iiinova — where your digital self keeps evolving

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Andrew Heiss @andrew.heiss.phd · 18/08/2026

My latest attempt at an AI/LLM policy in my intro to social science stats class. Basically two rules: 1. Everything human-facing must be human-generated 2. You are responsible for understanding, verifying, and citing everything an LLM generates quantf26.classes.andrewheiss.com/syllabus.htm...

Generative AI and LLMs
In this class, I have two general rules regarding LLMs:

Everything human-facing must be human-generated.
You are responsible for understanding, verifying, and citing everything an LLM generates.
I’ll explain what these mean below, but first, an important warning!

LLMs and learning
Phew, I can talk about the relationship between learning and LLMs for hours (see this for a more formal explanation of my thinking). LLMs and generative AI can be useful for statistical programming, but only when you already know what you are doing. They can be dangerous and counterproductive for beginners.

Additionally, using LLMs and generative AI to write does not lead to deeper learning. The point of writing is to help crystalize and organize your thinking. Pasting LLM-generated words into an assignment to make it look like you read and understood the content will not help you learn. Pasting LLM-generated code into an assignment and hoping that it works will not help you learn.

Rule 1: Everything human-facing must be human-generated
Generative AI tools like Gemini, ChatGPT, and Claude can be helpful for generating ideas or topics for your assignments and can even help you find existing research about topics you’re interested in. They are useful for debugging and troubleshooting code.

These uses are permitted in this course.

If you want to use LLM tools to document your code, clean up your notes, look up error messages, search for research related to your topic, and so on, cool. Do it. That’s fine. It’s unavoidable nowadays anyway, since every R-related Google search will give you a Gemini-generated answer at the top with mostly working R code.

Any writing and revisions, however, must be your own. You may not use AI tools to write any of the text you submit to me. AI text adds nothing to my understanding. I have no interest in engaging with it at all. There is nothing more disheartening for me than spending my time grading something that ChatGPT spat out in 10 seconds. I want to see good engagement with the readings. I want to see your thinking process. I want to see you make connections between the readings. I want to see your personal insights. I don’t want to see a bunch of words that look like a human wrote them. That’s not useful for future-you. That’s not useful for me. That’s a waste of time.

Thus, anything human-facing (i.e. not stuff done just for yourself, like your own personal notes, research, etc.) must be human-generated (i.e. written by you). You may not use AI tools to write any portion of your assignments. Using AI tools in this way, or failing to disclose the use of AI tools, will be treated as a case of plagiarism and referred to the Honor Council.

Rule 2: You are responsible for understanding, verifying, and citing everything an LLM generates
These tools are really good at working with code, but again, only if you know what you’re doing. Without guidance and expertise, LLMs love making lots of extraneous, convoluted, and weird code by default. The code ostensibly works, but it’s often strange and unnecessary and uses uncommon syntax and packages.

If you use LLMs for help with code, you must understand and verify what’s going on with your code and you must cite where it came from.

This means that you need to know what each line is doing. If the LLM uses a function or gives you an argument that you don’t understand or haven’t seen before, figure out why and figure out if it’s necessary. Look at the documentation for the function. Search Google for other examples. Ask the LLM about it, and then add comments to the code explaining what’s going on.

The citation doesn’t need to be anything formal (i.e. don’t worry about Chicago or APA guidelines)—it just needs to (1) say which LLM you used, and (2) mention what you asked the LLM.

You should do this by using code comments—inside your code chunk, add a # to the beginning of a line so that it’s treated as a comment (or text) instead of actual code.

Here’s an example of what this can look like:

Here’s an example of what this can look like:

# This calculates the average GDP per capita in each region in the dataset,
# for all countries after 2015. I couldn't remember how to use group_by() and
# summarise() together, so I asked Gemini:
#
# "I'm using tidyverse. I have a dataset named my_dataset and I'm filtering it
# to include all countries after 2015. I want to calculate region averages with
# group_by and summarise but cannot remember the syntax"

my_dataset |>
filter(year > 2015) |>
group_by(region) |>
# Gemini here included na.rm = TRUE, which omits any rows with missing values
# when calculating the average. That's okay and necessary here because
# South Sudan is missing some years of GDP data
summarise(avg_gdp = mean(gdp_per_cap, na.rm = TRUE))

# Gemini also included an extra ungroup() function at the end, but I don't need
# that because there are no groups leftover after summarising here

↑ That’s a lot of extra comments and you won’t always see stuff like that in real life code, but I want to see it here, and future-you will want to see it too.

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International Journal of Public Administration @ijpa2022.bsky.social · 29/06/2026

📢 We're pleased to welcome Dr. Sanghee Park as a new Associate Editor of IJPA. Dr. Park is an Associate Professor at Indiana University Indianapolis, specializing in public service delivery, representative bureaucracy, administrative reform, and AI in public management. @mengye.bsky.social

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Meng Ye @mengye.bsky.social · 08/06/2026

We will stop receiving applications at the end of day on 6/15 Eastern time. Thanks!

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Meng Ye @mengye.bsky.social · 03/06/2026

We are recruiting a new Social Media Editor @ijpa2022.bsky.social . Please share with those who might be interested! Feel free to reach out to me with any questions.

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University College Dublin @ucddublin.bsky.social · 17/03/2026

💙💛 Lá Fhéile Pádraig sona daoibh!

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Andrew Heiss @andrew.heiss.phd · 14/03/2026

This video is *bananas*—you can estimate π from a ton of random coin flips bc the expected prop of heads/total after 50%+ of the flips are heads = π/4 Here's an #rstats version I made of Matt's Python code in the video gist.github.com/andrewheiss/... With 10 million random flips I got 3.131381

youtube.com

Calculating pi from coin flips (without randomness)

YouTube video by Stand-up Maths

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Andrew Heiss @andrew.heiss.phd · 06/03/2026

Finally got around to adding fancy links to my different interactive teaching websites for showing things like p-hacking, p-value interpretations, and (still-in-draft-form) DAGs at www.andrewheiss.com/teaching/ #rstats #QuartoPub #statsky

Interactive resources

With the power of OJS and Quarto, I’ve created a few interactive websites to illustrate trickier statistical concepts when teaching. Check them out (and adapt and copy as much as you want!)

With links to three different websites (accessible at the main link in the post)

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International Journal of Public Administration @ijpa2022.bsky.social · 26/02/2026

📣 IJPA Award Highlight Congratulations to Mr. Nasir Uddin on receiving the #ASPA International Chapter Best Student Paper Award for his IJPA article “Impact of Crime and Insecurity on Citizen Trust in Public Institutions: Evidence from Bangladesh” (2025). tandfonline.com/doi/full/10....

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Anton Strezhnev @astrezh.bsky.social · 22/01/2026

The Spring 2026 course website for my grad causal inference class is up and running. Was inspired by @andrew.heiss.phd and @mattblackwell.bsky.social to move my materials over to a standalone site and use as little of Canvas as possible. www.antonstrezhnev.com/ps813/

antonstrezhnev.com

PoliSci 813

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Andrew Heiss @andrew.heiss.phd · 22/01/2026

Finally got around to removing broom::tidy(), broom::glance(), and broom::augment() from my class examples in favor of parameters::model_parameters(), performance::model_performance() and marginaleffects::predictions() because they're *so nice* for teaching! #rstats #easystats

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International Journal of Public Administration @ijpa2022.bsky.social · 07/01/2026

🌟 Thank you to our reviewers! As one of #IJPA’s Best Reviewers of 2025, we sincerely thank Ju Won Park from the University of Utah for the valuable contributions to the journal. Check out Ju Won Park’s profile here 📷 scholar.google.com/citations?hl...

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International Journal of Public Administration @ijpa2022.bsky.social · 06/01/2026

🌟 Thank you to our reviewers! As one of #IJPA’s Best Reviewers of 2025, we sincerely thank Frank Ohemeng from Concordia University for the valuable contributions to the journal. Check out Frank Ohemeng’s profile here 🙌https://scholar.google.com/citations?hl=zh-CN&user=KoHX16oAAAAJ

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Andrew Heiss @andrew.heiss.phd · 25/11/2025

I don't know how I've missed it because it's *right on the documentation home page*, but if you use {glue} for nice string interpolation in #rstats and you have {stringr} loaded (likely through the tidyverse), you can use str_glue() instead of glue::glue() or loading library(glue) glue.tidyverse.org

$Geordi LaForge rejecting this code library(tidyverse) penguins |> mutate( nice = glue::glue( "{species} on {island} Island ({body_mass} g)" ) ) |> select(nice) #> nice #> 1 Adelie on Torgersen Island (3750 g) #> 2 Adelie on Torgersen Island (3800 g) #> 3 Adelie on Torgersen Island (3250 g) Geordi LaForge approving this code library(tidyverse) penguins |> mutate( nice = str_glue( "{species} on {island} Island ({body_mass} g)" ) ) |> select(nice) #> nice #> 1 Adelie on Torgersen Island (3750 g) #> 2 Adelie on Torgersen Island (3800 g) #> 3 Adelie on Torgersen Island (3250 g)$

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Hadley Wickham @hadley.nz · 06/11/2025

Do you teach #rstats? Do your students complain about how lame and old-fashioned dplyr is? Don't worry: I have the solution for you: github.com/hadley/genzp.... genzplyr is dplyr, but bussin fr fr no cap.

github.com

GitHub - hadley/genzplyr: dplyr but make it bussin fr fr no cap

dplyr but make it bussin fr fr no cap. Contribute to hadley/genzplyr development by creating an account on GitHub.

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Georgia State Public Management and Policy Dept. @gsupmap.bsky.social · 05/11/2025

The AYSPS is excited to promote our doctoral students who are currently on the academic job market. Check out all their amazing research and hard work put in at Georgia State! 💙

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Andrew Heiss @andrew.heiss.phd · 04/11/2025

The {sf} package continues to blow my mind with how easy it is to make maps with #rstats and ggplot (code for that map here gist.github.com/andrewheiss/...)

Map showing points in Utah, North Carolina, Georgia, Jordan, Italy, and Egypt, with great circle paths plotted between them

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Nathan Yau @flowingdata.com · 31/10/2025

Halloween logicals, still the best 10/31 venn

Venn diagrams as jack-o-laterns showing trick/treat logicals: OR, AND, XOR, NOR, NAND, and XNOR

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Jess Calarco @jessicacalarco.com · 01/11/2025

We have progressed from data collection to data analysis.

My 11-year-old sitting with her pile of Halloween candy, sorting it into a bar graph

971344734087

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International Journal of Public Administration @ijpa2022.bsky.social · 24/10/2025

📚 The IJPA Editorial Team had a great time at the ABFM Conference in Atlanta! 🎉 Grateful for the inspiring discussions and connections with colleagues across the public administration community. 🤝 #ABFM2025 #IJPA

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Carl T. Bergstrom @carlbergstrom.com · 18/10/2025

This morning my ChatGPT quota was inexplicably exhausted. It took a while but I pieced it together. Voice mode somehow got activated when I went to bed. The bot then engaged in a 10 hour conversation with my snoring dog, answering questions the pup wasn’t asking and praising him for his insight.

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Meng Ye @mengye.bsky.social · 09/08/2025

Congratulations! The article has also been selected by the editors @ijpa2022.bsky.social to have free access until the end of October.

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International Journal of Public Administration @ijpa2022.bsky.social · 29/07/2025

🎉 Great news from Google Scholar Metrics 2025! IJPA has climbed from #8 to #7 among all PA journals globally! 🔝📈Proud to see IJPA continue its leading role in the field. See the full rankings here 👉 scholar.google.com/citations?vi... #PublicAdministration #ScholarMetrics

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Andrew Heiss @andrew.heiss.phd · 15/07/2025

I just discovered a new super easy way to constrain annotations to specific facets in {ggplot2} with at_panel() from @teunbrand.bsky.social's {ggh4x} teunbrand.github.io/ggh4x/refere... #rstats #dataviz

A plot with an annotation in three facets

library(tidyverse)
library(palmerpenguins)
penguins <- palmerpenguins::penguins |> drop_na(sex)

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) +
geom_point() +
guides(color = "none") +
facet_wrap(vars(species)) +
labs(title = "Labels in every facet :(") +
annotate(geom = "label", x = I(0.5), y = I(0.25), label = "big penguins!")

library(ggh4x)
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) +
geom_point() +
guides(color = "none") +
facet_wrap(vars(species)) +
labs(title = "Label in just one facet!") +
at_panel(
annotate(geom = "label", x = I(0.5), y = I(0.25), label = "big penguins!"),
species == "Gentoo"
)

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Andrew Heiss @andrew.heiss.phd · 16/07/2025

Newly published at NVSQ with @sch-ir.bsky.social and @marcdotson.com! A neat conjoint experiment (analyzed w/fancy multinomial Bayesian models) measuring the effect of anti-NGO crackdown on donor behavior! Prepreint+code: www.andrewheiss.com/research/art... Official version: doi.org/10.1177/0899...

Navigating Hostility: The Effect of Nonprofit Transparency and Accountability on Donor Preferences in the Face of Shrinking Civic Space

Donors unhappy
when states restrict NGOs—
’cept when transparent.

Suparna Chaudhry, Marc Dotson, and Andrew Heiss, “Navigating Hostility: The Effect of Nonprofit Transparency and Accountability on Donor Preferences in the Face of Shrinking Civic Space,” Nonprofit and Voluntary Sector Quarterly (2025), doi: 10.1177/08997640251348654

Navigating Hostility: The Effect of Nonprofit Transparency and Accountability on Donor Preferences in the Face of Shrinking Civic Space

Suparna Chaudhry, Marc Dotson, and Andrew Heiss

Abstract
Governments across the world have increasingly used laws to restrict the work of nonprofits, which has led to a reduction in public or official foreign aid directed towards these groups. Many international nonprofits, in response, have turned to individual donors to offset the loss of traditional funding. What are individual donors’ preferences regarding donating to legally besieged nonprofits abroad? We conducted a conjoint experiment on a nationally representative sample of likely donors in the US and found that learning about host government criticism and legal restrictions on nonprofits decreases individuals’ preferences to donate to them. However, organizational features such as financial transparency and accountability can protect against this dampening effect. Our results have important implications both for understanding private international philanthropy and how nonprofits can better frame their fundraising appeals at a time when they are facing restrictive civic spaces and hostile governments abroad.

Estimated marginal means and average marginal component effects for conjoint experiment results

Estimated marginal means and average marginal component effects for the interaction between transparency and crackdown

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AOM Public and Nonprofit Division (PNP) @aompnp.bsky.social · 15/07/2025

The PNP Division is pleased to announce the election results and 2025-2026 Executive Committee members. Congratulations to all and thank you to each candidate who ran for a position and thanks to everyone for voting! (@marlenewalk.bsky.social, @professajay.bsky.social , @stevebholt.bsky.social)

2025-26 PNP Division Election Results

Division Program Chair: **Emily Nwakpuda**, University of Texas, Arlington

Fundraising Chair: **Dan Heist**, Brigham Young University

Awards Chair: **Marlene Walk**, University of Freiburg

Chair, Best Article Award Committee: **Jason Coupet**, Georgia State University

Chair, Best Book Award Committee: **Stephen B. Holt**, University at Albany, SUNY

2025-26 PNP Division Election Results

Chair, Keith G. Provan Award Committee: **Donna Sedgwick**, Virginia Tech

Chair, Best Dissertation Award Committee: **Alexander Kroll**, Florida International University

Chair, Best International Conference Paper Award Committee: **Rebecca Tekula**, Pace University

Membership Committee: **Nicola Capolupo**, Università San Raffaele di Roma

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International Journal of Public Administration @ijpa2022.bsky.social · 26/06/2025

🎉 So glad we could all come together at IJPA’s Happy Hours for Asian Stories at #PMRC2025! Huge thanks to everyone who stopped by — great conversations, new ideas, and warm connections. @mengye.bsky.social @sethjmeyer.bsky.social

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International Journal of Public Administration @ijpa2022.bsky.social · 06/06/2025

🎉 Join IJPA’s Happy Hours for Asian Stories at #PMRC2025! 🗓️ June 26, 15:30–17:00 📍 Room 204, Building 57, SNU How can we bring Asian voices to the global stage? 🌏 Scan the code & sign up now! @pmra-1991.bsky.social @drspark0108.bsky.social @mengye.bsky.social @sethjmeyer.bsky.social

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Jocelyn Leitzinger @jocelynl.bsky.social · 02/06/2025

This past semester was the most stressful of my academic career. I had 180 students and about half cheated at some point in the semester They submit weekly reflections where the questions are opinion-based, graded only on effort, don't care about grammar SO MANY students submitted ChatGPT essays

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Andrew Heiss @andrew.heiss.phd · 24/04/2025

all this work just to get add some pretentious/fun little quotes

Screenshot of a draft PDF titled "Clarifying Correlation and Causation: A Guide to Modern Quantitative Causal Inference in Nonprofit Studies" with these two epigraphs:

Scientia et potentia humana in idem coincidunt, quia ignoratio causae destituit effectum. *(Human knowledge and human power meet in one; for where the cause is not known the effect cannot be produced.)*
—Francis Bacon, *Novum Organum*, part I, aphorism III

Don't let us forget that the causes of human actions are usually immeasurably more complex and varied than our subsequent explanations of them.
—Fyodor Dostoevsky, *The Idiot*, part IV, chapter 2

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International Journal of Public Administration @ijpa2022.bsky.social · 17/04/2025

🎥 Don’t miss the video abstract by IJPA Emerging Scholar Award winner @anadimand.bsky.social ! She unpacks key insights from her article “Understanding the Impact of Collaborative Governance on Sustainability”. 🌱🏛️ Read here: tandfonline.com/doi/full/10.... #IJPA #PublicAdmin #Sustainability

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Meng Ye @mengye.bsky.social · 14/04/2025

2025 Andrew Young School Honors Day

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Andrew Heiss @andrew.heiss.phd · 11/04/2025

yessss native penguins dataset in #rstats 4.5 ALSO the ability to load single functions from packages!

$# Namespaces! ## R 4.4 and earlier # -------------------------------------------------------------------------------- ### Handle it manually library(MASS) # load MASS first so that dplyr::select() takes over MASS::select() library(dplyr) model <- polr(...) ### Use {conflicted} library(dplyr) library(MASS) conflicted::conflicts_prefer(dplyr::select) model <- polr(...) ## R 4.5+ # -------------------------------------------------------------------------------- library(dplyr) use("MASS", c("polr")) model <- polr(...)$

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Vincent Arel-Bundock @vincentab.bsky.social · 10/04/2025

📚😅🎉 Yay!! I just submitted the complete manuscript of my upcoming book to the publisher! Learn to easily and clearly interpret (almost) any stats model w/ R or Python. Simple ideas, consistent workflow, powerful tools, detailed case studies. Read it for free @ marginaleffects.com #RStats #PyData

Model to Meaning: How to interpret statistical models with marginaleffects for R and Python

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Jaclyn Piatak @jaclynpiatak.bsky.social · 07/04/2025

Thanks so much for having me on the Fundraising School's First Day Podcast!! (youtu.be/x8zfph9fVZI?...) Great to talk about my book with @sowa75.bsky.social - Volunteer Management: A Strategic Approach www.routledge.com/Volunteer-Ma... #Volunteer #VolunteerSupport #VolunteerManagement

youtu.be

Volunteers and Fundraising

YouTube video by The Fund Raising School

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Andrew Heiss @andrew.heiss.phd · 02/04/2025

we've got a stew going

Scatterplot of 500,000 points, with discontinuities at 200,000 in revenue and 500,000 in assets, with 5 different discontinuities to look at

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International Journal of Public Administration @ijpa2022.bsky.social · 19/03/2025

📢 IJPA Best Article Award 2025! Congrats to @lotte-ba.bsky.social, @annemettekjeldsen.bsky.social for their winning paper: Crisis Intensity, Leadership Behavior & Employee Outcomes in Public Organizations This Open Access article is available here 👉 tandfonline.com/doi/full/10....

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Andrew Heiss @andrew.heiss.phd · 12/03/2025

New preprint! A general overview of stats in public policy research with this (oversimplified but still helpful) separation of methods into description, explanation, and prediction #policysky HTML/PDF: stats.andrewheiss.com/snoopy-spring/ SocArXiv: doi.org/10.31235/osf...

This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations. I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field.

Description Explanation Prediction
General question What exists? Why does it exist? How can it be influenced? What will happen next?
Focus of analysis Focus is on any variable—understanding different variables and their distributions and relationships Focus is on X —understanding the relationship between X and Y, often with an emphasis on causality Focus is on Y —forecasting or estimating the value of Y based on X, often without concern for causal mechanisms
Names for variable of interest — Explanatory variable
Independent variable
Predictor variable
Covariate Outcome variable
Dependent variable
Response variable
Goal of analysis Summarize and explore data to identify patterns, trends, and relationships Estimation: Test hypotheses or theories and make inferences about the relationship between one or more X variables and Y

Causal attribution: A special form of estimating—make inferences about the causal relationship between a single X of interest and Y through credible causal assumptions and identification strategies Generate accurate predictions; maximize the amount of explainable variation in Y while minimizing prediction error
Evaluation criteria — Confidence/credible intervals, coefficient significance, effect sizes, and theoretical consistency Metrics like root mean square error (RMSE) and R^2; out-of-sample performance
Typical approaches Univariate summary statistics like the mean, median, variance, and standard deviation; multivariate summary statistics like correlations and cross-tabulations t-tests, proportion tests, multivariate regression models; for causal attribution, careful identification through experiments, quasi-experiments, and other methods with observational data Multivariate regression models; more complex black-box approaches like machine learning and ensemble models

Table of contents
Introduction
Brief history of statistics in public policy
Core methodological approaches
Description
Explanation
Prediction
The pitfalls of counting, gathering, and learning from public data
Future directions
References

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International Journal of Public Administration @ijpa2022.bsky.social · 28/02/2025

📢 Call for Papers! Join Brian Byung Min, Jonathan Lubin, and Jihoon Jeong in IJPA’s Special Issue on Cross-Sector Collaboration Between Government and Civil Society Organizations. 🔗 More details: think.taylorandfrancis.com/special_issu...

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International Journal of Public Administration @ijpa2022.bsky.social · 24/02/2025

📢 Exciting News from IJPA! We’re excited to welcome Seth Meyer from Bridgewater State University as a new Associate Editor of IJPA! 🎉 Learn more about his work: bridgew.edu/department/p... Join us in congratulating him on this new role! 👏✨ #IJPA #PublicAdministration #AcademicCommunity

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Andrew Heiss @andrew.heiss.phd · 20/02/2025

Collider bias is the trickiest one of these—I use this example too: If the true relationship between niceness & appearance is zero but you only look at people you've dated (i.e. adjusted for that collider) you're missing a whole quadrant—the result is wrong and only valid for part of the population

Two scatterplots showing no relationship between niceness and appearance, with four quadrants: mean and attractive, nice and attractive, mean and ugly, nice and ugly

A scatterplot showing the relationship if you only look at people you've dated—the mean and ugly quadrant is omitted and the slope is negative

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AOM Public and Nonprofit Division (PNP) @aompnp.bsky.social · 18/02/2025

AOM-PMP is accepting nominations for leadership roles: •Program Chair •Fundraising Chair •Membership Team •Awards Chair •Chairs for best journal article, book, & dissertation •Provan Award Chair Please nominate yourself and others here by February 28: www.surveymonkey.com/r/NGRB3DV Thanks!

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International Journal of Public Administration @ijpa2022.bsky.social · 14/02/2025

📢 Exciting News from IJPA! We’re delighted to welcome BarbaraZyzak from NTNU – Norwegian University of Science and Technology as a Associate Editor of #IJPA! 🎉 Learn more about her work: ntnu.edu/employees/ba... Join us in congratulating her on this new role! 👏✨

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Andrew Heiss @andrew.heiss.phd · 27/01/2025

New blog post! Here's an #rstats guide for how to (1) get CPS data from IPUMS and (2) compare sample and population proportions both frequentistly and Bayesianly with {brms} (with ROPEs!), and (3) make pretty plots and tables #econsky #polisky #dataskyence www.andrewheiss.com/blog/2025/01...

Plot showing respondent demographic proportions compared to national proportions, overlaid on variable-specific regions of practical equivalence (ROPEs)

Table showing sample characteristics compared to nationally representative Current Population Survey (CPS) estimates, with probability that the sample is equivalent to the national proportion

Also, it’s important to check the variable details to check for availability. While basic demographic variables like age, sex, marital status, etc. are available in both the monthly surveys and in the annual ASEC, more specialized variables are not.

Variables related to philanthropy and volunteering are only available in September (since they’re part of a special CPS Volunteer Supplement), and only in some years:

[Screenshot from the IPUMS website showing the availability of the volunteer status CPS variable]

Contents for the post

Nationally representative demographic data
Accessing US Census data
ACS
CPS (and others!)
Getting started
Getting CPS data from the IPUMS website
Finding variables
Selecting samples
Downloading the data
More reproducible alternative: using the IPUMS API
Loading CPS data
Summarizing CPS data
Weighting
Calculating population-level proportions
Summarizing sample proportions
Testing sample vs. population proportions frequentist-ly
One-sample proportion test for age
One-sample proportion test for volunteering
Proportion tests and differences for everything all at once
Testing sample vs. population proportions Bayesian-ly
ew null hypothesis significance testing
Modeling proportions with a binomial distribution
Working with the posterior
The region of practical equivalence (ROPE)
Bayesian proportion test for volunteering
Posterior proportions, differences, and ROPEs for everything all at once

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Reposted by Meng Ye

Vincent Arel-Bundock @vincentab.bsky.social · 26/01/2025

{tinytable} 0.7.0 for #RStats is out! 🚀 This 📦 converts data frames to html, tex, docx, typ, or md tables. Super simple, ultra flexible, 0-dep, and the website hosts a billion tutorials. vincentarelbundock.github.io/tinytable/ 0.7.0 fixes bugs and adds some cool features. Please update!

419044

Reposted by Meng Ye

International Journal of Public Administration @ijpa2022.bsky.social · 23/01/2025

🌟 Exciting News! 🌟 #IJPA achieved an impressive 310,750 downloads in 2024! 📚🎉🚀A huge THANK YOU to our readers, authors, and contributors for supporting this journey. 🙌 We look forward to continuing to share impactful research in 2025 and beyond!

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Reposted by Meng Ye

International Journal of Public Administration @ijpa2022.bsky.social · 15/01/2025

🌟Calls for nominations We at #IJPA are thrilled to announce the launch of two inaugural awards: 1⃣Best Paper Award (shorturl.at/kMsz5) 2⃣Emerging Scholar's Award (shorturl.at/HOnPN) 🙌Nominations open soon! Please spread the word and encourage nominations!

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