Abstract
100 km ultra-marathons are increasingly popular among endurance athletes which, given the extreme distance and associated physical demands, raises the question of the role of emotional preparedness. This dataset is that of Samtleben (2021), who investigated the role of emotional intelligence (EI) on participants’ 100km ultra-marathon personal best times in multiple predictor analyses that adjusted for physical preparedness, age, and sex at birth. The sample size is n = 288 and there are p=10 variables. Measures of EI in this data include scores from three scales measuring EI: 1) the Trait Emotional Intelligence Questionnaire Short Form; 2) the Situational Test for Emotional Management Brief; and 3) the Situational Test for Emotional Understanding Brief. There are some missing values.
| Study Design | Topic | Statistical Method | Statistical Method | Statistical Method |
|---|---|---|---|---|
| Cross-Sectional | Ultra-Running and Emotional Intelligence | Description | Basic Inference | Linear Regression |
Contributor
The ultra running dataset was contributed by Eric Samtleben, Department of Psychology, Trent University, Peterborough Campus. Please refer to this resource as Samtleben, E. (2023) Ultrarunning dataset. Teaching of Statistics in the Health Sciences Resource Portal, Available at https://www.causeweb.org/tshs/ultra-running/.
Background
Emotional intelligence (EI), as defined by the American Psychological Association Dictionary of Psychology, is “the ability to process emotional information and use it in reasoning and other cognitive activities” (American Psychological Association, 2023). Extensive research on EI has yielded several models of EI. Their application has provided important insights into the role of EI in many aspects of our lives, including professional success, leadership, and success in interpersonal relationships. Recent research has also explored the role of EI in sports performance and, in particular, the role of EI in the performance of extreme endurance sports. An example is 100 km ultra-runs, which can reasonably be expected to elicit very strong emotions and a desire to quit. Samtleben (2021) investigated this question in a study of n=288 runners completing 100 km ultra-marathons using the tripartite model of emotional intelligence (Tripartite-EI; Mikolajczak, 2009). Briefly, the Tripartite-EI model posits that EI operates on three levels – knowledge, ability, and trait: 1) knowledge represents what we know about our own and others’ emotions, their source and how to manage them; 2) ability represents our skill at implementing our emotional knowledge in real time; and 3) trait represents our habitual patterns of responding to emotion. An illustration of this concept is a driver who becomes angry after being cut off by another driver. Knowledge is the driver’s awareness that such anger is often an over-reaction to fear caused by a near miss. Ability reflects whether the individual can use that knowledge to modify their natural angry response and avoid shouting at the other driver. In this illustration, if the driver habitually ignores such knowledge and ability and instead shouts at other drivers who cut them off, that habitual response pattern is their Trait response.
Objective
The study objective was to determine if Tripartite-EI had a statistically significant contribution to the prediction of 100 km ultra-marathon personal best times after adjustment for age, sex at birth and physical preparedness. Ancillary analyses sought to identify any mediating variables of the relationship between 100 km ultra-marathon personal best times and Tripartite-EI.
Subjects & Variables
| Subject | # Obs | # Var | Introduction | Data Dictionary |
|---|---|---|---|---|
| Ultra-Running | 288 | 10 | Ultra-Running Dataset Introduction | Ultra-Running Data Dictionary |
Data Downloads
| Posting Date | Contributor (email) |
|---|---|
| 8/21/23 | Eric Samtleben (ericsamtleben@trentu.ca) |