215 lines
7.6 KiB
Markdown
215 lines
7.6 KiB
Markdown
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# Mental Health in the EA Community using SSC's 2019 Survey
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If you naïvely run some regressions, you get a significant correlation between EA affiliation and mental conditions. If you look at the plots, this seems like maybe an artifact of different proportions of NAs.
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Anyways, the numerical results are:
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## Diagnosed + Intuited
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```
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x y %
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1 EA Yes 959 100.00000
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2 Has been diagnosed with a mental condition, or thinks they have one 580 60.47967
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3 Has not been diagnosed with a mental condition, and does not think they any 347 36.18352
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4 NA / Didn't answer 125 13.03441
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```
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```
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x y %
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1 EA Sorta 2223 100.000000
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2 Has been diagnosed with a mental condition, or thinks they have one 1354 60.908682
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3 Has not been diagnosed with a mental condition, and does not think they any 795 35.762483
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4 NA / Didn't answer 167 7.512371
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```
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```
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x y %
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1 EA No 4158 100.000000
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2 Has been diagnosed with a mental condition, or thinks they have one 2416 58.104858
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3 Has not been diagnosed with a mental condition, and does not think they any 1587 38.167388
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4 NA / Didn't answer 248 5.964406
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```
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## Diagnosed
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```
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x y %
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1 EA Yes 959 100.00000
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2 Has been diagnosed with a mental condition 314 32.74244
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3 Has not been diagnosed with a mental condition 613 63.92075
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4 NA / Didn't answer 125 13.03441
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```
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```
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x y %
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1 EA Sorta 2223 100.000000
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2 Has been diagnosed with a mental condition 718 32.298695
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3 Has not been diagnosed with a mental condition 1431 64.372470
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4 NA / Didn't answer 167 7.512371
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```
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```
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x y %
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1 EA No 4158 100.000000
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2 Has been diagnosed with a mental condition 1183 28.451178
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3 Has not been diagnosed with a mental condition 2820 67.821068
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4 NA / Didn't answer 248 5.964406
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```
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## Regressions
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### Linear
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```
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> # D$mentally_ill = Number of diagnosed mental ilnesses
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> # D$mentally_ill2= Number of mental ilnesses, diagnosed + intuited
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```
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```
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> summary(lm(D$mentally_ill ~ D$`EA ID`))
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Call:
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lm(formula = D$mentally_ill ~ D$`EA ID`)
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Residuals:
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Min 1Q Median 3Q Max
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-0.5717 -0.5514 -0.4689 0.4486 10.4283
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Coefficients:
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Estimate Std. Error t value Pr(>|t|)
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(Intercept) 0.46890 0.01424 32.935 < 2e-16 ***
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D$`EA ID`Sorta 0.08252 0.02409 3.426 0.000617 ***
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D$`EA ID`Yes 0.10284 0.03283 3.132 0.001742 **
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 0.9008 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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Multiple R-squared: 0.002421, Adjusted R-squared: 0.002139
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F-statistic: 8.587 on 2 and 7076 DF, p-value: 0.0001884
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```
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```
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> summary(lm(D$mentally_ill2 ~ D$`EA ID`))
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Call:
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lm(formula = D$mentally_ill2 ~ D$`EA ID`)
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Residuals:
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Min 1Q Median 3Q Max
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-1.3711 -1.2638 -0.2638 0.7362 9.6289
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Coefficients:
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Estimate Std. Error t value Pr(>|t|)
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(Intercept) 1.26380 0.02243 56.343 <2e-16 ***
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D$`EA ID`Sorta 0.09637 0.03795 2.539 0.0111 *
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D$`EA ID`Yes 0.10729 0.05173 2.074 0.0381 *
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 1.419 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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Multiple R-squared: 0.001216, Adjusted R-squared: 0.0009338
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F-statistic: 4.308 on 2 and 7076 DF, p-value: 0.0135
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```
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```
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> summary(lm(D$mentally_ill>0 ~ D$`EA ID`))
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Call:
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lm(formula = D$mentally_ill > 0 ~ D$`EA ID`)
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Residuals:
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Min 1Q Median 3Q Max
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-0.3387 -0.3341 -0.2955 0.6659 0.7045
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Coefficients:
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Estimate Std. Error t value Pr(>|t|)
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(Intercept) 0.295528 0.007323 40.354 < 2e-16 ***
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D$`EA ID`Sorta 0.038581 0.012391 3.114 0.00186 **
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D$`EA ID`Yes 0.043199 0.016889 2.558 0.01055 *
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 0.4633 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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Multiple R-squared: 0.001835, Adjusted R-squared: 0.001553
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F-statistic: 6.505 on 2 and 7076 DF, p-value: 0.001505
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```
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```
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> summary(lm(D$mentally_ill2>0 ~ D$`EA ID`))
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Call:
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lm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`)
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Residuals:
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Min 1Q Median 3Q Max
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-0.6301 -0.6036 0.3699 0.3965 0.3965
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Coefficients:
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Estimate Std. Error t value Pr(>|t|)
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(Intercept) 0.603547 0.007692 78.466 <2e-16 ***
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D$`EA ID`Sorta 0.026513 0.013014 2.037 0.0417 *
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D$`EA ID`Yes 0.022127 0.017738 1.247 0.2123
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 0.4867 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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Multiple R-squared: 0.0006657, Adjusted R-squared: 0.0003832
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F-statistic: 2.357 on 2 and 7076 DF, p-value: 0.09481
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```
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## Logistic
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```
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> summary(glm(D$mentally_ill>0 ~ D$`EA ID`, family=binomial(link='logit')))
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Call:
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glm(formula = D$mentally_ill > 0 ~ D$`EA ID`, family = binomial(link = "logit"))
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Deviance Residuals:
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Min 1Q Median 3Q Max
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-0.9095 -0.9018 -0.8370 1.4807 1.5614
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Coefficients:
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Estimate Std. Error z value Pr(>|z|)
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(Intercept) -0.86868 0.03464 -25.078 < 2e-16 ***
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D$`EA ID`Sorta 0.17902 0.05737 3.120 0.00181 **
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D$`EA ID`Yes 0.19971 0.07756 2.575 0.01003 *
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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(Dispersion parameter for binomial family taken to be 1)
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Null deviance: 8797.8 on 7078 degrees of freedom
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Residual deviance: 8784.8 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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AIC: 8790.8
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Number of Fisher Scoring iterations: 4
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```
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```
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> summary(glm(D$mentally_ill2>0 ~ D$`EA ID`, family=binomial(link='logit')))
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Call:
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glm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`, family = binomial(link = "logit"))
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Deviance Residuals:
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Min 1Q Median 3Q Max
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-1.4103 -1.3603 0.9612 1.0049 1.0049
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Coefficients:
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Estimate Std. Error z value Pr(>|z|)
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(Intercept) 0.42027 0.03231 13.007 <2e-16 ***
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D$`EA ID`Sorta 0.11221 0.05514 2.035 0.0419 *
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D$`EA ID`Yes 0.09344 0.07517 1.243 0.2139
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---
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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(Dispersion parameter for binomial family taken to be 1)
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Null deviance: 9439.1 on 7078 degrees of freedom
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Residual deviance: 9434.4 on 7076 degrees of freedom
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(354 observations deleted due to missingness)
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AIC: 9440.4
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Number of Fisher Scoring iterations: 4
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```
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## Plots:
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