mlsanityData to 7 Oct 2026

Deep Learning-Based Estimation and Goodness-of-Fit for Large-Scale Confirmatory Item Factor Analysis

arXiv 2109.09500 ↗First posted 20 Sep 2021stat.ML cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

We investigate novel parameter estimation and goodness-of-fit (GOF) assessment methods for large-scale confirmatory item factor analysis (IFA) with many respondents, items, and latent factors. For parameter estimation, we extend Urban and Bauer's (2021) deep learning algorithm for exploratory IFA to the confirmatory setting by showing how to handle constraints on loadings and factor correlations. For GOF assessment, we explore simulation-based tests and indices that extend the classifier two-sample test (C2ST), a method that tests whether a deep neural network can distinguish between observed data and synthetic data sampled from a fitted IFA model. Proposed extensions include a test of approximate fit wherein the user specifies what percentage of observed and synthetic data should be distinguishable as well as a relative fit index (RFI) that is similar in spirit to the RFIs used in structural equation modeling. Via simulation studies, we show that: (1) the confirmatory extension of Urban and Bauer's (2021) algorithm obtains comparable estimates to a state-of-the-art estimation procedure in less time; (2) C2ST-based GOF tests control the empirical type I error rate and detect when the latent dimensionality is misspecified; and (3) the sampling distribution of the C2ST-based RFI depends on the sample size.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 99 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

18 of 99 references.

References · 18

  1. 2016Variational Inference: A Review for Statisticianscited by 1,466
  2. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  3. 2019On the Convergence of Adam and Beyondcited by 834
  4. 2015Importance Weighted Autoencoderscited by 454
  5. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  6. 2016Revisiting Classifier Two-Sample Testscited by 258
  7. 2017Advances in Variational Inferencecited by 236
  8. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
  9. 2018On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimizationcited by 173
  10. 2016Interpretable Distribution Features with Maximum Testing Powercited by 63
  11. 2020not-MIWAE: Deep Generative Modelling with Missing not at Random Datacited by 61
  12. 2018Doubly Reparameterized Gradient Estimators for Monte Carlo Objectivescited by 41
6 more
  1. 2016Classification accuracy as a proxy for two sample testingcited by 39
  2. 2020Variational Item Response Theory: Fast, Accurate, and Expressivecited by 29
  3. 2019β3-IRT: A New Item Response Model and its Applicationscited by 17
  4. 2020A General Family of Stochastic Proximal Gradient Methods for Deep Learningcited by 6
  5. 2020A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysiscited by 5
  6. 2020Multicollinearity Correction and Combined Feature Effect in Shapley Valuescited by 3

Built on by

Not cited yet.