A Novel Approach for Developing Efficient and Convenient Short Assessments to Approximate a Long Assessment

Ant colony optimization for parallel test assembly

Ant colony optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to construct multiple parallel short scales that adhere to several competing and interacting criteria simultaneously. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at the domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of factual knowledge, and (e) are gender-fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions to metaheuristic test assembly and the equivalence of parallel knowledge tests in general.

A novel machine learning approach to shorten depression risk assessment for convenient uses

BACKGROUND

Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently and rapidly on a regular basis.

METHODS

We obtained a dataset of responses to the Depression, Anxiety, and Stress questionnaire (DASS-42) from a large sample of individuals worldwide (N = 31,715). With this dataset, important items from the questionnaire were extracted by applying feature selection techniques. Then, using the most important features, various machine learning algorithms were trained, tested, and validated in predicting depression status.

RESULTS

This study revealed that only seven items are needed to predict depression status with at least 90 % accuracy of the original full scale. This can be achieved through the Stacked Generalization Ensemble learning method of multiple models. The trained machine learning models from the best algorithm were then implemented as an online Depression Rapid Assessment tool, which allows the user to evaluate their current depression status conveniently and quickly (about 1 min).

LIMITATIONS

The sample size of the present study is still relatively small and has biases toward certain demographics (e.g., mostly young, Asian, and female). Further, memory issues with Stacked Generalization Ensemble prevent it from being trained in the same way as the other algorithm.

CONCLUSION

It is possible to produce very short assessments that approximate the accuracy of the full scale for convenient and rapid self-assessment of depression risks.