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Files for the project in which we compare the prevalence of statistical reporting inconsistencies between journals that implemented statcheck in their peer review process and matched control journals, before and after statcheck implementation.

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Meta-Data for the "Effectiveness of statcheck in Peer Review" Project

Michèle B. Nuijten & Jelte M. Wicherts

This GitHub/OSF project contains files for the project in which we compare the prevalence of statistical reporting inconsistencies between journals that implemented statcheck in their peer review process and matched control journals, before and after statcheck implementation.

Important links

Author roles

Conceived of idea: Michèle Nuijten (MN), Jelte Wicherts (JW)
Downloaded articles: Afra Kiliç (AK), Tsz Keung Wong (TKW), MN
Wrote preregistration: MN, JW
Scraped articles: MN
Analyzed data: MN
Wrote paper: MN Edited text: JW

Data origin

The data from this study are scraped from published psychology articles. Part of these articles were already downloaded for a previous project; the other part was downloaded for this project. All articles were scraped & checked with statcheck again for this project.

Ethical approval

Ethical approval obtained from the Ethics Review Board of TSB on 18-11-2022. Approval number: TSB_RP773.

Preregistration

This project was preregistered (a self-registration) on 30-11-2022. The preregistration, including power analysis, can be found here: https://osf.io/umwea.

Data package history and access

During the first period of this project, all files were stored on a SURFdrive folder that MN and JW could access. Writing was done in shared Google docs that could be accessed by MN and JW.

Nearing publication of the preprint in January 2023, we openly shared all relevant files necessary to reproduce our research publicly on GitHub and OSF.

For the full report (incl. exploratory analyses), we created a new version of statcheck that could deal with a specific type of spaces in JESP articles. We reran statcheck on all articles included in the sample and updated the data and analysis files accordingly.

Reasons for small changes and updates are either listed below the main overview table or, from January 13 2023 onwards, listed automatically in the form of GitHub commits.

What? When? Who?
Performed power analysis Nov 2022 MN
Wrote preregistration Oct/Nov 2022 MN, JW
Wrote downloading instructions for assistants Oct 2022 MN
Downloaded existing sample of articles from our GitHub page; PS 2003-2013; JPSP 2003-2013; JEPG 2003-2013; shortened some file names to avoid error messages when scraping Oct 2022 MN
Downloaded new sample of articles: PS 2013-2022; JEPG 2013-2022; JESP 2003-2022 Nov 2022 AK
Downloaded new sample of articles: JPSP (subsection ASC) 2013-2022 Nov/Dec 2022 TKW
Made list of all ASC titles of JPSP articles in existing sample Nov/Dec 2022 TKW
Scraped articles with statcheck (incl. refinements to the code) Dec 2022/Jan 2023 MN
Created new version of statcheck to deal with spacing in JESP April-June 2023 MN
Scraped articles with new version of statcheck June 2023 MN
Reran descriptive and confirmatory analyses & updated exploratory analyses June-July 2023 MN
Wrote and edited full report July 2023 MN, JW

Small changes and notes

  • 2-12-2022: In JPEG, there were some articles (39 articles in total) without HTML files. So, Afra created a list of articles without HTML files: https://catnip-ocelot-499.notion.site/Statcheck-io-the-articles-without-HTML-63712ffe996840dfb486b13a74bbed53
  • 13-12-2022: MN realized that there was a mistake in the downloading instructions and JESP 2003-2013 weren’t downloaded yet. These remaining articles were downloaded by MN & AK between 13-12 and 19-12.
  • 20-12-2022 to 23-12-2022: MN fixed issues with scraping dates from articles. JESP’s dates weren’t read by original regexes in code.
  • 23-12-2022: when analyzing descriptives, MN realized that 28 articles from JEPG published in 2013 had no “date received” information, because they were just the abstracts, not the full files. Redownloaded these.
  • 23-12-2022: MN realized that JPSP 2008 volume 1 was not downloaded. Downloaded & added to sample.
  • 23-12-2022: in list of ASC titles of JPSP, retracted articles and editorials were still included. Removed those.
  • 23-12-2022: reran scraping script on updated files.
  • 02-01-2023: reran scraping script on updated files: discovered a bug in the list of ASC titles; some titles had special characters that weren’t read by R and as a result ~50 articles were unjustly excluded.
  • 06-02-2023: found a bug in statcheck: it didn’t correctly check for correct rounding in negative test statistics. This required a bug fix in statcheck and a rerun of the scraping script & all analyses.

File descriptions

The file descriptions below are organized in the same way as the components and folders on the OSF page at https://osf.io/q84jn/.

02 Preregistration

File Name Description Created By
221130preregistration_effectivitystatcheck.pdf Preregistration of the project MN, JW

Power analysis

File Name Description Created By
00extract_dates.R Helper function with regexes to extract dates MN
00extract_pattern.R Helper function to extract a pattern from text MN
00html_to_txt.R Helper function to convert html files to plain txt MN
00scrape_html_dir.R Helper function that allows scraping a directory of html files MN
01scrape_articles.R Function to run statcheck & scrape additional meta-data from articles MN
02data_wrangling.R Function to clean & reorder raw data MN
2022-11-23scraped_articles.txt Raw, scraped data from articles MN
2022-11-28data_per_article.txt Data organized at article level MN
2022-11-28data_wrangled.txt Data with additional variables and missing values removed MN
power_analysis.R R script to run power simulation MN
powerplot_estimated.png Graph of power estimates per b3 value MN

03 Data

File Name Description Created By
00extract_dates.R Helper function with regexes to extract dates MN
00extract_pattern.R Helper function to extract a pattern from text MN
00html_to_txt_v2.R Helper function to convert html files to plain txt MN
00scrape_html_dir.R Helper function that allows scraping a directory of html files MN
01scrape_articles_v2.R Function to run statcheck & scrape additional meta-data from articles MN
02data_wrangling_v2.R Function to clean & reorder raw data MN
2023-06-01scraped_articles_v2.txt Raw statcheck data MN
2023-06-15data_per_article_with_missings.txt Data organized per article, including articles without NHST results MN
2023-06-15data_per_article_with_stats.txt Data organized per article, only including articles with NHST results MN
2023-06-15data_wrangled_no_missings.txt Clean data organized per NHST result, only including articles with NHST results MN
2023-06-15data_wrangled_with_missings.txt Clean data organized per NHST result, also including articles without NHST results (in these cases, only article level info is in the data frame) MN

04 Analysis

01 Descriptives

File Name Description Created By
descriptives_V2.R Main analysis file to create descriptive statistics, figures, and tables MN
fig1_violin_plots.png Figure 1 MN
fig2_line_graph_means.png Figure 2 MN
table2.txt Raw data from Table 2 MN
table3.txt Raw data from Table 3 MN

02 Confirmatory

File Name Description Created By
confirmatory_analyses_v2.R Main analysis script for confirmatory analyses MN
lm_dec_errors.rda R data object with output of the multilevel logistic model estimating the probability of a decision error MN
lm_errors.rda R data object with output of the multilevel logistic model estimating the probability of an error MN

03 Exploratory

File Name Description Created By
bayes_factors.R Calculating Bayes factors and posterior probabilities MN
fig3_nr_nhst_over_time.png Figure 3 MN
fig4_perc_articles_errors_over_time.png Figure 4 MN
separate_journal_pairs.R Fit regression models for journal pairs separately MN
trends_over_time.R Calculate and visualize trends over time MN

renv

File Name Description Created By
renv (folder), .Rprofile, renv.lock Files for syncing the same R package versions used in the data analysis MN

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Files for the project in which we compare the prevalence of statistical reporting inconsistencies between journals that implemented statcheck in their peer review process and matched control journals, before and after statcheck implementation.

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