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Affective Responses

 There is no single measure of affect that is "best" or appropriate in all research scenarios and for all study purposes. We recommend a three-step decision-making process, to help you choose an appropriate measure for your study. 

Step 1: Decide whether you wish to study core affect, emotion, or mood
The first, most fundamental decision pertains to identifying the target construct of your study, deciding among core affect, emotion, or mood. The boundaries between these three constructs are fuzzy and the lines of demarcation between the three domains are the subject of heated debates in the literature. To understand the differences between these substantively distinct constructs, it is crucial to study this issue in greater depth by referring to a guidebook (such as the corresponding chapter in The Measurement of Affect, Mood, and Emotion: A Guide for Health-Behavioral Research). 

Step 2: Choose the most appropriate theoretical framework for the chosen construct

Unlike many other concepts in psychology, which may be approached from a single theoretical perspective and assessed with a single measure, the domain of affect, mood, and emotion is characterized by enormous diversity of theoretical viewpoints and associated measures. From some perspectives, affective states are conceptualized as distinct. In other perspectives, affective states lay along dimensions that define a broad domain of content. In some cases, the dimensions are unipolar, whereas in other cases bipolar. In some cases, the dimensions are theorized to be orthogonal to each other. In other cases, affective states are theorized to have a specific pattern of interrelationships (e.g., forming a circumplex configuration). The differences between these theoretical perspectives are neither subtle nor trivial; the different perspectives are usually based on research traditions that extend over decades and their implications on the structure of the resultant measures (and therefore the interpretation of the resultant data) can be profound. Therefore, researchers are strongly encouraged to study the differences between the various theoretical perspectives, and understand their implications, before making a decision. 

Step 3: Select the psychometrically strongest measure based on the chosen theoretical framework

In most published reports, the measures of core affect, emotion, or mood appear as de facto choices, unaccompanied by any supporting conceptual rationale. In an effort to provide at least some type of evidence to support their choice, authors occasionally add certain psychometric data (e.g., an index of internal consistency or an index of the goodness of fit). While offering some rationale may seem better than offering no rationale at all, presenting psychometric data is often meaningless, even misleading, if this step is not preceded by the two previous steps outlined here. It makes little sense, for example, to argue that a certain factor model has been found to fit the data well if readers have been given no explanation for why that particular theoretical model was deemed appropriate for the study in the first place. Therefore, supporting psychometric information should be given only after it has been made clear (a) what the target construct of the study is and why, and (b) which theoretical model of the target construct was selected and why. It makes no sense to say that a certain measure has been found to be a "valid" measure of (for example) mood; it only makes sense to say that the measure has been found to be a "valid" measure of a particular theoretical model of the domain of mood. 

Three-step process for selecting a measure of core affect, mood, or emotion

An example of how this three-step decision-making process can be implemented in practice is shown below. 

Step 1


Step 2



Step 3

Given the specific goals of our research, we have determined that our target construct (in most cases) is core affect and the most appropriate conceptualization of the domain of core affect is the circumplex model. 

The circumplex model of core affect

The circumplex model of core affect serves as the template upon which the affective responses to exercise are mapped. According to the circumplex model, the affective space can be adequately defined by two orthogonal and bipolar dimensions, namely affective valence (pleasure-displeasure; see the horizontal dimension in the schematic) and activation (also referred to as arousal; see the vertical dimension in the schematic). When combined, these two dimensions divide the affective space into four meaningful quadrants: (a) pleasant high activation (e.g., excitement, energy), (b) pleasant low activation (e.g., calmness, relaxation), (c) unpleasant low activation (e.g., boredom, fatigue), and (d) unpleasant high activation (e.g., tension, distress). Because of its broad scope, balance, and unparalleled parsimony, the circumplex is a very useful investigative platform for studying the effects of various exercise stimuli on affect. Regardless of their exact nature and direction, which, in most cases, cannot be accurately predicted, affective responses to exercise can be plotted within this two-dimensional space, enabling the identification and basic description of their most salient experiential features.

For the assessment of the valence dimension, we primarily use either the Feeling Scale (FS; Hardy & Rejeski, 1989) or the Empirical Valence Scale (EVS; Lishner, Cooter, & Zald, 2008). For the assessment of the dimension of perceived activation, we use the Felt Arousal Scale (FAS; Svebak & Murgatroyd, 1985). Because the dimensions of affective valence and perceived activation are theorized to be orthogonal to each other within the framework of the circumplex model, in order to minimize common method variance, we strongly encourage researchers to present the scales assessing these dimensions (a) on separate pages (i.e., not side-by-side), (b) with different orientations (e.g., horizontally for the scale assessing valence, vertically for the scale assessing perceived activation), and (c) in randomized order (i.e., valence first, then activation, or activation first, then valence, in a random sequence). 

Sources of Cited Measures

  • Hardy, C. J., & Rejeski, W. J. (1989). Not what, but how one feels: The measurement of affect during exercise. Journal of Sport & Exercise Psychology, 11(3), 304–317. [DOI] 
  • Lishner, D. A., Cooter, A. B., & Zald, D. H. (2008). Addressing measurement limitations in affective rating scales: Development of an empirical valence scale. Cognition and Emotion, 22(1), 180–192. [DOI] 
  • Svebak, S., & Murgatroyd, S. (1985). Metamotivational dominance: A multimethod validation of reversal theory constructs. Journal of Personality and Social Psychology, 48(1), 107–116. [DOI] 

Recommended Further Readings on the Measurement of Affect

  • Ekkekakis, P. (2013). The measurement of affect, mood, and emotion: A guide for health-behavioral research. New York: Cambridge University Press. [DOI]
  • Ekkekakis, P., Ladwig, M.A., & Hartman, M.E. (2019). Physical activity and the "feel-good" effect: Challenges in researching the pleasure and displeasure people feel when they exercise. In S.R Bird (Ed.), Research methods in physical activity and health (pp. 210-229). New York: Routledge. [Link]
  • Ekkekakis, P., Zenko, Z., Ladwig, M.A., & Hartman, M.E. (2018). Affect as a potential determinant of physical activity and exercise: Critical appraisal of an emerging research field. In D.M. Williams, R.E. Rhodes, & M. Conner (Eds.), Affective determinants of health behavior (pp. 237-261). New York: Oxford University Press. [DOI]
  • Ekkekakis, P., & Zenko, Z. (2016). Measurement of affective responses to exercise: From "affectless arousal" to "the most well-characterized" relationship between the body and affect. In H.L. Meiselman (Ed.), Emotion measurement (pp. 299-321). Duxford, United Kingdom: Woodhead. [DOI]
  • Ekkekakis, P. (2012). Affect, mood, and emotion. In G. Tenenbaum, R.C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 321-332). Champaign, IL: Human Kinetics. [Link]