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Likert or Not, it's all Semantics Anyways...


One way in which attitudes are determined is through explicit measurements such as Likert scales and semantic differential scales. To enhance our learning in COMM 333, this blog is intended to discuss such scales. Gass and Seiter (2014) defined the Likert scale as "a series of statements about some attitude object, followed by a continuum of choices ranging from 'strongly agree' to 'strongly disagree'" (p. 45). An example of a measurable statement is "Dogs are the best choice of pet." A semantic differential scale "consists of a series of bipolar adjective pairs...opposites, such as light-dark...a respondent checks the 'semantic' space between each adjective pair that best reflects his or her overall attitude" (Gass and Seiter, 2014, p. 45). For example, if the researcher wanted to gauge attitudes about smoking they might pose the following pairs of words – life-death, expensive-cheap, calm-excite, smoker-nonsmoker. Both of these scales have positive and negative attributes.

The Likert scale is easy to manipulate to fit a wide breadth of purposes. Questions can be targeted to specific attitudes and do not require a large time commitment from participants. It is useful when gauging attitudes via statements and are utilized often in research done by degree seeking students at ODU because of their ease of use and potential for high participation. These scales are also useful for teacher evaluations. It can help to direct focus to specific things that the school board is looking for. One criticism is that it does not allow for the individual to give a full picture of their attitude. They are only allowed to give a one-dimensional answer to a single thought. Also, the wording of the questions or statements may be polarizing or biased in such a way to attempt to sway the participants answer.

The semantic differential scale allows the participant to indicate their attitude to a specific adjective instead of the implication of a statement. This can give researchers a more thorough understanding of what participants think about a topic. An example of this is a customer service improvement area identification survey. This scale can help to determine what components of the customer service need to be improved upon. One major criticism of this scale is that the potential exists for the participant to not give the adjectives the same meaning that the researcher has. Also, the participant must be critical of their attitudes to be able to apply each adjective to their attitude. So, there is some potential for skewed data.

One component of the theory of reasoned action (TRA) that can affect the results of these surveys is the subjective norm. Gass and Seiter (2014) state that this "is a person's perception of what others...think about the behavior in question" (p. 52). This is a very powerful component that can lead to skewed data. Therefore, this must be taken into account when selecting the appropriate scale. Objectivity by the researcher in both the creation and analysis of the data can combat the effects of the subjective norm.

The elaboration likelihood model (ELM) presents a challenge to attitude determination research because it is difficult to know which level of thinking the participant will be using. If the participant is engaged and critically thinking about the scale, then they are engaged in central processing (Gass and Seiter, 2014). This is an ideal scenario. However, if the participant is distracted away from the meaning of the scale but still completing it, then they are engaged in peripheral processing (Gass and Seiter, 2014). One way to deter the use of peripheral processing is to keep the scale short and to the point. This will make it easier for the participant to stay engaged for the entirety of their participation.

I have learned a lot about how scales can be used to determine attitudes. Provided that the research topic allows, I would use a mix of both of these scales. I would use the Likert scales to establish overall attitudes, followed by the semantic differential scale to elaborate and identify the components of the attitude. I think that it gives the most in-depth analysis, the ability to establish trends and assists in identifying skewed data. In regards to TRA, this attitude scale can provide more objectivity that allows the participant the ability to create their own meaning. This scale can also be used to encourage central processing that was determined to be the ideal ELM route.

References

Gass, R. H., & Seiter, J. S. (2014). Persuasion: Social influence and compliance gaining. Upper Saddle River, NJ: Pearson.


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