本研究藉由探討近年來常在大眾媒體、社群網絡間用於闡述新議題之相關數據時，經常作為數據呈現工具的資料視覺化與視覺資訊圖表於說服力及洞見提供效果之不同，進而確立針對不同背景之使用族群下，兩種數據呈現方式的使用時機。本研究框架建立於闡述使用者接收訊息後透過不同的處理路徑進而產生態度改變和說服效果的訊息處理論之一：思辨可能模式。本研究利用中華民國教育部所提供之國內大專校院畢業生就業薪資數據集透過資料視覺化與視覺資訊圖表兩種實驗媒介。實驗過程中受測者將隨機閱讀其中一種數據呈現形式並回答一系列涵蓋個人偏好，透過此數據呈現所獲得的洞見與態度上的轉變之問項。
　　實驗結果指出，閱讀資料視覺化之受測者較閱讀視覺資訊圖表之受測者普遍有較佳的洞見發現。然而受測者年齡也與洞見發現的效果有所影響。雖然近年圖表設計師等製作者較多傾向於利用視覺吸引較為豐富的視覺資訊圖表提高對議題涉入程度低之族群的參與度，本研究之實驗結果指出即便是議題涉入低族群，在資料視覺化中所獲得的洞見也較視覺資訊圖表佳。另一方面，在說服效果中，資料視覺化與視覺資訊圖表對涉入族群高低之族群所造成之影響雖相似，對議題有較高之涉入族群卻較易被說服。此外，實驗結果亦發現視覺呈現之品質及視覺吸引度也會提升數據呈現之說服效果。

The study investigates when to use data visualizations and when to use infographics, by comparing the two information charts for viewer insight and persuasion. Furthermore, this study explores how viewer involvement differs between data visualization and infographic. We used a research framework based on Elaboration Likelihood Model, which is an information processing theory that described how high and low involvement participants process information. We designed an experiment with treatments based on a dataset of starting salaries provided by Ministry of Education. In the experiment, participants were exposed to one of the treatments — data visualization or infographic — then asked to answer a variety of questions about perceptions, insight, and opinion change. The results show that data visualization is overall better at insight. However, participants’ age also influenced results. Although practitioners design infographics to engage low involvement viewers, our study found that even low involvement participants have better insight from data visualizations than infographics. On the other hand, the persuasiveness of the data visualization and infographic was similar between high and low involvement participants. The high involvement group was more easily persuaded. Furthermore, argument quality and attractiveness also increased persuasiveness.

CHAPTER 1 Introduction 1
CHAPTER 2 Literature Review 4
2.1 Debate between data visualization and infographic 4
2.1.1 Insight 6
2.2 Persuasion effect of visualization 6
2.3 Elaboration Likelihood Model 7
2.3.1 Involvement 8
2.3.2 Central route 8
2.3.3 Peripheral route 9
CHAPTER 3 Research Framework and Hypothesizes 10
3.1 Compare data visualization with infographic 10
3.2 Influence of involvement 10
3.3 Research framework and hypothesis scenarios 11
CHAPTER 4 Methodology 14
4.1 Research Design and Manipulations 14
4.1.1 Selected visualization topics 14
4.1.2 Finding from Tableau 14
4.1.3 Visualization drafts 15
4.1.4 Pilot test 16
4.1.5 Final version of visualization designs 17
4.1.6 Experiment process 22
4.2 Measurement of the constructs 23
4.3 Data collection 24
CHAPTER 5 Data Analysis 25
5.1 Measure the effect of insight in data visualization and infographic 25
5.1.1 Compare insight correction rate and how many “I don’t know” 25
5.1.2 Time Spent by Respondents 27
5.1.3 Variable correlations in data visualization and infographic 28
5.1.4 The effect of involvement and age to insight 30
5.2 The effect of persuasion in data visualization and infographic 32
5.2.1 Will people change their opinion? 32
5.2.2 Compare path coefficients to persuasion 32
5.2.3 The effect of involvement and age to persuasion 33
5.2.4 Attractiveness and argument quality to persuasion 33
5.2.5 Qualitative feedback 34
CHAPTER 6 Discussion 36
6.1 Theoretical Findings 36
6.1.1 Are data visualizations really better at providing insight than infographics? 36
6.1.2 Can data visualization also be persuasive? 37
6.1.3 How does viewers’ involvement alter the impact of data visualizations and infographics? 38
6.2 Practical strategies for designing information charts 40
CHAPTER 7 Conclusion 42
7.1 Study limitations and future research 42
7.2 Final thoughts 43
References 44
Appendix 46

Areni, C. S. and R. J. Lutzl (1988). "The Role of Argument Quality in the Elaboration Likelihood Model." Advances in Consumer Research 15: 197-203.

Bateman, S., et al. (2010). Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts. SIGCHI Conference on Human Factors in Computing Systems, ACM.

Borkin, M. A., et al. (2013). "What Makes a Visualization Memorable?" IEEE Transactions on Visualization and Computer Graphics 19(12): 2306-2315.

Card, S. K., et al. (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann.

Cawthon, N. and A. V. Moere (2007). The effect of aesthetic on the usability of data visualization. In Information Visualization. 11th International Conference Information Visualization, IEEE: 637-648.

Choe, E. K., et al. (2015). "Characterizing Visualization Insights from Quantified Selfers' Personal Data Presentations." IEEE computer graphics and applications 35(4): 28-37.

Cleveland, W. S. (1994). The elements of graphing data. Monterey, Wadsworth Advanced Books and Software.

Few, S. (2007). Data visualization: past, present, and future, IBM Cognos Innovation Center.

Friendly, M. (2005). "Milestones in the history of thematic cartography, statistical graphics, and data visualization." Classification—the Ubiquitous Challenge: 34-52.

Kosara, R. and J. Mackinlay (2013). Storytelling: The Next Step for Visualization. Computer: 44-50.

Krum, R. (2013). Cool infographics: Effective communication with data visualization and design, John Wiley & Sons.

Kulla-Mader (2007). Graphs via Ink: Understanding How the Amount of Non-data-ink in a Graph Affects Perception and Learning. Department of Information and Library Science, University of North Carolina. Master.

Lowry, P. B., et al. (2011). "Using an Elaboration Likelihood Approach to Better Understand the Persuasiveness ofWebsite Privacy Assurance Cues for Online Consumers." JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 63(4): 755-776.

Muehlenhaus, I. (2012). Beyond Biased: Exploring the Relationship between Map Design Style and Map Viewer Persuasion. AutoCarto2012.

North, C. (2006). "Toward Measuring Visualization Insight." IEEE computer graphics and applications 26(3): 6-9.

Pandey, A. V., et al. (2014). "The Persuasive Power of Data Visualization." IEEE Transactions on Visualization and Computer Graphics 20(12): 2211-2220.

Petty, R. E. and J. T. Cacioppo (1986). "THE ELABORATION LIKELIHOOD MODEL OF PERSUASION." ADVANCES IN EXPERIMENTAL SOCIAL PSYCHOLOGY 19: 124-181.

Quispel, A. and A. Maes (2014). "Would you prefer pie or cupcakes? Preferences for data visualization designs of professionals and laypeople in graphic design." Journal of Visual Languages and Computing 25(2): 107-116.

Saraiya, P., et al. (2005). "An Insight-Based Methodology for Evaluating Bioinformatics Visualizations." IEEE Transactions on Visualization and Computer Graphics 11(4): 443-456.

Simons, H. W. (1976). Persuasion: Understanding, practice, and analysis. , Addison Wesley Publishing Company.

Tufte, E. (1991). "Envisioning information." Optometry & Vision Science 68(4): 322-324.

Tufte, E. R. and P. R. Graves-Morris (1983). The Visual Display of Quantitative Information. Cheshire, Graphics press.

Yi, J. S., et al. (2008). Understanding and Characterizing Insights: How Do People Gain Insights Using Information Visualization? 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization, ACM.