1  Movement Advancement Project

Author

Affiliation

Seungju Kim

University of Illinois at Urbana-Champaign

Source Description

Full name: Movement Advancement Project Policy Tally
Website: https://www.mapresearch.org
Data type: Administrative/policy tracking
Unit of analysis: State-year
Coverage: 50 U.S. states + D.C. + territories, 2010–present

About the Original Data

Briefly describe what this dataset is and who collects it. What is the construct it’s measuring? Why is it relevant as a structural stigma indicator?

Original Data Collection

Feature	Details
Collection method	Policy tracking / expert coding
Sampling frame	All U.S. states and territories
Sample type	Census (not a sample)
Update frequency	Annual
Geographic coverage	State-level
Temporal coverage	2010–present

Note

Note any important caveats about how the original investigators collected or coded this data.

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Access and Download

How to Access

Describe where and how to obtain the raw data. Is it publicly available? Does it require registration? Is it scraped, downloaded, or accessed via API?

Feature	Details
Access type	Public download / API / web scrape / request
URL	https://…
Authentication required	Yes/No
Cost	Free / paid
Last accessed	r Sys.Date()

Download Procedure

Step-by-step description of how we obtained the data. Be specific enough that someone could reproduce this in 5 years when the website has changed.

# Any download code goes here

Warning

Note any known instability in access (e.g., website changes, file format shifts across years).

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Cleaning Procedure

Raw Data Structure

Describe what the raw data looks like before any processing.

• File format: .csv / .xlsx / .txt / .pdf
  
• Rows: One row per [state / state-year / indicator]
  
• Key variables: List the main variables used

Cleaning Steps

Describe each substantive cleaning decision in order. For each decision, explain both what was done and why.

1. [Step name, e.g., Variable Selection]

What variables were retained and why. What was excluded and why.

# Relevant code

2. [Step name, e.g., Handling Missing Data]

How missing values were identified and treated.

# Relevant code

3. [Step name, e.g., Recoding / Scoring]

Any recoding, reverse scoring, or composite construction.

# Relevant code

Important

Document any decisions that involve judgment calls — these are the decisions future you (or a reviewer) will ask about.

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Aggregation to State Level

Note

Skip this section if the data is already state-level (e.g., MAP).

Unit of Analysis in Raw Data

What is the original unit? (e.g., individual respondent, county, zip code)

Aggregation Method

Feature	Decision	Rationale
Statistic	Mean / Median / Sum	Why this statistic
Minimum n	e.g., n ≥ 50 per state	Why this threshold
Weighting	Unweighted / survey-weighted	Why
Missing states	Excluded / imputed	Why

# Aggregation code

Sensitivity Checks

Note any alternative aggregation approaches considered and why the chosen approach was preferred.

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Output Dataset

Variables in Final Output

Variable	Type	Description	Range
state	chr	Two-letter state abbreviation	—
year	int	Calendar year	2010–2024
map_so_total	dbl	Sexual orientation policy tally	-10 to 20
map_gi_total	dbl	Gender identity policy tally	-10 to 20.5

Output File

# Final save
saveRDS(map_clean, here("data", "clean", "map", "map_clean.rds"))

File location: data/clean/map/map_clean.rds
Rows: 51 (states + D.C.) × years available
Last generated: r Sys.Date()

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Known Limitations

• Limitation 1 and its implications for use
• Limitation 2
• Any known errors or inconsistencies in the source data

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References

List any papers, reports, or documentation from the original data collectors that should be cited when using this source.