Author: Hamid Bulut
Date: 2026-06-18

# Interactive Data Analysis with MAIHDA

#### Hamid Bulut

#### 2026-06-18

## Introduction

The MAIHDA package includes a fully-featured interactive Shiny Dashboard that provides a no-code alternative to exploring your data, building intersectional strata, fitting models, and analyzing inequality. This is particularly useful for rapid exploration.

## Launching the Application

### Online Version

You can access a live, cloud-hosted version of the MAIHDA interactive dashboard directly in your browser without installing R: https://hdbt.shinyapps.io/shiny/

### Local Version

You can start the interactive dashboard locally by running a single command:

    library(MAIHDA)
    run_maihda_app()

This will automatically launch the dashboard in your default web browser or the RStudio viewer.

Shiny Dashboard

## App Features

### 1. Data Import

- Upload Own Data: Easily upload datasets in `.csv`, Stata (`.dta`), or SPSS (`.sav`) formats.
- Use Included Data: Try out the app instantly by selecting the pre-loaded built-in NHANES health data (`maihda_health_data`) or PISA country data (`maihda_country_data`).
- View Data: The app includes an interactive data table letting you sort, filter, and inspect variables before analyzing.

### 2. Variable Selection & Strata Creation

- Choose a categorical/continuous outcome metric from your dataset.
- Select two or more categorical demographic variables (e.g., gender, race, education) to automatically generate intersectional strata.

### 3. Model Fitting & Settings

- Fit models with the lme4 engine used by the interactive dashboard. Bayesian brms models remain available from R code via `fit_maihda(engine = "brms")`.
- Select covariates to control for within your models.
- Choose whether to calculate parametric bootstrap confidence intervals to quantify uncertainty. When enabled, the dashboard reports bootstrap intervals for both the Variance Partition Coefficient (VPC / ICC) – shown in the Model Summary and Interactive Explorer tabs – and the Proportional Change in Variance (PCV), shown in the PCV Results tab.

### 4. Interactive Visualizations

Once a model is fit, you can navigate across multiple tabs:

- Predicted Values: Visually evaluate stratum-level predictions relative to the overall mean with dynamic prediction intervals.
- VPC Decomposition: Examine how much of your outcome’s variance is attributed to between-stratum differences versus within-stratum individual heterogeneity.
- Observed vs. Shrunken Estimates: Compare raw unadjusted group means to your model’s shrinkage estimates to see the protective mechanism of multilevel modeling.

### 5. Stepwise Variance Analysis (PCV)

The dashboard calculates stepwise Proportional Change in Variance (PCV) tables:

- See the proportional change in between-stratum variance as covariates are added sequentially (a model-dependent, descriptive comparison, not a causal decomposition of inequality).
- Uncover masking/suppression effects directly inside the app by comparing partial PCV values across models.

## Summary

The MAIHDA interactive dashboard is designed to make modeling health and social inequalities accessible without needing to write code. It provides a platform for exploring intersectional data, fitting multilevel models, and visualizing results in a user-friendly way.