Dataset Information

Assessing Spatial and Temporal Differences in State-Level Child Well-Being Based on Tests of Statistical Significance.

ABSTRACT: The concept of child well-being is central to the study of children and are reflected in data books, statistical indices, and report cards. Statistical indicators of child well-being are increasingly used to examine the quality of life for children. Such reports are often used to examine differences across geographic areas (spatial differences) and changes over time (temporal differences). In this study, indicators from a widely used report on child well-being are used to compare spatial differences and temporal differences among states in the U.S. based on tests of statistical significance. Results show that currently available indicators are better at detecting differences in child well-being between states at one point in time rather than state-level changes over time. Additionally, a state index of child well-being is constructed using only indicators that are statistically significant different than the national rate; the results of the new index proved to be similar to the more traditional z-score method.

SUBMITTER: Patterson SE

PROVIDER: S-EPMC5300055 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing Spatial and Temporal Differences in State-Level Child Well-Being Based on Tests of Statistical Significance.

Patterson Sarah E SE O'Hare William P WP

Child indicators research 20151209 4

The concept of child well-being is central to the study of children and are reflected in data books, statistical indices, and report cards. Statistical indicators of child well-being are increasingly used to examine the quality of life for children. Such reports are often used to examine differences across geographic areas (spatial differences) and changes over time (temporal differences). In this study, indicators from a widely used report on child well-being are used to compare spatial differe ...[more]

PMID: 28191265

Similar Datasets

Project description:Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.We present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

Dataset Information

Assessing Spatial and Temporal Differences in State-Level Child Well-Being Based on Tests of Statistical Significance.

Publications

Assessing Spatial and Temporal Differences in State-Level Child Well-Being Based on Tests of Statistical Significance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets