mda.biber

An R package for conducting Multi-Dimensional Analysis (MDA), a statistical procedure developed by Douglas Biber for analyzing linguistic variation across genres, registers, and text types.

About Multi-Dimensional Analysis

Multi-Dimensional Analysis (MDA) is a specialized application of factor analysis that identifies co-occurring patterns in linguistic features. It is based on the principle that some linguistic variables co-occur (e.g., nouns and adjectives) while others inversely co-occur (e.g., nouns and pronouns).

MDA typically involves:

1. Feature extraction: Measuring syntactic categories (passive constructions), lexical classes (nouns), or functional categories (hedges)
2. Factor analysis: Identifying underlying dimensions of linguistic variation
3. Dimension scoring: Computing scores for texts along each dimension
4. Interpretation: Understanding what linguistic patterns each dimension represents

The linguistic features used in this package are based on Biber's original research, documented in the corpora and pseudobibeR packages.

Key Features

• Core MDA function: mda_loadings() performs factor analysis and calculates dimension scores
• Visualization tools: Multiple plotting functions for different aspects of MDA results
• Built-in dataset: MICUSP corpus data tagged with 67 linguistic features
• Flexible parameters: Customizable correlation thresholds and factor loadings
• Comprehensive output: Dimension scores, group means, and factor loadings

Installation

Install the development version from GitHub:

# Install from GitHub
devtools::install_github("browndw/mda.biber")

# Load the package
library(mda.biber)

Quick Start

library(mda.biber)
library(dplyr)

# Load and prepare the built-in MICUSP dataset
data(micusp_biber)

# Extract discipline codes and convert to factor
d <- micusp_biber %>%
  mutate(doc_id = stringr::str_extract(doc_id, "^[A-Z]+")) %>%
  mutate(doc_id = as.factor(doc_id)) %>%
  select(where(~ any(. != 0)))  # Remove zero-only columns

# Determine optimal number of factors
screeplot_mda(d)

# Perform MDA with 2 factors
mda_result <- mda_loadings(d, n_factors = 2)

# View dimension scores
head(mda_result)

# Access group means and factor loadings
attributes(mda_result)$group_means
attributes(mda_result)$loadings

Visualization

The package provides several visualization functions:

Stick Plot

# Show category means along a dimension
stickplot_mda(mda_result, n_factor = 1)

Heatmap

# Combine stick plot with factor loadings heatmap
heatmap_mda(mda_result, n_factor = 1)

Boxplot

# Show dimension scores with contributing variables
boxplot_mda(mda_result, n_factor = 1)

Dataset

The package includes micusp_biber, a dataset containing:

• 828 texts from the Michigan Corpus of Upper-Level Student Papers (MICUSP)
• 67 linguistic features tagged using the pseudobibeR package
• Multiple disciplines represented (Biology, Engineering, Philosophy, etc.)
• Standardized rates per 1,000 tokens for most features

Functions Overview

Documentation

• Package documentation
• Full methodology description: Multi-dimensional analysis tutorial

Citation

If you use this package in your research, please cite:

Brown, D. (2024). mda.biber: Functions for Multi-Dimensional Analysis. 
R package version 1.0.1. https://github.com/browndw/mda.biber

License

This package is licensed under the MIT License.