Go for the gold: Hurdles and winning strategies for conducting longitudinal and nonlinear research in undergraduate leader development

Abstract

This article makes a case for longitudinal and non‐linear methods when researching or evaluating student leadership development. After a primer on longitudinal methodology, barriers and aligned solutions to methodological challenges are presented.

Assessing and measuring leadership identity

“…If the intent is to assess or measure identity development to evaluate whether the leadership development program met its intended objective, then diagnosing development is about measuring the accomplishments of stated learning objectives and program outcomes related to leader or leadership identity development and placing value on that assessment data to determine the effectiveness of the program in meeting its intended outcomes. The good news for leadership educators is that leader identity is considered a proximal outcome of leadership development (Diaz et al., 2022), and therefore, could be reasonably included in stated program outcomes. The key is to be specific in setting up program goals and learning objectives so assessment questions can address specific leader or leadership identity changes, while avoiding assessment issues such as the ‘Halo’ or ‘Hallmark’ effects (see Rosch & Schwartz, 2009).…”

“…If the intent is to measure leader or leadership identity development for research purposes, then precise measurement is critical and likely to involve true longitudinal research via three or more waves of data, outcome tracking over time, and practical time metrics (Day, 2011). While leader identity as a proximal outcome of leadership development (Diaz et al., 2022) is good news for leadership educators, the bad news is that leader identity tends to develop over time in nonlinear fashions (Day & Sin, 2011). When diagnosing development in leader or leadership identity over time is the desired goal, it will be important to select longitudinal methods and appropriate associated analytic techniques (e.g., latent change score modeling) that account for nonlinear growth trajectories.…”