Fetching contributors…
Cannot retrieve contributors at this time
74 lines (60 sloc) 2.83 KB

+++ title = "A hierarchical IRT model for identifying group-level aberrant growth" date = 2018-04-01T00:00:00 draft = false

Authors. Comma separated list, e.g. ["Bob Smith", "David Jones"].

authors = ["Jennifer A. Brussow", "William P. Skorupski", "W. Jake Thompson"]

Publication type.


0 = Uncategorized

1 = Conference paper

2 = Journal article

3 = Manuscript

4 = Report

5 = Book

6 = Book section

7 = Encyclopedia entry

publication_types = ["1"]

Publication name and optional abbreviated version.

publication = "National Council on Measurement in Education" publication_short = "NCME"

Abstract and optional shortened version.

abstract = "As cheating on high-stakes tests continues to threaten the validity of score interpretations, approaches for detecting cheating proliferate. Most research focuses on individual scores, but recent events show group-level cheating is also occurring. The present IRT simulation study extends the Bayesian Hierarchical Linear Model (BHLM) for detecting group-level aberrance to a hierarchical IRT model. Results show good parameter recovery across conditions, suggesting that the model could be successfully applied to real world data. An evaluation of decisions reached using various plausible decision thresholds shows that decision-makers should carefully consider their assessment and population before deciding how to flag groups as possible cheaters. Higher decision thresholds provide lower false positive rates and improved precision at the expense of power; this trade-off seems worth it given the high stakes associated with decisions about cheating."

Is this a selected publication? (true/false)

selected = false

Projects (optional).

Associate this publication with one or more of your projects.

Simply enter your project's folder or file name without extension.

E.g. projects = ["deep-learning"] references


Otherwise, set projects = [].

projects = []

Tags (optional).

Set tags = [] for no tags, or use the form tags = ["A Tag", "Another Tag"] for one or more tags.

tags = ["educational-assessment"]

Links (optional).

url_pdf = "pub/2018-Brussow-NCME-Aberrant-Growth.pdf" url_preprint = "" url_code = "" url_dataset = "" url_project = "" url_slides = "" url_video = "" url_poster = "" url_source = ""

Custom links (optional).

Uncomment line below to enable. For multiple links, use the form [{...}, {...}, {...}].

url_custom = []

Digital Object Identifier (DOI)

doi = ""

Does this page contain LaTeX math? (true/false)

math = true

Featured image

To use, add an image named featured.jpg/png to your page's folder.


Caption (optional)

caption = ""

Focal point (optional)

Options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight

focal_point = "" +++