Two-dimensional affective space: a new approach to orienting the axes

Area of Science:

• Psychology
• Affective Science
• Quantitative Psychology

Background:

• Debate exists on the fundamental constructs underlying affective experiences.
• Previous research utilized two-mode factor analysis, yielding different axis orientations (Valence-Activation vs. Positive-Negative Activation).
• The limitations of traditional factor analysis in determining the best-fitting dimensional structure for affective experiences are evident.

Purpose of the Study:

• To apply three-mode parallel factor analysis (PARAFAC) to identify the most appropriate constructs for representing affective experiences.
• To resolve the debate regarding the underlying dimensions of emotion.
• To determine the optimal dimensional solution for affective experiences.

Main Methods:

• Employed three-mode (People x Adjectives x Occasions) parallel factor analysis (PARAFAC).
• Conducted simulation studies (Study 1) to assess PARAFAC's ability to recover factor structures.
• Collected self-reported emotional state data from undergraduate students across various occasions (Study 2: N=112, Study 3: N=349).

Main Results:

• PARAFAC analysis identified Valence and Activation as the best-fitting axes for a two-dimensional representation of affective experiences in both studies.
• Higher-dimensional analysis in Study 3 revealed a three-factor solution.
• The three-factor solution included an activation factor and supported the distinct separation of positive and negative emotions.

Conclusions:

• Valence and Activation provide a robust two-dimensional framework for understanding affective experiences.
• A three-factor solution, separating positive and negative valence alongside activation, offers a more nuanced representation of emotional states.
• PARAFAC analysis is a powerful tool for resolving structural debates in affective science.