SMI205 Replication Project (2023)

[Project title here]

Rpubs link: [copy Rpubs url address here]

GitHub Repository: [add url here if you have created a data or code repository, if not delete this line]

Study Preregistration form: [copy Rpubs url address here or delete]

Information about this replication project

• Replication project based on paper [add full citation here and link to its published online version]
• Replication method (select one from below):
  • Used replication package available at [add citation + repository link here]
  • Used materials obtained from authors
  • Own replication following methods section of the paper
  • Other - explain

Workspace setup

YAML settings

output:
  html_document:
   code_download: true
    toc: true
    toc_depth: 2
    toc_float:
     collapsed: false
     smooth_scroll: true

Global settings of R chunks

# Global options
opts_chunk$set(echo=TRUE,
                 cache=TRUE,
               comment=NA,
               message=FALSE,
               warning=FALSE)

Libraries

# All used libraries
library("rmarkdown")
library("knitr")

Versions of used packages

$rmarkdown
[1] '2.20'

$knitr
[1] '1.42'

My enviroment

[1] "R version 4.2.2 (2022-10-31 ucrt)"

1. Introduction

Code and outputs are not included in the wordcount

500 words

Critical and evidence-based reflections on the paper and your new research question(s).

• Clearly state which claim (finding/argument) from the replicated paper you discuss or test robustness of any methodological assumptions.
• What the authors argued, and what could be counter-argued, and why.
• Use at least 5 scientific references to support your critique and your idea for research extension.

Refer to the four types of replications as discussed in Freese and Peterson (2017) and explain what aspects of the original research your replication tests.

Briefly summarise, what results you expected to find and preregistered.

2. Data and methods

2.1. Data

250 words

Transparently describe data used in the original paper and in your replication project: full dataset names, years of data collection, sources (who created data), sample sizes, variables used in the model you replicate.

Embed R code to upload your data (recommendation - show code only using results='hide' in the chunk options).

Remember to add all datasets with their doi number to the list of references. See examples in the conditions of use for the ESS data here.

Report all steps you have undertaken in data transformation:

• Subsetting data by selecting a lower number of variables or excluding any cases.
• Merging data files.
• Steps in recoding and renaming all variables.
• Embed R code below to present the steps in data manipulation. If this would be very long chunk of R code (over 1 page), show just some examples and hide the rest of the code in text (include=FALSE option). The entire code will be still displayed in Appendix 2.

You have to display some summary statistics of your key variables of interest. This could be done as a table/s or a graph/s presenting distribution of your dependent variable/s for the entire sample or by a group of interest, boxplots, correlation plots or graphs with means values.

2.2. Methods

250 words

Describe what methods were used in the original paper and what methods you used and why.

Support any changes you made in the analytical strategy with literature and/or displaying any relevant data diagnostics.

If suitable, embed statistical tests comparing model fit of both methods.

3. Results

500 words

Display and describe your research results.

Show R code and professionally looking outputs, e.g. tables prepared in tab_model or stargazer functions, or graphs as coefplots or predicted values.

Provide in-depth discussion and interpretation of your results.

4. Conclusions

250 words

Discuss you findings in the light of literature you introduced in the introduction.

Reflect why your results are similar/different from the original study.

References

min. 5 scientific references, excluding the replicated paper

Freese, J., & Peterson, D. (2017). Replication in social science. Annual Review of Sociology, 43, 147-165, doi: 10.1146.

Appendix

Appendix 1. My enviroment (full information)

# Detailed information about my environment
sessionInfo()

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.utf8 
[2] LC_CTYPE=English_United Kingdom.utf8   
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] knitr_1.42     rmarkdown_2.20

loaded via a namespace (and not attached):
 [1] codetools_0.2-18 digest_0.6.31    R6_2.5.1         jsonlite_1.8.4  
 [5] evaluate_0.20    cachem_1.0.6     rlang_1.0.6      cli_3.6.0       
 [9] rstudioapi_0.14  jquerylib_0.1.4  bslib_0.4.2      tools_4.2.2     
[13] xfun_0.37        yaml_2.3.7       fastmap_1.1.0    compiler_4.2.2  
[17] htmltools_0.5.4  sass_0.4.5      

Appendix 2. Entire R code used in the project

# Opening key libraries first
library(rmarkdown)
library(knitr)
# Global options
opts_chunk$set(echo=TRUE,
                 cache=TRUE,
               comment=NA,
               message=FALSE,
               warning=FALSE)
# All used libraries
library("rmarkdown")
library("knitr")
# Versions of used packages
packages <- c("rmarkdown", "knitr")
names(packages) <- packages
lapply(packages, packageVersion)
# What is my R version?
version[['version.string']]
# Detailed information about my environment
sessionInfo()