tiffanyxwu.bsky.social
Tiffany Wu
@tiffanyxwu.bsky.social
810 followers 510 following 15 posts
Edpsych PhD & Stats dual master's @UMich | IES Predoc Fellow | #rstats enthusiast | Former teacher | @NorthwesternU & @UChicago alumna | 🇹🇼🇺🇸 Website: https://tiffany-wu.github.io/
tiffany-wu.github.io
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · Nov 15
Hello new friends! 👋 I’m Tiffany. I’m a PhD candidate at UMich interested in edpsych 🧠, early childhood 🎒, and machine learning 🖥️. I make pretty pictures using #rstats once in a while (👇). Looking forward to (re)building community & learning from + with y'all! #EduSky #introduction
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paul-bruno.com
Paul Bruno @paul-bruno.com · 16d
The Relationship Between Student Attendance and Achievement, Pre- and Post-COVID journals.sagepub.com/doi/abs/10.1...
Sage Journals: Discover world-class research
Subscription and open access journals from Sage, the world's leading independent academic publisher.
journals.sagepub.com
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aaronsojourner.org
Aaron Sojourner @aaronsojourner.org · Mar 12
We invest 9X less per child-year in care & education in the first 5 years of life than the next 13.

This gap in public investment is why K12 is free for parents & early care & education is expensive.
www.hamiltonproject.org/publication/...
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doughesm.bsky.social
Shaun M. Dougherty @doughesm.bsky.social · Aug 29
🚨Interested in a PhD focusing on quant methods and education & social policy? I'm recruiting this year at @bclynchschool in the Measurement, Evaluation, Statistics, and Assessment program www.bc.edu/bc-web/schoo...
Doctor of Philosophy (Ph.D.) in Measurement, Evaluation, Statistics, and Assessment - Lynch School of Education and Human Development - Boston College
MESA has been training students to examine educational programs, design quantitative research studies, develop assessment instruments, and analyze educational data to help inform policy-making for ove...
www.bc.edu
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annenberginstitute.bsky.social
Annenberg Institute @annenberginstitute.bsky.social · Aug 25
📢 #EdWorkingPapers: How can we measure student behavior at scale?

@tiffanyxwu.bsky.social, @weilanch.bsky.social, @mattadiemer.bsky.social, Rebecca Unterman, @annakshapiro.bsky.social, & Thomas Staines use PCA and factor analysis to build behavior composites from admin data.

📄 bit.ly/4ofre5b
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joshua-goodman.com
Joshua Goodman @joshua-goodman.com · Jun 25
🔥What’s the only thing hotter than this week’s weather?🔥

Our new @wheelockpolicybu.bsky.social working paper:

“School Enrollment Shifts Five Years After the Pandemic”

In it, BU Wheelock PhD @abbyfrancis.bsky.social and I ask:

Has the pandemic permanently changed families' schooling choices?
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paul-bruno.com
Paul Bruno @paul-bruno.com · Jun 16
If we're heading into summer that means academic job market season is looming. As a reminder, my collected tips are here (along with my standing offer to set up time to chat with folks): www.paul-bruno.com/2021/07/tips...
Some Basic Tips About the Academic Job Market – Paul Bruno
www.paul-bruno.com
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joshua-goodman.com
Joshua Goodman @joshua-goodman.com · Jun 11
Re-upping my advice about how to write a good title and abstract for an academic paper, appropriately called:

"How to Write a Title and Abstract"

Feel free to share this thread, which will focus on titles.

#EconSky #AcademicSky
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
A special thank you to Christina Claiborne, @aizatnurshat.bsky.social ‬, and the the entire #EdWorkingPapers and #EdExchange teams at @annenberginstitute.bsky.social for featuring our study in their new series, and for creating such a valuable platform to connect research and practice!
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
Huge thanks to our research-practice partners at Boston Public Schools and Massachusetts Department of Education, and the team at EPI, without whom this work would not be possible.
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
Led by our fearless leader @weilanch.bsky.social, alongside amazing co-authors Rebecca Unterman, @annakshapiro.bsky.social‬, Shekinah Lightner, Thomas Staines, and Annie Taylor.
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
Compared to CTL group, TRT compliers were:
✅ More likely to remain enrolled in BPS throughout middle school
✅ More likely to apply to exam schools
✅ Less likely to be suspended in 7th grade
✅ More likely to complete Algebra I by the end of 8th grade
✅ Higher-performing on 7th grade math tests
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
We find that winning an oversubscribed seat in one of Boston Public Schools’ (BPS) high-quality Pre-K programs shaped students’ middle school trajectories in meaningful ways, despite nearly all control group children attending other preschool programs.
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
📄 Don’t have time for all 86 pages?
Check out our short summary featured in the new #EdWorkingPapers Policy & Practice Series by @annenberginstitute.bsky.social, designed for education leaders and decision-makers:
🔗 edworkingpapers.com/policy-practice-series/ai25-1194
Impacts of Oversubscribed Boston Pre-K Programs through Middle School
This study finds that Boston Public Schools’ (BPS) Pre-K program leads to stronger middle school outcomes than other preschool options. Although differences in outcomes between BPS Pre-K participants ...
edworkingpapers.com
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tiffanyxwu.bsky.social
Tiffany Wu @tiffanyxwu.bsky.social · May 28
🤔 What kind of Pre-K experience sets students up for long-term success?

Our new working paper offers important new evidence to help answer this question!

🔗 edworkingpapers.com/sites/default/files/ai25-1194.pdf

And 🧵👇
A screenshot of the abstract of the working paper Figure 1: School Enrollment Pathways for Lottery Winners and Control Group Students through 8th Grade. This figure represents school enrollment pathways across different school types (BPS, BPS Exam School, non-BPS district school, charter school, and other) for both lottery winners and the control group. The thickness of the lines represents the percentage of lottery winners/control group students that are enrolled in each type of school. Blue lines show the pathways taken by lottery winners, and BPS-specific school categories are highlighted in the light blue boxes. BPS = Boston Public Schools.
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annenberginstitute.bsky.social
Annenberg Institute @annenberginstitute.bsky.social · May 27
🚀 Launching the EdWorkingPapers Policy & Practice Series!

Too much good research never reaches the people making real decisions.

Our new series changes that. Each 2-pager highlights key findings from #EdWorkingPapers, made for busy leaders and policymakers.

📄 Read the first three: bit.ly/3Z1Kok2
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weilanch.bsky.social
Christina Weiland @weilanch.bsky.social · May 22
Using data from Michigan, we find that third grade retention for struggling readers may be a much less important component of the benefits of literacy reforms than previously understood.

(Thx, @annenberginstitute.bsky.social, for the dissemination bump!)
annenberginstitute.bsky.social
Annenberg Institute @annenberginstitute.bsky.social · May 22
📢 #EdWorkingPapers: Flagged for retention, but not retained: Michigan’s 3rd-grade retention policy raised reading scores largely by triggering extra help, not holding kids back.

🔍Jordan Berne, Brian Jacob, @weilanch.bsky.social, Katharine Strunk

📄 edworkingpapers.com/ai25-1188
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johnholbein1.bsky.social
John Holbein @johnholbein1.bsky.social · May 12
Wow!

Each $1 spent on Universal Pre-Kindergarten generates between $3-$20 dollars in aggregate earnings.

That's enough to offset the costs of Universal Pre-Kindergarten through higher tax revenues.
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andrew.heiss.phd
Andrew Heiss @andrew.heiss.phd · Apr 18
Thing I just learned in #rstats: unz() lets you connect to a .zip and load files from inside it without actually unzipping it (great for a file I'm working with that's 30 MB zipped and 1+ GB unzipped, with multiple CSVs in it)
# unz() lets you connect to a .zip and treat it like a mini file system, # and you can load files from inside it one_zipped_csv_among_others <- readr::read_csv( unz("lotsa_zipped_csvs.zip"), "one_csv.csv" ) # readr::read_csv() can read a .zip with a single CSV in it one_zipped_csv <- readr::read_csv("big_zipped_file.zip")
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chloergibbs.bsky.social
chloe gibbs @chloergibbs.bsky.social · Apr 18
I hope it is not lost, in all of the chaos at the federal level, that there are many longstanding, discretionary programs that are on the Administration's chopping block, either directly or through the erosion of agency staff, expertise, and capacity. Programs enacted and reauthorized through...
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vincentab.bsky.social
Vincent Arel-Bundock @vincentab.bsky.social · Apr 10
📚😅🎉

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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andrew.heiss.phd
Andrew Heiss @andrew.heiss.phd · Mar 20
I’ve long used FiveThirtyEight’s interactive “Hack Your Way To Scientific Glory” to illustrate the idea of p-hacking when I teach statistics. But ABC/Disney killed the site earlier this month :(

So I made my own with #rstats and Observable and #QuartoPub ! stats.andrewheiss.com/hack-your-way/
Screenshot of the linked Quarto website, with input checkboxes to change different conditions for a regression model that predicts economic performance based on US political party, with a reported p-value
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dynarski.bsky.social
Prof Dynarski @dynarski.bsky.social · Mar 15
If you have research dependent on these data here is my suggestion
1/N

Big picture:

Create a dataset of cell means (cells must be big enough to pass disclosure)

- load these cell means into many, many tables & put through review

-These cell means can then be used in OLS - which runs on means
andrewdeanho.bsky.social
Andrew Ho @andrewdeanho.bsky.social · Mar 14
Research with restricted-use data is more precise, powerful, and relevant. Faculty and staff will be working through the weekend to answer important research questions before we no longer can. This is not normal, necessary, or efficient.
aeraedresearch.bsky.social
AERA @aeraedresearch.bsky.social · Mar 14
AERA has just learned that all restricted-use NCES data licenses will be cancelled, possibly as early as March 20. We urgently request that all AERA members and others in the research community with restricted-use licenses take these two actions: www.aera.net/Research-Pol...
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carajackson.bsky.social
Cara Jackson @carajackson.bsky.social · Mar 12
Objective, comprehensive, rigorous, timely, and useful:
A digital hub for evidence-based education policy research.

#EdResearch #EdPolicy

livehandbook.org
Evidence-Based Education Policy Research | Live Handbook
Explore evidence-based education policy research at our digital hub. Access data-driven insights to improve learning outcomes and shape effective policies.
livehandbook.org
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andrew.heiss.phd
Andrew Heiss @andrew.heiss.phd · Mar 12
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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bethschueler.bsky.social
Beth Schueler @bethschueler.bsky.social · Mar 5
It's that time of year to circulate this list of orgs for those seeking ed policy jobs/internships. Please let me know if there are opportunities/orgs I should add. Good luck out there! docs.google.com/spreadsheets...
The Unofficial List of Ed Policy Orgs for Job/Internship Seekers
docs.google.com
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