Andrew Zieffler

Academic. Data lover. Statistics enthusiast.

Selected Publications

(2018). Every Rose has It's Thorn: Secondary Teachers' Reasoning about Statistical Models. ZDM—The International Journal on Mathematics Education.


(2017). What is Statistics Education?. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), The International Handbook of Research in Statistics Education (pp. 37–70). Cham, Switzerland: Springer International Publishing.

(2017). Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE). CBE—Life Sciences Education, 16(4), ar:66.

Project Source Document

(2017). Statistics Graduate Teaching Assistants' Beliefs, Practices, and Preparation for Teaching Introductory Statistics. Statistics Education Research Journal, 16(1), 294–319.


(2015). Assessing Learning Outcomes: An Analysis of the GOALS-2 Instrument. Statistics Education Research Journal, 14(2), 93–116.

PDF Project

(2015). A Catalyst for Change in the High School Math Curriculum. CHANCE, 28(3), 44–49.


(2015). Reasoning about Uncertainty: Learning and Teaching Informal Inferential Reasoning. Minneapolis, MN: Catalyst Press.


(2014). Developing Students' Reasoning about Samples and Sampling Variability as a Path to Expert Statistical Thinking. Educational Studies in Mathematics, 88(3), 327–342.

Project Source Document

(2014). Using TinkerPlots to Develop Tertiary Students' Statistical Thinking in a Modeling-Based Introductory Statistics Class. In T. Wassong, D. Frischemeier, P. R. Fischer, R. Hochmuth, & P. Bender (Eds.), Mit Werkzeugen, Mathematik und Stochastik lernen—Using tools for Learning mathematics and statistics (pp. 405–420). Wiesbaden, Germany: Springer Spektrum.

(2013). The Course as Textbook: A Symbiotic Relationship in the Introductory Statistics Class. Technology Innovations in Statistics Education, 7(3).

PDF Project

(2012). Developing Statistical Modelers and Thinkers in an Introductory, Tertiary-Level Statistics Course. ZDM—The International Journal on Mathematics Education, 44(4), 883–898.


(2012). Automated Assembly of Optimally Spaced and Balanced Paired Comparisons: Controlling Order Effects. Behavioral Research Methods, 44(3), 753–764.


(2012). The Statistics Teaching Inventory: A Survey on Statistics Teachers' Classroom Practices and Beliefs. Journal of Statistics Education, 20(1).

PDF Project

(2011). Rethinking Assessment of Student Learning in Statistics Courses. The American Statistician, 65(1), 1–10.

(2011). Comparing Groups: Randomization and Bootstrap Methods using R. New York: Wiley.

PDF Code Dataset

(2010). Assessing Important Learning Outcomes in Introductory Tertiary Statistics Courses. In P. Bidgood, N. Hunt, & F. Jolliffe (Eds.), Assessment Methods in Statistical Education: An International Perspective (pp. 75–86). Chichester, West Sussex, England: John Wiley & Sons Ltd.

(2009). Modeling the Growth of Students' Covariational Reasoning during an Introductory Statistics Course. Statistics Education Research Journal, 8(1), 7–31.


(2008). A Framework to Support Research on Informal Inferential Reasoning. Statistics Education Research Journal, 7(2), 40–58.


(2008). Implementing New Reform Guidelines in Teaching Introductory College Statistics Courses. Teaching Statistics, 30(3), 66–70.

(2008). What does Research Suggest about the Teaching and Learning of Introductory Statistics at the College Level? A Review of the Literature. Journal of Statistics Education, 16(2).


(2006). Research in the Statistics Classroom: Learning from Teaching Experiments. In G. Burrill, & P. C. Elliott (Eds.), Thinking and Reasoning with Data and Chance: 68th NCTM Yearbook (pp. 467–482). Reston, VA: National Council of Teachers of Mathematics.


Recent Posts

More Posts

On Saturday August 25, 2018 at 8:08 PM I finally hit Inbox Zero! Inbox Zero I did it by immediately copying the snippets of email I wanted into Evernote notes (my notetaking system). I also attended to to-dos more immediately, or added them to a note of “To-Dos”. I doubt I will stay at zero emails in my inbox, but I have been at fewer than 10 emails all summer.


I am giving a talk at the 46th Annual Meeting of the Statistical Society of Canada in Montreal on June 05, 2018. The talk is part of an invited session on Teaching Statistics to Graduate Students in the Health and Social Sciences. Information, including the slides, is available below. Title: Statistical Computing: Non-Ignorable Missingness in the Graduate-Level Social Science Curriculum Abstract: In 2010, Nolan and Temple Lang pointed out that "computational literacy and programming are as fundamental to statistical practice and research as mathematics".


R Markdown is a great way to integrate R code into a document. An example of the default theme used in R Markdown HTML documents is shown below. Pre-Packaged Themes There are several other canned themes you can use rather than the default theme. There are 12 additional themes that you can use without installing any other packages: “cerulean”, “cosmo”, “flatly”, “journal”, “lumen”, “paper”, “readable”, “sandstone”, “simplex”, “spacelab”, “united”, and “yeti”.


It feels like this spring has been especially terrible weather-wise. We have gotten a lot of snow and it has been cold. To evaluate whether this is the case or whether I have hindsight bias, I pulled some historical weather data for the month of April from Weather Underground. library(dplyr) library(forcats) library(ggplot2) library(ggridges) library(readr) library(viridis) # Read in data april = read_csv("~/Documents/github/Public-Stuff/data/april-weather.csv") # Filter dates april = april %>% filter(date <= 11) I grabbed data back to 2008 (avialable at https://raw.


This post is the fourth (and last) in a series of blogposts in which I respond to questions from the students in the Becoming a Teacher of Statistics course. In today’s posting I respond to questions that asked me for predictions about the future of statistics teaching and statistics education research. Before I get into the Q&A, let me just state: Prediction is hard. Leland Wilkinson in The Future of Statistical Computing reminded us of this when he cited a prediction about computers that Andrew Hamilton made in a 1949 issue of Popular Mechanics



e-ATLAS Project

Evaluation and Assessment of Teaching and Learning About Statistics (e-ATLAS) was an NSF-funded project from 2011–2013 that developed high-quality instruments to help evaluate the effectiveness of past and on-going efforts to reform the teaching and learning of introductory statistics at the tertiary level (DUE-1044812 & 1043141).

AIMS Project

Adapting and Implementing Innovative Material in Statistics (AIMS) was an NSF-funded project from 2006–2010 that developed lesson plans and activities based on innovative materials that have been produced for introductory statistics courses (DUE-0535912).


BioSQuaRE is an instrument, created via an HHMI grant, to assess the quantitative readiness of students planning on majoring in biology or the life sciences.


Change Agents for Teaching and Learning Statistics (CATALST) was an NSF-funded project from 2008–2012 that developed materials for teaching a radically different introductory statistics course based on randomization and bootstrap methods to provide students a deep understanding of statistical inference (DUE-0814433).


I teach (have taught) the following courses at the University of Minnesota:

  • EPsy 1261: Understanding data stories through visualization and computing
  • EPsy 3264: Basic and applied statistics
  • EPsy 5244: Survey design, sampling, and implementation
  • EPsy 8220: Methods for categorical response data in educational research
  • EPsy 8251: Methods in data analysis for educational research I
  • EPsy 8252: Methods in data analysis for educational research II
  • EPsy 8282: Statistical analysis of longitudinal data I


location, email, what-not

  • zief0002
  • 178 EdSciB
    56 East River Road
    Minneapolis, MN 55455
  • Tuesday 9:30 to 10:30 or email for appointment


Blogs I follow: