Graduate project implementing a pattern-based approach on organizational data
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Updated
Apr 9, 2021 - R
Graduate project implementing a pattern-based approach on organizational data
Resources to estimate usual alcohol consumption from the Extended AUDIT-C
An R adaptation of Multidimentional Top Scoring method presented by Forthmann, Karwowski and Beaty (2023).
Psychometric techniques, including functions to analyze dichotomous variables, and applied examples of factor analysis and discriminant analysis. Produced within the classes "Principles and Methods of Measurement" and "Public Opinion", both taught at the University of Chicago in the Spring of 2021.
Developmental version of R package BayesTwin
🚨[WIP]🚨Classes and Algorithms used across Exploratory Diagnostic Modeling Framework
Inject Missing Values Not-At-Random to Simulated Likert Data Sets
Factor analytics techniques employed in R, including EFA and CFA, to analyze Martin & Doris's (2003) research on the development of a psychometric instrument measuring individual styles of humor.
Evaluating solutions to the label-switching issue when estimating latent variable models with the NUTS algorithm
Scale development project assessing human-machine preferences
Kernel Equating Without Pre-Smoothing
Computing Reliable Change Index (RCI) for Clinically-Significant Differences [R]
📊 Contains code used to analyze data about HCL-32 instrument (Hypomania Checklist). Functions related to confirmatory factor analysis and internal consistency.
Interpreting Differences Between Mean ACT Scores
Limited Information Goodness of Fit Tests for Binary Factor Models
This R package visualizes a correlation matrix as a Venn diagram. The overlap of circles represents the shared variance between variables, and (optionally) the distance between circles is equivalent to the correlation between variables.
R and C++ code useful for time series analysis on dichotomous data
An introduction to graphical models in psychometrics.
R Package for modeling omega-reliability coefficient from exogenous or latent space using Gaussian Processes or linear models.
The project uncovers distinct groups of voter profiles utilizing COVID-19 orientations in a representative sample of German voters. The distinct groups in the population differ in their beliefs towards the effectiveness of government measures, compliance with a possible curfew, and trust in various institutions.
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