Under social sciences contexts, factors exhibit various forms of correlation, and it’s not easy for reseachers to capture the correct relationships. Incorrect model specification can bring unmodeled interactions which further causes bias in estimation and predictions. In this project, We focused on the logistic regression and tried to utilize Neural Networks as a supplementary tool to help to identify the model specification.
We used a multiple single-case experimental design to examine the use of DTTC in children with childhood apraxia of speech over six weeks of intervention. A linear mixed effect model was used to estimate the change in word accuracy for treated and untreated words across all children from Baseline to Post- treatment and to Maintenance. A Quasi-Poisson regression model was used to estimate mean change and calculate the effect size per individual child and at the word level.
This research examined the relationship between auditory-perceptual ratings of word accuracy and measures of speech motor timing and variability at pre- and post-treatment in children with childhood apraxia of speech (CAS). We found 1) a negative relationship between auditory-perceptual measures of word accuracy and movement variability; 2) participants who demonstrated the poorest performance at treatment onset displayed the most significant gains.
This tracker monitors NYC’s revenue, and how this flows through to its expenditure on government public services domain. This project aims to provide insight into the fiscal operations of the city government and can be used by advocates, policymakers and individuals who want to learn about the city’s funding decisions.
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Lab sessions, New York University, Department of Applied Statistics, Social Science, and Humanities, 2022