Associate Professor of Business Analytics

Nicholas P. Danks

Trinity Business School, Trinity College Dublin

I am an Associate Professor of Business Analytics at Trinity Business School, Trinity College Dublin. My research focuses on the methodology of partial least squares structural equation modeling (PLS-SEM), predictive modeling, and model selection techniques. I am the co-creator and maintainer of the SEMinR R package, which provides a domain-specific language for building and estimating structural equation models in R. I hold a PhD from National Tsing Hua University, Taiwan, and also serve as Program Director for the MSc in Business Analytics.

PLS-SEM Methodology Predictive Modeling Model Selection Information Systems Statistical Software Development

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Research Focus

My research sits at the intersection of statistical methodology and business analytics.

PLS-SEM Methodology

Advancing the methodology of partial least squares structural equation modeling, including estimation, prediction, and model selection techniques.

Statistical Software

Building open-source R packages (SEMinR, seminrExtras) that make advanced statistical methods accessible through intuitive domain-specific languages.

Predictive Modeling

Developing frameworks for out-of-sample prediction, model selection uncertainty, and multimodel inference in business and social science research.

SEMinR

A domain-specific language for building and estimating structural equation models in R.

example.R

library(seminr)

# Define measurement model
measurements <- constructs(
  composite("Satisfaction", multi_items("sat", 1:3)),
  composite("Loyalty",      multi_items("loy", 1:4)),
  composite("Quality",     multi_items("qual", 1:5))
)

# Define structural model
structure <- relationships(
  paths("Quality",       to = "Satisfaction"),
  paths("Satisfaction",  to = "Loyalty")
)

# Estimate the model
model <- estimate_pls(
  data          = survey_data,
  measurement   = measurements,
  structure     = structure
)

CRAN Package 70,000+ downloads

SEMinR provides an intuitive, natural syntax for specifying and estimating PLS structural equation models in R. The domain-specific language lets researchers express models using familiar SEM terminology.