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Since people are different, it implies the impossibility of counting on them to behave or respond in the same way under any given set of circumstances. Consequently, researchers in communication must be aware that participants’ characteristics may influence their findings. Predictive truths can be complicated to develop and support in the context of certain phenomena. It is possible to measure individual differences through surveys and questionnaires. It is possible to account for individual differences in the design of an experiment using a within-subjects design. Each participant will go through a series of comparison and manipulation tests as part of the study (Cascio, 2018). Using the standard deviation, it is possible to see how much variation among these groups. To determine which individuals are more or less susceptible to manipulation, you can examine the correlation between their attributes and the outcome variable they are exposed.
Each participant’s difference score needs to be averaged across all participants in each condition to calculate the overall causal effect of manipulation. When participants are randomly assigned to either the experimental or control groups, assuming that individual differences will be averaged across groups, individual differences are sacrificed in favor of internal validity. Statistical techniques and research designs can address these issues, but they aren’t widely used. Most communication research experiments assume that individual characteristics do not influence the outcomes. It is essential to note that the case described does not always hold. Taking action here is essential. The measurement scale matters most when it comes to long-term precision and error levels. It helps understand the susceptibility and determines the bias and effectiveness of the various research methods.
Reference
Cascio, W. F., & Aguinis, H. (2018). Applied psychology in talent management. SAGE Publications. Web.
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