Understanding how to handle complex survey design is crucial for researchers and analysts who work with survey data, particularly those who want to produce accurate and representative estimates of population parameters. Properly accounting for concepts such as primary survey units, clusters, strata, and sampling weights, as well as using specialized software packages designed for complex survey data analysis, is essential for producing reliable results. This article will teach you how to use R to handle complex survey design in 10 minutes.

Writing psychodiagnostic reports can be a cumbersome and time-consuming process. It can take up to four hours to write a full report, which is an essential component of patient care management. Writing reports in clear, neutral medical language is important, especially since patients tend to be transferred between different levels of care. However, Natural Language Processing (NLP) has the potential to automate the report generation process, saving time and effort for healthcare providers. Here is a quick demo of how to combine NLP with medical report generation.

When discussing treatment outcomes, we tend to focus solely on symptom reduction. We typically assume that other aspects of improvements always follow core symptom reduction. However, researchers rarely examine the overlaps between core symptom reduction and different types of outcome improvements. Here, we examined whether other types of outcome improvements follow core symptom reduction (here, depressive symptoms) in patients with depression after completing a partial hospital program.

Heavily influenced by the medical model, we tend to conceptualize patient improvement as symptom reduction. The fewer symptoms a person has, the happier they should be. However, there are certainly many different aspects to patient-perceived satisfaction and improvement. Let's use simple data analytic techniques to decompose patient perspectives.

Sick of deleting all rows with missing data? Learn how to deal with missing data with multiple imputation!

Network analysis has been widely used in multidisciplinary data science. Learn how to apply it on empirical data within 30 minutes!