EDUC/PSY 7610

Chapter 11 talks about the problem of multiple tests. These examples will be fairly short but will highlight a few ways to adjust for multiple tests.

- Let’s start by loading the
`tidyverse`

package and the`educ7610`

package.

```
library(tidyverse)
library(educ7610)
```

- Import
`gss`

into R.

`data(gss)`

- Let’s do a multiple with degree, race, and education predicting income (
`income06`

). Our first adjustment is using Bonferroni’s adjustment. To do so, we will grab the p-values from the`summary()`

and feed that to`p.adjust()`

.

```
fit <- gss %>%
lm(income06 ~ degree + race + educ, data = .)
mt_adjust(fit, "bonferroni")
```

```
## Estimate Std..Error t.value Pr...t..
## (Intercept) 7885.8124 4776.5311 1.650950 7.914133e-01
## degreeAssociates 29800.3711 4442.5264 6.707978 2.118946e-10
## degreeBachelors 45455.5562 4227.3359 10.752766 2.912338e-25
## degreeGraduate 57663.9272 5123.0925 11.255687 1.573189e-27
## degreeHS 20296.5103 3067.3139 6.617031 3.875839e-10
## raceOther 13466.5777 4026.0555 3.344856 6.723918e-03
## raceWhite 16327.4677 2740.7819 5.957230 2.471381e-08
## educ 706.0966 366.1085 1.928654 4.314717e-01
```

- Are there significant mean differences between the non-reference levels and the reference level regarding income for either degree or race now that we have adjusted for multiple tests? What about the effect of education?
- Let’s try a different approach; namely, let’s try
`"fdr"`

(the false discovery rate). This approach controls the number of false discoveries (as the name inplies). This approach is less conservative than Bonferroni’s.

`mt_adjust(fit, "fdr")`

```
## Estimate Std..Error t.value Pr...t..
## (Intercept) 7885.8124 4776.5311 1.650950 9.892666e-02
## degreeAssociates 29800.3711 4442.5264 6.707978 7.063154e-11
## degreeBachelors 45455.5562 4227.3359 10.752766 1.456169e-25
## degreeGraduate 57663.9272 5123.0925 11.255687 1.573189e-27
## degreeHS 20296.5103 3067.3139 6.617031 9.689598e-11
## raceOther 13466.5777 4026.0555 3.344856 1.120653e-03
## raceWhite 16327.4677 2740.7819 5.957230 4.942763e-09
## educ 706.0966 366.1085 1.928654 6.163882e-02
```

- Do any conclusions change based on using the false discovery rate rather than Bonferroni’s? Which change? Why?

Adjusting for multiple comparisons is often an important consideration. In R, there are several approaches that can be quickly applied. Although important to adjust, possibly more important is the consideration of which tests to run and the likelihood of the effect prior to making any tests.