This is the first of two Blogs on the Hiller Vagueness Dictionary
It all started back in the late sixties and early seventies when J.H. Hiller and his collaborators wrote a series of papers exploring communication vagueness. Back in those early days they used computer-assisted content analysis to analyze lectures, students papers and presentations to measure vagueness and its impacts in different situation, establishing a correlation between verbal fluency and vagueness in teacher lectures. Hiller, Fisher & Kaess (1969) found that when verbal fluency increased, and vagueness score decreased, the students test scores improved. In another study, Hiller, Marcotte & Martin (1969) analyzed 256 high school essays and found that vagueness and grading scores had a direct correlation: More specific or less vague papers received higher marks.
One of the lasting results of those studies was a communication vagueness dictionary that is still being used today to analyze various forms of communication in financial reporting, corporate strategy, federal reserve speak, political statements, multiple choice tests, coping mechanisms of PhD students and the list goes on.
When people are vague, they might not be communicating something specific but that doesn’t mean they aren’t saying a lot. Vagueness itself is not easy to nail down and its use and impact vary depending on the field of study. According to Goss and William (1973),
“Vagueness has many definitions and whose cause and effect relate primary to the particular discipline. To the English teacher, vagueness and ambiguity in writing are undesirable. To the poet, they are vehicles for poetic imagery. To the social psychologist, they are social forces beckoning cognitive resolution. To the philosopher, like Alston, vagueness is a necessary condition of language which is “not always undesirable.” To the communincologists, vagueness can become, if used deliberately, a rhetorical strategy.”
Hiller’s dictionary is still used by researchers in many papers today as a primary or secondary dictionary source.
Several papers use the dictionary to analyze financial or corporate communication. Guo, Yu & Gimeno (2017) looked at how companies facing added competition or new entrants into their markets tended to use vague language concerning their business strategy. Studying annual reports in the U.S. airline industry they identified vague words and phrases by using Hiller’s communication vagueness dictionary with WordStat to see if this was a strategy by companies to make it more difficult for their rivals to gain strategic insight into their business.
Want to be vague to try to duck poor performance? Maybe, maybe not. To test this obfuscation hypothesis, Datar, Mitsuhashi & Nakamura (2019, July) used Hiller’s communication vagueness dictionary and conducted content analysis on the 10-Ks of U.S. high-tech firms. They found support instead to a persuasiveness hypothesis, that firms use vague language less when they receive negative performance-feedback.
Ever wonder why your paper isn’t getting published? Maybe you aren’t telling us anything new about the subject or maybe you’re just being too vague. Onwuegbuzie (2018) used Hiller’s dictionary in WordStat to identifies 10 categories of communication vagueness and found that the high prevalence of some of those categories in submitted papers to be related to a higher risk for rejection by publishers.
Vagueness in diplomacy is well known. It is often used to varying degrees depending on the situation or the strategy. Measuring it can also help researchers access the risk of conflict. Hogenraad & Garagozov (2014) used Hiller’s Communication Vagueness dictionary in their analysis of political speeches of the U.S., Iran, and Israel. Hiller’s dictionary was just one component in their assessment of the risk of war from these speeches. The authors also constructed a category-based motive imagery dictionary and noted cultural influences in the use of language in the U.S. Iran and Israel.
“Playing martial language against vagueness of language, we noticed the rate of vagueness increased in Ahmadinejad, Clinton, and Netanyahu. But the context of the increases does not mean the same for each of them. Being vague in Iran is not the same as being vague in Washington or Jerusalem.”
Hiller’s dictionary was also one of the tools used by Ford et al. (2000) in a content analysis of multiple-choice tests using WordStat that helped identify vagueness in questions. The study, from the U.S. Office of Personnel Management, looked at the U.S. government’s Basic-Level-Knowledge test which was given as part of the hiring process. The test was pre-reviewed by subject matter experts and then subjected to automated content analysis. The results were used to help reviewers improve the questions by removing vagueness or inappropriate wording. The results of automated content analysis and the use of Hiller’s dictionary on the draft tests were most appropriate in this case “to summarize content and present it to reviewers for final appropriateness judgments.”
These are just a few of the papers we found on Google Scholar published between 2000 and 2020. There are many more that directly use the Hiller dictionary in one way or another. While vagueness might be hard to define, a dictionary developed 50 years ago is still at the forefront today and will likely be there for many more years to come.
You can click on the following links to get more information about the Hiller Communication Vagueness Dictionary and other WordStat dictionaries. If you have suggestions for other dictionaries that we could feature, or if you have built one for your research and would like to share it, please contact us at .
Our next blog is written by John Ford, Public Sector Research Psychologist. In this Blog John provides a first-hand perspective on using the Hiller Vagueness scales in his research.
Datar, A., Mitsuhashi, H., & Nakamura, A. (2019, July). To be Vague, or Not to Be: Firms’ Avoidance of Vague Language and Performance below Aspirations. In Academy of Management Proceedings (Vol. 2019, No. 1, p. 18725). Briarcliff Manor, NY 10510: Academy of Management.
Ford, J. M., Stetz, T. A., Bott, M. M., & O’Leary, B. S. (2000). Automated content analysis of multiple-choice test item banks. Social science computer review, 18(3), 258-271
Guo, W., Yu, T., & Gimeno, J. (2017). Language and competition: Communication vagueness, interpretation difficulties, and market entry. Academy of Management Journal, 60(6), 2073-2098.
Hiller, J. H. 2014. Communication vagueness dictionary (WordStat version 7) [Computer software]. Montreal, Quebec, Canada: Provalis Research.
Hiller, J. H., Fisher, G. A., & Kaess, W. (1969). A Computer Investigation of Verbal Characteristics of Effective Classroom Lecturing. American Educational Research Journal, 6(4), 661-675.
Hiller, J. H., Marcotte, D. R., & Martin, T. 1969. Opinionation, vagueness, and specificity-distinctions: Essay traits measured by computer. American Educational Research Journal, 6: 271–286.
Hogenraad, R. L., & Garagozov, R. R. (2014). Textual fingerprints of risk of war. Literary and Linguistic Computing, 29(1), 41-55.
Guo, W., Yu, T., & Gimeno, J. (2014). Language and Competition: Exploring the Antecedents and Consequences of Firm Verbal Signals. In Academy of Management Proceedings (Vol. 2014, No. 1, p. 17326). Briarcliff Manor, NY 10510: Academy of Management.
Goss, B., & Williams, L. (1973). The effects of equivocation on perceived source credibility. Communication Studies, 24(3), 162-167.
Onwuegbuzie, A. J. (2018). Communication Vagueness in the Literature Review Section of Journal Article Submissions. Journal of Educational Issues, 4(1), 174-190.