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Causal inference with large-scale assessments in education from a Bayesian perspective: a review and synthesis

Overview of attention for article published in Large-scale Assessments in Education, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
37 Mendeley
Title
Causal inference with large-scale assessments in education from a Bayesian perspective: a review and synthesis
Published in
Large-scale Assessments in Education, May 2016
DOI 10.1186/s40536-016-0022-6
Pubmed ID
Authors

David Kaplan

Abstract

This paper reviews recent research on causal inference with large-scale assessments in education from a Bayesian perspective. I begin by adopting the potential outcomes model of Rubin (J Educ Psychol 66:688-701, 1974) as a framework for causal inference that I argue is appropriate with large-scale educational assessments. I then discuss the elements of Bayesian inference arguing that methods and models of causal inference can benefit from the Bayesian approach to quantifying uncertainty. Next I outline one method of causal inference that I believe is fruitful for addressing causal questions with large-scale educational assessments within the potential outcomes framework- namely, propensity score analysis. I then discuss the quantification of uncertainty in propensity score analysis through a Bayesian approach. Next, I discuss a series of necessary conditions for addressing causal questions with large-scale educational assessments. The paper closes with a discussion of the implications for the design of large-scale educational assessments when the goal is in asking causal questions and warranting causal claims.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Researcher 6 16%
Student > Master 5 14%
Student > Doctoral Student 3 8%
Professor > Associate Professor 2 5%
Other 2 5%
Unknown 10 27%
Readers by discipline Count As %
Social Sciences 11 30%
Mathematics 3 8%
Psychology 3 8%
Arts and Humanities 2 5%
Economics, Econometrics and Finance 2 5%
Other 4 11%
Unknown 12 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 July 2018.
All research outputs
#1,391,374
of 13,266,991 outputs
Outputs from Large-scale Assessments in Education
#21
of 71 outputs
Outputs of similar age
#38,409
of 264,639 outputs
Outputs of similar age from Large-scale Assessments in Education
#1
of 1 outputs
Altmetric has tracked 13,266,991 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 71 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,639 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them