Model Uncertainty and the Crisis in Science. Young, C. Socius: Sociological Research for a Dynamic World, 4:237802311773720, January, 2018. 00023
Model Uncertainty and the Crisis in Science [link]Paper  doi  abstract   bibtex   
The “crisis in science” today is rooted in genuine problems of model uncertainty and lack of transparency. Researchers estimate a large number of models in the course of their research but only publish a small number of preferred results. Authors have much influence on the results of an empirical study through their choices about model specification. I advance methods to quantify the influence of the author—or at least demonstrate the scope an author has to choose a preferred result. Multimodel analysis, combined with modern computational power, allows authors to present their preferred estimate alongside a distribution of estimates from many other plausible models. I demonstrate the method using new software and applied empirical examples. When evaluating research results, accounting for model uncertainty and model robustness is at least as important as statistical significance.
@article{young_model_2018,
	title = {Model {Uncertainty} and the {Crisis} in {Science}},
	volume = {4},
	issn = {2378-0231, 2378-0231},
	url = {http://journals.sagepub.com/doi/10.1177/2378023117737206},
	doi = {10.1177/2378023117737206},
	abstract = {The “crisis in science” today is rooted in genuine problems of model uncertainty and lack of transparency. Researchers estimate a large number of models in the course of their research but only publish a small number of preferred results. Authors have much influence on the results of an empirical study through their choices about model specification. I advance methods to quantify the influence of the author—or at least demonstrate the scope an author has to choose a preferred result. Multimodel analysis, combined with modern computational power, allows authors to present their preferred estimate alongside a distribution of estimates from many other plausible models. I demonstrate the method using new software and applied empirical examples. When evaluating research results, accounting for model uncertainty and model robustness is at least as important as statistical significance.},
	language = {en},
	urldate = {2020-10-02},
	journal = {Socius: Sociological Research for a Dynamic World},
	author = {Young, Cristobal},
	month = jan,
	year = {2018},
	note = {00023},
	pages = {237802311773720},
}
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