-
Notifications
You must be signed in to change notification settings - Fork 27
/
README.Rmd
64 lines (44 loc) · 3.31 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
<!-- after editing README.Rmd, run devtools::build_readme() -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
```{r echo=FALSE, results="hide", message=FALSE}
library("badger")
```
# statcheck <a href='http://statcheck.io'><img src='man/figures/logo.jpg' align="right" height="100" /></a>
<!-- badges: start -->
```{r, echo = FALSE, results='asis'}
cat(
badge_cran_release("statcheck", "green"),
badge_cran_download("statcheck", "grand-total", "blue"),
badge_github_version("MicheleNuijten/statcheck", "yellow")
)
```
<!-- badges: end -->
## What is statcheck?
`statcheck` is a "spellchecker" for statistics. It checks whether your *p*-values match their accompanying test statistic and degrees of freedom.
`statcheck` searches for null-hypothesis significance test (NHST) in APA style (e.g., *t*(28) = 2.2, *p* < .05). It recalculates the p-value using the reported test statistic and degrees of freedom. If the reported and computed p-values don't match, `statcheck` will flag the result as an error.
## What can I use statcheck for?
`statcheck` is mainly useful for:
1. **Self-checks**: you can use `statcheck` to make sure your manuscript doesn't contain copy-paste errors or other inconsistencies before you submit it to a journal.
2. **Peer review**: editors and reviewers can use `statcheck` to check submitted manuscripts for statistical inconsistencies. They can ask authors for a correction or clarification before publishing a manuscript.
3. **Research**: `statcheck` can be used to automatically extract statistical test results from articles that can then be analyzed. You can for instance investigate whether you can predict statistical inconsistencies (see e.g., Nuijten et al., 2017 <doi:10.1525/collabra.102>), or use it to analyze p-value distributions (see e.g., Hartgerink et al., 2016 <doi:10.7717/peerj.1935>).
## How does statcheck work?
The algorithm behind `statcheck` consists of four basic steps:
1. **Convert** pdf and html articles to plain text files.
2. **Search** the text for instances of NHST results. Specifically, `statcheck` can recognize *t*-tests, *F*-tests, correlations, *z*-tests, $\chi^2$ -tests, and Q-tests (from meta-analyses) if they are reported completely (test statistic, degrees of freedom, and *p*-value) and in APA style.
3. **Recompute** the *p*-value using the reported test statistic and degrees of freedom.
4. **Compare** the reported and recomputed *p*-value. If the reported *p*-value does not match the computed one, the result is marked as an *inconsistency* (`error` in the output). If the reported *p*-value is significant and the computed is not, or vice versa, the result is marked as a *gross inconsistency* (`decision_error` in the output).
`statcheck` takes into account correct rounding of the test statistic, and has the option to take into account one-tailed testing. See the [manual](http://rpubs.com/michelenuijten/statcheckmanual) for details.
## Installation and use
For detailed information about installing and using `statcheck`, see the [manual on RPubs](http://rpubs.com/michelenuijten/statcheckmanual).
Also see [statcheck.io](http://statcheck.io/), a web-based interface for statcheck.