Draft:Influence relationship
| Submission declined on 10 August 2024 by DoubleGrazing (talk). This submission is not adequately supported by reliable sources. Reliable sources are required so that information can be verified. If you need help with referencing, please see Referencing for beginners and Citing sources. This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
Influence relationship is defined as a directional relationship in the category of correlation.[1][2] For example, the environment influences emotion, the child's age influences athletic ability, and childhood trauma influences adolescent adjustment. The variable that appears earlier is called the influence factor and the variable that emerges later is the outcome.
Correlation is covariation between variables, reflecting the association between variables. Directionality is a sequential order between variables, reflecting the effect of one variable on another.[3] Relationships between variables in many statistical models (e.g., mediated effects, moderated effects models) usually satisfy both, the arrow between variables based on well-founded assumptions (directionality), and the statistical significance of the coefficients of the paths between variables (covariation).
The associations among influence, correlation, and causation are shown in Figure 1. Causation must be influence while influence must be correlation. In terms of extension, the three types of relationships are inclusive, with correlation encompassing influence and influence encompassing causation. The connotations of the three are just the opposite, with correlation having the least connotations and causation having the most.
Figure 1 Connotations and denotations of causation, influence and correlation relationships
In the fields of epidemiology and clinical psychology, the concept of risk factor[4] is often mentioned. Risk factors are all influence factors, but the relationship between risk factors and outcomes is not necessarily causal. The opposite of risk factors is protective factors, which can simply be understood as influences that reduce the occurrence of negative outcomes. That is, both risk factors and protective factors are influences on negative outcomes, and negative outcomes will increase as the level of risk factors rises and decrease as the level of protective factors drops.
Influence relationship is established according to two criteria, directionality and correlation. Statistical analyses of influence relationship include the commonly used test of variance, correlation analysis, regression analysis, and path analysis, depending on the type of variables and the number of influence factors.
The key to modeling influence relationships is to find evidence for directionality. The following approaches can be used to find evidence for determining the termporal order of variables: (1) determining the direction based on the chronological order of variable occurrences; (2) reversing the termporal order of two variables to see which one is easier to explain; (3) following the rule that object variables influence subject variables; (4) determining according to attributes of variables, i.e., essential attributes may influence state attributes, long-term attributes impact temporary attributes, and stable attributes affect unstable attributes; (5) gaining support from theories or empirical literature; (6) summarizing from life experience and common sense; (7) reasoning through analogies; (8) using the principles of cross-lag analysis to identify dominant factors.
See also
[edit]References
[edit]- ^ Wen, Zhonglin; Wang, Yifan; Ma, Peng; Meng, Jin; Liu, Xiqin (2024). "The influence relationship among variables and types of multiple influence factors working together". Acta Psychologica Sinica. 56 (10): 1462–1470. doi:10.3724/SP.J.1041.2024.01462.
- ^ "OSF".
- ^ Davis, James A (1985). The Logic of Causal Order. Newbury Park, CA: Sage Publications.
- ^ Kraemer, Helena C; Stice, Eric; Kazdin, Alan; Offord, David; Kupfer, David (2001). "How do risk factors work together? mediators, moderators, and independent, overlapping, and proxy risk factors". American Journal of Psychiatry. 158 (6): 848−856. doi:10.1176/appi.ajp.158.6.848. PMID 11384888.