 0.202 0.006 0.049
## Best_MPS 0.010 0.107 0.097 0.923 0.010 0.004
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.217 0.059 -3.681 0.000 -0.217 -0.160
## participat_c_3 0.008 0.005 1.558 0.119 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.107 0.111 -0.959 0.337 -0.107 -0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_int_1 5.087 0.057 89.746 0.000 5.087 3.419
## .pre_int_2 5.031 0.057 88.362 0.000 5.031 3.368
## .pre_int_3 4.732 0.059 80.832 0.000 4.732 3.083
## .pre_int_4 4.749 0.056 84.284 0.000 4.749 3.214
## .pre_int_5 5.106 0.056 91.238 0.000 5.106 3.477
## .post_int_1 3.525 0.346 10.197 0.000 3.525 2.148
## .post_int_2 3.455 0.348 9.913 0.000 3.455 2.107
## .post_int_3 3.143 0.347 9.047 0.000 3.143 1.883
## .post_int_4 3.336 0.320 10.417 0.000 3.336 2.039
## .post_int_5 3.503 0.350 10.018 0.000 3.503 2.134
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.888 0.000 0.083 0.300
## Best_MPS 19.994 0.233 85.852 0.000 19.994 4.052
## eoc_int_avg 3.813 0.088 43.100 0.000 3.813 2.481
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_interest 0.000 0.000 0.000
## .post_interest 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_int_1 0.339 0.025 13.788 0.000 0.339 0.153
## .pre_int_2 0.385 0.027 14.364 0.000 0.385 0.173
## .pre_int_3 0.599 0.038 15.871 0.000 0.599 0.254
## .pre_int_4 0.734 0.044 16.766 0.000 0.734 0.336
## .pre_int_5 0.277 0.022 12.713 0.000 0.277 0.128
## .post_int_1 0.452 0.036 12.631 0.000 0.452 0.168
## .post_int_2 0.405 0.033 12.081 0.000 0.405 0.151
## .post_int_3 0.523 0.041 12.813 0.000 0.523 0.188
## .post_int_4 0.775 0.054 14.254 0.000 0.775 0.289
## .post_int_5 0.393 0.033 11.880 0.000 0.393 0.146
## eoc_int_avg 2.362 0.207 11.433 0.000 2.362 1.000
## pre_interest 1.874 0.119 15.739 0.000 1.000 1.000
## .post_interest 0.936 0.081 11.562 0.000 0.418 0.418
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.585 0.000 0.076 1.000
## Best_MPS 24.345 1.615 15.078 0.000 24.345 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.972 0.966 0.057 0.062## Warning in lavaan::lavaan(model = Expectancies_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 41
##
## Number of observations 698
## Number of missing patterns 32
##
## Model Test User Model:
##
## Test statistic 72.988
## Degrees of freedom 36
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_expectancies =~
## pre_exp_1 1.000 0.912 0.782
## pre_exp_2 0.926 0.047 19.858 0.000 0.844 0.809
## pre_exp_3 1.069 0.054 19.799 0.000 0.975 0.806
## post_expectancies =~
## post_exp_1 1.000 1.006 0.781
## post_exp_2 1.042 0.065 16.094 0.000 1.048 0.766
## post_exp_3 1.088 0.072 15.135 0.000 1.094 0.772
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_expectancies ~
## female -0.162 0.091 -1.778 0.075 -0.161 -0.076
## urm 0.229 0.171 1.337 0.181 0.228 0.063
## Best_MPS 0.038 0.012 3.223 0.001 0.038 0.187
## pre_expectancs 0.585 0.064 9.078 0.000 0.530 0.530
## eoc_comp_avg 0.019 0.070 0.270 0.787 0.019 0.024
## participat_c_3 0.065 0.090 0.723 0.470 0.065 0.032
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_expectancies ~~
## eoc_comp_avg 0.235 0.080 2.939 0.003 0.257 0.201
## female ~~
## urm 0.006 0.005 1.276 0.202 0.006 0.049
## Best_MPS 0.019 0.107 0.180 0.857 0.019 0.008
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.215 0.059 -3.661 0.000 -0.215 -0.159
## participat_c_3 0.008 0.005 1.556 0.120 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.106 0.111 -0.955 0.340 -0.106 -0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_exp_1 5.630 0.045 126.240 0.000 5.630 4.827
## .pre_exp_2 5.829 0.040 146.036 0.000 5.829 5.585
## .pre_exp_3 5.462 0.046 118.121 0.000 5.462 4.515
## .post_exp_1 4.527 0.389 11.645 0.000 4.527 3.514
## .post_exp_2 4.506 0.406 11.102 0.000 4.506 3.295
## .post_exp_3 4.257 0.426 9.989 0.000 4.257 3.005
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.891 0.000 0.083 0.300
## Best_MPS 19.992 0.233 85.917 0.000 19.992 4.049
## eoc_comp_avg 4.613 0.079 58.543 0.000 4.613 3.614
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_expectancs 0.000 0.000 0.000
## .post_expectncs 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_exp_1 0.528 0.042 12.712 0.000 0.528 0.388
## .pre_exp_2 0.376 0.032 11.613 0.000 0.376 0.346
## .pre_exp_3 0.513 0.044 11.769 0.000 0.513 0.351
## .post_exp_1 0.648 0.063 10.277 0.000 0.648 0.391
## .post_exp_2 0.772 0.071 10.892 0.000 0.772 0.413
## .post_exp_3 0.809 0.078 10.315 0.000 0.809 0.403
## eoc_comp_avg 1.629 0.142 11.462 0.000 1.629 1.000
## pre_expectancs 0.832 0.074 11.233 0.000 1.000 1.000
## .post_expectncs 0.680 0.081 8.377 0.000 0.672 0.672
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.586 0.000 0.076 1.000
## Best_MPS 24.378 1.619 15.061 0.000 24.378 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.976 0.967 0.038 0.045## Warning in lavaan::lavaan(model = Value_1, data = d, missing = "ML.x",
## model.type = "sem", : lavaan WARNING: syntax contains parameters involving
## exogenous covariates; switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 86 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 41
##
## Number of observations 698
## Number of missing patterns 34
##
## Model Test User Model:
##
## Test statistic 61.850
## Degrees of freedom 36
## P-value (Chi-square) 0.005
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_value =~
## pre_val_1 1.000 0.970 0.830
## pre_val_2 0.942 0.046 20.408 0.000 0.913 0.780
## pre_val_3 0.987 0.049 20.248 0.000 0.958 0.795
## post_value =~
## post_val_1 1.000 1.162 0.819
## post_val_2 0.934 0.052 17.929 0.000 1.085 0.785
## post_val_3 1.006 0.054 18.694 0.000 1.168 0.829
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_value ~
## female -0.060 0.102 -0.587 0.557 -0.051 -0.024
## urm -0.082 0.190 -0.432 0.666 -0.071 -0.019
## Best_MPS 0.039 0.012 3.094 0.002 0.033 0.164
## pre_value 0.436 0.074 5.865 0.000 0.364 0.364
## eoc_val_avg 0.281 0.063 4.459 0.000 0.242 0.340
## participat_c_3 0.159 0.102 1.558 0.119 0.137 0.067
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_value ~~
## eoc_val_avg 0.501 0.093 5.382 0.000 0.517 0.368
## female ~~
## urm 0.006 0.005 1.276 0.202 0.006 0.049
## Best_MPS 0.014 0.107 0.133 0.894 0.014 0.006
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.224 0.059 -3.799 0.000 -0.224 -0.165
## participat_c_3 0.008 0.005 1.556 0.120 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.104 0.111 -0.933 0.351 -0.104 -0.043
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_val_1 5.800 0.045 129.987 0.000 5.800 4.966
## .pre_val_2 5.590 0.045 124.965 0.000 5.590 4.774
## .pre_val_3 5.601 0.046 121.543 0.000 5.601 4.648
## .post_val_1 3.234 0.401 8.055 0.000 3.234 2.279
## .post_val_2 3.150 0.375 8.391 0.000 3.150 2.280
## .post_val_3 3.050 0.403 7.566 0.000 3.050 2.165
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.891 0.000 0.083 0.300
## Best_MPS 20.019 0.233 85.948 0.000 20.019 4.056
## eoc_val_avg 4.719 0.084 55.938 0.000 4.719 3.358
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_value 0.000 0.000 0.000
## .post_value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_val_1 0.423 0.040 10.571 0.000 0.423 0.310
## .pre_val_2 0.537 0.041 13.029 0.000 0.537 0.392
## .pre_val_3 0.535 0.044 12.119 0.000 0.535 0.368
## .post_val_1 0.664 0.065 10.184 0.000 0.664 0.330
## .post_val_2 0.732 0.064 11.489 0.000 0.732 0.384
## .post_val_3 0.620 0.064 9.761 0.000 0.620 0.312
## eoc_val_avg 1.975 0.179 11.018 0.000 1.975 1.000
## pre_value 0.941 0.077 12.156 0.000 1.000 1.000
## .post_value 0.847 0.101 8.421 0.000 0.628 0.628
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.585 0.000 0.076 1.000
## Best_MPS 24.366 1.617 15.071 0.000 24.366 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.984 0.979 0.032 0.039## Warning in lavaan::lavaan(model = TE_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 75 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 53
##
## Number of observations 698
## Number of missing patterns 42
##
## Model Test User Model:
##
## Test statistic 165.335
## Degrees of freedom 82
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_te_cost =~
## pre_cost_te_1 1.000 1.193 0.847
## pre_cost_te_2 0.972 0.039 25.071 0.000 1.159 0.803
## pre_cost_te_3 0.974 0.034 28.285 0.000 1.162 0.857
## pre_cost_te_4 0.969 0.037 26.001 0.000 1.155 0.824
## pre_cost_te_5 0.992 0.039 25.646 0.000 1.183 0.818
## post_te_cost =~
## post_cost_te_1 1.000 1.341 0.865
## post_cost_te_2 0.953 0.043 22.292 0.000 1.278 0.791
## post_cost_te_3 1.052 0.039 27.165 0.000 1.410 0.878
## post_cost_te_4 1.009 0.039 26.044 0.000 1.354 0.865
## post_cost_te_5 0.991 0.038 25.846 0.000 1.329 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_te_cost ~
## female 0.072 0.104 0.699 0.485 0.054 0.026
## urm -0.121 0.194 -0.626 0.532 -0.091 -0.025
## Best_MPS -0.038 0.013 -2.850 0.004 -0.028 -0.140
## pre_te_cost 0.297 0.065 4.557 0.000 0.264 0.264
## eoc_cost_te_vg 0.451 0.061 7.427 0.000 0.336 0.480
## participat_c_3 -0.211 0.106 -1.996 0.046 -0.158 -0.077
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_te_cost ~~
## eoc_cost_te_vg 0.994 0.104 9.606 0.000 0.834 0.584
## female ~~
## urm 0.006 0.005 1.275 0.202 0.006 0.049
## Best_MPS 0.019 0.107 0.176 0.860 0.019 0.008
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.224 0.059 -3.802 0.000 -0.224 -0.165
## participat_c_3 0.008 0.005 1.558 0.119 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.097 0.112 -0.865 0.387 -0.097 -0.040
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_te_1 3.282 0.054 61.090 0.000 3.282 2.332
## .pre_cost_te_2 3.434 0.055 62.367 0.000 3.434 2.379
## .pre_cost_te_3 3.241 0.052 62.604 0.000 3.241 2.391
## .pre_cost_te_4 3.385 0.054 63.224 0.000 3.385 2.413
## .pre_cost_te_5 3.257 0.055 59.040 0.000 3.257 2.253
## .post_cost_te_1 2.862 0.366 7.820 0.000 2.862 1.845
## .post_cost_te_2 2.954 0.350 8.434 0.000 2.954 1.829
## .post_cost_te_3 2.816 0.384 7.325 0.000 2.816 1.752
## .post_cost_te_4 2.835 0.369 7.674 0.000 2.835 1.812
## .post_cost_te_5 2.869 0.363 7.910 0.000 2.869 1.872
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.888 0.000 0.083 0.300
## Best_MPS 20.044 0.234 85.757 0.000 20.044 4.057
## eoc_cost_te_vg 3.060 0.079 38.674 0.000 3.060 2.144
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_te_cost 0.000 0.000 0.000
## .post_te_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_te_1 0.558 0.039 14.201 0.000 0.558 0.282
## .pre_cost_te_2 0.738 0.048 15.425 0.000 0.738 0.354
## .pre_cost_te_3 0.488 0.035 13.758 0.000 0.488 0.265
## .pre_cost_te_4 0.633 0.042 14.970 0.000 0.633 0.322
## .pre_cost_te_5 0.690 0.046 14.995 0.000 0.690 0.330
## .post_cost_te_1 0.607 0.049 12.451 0.000 0.607 0.252
## .post_cost_te_2 0.974 0.069 14.061 0.000 0.974 0.374
## .post_cost_te_3 0.593 0.049 12.058 0.000 0.593 0.230
## .post_cost_te_4 0.616 0.049 12.596 0.000 0.616 0.252
## .post_cost_te_5 0.584 0.047 12.393 0.000 0.584 0.248
## eoc_cost_te_vg 2.039 0.167 12.236 0.000 2.039 1.000
## pre_te_cost 1.423 0.106 13.449 0.000 1.000 1.000
## .post_te_cost 0.947 0.097 9.762 0.000 0.526 0.526
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.585 0.000 0.076 1.000
## Best_MPS 24.405 1.622 15.048 0.000 24.405 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.982 0.978 0.038 0.036## Warning in lavaan::lavaan(model = OE_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 47
##
## Number of observations 698
## Number of missing patterns 34
##
## Model Test User Model:
##
## Test statistic 93.894
## Degrees of freedom 57
## P-value (Chi-square) 0.002
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_oe_cost =~
## pre_cost_oe_1 1.000 1.068 0.818
## pre_cost_oe_2 1.000 0.043 23.203 0.000 1.068 0.814
## pre_cost_oe_3 1.001 0.041 24.205 0.000 1.069 0.832
## pre_cost_oe_4 0.966 0.043 22.430 0.000 1.032 0.789
## post_oe_cost =~
## post_cost_oe_1 1.000 1.226 0.861
## post_cost_oe_2 1.089 0.043 25.101 0.000 1.336 0.871
## post_cost_oe_3 0.959 0.044 21.757 0.000 1.176 0.797
## post_cost_oe_4 1.028 0.041 24.803 0.000 1.261 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_oe_cost ~
## female 0.039 0.099 0.393 0.695 0.032 0.015
## urm -0.404 0.186 -2.172 0.030 -0.329 -0.091
## Best_MPS -0.035 0.012 -2.769 0.006 -0.028 -0.139
## pre_oe_cost 0.258 0.069 3.745 0.000 0.225 0.225
## eoc_cost_oe_vg 0.479 0.063 7.589 0.000 0.391 0.503
## participat_c_3 0.000 0.101 0.004 0.997 0.000 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_oe_cost ~~
## eoc_cost_oe_vg 0.762 0.087 8.804 0.000 0.713 0.553
## female ~~
## urm 0.006 0.005 1.277 0.202 0.006 0.049
## Best_MPS 0.028 0.107 0.258 0.796 0.028 0.012
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.227 0.059 -3.845 0.000 -0.227 -0.167
## participat_c_3 0.008 0.005 1.556 0.120 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.106 0.112 -0.947 0.343 -0.106 -0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_oe_1 2.831 0.050 56.833 0.000 2.831 2.169
## .pre_cost_oe_2 2.859 0.050 57.077 0.000 2.859 2.178
## .pre_cost_oe_3 2.781 0.049 56.772 0.000 2.781 2.165
## .pre_cost_oe_4 2.790 0.050 55.871 0.000 2.790 2.134
## .post_cost_oe_1 2.447 0.325 7.522 0.000 2.447 1.718
## .post_cost_oe_2 2.530 0.354 7.145 0.000 2.530 1.650
## .post_cost_oe_3 2.573 0.313 8.226 0.000 2.573 1.743
## .post_cost_oe_4 2.477 0.335 7.404 0.000 2.477 1.704
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.889 0.000 0.083 0.300
## Best_MPS 20.050 0.234 85.642 0.000 20.050 4.056
## eoc_cost_oe_vg 2.782 0.073 38.187 0.000 2.782 2.159
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_oe_cost 0.000 0.000 0.000
## .post_oe_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_oe_1 0.563 0.041 13.696 0.000 0.563 0.330
## .pre_cost_oe_2 0.582 0.042 13.864 0.000 0.582 0.337
## .pre_cost_oe_3 0.507 0.039 13.117 0.000 0.507 0.307
## .pre_cost_oe_4 0.646 0.044 14.535 0.000 0.646 0.378
## .post_cost_oe_1 0.524 0.045 11.669 0.000 0.524 0.258
## .post_cost_oe_2 0.567 0.050 11.309 0.000 0.567 0.241
## .post_cost_oe_3 0.795 0.059 13.428 0.000 0.795 0.365
## .post_cost_oe_4 0.524 0.046 11.446 0.000 0.524 0.248
## eoc_cost_oe_vg 1.661 0.136 12.190 0.000 1.661 1.000
## pre_oe_cost 1.141 0.091 12.494 0.000 1.000 1.000
## .post_oe_cost 0.824 0.087 9.432 0.000 0.548 0.548
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.580 0.000 0.076 1.000
## Best_MPS 24.432 1.625 15.032 0.000 24.432 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.988 0.985 0.030 0.033## Warning in lavaan::lavaan(model = LV_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 47
##
## Number of observations 698
## Number of missing patterns 33
##
## Model Test User Model:
##
## Test statistic 58.848
## Degrees of freedom 57
## P-value (Chi-square) 0.408
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_lv_cost =~
## pre_cost_lv_1 1.000 1.036 0.774
## pre_cost_lv_2 1.092 0.049 22.295 0.000 1.131 0.835
## pre_cost_lv_3 1.155 0.051 22.813 0.000 1.197 0.862
## pre_cost_lv_4 0.980 0.057 17.139 0.000 1.015 0.661
## post_lv_cost =~
## post_cost_lv_1 1.000 1.193 0.818
## post_cost_lv_2 1.048 0.048 21.700 0.000 1.250 0.852
## post_cost_lv_3 1.102 0.050 22.145 0.000 1.315 0.856
## post_cost_lv_4 1.093 0.054 20.118 0.000 1.304 0.805
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_lv_cost ~
## female -0.014 0.097 -0.146 0.884 -0.012 -0.006
## urm -0.424 0.183 -2.312 0.021 -0.355 -0.098
## Best_MPS -0.033 0.012 -2.719 0.007 -0.028 -0.138
## pre_lv_cost 0.283 0.065 4.347 0.000 0.246 0.246
## eoc_cost_lv_vg 0.426 0.056 7.635 0.000 0.357 0.490
## participat_c_3 -0.159 0.100 -1.601 0.109 -0.134 -0.065
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_lv_cost ~~
## eoc_cost_lv_vg 0.695 0.090 7.765 0.000 0.671 0.490
## female ~~
## urm 0.006 0.005 1.282 0.200 0.006 0.049
## Best_MPS 0.018 0.107 0.164 0.870 0.018 0.008
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.225 0.059 -3.821 0.000 -0.225 -0.166
## participat_c_3 0.008 0.005 1.558 0.119 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.099 0.112 -0.887 0.375 -0.099 -0.041
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_lv_1 2.879 0.051 56.381 0.000 2.879 2.152
## .pre_cost_lv_2 3.044 0.052 58.915 0.000 3.044 2.248
## .pre_cost_lv_3 3.027 0.053 57.052 0.000 3.027 2.180
## .pre_cost_lv_4 3.690 0.059 62.975 0.000 3.690 2.403
## .post_cost_lv_1 2.720 0.318 8.541 0.000 2.720 1.864
## .post_cost_lv_2 2.704 0.333 8.117 0.000 2.704 1.842
## .post_cost_lv_3 2.755 0.350 7.872 0.000 2.755 1.793
## .post_cost_lv_4 2.981 0.348 8.564 0.000 2.981 1.840
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.888 0.000 0.083 0.300
## Best_MPS 20.033 0.234 85.770 0.000 20.033 4.056
## eoc_cost_lv_vg 2.751 0.079 34.891 0.000 2.751 2.006
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_lv_cost 0.000 0.000 0.000
## .post_lv_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_lv_1 0.717 0.049 14.723 0.000 0.717 0.401
## .pre_cost_lv_2 0.555 0.044 12.473 0.000 0.555 0.302
## .pre_cost_lv_3 0.496 0.045 11.034 0.000 0.496 0.257
## .pre_cost_lv_4 1.329 0.080 16.603 0.000 1.329 0.564
## .post_cost_lv_1 0.705 0.056 12.484 0.000 0.705 0.331
## .post_cost_lv_2 0.591 0.052 11.373 0.000 0.591 0.274
## .post_cost_lv_3 0.632 0.057 11.185 0.000 0.632 0.268
## .post_cost_lv_4 0.924 0.072 12.782 0.000 0.924 0.352
## eoc_cost_lv_vg 1.880 0.155 12.134 0.000 1.880 1.000
## pre_lv_cost 1.073 0.093 11.501 0.000 1.000 1.000
## .post_lv_cost 0.788 0.088 8.935 0.000 0.553 0.553
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.399 1.621 15.052 0.000 24.399 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.999 0.999 0.007 0.027## Warning in lavaan::lavaan(model = EM_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 83 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 59
##
## Number of observations 698
## Number of missing patterns 38
##
## Model Test User Model:
##
## Test statistic 272.895
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_em_cost =~
## pre_cost_em_1 1.000 1.253 0.754
## pre_cost_em_2 0.906 0.043 21.156 0.000 1.136 0.796
## pre_cost_em_3 1.026 0.049 20.950 0.000 1.286 0.789
## pre_cost_em_4 0.956 0.045 21.409 0.000 1.198 0.803
## pre_cost_em_5 1.025 0.046 22.411 0.000 1.285 0.838
## pre_cost_em_6 0.994 0.047 21.001 0.000 1.246 0.781
## post_em_cost =~
## post_cost_em_1 1.000 1.407 0.773
## post_cost_em_2 1.012 0.047 21.384 0.000 1.424 0.857
## post_cost_em_3 1.089 0.051 21.210 0.000 1.532 0.852
## post_cost_em_4 1.030 0.048 21.314 0.000 1.450 0.857
## post_cost_em_5 1.058 0.048 21.927 0.000 1.489 0.869
## post_cost_em_6 1.089 0.052 21.076 0.000 1.533 0.842
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_em_cost ~
## female 0.095 0.106 0.897 0.370 0.067 0.032
## urm -0.257 0.194 -1.325 0.185 -0.183 -0.050
## Best_MPS -0.033 0.014 -2.423 0.015 -0.024 -0.117
## pre_em_cost 0.236 0.060 3.937 0.000 0.210 0.210
## eoc_cost_em_vg 0.525 0.052 10.013 0.000 0.373 0.594
## participat_c_3 -0.281 0.110 -2.557 0.011 -0.200 -0.098
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_em_cost ~~
## eoc_cost_em_vg 1.131 0.124 9.151 0.000 0.903 0.566
## female ~~
## urm 0.006 0.005 1.278 0.201 0.006 0.049
## Best_MPS 0.017 0.107 0.161 0.872 0.017 0.007
## participat_c_3 -0.004 0.009 -0.405 0.686 -0.004 -0.015
## urm ~~
## Best_MPS -0.220 0.059 -3.738 0.000 -0.220 -0.162
## participat_c_3 0.008 0.005 1.560 0.119 0.008 0.059
## Best_MPS ~~
## participat_c_3 -0.107 0.112 -0.959 0.337 -0.107 -0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_em_1 3.986 0.063 62.777 0.000 3.986 2.397
## .pre_cost_em_2 3.400 0.054 62.429 0.000 3.400 2.382
## .pre_cost_em_3 3.818 0.062 61.413 0.000 3.818 2.344
## .pre_cost_em_4 3.453 0.057 60.643 0.000 3.453 2.313
## .pre_cost_em_5 3.607 0.059 61.630 0.000 3.607 2.354
## .pre_cost_em_6 3.741 0.061 61.413 0.000 3.741 2.344
## .post_cost_em_1 3.249 0.361 8.990 0.000 3.249 1.784
## .post_cost_em_2 2.603 0.364 7.159 0.000 2.603 1.566
## .post_cost_em_3 2.817 0.392 7.190 0.000 2.817 1.567
## .post_cost_em_4 2.711 0.370 7.320 0.000 2.711 1.603
## .post_cost_em_5 2.787 0.380 7.338 0.000 2.787 1.627
## .post_cost_em_6 2.932 0.392 7.482 0.000 2.932 1.610
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.886 0.000 0.083 0.300
## Best_MPS 20.044 0.234 85.566 0.000 20.044 4.058
## eoc_cost_em_vg 3.152 0.089 35.455 0.000 3.152 1.978
## participat_c_3 0.391 0.018 21.175 0.000 0.391 0.801
## pre_em_cost 0.000 0.000 0.000
## .post_em_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_em_1 1.194 0.074 16.139 0.000 1.194 0.432
## .pre_cost_em_2 0.747 0.048 15.432 0.000 0.747 0.367
## .pre_cost_em_3 1.000 0.064 15.595 0.000 1.000 0.377
## .pre_cost_em_4 0.792 0.052 15.313 0.000 0.792 0.356
## .pre_cost_em_5 0.698 0.049 14.314 0.000 0.698 0.297
## .pre_cost_em_6 0.994 0.063 15.668 0.000 0.994 0.390
## .post_cost_em_1 1.337 0.093 14.448 0.000 1.337 0.403
## .post_cost_em_2 0.734 0.056 13.131 0.000 0.734 0.266
## .post_cost_em_3 0.884 0.067 13.189 0.000 0.884 0.273
## .post_cost_em_4 0.757 0.058 13.121 0.000 0.757 0.265
## .post_cost_em_5 0.719 0.056 12.760 0.000 0.719 0.245
## .post_cost_em_6 0.965 0.072 13.473 0.000 0.965 0.291
## eoc_cost_em_vg 2.541 0.206 12.309 0.000 2.541 1.000
## pre_em_cost 1.571 0.140 11.258 0.000 1.000 1.000
## .post_em_cost 0.866 0.107 8.118 0.000 0.437 0.437
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.585 0.000 0.076 1.000
## Best_MPS 24.403 1.622 15.046 0.000 24.403 1.000
## participat_c_3 0.238 0.013 18.682 0.000 0.238 1.000## cfi tli rmsea srmr
## 0.970 0.964 0.046 0.056With signals responded as tertiles
## Warning in lavaan::lavaan(model = Interest_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 99 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
##
## Number of observations 698
## Number of missing patterns 40
##
## Model Test User Model:
##
## Test statistic 291.654
## Degrees of freedom 92
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_interest =~
## pre_int_1 1.000 1.369 0.920
## pre_int_2 0.992 0.025 40.000 0.000 1.359 0.910
## pre_int_3 0.968 0.028 34.723 0.000 1.326 0.864
## pre_int_4 0.880 0.029 30.485 0.000 1.204 0.815
## pre_int_5 1.002 0.023 44.022 0.000 1.371 0.934
## post_interest =~
## post_int_1 1.000 1.497 0.912
## post_int_2 1.009 0.028 35.552 0.000 1.511 0.922
## post_int_3 1.005 0.030 33.408 0.000 1.504 0.901
## post_int_4 0.921 0.033 28.209 0.000 1.379 0.843
## post_int_5 1.014 0.028 35.870 0.000 1.518 0.924
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_interest ~
## female -0.169 0.103 -1.636 0.102 -0.113 -0.054
## urm -0.101 0.185 -0.543 0.587 -0.067 -0.018
## Best_MPS 0.034 0.012 2.759 0.006 0.023 0.113
## pre_interest 0.715 0.052 13.860 0.000 0.654 0.654
## eoc_int_avg 0.168 0.060 2.811 0.005 0.112 0.173
## second_tertile -0.130 0.201 -0.645 0.519 -0.087 -0.040
## third_tertile 0.038 0.193 0.194 0.846 0.025 0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_interest ~~
## eoc_int_avg 1.012 0.136 7.450 0.000 0.739 0.481
## female ~~
## urm 0.006 0.005 1.276 0.202 0.006 0.049
## Best_MPS 0.011 0.107 0.101 0.920 0.011 0.005
## second_tertile 0.002 0.013 0.169 0.866 0.002 0.010
## third_tertile 0.038 0.013 2.810 0.005 0.038 0.170
## urm ~~
## Best_MPS -0.216 0.059 -3.669 0.000 -0.216 -0.159
## second_tertile 0.001 0.007 0.112 0.911 0.001 0.006
## third_tertile -0.010 0.007 -1.454 0.146 -0.010 -0.079
## Best_MPS ~~
## second_tertile 0.150 0.157 0.956 0.339 0.150 0.066
## third_tertile -0.132 0.166 -0.795 0.427 -0.132 -0.057
## second_tertile ~~
## third_tertile -0.103 0.015 -7.050 0.000 -0.103 -0.471
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_int_1 5.087 0.057 89.739 0.000 5.087 3.419
## .pre_int_2 5.031 0.057 88.355 0.000 5.031 3.368
## .pre_int_3 4.732 0.059 80.825 0.000 4.732 3.083
## .pre_int_4 4.749 0.056 84.278 0.000 4.749 3.213
## .pre_int_5 5.106 0.056 91.229 0.000 5.106 3.476
## .post_int_1 3.522 0.366 9.609 0.000 3.522 2.146
## .post_int_2 3.451 0.370 9.336 0.000 3.451 2.105
## .post_int_3 3.140 0.368 8.527 0.000 3.140 1.881
## .post_int_4 3.334 0.339 9.827 0.000 3.334 2.037
## .post_int_5 3.500 0.371 9.432 0.000 3.500 2.132
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.899 0.000 0.083 0.300
## Best_MPS 19.966 0.233 85.785 0.000 19.966 4.047
## eoc_int_avg 3.819 0.088 43.631 0.000 3.819 2.486
## second_tertile 0.315 0.028 11.202 0.000 0.315 0.679
## third_tertile 0.330 0.028 11.742 0.000 0.330 0.704
## pre_interest 0.000 0.000 0.000
## .post_interest 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_int_1 0.339 0.025 13.782 0.000 0.339 0.153
## .pre_int_2 0.385 0.027 14.364 0.000 0.385 0.173
## .pre_int_3 0.599 0.038 15.874 0.000 0.599 0.254
## .pre_int_4 0.734 0.044 16.766 0.000 0.734 0.336
## .pre_int_5 0.277 0.022 12.717 0.000 0.277 0.128
## .post_int_1 0.452 0.036 12.627 0.000 0.452 0.168
## .post_int_2 0.404 0.033 12.077 0.000 0.404 0.150
## .post_int_3 0.524 0.041 12.817 0.000 0.524 0.188
## .post_int_4 0.776 0.054 14.257 0.000 0.776 0.290
## .post_int_5 0.392 0.033 11.866 0.000 0.392 0.145
## eoc_int_avg 2.360 0.206 11.439 0.000 2.360 1.000
## pre_interest 1.875 0.119 15.740 0.000 1.000 1.000
## .post_interest 0.933 0.081 11.524 0.000 0.416 0.416
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.339 1.614 15.080 0.000 24.339 1.000
## second_tertile 0.216 0.018 11.682 0.000 0.216 1.000
## third_tertile 0.219 0.019 11.705 0.000 0.219 1.000## cfi tli rmsea srmr
## 0.970 0.965 0.056 0.064## Warning in lavaan::lavaan(model = Expectancies_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 698
## Number of missing patterns 35
##
## Model Test User Model:
##
## Test statistic 71.001
## Degrees of freedom 42
## P-value (Chi-square) 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_expectancies =~
## pre_exp_1 1.000 0.912 0.782
## pre_exp_2 0.926 0.047 19.869 0.000 0.844 0.809
## pre_exp_3 1.070 0.054 19.797 0.000 0.976 0.807
## post_expectancies =~
## post_exp_1 1.000 1.006 0.781
## post_exp_2 1.040 0.065 16.114 0.000 1.046 0.765
## post_exp_3 1.088 0.072 15.147 0.000 1.094 0.773
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_expectancies ~
## female -0.240 0.098 -2.462 0.014 -0.239 -0.113
## urm 0.292 0.173 1.686 0.092 0.291 0.080
## Best_MPS 0.038 0.012 3.212 0.001 0.038 0.186
## pre_expectancs 0.583 0.064 9.039 0.000 0.528 0.528
## eoc_comp_avg 0.032 0.068 0.474 0.635 0.032 0.041
## second_tertile 0.126 0.201 0.626 0.532 0.125 0.058
## third_tertile 0.428 0.187 2.289 0.022 0.425 0.200
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_expectancies ~~
## eoc_comp_avg 0.238 0.080 2.980 0.003 0.261 0.204
## female ~~
## urm 0.006 0.005 1.275 0.202 0.006 0.049
## Best_MPS 0.020 0.107 0.183 0.855 0.020 0.008
## second_tertile 0.002 0.013 0.129 0.897 0.002 0.008
## third_tertile 0.039 0.013 2.909 0.004 0.039 0.175
## urm ~~
## Best_MPS -0.215 0.059 -3.649 0.000 -0.215 -0.158
## second_tertile 0.001 0.007 0.107 0.915 0.001 0.006
## third_tertile -0.009 0.007 -1.349 0.177 -0.009 -0.073
## Best_MPS ~~
## second_tertile 0.153 0.157 0.971 0.332 0.153 0.067
## third_tertile -0.115 0.165 -0.697 0.486 -0.115 -0.049
## second_tertile ~~
## third_tertile -0.103 0.015 -7.026 0.000 -0.103 -0.471
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_exp_1 5.630 0.045 126.233 0.000 5.630 4.826
## .pre_exp_2 5.829 0.040 146.027 0.000 5.829 5.584
## .pre_exp_3 5.462 0.046 118.115 0.000 5.462 4.515
## .post_exp_1 4.323 0.401 10.783 0.000 4.323 3.357
## .post_exp_2 4.296 0.418 10.284 0.000 4.296 3.143
## .post_exp_3 4.035 0.440 9.176 0.000 4.035 2.849
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.901 0.000 0.083 0.301
## Best_MPS 19.960 0.233 85.814 0.000 19.960 4.044
## eoc_comp_avg 4.611 0.079 58.548 0.000 4.611 3.611
## second_tertile 0.316 0.028 11.220 0.000 0.316 0.681
## third_tertile 0.329 0.028 11.738 0.000 0.329 0.700
## pre_expectancs 0.000 0.000 0.000
## .post_expectncs 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_exp_1 0.529 0.042 12.730 0.000 0.529 0.389
## .pre_exp_2 0.377 0.032 11.634 0.000 0.377 0.346
## .pre_exp_3 0.512 0.044 11.742 0.000 0.512 0.350
## .post_exp_1 0.647 0.063 10.269 0.000 0.647 0.390
## .post_exp_2 0.776 0.071 10.964 0.000 0.776 0.415
## .post_exp_3 0.807 0.078 10.309 0.000 0.807 0.403
## eoc_comp_avg 1.631 0.142 11.451 0.000 1.631 1.000
## pre_expectancs 0.832 0.074 11.228 0.000 1.000 1.000
## .post_expectncs 0.650 0.081 8.038 0.000 0.643 0.643
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.365 1.617 15.067 0.000 24.365 1.000
## second_tertile 0.216 0.018 11.682 0.000 0.216 1.000
## third_tertile 0.220 0.019 11.644 0.000 0.220 1.000## cfi tli rmsea srmr
## 0.981 0.974 0.031 0.045## Warning in lavaan::lavaan(model = Value_1, data = d, missing = "ML.x",
## model.type = "sem", : lavaan WARNING: syntax contains parameters involving
## exogenous covariates; switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 48
##
## Number of observations 698
## Number of missing patterns 37
##
## Model Test User Model:
##
## Test statistic 66.505
## Degrees of freedom 42
## P-value (Chi-square) 0.009
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_value =~
## pre_val_1 1.000 0.970 0.831
## pre_val_2 0.941 0.046 20.411 0.000 0.913 0.779
## pre_val_3 0.988 0.049 20.242 0.000 0.958 0.795
## post_value =~
## post_val_1 1.000 1.163 0.818
## post_val_2 0.940 0.052 17.941 0.000 1.093 0.790
## post_val_3 1.005 0.054 18.766 0.000 1.169 0.828
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_value ~
## female -0.148 0.106 -1.387 0.165 -0.127 -0.060
## urm -0.022 0.193 -0.116 0.908 -0.019 -0.005
## Best_MPS 0.037 0.013 2.963 0.003 0.032 0.158
## pre_value 0.435 0.074 5.901 0.000 0.363 0.363
## eoc_val_avg 0.288 0.061 4.732 0.000 0.248 0.349
## second_tertile 0.399 0.207 1.925 0.054 0.343 0.159
## third_tertile 0.506 0.195 2.590 0.010 0.435 0.204
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_value ~~
## eoc_val_avg 0.507 0.093 5.436 0.000 0.522 0.371
## female ~~
## urm 0.006 0.005 1.274 0.203 0.006 0.049
## Best_MPS 0.014 0.107 0.134 0.894 0.014 0.006
## second_tertile 0.001 0.013 0.072 0.943 0.001 0.004
## third_tertile 0.036 0.013 2.706 0.007 0.036 0.164
## urm ~~
## Best_MPS -0.223 0.059 -3.789 0.000 -0.223 -0.164
## second_tertile 0.001 0.007 0.132 0.895 0.001 0.007
## third_tertile -0.010 0.007 -1.462 0.144 -0.010 -0.080
## Best_MPS ~~
## second_tertile 0.148 0.158 0.936 0.350 0.148 0.064
## third_tertile -0.136 0.165 -0.821 0.412 -0.136 -0.059
## second_tertile ~~
## third_tertile -0.102 0.015 -7.041 0.000 -0.102 -0.471
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_val_1 5.800 0.045 129.994 0.000 5.800 4.966
## .pre_val_2 5.591 0.045 124.973 0.000 5.591 4.774
## .pre_val_3 5.601 0.046 121.560 0.000 5.601 4.648
## .post_val_1 3.018 0.399 7.572 0.000 3.018 2.123
## .post_val_2 2.934 0.377 7.787 0.000 2.934 2.120
## .post_val_3 2.834 0.400 7.086 0.000 2.834 2.007
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.901 0.000 0.083 0.301
## Best_MPS 19.986 0.233 85.819 0.000 19.986 4.048
## eoc_val_avg 4.698 0.082 57.112 0.000 4.698 3.340
## second_tertile 0.314 0.028 11.149 0.000 0.314 0.676
## third_tertile 0.327 0.028 11.658 0.000 0.327 0.698
## pre_value 0.000 0.000 0.000
## .post_value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_val_1 0.423 0.040 10.559 0.000 0.423 0.310
## .pre_val_2 0.538 0.041 13.059 0.000 0.538 0.393
## .pre_val_3 0.534 0.044 12.097 0.000 0.534 0.368
## .post_val_1 0.669 0.065 10.293 0.000 0.669 0.331
## .post_val_2 0.721 0.064 11.344 0.000 0.721 0.376
## .post_val_3 0.626 0.063 9.894 0.000 0.626 0.314
## eoc_val_avg 1.979 0.180 11.011 0.000 1.979 1.000
## pre_value 0.941 0.077 12.158 0.000 1.000 1.000
## .post_value 0.801 0.100 8.029 0.000 0.592 0.592
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.371 1.618 15.067 0.000 24.371 1.000
## second_tertile 0.216 0.018 11.673 0.000 0.216 1.000
## third_tertile 0.219 0.019 11.701 0.000 0.219 1.000## cfi tli rmsea srmr
## 0.985 0.980 0.029 0.042## Warning in lavaan::lavaan(model = TE_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 81 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
##
## Number of observations 698
## Number of missing patterns 45
##
## Model Test User Model:
##
## Test statistic 177.570
## Degrees of freedom 92
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_te_cost =~
## pre_cost_te_1 1.000 1.193 0.848
## pre_cost_te_2 0.972 0.039 25.069 0.000 1.160 0.803
## pre_cost_te_3 0.974 0.034 28.283 0.000 1.162 0.857
## pre_cost_te_4 0.969 0.037 26.009 0.000 1.156 0.824
## pre_cost_te_5 0.992 0.039 25.643 0.000 1.183 0.818
## post_te_cost =~
## post_cost_te_1 1.000 1.339 0.864
## post_cost_te_2 0.953 0.043 22.267 0.000 1.275 0.791
## post_cost_te_3 1.052 0.039 27.160 0.000 1.408 0.877
## post_cost_te_4 1.010 0.039 26.046 0.000 1.352 0.865
## post_cost_te_5 0.992 0.038 25.854 0.000 1.327 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_te_cost ~
## female 0.136 0.109 1.255 0.210 0.102 0.048
## urm -0.200 0.196 -1.020 0.308 -0.149 -0.041
## Best_MPS -0.038 0.013 -2.852 0.004 -0.028 -0.141
## pre_te_cost 0.291 0.065 4.475 0.000 0.259 0.259
## eoc_cost_te_vg 0.455 0.060 7.535 0.000 0.340 0.486
## second_tertile 0.068 0.185 0.368 0.713 0.051 0.024
## third_tertile -0.305 0.178 -1.714 0.087 -0.228 -0.107
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_te_cost ~~
## eoc_cost_te_vg 0.999 0.104 9.644 0.000 0.837 0.586
## female ~~
## urm 0.006 0.005 1.273 0.203 0.006 0.048
## Best_MPS 0.020 0.107 0.183 0.855 0.020 0.008
## second_tertile 0.002 0.013 0.115 0.909 0.002 0.007
## third_tertile 0.039 0.013 2.894 0.004 0.039 0.175
## urm ~~
## Best_MPS -0.223 0.059 -3.780 0.000 -0.223 -0.164
## second_tertile 0.001 0.007 0.085 0.933 0.001 0.005
## third_tertile -0.010 0.007 -1.380 0.167 -0.010 -0.075
## Best_MPS ~~
## second_tertile 0.164 0.157 1.043 0.297 0.164 0.071
## third_tertile -0.107 0.165 -0.648 0.517 -0.107 -0.046
## second_tertile ~~
## third_tertile -0.103 0.015 -7.051 0.000 -0.103 -0.471
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_te_1 3.282 0.054 61.075 0.000 3.282 2.331
## .pre_cost_te_2 3.433 0.055 62.352 0.000 3.433 2.379
## .pre_cost_te_3 3.241 0.052 62.590 0.000 3.241 2.390
## .pre_cost_te_4 3.385 0.054 63.210 0.000 3.385 2.413
## .pre_cost_te_5 3.257 0.055 59.026 0.000 3.257 2.252
## .post_cost_te_1 2.814 0.370 7.596 0.000 2.814 1.816
## .post_cost_te_2 2.909 0.354 8.205 0.000 2.909 1.803
## .post_cost_te_3 2.765 0.389 7.104 0.000 2.765 1.723
## .post_cost_te_4 2.787 0.374 7.450 0.000 2.787 1.783
## .post_cost_te_5 2.822 0.367 7.681 0.000 2.822 1.843
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.898 0.000 0.083 0.300
## Best_MPS 20.012 0.234 85.674 0.000 20.012 4.051
## eoc_cost_te_vg 3.101 0.076 40.868 0.000 3.101 2.169
## second_tertile 0.319 0.028 11.297 0.000 0.319 0.686
## third_tertile 0.325 0.028 11.611 0.000 0.325 0.695
## pre_te_cost 0.000 0.000 0.000
## .post_te_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_te_1 0.558 0.039 14.198 0.000 0.558 0.282
## .pre_cost_te_2 0.738 0.048 15.426 0.000 0.738 0.354
## .pre_cost_te_3 0.488 0.035 13.761 0.000 0.488 0.265
## .pre_cost_te_4 0.632 0.042 14.962 0.000 0.632 0.321
## .pre_cost_te_5 0.690 0.046 14.998 0.000 0.690 0.330
## .post_cost_te_1 0.608 0.049 12.461 0.000 0.608 0.253
## .post_cost_te_2 0.976 0.069 14.069 0.000 0.976 0.375
## .post_cost_te_3 0.593 0.049 12.053 0.000 0.593 0.230
## .post_cost_te_4 0.616 0.049 12.594 0.000 0.616 0.252
## .post_cost_te_5 0.582 0.047 12.385 0.000 0.582 0.248
## eoc_cost_te_vg 2.043 0.167 12.231 0.000 2.043 1.000
## pre_te_cost 1.423 0.106 13.448 0.000 1.000 1.000
## .post_te_cost 0.928 0.096 9.660 0.000 0.518 0.518
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.402 1.621 15.049 0.000 24.402 1.000
## second_tertile 0.216 0.018 11.689 0.000 0.216 1.000
## third_tertile 0.219 0.019 11.707 0.000 0.219 1.000## cfi tli rmsea srmr
## 0.982 0.978 0.037 0.038## Warning in lavaan::lavaan(model = OE_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 81 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 54
##
## Number of observations 698
## Number of missing patterns 37
##
## Model Test User Model:
##
## Test statistic 97.732
## Degrees of freedom 65
## P-value (Chi-square) 0.005
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_oe_cost =~
## pre_cost_oe_1 1.000 1.068 0.818
## pre_cost_oe_2 1.000 0.043 23.205 0.000 1.069 0.814
## pre_cost_oe_3 1.001 0.041 24.210 0.000 1.069 0.832
## pre_cost_oe_4 0.966 0.043 22.430 0.000 1.032 0.789
## post_oe_cost =~
## post_cost_oe_1 1.000 1.224 0.862
## post_cost_oe_2 1.087 0.043 25.106 0.000 1.330 0.869
## post_cost_oe_3 0.957 0.044 21.759 0.000 1.172 0.795
## post_cost_oe_4 1.028 0.041 24.862 0.000 1.258 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_oe_cost ~
## female 0.130 0.103 1.265 0.206 0.107 0.051
## urm -0.474 0.187 -2.538 0.011 -0.387 -0.107
## Best_MPS -0.035 0.012 -2.834 0.005 -0.028 -0.141
## pre_oe_cost 0.265 0.068 3.887 0.000 0.232 0.232
## eoc_cost_oe_vg 0.469 0.063 7.451 0.000 0.383 0.493
## second_tertile -0.191 0.171 -1.119 0.263 -0.156 -0.073
## third_tertile -0.521 0.165 -3.155 0.002 -0.425 -0.199
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_oe_cost ~~
## eoc_cost_oe_vg 0.762 0.087 8.803 0.000 0.713 0.553
## female ~~
## urm 0.006 0.005 1.276 0.202 0.006 0.049
## Best_MPS 0.028 0.107 0.262 0.793 0.028 0.012
## second_tertile 0.002 0.013 0.143 0.886 0.002 0.009
## third_tertile 0.038 0.013 2.846 0.004 0.038 0.171
## urm ~~
## Best_MPS -0.226 0.059 -3.829 0.000 -0.226 -0.166
## second_tertile 0.001 0.007 0.124 0.902 0.001 0.007
## third_tertile -0.010 0.007 -1.415 0.157 -0.010 -0.077
## Best_MPS ~~
## second_tertile 0.160 0.158 1.015 0.310 0.160 0.070
## third_tertile -0.114 0.164 -0.698 0.485 -0.114 -0.049
## second_tertile ~~
## third_tertile -0.103 0.015 -7.063 0.000 -0.103 -0.472
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_oe_1 2.831 0.050 56.829 0.000 2.831 2.169
## .pre_cost_oe_2 2.859 0.050 57.074 0.000 2.859 2.178
## .pre_cost_oe_3 2.781 0.049 56.768 0.000 2.781 2.165
## .pre_cost_oe_4 2.790 0.050 55.867 0.000 2.790 2.133
## .post_cost_oe_1 2.711 0.329 8.233 0.000 2.711 1.908
## .post_cost_oe_2 2.819 0.358 7.881 0.000 2.819 1.842
## .post_cost_oe_3 2.827 0.316 8.946 0.000 2.827 1.919
## .post_cost_oe_4 2.749 0.338 8.121 0.000 2.749 1.895
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.899 0.000 0.083 0.300
## Best_MPS 20.018 0.234 85.576 0.000 20.018 4.051
## eoc_cost_oe_vg 2.769 0.070 39.840 0.000 2.769 2.149
## second_tertile 0.316 0.028 11.208 0.000 0.316 0.680
## third_tertile 0.331 0.028 11.867 0.000 0.331 0.707
## pre_oe_cost 0.000 0.000 0.000
## .post_oe_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_oe_1 0.562 0.041 13.694 0.000 0.562 0.330
## .pre_cost_oe_2 0.582 0.042 13.865 0.000 0.582 0.337
## .pre_cost_oe_3 0.507 0.039 13.119 0.000 0.507 0.307
## .pre_cost_oe_4 0.646 0.044 14.540 0.000 0.646 0.378
## .post_cost_oe_1 0.521 0.045 11.631 0.000 0.521 0.258
## .post_cost_oe_2 0.571 0.050 11.378 0.000 0.571 0.244
## .post_cost_oe_3 0.798 0.059 13.451 0.000 0.798 0.368
## .post_cost_oe_4 0.521 0.046 11.419 0.000 0.521 0.248
## eoc_cost_oe_vg 1.661 0.136 12.184 0.000 1.661 1.000
## pre_oe_cost 1.141 0.091 12.495 0.000 1.000 1.000
## .post_oe_cost 0.788 0.085 9.314 0.000 0.525 0.525
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.578 0.000 0.076 1.000
## Best_MPS 24.414 1.623 15.042 0.000 24.414 1.000
## second_tertile 0.216 0.018 11.684 0.000 0.216 1.000
## third_tertile 0.219 0.019 11.731 0.000 0.219 1.000## cfi tli rmsea srmr
## 0.990 0.987 0.027 0.030## Warning in lavaan::lavaan(model = LV_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 77 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 54
##
## Number of observations 698
## Number of missing patterns 36
##
## Model Test User Model:
##
## Test statistic 72.679
## Degrees of freedom 65
## P-value (Chi-square) 0.240
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_lv_cost =~
## pre_cost_lv_1 1.000 1.035 0.774
## pre_cost_lv_2 1.093 0.049 22.287 0.000 1.131 0.835
## pre_cost_lv_3 1.156 0.051 22.811 0.000 1.197 0.862
## pre_cost_lv_4 0.980 0.057 17.132 0.000 1.014 0.660
## post_lv_cost =~
## post_cost_lv_1 1.000 1.192 0.818
## post_cost_lv_2 1.047 0.048 21.702 0.000 1.248 0.851
## post_cost_lv_3 1.101 0.050 22.133 0.000 1.312 0.855
## post_cost_lv_4 1.093 0.054 20.140 0.000 1.303 0.805
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_lv_cost ~
## female 0.054 0.102 0.524 0.600 0.045 0.021
## urm -0.490 0.185 -2.645 0.008 -0.411 -0.113
## Best_MPS -0.033 0.012 -2.682 0.007 -0.028 -0.136
## pre_lv_cost 0.282 0.065 4.325 0.000 0.245 0.245
## eoc_cost_lv_vg 0.426 0.056 7.639 0.000 0.357 0.490
## second_tertile -0.051 0.170 -0.297 0.766 -0.042 -0.020
## third_tertile -0.335 0.164 -2.048 0.041 -0.281 -0.132
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_lv_cost ~~
## eoc_cost_lv_vg 0.697 0.090 7.778 0.000 0.673 0.491
## female ~~
## urm 0.006 0.005 1.281 0.200 0.006 0.049
## Best_MPS 0.018 0.107 0.169 0.866 0.018 0.008
## second_tertile 0.001 0.013 0.109 0.913 0.001 0.007
## third_tertile 0.039 0.013 2.931 0.003 0.039 0.177
## urm ~~
## Best_MPS -0.224 0.059 -3.803 0.000 -0.224 -0.165
## second_tertile 0.001 0.007 0.112 0.911 0.001 0.006
## third_tertile -0.010 0.007 -1.410 0.158 -0.010 -0.077
## Best_MPS ~~
## second_tertile 0.153 0.158 0.969 0.333 0.153 0.067
## third_tertile -0.116 0.165 -0.702 0.483 -0.116 -0.050
## second_tertile ~~
## third_tertile -0.103 0.015 -7.048 0.000 -0.103 -0.471
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_lv_1 2.879 0.051 56.375 0.000 2.879 2.152
## .pre_cost_lv_2 3.044 0.052 58.908 0.000 3.044 2.248
## .pre_cost_lv_3 3.027 0.053 57.047 0.000 3.027 2.180
## .pre_cost_lv_4 3.690 0.059 62.970 0.000 3.690 2.403
## .post_cost_lv_1 2.748 0.325 8.468 0.000 2.748 1.886
## .post_cost_lv_2 2.733 0.339 8.056 0.000 2.733 1.865
## .post_cost_lv_3 2.786 0.356 7.817 0.000 2.786 1.815
## .post_cost_lv_4 3.012 0.355 8.485 0.000 3.012 1.861
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.898 0.000 0.083 0.300
## Best_MPS 20.001 0.233 85.681 0.000 20.001 4.050
## eoc_cost_lv_vg 2.781 0.075 37.003 0.000 2.781 2.027
## second_tertile 0.317 0.028 11.237 0.000 0.317 0.682
## third_tertile 0.327 0.028 11.659 0.000 0.327 0.697
## pre_lv_cost 0.000 0.000 0.000
## .post_lv_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_lv_1 0.718 0.049 14.735 0.000 0.718 0.401
## .pre_cost_lv_2 0.555 0.044 12.473 0.000 0.555 0.302
## .pre_cost_lv_3 0.494 0.045 11.007 0.000 0.494 0.256
## .pre_cost_lv_4 1.330 0.080 16.606 0.000 1.330 0.564
## .post_cost_lv_1 0.704 0.056 12.474 0.000 0.704 0.331
## .post_cost_lv_2 0.592 0.052 11.390 0.000 0.592 0.275
## .post_cost_lv_3 0.635 0.057 11.210 0.000 0.635 0.269
## .post_cost_lv_4 0.921 0.072 12.762 0.000 0.921 0.352
## eoc_cost_lv_vg 1.882 0.155 12.128 0.000 1.882 1.000
## pre_lv_cost 1.072 0.093 11.495 0.000 1.000 1.000
## .post_lv_cost 0.773 0.087 8.869 0.000 0.545 0.545
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.582 0.000 0.076 1.000
## Best_MPS 24.388 1.620 15.058 0.000 24.388 1.000
## second_tertile 0.216 0.018 11.685 0.000 0.216 1.000
## third_tertile 0.220 0.019 11.698 0.000 0.220 1.000## cfi tli rmsea srmr
## 0.997 0.997 0.013 0.033## Warning in lavaan::lavaan(model = EM_Cost_1, data = d, missing = "ML.x", :
## lavaan WARNING: syntax contains parameters involving exogenous covariates;
## switching to fixed.x = FALSE## lavaan 0.6-7 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 66
##
## Number of observations 698
## Number of missing patterns 41
##
## Model Test User Model:
##
## Test statistic 289.445
## Degrees of freedom 123
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_em_cost =~
## pre_cost_em_1 1.000 1.253 0.754
## pre_cost_em_2 0.907 0.043 21.156 0.000 1.136 0.796
## pre_cost_em_3 1.027 0.049 20.953 0.000 1.286 0.790
## pre_cost_em_4 0.956 0.045 21.407 0.000 1.199 0.803
## pre_cost_em_5 1.025 0.046 22.395 0.000 1.284 0.838
## pre_cost_em_6 0.994 0.047 21.004 0.000 1.246 0.781
## post_em_cost =~
## post_cost_em_1 1.000 1.408 0.773
## post_cost_em_2 1.010 0.047 21.383 0.000 1.422 0.856
## post_cost_em_3 1.088 0.051 21.230 0.000 1.531 0.852
## post_cost_em_4 1.030 0.048 21.334 0.000 1.449 0.857
## post_cost_em_5 1.059 0.048 21.984 0.000 1.490 0.870
## post_cost_em_6 1.088 0.052 21.086 0.000 1.532 0.841
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_em_cost ~
## female 0.172 0.111 1.555 0.120 0.122 0.058
## urm -0.291 0.197 -1.476 0.140 -0.207 -0.057
## Best_MPS -0.030 0.014 -2.155 0.031 -0.021 -0.105
## pre_em_cost 0.228 0.060 3.821 0.000 0.203 0.203
## eoc_cost_em_vg 0.529 0.052 10.095 0.000 0.376 0.600
## second_tertile -0.409 0.182 -2.249 0.025 -0.291 -0.135
## third_tertile -0.404 0.176 -2.292 0.022 -0.287 -0.134
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pre_em_cost ~~
## eoc_cost_em_vg 1.139 0.124 9.208 0.000 0.909 0.569
## female ~~
## urm 0.006 0.005 1.277 0.202 0.006 0.049
## Best_MPS 0.018 0.107 0.163 0.870 0.018 0.007
## second_tertile 0.001 0.013 0.068 0.946 0.001 0.004
## third_tertile 0.037 0.013 2.765 0.006 0.037 0.168
## urm ~~
## Best_MPS -0.220 0.059 -3.723 0.000 -0.220 -0.162
## second_tertile 0.001 0.007 0.184 0.854 0.001 0.010
## third_tertile -0.010 0.007 -1.429 0.153 -0.010 -0.078
## Best_MPS ~~
## second_tertile 0.171 0.157 1.092 0.275 0.171 0.075
## third_tertile -0.135 0.165 -0.818 0.414 -0.135 -0.058
## second_tertile ~~
## third_tertile -0.103 0.015 -7.070 0.000 -0.103 -0.473
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_em_1 3.985 0.063 62.767 0.000 3.985 2.396
## .pre_cost_em_2 3.400 0.054 62.417 0.000 3.400 2.382
## .pre_cost_em_3 3.817 0.062 61.403 0.000 3.817 2.343
## .pre_cost_em_4 3.453 0.057 60.631 0.000 3.453 2.313
## .pre_cost_em_5 3.607 0.059 61.618 0.000 3.607 2.353
## .pre_cost_em_6 3.741 0.061 61.404 0.000 3.741 2.344
## .post_cost_em_1 3.258 0.372 8.761 0.000 3.258 1.789
## .post_cost_em_2 2.613 0.374 6.992 0.000 2.613 1.573
## .post_cost_em_3 2.827 0.403 7.016 0.000 2.827 1.573
## .post_cost_em_4 2.720 0.381 7.142 0.000 2.720 1.609
## .post_cost_em_5 2.796 0.391 7.150 0.000 2.796 1.632
## .post_cost_em_6 2.942 0.403 7.304 0.000 2.942 1.617
## female 0.342 0.018 19.064 0.000 0.342 0.722
## urm 0.083 0.010 7.896 0.000 0.083 0.300
## Best_MPS 20.006 0.234 85.447 0.000 20.006 4.050
## eoc_cost_em_vg 3.222 0.083 38.745 0.000 3.222 2.018
## second_tertile 0.312 0.028 11.064 0.000 0.312 0.672
## third_tertile 0.326 0.028 11.600 0.000 0.326 0.697
## pre_em_cost 0.000 0.000 0.000
## .post_em_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_cost_em_1 1.195 0.074 16.140 0.000 1.195 0.432
## .pre_cost_em_2 0.746 0.048 15.427 0.000 0.746 0.366
## .pre_cost_em_3 0.999 0.064 15.590 0.000 0.999 0.376
## .pre_cost_em_4 0.792 0.052 15.310 0.000 0.792 0.355
## .pre_cost_em_5 0.700 0.049 14.330 0.000 0.700 0.298
## .pre_cost_em_6 0.993 0.063 15.662 0.000 0.993 0.390
## .post_cost_em_1 1.334 0.092 14.441 0.000 1.334 0.402
## .post_cost_em_2 0.738 0.056 13.145 0.000 0.738 0.268
## .post_cost_em_3 0.884 0.067 13.189 0.000 0.884 0.274
## .post_cost_em_4 0.758 0.058 13.126 0.000 0.758 0.265
## .post_cost_em_5 0.714 0.056 12.732 0.000 0.714 0.243
## .post_cost_em_6 0.967 0.072 13.482 0.000 0.967 0.292
## eoc_cost_em_vg 2.549 0.207 12.303 0.000 2.549 1.000
## pre_em_cost 1.571 0.140 11.256 0.000 1.000 1.000
## .post_em_cost 0.849 0.105 8.081 0.000 0.428 0.428
## female 0.225 0.012 18.682 0.000 0.225 1.000
## urm 0.076 0.004 18.584 0.000 0.076 1.000
## Best_MPS 24.404 1.622 15.044 0.000 24.404 1.000
## second_tertile 0.216 0.018 11.682 0.000 0.216 1.000
## third_tertile 0.219 0.019 11.719 0.000 0.219 1.000## cfi tli rmsea srmr
## 0.969 0.964 0.044 0.055CFA for pre and post
## lavaan 0.6-7 ended normally after 70 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
##
## Used Total
## Number of observations 643 698
##
## Model Test User Model:
##
## Test statistic 910.555
## Degrees of freedom 384
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 15516.106
## Degrees of freedom 435
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.965
## Tucker-Lewis Index (TLI) 0.960
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -26104.246
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 52370.492
## Bayesian (BIC) 52732.249
## Sample-size adjusted Bayesian (BIC) 52475.079
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent confidence interval - lower 0.042
## 90 Percent confidence interval - upper 0.050
## P-value RMSEA <= 0.05 0.948
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.034
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## pre_interest =~
## pre_int_1 1.000
## pre_int_2 0.982 0.024 40.445 0.000
## pre_int_3 0.954 0.028 34.389 0.000
## pre_int_4 0.867 0.029 29.824 0.000
## pre_int_5 0.988 0.023 43.866 0.000
## pre_expectancies =~
## pre_exp_1 1.000
## pre_exp_2 0.957 0.048 19.790 0.000
## pre_exp_3 1.120 0.056 19.927 0.000
## pre_value =~
## pre_val_1 1.000
## pre_val_2 1.046 0.049 21.374 0.000
## pre_val_3 1.032 0.050 20.506 0.000
## pre_te_cost =~
## pre_cost_te_1 1.000
## pre_cost_te_2 0.959 0.039 24.367 0.000
## pre_cost_te_3 0.983 0.035 28.328 0.000
## pre_cost_te_4 0.973 0.037 26.489 0.000
## pre_cost_te_5 0.984 0.039 25.456 0.000
## pre_oe_cost =~
## pre_cost_oe_1 1.000
## pre_cost_oe_2 1.015 0.044 23.328 0.000
## pre_cost_oe_3 1.011 0.042 24.037 0.000
## pre_cost_oe_4 0.976 0.044 22.285 0.000
## pre_lv_cost =~
## pre_cost_lv_1 1.000
## pre_cost_lv_2 1.013 0.042 23.965 0.000
## pre_cost_lv_3 1.054 0.043 24.330 0.000
## pre_cost_lv_4 0.903 0.052 17.292 0.000
## pre_em_cost =~
## pre_cost_em_1 1.000
## pre_cost_em_2 0.967 0.046 21.210 0.000
## pre_cost_em_3 1.021 0.052 19.476 0.000
## pre_cost_em_4 0.980 0.048 20.578 0.000
## pre_cost_em_5 1.053 0.049 21.582 0.000
## pre_cost_em_6 0.987 0.051 19.235 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## pre_interest ~~
## pre_expectancs 0.691 0.064 10.867 0.000
## pre_value 0.860 0.069 12.415 0.000
## pre_te_cost -0.482 0.073 -6.585 0.000
## pre_oe_cost -0.380 0.065 -5.825 0.000
## pre_lv_cost -0.351 0.067 -5.267 0.000
## pre_em_cost -0.598 0.078 -7.638 0.000
## pre_expectancies ~~
## pre_value 0.521 0.047 11.107 0.000
## pre_te_cost -0.520 0.054 -9.579 0.000
## pre_oe_cost -0.463 0.049 -9.435 0.000
## pre_lv_cost -0.411 0.048 -8.502 0.000
## pre_em_cost -0.542 0.058 -9.387 0.000
## pre_value ~~
## pre_te_cost -0.296 0.050 -5.884 0.000
## pre_oe_cost -0.302 0.046 -6.559 0.000
## pre_lv_cost -0.229 0.046 -4.986 0.000
## pre_em_cost -0.249 0.051 -4.874 0.000
## pre_te_cost ~~
## pre_oe_cost 0.996 0.076 13.095 0.000
## pre_lv_cost 1.147 0.082 13.948 0.000
## pre_em_cost 1.287 0.097 13.228 0.000
## pre_oe_cost ~~
## pre_lv_cost 0.954 0.072 13.218 0.000
## pre_em_cost 0.875 0.077 11.418 0.000
## pre_lv_cost ~~
## pre_em_cost 1.101 0.087 12.679 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pre_int_1 0.316 0.024 13.221 0.000
## .pre_int_2 0.367 0.026 14.038 0.000
## .pre_int_3 0.601 0.038 15.719 0.000
## .pre_int_4 0.744 0.045 16.466 0.000
## .pre_int_5 0.261 0.021 12.381 0.000
## .pre_exp_1 0.557 0.039 14.346 0.000
## .pre_exp_2 0.335 0.027 12.375 0.000
## .pre_exp_3 0.436 0.036 12.091 0.000
## .pre_val_1 0.448 0.034 13.052 0.000
## .pre_val_2 0.436 0.035 12.437 0.000
## .pre_val_3 0.531 0.039 13.559 0.000
## .pre_cost_te_1 0.562 0.037 15.028 0.000
## .pre_cost_te_2 0.797 0.050 16.063 0.000
## .pre_cost_te_3 0.468 0.032 14.550 0.000
## .pre_cost_te_4 0.607 0.039 15.393 0.000
## .pre_cost_te_5 0.721 0.046 15.750 0.000
## .pre_cost_oe_1 0.566 0.040 14.295 0.000
## .pre_cost_oe_2 0.582 0.041 14.293 0.000
## .pre_cost_oe_3 0.504 0.037 13.742 0.000
## .pre_cost_oe_4 0.652 0.044 14.934 0.000
## .pre_cost_lv_1 0.572 0.039 14.592 0.000
## .pre_cost_lv_2 0.595 0.041 14.640 0.000
## .pre_cost_lv_3 0.604 0.042 14.404 0.000
## .pre_cost_lv_4 1.382 0.082 16.842 0.000
## .pre_cost_em_1 1.253 0.076 16.388 0.000
## .pre_cost_em_2 0.642 0.043 15.090 0.000
## .pre_cost_em_3 1.100 0.068 16.097 0.000
## .pre_cost_em_4 0.779 0.050 15.533 0.000
## .pre_cost_em_5 0.684 0.046 14.769 0.000
## .pre_cost_em_6 1.087 0.067 16.196 0.000
## pre_interest 1.910 0.124 15.409 0.000
## pre_expectancs 0.722 0.068 10.565 0.000
## pre_value 0.797 0.069 11.560 0.000
## pre_te_cost 1.429 0.108 13.175 0.000
## pre_oe_cost 1.116 0.092 12.196 0.000
## pre_lv_cost 1.167 0.094 12.363 0.000
## pre_em_cost 1.483 0.139 10.657 0.000## lavaan 0.6-7 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
##
## Used Total
## Number of observations 487 698
##
## Model Test User Model:
##
## Test statistic 846.564
## Degrees of freedom 384
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 13917.278
## Degrees of freedom 435
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.966
## Tucker-Lewis Index (TLI) 0.961
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -20570.635
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 41303.270
## Bayesian (BIC) 41642.520
## Sample-size adjusted Bayesian (BIC) 41385.429
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.050
## 90 Percent confidence interval - lower 0.045
## 90 Percent confidence interval - upper 0.054
## P-value RMSEA <= 0.05 0.531
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.035
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## post_interest =~
## post_int_1 1.000
## post_int_2 1.016 0.028 36.178 0.000
## post_int_3 1.014 0.030 33.946 0.000
## post_int_4 0.927 0.033 28.134 0.000
## post_int_5 1.013 0.027 37.200 0.000
## post_expectancies =~
## post_exp_1 1.000
## post_exp_2 1.043 0.062 16.787 0.000
## post_exp_3 1.118 0.065 17.145 0.000
## post_value =~
## post_val_1 1.000
## post_val_2 1.011 0.055 18.437 0.000
## post_val_3 1.069 0.055 19.292 0.000
## post_te_cost =~
## post_cost_te_1 1.000
## post_cost_te_2 0.938 0.041 22.854 0.000
## post_cost_te_3 1.043 0.037 28.351 0.000
## post_cost_te_4 0.985 0.036 27.093 0.000
## post_cost_te_5 0.945 0.037 25.799 0.000
## post_oe_cost =~
## post_cost_oe_1 1.000
## post_cost_oe_2 1.097 0.042 25.809 0.000
## post_cost_oe_3 0.960 0.044 21.765 0.000
## post_cost_oe_4 1.006 0.041 24.641 0.000
## post_lv_cost =~
## post_cost_lv_1 1.000
## post_cost_lv_2 1.011 0.045 22.325 0.000
## post_cost_lv_3 1.085 0.047 23.197 0.000
## post_cost_lv_4 1.076 0.052 20.872 0.000
## post_em_cost =~
## post_cost_em_1 1.000
## post_cost_em_2 1.036 0.049 21.352 0.000
## post_cost_em_3 1.081 0.053 20.446 0.000
## post_cost_em_4 1.035 0.049 21.043 0.000
## post_cost_em_5 1.065 0.050 21.489 0.000
## post_cost_em_6 1.077 0.054 19.992 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## post_interest ~~
## post_expectncs 0.913 0.094 9.701 0.000
## post_value 1.214 0.108 11.207 0.000
## post_te_cost -0.732 0.105 -6.968 0.000
## post_oe_cost -0.427 0.091 -4.666 0.000
## post_lv_cost -0.552 0.092 -6.005 0.000
## post_em_cost -0.914 0.115 -7.962 0.000
## post_expectancies ~~
## post_value 0.820 0.080 10.246 0.000
## post_te_cost -0.739 0.084 -8.823 0.000
## post_oe_cost -0.611 0.074 -8.207 0.000
## post_lv_cost -0.632 0.074 -8.501 0.000
## post_em_cost -0.809 0.091 -8.881 0.000
## post_value ~~
## post_te_cost -0.557 0.082 -6.775 0.000
## post_oe_cost -0.438 0.073 -5.988 0.000
## post_lv_cost -0.472 0.073 -6.474 0.000
## post_em_cost -0.593 0.087 -6.819 0.000
## post_te_cost ~~
## post_oe_cost 1.379 0.112 12.292 0.000
## post_lv_cost 1.528 0.118 12.927 0.000
## post_em_cost 1.692 0.140 12.063 0.000
## post_oe_cost ~~
## post_lv_cost 1.263 0.103 12.243 0.000
## post_em_cost 1.194 0.113 10.573 0.000
## post_lv_cost ~~
## post_em_cost 1.362 0.120 11.334 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .post_int_1 0.402 0.033 12.192 0.000
## .post_int_2 0.394 0.033 12.007 0.000
## .post_int_3 0.507 0.040 12.834 0.000
## .post_int_4 0.785 0.056 14.125 0.000
## .post_int_5 0.346 0.030 11.522 0.000
## .post_exp_1 0.663 0.057 11.654 0.000
## .post_exp_2 0.736 0.063 11.727 0.000
## .post_exp_3 0.753 0.067 11.247 0.000
## .post_val_1 0.702 0.058 12.026 0.000
## .post_val_2 0.680 0.058 11.823 0.000
## .post_val_3 0.591 0.055 10.709 0.000
## .post_cost_te_1 0.534 0.041 13.146 0.000
## .post_cost_te_2 0.967 0.067 14.433 0.000
## .post_cost_te_3 0.565 0.043 13.074 0.000
## .post_cost_te_4 0.603 0.045 13.505 0.000
## .post_cost_te_5 0.664 0.048 13.857 0.000
## .post_cost_oe_1 0.528 0.043 12.228 0.000
## .post_cost_oe_2 0.516 0.045 11.443 0.000
## .post_cost_oe_3 0.786 0.058 13.537 0.000
## .post_cost_oe_4 0.539 0.044 12.261 0.000
## .post_cost_lv_1 0.624 0.047 13.347 0.000
## .post_cost_lv_2 0.656 0.049 13.415 0.000
## .post_cost_lv_3 0.642 0.049 12.997 0.000
## .post_cost_lv_4 0.959 0.069 13.935 0.000
## .post_cost_em_1 1.359 0.094 14.398 0.000
## .post_cost_em_2 0.658 0.051 12.909 0.000
## .post_cost_em_3 0.930 0.069 13.534 0.000
## .post_cost_em_4 0.721 0.055 13.151 0.000
## .post_cost_em_5 0.667 0.052 12.790 0.000
## .post_cost_em_6 1.040 0.076 13.769 0.000
## post_interest 2.250 0.169 13.293 0.000
## post_expectncs 1.011 0.106 9.581 0.000
## post_value 1.182 0.118 10.012 0.000
## post_te_cost 1.849 0.150 12.291 0.000
## post_oe_cost 1.491 0.128 11.675 0.000
## post_lv_cost 1.402 0.126 11.140 0.000
## post_em_cost 1.972 0.198 9.946 0.000##
## 1 2 3 4
## 67 110 95 426##
## 1 2 3 4 5 <NA>
## 565 93 24 6 5 5##
## 1 2 3
## 0 6 391 28
## 1 215 55 2##
## 1 2 3
## 0 4 283 20
## 1 201 11 1##
## MTH
## 34 664Female by condition
##
## 0 1
## 0 277 148
## 1 182 91##
## Asian (non-Hispanic) Black or African American (non-Hispanic)
## 0 37 12
## 1 19 15
##
## Hispanic Ethnicity International Not Reported
## 0 17 62 6
## 1 13 48 1
##
## Two or more races (non-Hispanic) White (non-Hispanic)
## 0 14 277
## 1 4 173##
## 0 1
## 459 239Proportions and means by group for Pre Survey eoc_participate
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc | prop_female | mean_pre_interest | mean_pre_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 4.840 | 4.922 | 5.563 | 5.874 | 3.328 | 2.892 | 3.152 | 3.671 |
| 1 | 0.333 | 5.097 | 4.985 | 5.820 | 5.977 | 3.308 | 2.693 | 3.170 | 3.655 |
Proportions and means by group for Post Survey eoc_participate
## `summarise()` ungrouping output (override with `.groups` argument)| participate_eoc | prop_female | mean_Grade | mean_post_interest | mean_post_confidence | mean_val | mean_stem_int | mean_cost_te | mean_cost_oe | mean_cost_lv | mean_cost_em |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.348 | 2.813 | 4.592 | 5.059 | 5.179 | 5.632 | 3.484 | 3.158 | 3.268 | 3.875 |
| 1 | 0.333 | 2.922 | 4.752 | 5.268 | 5.423 | 5.770 | 3.254 | 3.026 | 3.081 | 3.548 |