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Parsing the Achievement Gap II (2009)
More research at https://www.ets.org/s/achievement_gap/research/index.html

"From the research and statistics now available to us, we have determined that it remains clear that minority students and poor students continue to face conditions that undermine school achievement. At different points in their lives they will, on average, lag behind their peers in cognitive development. The achievement gap has deep roots — deep in out-of-school experiences and deep in the structures of schools."
education  paper  social_science  public_policy 
4 weeks ago by loimprevisto
Extending the Use and Prediction Precision of Subnational Public Opinion Estimation - Leemann - 2017 - American Journal of Political Science - Wiley Online Library
The comparative study of subnational units is on the rise. Multilevel regression and poststratification (MrP) has become the standard method for estimating subnational public opinion. Unfortunately, MrP comes with stringent data demands. As a consequence, scholars cannot apply MrP in countries without detailed census data, and when such data are available, the modeling is restricted to a few variables. This article introduces multilevel regression with synthetic poststratification (MrsP), which relaxes the data requirement of MrP to marginal distributions, substantially increases the prediction precision of the method, and extends its use to countries without census data. The findings of Monte Carlo, U.S., and Swiss analyses show that, using the same predictors, MrsP usually performs in standard applications as well as the currently used standard approach, and it is superior when additional predictors are modeled. The better performance and the more straightforward implementation promise that MrsP will further stimulate subnational research.
public_opinion  spatial_statistics  methods  social_science  via:nyhan 
6 weeks ago by rvenkat
The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About ItPersonality and Social Psychology Bulletin - Andrew Gelman, 2017
A standard mode of inference in social and behavioral science is to establish stylized facts using statistical significance in quantitative studies. However, in a world in which measurements are noisy and effects are small, this will not work: selection on statistical significance leads to effect sizes which are overestimated and often in the wrong direction. After a brief discussion of two examples, one in economics and one in social psychology, we consider the procedural solution of open postpublication review, the design solution of devoting more effort to accurate measurements and within-person comparisons, and the statistical analysis solution of multilevel modeling and reporting all results rather than selection on significance. We argue that the current replication crisis in science arises in part from the ill effects of null hypothesis significance testing being used to study small effects with noisy data. In such settings, apparent success comes easy but truly replicable results require a more serious connection between theory, measurement, and data.
statistics  social_science  critique  replication_of_studies  meta-analysis  methods  andrew.gelman 
september 2017 by rvenkat

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