replicatorbench / 15 /gt /expected_post_registration_3.json
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{
"original_study": {
"claim": {
"hypothesis": "The relationship between (in)security and election fraud should be in the form of an inverted U-shape. Fraud increases with violence up to a certain level, but then decreases again.",
"hypothesis_location": "Section: MANIPULATION TACTICS; Subsection: Violence and Fraud; p. 58",
"statement": "We find support…for the inverted U-shaped relationship between violence and fraud",
"statement_location": "Section: CONCLUSION AND POLICY IMPLICATIONS; p. 74",
"study_type": "Observational"
},
"data": [
{
"source": "International Election Commission",
"wave_or_subset": "1-3",
"sample_size": "27,163",
"unit_of_analysis": "polling-station level",
"access_details": "not stated",
"notes": "After removing missing, sample size drops to 22,858."
},
{
"source": "SIGACT reports",
"wave_or_subset": "not stated",
"sample_size": "not stated",
"unit_of_analysis": "violent incident",
"access_details": "not stated",
"notes": "The authors discuss that they use this data, but do not describe the characteristics of the data (e.g. sample size)."
},
{
"source": "National Risk and Vulnerability Assessment",
"wave_or_subset": "2007",
"sample_size": "20,576",
"unit_of_analysis": "household",
"access_details": "not stated",
"notes": ""
},
{
"source": "GTOPO30",
"wave_or_subset": "NA",
"sample_size": "NA",
"unit_of_analysis": "elevation",
"access_details": "Available at http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html. 2007.",
"notes": "This is geological raster data for Afghanistan so there is not real sample size."
},
{
"source": "LandScan population data",
"wave_or_subset": "not stated",
"sample_size": "not stated",
"unit_of_analysis": "30 x 30 latitude/longitude cells",
"access_details": "not stated",
"notes": "They do not directly discuss access, but they leave the reference to the data: Oak Ridge National Laboratory, LandScan Global Population database, 2008. For this paper, the data is aggregated to the district level."
}
]
},
"method": {
"description": "Use logit (for the last-digit fraud measure) and OLS models (for the recount-based fraud measure) and regress fraud on violence and its squared term to test the inverted U-shaped prediction.",
"steps": "(1) Clean, merge, and aggregate the data (remove missing, etc.) when necessary; (2) Group polling stations by district and apply the Beber–Scacco last-digit test to the total vote count; (3) Code a binary dependent variable for fraud; (4) Estimate the effects using logit and OLS models.",
"models": "logit and OLS",
"outcome_variable": "Fraud",
"independent_variables": "Violence; Violence squared",
"control_variables": "number of closed centres, economic development and geographic accessibility",
"tools_software": "not stated"
},
"results": {
"summary": "Since the linear term of the violence measure receives a positive and significant coefficient, and the squared term a negative one, this implies the U-shaped prediction is credible.",
"numerical_results": [
{
"outcome_name": "Election Fraud (last-digit)",
"value": "13.748",
"unit": "not stated",
"effect_size": "not stated",
"confidence_interval": {
"lower": "not stated",
"upper": "not stated",
"level": "not stated"
},
"p_value": "<0.001",
"statistical_significance": "0.1% level",
"direction": "Negative",
"notes": "Confidence intervals are not presented, but standard errors are presented instead (4.720)."
},
{
"outcome_name": "Election Fraud (Recount)",
"value": "1.438",
"unit": "not stated",
"effect_size": "not stated",
"confidence_interval": {
"lower": "not stated",
"upper": "not stated",
"level": "not stated"
},
"p_value": "<0.001",
"statistical_significance": "0.1% level",
"direction": "Negative",
"notes": "Confidence intervals are not presented, but standard errors are presented instead (0.375)."
}
]
},
"metadata": {
"original_paper_id": "10.1017/S0007123412000191",
"original_paper_title": "Violence and Election Fraud: Evidence from Afghanistan",
"original_paper_code": "http://dvn.iq.harvard.edu/dvn/dv/nilsw",
"original_paper_data": "http://dvn.iq.harvard.edu/dvn/dv/nilsw"
}
}