Wellness LPA Analyses Output

Demographics

Majors

Race/Ethnicity

Gender

GPA

## 
##    0  0.5  0.7    1  1.1  1.2  1.3  1.5  1.6  1.7  1.8  1.9    2  2.1  2.2 
##   18    1    1    2    1    1    1    3    1    4    5    6   16    9    9 
##  2.3  2.4  2.5  2.6  2.7  2.8  2.9    3  3.1  3.2  3.3  3.4  3.5  3.6  3.7 
##   20   26   52   43   50   91   62  189   98  129   97  112  191  125  158 
##  3.8  3.9    4  4.1  4.2  4.4  4.7  5.2 <NA> 
##  171  134  253    1    1    1    1    1  255

## 
##    0    1    2    3    4    5 <NA> 
##   19    7  142  933  981    2  255

## Warning: Removed 255 rows containing non-finite values (stat_bin).

## Warning: Removed 255 rows containing non-finite values (stat_bin).

Normality & Outliers

Univariate Normality

Checked the skewness and kurtosis of the chosen items, everything is good to proceed.

Outlier Analysis

Outliers can severely distort LPA results, so even though the data had already been screened for outliers I checked for them again using the LPA variables. Cutoff of 2 was used, per recommendations from the literature (Tabachnik).

##  [1] "major"        "raceeth"      "raceeth_text" "eth_text"    
##  [5] "gender"       "gender_gq"    "gender_text"  "Q46"         
##  [9] "Q46_5_TEXT"   "Q49a"         "Q49b"         "Q57"         
## [13] "Q57_4_TEXT"   "Q57_10_TEXT"  "UID"          "gpa"         
## [17] "raceeth_rc"   "gender_rc"    "gender_bin"   "gender_bin2" 
## [21] "gpa_r1"       "gpa_r2"

##  [1] "UID2"           "pos_class"      "pos_pp"         "neg_class"     
##  [5] "neg_pp"         "UID.x"          "meaningpurpose" "gratitude"     
##  [9] "motExpeng"      "mindfulness"    "TestAnx"        "stressChanges" 
## [13] "stressConflict" "stressFrust"    "stressReac"     "stressSupp"    
## [17] "UID.y"          "belong_c"       "future_c"       "UID"           
## [21] "gpa"            "raceeth_rc"     "gender_rc"      "gender_bin"    
## [25] "gpa_r2"

## 
##   No  Yes 
## 2285   54

Correlations

Correlation Matrix

Observing the correlations between the LPA items.

##                meaningpurpose    gratitude    motExpeng  mindfulness
## meaningpurpose   0.000000e+00 1.039319e-52 2.470569e-59 1.619871e-41
## gratitude        2.886997e-54 0.000000e+00 3.656565e-58 9.387786e-12
## motExpeng        6.176424e-61 9.375808e-60 0.000000e+00 1.047721e-24
## mindfulness      5.043783e-43 5.522227e-13 4.190885e-26 0.000000e+00
## TestAnx          1.584257e-13 2.600324e-01 2.469247e-37 2.476120e-49
## stressChanges    4.145602e-19 2.685202e-02 6.525155e-10 2.736809e-50
## stressConflict   2.674689e-03 8.053918e-03 4.084215e-01 8.402075e-13
## stressFrust      3.140736e-17 7.088953e-01 8.547784e-17 3.106848e-39
## stressReac       4.370579e-19 4.091471e-04 5.083826e-16 2.254930e-37
## stressSupp       1.874068e-26 6.039696e-27 1.696567e-04 1.276931e-03
##                      TestAnx stressChanges stressConflict   stressFrust
## meaningpurpose  2.851662e-12  9.534885e-18   2.139751e-02  6.595545e-16
## gratitude       7.800972e-01  1.342601e-01   4.832351e-02  8.168431e-01
## motExpeng       7.160817e-36  9.787732e-09   8.168431e-01  1.709557e-15
## mindfulness     8.418808e-48  9.578833e-49   1.344332e-11  9.631229e-38
## TestAnx         0.000000e+00  1.619871e-41   1.724284e-04  1.153150e-75
## stressChanges   4.908699e-43  0.000000e+00   9.199381e-57 3.848376e-112
## stressConflict  1.231631e-05  2.420890e-58   0.000000e+00  9.401733e-54
## stressFrust     2.812562e-77 8.746310e-114   2.541009e-55  0.000000e+00
## stressReac     1.330841e-130 3.161791e-100   3.124512e-32  2.275868e-87
## stressSupp      3.780676e-03  2.114950e-03   6.232789e-04  1.465641e-01
##                   stressReac   stressSupp
## meaningpurpose  9.615274e-18 4.872576e-25
## gratitude       4.909765e-03 1.630718e-25
## motExpeng       9.659270e-15 2.205537e-03
## mindfulness     6.764791e-36 1.276931e-02
## TestAnx        5.988783e-129 2.646474e-02
## stressChanges   1.359570e-98 1.903455e-02
## stressConflict  8.748632e-31 6.856067e-03
## stressFrust     9.558644e-86 5.862565e-01
## stressReac      0.000000e+00 1.161332e-19
## stressSupp      4.838882e-21 0.000000e+00

Correlation Plot

Plot suggests that there are two clusters, the positive and negative variables. This grouping was used in the LPA analysis when running all 10 items together failed to converge.

LPA Output

LPA Plots - Positive Variables

Barplots of the positive variables, grouped by their latent profile assignment.

• Group 1: Low Positive Wellness, n = 290
• Group 2: High Positive Wellness, n = 258
• Group 3: High Stress Support, n = 426
• Group 4: Average Positive Wellness, n = 829
• Group 5: Low Stress Support and High Expectancy, n = 536

LPA Plots - Negative Variables

Barplots of the negative variables, grouped by their latent profile assignment.

• Group 1: High Anxiety & Reactivity, n = 425
• Group 2: Low Anxiety & Reactivity, n = 560
• Group 3: High Stress, n = 304
• Group 4: Low stress, n = 356
• Group 5: Average Stress, n = 694

LPA Plots - All Variables

First table is by positive class, second is by negative class.

Chi-Squares (Race/Gen)

## [1] 2097

## 
##  Pearson's Chi-squared test
## 
## data:  table
## X-squared = 100.89, df = 20, p-value = 8.712e-13

## [1] 2097

## 
##  Pearson's Chi-squared test
## 
## data:  table
## X-squared = 125.71, df = 20, p-value < 2.2e-16

Chi-Square 2 (GPA)

table(df2$gpa_r2, df2$pos_class, useNA = "always")

##       
##          1   2   3   4   5 <NA>
##   0      2   2   2  10   1    0
##   1      1   0   0   5   1    0
##   2     18   9  24  48  26    0
##   3    110  83 146 321 188    0
##   4     66 125 173 308 213    0
##   5      0   1   1   0   0    0
##   <NA>  39  16  44  66  48    0

table(df2$gpa_r2, df2$neg_class, useNA = "always")

##       
##          1   2   3   4   5 <NA>
##   0      3   7   2   3   2    0
##   1      0   1   2   0   4    0
##   2     25  19  31  10  40    0
##   3    174 179 129 117 249    0
##   4    128 244  69 160 284    0
##   5      0   0   0   0   2    0
##   <NA>  40  48  30  30  65    0

df3 <- df2
df3$gpa_r2[df2$gpa_r2 == 0 | df2$gpa_r2 == 1 | df2$gpa_r2 == 2] <- "<= 2"
df3$gpa_r2[df2$gpa_r2 == 3] <- "3"
df3$gpa_r2[df2$gpa_r2 == 4 | df2$gpa_r2 == 5] <- ">= 4"
df3$gpa_r2[is.na(df2$gpa_r2)] <- "NA"
df3$gpa_r2 <- factor(df3$gpa_r2, levels = c("<= 2","3",">= 4","NA"))

table <- table(df3$gpa_r2, df3$pos_class)
n <- colSums(table)
sum(unlist(n))

## [1] 2097

cs <- chisq.test(table)
cs

## 
##  Pearson's Chi-squared test
## 
## data:  table
## X-squared = 45.329, df = 12, p-value = 9.051e-06

sr <- as.data.frame.matrix(cs$stdres)

datatable(as.data.frame.matrix(table), options = list(pageLength = 50),
  rownames = T, class = "cell-border stripe")

datatable(sr, options = list(pageLength = 50),
  rownames = T, class = "cell-border stripe")

sr$gpa <- rownames(sr)
sr_long <- sr %>% gather(profile, sr, 1:5)
sr_long$srabs <- abs(sr_long$sr)
sr_long$ou <- "n"
sr_long$ou[sr_long$sr > 3] <- "o"
sr_long$ou[sr_long$sr < -3] <- "u"
sr_long$gpa <- factor(sr_long$gpa, levels = c("<= 2","3",">= 4","NA"))

ggplot(sr_long, aes(x = gpa, y = srabs, group = profile, fill = ou, label = profile)) +
  facet_grid(.~profile) +
  geom_bar(stat="identity", width=.8, position=position_dodge(width=.9)) +
  geom_hline(aes(yintercept=3), color="#000000", linetype="dashed") +
  geom_text(aes(label=ifelse(sr>3,as.character(round(sr, digits = 1)),'')), position=position_dodge(width=.9), vjust=-1, cex=2.5) +
  geom_text(aes(label=ifelse(-3>sr,as.character(round(sr, digits = 1)),'')), position=position_dodge(width=.9), vjust=-1, cex=2.5) +
  theme_bw()

table <- table(df3$gpa_r2, df3$neg_class)
n <- colSums(table)
sum(unlist(n))

## [1] 2097

cs <- chisq.test(table)
cs

## 
##  Pearson's Chi-squared test
## 
## data:  table
## X-squared = 66.04, df = 12, p-value = 1.75e-09

sr <- as.data.frame.matrix(cs$stdres)

datatable(as.data.frame.matrix(table), options = list(pageLength = 50),
  rownames = T, class = "cell-border stripe")

datatable(sr, options = list(pageLength = 50),
  rownames = T, class = "cell-border stripe")

sr$gpa <- rownames(sr)
sr_long <- sr %>% gather(profile, sr, 1:5)
sr_long$srabs <- abs(sr_long$sr)
sr_long$ou <- "n"
sr_long$ou[sr_long$sr > 3] <- "o"
sr_long$ou[sr_long$sr < -3] <- "u"
sr_long$gpa <- factor(sr_long$gpa, levels = c("<= 2","3",">= 4","NA"))

ggplot(sr_long, aes(x = gpa, y = srabs, group = profile, fill = ou, label = profile)) +
  facet_grid(.~profile) +
  geom_bar(stat="identity", width=.8, position=position_dodge(width=.9)) +
  geom_hline(aes(yintercept=3), color="#000000", linetype="dashed") +
  geom_text(aes(label=ifelse(sr>3,as.character(round(sr, digits = 1)),'')), position=position_dodge(width=.9), vjust=-1, cex=2.5) +
  geom_text(aes(label=ifelse(-3>sr,as.character(round(sr, digits = 1)),'')), position=position_dodge(width=.9), vjust=-1, cex=2.5) +
  theme_bw()

Wellbeing and Perceptions of Belonging and Future in Engineering

Regression (Pos Belonging)

Reference group is the Average Group (group 4).

## Warning: package 'mctest' was built under R version 3.6.1

## Warning: package 'sjPlot' was built under R version 3.6.1

## Learn more about sjPlot with 'browseVignettes("sjPlot")'.

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7712 -0.3897  0.1046  0.4705  2.4466 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.002255   0.015454   0.146  0.88402    
## meaningpurpose  0.173354   0.017732   9.776  < 2e-16 ***
## gratitude       0.217760   0.018240  11.938  < 2e-16 ***
## motExpeng       0.450228   0.017795  25.301  < 2e-16 ***
## mindfulness     0.051895   0.016524   3.141  0.00171 ** 
## stressSupp     -0.076135   0.016272  -4.679 3.06e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7383 on 2279 degrees of freedom
## Multiple R-squared:  0.4225, Adjusted R-squared:  0.4212 
## F-statistic: 333.5 on 5 and 2279 DF,  p-value: < 2.2e-16

## $meaningpurpose

## 
## $gratitude

## 
## $motExpeng

## 
## $mindfulness

## 
## $stressSupp

## 
## Call:
## lm(formula = belong_c ~ pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4356 -0.4863  0.0808  0.5902  2.2237 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.14162    0.02991  -4.734 2.34e-06 ***
## pos_class_r1 -0.82834    0.06181 -13.402  < 2e-16 ***
## pos_class_r2  0.87521    0.06162  14.203  < 2e-16 ***
## pos_class_r3  0.18814    0.05147   3.656 0.000262 ***
## pos_class_r4  0.50942    0.04792  10.630  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8603 on 2280 degrees of freedom
## Multiple R-squared:  0.2155, Adjusted R-squared:  0.2141 
## F-statistic: 156.6 on 4 and 2280 DF,  p-value: < 2.2e-16

## $pos_class_r

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp + pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8243 -0.3893  0.1092  0.4664  2.5223 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.04290    0.03012   1.425  0.15442    
## meaningpurpose  0.16400    0.01886   8.695  < 2e-16 ***
## gratitude       0.23060    0.02612   8.827  < 2e-16 ***
## motExpeng       0.45612    0.02098  21.737  < 2e-16 ***
## mindfulness     0.04501    0.01711   2.630  0.00859 ** 
## stressSupp     -0.09157    0.02154  -4.251 2.21e-05 ***
## pos_class_r1   -0.08453    0.06197  -1.364  0.17269    
## pos_class_r2   -0.02606    0.07269  -0.359  0.71997    
## pos_class_r3   -0.03023    0.06100  -0.495  0.62031    
## pos_class_r4   -0.09938    0.05644  -1.761  0.07837 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7381 on 2275 degrees of freedom
## Multiple R-squared:  0.4238, Adjusted R-squared:  0.4215 
## F-statistic: 185.9 on 9 and 2275 DF,  p-value: < 2.2e-16

## $meaningpurpose

## 
## $gratitude

## 
## $motExpeng

## 
## $mindfulness

## 
## $stressSupp

## 
## $pos_class_r

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose * pos_class_r + gratitude * 
##     pos_class_r + motExpeng * pos_class_r + mindfulness * pos_class_r + 
##     stressSupp * pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7943 -0.3927  0.0973  0.4673  2.5337 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.052301   0.040640   1.287 0.198248    
## meaningpurpose               0.194174   0.040332   4.814 1.57e-06 ***
## pos_class_r1                 0.052334   0.116605   0.449 0.653609    
## pos_class_r2                -0.165492   0.251846  -0.657 0.511173    
## pos_class_r3                -0.023696   0.114773  -0.206 0.836448    
## pos_class_r4                -0.063243   0.085069  -0.743 0.457299    
## gratitude                    0.257821   0.048377   5.329 1.08e-07 ***
## motExpeng                    0.454636   0.038766  11.728  < 2e-16 ***
## mindfulness                  0.039900   0.035110   1.136 0.255900    
## stressSupp                  -0.111197   0.030958  -3.592 0.000335 ***
## meaningpurpose:pos_class_r1 -0.047666   0.057881  -0.824 0.410304    
## meaningpurpose:pos_class_r2  0.219370   0.153458   1.430 0.152995    
## meaningpurpose:pos_class_r3  0.023919   0.064548   0.371 0.711003    
## meaningpurpose:pos_class_r4 -0.070165   0.052534  -1.336 0.181817    
## pos_class_r1:gratitude      -0.041702   0.065380  -0.638 0.523644    
## pos_class_r2:gratitude      -0.184304   0.222131  -0.830 0.406791    
## pos_class_r3:gratitude      -0.016699   0.105336  -0.159 0.874055    
## pos_class_r4:gratitude       0.069363   0.086868   0.798 0.424674    
## pos_class_r1:motExpeng       0.117916   0.056913   2.072 0.038392 *  
## pos_class_r2:motExpeng      -0.067066   0.105867  -0.633 0.526480    
## pos_class_r3:motExpeng      -0.015137   0.058967  -0.257 0.797436    
## pos_class_r4:motExpeng      -0.198389   0.069370  -2.860 0.004277 ** 
## pos_class_r1:mindfulness     0.043575   0.064782   0.673 0.501251    
## pos_class_r2:mindfulness     0.012027   0.075158   0.160 0.872882    
## pos_class_r3:mindfulness     0.020882   0.050730   0.412 0.680655    
## pos_class_r4:mindfulness    -0.010424   0.048314  -0.216 0.829194    
## pos_class_r1:stressSupp      0.048763   0.065163   0.748 0.454344    
## pos_class_r2:stressSupp      0.066970   0.101341   0.661 0.508785    
## pos_class_r3:stressSupp      0.005034   0.085675   0.059 0.953149    
## pos_class_r4:stressSupp      0.012230   0.058069   0.211 0.833209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7354 on 2255 degrees of freedom
## Multiple R-squared:  0.4331, Adjusted R-squared:  0.4258 
## F-statistic:  59.4 on 29 and 2255 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ 1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5940 -0.5746  0.2305  0.6331  1.2370 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01679    0.02030   0.827    0.408
## 
## Residual standard error: 0.9704 on 2284 degrees of freedom

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7712 -0.3897  0.1046  0.4705  2.4466 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.002255   0.015454   0.146  0.88402    
## meaningpurpose  0.173354   0.017732   9.776  < 2e-16 ***
## gratitude       0.217760   0.018240  11.938  < 2e-16 ***
## motExpeng       0.450228   0.017795  25.301  < 2e-16 ***
## mindfulness     0.051895   0.016524   3.141  0.00171 ** 
## stressSupp     -0.076135   0.016272  -4.679 3.06e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7383 on 2279 degrees of freedom
## Multiple R-squared:  0.4225, Adjusted R-squared:  0.4212 
## F-statistic: 333.5 on 5 and 2279 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4356 -0.4863  0.0808  0.5902  2.2237 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.14162    0.02991  -4.734 2.34e-06 ***
## pos_class_r1 -0.82834    0.06181 -13.402  < 2e-16 ***
## pos_class_r2  0.87521    0.06162  14.203  < 2e-16 ***
## pos_class_r3  0.18814    0.05147   3.656 0.000262 ***
## pos_class_r4  0.50942    0.04792  10.630  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8603 on 2280 degrees of freedom
## Multiple R-squared:  0.2155, Adjusted R-squared:  0.2141 
## F-statistic: 156.6 on 4 and 2280 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp + pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8243 -0.3893  0.1092  0.4664  2.5223 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.04290    0.03012   1.425  0.15442    
## meaningpurpose  0.16400    0.01886   8.695  < 2e-16 ***
## gratitude       0.23060    0.02612   8.827  < 2e-16 ***
## motExpeng       0.45612    0.02098  21.737  < 2e-16 ***
## mindfulness     0.04501    0.01711   2.630  0.00859 ** 
## stressSupp     -0.09157    0.02154  -4.251 2.21e-05 ***
## pos_class_r1   -0.08453    0.06197  -1.364  0.17269    
## pos_class_r2   -0.02606    0.07269  -0.359  0.71997    
## pos_class_r3   -0.03023    0.06100  -0.495  0.62031    
## pos_class_r4   -0.09938    0.05644  -1.761  0.07837 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7381 on 2275 degrees of freedom
## Multiple R-squared:  0.4238, Adjusted R-squared:  0.4215 
## F-statistic: 185.9 on 9 and 2275 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ meaningpurpose * pos_class_r + gratitude * 
##     pos_class_r + motExpeng * pos_class_r + mindfulness * pos_class_r + 
##     stressSupp * pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7943 -0.3927  0.0973  0.4673  2.5337 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.052301   0.040640   1.287 0.198248    
## meaningpurpose               0.194174   0.040332   4.814 1.57e-06 ***
## pos_class_r1                 0.052334   0.116605   0.449 0.653609    
## pos_class_r2                -0.165492   0.251846  -0.657 0.511173    
## pos_class_r3                -0.023696   0.114773  -0.206 0.836448    
## pos_class_r4                -0.063243   0.085069  -0.743 0.457299    
## gratitude                    0.257821   0.048377   5.329 1.08e-07 ***
## motExpeng                    0.454636   0.038766  11.728  < 2e-16 ***
## mindfulness                  0.039900   0.035110   1.136 0.255900    
## stressSupp                  -0.111197   0.030958  -3.592 0.000335 ***
## meaningpurpose:pos_class_r1 -0.047666   0.057881  -0.824 0.410304    
## meaningpurpose:pos_class_r2  0.219370   0.153458   1.430 0.152995    
## meaningpurpose:pos_class_r3  0.023919   0.064548   0.371 0.711003    
## meaningpurpose:pos_class_r4 -0.070165   0.052534  -1.336 0.181817    
## pos_class_r1:gratitude      -0.041702   0.065380  -0.638 0.523644    
## pos_class_r2:gratitude      -0.184304   0.222131  -0.830 0.406791    
## pos_class_r3:gratitude      -0.016699   0.105336  -0.159 0.874055    
## pos_class_r4:gratitude       0.069363   0.086868   0.798 0.424674    
## pos_class_r1:motExpeng       0.117916   0.056913   2.072 0.038392 *  
## pos_class_r2:motExpeng      -0.067066   0.105867  -0.633 0.526480    
## pos_class_r3:motExpeng      -0.015137   0.058967  -0.257 0.797436    
## pos_class_r4:motExpeng      -0.198389   0.069370  -2.860 0.004277 ** 
## pos_class_r1:mindfulness     0.043575   0.064782   0.673 0.501251    
## pos_class_r2:mindfulness     0.012027   0.075158   0.160 0.872882    
## pos_class_r3:mindfulness     0.020882   0.050730   0.412 0.680655    
## pos_class_r4:mindfulness    -0.010424   0.048314  -0.216 0.829194    
## pos_class_r1:stressSupp      0.048763   0.065163   0.748 0.454344    
## pos_class_r2:stressSupp      0.066970   0.101341   0.661 0.508785    
## pos_class_r3:stressSupp      0.005034   0.085675   0.059 0.953149    
## pos_class_r4:stressSupp      0.012230   0.058069   0.211 0.833209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7354 on 2255 degrees of freedom
## Multiple R-squared:  0.4331, Adjusted R-squared:  0.4258 
## F-statistic:  59.4 on 29 and 2255 DF,  p-value: < 2.2e-16

## Analysis of Variance Table
## 
## Model 1: belong_c ~ 1
## Model 2: belong_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 3: belong_c ~ pos_class_r
## Model 4: belong_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp + pos_class_r
## Model 5: belong_c ~ meaningpurpose * pos_class_r + gratitude * pos_class_r + 
##     motExpeng * pos_class_r + mindfulness * pos_class_r + stressSupp * 
##     pos_class_r
##   Res.Df    RSS Df Sum of Sq        F  Pr(>F)    
## 1   2284 2150.9                                  
## 2   2279 1242.2  5    908.79 336.1040 < 2e-16 ***
## 3   2280 1687.4 -1   -445.25 823.3457 < 2e-16 ***
## 4   2275 1239.4  5    447.98 165.6793 < 2e-16 ***
## 5   2255 1219.5 20     19.97   1.8461 0.01247 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: belong_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 2: belong_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp + pos_class_r
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2279 1242.2                           
## 2   2275 1239.4  4    2.7315 1.2534 0.2862

## Analysis of Variance Table
## 
## Model 1: belong_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 2: belong_c ~ meaningpurpose * pos_class_r + gratitude * pos_class_r + 
##     motExpeng * pos_class_r + mindfulness * pos_class_r + stressSupp * 
##     pos_class_r
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1   2279 1242.2                              
## 2   2255 1219.5 24    22.698 1.7489 0.01362 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Regression (Neg Belonging)

Reference group is the Average Group (group 5)

## Warning: package 'ppcor' was built under R version 3.6.3

## Loading required package: MASS

## 
## Call:
## lm(formula = belong_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7622 -0.5518  0.1167  0.6964  1.7995 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.01520    0.01970   0.772   0.4403    
## TestAnx        -0.12085    0.02340  -5.164 2.62e-07 ***
## stressChanges  -0.10249    0.02402  -4.267 2.06e-05 ***
## stressConflict  0.09345    0.02220   4.210 2.66e-05 ***
## stressFrust    -0.05785    0.02430  -2.381   0.0173 *  
## stressReac     -0.06139    0.02467  -2.489   0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9412 on 2279 degrees of freedom
## Multiple R-squared:  0.06136,    Adjusted R-squared:  0.0593 
## F-statistic:  29.8 on 5 and 2279 DF,  p-value: < 2.2e-16

## $TestAnx

## 
## $stressChanges

## 
## $stressConflict

## 
## $stressFrust

## 
## $stressReac

## 
## Call:
## lm(formula = belong_c ~ neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7822 -0.5615  0.0723  0.7114  1.5165 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.08612    0.03597  -2.394  0.01675 *  
## neg_class_r1 -0.06913    0.05874  -1.177  0.23933    
## neg_class_r2  0.29109    0.05444   5.347 9.85e-08 ***
## neg_class_r3 -0.17662    0.06591  -2.680  0.00742 ** 
## neg_class_r4  0.46239    0.06240   7.410 1.76e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.947 on 2280 degrees of freedom
## Multiple R-squared:  0.04939,    Adjusted R-squared:  0.04772 
## F-statistic: 29.61 on 4 and 2280 DF,  p-value: < 2.2e-16

## $neg_class_r

## 
## Call:
## lm(formula = belong_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac + neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8027 -0.5509  0.0928  0.6857  1.7134 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.09878    0.03700  -2.670 0.007648 ** 
## TestAnx        -0.09927    0.02945  -3.371 0.000761 ***
## stressChanges  -0.10713    0.02959  -3.620 0.000301 ***
## stressConflict  0.07970    0.02417   3.297 0.000991 ***
## stressFrust    -0.05512    0.02576  -2.140 0.032469 *  
## stressReac     -0.05383    0.02817  -1.911 0.056157 .  
## neg_class_r1    0.12570    0.07233   1.738 0.082382 .  
## neg_class_r2    0.19854    0.06374   3.115 0.001863 ** 
## neg_class_r3    0.12045    0.09645   1.249 0.211860    
## neg_class_r4    0.19180    0.08581   2.235 0.025510 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9389 on 2275 degrees of freedom
## Multiple R-squared:  0.06754,    Adjusted R-squared:  0.06385 
## F-statistic: 18.31 on 9 and 2275 DF,  p-value: < 2.2e-16

## $TestAnx

## 
## $stressChanges

## 
## $stressConflict

## 
## $stressFrust

## 
## $stressReac

## 
## $neg_class_r

## 
## Call:
## lm(formula = belong_c ~ TestAnx * neg_class_r + stressChanges * 
##     neg_class_r + stressConflict * neg_class_r + stressFrust * 
##     neg_class_r + stressReac * neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9280 -0.5255  0.1080  0.6698  1.8731 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.1225353  0.0445946  -2.748 0.006048 ** 
## TestAnx                     -0.1163444  0.0594691  -1.956 0.050543 .  
## neg_class_r1                 0.0983633  0.1481523   0.664 0.506801    
## neg_class_r2                 0.1026153  0.0823103   1.247 0.212641    
## neg_class_r3                -0.4065657  0.2855943  -1.424 0.154707    
## neg_class_r4                -0.0278890  0.2117988  -0.132 0.895252    
## stressChanges               -0.1913306  0.0556231  -3.440 0.000593 ***
## stressConflict               0.0596561  0.0925793   0.644 0.519396    
## stressFrust                 -0.0547035  0.0540566  -1.012 0.311663    
## stressReac                  -0.0668590  0.0557746  -1.199 0.230756    
## TestAnx:neg_class_r1         0.1484774  0.1196115   1.241 0.214613    
## TestAnx:neg_class_r2        -0.0585424  0.0805625  -0.727 0.467503    
## TestAnx:neg_class_r3         0.0055840  0.0938672   0.059 0.952569    
## TestAnx:neg_class_r4         0.0668871  0.0892108   0.750 0.453475    
## neg_class_r1:stressChanges   0.0004798  0.0826229   0.006 0.995367    
## neg_class_r2:stressChanges   0.2090608  0.0738549   2.831 0.004686 ** 
## neg_class_r3:stressChanges   0.4817692  0.1576047   3.057 0.002263 ** 
## neg_class_r4:stressChanges  -0.1767318  0.1301632  -1.358 0.174672    
## neg_class_r1:stressConflict -0.0259816  0.1032517  -0.252 0.801347    
## neg_class_r2:stressConflict  0.0618067  0.1035449   0.597 0.550629    
## neg_class_r3:stressConflict  0.1619407  0.1160181   1.396 0.162905    
## neg_class_r4:stressConflict  0.0160361  0.1046975   0.153 0.878281    
## neg_class_r1:stressFrust     0.0277101  0.0871511   0.318 0.750549    
## neg_class_r2:stressFrust     0.0104440  0.0687224   0.152 0.879221    
## neg_class_r3:stressFrust     0.0194894  0.1197606   0.163 0.870741    
## neg_class_r4:stressFrust    -0.0706837  0.0788392  -0.897 0.370052    
## neg_class_r1:stressReac     -0.1112294  0.1018516  -1.092 0.274918    
## neg_class_r2:stressReac     -0.0107444  0.0706760  -0.152 0.879182    
## neg_class_r3:stressReac     -0.0554054  0.1551920  -0.357 0.721116    
## neg_class_r4:stressReac      0.1431435  0.0884099   1.619 0.105568    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9347 on 2255 degrees of freedom
## Multiple R-squared:  0.08416,    Adjusted R-squared:  0.07238 
## F-statistic: 7.145 on 29 and 2255 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ 1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5940 -0.5746  0.2305  0.6331  1.2370 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01679    0.02030   0.827    0.408
## 
## Residual standard error: 0.9704 on 2284 degrees of freedom

## 
## Call:
## lm(formula = belong_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7622 -0.5518  0.1167  0.6964  1.7995 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.01520    0.01970   0.772   0.4403    
## TestAnx        -0.12085    0.02340  -5.164 2.62e-07 ***
## stressChanges  -0.10249    0.02402  -4.267 2.06e-05 ***
## stressConflict  0.09345    0.02220   4.210 2.66e-05 ***
## stressFrust    -0.05785    0.02430  -2.381   0.0173 *  
## stressReac     -0.06139    0.02467  -2.489   0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9412 on 2279 degrees of freedom
## Multiple R-squared:  0.06136,    Adjusted R-squared:  0.0593 
## F-statistic:  29.8 on 5 and 2279 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7822 -0.5615  0.0723  0.7114  1.5165 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.08612    0.03597  -2.394  0.01675 *  
## neg_class_r1 -0.06913    0.05874  -1.177  0.23933    
## neg_class_r2  0.29109    0.05444   5.347 9.85e-08 ***
## neg_class_r3 -0.17662    0.06591  -2.680  0.00742 ** 
## neg_class_r4  0.46239    0.06240   7.410 1.76e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.947 on 2280 degrees of freedom
## Multiple R-squared:  0.04939,    Adjusted R-squared:  0.04772 
## F-statistic: 29.61 on 4 and 2280 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac + neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8027 -0.5509  0.0928  0.6857  1.7134 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.09878    0.03700  -2.670 0.007648 ** 
## TestAnx        -0.09927    0.02945  -3.371 0.000761 ***
## stressChanges  -0.10713    0.02959  -3.620 0.000301 ***
## stressConflict  0.07970    0.02417   3.297 0.000991 ***
## stressFrust    -0.05512    0.02576  -2.140 0.032469 *  
## stressReac     -0.05383    0.02817  -1.911 0.056157 .  
## neg_class_r1    0.12570    0.07233   1.738 0.082382 .  
## neg_class_r2    0.19854    0.06374   3.115 0.001863 ** 
## neg_class_r3    0.12045    0.09645   1.249 0.211860    
## neg_class_r4    0.19180    0.08581   2.235 0.025510 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9389 on 2275 degrees of freedom
## Multiple R-squared:  0.06754,    Adjusted R-squared:  0.06385 
## F-statistic: 18.31 on 9 and 2275 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = belong_c ~ TestAnx * neg_class_r + stressChanges * 
##     neg_class_r + stressConflict * neg_class_r + stressFrust * 
##     neg_class_r + stressReac * neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9280 -0.5255  0.1080  0.6698  1.8731 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.1225353  0.0445946  -2.748 0.006048 ** 
## TestAnx                     -0.1163444  0.0594691  -1.956 0.050543 .  
## neg_class_r1                 0.0983633  0.1481523   0.664 0.506801    
## neg_class_r2                 0.1026153  0.0823103   1.247 0.212641    
## neg_class_r3                -0.4065657  0.2855943  -1.424 0.154707    
## neg_class_r4                -0.0278890  0.2117988  -0.132 0.895252    
## stressChanges               -0.1913306  0.0556231  -3.440 0.000593 ***
## stressConflict               0.0596561  0.0925793   0.644 0.519396    
## stressFrust                 -0.0547035  0.0540566  -1.012 0.311663    
## stressReac                  -0.0668590  0.0557746  -1.199 0.230756    
## TestAnx:neg_class_r1         0.1484774  0.1196115   1.241 0.214613    
## TestAnx:neg_class_r2        -0.0585424  0.0805625  -0.727 0.467503    
## TestAnx:neg_class_r3         0.0055840  0.0938672   0.059 0.952569    
## TestAnx:neg_class_r4         0.0668871  0.0892108   0.750 0.453475    
## neg_class_r1:stressChanges   0.0004798  0.0826229   0.006 0.995367    
## neg_class_r2:stressChanges   0.2090608  0.0738549   2.831 0.004686 ** 
## neg_class_r3:stressChanges   0.4817692  0.1576047   3.057 0.002263 ** 
## neg_class_r4:stressChanges  -0.1767318  0.1301632  -1.358 0.174672    
## neg_class_r1:stressConflict -0.0259816  0.1032517  -0.252 0.801347    
## neg_class_r2:stressConflict  0.0618067  0.1035449   0.597 0.550629    
## neg_class_r3:stressConflict  0.1619407  0.1160181   1.396 0.162905    
## neg_class_r4:stressConflict  0.0160361  0.1046975   0.153 0.878281    
## neg_class_r1:stressFrust     0.0277101  0.0871511   0.318 0.750549    
## neg_class_r2:stressFrust     0.0104440  0.0687224   0.152 0.879221    
## neg_class_r3:stressFrust     0.0194894  0.1197606   0.163 0.870741    
## neg_class_r4:stressFrust    -0.0706837  0.0788392  -0.897 0.370052    
## neg_class_r1:stressReac     -0.1112294  0.1018516  -1.092 0.274918    
## neg_class_r2:stressReac     -0.0107444  0.0706760  -0.152 0.879182    
## neg_class_r3:stressReac     -0.0554054  0.1551920  -0.357 0.721116    
## neg_class_r4:stressReac      0.1431435  0.0884099   1.619 0.105568    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9347 on 2255 degrees of freedom
## Multiple R-squared:  0.08416,    Adjusted R-squared:  0.07238 
## F-statistic: 7.145 on 29 and 2255 DF,  p-value: < 2.2e-16

## Analysis of Variance Table
## 
## Model 1: belong_c ~ 1
## Model 2: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 3: belong_c ~ neg_class_r
## Model 4: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac + neg_class_r
## Model 5: belong_c ~ TestAnx * neg_class_r + stressChanges * neg_class_r + 
##     stressConflict * neg_class_r + stressFrust * neg_class_r + 
##     stressReac * neg_class_r
##   Res.Df    RSS Df Sum of Sq       F    Pr(>F)    
## 1   2284 2150.9                                   
## 2   2279 2019.0  5   131.981 30.2162 < 2.2e-16 ***
## 3   2280 2044.7 -1   -25.755 29.4823 6.248e-08 ***
## 4   2275 2005.7  5    39.050  8.9403 2.019e-08 ***
## 5   2255 1969.9 20    35.740  2.0456  0.004061 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 2: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac + neg_class_r
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1   2279 2019.0                                
## 2   2275 2005.7  4    13.295 3.7701 0.004625 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 2: belong_c ~ TestAnx * neg_class_r + stressChanges * neg_class_r + 
##     stressConflict * neg_class_r + stressFrust * neg_class_r + 
##     stressReac * neg_class_r
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1   2279 2019.0                                  
## 2   2255 1969.9 24    49.035 2.3388 0.0002506 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: belong_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac + neg_class_r
## Model 2: belong_c ~ TestAnx * neg_class_r + stressChanges * neg_class_r + 
##     stressConflict * neg_class_r + stressFrust * neg_class_r + 
##     stressReac * neg_class_r
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1   2275 2005.7                                
## 2   2255 1969.9 20     35.74 2.0456 0.004061 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Regression (Pos Future)

## 
## Call:
## lm(formula = future_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0809 -0.4203  0.1498  0.5261  2.4079 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.000378   0.017126   0.022  0.98239    
## meaningpurpose  0.226916   0.019651  11.547  < 2e-16 ***
## gratitude       0.227625   0.020214  11.261  < 2e-16 ***
## motExpeng       0.309196   0.019720  15.679  < 2e-16 ***
## mindfulness    -0.002430   0.018311  -0.133  0.89442    
## stressSupp     -0.056456   0.018033  -3.131  0.00177 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8181 on 2279 degrees of freedom
## Multiple R-squared:  0.3024, Adjusted R-squared:  0.3009 
## F-statistic: 197.6 on 5 and 2279 DF,  p-value: < 2.2e-16

## $meaningpurpose

## 
## $gratitude

## 
## $motExpeng

## 
## $mindfulness

## 
## $stressSupp

## 
## Call:
## lm(formula = future_c ~ pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7496 -0.4852  0.1272  0.6420  2.0005 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.13305    0.03127  -4.255 2.18e-05 ***
## pos_class_r1 -0.72610    0.06461 -11.238  < 2e-16 ***
## pos_class_r2  0.72104    0.06441  11.194  < 2e-16 ***
## pos_class_r3  0.24996    0.05380   4.646 3.57e-06 ***
## pos_class_r4  0.43498    0.05010   8.683  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8993 on 2280 degrees of freedom
## Multiple R-squared:  0.1569, Adjusted R-squared:  0.1554 
## F-statistic:   106 on 4 and 2280 DF,  p-value: < 2.2e-16

## $pos_class_r

## Contrasts set to contr.sum for the following variables: pos_class_r

## Anova Table (Type 3 tests)
## 
## Response: future_c
##        Effect      df  MSE          F ges p.value
## 1 pos_class_r 4, 2280 0.81 106.04 *** .16  <.0001
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

##  pos_class_r emmean     SE   df lower.CL upper.CL
##  0           -0.133 0.0313 2280  -0.1944  -0.0717
##  1           -0.859 0.0565 2280  -0.9700  -0.7483
##  2            0.588 0.0563 2280   0.4776   0.6984
##  3            0.117 0.0438 2280   0.0311   0.2028
##  4            0.302 0.0391 2280   0.2252   0.3787
## 
## Confidence level used: 0.95

##  contrast estimate     SE   df t.ratio p.value
##  0 - 1       0.726 0.0646 2280  11.238 <.0001 
##  0 - 2      -0.721 0.0644 2280 -11.194 <.0001 
##  0 - 3      -0.250 0.0538 2280  -4.646 <.0001 
##  0 - 4      -0.435 0.0501 2280  -8.683 <.0001 
##  1 - 2      -1.447 0.0798 2280 -18.135 <.0001 
##  1 - 3      -0.976 0.0715 2280 -13.650 <.0001 
##  1 - 4      -1.161 0.0688 2280 -16.886 <.0001 
##  2 - 3       0.471 0.0713 2280   6.604 <.0001 
##  2 - 4       0.286 0.0686 2280   4.171 0.0003 
##  3 - 4      -0.185 0.0587 2280  -3.151 0.0142 
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

## 
## Call:
## lm(formula = future_c ~ meaningpurpose + gratitude + motExpeng + 
##     stressSupp + mindfulness + pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0679 -0.4204  0.1435  0.5298  2.4839 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.0182249  0.0333697   0.546  0.58502    
## meaningpurpose  0.2320806  0.0208981  11.105  < 2e-16 ***
## gratitude       0.2201997  0.0289449   7.608 4.06e-14 ***
## motExpeng       0.3081351  0.0232486  13.254  < 2e-16 ***
## stressSupp     -0.0683742  0.0238668  -2.865  0.00421 ** 
## mindfulness     0.0005119  0.0189596   0.027  0.97846    
## pos_class_r1   -0.0949401  0.0686598  -1.383  0.16687    
## pos_class_r2   -0.0778756  0.0805402  -0.967  0.33369    
## pos_class_r3    0.0452931  0.0675911   0.670  0.50286    
## pos_class_r4   -0.0287974  0.0625280  -0.461  0.64516    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8178 on 2275 degrees of freedom
## Multiple R-squared:  0.3043, Adjusted R-squared:  0.3015 
## F-statistic: 110.5 on 9 and 2275 DF,  p-value: < 2.2e-16

## $meaningpurpose

## 
## $gratitude

## 
## $motExpeng

## 
## $stressSupp

## 
## $mindfulness

## 
## $pos_class_r

## 
## Call:
## lm(formula = future_c ~ meaningpurpose * pos_class_r + gratitude * 
##     pos_class_r + motExpeng * pos_class_r + stressSupp * pos_class_r + 
##     mindfulness * pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9987 -0.4238  0.1501  0.5289  2.8572 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.0083815  0.0451367   0.186   0.8527    
## meaningpurpose               0.2448416  0.0447946   5.466 5.11e-08 ***
## pos_class_r1                 0.1967383  0.1295076   1.519   0.1289    
## pos_class_r2                -0.1657714  0.2797137  -0.593   0.5535    
## pos_class_r3                 0.0409595  0.1274731   0.321   0.7480    
## pos_class_r4                -0.0363049  0.0944818  -0.384   0.7008    
## gratitude                    0.2099084  0.0537305   3.907 9.63e-05 ***
## motExpeng                    0.2997679  0.0430559   6.962 4.37e-12 ***
## stressSupp                  -0.0867208  0.0343834  -2.522   0.0117 *  
## mindfulness                 -0.0004543  0.0389954  -0.012   0.9907    
## meaningpurpose:pos_class_r1 -0.0499192  0.0642856  -0.777   0.4375    
## meaningpurpose:pos_class_r2  0.1785125  0.1704383   1.047   0.2950    
## meaningpurpose:pos_class_r3  0.0093523  0.0716903   0.130   0.8962    
## meaningpurpose:pos_class_r4 -0.0150685  0.0583476  -0.258   0.7962    
## pos_class_r1:gratitude       0.0595534  0.0726146   0.820   0.4122    
## pos_class_r2:gratitude      -0.0628391  0.2467104  -0.255   0.7990    
## pos_class_r3:gratitude      -0.0514308  0.1169921  -0.440   0.6603    
## pos_class_r4:gratitude       0.1064631  0.0964805   1.103   0.2699    
## pos_class_r1:motExpeng       0.1445992  0.0632111   2.288   0.0223 *  
## pos_class_r2:motExpeng       0.0066985  0.1175818   0.057   0.9546    
## pos_class_r3:motExpeng      -0.0582802  0.0654919  -0.890   0.3736    
## pos_class_r4:motExpeng      -0.1059009  0.0770456  -1.375   0.1694    
## pos_class_r1:stressSupp      0.1009664  0.0723735   1.395   0.1631    
## pos_class_r2:stressSupp     -0.0723301  0.1125552  -0.643   0.5205    
## pos_class_r3:stressSupp      0.0774585  0.0951554   0.814   0.4157    
## pos_class_r4:stressSupp     -0.0105106  0.0644948  -0.163   0.8706    
## pos_class_r1:mindfulness     0.0719498  0.0719506   1.000   0.3174    
## pos_class_r2:mindfulness     0.0504249  0.0834740   0.604   0.5459    
## pos_class_r3:mindfulness     0.0205824  0.0563437   0.365   0.7149    
## pos_class_r4:mindfulness    -0.0467206  0.0536601  -0.871   0.3840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8167 on 2255 degrees of freedom
## Multiple R-squared:  0.3121, Adjusted R-squared:  0.3033 
## F-statistic: 35.29 on 29 and 2255 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = future_c ~ 1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4614 -0.5932  0.1716  0.7453  1.1277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01370    0.02047   0.669    0.504
## 
## Residual standard error: 0.9785 on 2284 degrees of freedom

## 
## Call:
## lm(formula = future_c ~ meaningpurpose + gratitude + motExpeng + 
##     mindfulness + stressSupp, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0809 -0.4203  0.1498  0.5261  2.4079 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.000378   0.017126   0.022  0.98239    
## meaningpurpose  0.226916   0.019651  11.547  < 2e-16 ***
## gratitude       0.227625   0.020214  11.261  < 2e-16 ***
## motExpeng       0.309196   0.019720  15.679  < 2e-16 ***
## mindfulness    -0.002430   0.018311  -0.133  0.89442    
## stressSupp     -0.056456   0.018033  -3.131  0.00177 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8181 on 2279 degrees of freedom
## Multiple R-squared:  0.3024, Adjusted R-squared:  0.3009 
## F-statistic: 197.6 on 5 and 2279 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = future_c ~ pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7496 -0.4852  0.1272  0.6420  2.0005 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.13305    0.03127  -4.255 2.18e-05 ***
## pos_class_r1 -0.72610    0.06461 -11.238  < 2e-16 ***
## pos_class_r2  0.72104    0.06441  11.194  < 2e-16 ***
## pos_class_r3  0.24996    0.05380   4.646 3.57e-06 ***
## pos_class_r4  0.43498    0.05010   8.683  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8993 on 2280 degrees of freedom
## Multiple R-squared:  0.1569, Adjusted R-squared:  0.1554 
## F-statistic:   106 on 4 and 2280 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = future_c ~ meaningpurpose + gratitude + motExpeng + 
##     stressSupp + mindfulness + pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0679 -0.4204  0.1435  0.5298  2.4839 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.0182249  0.0333697   0.546  0.58502    
## meaningpurpose  0.2320806  0.0208981  11.105  < 2e-16 ***
## gratitude       0.2201997  0.0289449   7.608 4.06e-14 ***
## motExpeng       0.3081351  0.0232486  13.254  < 2e-16 ***
## stressSupp     -0.0683742  0.0238668  -2.865  0.00421 ** 
## mindfulness     0.0005119  0.0189596   0.027  0.97846    
## pos_class_r1   -0.0949401  0.0686598  -1.383  0.16687    
## pos_class_r2   -0.0778756  0.0805402  -0.967  0.33369    
## pos_class_r3    0.0452931  0.0675911   0.670  0.50286    
## pos_class_r4   -0.0287974  0.0625280  -0.461  0.64516    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8178 on 2275 degrees of freedom
## Multiple R-squared:  0.3043, Adjusted R-squared:  0.3015 
## F-statistic: 110.5 on 9 and 2275 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = future_c ~ meaningpurpose * pos_class_r + gratitude * 
##     pos_class_r + motExpeng * pos_class_r + stressSupp * pos_class_r + 
##     mindfulness * pos_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9987 -0.4238  0.1501  0.5289  2.8572 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.0083815  0.0451367   0.186   0.8527    
## meaningpurpose               0.2448416  0.0447946   5.466 5.11e-08 ***
## pos_class_r1                 0.1967383  0.1295076   1.519   0.1289    
## pos_class_r2                -0.1657714  0.2797137  -0.593   0.5535    
## pos_class_r3                 0.0409595  0.1274731   0.321   0.7480    
## pos_class_r4                -0.0363049  0.0944818  -0.384   0.7008    
## gratitude                    0.2099084  0.0537305   3.907 9.63e-05 ***
## motExpeng                    0.2997679  0.0430559   6.962 4.37e-12 ***
## stressSupp                  -0.0867208  0.0343834  -2.522   0.0117 *  
## mindfulness                 -0.0004543  0.0389954  -0.012   0.9907    
## meaningpurpose:pos_class_r1 -0.0499192  0.0642856  -0.777   0.4375    
## meaningpurpose:pos_class_r2  0.1785125  0.1704383   1.047   0.2950    
## meaningpurpose:pos_class_r3  0.0093523  0.0716903   0.130   0.8962    
## meaningpurpose:pos_class_r4 -0.0150685  0.0583476  -0.258   0.7962    
## pos_class_r1:gratitude       0.0595534  0.0726146   0.820   0.4122    
## pos_class_r2:gratitude      -0.0628391  0.2467104  -0.255   0.7990    
## pos_class_r3:gratitude      -0.0514308  0.1169921  -0.440   0.6603    
## pos_class_r4:gratitude       0.1064631  0.0964805   1.103   0.2699    
## pos_class_r1:motExpeng       0.1445992  0.0632111   2.288   0.0223 *  
## pos_class_r2:motExpeng       0.0066985  0.1175818   0.057   0.9546    
## pos_class_r3:motExpeng      -0.0582802  0.0654919  -0.890   0.3736    
## pos_class_r4:motExpeng      -0.1059009  0.0770456  -1.375   0.1694    
## pos_class_r1:stressSupp      0.1009664  0.0723735   1.395   0.1631    
## pos_class_r2:stressSupp     -0.0723301  0.1125552  -0.643   0.5205    
## pos_class_r3:stressSupp      0.0774585  0.0951554   0.814   0.4157    
## pos_class_r4:stressSupp     -0.0105106  0.0644948  -0.163   0.8706    
## pos_class_r1:mindfulness     0.0719498  0.0719506   1.000   0.3174    
## pos_class_r2:mindfulness     0.0504249  0.0834740   0.604   0.5459    
## pos_class_r3:mindfulness     0.0205824  0.0563437   0.365   0.7149    
## pos_class_r4:mindfulness    -0.0467206  0.0536601  -0.871   0.3840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8167 on 2255 degrees of freedom
## Multiple R-squared:  0.3121, Adjusted R-squared:  0.3033 
## F-statistic: 35.29 on 29 and 2255 DF,  p-value: < 2.2e-16

## Analysis of Variance Table
## 
## Model 1: future_c ~ 1
## Model 2: future_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 3: future_c ~ pos_class_r
## Model 4: future_c ~ meaningpurpose + gratitude + motExpeng + stressSupp + 
##     mindfulness + pos_class_r
## Model 5: future_c ~ meaningpurpose * pos_class_r + gratitude * pos_class_r + 
##     motExpeng * pos_class_r + stressSupp * pos_class_r + mindfulness * 
##     pos_class_r
##   Res.Df    RSS Df Sum of Sq        F Pr(>F)    
## 1   2284 2186.9                                 
## 2   2279 1525.5  5    661.42 198.3032 <2e-16 ***
## 3   2280 1843.8 -1   -318.39 477.2970 <2e-16 ***
## 4   2275 1521.5  5    322.35  96.6459 <2e-16 ***
## 5   2255 1504.2 20     17.24   1.2921 0.1726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: future_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 2: future_c ~ meaningpurpose + gratitude + motExpeng + stressSupp + 
##     mindfulness + pos_class_r
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2279 1525.5                           
## 2   2275 1521.5  4    3.9576 1.4794 0.2058

## Analysis of Variance Table
## 
## Model 1: future_c ~ meaningpurpose + gratitude + motExpeng + mindfulness + 
##     stressSupp
## Model 2: future_c ~ meaningpurpose * pos_class_r + gratitude * pos_class_r + 
##     motExpeng * pos_class_r + stressSupp * pos_class_r + mindfulness * 
##     pos_class_r
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2279 1525.5                           
## 2   2255 1504.2 24    21.196 1.3239 0.1344

Regression (Neg Future)

## 
## Call:
## lm(formula = future_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5398 -0.5620  0.2040  0.7502  1.3767 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.01275    0.02037   0.626  0.53132   
## TestAnx        -0.01669    0.02420  -0.690  0.49042   
## stressChanges  -0.07425    0.02484  -2.989  0.00283 **
## stressConflict  0.03519    0.02296   1.533  0.12547   
## stressFrust    -0.01926    0.02513  -0.767  0.44340   
## stressReac     -0.04228    0.02551  -1.657  0.09758 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9734 on 2279 degrees of freedom
## Multiple R-squared:  0.01267,    Adjusted R-squared:  0.0105 
## F-statistic: 5.847 on 5 and 2279 DF,  p-value: 2.247e-05

## $TestAnx

## 
## $stressChanges

## 
## $stressConflict

## 
## $stressFrust

## 
## $stressReac

## 
## Call:
## lm(formula = future_c ~ neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5230 -0.5465  0.2183  0.7920  1.2442 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.06879    0.03695  -1.862   0.0628 .  
## neg_class_r1  0.03579    0.06034   0.593   0.5531    
## neg_class_r2  0.14409    0.05593   2.576   0.0100 *  
## neg_class_r3 -0.03400    0.06771  -0.502   0.6156    
## neg_class_r4  0.30788    0.06410   4.803 1.66e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9728 on 2280 degrees of freedom
## Multiple R-squared:  0.01334,    Adjusted R-squared:  0.01161 
## F-statistic: 7.707 on 4 and 2280 DF,  p-value: 3.631e-06

## $neg_class_r

## Contrasts set to contr.sum for the following variables: neg_class_r

## Anova Table (Type 3 tests)
## 
## Response: future_c
##        Effect      df  MSE        F ges p.value
## 1 neg_class_r 4, 2280 0.95 7.71 *** .01  <.0001
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

##  neg_class_r  emmean     SE   df lower.CL upper.CL
##  0           -0.0688 0.0370 2280 -0.14126  0.00367
##  1           -0.0330 0.0477 2280 -0.12654  0.06053
##  2            0.0753 0.0420 2280 -0.00702  0.15762
##  3           -0.1028 0.0567 2280 -0.21405  0.00847
##  4            0.2391 0.0524 2280  0.13638  0.34179
## 
## Confidence level used: 0.95

##  contrast estimate     SE   df t.ratio p.value
##  0 - 1     -0.0358 0.0603 2280 -0.593  0.9761 
##  0 - 2     -0.1441 0.0559 2280 -2.576  0.0751 
##  0 - 3      0.0340 0.0677 2280  0.502  0.9872 
##  0 - 4     -0.3079 0.0641 2280 -4.803  <.0001 
##  1 - 2     -0.1083 0.0635 2280 -1.705  0.4313 
##  1 - 3      0.0698 0.0741 2280  0.942  0.8806 
##  1 - 4     -0.2721 0.0708 2280 -3.841  0.0012 
##  2 - 3      0.1781 0.0706 2280  2.523  0.0858 
##  2 - 4     -0.1638 0.0671 2280 -2.440  0.1052 
##  3 - 4     -0.3419 0.0772 2280 -4.428  0.0001 
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

## 
## Call:
## lm(formula = future_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac + neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4650 -0.5612  0.2053  0.7601  1.3471 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.084711   0.038301  -2.212   0.0271 *
## TestAnx         0.003843   0.030481   0.126   0.8997  
## stressChanges  -0.077069   0.030630  -2.516   0.0119 *
## stressConflict  0.026462   0.025018   1.058   0.2903  
## stressFrust    -0.015794   0.026661  -0.592   0.5537  
## stressReac     -0.034831   0.029160  -1.194   0.2324  
## neg_class_r1    0.088745   0.074874   1.185   0.2360  
## neg_class_r2    0.157763   0.065977   2.391   0.0169 *
## neg_class_r3    0.120315   0.099836   1.205   0.2283  
## neg_class_r4    0.190254   0.088827   2.142   0.0323 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9719 on 2275 degrees of freedom
## Multiple R-squared:  0.01733,    Adjusted R-squared:  0.01345 
## F-statistic: 4.459 on 9 and 2275 DF,  p-value: 8.114e-06

## $TestAnx

## 
## $stressChanges

## 
## $stressConflict

## 
## $stressFrust

## 
## $stressReac

## 
## $neg_class_r

## 
## Call:
## lm(formula = future_c ~ TestAnx * neg_class_r + stressChanges * 
##     neg_class_r + stressConflict * neg_class_r + stressFrust * 
##     neg_class_r + stressReac * neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5862 -0.5509  0.1948  0.7359  1.6497 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                 -0.077377   0.046204  -1.675   0.0941 .
## TestAnx                     -0.021174   0.061616  -0.344   0.7311  
## neg_class_r1                 0.069656   0.153500   0.454   0.6500  
## neg_class_r2                 0.039557   0.085281   0.464   0.6428  
## neg_class_r3                -0.559380   0.295903  -1.890   0.0588 .
## neg_class_r4                -0.123598   0.219444  -0.563   0.5733  
## stressChanges               -0.141606   0.057631  -2.457   0.0141 *
## stressConflict               0.115496   0.095921   1.204   0.2287  
## stressFrust                 -0.044624   0.056008  -0.797   0.4257  
## stressReac                  -0.012762   0.057788  -0.221   0.8252  
## TestAnx:neg_class_r1         0.178028   0.123929   1.437   0.1510  
## TestAnx:neg_class_r2        -0.057260   0.083470  -0.686   0.4928  
## TestAnx:neg_class_r3         0.095115   0.097255   0.978   0.3282  
## TestAnx:neg_class_r4         0.009890   0.092431   0.107   0.9148  
## neg_class_r1:stressChanges  -0.003139   0.085605  -0.037   0.9708  
## neg_class_r2:stressChanges   0.160423   0.076521   2.096   0.0362 *
## neg_class_r3:stressChanges   0.411133   0.163293   2.518   0.0119 *
## neg_class_r4:stressChanges  -0.149842   0.134861  -1.111   0.2667  
## neg_class_r1:stressConflict -0.134167   0.106979  -1.254   0.2099  
## neg_class_r2:stressConflict -0.049779   0.107282  -0.464   0.6427  
## neg_class_r3:stressConflict  0.051433   0.120206   0.428   0.6688  
## neg_class_r4:stressConflict -0.139066   0.108477  -1.282   0.2000  
## neg_class_r1:stressFrust     0.077937   0.090297   0.863   0.3882  
## neg_class_r2:stressFrust     0.029838   0.071203   0.419   0.6752  
## neg_class_r3:stressFrust     0.074514   0.124083   0.601   0.5482  
## neg_class_r4:stressFrust    -0.026003   0.081685  -0.318   0.7503  
## neg_class_r1:stressReac     -0.247382   0.105528  -2.344   0.0192 *
## neg_class_r2:stressReac     -0.031570   0.073227  -0.431   0.6664  
## neg_class_r3:stressReac     -0.001274   0.160794  -0.008   0.9937  
## neg_class_r4:stressReac      0.069478   0.091601   0.758   0.4482  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9684 on 2255 degrees of freedom
## Multiple R-squared:  0.033,  Adjusted R-squared:  0.02057 
## F-statistic: 2.654 on 29 and 2255 DF,  p-value: 4.205e-06

## 'data.frame':    2285 obs. of  30 variables:
##  $ UID2          : int  1 2 3 4 5 6 7 8 9 11 ...
##  $ pos_class     : Factor w/ 5 levels "1","2","3","4",..: 2 4 3 3 4 2 2 5 5 4 ...
##  $ pos_pp        : num  0.897 0.923 0.721 0.82 0.885 ...
##  $ neg_class     : Factor w/ 5 levels "1","2","3","4",..: 3 3 5 2 2 4 4 3 5 5 ...
##  $ neg_pp        : num  0.831 0.571 0.625 0.438 0.949 ...
##  $ UID.x         : Factor w/ 2339 levels "201803_AEG2344EJV8806",..: 1 2 3 4 5 6 7 8 9 11 ...
##  $ meaningpurpose: num  1.2208 -0.2311 -1.0608 -0.0237 0.5986 ...
##  $ gratitude     : num  0.537 -0.693 0.783 0.537 -0.447 ...
##  $ motExpeng     : num  0.918 -0.436 0.41 -0.605 -0.605 ...
##  $ mindfulness   : num  0.6617 -0.6154 -0.2505 0.4792 -0.0681 ...
##  $ TestAnx       : num  0.24 0.24 -0.508 -0.383 -0.383 ...
##  $ stressChanges : num  1.114 1.314 -0.884 1.314 -1.083 ...
##  $ stressConflict: num  0.5582 0.5582 0.0237 -0.5109 1.8946 ...
##  $ stressFrust   : num  0.4321 -0.0826 -1.1122 0.6895 -0.5974 ...
##  $ stressReac    : num  1.123 0.907 -0.176 0.257 1.123 ...
##  $ stressSupp    : num  0.742 0.144 0.742 1.042 -0.454 ...
##  $ UID.y         : Factor w/ 2339 levels "201803_AEG2344EJV8806",..: 1 2 3 4 5 6 7 8 9 11 ...
##  $ belong_c      : num  0.851 0.247 0.449 0.046 1.254 ...
##  $ future_c      : num  -0.388 -0.197 0.185 -0.197 1.141 ...
##  $ UID           : Factor w/ 2339 levels "201803_AEG2344EJV8806",..: 1 2 3 4 5 6 7 8 9 11 ...
##  $ gpa           : num  2.48 3.14 4.68 3.08 3.54 2.07 3.57 3.3 3.61 3.6 ...
##  $ raceeth_rc    : chr  "White" "Asian" "Asian" "White" ...
##  $ gender_rc     : chr  "Not Indicated" "Not Indicated" "Not Indicated" "Not Indicated" ...
##  $ gender_bin    : chr  "Female" "Male" "Female" "Male" ...
##  $ gpa_r2        : num  2 3 5 3 4 2 4 3 4 4 ...
##  $ MD            : num  5.4 3.3 6.3 6.2 13.2 9 5.9 13.1 6.1 4 ...
##  $ outlier       : chr  "No" "No" "No" "No" ...
##  $ gender_fin    : num  1 0 1 0 0 NA NA 1 0 0 ...
##  $ pos_class_r   : Factor w/ 5 levels "0","1","2","3",..: 3 1 4 4 1 3 3 5 5 1 ...
##  $ neg_class_r   : Factor w/ 5 levels "0","1","2","3",..: 4 4 1 3 3 5 5 4 1 1 ...

## 
## Call:
## lm(formula = future_c ~ 1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4614 -0.5932  0.1716  0.7453  1.1277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01370    0.02047   0.669    0.504
## 
## Residual standard error: 0.9785 on 2284 degrees of freedom

## 
## Call:
## lm(formula = future_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5398 -0.5620  0.2040  0.7502  1.3767 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.01275    0.02037   0.626  0.53132   
## TestAnx        -0.01669    0.02420  -0.690  0.49042   
## stressChanges  -0.07425    0.02484  -2.989  0.00283 **
## stressConflict  0.03519    0.02296   1.533  0.12547   
## stressFrust    -0.01926    0.02513  -0.767  0.44340   
## stressReac     -0.04228    0.02551  -1.657  0.09758 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9734 on 2279 degrees of freedom
## Multiple R-squared:  0.01267,    Adjusted R-squared:  0.0105 
## F-statistic: 5.847 on 5 and 2279 DF,  p-value: 2.247e-05

## 
## Call:
## lm(formula = future_c ~ neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5230 -0.5465  0.2183  0.7920  1.2442 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.06879    0.03695  -1.862   0.0628 .  
## neg_class_r1  0.03579    0.06034   0.593   0.5531    
## neg_class_r2  0.14409    0.05593   2.576   0.0100 *  
## neg_class_r3 -0.03400    0.06771  -0.502   0.6156    
## neg_class_r4  0.30788    0.06410   4.803 1.66e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9728 on 2280 degrees of freedom
## Multiple R-squared:  0.01334,    Adjusted R-squared:  0.01161 
## F-statistic: 7.707 on 4 and 2280 DF,  p-value: 3.631e-06

## 
## Call:
## lm(formula = future_c ~ TestAnx + stressChanges + stressConflict + 
##     stressFrust + stressReac + neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4650 -0.5612  0.2053  0.7601  1.3471 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.084711   0.038301  -2.212   0.0271 *
## TestAnx         0.003843   0.030481   0.126   0.8997  
## stressChanges  -0.077069   0.030630  -2.516   0.0119 *
## stressConflict  0.026462   0.025018   1.058   0.2903  
## stressFrust    -0.015794   0.026661  -0.592   0.5537  
## stressReac     -0.034831   0.029160  -1.194   0.2324  
## neg_class_r1    0.088745   0.074874   1.185   0.2360  
## neg_class_r2    0.157763   0.065977   2.391   0.0169 *
## neg_class_r3    0.120315   0.099836   1.205   0.2283  
## neg_class_r4    0.190254   0.088827   2.142   0.0323 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9719 on 2275 degrees of freedom
## Multiple R-squared:  0.01733,    Adjusted R-squared:  0.01345 
## F-statistic: 4.459 on 9 and 2275 DF,  p-value: 8.114e-06

## 
## Call:
## lm(formula = future_c ~ TestAnx * neg_class_r + stressChanges * 
##     neg_class_r + stressConflict * neg_class_r + stressFrust * 
##     neg_class_r + stressReac * neg_class_r, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5862 -0.5509  0.1948  0.7359  1.6497 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                 -0.077377   0.046204  -1.675   0.0941 .
## TestAnx                     -0.021174   0.061616  -0.344   0.7311  
## neg_class_r1                 0.069656   0.153500   0.454   0.6500  
## neg_class_r2                 0.039557   0.085281   0.464   0.6428  
## neg_class_r3                -0.559380   0.295903  -1.890   0.0588 .
## neg_class_r4                -0.123598   0.219444  -0.563   0.5733  
## stressChanges               -0.141606   0.057631  -2.457   0.0141 *
## stressConflict               0.115496   0.095921   1.204   0.2287  
## stressFrust                 -0.044624   0.056008  -0.797   0.4257  
## stressReac                  -0.012762   0.057788  -0.221   0.8252  
## TestAnx:neg_class_r1         0.178028   0.123929   1.437   0.1510  
## TestAnx:neg_class_r2        -0.057260   0.083470  -0.686   0.4928  
## TestAnx:neg_class_r3         0.095115   0.097255   0.978   0.3282  
## TestAnx:neg_class_r4         0.009890   0.092431   0.107   0.9148  
## neg_class_r1:stressChanges  -0.003139   0.085605  -0.037   0.9708  
## neg_class_r2:stressChanges   0.160423   0.076521   2.096   0.0362 *
## neg_class_r3:stressChanges   0.411133   0.163293   2.518   0.0119 *
## neg_class_r4:stressChanges  -0.149842   0.134861  -1.111   0.2667  
## neg_class_r1:stressConflict -0.134167   0.106979  -1.254   0.2099  
## neg_class_r2:stressConflict -0.049779   0.107282  -0.464   0.6427  
## neg_class_r3:stressConflict  0.051433   0.120206   0.428   0.6688  
## neg_class_r4:stressConflict -0.139066   0.108477  -1.282   0.2000  
## neg_class_r1:stressFrust     0.077937   0.090297   0.863   0.3882  
## neg_class_r2:stressFrust     0.029838   0.071203   0.419   0.6752  
## neg_class_r3:stressFrust     0.074514   0.124083   0.601   0.5482  
## neg_class_r4:stressFrust    -0.026003   0.081685  -0.318   0.7503  
## neg_class_r1:stressReac     -0.247382   0.105528  -2.344   0.0192 *
## neg_class_r2:stressReac     -0.031570   0.073227  -0.431   0.6664  
## neg_class_r3:stressReac     -0.001274   0.160794  -0.008   0.9937  
## neg_class_r4:stressReac      0.069478   0.091601   0.758   0.4482  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9684 on 2255 degrees of freedom
## Multiple R-squared:  0.033,  Adjusted R-squared:  0.02057 
## F-statistic: 2.654 on 29 and 2255 DF,  p-value: 4.205e-06

## Analysis of Variance Table
## 
## Model 1: future_c ~ 1
## Model 2: future_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 3: future_c ~ neg_class_r
## Model 4: future_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac + neg_class_r
## Model 5: future_c ~ TestAnx * neg_class_r + stressChanges * neg_class_r + 
##     stressConflict * neg_class_r + stressFrust * neg_class_r + 
##     stressReac * neg_class_r
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1   2284 2186.9                                  
## 2   2279 2159.2  5    27.699 5.9075 1.966e-05 ***
## 3   2280 2157.7 -1     1.477                     
## 4   2275 2149.0  5     8.732 1.8623   0.09774 .  
## 5   2255 2114.7 20    34.266 1.8270   0.01383 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: future_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 2: future_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac + neg_class_r
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1   2279 2159.2                              
## 2   2275 2149.0  4    10.209 2.7018 0.02907 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Model 1: future_c ~ TestAnx + stressChanges + stressConflict + stressFrust + 
##     stressReac
## Model 2: future_c ~ TestAnx * neg_class_r + stressChanges * neg_class_r + 
##     stressConflict * neg_class_r + stressFrust * neg_class_r + 
##     stressReac * neg_class_r
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1   2279 2159.2                                
## 2   2255 2114.7 24    44.475 1.9761 0.003201 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1