This is a commentary on a paper entitled “The influence relationship among variables and types of multiple influence factors working together” by Wen et al., published in Acta Psychologica Sinica in October 2024. They proposed a new concept called the “influence relationship.” This new concept is problematic. First, Wen et al. provided no definition for the “influence relationship,” which is unacceptable for a new-conception paper. Second, according to their proposed inference requirement, if researchers fail to disprove alternative explanations that threaten causal inferences, then they can use the term, “influence relationship,” when reporting their studies. However, this argument is a manifestation of the misunderstanding of inference requirements of causal relationships. Third, “influence” is a term that poses causal meanings according to Chinese and English dictionaries, previous academic articles, and empirical evidence. Thus, the suggestion by Wen et al. to describe a noncausal relationship using “influence” can result in an overstatement of research significance and misunderstanding among fellow academics and public readers. This scenario is contradictory to the increasing expectations of researchers of more rigorous scientific language. Fourth, Wen et al. were confused with goals and the realization of such goals. Failure to disprove alternative explanations is a compromise or a limitation in methods instead of a unique goal.
Wen et al. stressed that a “directional correlation” lacked an appropriate name in academia. Therefore, they called it the “influence relationship.” This stance is seemingly an unfair description of the academic status quo because researchers typically adopt the word, “predict,” to describe a directional correlation. Based on previous articles, this commentary proposes another framework for the categorization of variable relationships. At the goal level, causal goals—in which researchers hypothesize a difference in Y if X is deliberately changed—can be distinguished from noncausal goals. Furthermore, noncausal goals can be classified as predictive goals (e.g., using texts to predict mental disorder risks or test scores to predict future performance) and purely correlational goals (e.g., a shopping basket analysis or a correlation analysis between a newly proposed personality construct and the Big Five). Neither is concerned with alterations to X. At the realization level, if a researcher opts for a causal goal but fails to provide sufficient evidence to support causal relationships, then they are expected to avoid causal language (e.g., “influence”) when reporting results and key conclusions. Alternatively, they can use terms such as “be associated with” and “predict” if appropriate.
Moreover, this commentary provides authors and reviewers with several practical suggestions. (A) Clearly define research goals because the different criteria to evaluate causal, predictive, and purely correlational studies should be followed. (B) Enable researchers to discuss causal meanings conveyed by their results even if they fail to offer sufficient causal evidence when targeting causal goals. This statement does not mean an encouragement of overstatement; conversely, only if researchers clearly define their causal goals can they admit the extent to which they are realizing such goals. (C) Use noncausal language to report noncausal results frankly rather than using euphemisms as a strategy for impression management. (D) Avoid an all-or-none attitude toward causal evidence; instead, value every effort that helps disprove alternative explanations and provides more confidence in causal propositions. (E) Do not rely on a single study (even a randomized experiment) to provide conclusive answers to causal questions; instead, value the accumulation of evidence and triangulation.
