# Mental Health in the EA Community using SSC’s 2019 Survey

If you run some regressions, you get a significant correlation between EA affiliation and mental conditions; respondents who identified as EA differed from non-EAs by ~2-4% (see below). Note that the SSC Survey is subject to fewer biases than the EA Mental Health survey, and also note that it’s still difficult to extract causal conclusions. See also: that EA Mental Health Survey. Data available here

## Plots:

## Diagnosed + Intuited

```
x y %
1 EA Yes 959 100.00000
2 Has been diagnosed with a mental condition, or thinks they have one 580 60.47967
3 Has not been diagnosed with a mental condition, and does not think they any 347 36.18352
4 NA / Didn't answer 125 13.03441
```

```
x y %
1 EA Sorta 2223 100.000000
2 Has been diagnosed with a mental condition, or thinks they have one 1354 60.908682
3 Has not been diagnosed with a mental condition, and does not think they any 795 35.762483
4 NA / Didn't answer 167 7.512371
```

```
x y %
1 EA No 4158 100.000000
2 Has been diagnosed with a mental condition, or thinks they have one 2416 58.104858
3 Has not been diagnosed with a mental condition, and does not think they any 1587 38.167388
4 NA / Didn't answer 248 5.964406
```

## Diagnosed

```
x y %
1 EA Yes 959 100.00000
2 Has been diagnosed with a mental condition 314 32.74244
3 Has not been diagnosed with a mental condition 613 63.92075
4 NA / Didn't answer 125 13.03441
```

```
x y %
1 EA Sorta 2223 100.000000
2 Has been diagnosed with a mental condition 718 32.298695
3 Has not been diagnosed with a mental condition 1431 64.372470
4 NA / Didn't answer 167 7.512371
```

```
x y %
1 EA No 4158 100.000000
2 Has been diagnosed with a mental condition 1183 28.451178
3 Has not been diagnosed with a mental condition 2820 67.821068
4 NA / Didn't answer 248 5.964406
```

## Regressions

### Linear

```
> # D$mentally_ill = Number of diagnosed mental ilnesses
> # D$mentally_ill2= Number of mental ilnesses, diagnosed + intuited
```

```
> summary(lm(D$mentally_ill ~ D$`EA ID`))
Call:
lm(formula = D$mentally_ill ~ D$`EA ID`)
Residuals:
Min 1Q Median 3Q Max
-0.5717 -0.5514 -0.4689 0.4486 10.4283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46890 0.01424 32.935 < 2e-16 ***
D$`EA ID`Sorta 0.08252 0.02409 3.426 0.000617 ***
D$`EA ID`Yes 0.10284 0.03283 3.132 0.001742 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9008 on 7076 degrees of freedom
(354 observations deleted due to missingness)
Multiple R-squared: 0.002421, Adjusted R-squared: 0.002139
F-statistic: 8.587 on 2 and 7076 DF, p-value: 0.0001884
```

```
> summary(lm(D$mentally_ill2 ~ D$`EA ID`))
Call:
lm(formula = D$mentally_ill2 ~ D$`EA ID`)
Residuals:
Min 1Q Median 3Q Max
-1.3711 -1.2638 -0.2638 0.7362 9.6289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.26380 0.02243 56.343 <2e-16 ***
D$`EA ID`Sorta 0.09637 0.03795 2.539 0.0111 *
D$`EA ID`Yes 0.10729 0.05173 2.074 0.0381 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.419 on 7076 degrees of freedom
(354 observations deleted due to missingness)
Multiple R-squared: 0.001216, Adjusted R-squared: 0.0009338
F-statistic: 4.308 on 2 and 7076 DF, p-value: 0.0135
```

```
> summary(lm(D$mentally_ill>0 ~ D$`EA ID`))
Call:
lm(formula = D$mentally_ill > 0 ~ D$`EA ID`)
Residuals:
Min 1Q Median 3Q Max
-0.3387 -0.3341 -0.2955 0.6659 0.7045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.295528 0.007323 40.354 < 2e-16 ***
D$`EA ID`Sorta 0.038581 0.012391 3.114 0.00186 **
D$`EA ID`Yes 0.043199 0.016889 2.558 0.01055 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4633 on 7076 degrees of freedom
(354 observations deleted due to missingness)
Multiple R-squared: 0.001835, Adjusted R-squared: 0.001553
F-statistic: 6.505 on 2 and 7076 DF, p-value: 0.001505
```

```
> summary(lm(D$mentally_ill2>0 ~ D$`EA ID`))
Call:
lm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`)
Residuals:
Min 1Q Median 3Q Max
-0.6301 -0.6036 0.3699 0.3965 0.3965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.603547 0.007692 78.466 <2e-16 ***
D$`EA ID`Sorta 0.026513 0.013014 2.037 0.0417 *
D$`EA ID`Yes 0.022127 0.017738 1.247 0.2123
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4867 on 7076 degrees of freedom
(354 observations deleted due to missingness)
Multiple R-squared: 0.0006657, Adjusted R-squared: 0.0003832
F-statistic: 2.357 on 2 and 7076 DF, p-value: 0.09481
```

## Logistic

```
> summary(glm(D$mentally_ill>0 ~ D$`EA ID`, family=binomial(link='logit')))
Call:
glm(formula = D$mentally_ill > 0 ~ D$`EA ID`, family = binomial(link = "logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-0.9095 -0.9018 -0.8370 1.4807 1.5614
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.86868 0.03464 -25.078 < 2e-16 ***
D$`EA ID`Sorta 0.17902 0.05737 3.120 0.00181 **
D$`EA ID`Yes 0.19971 0.07756 2.575 0.01003 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 8797.8 on 7078 degrees of freedom
Residual deviance: 8784.8 on 7076 degrees of freedom
(354 observations deleted due to missingness)
AIC: 8790.8
Number of Fisher Scoring iterations: 4
```

```
> summary(glm(D$mentally_ill2>0 ~ D$`EA ID`, family=binomial(link='logit')))
Call:
glm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`, family = binomial(link = "logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4103 -1.3603 0.9612 1.0049 1.0049
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.42027 0.03231 13.007 <2e-16 ***
D$`EA ID`Sorta 0.11221 0.05514 2.035 0.0419 *
D$`EA ID`Yes 0.09344 0.07517 1.243 0.2139
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 9439.1 on 7078 degrees of freedom
Residual deviance: 9434.4 on 7076 degrees of freedom
(354 observations deleted due to missingness)
AIC: 9440.4
Number of Fisher Scoring iterations: 4
```