Center for Innovative Research Methodology (CIRM)

CIRM Co-Directors

Donna Surges Tatum, PhD, CAE, CAEd
Founder & Psychometrician
Meaningful Measurement, Inc.

Todd D. Little, PhD
Professor and Director of the Research, Evaluation, Measurement, and Statistics program, Texas Tech University (TTU)

As an active agent for facilitating and encouraging transdisciplinary research in healthcare quality and safety, ISCOME brings together scholars and thought leaders to create new theories and generate new empirical evidence for impactful frontline systemic improvements. With the addition of “The Golden Bridge” of communication science to existing groundwork, a paradigm shift is imminent.

The Center for Innovative Research Methodology (CIRM) exists to support ISCOME’s active interdisciplinary research teams. Using the most modern, proven methodological tools available, theory and evidence-based knowledge are turned into implementations that are actionable and measureable. Expert quantitative specialists and psychometricians assist in the creation of assessment tools and measurement instruments to provide the needed evidence for cutting-edge, evidence-based implementations.

To perform any creditable research, it is mandatory that the scientific principles of measurement are used to create a legitimate framework. Moving beyond traditional statistics, CIRM uses the most appropriate quantitative and qualitative techniques to delve deeply into data to uncover the best information upon which to base decisions. This allows theory and evidence-based knowledge to be put into practice to create rating scales, assessments, and other instruments which in turn produce standardized measures. These instruments can be used globally to investigate theory and to measure improvements, differences, levels, abilities, and outcomes.

In order to operationalize theories, evaluate and validate empirical evidence, and measure the longitudinal impact of evidence-based front-line implementations, the following leading-edge methods are employed:

  • Item Response Theory/Rasch Measurement using dichotomous, polytomous, many-faceted, partial credit, and multidimensional models
  • Structural Equation Modelling (SEM)
  • Big data analytical techniques
  • Techniques for the analysis of complex, nested data sets
  • Analyses of longitudinal and dyadic data
  • Mean and Covariances Structures (MACS)
  • Modern methods for the treatment of unplanned missing data
  • Planned missing data designs and analyses
  • Latent growth modelling
  • P-technique modelling
  • Techniques to calibrate qualitative data
  • Thematic analysis