Neural Network Analysis of Psychological Data: A Step-by-Step Guide
Saved in:
| Title: | Neural Network Analysis of Psychological Data: A Step-by-Step Guide |
|---|---|
| Language: | English |
| Authors: | Lingbo Tong, Zhiyong Zhang |
| Source: | Grantee Submission. 2026. |
| Peer Reviewed: | Y |
| Page Count: | 23 |
| Publication Date: | 2026 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305D210023 |
| Document Type: | Reports - Evaluative |
| Descriptors: | Artificial Intelligence, Data, Psychological Studies, Network Analysis, Data Analysis, Models, Statistical Inference, Regression (Statistics) |
| DOI: | 10.1080/00273171.2025.2587379 |
| Abstract: | Artificial neural networks (ANN) have attracted increasing attention in the field of psychology. With the availability of software programs, the wide application of ANN becomes possible. However, without a firm understanding of the basics of the ANN, issues can easily arise. This article presents a step-by-step guide for implementing a feed-forward neural network (FNN) on a psychological data set to illustrate the critical steps in building, estimating, and interpreting a neural network model. We start with a concrete example of a basic 3-layer FNN, illustrating the core concepts, the matrix representation, and the whole optimization process. By adjusting parameters and changing the model structure, we examine their effects on model performance. Then, we introduce accessible methods for interpreting model results and making inferences. Through the guide, we hope to help researchers avoid common problems in applying neural network models and machine learning methods in general. [This is the online first version of an article published in "Multivariate Behavioral Research." This work was also supported by the Lucy Family Institute for Data and Society and Notre Dame Global.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2026 |
| Accession Number: | ED679891 |
| Database: | ERIC |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
