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Data Analysis & Visualization Master’s Theses and Capstone Projects

Dissertations/theses/capstones from 2024 2024.

Assessing Job Vulnerability and Employment Growth in the Era of Large Language Models (LLMs) , Prudence P. Brou

The Charge Forward: An Assessment of Electric Vehicle Charging Infrastructure in New York City , Christopher S. Cali

Visualizing a Life, Uprooted: An Interactive, Web-Map and Scroll-Driven Exploration of the Oral History of my Great-Grandfather – from Ottoman Cilicia to Lebanon and Beyond , Alyssa Campbell

Examining the Health Risks of Particulate Matter 2.5 in New York City: How it Affects Marginalized Groups and the Steps Needed to Reduce Air Pollution , Freddy Castro

Clustering of Patients with Heart Disease , Mukadder Cinar

Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors , Livia Clarete

The Complete Sight and Sound Greatest Films of All Time Database , Katie Donia

Wrapped Insights: A Data-Driven Approach to Personalizing User Experiences in a Digital Tipping Platform , Hamza Habeeb

The Efficacy of Using Machine Learning Techniques for Identifying and Classifying “Fake News” , Muhammad Islam

Invisible Hand of Socioeconomic Factors in Rising Trend of Maternal Mortality Rates in the U.S. , Disha Kanada

Factors that Impact New York City Public High School Graduation: Finding Barriers to Education through Data Analysis and Visualization , Kyoung Kang

Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model , Ethan Lo

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth , William Mahoney Luckman

Making Sense of Making Parole in New York , Alexandra McGlinchy

Employment Outcomes in Higher Education , Yunxia Wei

Dissertations/Theses/Capstones from 2023 2023

Phantom Shootings , Allan Ambris

Naming Venus: An Exploration of Goddesses, Heroines, and Famous Women , Kavya Beheraj

Social Impacts of Robotics on the Labor and Employment Market , Kelvin Espinal

Fighting the Invisibility of Domestic Violence , Yesenny Fernandez

Navigating Through World’s Military Spending Data with Scroll-Event Driven Visualization , Hong Beom Hur

Evocative Visualization of Void and Fluidity , Tomiko Karino

Analyzing Relationships with Machine Learning , Oscar Ko

Analyzing ‘Fight the Power’ Part 1: Music and Longevity Across Evolving Marketing Eras , Shokolatte Tachikawa

Stand-up Comedy Visualized , Berna Yenidogan

Dissertations/Theses/Capstones from 2022 2022

El Ritmo del Westside: Exploring the Musical Landscape of San Antonio’s Historic Westside , Valeria Alderete

A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics , Alexander Avdeev

A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women , Meriem Benarbia

Disrepair, Displacement and Distress: Finding Housing Stories Through Data Visualizations , Jennifer Cheng

Blockchain: Key Principles , Nadezda Chikurova

Data for Power: A Visual Tool for Organizing Unions , Shay Culpepper

Happiness From a Different Perspective , Suparna Das

Happiness and Policy Implications: A Sociological View , Sarah M. Kahl

Heating Fire Incidents in New York City , Merissa K. Lissade

NYC vs. Covid-19: The Human and Financial Resources Deployed to Fight the Most Expensive Health Emergency in History in NYC during the Year 2020 , Elmer A. Maldonado Ramirez

Slices of the Big Apple: A Visual Explanation and Analysis of the New York City Budget , Joanne Ramadani

The Value of NFTs , Angelina Tham

Air Pollution, Climate Change, and Our Health , Kathia Vargas Feliz

Peru's Fishmeal Industry: Its Societal and Environmental Impact , Angel Vizurraga

Why, New York City? Gauging the Quality of Life Through the Thoughts of Tweeters , Sheryl Williams

Dissertations/Theses/Capstones from 2021 2021

Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology , Quinn Bolewicki

Remaking Cinema: Black Hollywood Films, Filmmakers, and Finances , Kiana A. Carrington

Detecting Stance on Covid-19 Vaccine in a Polarized Media , Rodica Ceslov

Dota 2 Hero Selection Analysis , Zhan Gong

An Analysis of Machine Learning Techniques for Economic Recession Prediction , Sheridan Kamal

Black Women in Romance , Vianny C. Lugo Aracena

The Public Innovations Explorer: A Geo-Spatial & Linked-Data Visualization Platform For Publicly Funded Innovation Research In The United States , Seth Schimmel

Making Space for Unquantifiable Data: Hand-drawn Data Visualization , Eva Sibinga

Who Pays? New York State Political Donor Matching with Machine Learning , Annalisa Wilde

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The effects of visualization on judgment and decision-making: a systematic literature review

• Open access
• Published: 25 August 2021
• Volume 73 , pages 167–214, ( 2023 )

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• Karin Eberhard   ORCID: orcid.org/0000-0001-6676-8889 1  

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The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

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1 Introduction

A visualization is defined as a visual representation of information or concepts designed to effectively communicate the content or message (Padilla et al. 2018 ) and improve understanding in the audience (Alhadad 2018 ). This representation can manifest in a range of imagery, from quantitative graphs (Tang et al. 2014 ) to qualitative diagrams (Yildiz and Boehme 2017 ), to abstract visual metaphors (Eppler and Aeschimann 2009 ) or artistic imagery. Visualization design may also intend to promote a specific behavior in the audience (Correll and Gleicher 2014 ). The visualization of information is associated with effective communication in terms of clarity (Suwa and Tversky 2002 ), speed (Perdana et al. 2018 ), and the understanding of complex concepts (Wang et al. 2017 ). Research shows, for example, that visualized risk data require less cognitive effort in interpretation than textual alternatives and are therefore comprehended more easily (Smerecnik et al. 2010 ), and complex sentiment data visualized in a scatterplot improve the accuracy in law enforcement decisions compared to raw data (Cassenti et al. 2019 ).

Visual experiences are the dominant sensory input for cognitive reasoning in everyday life, business, and science (Gooding 2006 ). As Davis ( 1986 ) points out, image creation and perception are part of the “unique and quintessential competencies of homo sapiens sapiens”. Hence, the visualization of information is an integral research subject in the domains of cognitive psychology, education (Alfred and Kraemer 2017 ), management (Tang et al. 2014 ) including financial reporting, strategic management, and controlling, marketing (Hutchinson et al. 2010 ), as well as information science (Correll and Gleicher 2014 ).

Management researchers study visualizations from a business perspective. First, the field of financial reporting considers the effect of financial graphs on investor perception (Beattie and Jones 2008 ; Pennington and Tuttle 2009 ). Second, the potential consequences of visualizations on decision-making are examined in the area of managerial decision support, with a focus on judgments based on quantitative data such as financial decisions (Tang et al. 2014 ) and performance controlling (Ballard 2020 ). Finally, a small number of works investigate more complex decision-making based on qualitative, multivariate, and relational information (Platts and Tan 2004 ). Altogether visualizations fulfill a variety of functions, from focusing attention to sharing thoughts to identifying data structures, trends, and patterns (Platts and Tan 2004 ).

The vast majority of existing research in visualization, however, arises from the two domains of information science and cognitive psychology. Information science research on how to design visualizations for effective user cognition stretches back almost one century (Washburne 1927 ). While early research focuses on comparing tables and simple graphs, newer research on human–computer interfaces covers advanced data visualizations facilitated by computing power (Conati et al. 2014 ). For example, interactive visualization software enables users to manipulate data directly. While promising in terms of analytic capability, the potential for biases and overconfidence is suggested as a downside (Ajayi 2014 ). Equally, cognitive psychology research notes that visual information may be superior over verbal alternatives in certain cognitive tasks since they can be encoded in their original form, where spatial and relational data is preserved. Thereby, visual input is inherently richer than verbal and symbolic information, which is automatically reductionistic (Meyer 1991 ), but more suited for discrete information retrieval due to its simplicity (Vessey and Galletta 1991 ). However, the processes behind visual cognition remain largely unclear (Vila and Gomez 2016 ).

Despite the ubiquity of visualizations in research and practice, there is no comprehensive understanding of the potential and limits of information visualization for decision-making. Although at times converging, insights from research of different areas are seldom synthesized (Padilla et al. 2018 ), and there has been no effort for a systematic review or overarching framework (Zabukovec and Jaklič 2015 ). However, a synthesis of existing research is essential and timely due to three reasons. First, information visualization is ubiquitous both in the scientific and business community, yet there are conflicting findings on its powers and limits in support of judgment and decision-making. Second, cognitive psychology research provides several promising suggestions to explain observable effects of visualizations, yet these are rarely integrated into research in other domains, including strategic decision-making. Third, the barriers to using information visualization software have fallen to a minimum, making it available to a wide range of producers and users. This raises the issue of the validity of positive effects for various task and user configurations. The goal of this paper is therefore to provide an overview of the fragmented existing research on visualizations across the social and information sciences and generate insights and a timely research agenda for its applicability to strategic management decisions.

My study advances visualization research on three paths. First, I establish a framework to summarize the numerous effects and variable interactions surrounding the use of visualizations. Second, I conduct a systematic literature review across the social and information sciences and summarize and discuss this plethora of findings along with the aforementioned structure. Third, I utilize this work as a basis for identifying and debating gaps in existing research and resulting potential avenues for future research, with a focus on the area of strategic management decisions.

The structure of the article is as follows. The next chapter briefly describes the research field, followed by the methodology of my literature search. Next, I analyze the results of my search and discuss common insights. In the ensuing chapter, I develop an agenda for management research by building on particularly relevant ideas with conflicting or incomplete evidence. Finally, I conclude my review and discuss contributions and implications for practice.

2 Definition of the research field

2.1 definition of key terms.

Information visualizations support the exploration, judgment, and communication of ideas and messages (Yildiz and Boehme 2017 ). The term “graph” is often used as a synonym for information visualization in general (Meyer 1991 ) as well as describing quantitative data presentation specifically (Washburne 1927 ). As my review exhibits, these graphs constitute the prevalent form of information visualization. Common quantitative visualizations are line and bar charts, often showcasing a development over time and regularly used in financial reporting (Cardoso et al. 2018 ) and controlling (Hutchinson et al. 2010 ). In scientific literature, probabilistic charts such as scatterplots, boxplots, and probability distribution charts (Allen et al. 2014 ) frequently depict risk and uncertainty. More specialized charts include decision trees to depict conditional logic (Subramanian et al. 1992 ), radar charts to display complex multivariate information (Peebles 2008 ), or cluster charts and perceptual maps for marketing decision support (Cornelius et al. 2010 ).

Despite the breadth of existing visualization research, its application to strategic decisions is narrow and there is an abundance of research limited to elementary tasks and choices. To provide a clear distinction, I focus my search on decisions, judgments, and inferential reasoning as more advanced forms of cognitive processing. Decision-making can be broadly defined as choosing between several alternative courses of action (Padilla et al. 2018 ). On the other hand, reasoning and judgment refer to the evaluation of a set of alternatives (Reani et al. 2019 ), without actions necessarily being attached as for decision-making. Such efforts are cognitively demanding and complex when compared to more elementary tasks, such as a choice between options (Tuttle and Kershaw 1998 ), and include the rigorous evaluation of alternatives across a range of attributes, which is characteristic for strategic decisions (Bajracharya et al. 2014 ). For this reason, I include studies that examine the influence of visualizations on some form of decision or judgment outcome. Mason and Mitroff ( 1981 ) highlight that strategic decisions, in management and elsewhere, involve complex and ambiguous information environments. Information visualization may relate to decision quality in this context since one critical factor in the effectiveness of strategic decisions is the objective and comprehensive acquisition and analysis of relevant information to define and evaluate alternatives (Dean and Sharfman 1996 ).

2.2 Perspectives in literature

Visualization research exists within a range of domains in the social and information sciences, which reflects the diversity of the empirical application. I identify psychology (cognitive and educational), management (financial reporting, strategic management decisions, and controlling), marketing, and information science as the primary areas of research. This heterogeneity in terms of application area provides the first dimension in my literature review. Second, I classify existing studies along the type of variable interaction they primarily investigate. Based on the framework first introduced by DeSanctis ( 1984 ), I hereby differentiate four categories: Works principally focused on (1) the effects of visualizations on comprehension and decisions as dependent variables provide the basis of all research. This relationship is then investigated through: (2) User characteristics as moderators; (3) task and format characteristics as moderators; and (4) cognitive processing as mediator. An overview of this classification, including the prevalence of extant findings across domains, is given in Fig.  1 .

Visualization research structured by domain and variables primarily investigated

First, the investigation of visualization effects on decisions and judgments is established across all research areas mentioned, and primarily studies outcome variables such as decision accuracy (Sen and Boe 1991 ), speed (Falschlunger et al. 2015a ), and confidence (Correll and Gleicher 2014 ). While these studies contribute examples for graphs influencing observable decision effectiveness and efficiency across a range of contexts, they do not investigate moderating or mediating factors.

Second, psychology research pushes this investigation further towards including moderating effects of user characteristics , such as domain expertise and training (Hegarty 2013 ), and measures of cognitive ability such as numeracy (Honda et al. 2015 ) or literacy (Okan et al. 2018a ). The relevance of these moderating factors is validated both in studies focusing on cognition as well as experiments in educational research, for example by providing evidence that the quality of a judgment made based on a graph may depend more on the user than the format itself (Mayer and Gallini 1990 ).

Similarly, human–computer interface research spearheads further insights into moderating factors of task and format characteristics, such as task type (Porat et al. 2009 ), task complexity (Meyer et al. 1997 ), data structure (Meyer et al. 1999 ), and the graphical saliency of features (Fabrikant et al. 2010 ) through rigorous user testing. At the same time, Vessey ( 1991 ) developed the theory of cognitive fit as a concept bridging cognitive and information systems research, stating that positive effects of graphs depend on a fit between task type and format type, differentiating between symbolic and spatial archetypes.

Finally, cognitive psychology research aims at explaining the observable effects of visualization in terms of mediating cognitive mechanisms . Here, cognitive load theory provides the foundation, stating that an individual’s working memory capacity is limited, and performance in a task or judgment depends on the cognitive load they experience while assessing information. According to this logic, cognitive load that is too high damages performance (Chandler and Sweller 1991 ). Reducing cognitive load by providing visualizations in complex environments is therefore often stated as a key goal of graph design (Smerecnik et al. 2010 ).

Importantly, the boundaries between these variable categories are fluid. Many studies investigate more than one relationship and the inclusion of moderating variables has become common. Various application areas covering these interdependencies attest to the heterogeneous nature of visualization research. However, previous reviews highlight that insights are seldom shared across fields and call for the integration of findings into new studies (Padilla et al. 2018 ). In particular, strategic management research does not yet follow such a holistic approach.

3 Method of literature search

3.1 search design.

The methodological basis of this paper is a systematic literature search as a means to collect and evaluate the existing findings in a systematic, transparent, and reproducible way on the specified topic (Fisch and Block 2018 ) in order to produce a more complete and objective knowledge presentation than in traditional reviews (Clark et al. 2021 ). I conduct a keyword search on the online search engines EBSCOhost and ProQuest, limited to English-language works that have been peer-reviewed, in order to ensure the quality of the sources. Gusenbauer and Haddaway ( 2020 ) identify both search engines as principal academic search systems as they fulfill all essential performance requirements for systematic reviews. On EBSCOhost, I use the databases Business Source Premier , Education Research Complete , EconLit , APA PsycInfo , APA PsycArticles , and OpenDissertations to search for empirical works; on ProQuest, I use the databases British Periodicals , International Bibliography of the Social Sciences (IBSS) , Periodicals Archive Online , and Periodicals Index Online with a filter on articles to cover the social sciences comprehensively. The keyword used is the concatenated term “(visualization OR graph OR chart) AND (decision OR judgment OR reasoning)”, searched for in abstracts. Footnote 1 The terms were chosen as “visualization” is commonly used as a category name for visualized information (Brodlie et al. 2012 ), and the “graph” is the focus of traditional visualization research (Vessey 1991 ). The term “chart” is a synonym for both quantitative and qualitative graphs which has seen increasing use particularly in the 2000s (Semmler and Brewer 2002 ). The terms “judgment OR decision OR reasoning” were added to ensure that studies examining observable outcomes of visualization use, as opposed to cognitive processes such as comprehension only, were highlighted. After a review of the evolution of visualization research over time, I focus my search to articles published from the year 1990 in order to capture the recent advancements covering modern modes of information visualization. Footnote 2 This search results in 1658 articles combined, after removing duplicates 1505 articles remain.

Next, I review all article abstracts based on the three content criteria defined in the following. I include all articles rooted in the (1) social sciences or information sciences , where the focus of the study lies on (2) how a visualization per se or a variation within related visualizations affects a user's or audience's decision or judgment in a given task , and the topic is studied through (3) original empirical works. Most articles are excluded in this process and 116 studies remain due to the prevalence of graphs as auxiliary means, not the subject of research, in various domains, particularly in medical research. I repeat this exclusion process by reading the full texts of all articles and narrow down the selection further to 81 papers.

Building on this systematic search, I conducted a supplementary search through citation and reference tracking, as well as supplementary search engines, such as JSTOR (Gusenbauer and Haddaway 2020 ). Footnote 3 This includes gray literature such as conference proceedings or dissertations, which lie outside of traditional academic publishing. In addition, I limit the inclusion of gray literature to studies by researchers included in my systematic search and completed within the last 10 years in order to gather a comprehensive and up-to-date overview of the findings of working groups particularly relevant to visualization research. Thereby I identify 52 additional articles, resulting in a total of 133 articles included.

3.2 Limitations of search

Due to the plethora of existing literature mentioning the topic of visualization in various contexts and degrees of quality, I subject my search to well-defined limitations. First, I only include peer-reviewed articles in my systematic search. These are studies that have been thoroughly validated and represent the major theories within a field (Podsakoff et al. 2005 ). However, I incorporate gray literature of comparable quality as part of my additional exploratory search.

Second, I limit the search to information and social sciences to deliberately omit results from the broad areas of medicine and natural sciences. In these, various specific concepts are visualized as a means within research, yet not investigating the visualization itself. For the same reason, I only apply the search terms to article abstracts, since the terms “graph” and “chart” in particular will result in a high number of results when searched for in the full text, due to the common use of graphs in presenting concepts and results.

Third, I only include original empirical work in order to enable the synthesis and critical validation of empirical findings across research areas. At the same time, I acknowledge the existence of several highly relevant theoretical works, which inform my search design and structure while being excluded from the systematic literature search and analysis.

4.1 Overview of results

I identify a total of 133 articles, published between 1990 and 2020. Interest in visualization research gained initial momentum in the early 1990s (Fig.  2 ). More recently, the number of studies rises starting around 2008, with the continued publication of five to ten papers per year since and a visible peak in interest around 2014/15. A significant share of recent works stems from the information science literature, and the wealth of publications around 2014 coincides with the advent of mainstream interest in big data (Arunachalam et al. 2018 ), which is closely linked to information visualization for subsequent analysis and decision-making (Keahey 2013 ). In addition, a cluster of publications by one group of authors (Falschlunger et al. 2014 , 2015a , c, b) in the financial reporting domain enhances the observed peak in publications, which is therefore not indicative of a larger trend. Instead, the continued wealth of publications in the last decade shows the contemporary relevance of and interest in visualization research.

Articles included in systematic search by publication year and area of research

Next to the information sciences, the largest share of the studies identified originates in cognitive psychology research. Furthermore, management literature discusses visualization and graphs continuously throughout the last three decades, with notable peaks in interest around the year 2000 in the domain of annual reporting (Beattie and Jones 2000 , 2002a , b ; Arunachalam et al. 2002 ; Amer 2005 ; Xu 2005 ) and internal management reporting with classic bar and line graphs around the year 2015 (Falschlunger et al. 2014 , 2015a , c ; Tang et al. 2014 ; Hirsch et al. 2015 ; Zabukovec and Jaklič 2015 ). Consumer research in marketing constitutes a further domain regularly discussing visualizations and their effect on decisions and judgment (Symmank 2019 ), albeit to a smaller extent. This heterogeneity in research areas is reflected by the journals identified in my search, where the 133 articles spread across 83 different journals, complemented by ten studies from conference proceedings and three papers included in doctoral dissertations (Table 1 ). Apart from the articles in conference proceedings added through the supplementary exploratory search, the studies were published in journals with a SCIMAGO Journal Rank indicator ranging from 0.253 (Informing Science) to 8.916 (Journal of Consumer Research). All but four journals received Q1 and Q2 ratings, which equals the top half of all SCImago rated journals. The h-index ranges from 6 (Journal of Education for Library and Information Science) to 332 (PLoS ONE) (Scimago Lab 2021 ).

In the 133 articles identified, experiments are by far the most common method for data collection, with 113 (85%) of studies conducting a total of 182 controlled experiments with over 28,000 participants (Fig.  3 ). In addition, I find seven instances of archival research covering over 600 companies, six instances of surveys with almost 1000 participants in total, four quasi experiments, two natural experiments, and one field experiment to complete the picture.

Articles included in systematic search by methodology

Of the 182 experiments conducted, the majority works with students as subjects (125 or 69%). The largest remaining share investigates a sample of the general (online) population (32 or 18%) and only 13% study the effect of visualization with practitioners in their respective domain (24). In contrast, four out of the six surveys were conducted with practitioners that were addressed explicitly. Besides, one survey each was conducted with students and subjects from the general population.

Following the advice by Fisch and Block ( 2018 ), I categorize the results from literature in a concept-centric manner, based on the primary variable interaction investigated. I further distinguish by the four application domains and seven subdomains discussed and present a structured overview at the end of each subchapter. The independent variable in all cases is the use of a visual representation designed for a specific use case, either as opposed to non-visual representation methods such as verbal descriptions [e.g. Vessey and Galletta ( 1991 )], or traditional visualizations that the research aims to improve on [e.g. Dull and Tegarden ( 1999 )].

4.2 Effects of visualizations on decisions and judgments

4.2.1 judgment/decision accuracy.

The most common dependent variable investigated in visualization research is the accuracy of the subjects on a given comprehension, judgment, or decision task. Most studies are in psychology research, with positive effects dominating. In cognitive psychology, experiments show that well-designed visualizations can improve problem comprehension (Chandler and Sweller 1991 ; Huang and Eades 2005 ; Nadav-Greenberg et al. 2008 ; Okan et al. 2018b ). For example, Dong and Hayes ( 2012 ) show in their experiment with 22 practitioners that a decision support system visualizing uncertainty improves the identification and understanding of ambiguous decision situations. Likewise, visualizations improve decision (Pfaff et al. 2013 ) and judgment accuracy (Semmler and Brewer 2002 ; Tak et al. 2015 ; Wu et al. 2017 ) and improve the quality of inferences made from data (Sato et al. 2019 ). Findings in educational psychology support this claim. In teaching, visual materials improve understanding and retention (Dori and Belcher 2005 ; Brusilovsky et al. 2010 ; Binder et al. 2015 ; Chen et al. 2018 ) in students, and support the judgment accuracy of educators when analyzing learning progress quantitatively (Lefebre et al. 2008 ; Van Norman et al. 2013 ; Géryk 2017 ; Nelson et al. 2017 ). Furthermore, Yoon’s longitudinal classroom intervention (2011) using social network graphs enables students to make more reflected and information-driven strategic decisions. However, other studies arrive at more mixed or opposing findings. In their experiment, Rebotier et al. ( 2003 ) find that visual cues do not improve judgment accuracy over verbal cues in imagery processing. Other experiments even demonstrate verbal information to be superior over graphs in comprehension (Parrott et al. 2005 ) as well as judgment accuracy (Sanfey and Hastie 1998 ). Some graphs appear unsuitable for specific content, such as bar graphs depicting probabilities (Newman and Scholl 2012 ) and bubble charts encoding information in circle area size (Raidvee et al. 2020 ). In addition, more complex charts like boxplots, histograms (Lem et al. 2013 ), and tree charts (Bruckmaier et al. 2019 ) appear less effective for the accurate interpretation of statistical data in some experiments, presumably as they elicit errors and confusion in insufficiently trained students.

Studies in management and business research arrive at further, more pessimistic results. While Dull and Tegarden ( 1999 ) find in their experiment with students that three-dimensional visuals can improve the prediction accuracy in financial reporting contexts, and Yildiz and Boehme ( 2017 ) observe in their practitioner survey that a graphical model of a corporate security decision problem improves risk perception when compared to a textual description, most other studies present a less positive picture. Several studies do not find graphs superior over tables in financial judgments (Chan 2001 ; Tang et al. 2014 ; Volkov and Laing 2012 ), and in consumer research (Artacho-Ramírez et al. 2008 ). In financial reporting, a dedicated school of research investigates the effect of distorted graphs lowering financial judgment accuracy (Arunachalam et al. 2002 ; Beattie and Jones 2002a , b ; Amer 2005 ; Xu 2005 ; Pennington and Tuttle 2009 ; Falschlunger et al. 2014 ), irrespective of whether the distortion is intended by the designer. Chandar et al. ( 2012 ) elaborate on the positive effect of the introduction of graphs and statistics in performance management for AT&T in the 1920s, but more recent case study examples are rare.

By contrast, several experimental studies from human–computer interaction research largely contribute evidence for a positive effect. Targeted visual designs lead to higher judgment accuracy in specific tasks (Subramanian et al. 1992 ; Butavicius and Lee 2007 ; Van der Linden et al. 2014 ; Perdana et al. 2018 ) and improve decision-making (Peng et al. 2019 ). For example, probabilistic gradient plots and violin plots enable higher accuracy in statistical inference judgments in the online experiment by Correll and Gleicher ( 2014 ) than traditional bar charts. However, experiments by Sen and Boe ( 1991 ) and Hutchinson et al. ( 2010 ) equally lack a significant effect on data-based decision-making quality. Amer and Ravindran ( 2010 ) find a potential for visual illusions degrading judgment accuracy similar to results from financial reporting, and McBride and Caldara ( 2013 ) find that visuals lower accuracy in law enforcement judgments when compared to raw data presentation (Table 2 ).

4.2.2 Response time

The next most common outcome variable investigated in visualization research is response time , often referred to as efficiency. Across the board, experimenters observe that information visualization lowers response time in various judgment and decision tasks. In psychology, this includes decision-making in complex information environments (Sun et al. 2016 ; Géryk 2017 ). The opposite effect emerges from only one study, where Pfaff et al. ( 2013 ) find that a decision support system visualizing complex uncertainty information requires a longer time to use than one omitting this graphical information. In management research, Falschlunger et al. ( 2015a ) find that visually optimized financial reports can speed up judgment both for students and practitioners. Studies originating in information science validate this picture, observing that well-designed visualizations reduce response time in quantitative (Perdana et al. 2018 ) as well as geospatial judgment tasks (MacEachren 1992 ). Furthermore, McBride and Caldara ( 2013 ) observe that students in their experiments arrive at faster judgments when provided with a network graph as opposed to a table (Table 3 ).

4.2.3 Decision confidence

Next to these directly observable metrics, experimenters regularly elicit measures of decision confidence in visualization research based on subjects’ self-assessment. From a cognitive psychology perspective, Andrade ( 2011 ) finds that subjects display excessive confidence in estimates based on visualizations, which biases subsequent decision-making. On the other hand, Dong and Hayes ( 2012 ) show that a visual decision support system depicting uncertainty in engineering design leads to marginally lower decision confidence, compared to traditional methods omitting uncertainty information. In management research, Tang et al. ( 2014 ) present an increase in confidence in the context of financial decision-making, and Yildiz and Böhme (2017) find in their practitioner survey that an appealing visual increases decision confidence in a managerial setting without changing the actual decision outcome. Similarly, further experiments in information science provide evidence for increased confidence with a link to increased judgment accuracy (Butavicius and Lee 2007 ) or without (Sen and Boe 1991 ; Wesslen et al. 2019 ). In the context of uncertainty, Arshad et al. ( 2015 ) once again report novice subjects having lower confidence in the use of graphs with uncertainty visualized, however, this effect does not occur for practitioners (Table 4 ).

4.2.4 Prevalence of biases

Several studies investigate the prevalence of biases by searching for distinct patterns of deviations in judgment and decision accuracy with largely mixed results. Through a total of seven cognitive psychology experiments, Sun et al. ( 2010 , 2016 ) and Radley et al. ( 2018 ) find that varying scale proportions in graphs change the resulting decision-making since data points are evaluated in a cognitively biased manner based on their distance to other chart elements. Furthermore, Padilla et al. ( 2015 ) demonstrate that uncertainty is understood to a disparate extent when it is encoded through spatial glyphs, color, or brightness. In human–computer interaction research, experiments observe similar framing biases through salient graphical features (Diamond and Lerch 1992 ) such as color schemes (Klockow-McClain et al. 2020 ). Lawrence and O’Connor ( 1993 ) also show that graph scaling affects judgment and relate this to the anchoring heuristic. Finally, financial reporting research extensively dedicates its field of impression management on the observation that such biases are prevalent and possibly intended in annual report graphics, including through distorted graph axes (Falschlunger et al. 2015b ) and an intentional selection of information to visualize (Beattie and Jones 1992 , 2000 ; Dilla and Janvrin 2010 ; Jones 2011 ; Cho et al. 2012a , b ). Two further experiments compare the prevalence of cognitive biases with graphs compared to text directly and find no difference for the recency bias in financial reporting (Hellmann et al. 2017 ) as well as for other heuristics in data-based managerial decision-making (Hutchinson et al. 2010 ) (Table 5 ).

4.2.5 Attitude change and willingness to act

Observations on attitude change and the willingness to act on information constitute the final category of outcome variables found in visualization research. Cognitive psychology research observes an effect of visualizations on risk attitude, where salient graphs can either enhance risk aversion (Dambacher et al. 2016 ) or risk-seeking (Okan et al. 2018b ), depending on the information that is highlighted most saliently. Similarly, varied financial graphs change investors’ risk perception and subsequent investment recommendations (Diacon and Hasseldine 2007 ). In the area of performance management, the visualization of KPIs motivates managers’ intention to act on the information when compared to text (Ballard 2020 ). Consumer research investigates such phenomena commonly, where brand attitude and the intention to purchase a product represent specific cases of judgment and decision-making. Miniard et al. ( 1991 ) were among the first to show that different pictures can result in different attitudes, while Gkiouzepas and Hogg ( 2011 ) extend this investigation to visual metaphors. Finally, information science research provides further insights. King Jr et al. (1991) find that visualizations are more persuasive in attitude change than text, and Perdana et al. ( 2018 ) increase student subjects’ willingness to invest in their experimental setting through visualization software. On the other hand, Phillips et al. ( 2014 ) find their subjects to be less willing to seek out additional information in ambiguous decision settings (Table 6 ).

4.3 User characteristics as moderating variables

4.3.1 expertise and training.

Common moderating variables investigated both in psychological and information science research are the users’ expertise or training experience in a given domain. Experimenters widely encounter a positive impact of experience on the influence of visualizations on judgment accuracy and efficiency. In cognitive psychology, Hilton et al. ( 2017 ) find that graphs of statistical risk improve decision quality for more experienced practitioners alone. On the other hand, some results from educational psychology point towards the opposite effect of experience. Mayer and Gallini ( 1990 ) find in their student experiments that learners with higher pre-test performance benefit less from visual aids than learners on a lower level. In the information sciences, Conati et al. ( 2014 ) find in their testing of computer interfaces that experience with visualizations leads to a pronounced advantage in judgment accuracy. Training sessions (Raschke and Steinbart 2008 ) and experience through task repetition (Meyer 2000 ) enhance the positive effects of graphs (Table 7 ).

4.3.2 Cognitive ability

Another user characteristic regularly investigated in the social sciences is the measurement of cognitive ability . In psychology studies, Honda et al. ( 2015 ) and Cardoso et al. ( 2018 ) find that reflective ability determines in part how well subjects translate visualizations into accurate judgments. Visual working memory (Tintarev and Masthoff 2016 ) and numeracy (Honda et al. 2015 ) are further traits related to cognitive ability in dealing with visualizations and found to enhance the benefits of visualizations on judgment effectiveness and efficiency. The only study presenting contrary results consists of three experiments by Okan et al. ( 2018a ), where subjects with higher graph literacy are more prone to specific biases when shown bar graphs of health risk data, and thereby make less accurate judgments. On the other hand, experiments in financial reporting (Cardoso et al. 2018 ) confirm the positive effect of the reflective ability. Conati and Maclaren ( 2008 ) and Conati et al. ( 2014 ) extend this idea to perceptual speed in the area of consumer research (Table 8 ).

4.3.3 User preferences

Finally, experimenters investigate user preferences at times. In the adjacent field of musical education, for example, Korenman and Peynircioglu ( 2007 ) demonstrate that the visual presentation of learning material is only helpful to students with the respective learning style. In cognitive psychology, Daron et al. ( 2015 ) observe a variation in user preferences when presented with visualization options, however without a significant effect on decision performance. This result is replicated in an online survey on human–computer interaction by Lorenz et al. ( 2015 ). O’Keefe and Pitt ( 1991 ) operationalize cognitive style from the MBTI framework and find a weak association with the subjects’ reported preferences for text or specific chart types. However, no relation to actual judgment accuracy or efficiency is found (Table 9 ).

4.4 Task and format characteristics as moderating variables

4.4.1 task type.

One common task characteristic identified as a moderating variable is the task type , originally defined in the information sciences. In her seminal theoretical paper, Vessey ( 1991 ) identifies spatial and symbolic tasks as the two archetypes, which correspond to spatial and symbolic types of cognitive processing and spatial (graphical) and symbolic (textual/numerical) representations. She hypothesizes that visualizations improve judgment effectiveness where these three manifestations align, which she defines as cognitive fit and validates through experiments (Vessey and Galletta 1991 ), including in the sphere of multiattribute management decisions (Umanath and Vessey 1994 ). Further research in information science widely supports this moderating effect by comparing tables and standard quantitative graphs in judgment tasks of increasing complexity (Coll et al. 1994 ; Tuttle and Kershaw 1998 ; Speier 2006 ; Porat et al. 2009 ). On the other hand, experiments in managerial forecasting (Carey and White 1991 ) and financial reporting (Hirsch et al. 2015 ) present the effectiveness of graphical displays in spatial decisions, based on cognitive fit theory. Fischer et al. ( 2005 ) provide further evidence from the domain of cognitive psychology, showing that bar graphs support spatial-numerical judgments particularly well when the chart orientation equals the cognitive processing by following a left-to-right direction (Table 10 ).

4.4.2 Level of data structure

I identify two other task characteristics investigated in the literature, albeit infrequently. First, the level of data structure has been investigated only once in the information science domain. Meyer et al. ( 1999 ) find line charts superior over tables in judgment tasks when the underlying data is structured, with the opposite effect for unstructured data (Table 11 ).

4.4.3 Task complexity

Second, two further experiments observe task complexity as a moderating effect. Meyer et al. ( 1997 ) demonstrate that the speed advantage they find for tables over bar graphs in their computer interface tasks becomes more pronounced with increasing task complexity. However, the same effect does not occur for line graphs. On the other hand, Falschlunger et al. ( 2015c ) find task complexity to be the main factor in predicting task efficiency and effectiveness in handling financial reports but do not observe interaction effects with the visualization (Table 12 ).

4.4.4 Graphical saliency of relevant data

Finally, various studies investigate modifications in the graph format as a variable, with a focus on the graphical saliency of relevant data . This area of research is bridging the two domains of cognitive psychology and information science with widely overlapping results. For example, Verovszek et al. (2013) observe in their information science experiment that colored visualizations are less effective in supporting laypeople’s judgments on urban planning than simple black-and-white line drawings since colorful, irrelevant features distract from the core information. Van den Berg et al. ( 2007 ) identify color as a more powerful feature to highlight salient information in graphs than other variables, such as size. Spence et al. ( 1999 ) find that variations in brightness lead to faster response times in comparison tasks than variations in color. Breslow et al ( 2009 ) demonstrate that the moderating effect of the use of color on judgment speed depends on the task type, with multicolored visuals ideal for identification tasks and black-and-white brightness scales preferable for comparison tasks. Finally, MacEachran et al. (2012) find colorless suited to represent uncertainty when compared to features such as fuzziness or transparency in their surveys with students and practitioners.

Next to color, three-dimensional depth cues have received attention in research. Several psychology experiments find that three-dimensional depth cues irrelevant to the information visualized lower judgment accuracy (Zacks et al. 1998 ; Edwards et al. 2012 ) as well as speed (Fischer 2000 ). Negative effects occur equally for other irrelevant visual cues lowering the saliency of actually relevant information (Fischer 2000 ). Further studies show that increasing the saliency of relevant features can enhance the tendency to make compensatory choices (Dilla and Steinbart 2005 ) and shorten response time (Fabrikant et al. 2010 ), while visual clutter decreases judgment accuracy and boosts response times (Ognjanovic et al. 2019 ). Several other studies test the suitability of a specific set of graphs for unique judgment areas such as uncertainty simulation in urban development (Aerts et al. 2003 ), risk communication (Stone et al. 2017 ; Stone et al. 2018 ), and performance management (Peebles 2008 ) (Table 13 ).

4.5 Cognitive aspects as mediating variables

4.5.1 cognitive load.

Cognitive psychology research introduces the idea of cognitive processes mediating the influence of visualizations on judgment performance, with a focus on cognitive load . Jolicœur and Dell’Acqua ( 1999 ) show in their experiment that the perception of visualizations is subject to structural constraints in working memory capacity, and Allen et al. ( 2014 ) manipulate cognitive load as a dependent variable to demonstrate that judgment accuracy and speed using visualizations decrease under higher cognitive load. Subsequently, psychology experiments provide evidence that visualizations improve decision performance by reducing cognitive load as a mediating factor, operationalized and measured either through pupil size and dilation (Smerecnik et al. 2010 ; Toker and Conati 2017 ) or self-reported load (Cassenti et al. 2019 ). In management research, Ajayi ( 2014 ) investigates this relationship in the context of a proprietary visualization tool for financial data but finds no effect of the visualization component on cognitive load or judgment accuracy. Two further experiments in human–computer interface research operationalize cognitive load based on subjective reporting (Anderson et al. 2011 ) and performance in a secondary task (Block 2013 ) and demonstrate that cognitive load mediates the relationship between visualization use and judgment accuracy and speed, with some types of graphics better suited than others (Table 14 ).

4.5.2 Gazing behavior

Another concept frequently operationalized to represent working memory capacity is gazing behavior , which more recent experiments observe through the use of eye-tracking technology, pioneered by the information sciences. Reani et al. ( 2019 ) observe in their experiment with 49 students that gazing behavior is associated with judgment accuracy, where subjects that pay more attention to relevant visual areas deliver more accurate answers. Similarly, Lohse ( 1997 ) finds that in the more complex decision environment of a budget allocation simulation, decision accuracy is related to efficient gazing behavior and can be improved through the use of colors to reduce the time subjects spend looking at the chart legend. Psychology experiments validate that well-designed graphs enable subjects to focus their attention on relevant information and subsequently improve decision accuracy (Huestegge and Pötzsch 2018 ) and response time (Vila and Gomez 2016 ) (Table 15 ).

4.5.3 Attention

Another variable operationalized at times in eye-tracking experiments is attention, which is elicited through metrics such as the average gazing duration on a specific visual element (Pieters et al. 2010 ). In their cognitive psychology experiment, Smerecnik et al. ( 2010 ) observe that graphs attract more attention in risk communication compared to tables and text and are associated with more accurate judgments. Applying this idea to consumer research, Pieters et al. ( 2010 ) study the consumer’s attention towards visual advertisements and observe that visual complexity based on features such as decorative color can hurt attention, while well-structured complexity such as arrangements of relevant information enhances attention and the attitude toward the brand (Table 16 ).

4.5.4 Affect

Finally, some research emerges into the potential mediating role of affect . Harrison ( 2013 ) shows in her large-scale online experiment that affective priming can significantly influence judgment accuracy in tasks supported visually and that the graphs themselves can cause a change in affect valence. Similarly, Plass et al. ( 2014 ) demonstrate in their educational research that color and shape in visualizations can evoke positive affect and are associated with better student learning (Table 17 ).

5 Discussion

In this paper, I have presented a systematic and integrative review of the current state of research on the effect of information visualization in the social and information sciences. I structured and summarized the results of my systematic literature review along the type of variable interactions present in experimental research. In order to discuss and synthesize the variety of literature insights, I categorize them into three groups: Descriptions of the positive effects for visualizations within decision-making, elaborations on moderators of this potential, and insights into negative effects of misguided visualization use. Table 18 highlights this categorization of results by application domain.

5.1 Positive Effect 1: Information visualization improves decision accuracy and quality

Research findings overwhelmingly confirm the hypothesis that visualizations enable the user to comprehend information more effectively, subsequently improving performance in judgments and decisions. The reason behind this effect is most commonly attributed to cognitive mechanisms. Suwa and Tversky ( 2002 ) point out that based on cognitive load theory, less working memory is needed when visuals provide external representations of concepts, which one can easily refer back to and thereby need not keep in mind, leading to improved judgments. Allen et al. ( 2014 ) show in their experiment that under externally induced cognitive load, well-designed charts suffer less than cluttered ones. Furthermore, graphs enable a simpler gazing pattern than text, which can be used as an indicator of cognitive effort (Smerecnik et al. 2010 ). Based on the concept of cognitive load reduction, visualizations are effectively used in various application areas including management research (Falschlunger et al. 2014 ) and more specifically managerial decision-making (Yildiz and Boehme 2017 ), next to psychology and information sciences more broadly.

5.2 Positive Effect 2: Information visualization steers attention towards uncertainty

A large share of studies identified points towards the strength of visualizations in enhancing uncertainty and risk features in a data set. Beyond increasing the awareness of uncertainty (Dong and Hayes 2012 ), the question of whether visualizations can also improve the reasoning with probabilistic information is studied extensively. Various studies show that visualizations can reduce typical comprehension issues, resulting in the more accurate use of probabilities from a statistical perspective (Allen et al. 2014 ; Wu et al. 2017 ; Stone et al. 2018 ). Positive effects in risk understanding are evaluated particularly in the contexts of safety, such as food safety (Honda et al. 2015 ) and violence risk (Hilton et al. 2017 ). Studies investigating the cognitive processes more closely provide evidence that simpler charts indeed perform best (Edwards et al. 2012 ) since they can reduce cognitive load (Anderson et al. 2011 ) and ultimately improve the internal processing of probabilistic models (Tak et al. 2015 ). As Quattrone ( 2017 ) points out, ambiguity and uncertainty are inherent in managerial decision-making and should be embraced by information visualization, but research on this insight in management is scarce.

5.3 Positive Effect 3: Information Visualization Speeds Up Cognitive Processing

There is evidence that graphs lead to faster processing, learning, and decision-making (Block 2013 ), as judgment and decision efficiency are measured and operationalized as the response time in various experiments. Utilizing eye-tracking technology, Reani et al. ( 2019 ) point out that different types of graphs result in varying gazing patterns in users and hypothesize a link to the reasoning processes. Based on the principle of saliency, multiple studies show that graphs optimally designed to focus attention on the most relevant information lead to more efficient and thereby faster gazing (Falschlunger et al. 2014 , 2015a ), since more time can be spent focusing on highly relevant information (Vila and Gomez 2016 ). Much of this existing work stems from the area of management reporting, investigating quantitative financial data. Overall, the evidence for visual aids speeding up cognitive processing and decision-making appears robust and applicable to management research.

5.4 Moderator 1: The effects of visualization depend on cognitive fit within the decision context

Cognitive fit is a moderator in the effectiveness of visualizations that has been well validated across psychological, management, and information science. Introducing cognitive fit theory, Vessey ( 1991 ) explains many existing research findings in the graph versus table literature claiming that graphs are not (always) more effective, most notably by DeSanctis ( 1984 ). Cognitive fit theory is validated widely (Vessey and Galletta 1991 ; Carey and White 1991 ; Coll et al. 1994 ; Meyer et al. 1997 ; Meyer 2000 ; Porat et al. 2009 ; Perdana et al. 2019 ). Padilla (2018) recognizes that this well-documented effect arises because a cognitive mismatch between data, task, and approach (format) requires more working memory, which negatively affects cognitive processing effectiveness and efficiency. Though highly reliable, many studies investigate elementary processing tasks with limited external validity for more complex decision-making in practice. Umanath and Vessey ( 1994 ) and others (Tuttle and Kershaw 1998 ; Hirsch et al. 2015 ) extend the original cognitive fit theory and successfully apply it to multi-attribute judgments—though at a potential time-accuracy tradeoff. Finally, the idea of matching task and format complexity can be seen as an extension to cognitive fit theory, where graphs are only helpful when they represent as much data complexity as necessary to complete the respective task, but as little as possible (Pieters et al. 2010 ; Van der Linden et al. 2014 ; Géryk 2017 ).

5.5 Moderator 2: Differences within users can be more relevant than the visualization design

Task complexity in relation to user ability needs to be strictly controlled for as a moderator of positive visualization effects. Early studies including individual differences hypothesize that graph potential may be limited to users with a high level of ability (Subramanian et al. 1992 ). Other studies claim that the positive effects of visualizations may be more significant for (McIntire et al. 2014 ) or even limited to (Mayer and Gallini 1990 ) less-skilled individuals. However, these seemingly conflicting results can be explained by the idea that since graphs are effective by requiring less working memory than other formats, improvements are only visible where working memory capacity is limited and needed elsewhere (Lohse 1997 ).

Furthermore, the majority of studies including user factors emphasize the importance of training and expertise, as opposed to inherent ability. Various studies support the claim that experience significantly enhances the contribution of visuals (Porat et al. 2009 ; Edwards et al. 2012 ; Falschlunger et al. 2015a ; Ognjanovic et al. 2019 ), with some claiming that training constitutes a requirement (Géryk 2017 ; Hilton et al. 2017 ) or that users without training are subject to stronger biases (Raschke and Steinbart 2008 ). Consequently, the training factor needs to be closely monitored particularly for a novel or complex visualization. However, extensive training of users is frequently time-consuming and costly. Therefore, the imperative arises for interactive visualization interfaces to accommodate for varying user needs in demanding decision situations. Interactive data visualization software is shown to improve investment decisions (Perdana et al. 2018 ) and judgments by reducing cognitive load (Ajayi 2014 ), for example with flexible performance management dashboards that reduce information load while hosting a full set of KPIs (Yigitbasioglu and Velcu 2012 ). Contrary to much of the early research on static visualizations, the progress in interactivity studies has been driven by practice and case studies, with calls for science to follow suit (Marchak 1994 ; McInerny et al. 2014 ). Overall, I conclude that a match in ability and training with format complexity and novelty, respectively, is a significant determinant of the effectiveness of visualizations. However, there has been little to no empirical research on the subject in the domain of management.

5.6 Negative Effect 1: Visualizations May Not Always Be Helpful: Risk to Impair Decision Making by Misguiding Attention

Several studies, including in management research, argue that visualizations misguide attention even in the presence of cognitive and user fit. For example, Hutchinson (2010) finds graphs to be as exposed to cognitive biases as tables in data-based managerial decision-making. Similarly, other studies identify graphical representations as equally or less effective than verbal formats in financial reports (Volkov and Laing 2012 ), forecasting (Chan 2001 ), probabilistic comprehension (Parrott et al. 2005 ), evidence evaluation (Sanfey and Hastie 1998 ), and communication (Rose 1966 ). The common denominator in these studies is the suboptimal use of salient visual elements, leading to distraction. For example, overly realistic visualizations encompassing color and higher complexity (DeSanctis 1984 ), may lead to visual clutter that decreases performance (Alhadad 2018 ). As Padilla et al. ( 2018 ) argue, visualizations are powerful because they attract fast cognitive bottom-up processing. However, when this superficial processing is focused on irrelevant elements, decision quality can suffer. A well-studied example of this effect is the addition of superfluous three-dimensional cues to quantitative graphs, which lowers accuracy in using the graph (Zacks et al. 1998 ; Fischer 2000 ).

5.7 Negative Effect 2: Visualizations can increase decision-maker overconfidence

The most documented cognitive bias in my review is overconfidence, which can be aggravated by the use of visualizations (O’Keefe and Pitt 1991 ). Multiple studies demonstrate that graph use can increase decision confidence without enhancing decision quality to the same extent in the context of management and finance (Tang et al. 2014 ; Yildiz and Boehme 2017 ; Wesslen et al. 2019 ). This may result from the perception that visualizations show more information at once (Miettinen 2014 ), thereby seemingly requiring less search for additional information (Phillips et al. 2014 ). In particular, this can be the case when graphs appear to visually simplify a problem and the decision-maker fails to adjust his confidence to the underlying complexity (Sen and Boe 1991 ). There is some research with inconclusive results (Pfaff et al. 2013 ), showing no difference in confidence (Hirsch et al. 2015 ) or even lowered confidence (Dong and Hayes 2012 ; Arshad et al. 2015 ). However, the majority of these studies deal with uncertainty communication, which is inherently tied to a decrease in confidence (Watkins 2000 ). Overall, the evidence demonstrates that unless highlighting uncertainty, visual aids result in higher decision confidence. The case of overconfidence is particularly well established in the area of management controlling and financial reporting but understudied for strategic decisions.

6 Research agenda

In summary, there is ample evidence for the potential of information visualization to improve decision-making in terms of effectiveness and efficiency, yet my review highlights possible limitations and risks where its use is misguided or inappropriate. I argue that several of these are particularly critical for further research since there is little to no application to the domain of strategic management decisions, despite the ubiquity of visualizations to support these in practice. Based on the summary of my insights by application domain in Table 18, I identify five research gaps in the field of strategic management decisions.

First, there is conflicting evidence regarding the effect of information visualization on decision-making under uncertainty, and existing research is mostly limited to information science (Aerts et al. 2003 ). Depending on the context and design, visualization use can increase or reduce risk-taking (Dambacher et al. 2016 ) but has the potential to improve probabilistic reasoning in an objective manner (Allen et al. 2014 ). Given the importance of uncertainty as a defining factor of strategic management decisions (Quattrone 2017 ), the possibility of information visualizations to improve risk understanding in the management context deserves closer evaluation. For example, the framing bias is a well-documented phenomenon in strategic decision-making (Hodgkinson et al. 1999 ), leading to different subjective risk interpretations and subsequent decisions based on the presentation of information. Naturally, the question arises whether information visualization can mitigate this bias and which salient visual features are beneficial. I suggest exploring this question through experiments with strategic management decision vignettes.

Research Gap 1: How can information visualization mitigate the framing bias and improve risk understanding in strategic management decisions?

Second, my review has made clear that the effectiveness of information visualization depends in large parts on user characteristics such as expertise (Hilton et al. 2017 ), numeracy (Honda et al. 2015 ), and graph literacy (Okan et al. 2018b ), yet there exists no transfer of this insight towards individual managerial traits. At the same time, well-established concepts such as the Upper Echelons Theory (Hambrick 2007 ) highlight the relevance of CEO characteristics, both observable and psychological for strategic managerial choices and, subsequently, company performance. While some concepts such as experience may be transferrable from existing visualization research (Falschlunger et al. 2015c ) requiring validation only, others, such as group position or individual values, present opportunities to extend theory substantially. I suggest exploring this area through a dedicated analysis of relevant CEO characteristics and corresponding empirical research with practitioner subjects.

Research Gap 2: How do CEO characteristics influence the effectiveness of information visualization in strategic management decisions?

Third, while the prevalence of visualization use for impression management in financial reporting is well-established (Falschlunger et al. 2015b ), there is a complete lack of transfer of this phenomenon to the realm of strategic management decisions. As Whittington et al. ( 2016 ) highlight, strategy presentations can be seen as an effective tool for CEO impression management. Given the popularity of visualizations in this communication medium – both through quantitative charts and schematic diagrams (Zelazny 2001 ), the question arises to what degree impression management also takes place in this case, for example through the reporting bias (Beattie and Jones 2000 ). I suggest investigating this subject empirically, for example through archival studies.

Research Gap 3: To what extent does CEO impression management occur through visualization use in strategy presentations?

Fourth, while overconfidence in managerial decision-making is a commonly reported issue with significant efforts to develop corrective feedback as a remedy (Chen et al. 2015 ), there is little understanding of the role of information visualization in this matter. My review has demonstrated that visual aids often increase decision confidence as much as they improve the judgment itself (Yildiz and Boehme 2017 ) or even more (Sen and Boe 1991 ), but can also reduce confidence, particularly where uncertainty information is depicted (Dong and Hayes 2012 ). However, the latter effect was only studied for topics unrelated to management. Therefore, there is a complete lack of understanding of the effects of visualizations on managerial overconfidence, and I suggest exploring this research gap empirically with practitioners.

Research Gap 4: How do visual aids influence overconfidence in managerial decision-making?

Finally, a large share of cognitive psychology research discusses the effectiveness of visualization use through the reduction of cognitive load, yet they usually start off with low-load contexts, which is the opposite of high-stress managerial decision-making (Laamanen et al. 2018 ). Allen et al. ( 2014 ) find evidence that the effectiveness of distinct graph types changes with the level of externally induced cognitive load, raising the question to what extent previous insights on helpful visual aids are applicable to managerial decisions in a high-stakes environment filled with distractions and parallel issues requiring attention. Therefore, I suggest studying visualization use in experimental environments with varying levels of cognitive load as the independent variable, ideally with management practitioners and a realistic strategic task setting.

Research Gap 5: How does cognitive load influence the effectiveness of information visualization in strategic management decisions?

7 Conclusion

Information visualization has become ubiquitous in our daily professional and private lives, even more so with the advent of accessible and powerful computer graphics. However, the impact that visualizations have on human cognition and ultimately decisions stills remains unclear to a large extent. While the prevalence of visualization research across a plethora of application domains shows its pertinence, the decentralized approach has led to a scattered and unstructured field of theories and empirical evidence. My literature review thus sought to provide a far-reaching overview of this work and a detailed research agenda. As a result, three contributions arise from my review.

First, I provide an overarching structure to summarize the range of effects and interacting variables that can be found surrounding visualization research. This includes a wide set of dependent variables ranging from decision quality and speed to confidence and attitudes, as well as complex moderating and mediating effects that are crucial to understanding the overall power of visualizations. This precise framework is paramount to a holistic and comprehensive review of the scattered existing literature.

Second, to the best of my knowledge, my systematic literature review is the first on visualizations spanning the whole of social and information sciences simultaneously. While some previous reviews such as the one by Yigitbasioglu and Velcu ( 2012 ) utilize a multidisciplinary approach, they usually define the visualization type investigated more narrowly, for example by focusing on dashboards only. I believe that my integrative overview provides a valid contribution to the ongoing work to synthesize the mixed results in visualization research.

Third, I demonstrate that despite the plethora of evidence at first sight, visualization research is far from complete due to its multitude of moderating variables and at times conflicting results. Building on my systematic review of existing literature, I specify an agenda of potential research directions for future studies to follow in order to advance our understanding of the cognitive implications of visualizations in the context of managerial decision making in particular.

This paper also has direct implications for management practice. As Zhang ( 1998 ) points out, managerial decision-making is particularly well-positioned to profit from good visualizations since it often utilizes unstructured, large sets of information that are computer-centered, dynamic, and need to be interpreted constantly under time pressure. However, the interaction of visualization use with various factors should not be underestimated in the design of computer graphics for decision support. The high validity of the cognitive fit theory and the contingency on user characteristics found in the literature demonstrates that the designer should spend extensive time on clarifying for whom and what the visualization is intended. Furthermore, the potential for overconfidence and automatic processing based on visualized information may result in decision-makers skipping on more elaborate thought, which may be desirable in some, but certainly not all situations.

Availability of data and material

Not applicable.

Code availability

Thanks to the anonymous reviewer for encouraging me to extend my keyword search.

Thanks to the anonymous reviewer for this valuable impulse.

Thanks to the anonymous reviewer for pointing me towards additional, highly relevant articles.

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Eberhard, K. The effects of visualization on judgment and decision-making: a systematic literature review. Manag Rev Q 73 , 167–214 (2023). https://doi.org/10.1007/s11301-021-00235-8

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The Relationship Between Data Visualization and Task Performance

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We are entering an era of business intelligence and big data where simple tables and other traditional means of data display cannot deal with the vast amounts of data required to meet the decision-making needs of businesses and their clients. Graphical figures constructed with modern visualization software can convey more information than a table because there is a limit to the table size that is visually usable. Contemporary decision performance is influenced by the task domain, the user experience, and the visualizations themselves. Utilizing data visualization in task performance to aid in decision making is a complex process. We develop … continued below

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• Discipline: Business Computer Information Systems
• Level: Doctoral
• Name: Doctor of Philosophy
• Grantor: University of North Texas
• PublicationType: Doctoral Dissertation

We are entering an era of business intelligence and big data where simple tables and other traditional means of data display cannot deal with the vast amounts of data required to meet the decision-making needs of businesses and their clients. Graphical figures constructed with modern visualization software can convey more information than a table because there is a limit to the table size that is visually usable. Contemporary decision performance is influenced by the task domain, the user experience, and the visualizations themselves. Utilizing data visualization in task performance to aid in decision making is a complex process. We develop and test a decision-making framework to examine task performance in a visual and non-visual aided decision-making by using three experiments to test this framework. Studies 1 and 2 investigate DV formats and how complexity and design affects the proposed visual decision making framework. The studies also examine how DV formats affect task performance, as measured by accuracy and timeliness, and format preference. Additionally, these studies examine how DV formats influence the constructs in the proposed decision making framework which include information usefulness, decision confidence, cognitive load, visual aesthetics, information seeking intention, and emotion. Preliminary findings indicate that graphical DV allows individuals to respond faster and more accurately, resulting in improved task fit and performance. Anticipated implications of this research are as follows. Visualizations are independent of the size of the data set but can be increasingly complex as the data complexity increases. Furthermore, well designed visualizations let you see through the complexity and simultaneously mine the complexity with drill down technologies such as OLAP.

• Task performance
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The Design Activity Framework: Investigating the Data Visualization Design Process

This dissertation establishes a new visualization design process model devised to guide visualization designers in building more effective and useful visualization systems and tools. The novelty of this framework includes its flexibility for iteration, actionability for guiding visualization designers with concrete steps, concise yet methodical definitions, and connections to other visualization design models commonly used in the field of data visualization. In summary, the design activity framework breaks down the visualization design process into a series of four design activities: understand , ideate , make , and deploy . For each activity, the framework prescribes a descriptive motivation, list of design methods, and expected visualization artifacts.

To elucidate the framework, two case studies for visualization design illustrate these concepts, methods, and artifacts in real-world projects in the field of cybersecurity. For example, these projects employ user-centered design methods, such as personas and data sketches, which emphasize our teams' motivations and visualization artifacts with respect to the design activity framework. These case studies also serve as examples for novice visualization designers, and we hypothesized that the framework could serve as a pedagogical tool for teaching and guiding novices through their own design process to create a visualization tool.

To externally evaluate the efficacy of this framework, we created worksheets for each design activity, outlining a series of concrete, tangible steps for novices. In order to validate the design worksheets, we conducted 13 student observations over the course of two months, received 32 online survey responses, and performed a qualitative analysis of 11 in-depth interviews. Students found the worksheets both useful and effective for framing the visualization design process. Next, by applying the design activity framework to technique-driven and evaluation-based research projects, we brainstormed possible extensions to the design model. Lastly, we examined implications of the design activity framework and present future work in this space. The visualization community is challenged to consider on how to more effectively describe, capture, and communicate the complex, iterative nature of data visualization design throughout research, design, development, and deployment of visualization systems and tools.

Project Website

•   Design Activity Framework Website
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•   Recording of the Defense

How to Use Creative Data Visualization Techniques for Easy Comprehension of Qualitative Research

“A picture is worth a thousand words!”—an adage used so often stands true even whilst reporting your research data. Research studies with overwhelming data can perhaps be difficult to comprehend by some readers or can even be time-consuming. While presenting quantitative research data becomes easier with the help of graphs, pie charts, etc. researchers face an undeniable challenge whilst presenting qualitative research data. In this article, we will elaborate on effectively presenting qualitative research using data visualization techniques .

Table of Contents

What is Data Visualization?

Data visualization is the process of converting textual information into graphical and illustrative representations. It is imperative to think beyond numbers to get a holistic and comprehensive understanding of research data. Hence, this technique is adopted to help presenters communicate relevant research data in a way that’s easy for the viewer to interpret and draw conclusions.

What Is the Importance of Data Visualization in Qualitative Research?

According to the form in which the data is collected and expressed, it is broadly divided into qualitative data and quantitative data. Quantitative data expresses the size or quantity of data in a countable integer. Unlike quantitative data, qualitative data cannot be expressed in continuous integer values; it refers to data values ​​described in the non-numeric form related to subjects, places, things, events, activities, or concepts.

What Are the Advantages of Good Data Visualization Techniques?

Excellent data visualization techniques have several benefits:

• Human eyes are often drawn to patterns and colors. Moreover, in this age of Big Data , visualization can be considered an asset to quickly and easily comprehend large amounts of data generated in a research study.
• Enables viewers to recognize emerging trends and accelerate their response time on the basis of what is seen and assimilated.
• Illustrations make it easier to identify correlated parameters.
• Allows the presenter to narrate a story whilst helping the viewer understand the data and draw conclusions from it.
• As humans can process visual images better than texts, data visualization techniques enable viewers to remember them for a longer time.

Different Types of Data Visualization Techniques in Qualitative Research

Here are several data visualization techniques for presenting qualitative data for better comprehension of research data.

1. Word Clouds

• Word Clouds is a type of data visualization technique which helps in visualizing one-word descriptions.
• It is a single image composing multiple words associated with a particular text or subject.
• The size of each word indicates its importance or frequency in the data.
• Wordle and Tagxedo are two majorly used tools to create word clouds.

2. Graphic Timelines

• Graphic timelines are created to present regular text-based timelines with pictorial illustrations or diagrams, photos, and other images.
• It visually displays a series of events in chronological order on a timescale.
• Furthermore, showcasing timelines in a graphical manner makes it easier to understand critical milestones in a study.

3. Icons Beside Descriptions

• Rather than writing long descriptive paragraphs, including resembling icons beside brief and concise points enable quick and easy comprehension.

4. Heat Map

• Using a heat map as a data visualization technique better displays differences in data with color variations.
• The intensity and frequency of data is well addressed with the help of these color codes.
• However, a clear legend must be mentioned alongside the heat map to correctly interpret a heat map.
• Additionally, it also helps identify trends in data.

5. Mind Map

• A mind map helps explain concepts and ideas linked to a central idea.
• Allows visual structuring of ideas without overwhelming the viewer with large amounts of text.
• These can be used to present graphical abstracts

Do’s and Don’ts of Data Visualization Techniques

It perhaps is not easy to visualize qualitative data and make it recognizable and comprehensible to viewers at a glance. However, well-visualized qualitative data can be very useful in order to clearly convey the key points to readers and listeners in presentations.

Are you struggling with ways to display your qualitative data? Which data visualization techniques have you used before? Let us know about your experience in the comments section below!

nicely explained

None. And I want to use it from now.

Would it be ideal or suggested to use these techniques to display qualitative data in a thesis perhaps?

Using data visualization techniques in a qualitative research thesis can help convey your findings in a more engaging and comprehensible manner. Here’s a brief overview of how to incorporate data visualization in such a thesis:

Select Relevant Visualizations: Identify the types of data you have (e.g., textual, audio, visual) and the appropriate visualization techniques that can represent your qualitative data effectively. Common options include word clouds, charts, graphs, timelines, and thematic maps.

Data Preparation: Ensure your qualitative data is well-organized and coded appropriately. This might involve using qualitative analysis software like NVivo or Atlas.ti to tag and categorize data.

Create Visualizations: Generate visualizations that illustrate key themes, patterns, or trends within your qualitative data. For example: Word clouds can highlight frequently occurring terms or concepts. Bar charts or histograms can show the distribution of specific themes or categories. Timeline visualizations can help display chronological trends. Concept maps can illustrate the relationships between different concepts or ideas.

Integrate Visualizations into Your Thesis: Incorporate these visualizations within your thesis to complement your narrative. Place them strategically to support your arguments or findings. Include clear and concise captions and labels for each visualization, providing context and explaining their significance.

Interpretation: In the text of your thesis, interpret the visualizations. Explain what patterns or insights they reveal about your qualitative data. Offer meaningful insights and connections between the visuals and your research questions or hypotheses.

Maintain Consistency: Maintain a consistent style and formatting for your visualizations throughout the thesis. This ensures clarity and professionalism.

Ethical Considerations: If your qualitative research involves sensitive or personal data, consider ethical guidelines and privacy concerns when presenting visualizations. Anonymize or protect sensitive information as needed.

Review and Refinement: Before finalizing your thesis, review the visualizations for accuracy and clarity. Seek feedback from peers or advisors to ensure they effectively convey your qualitative findings.

Appendices: If you have a large number of visualizations or detailed data, consider placing some in appendices. This keeps the main body of your thesis uncluttered while providing interested readers with supplementary information.

Cite Sources: If you use specific software or tools to create your visualizations, acknowledge and cite them appropriately in your thesis.

Hope you find this helpful. Happy Learning!

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5 Data Visualization Dissertations Worth a Look

It’s coming to the end of the academic year, which means there are lots of graduate students frantically finishing up their dissertations, defending, and earning their degrees (yay!). Here are some tasty visualization dissertations, new and old, worth thumbing through.

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I’ll not-so-humbly put forth my own master’s thesis for consideration. The focus is qualitative representation, not quantitative but, the foundations in human perception and cognition are 100% applicable to the design of any knowledge visualization.

Generation of Complex Diagrams: How to Make Lasagna Instead of Spaghetti

Fantastic idea for a post Nathan!

Ben Fry’s Masters thesis “Organic Information Design” is also an essential read.. it is (expectedly) a lot looser than “Computational Information Design” and fun to read now considering how popular visualization has become. The history/precedents that he tracks in it reads as quite an essential list of early web based work and applications.

Personally, I’m really excited to read Mike Danziger’s thesis. His writing at Visual Methods is fantastic so I look forward to seeing his more formal research.

@Greg: yup. computational information design is one of the few dissertations i’ve read cover-to-cover. really fun read.

Thanks for the link; another really interesting and original work is Yuri Engelhardt’s dissertation “The Language of Graphics” , where information design gets a thorough linguistic treatment.

wow, great list, thanks

@Moritz: thanks for the pointer! the name sounds really familiar, but i can’t quite figure out where from..

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Data Visualization for Interdisciplinary Thesis

Teaching with data: data visualization for interdisciplinary thesis.

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Faculty Author: Virginia Kuhn

Course: IML 440: Interdisciplinary Thesis

Department or School: Media Arts and Practice, School of Cinematic Arts

Student Population: Undergraduate seniors

Duration: semester

Deliverables:

• Project plan with chosen data set
• Data set visualized in 3 ways
• Written report describing the visualizations

Keywords: comparative data visualization, thesis, visual literacy, images, infographics

Summary:   Students identify a data set that serves their senior thesis project’s main research question, visualize the data in 3 ways (e.g. pie chart word cloud, scatter plot, bar graph), and write a 750-­‐1,000-­‐word report describing how each visualization changes the meaning of the information based on the way it looks.

Assignment Goals:   The information visualization assignment asks you to explore the ways in which data become information and, further, the ways in which information shifts its meaning depending on its context and presentation. This assignment helps students to do the following:  

• move from research into production
• critically decode and encode digital, image-­‐based media
• acquire data literacy, as well as visual literacy

For full instructions, see:  https://virginiakuhn.net/

Recommended Tools:

• LA City Open Data
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• Wordl word clouds
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• Visual Complexity
• NodeXl (works with Excel)
• Visualizing economics
• Flowing Data
• Tableau Public

Faculty Author Advice:   Allow more time for students to cull data between project plan and when it’s due; about a week more should be spent on this part. The perception is that data is very easy to get at and that it is cleaned up in the way you want it. For example, a student couldn’t find data on her topic and needed to create a data set. It was hard for students to get the data they needed from sources like the New York Times from the 1960s because they weren’t searchable. There are numerous cases like that for web scraping for APIs. That was the best lesson and that’s why the data literacy aspect was so key and led them to think about who decides what a data point is, and what is data, really.

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Information Design and Visualization Master's Theses Collection

http://hdl.handle.net/2047/D20207599

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Study Postgraduate

Data visualisation (masc/pgdip) (2025 entry).

Course code

P-L995 (MASc);

P-L996 (PGDip)

29 September 2025 

1 year full-time or 2 years part-time (MASc); 9 months full-time or 21 months part-time (PGDip)

Qualification

MASc (Masters in Arts and Science); PG Diploma

Centre for Interdisciplinary Methodologies

University of Warwick

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Explore our Data Visualisation taught Master's degree at Warwick

Warwick’s Data Visualisation MASc focuses on the skills and knowledge needed to design, develop, deploy and interpret data visualisations. Open up diverse career opportunities by developing a Data Visualisation portfolio through studies spanning the Sciences, Arts, Humanities and Social Sciences at the Centre for Interdisciplinary Methodologies.

Course overview

The MASc in Data Visualisation is an innovative, interdisciplinary course which enables students to acquire crucial knowledge and skills in visualisation as a methodology for data-intensive research, communication and engagement. Students will be trained in concepts, methods and techniques from data science, digital humanities and design research, whilst developing a portfolio of work that prepares them for diverse career opportunities.

The programme aims to develop the methodological, conceptual and practical skills needed to design, deploy and interpret data visualisations successfully in academic, policy and public contexts. The course combines academic training in methodological and conceptual aspects with the development of technical, creative and practical competences in data visualisation as a way of communicating knowledge, as a form of engagement, and as a way of seeing the world.

During the programme you will develop:

• End-to-end skills and joined-up understanding that enable you to design, create and code visualisations, work with data, and analyse and understand your data visualisations and those of others.
• Critical, interdisciplinary perspectives required by employers, that integrate expertise in tools, techniques, knowledges and methods of analysis, leading to a 360 view of what data visualisations are and do, and the limits of this medium.
• A portfolio of work to kickstart your career, progressed through diverse projects in your modules, a practice- or theory-led dissertation, and within the Data-Design Camp.
• Expertise in the interactions between Data + Code + Design + Theory developed through learn to code as a basis for creating visualisations, as well as furthering your understanding of visualisations through critique and analysis.

What is an MASc?

The MASc is a flexible degree where students customise their learning trajectory through interdisciplinary topics and modules that might usually be isolated to either MA or MSc qualifications. Through optional module choices, project directions and final dissertation, you can tune your degree to fit your learning and career goals.

Skills from this degree

• Coding and software skills for visualisation
• Design and visual analysis skills building on the fundamentals of data visualisation
• Substantial design experience through project work
• Analytical skills to conceptually frame and relate visualisation designs to wider societal, cultural, and political debates
• Writing and communication skills for analysis/discussing technical content
• Critical academic research skills with an interdisciplinary focus

General entry requirements

Minimum requirements.

2:1 undergraduate degree (or equivalent). There is no requirement for prior knowledge of coding or programming.

English language requirements

You can find out more about our English language requirements Link opens in a new window . This course requires the following:

• IELTS overall score of 7.0, minimum component scores of two at 6.0/6.5 and the rest at 7.0 or above.

International qualifications

We welcome applications from students with other internationally recognised qualifications.

For more information, please visit the international entry requirements page Link opens in a new window .

Additional requirements

There are no additional entry requirements for this course.

Core modules

Visualisation Foundations

Data visualisations (graphs, maps, networks) have become a fundamental currency for the exploration of data and the exchange of information. This module develops foundational understanding in what visualisations are and how they operate. Coding skills are developed alongside the conceptual understanding, allowing students to develop visualisations and their understanding in terms of design, theory, data and code.

As visualisation is such an interdisciplinary topic, students will engage with diverse topics spanning data science and psychology, graphic design and the arts, and critical cartography and data feminism.

Data Visualisation in Science, Culture and Public Policy

The module introduces concepts, methods and empirical cases that enable an understanding of the affordances, power and limitations of data visualisation in science, culture, and public policy.

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• v.1(9); 2020 Dec 11

Principles of Effective Data Visualization

Stephen r. midway.

1 Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many figures incorrectly present information or, when not incorrect, still use suboptimal data visualization practices. Presented here are ten principles that serve as guidance for authors who seek to improve their visual message. Some principles are less technical, such as determining the message before starting the visual, while other principles are more technical, such as how different color combinations imply different information. Because figure making is often not formally taught and figure standards are not readily enforced in science, it is incumbent upon scientists to be aware of best practices in order to most effectively tell the story of their data.

The Bigger Picture

Visuals are an increasingly important form of science communication, yet many scientists are not well trained in design principles for effective messaging. Despite challenges, many visuals can be improved by taking some simple steps before, during, and after their creation. This article presents some sequential principles that are designed to improve visual messages created by scientists.

Many scientific visuals are not as effective as they could be because scientists often lack basic design principles. This article reviews the importance of effective data visualization and presents ten principles that scientists can use as guidance in developing effective visual messages.

Introduction

Visual learning is one of the primary forms of interpreting information, which has historically combined images such as charts and graphs (see Box 1 ) with reading text. 1 However, developments on learning styles have suggested splitting up the visual learning modality in order to recognize the distinction between text and images. 2 Technology has also enhanced visual presentation, in terms of the ability to quickly create complex visual information while also cheaply distributing it via digital means (compared with paper, ink, and physical distribution). Visual information has also increased in scientific literature. In addition to the fact that figures are commonplace in scientific publications, many journals now require graphical abstracts 3 or might tweet figures to advertise an article. Dating back to the 1970s when computer-generated graphics began, 4 papers represented by an image on the journal cover have been cited more frequently than papers without a cover image. 5

Regarding terminology, the terms graph , plot , chart , image , figure , and data visual(ization) are often used interchangeably, although they may have different meanings in different instances. Graph , plot , and chart often refer to the display of data, data summaries, and models, while image suggests a picture. Figure is a general term but is commonly used to refer to visual elements, such as plots, in a scientific work. A visual , or data visualization , is a newer and ostensibly more inclusive term to describe everything from figures to infographics. Here, I adopt common terminology, such as bar plot, while also attempting to use the terms figure and data visualization for general reference.

There are numerous advantages to quickly and effectively conveying scientific information; however, scientists often lack the design principles or technical skills to generate effective visuals. Going back several decades, Cleveland 6 found that 30% of graphs in the journal Science had at least one type of error. Several other studies have documented widespread errors or inefficiencies in scientific figures. 7 , 8 , 9 In fact, the increasing menu of visualization options can sometimes lead to poor fits between information and its presentation. These poor fits can even have the unintended consequence of confusing the readers and setting them back in their understanding of the material. While objective errors in graphs are hopefully in the minority of scientific works, what might be more common is suboptimal figure design, which takes place when a design element may not be objectively wrong but is ineffective to the point of limiting information transfer.

Effective figures suggest an understanding and interpretation of data; ineffective figures suggest the opposite. Although the field of data visualization has grown in recent years, the process of displaying information cannot—and perhaps should not—be fully mechanized. Much like statistical analyses often require expert opinions on top of best practices, figures also require choice despite well-documented recommendations. In other words, there may not be a singular best version of a given figure. Rather, there may be multiple effective versions of displaying a single piece of information, and it is the figure maker's job to weigh the advantages and disadvantages of each. Fortunately, there are numerous principles from which decisions can be made, and ultimately design is choice. 7

The data visualization literature includes many great resources. While several resources are targeted at developing design proficiency, such as the series of columns run by Nature Communications , 10 Wilkinson's The Grammar of Graphics 11 presents a unique technical interpretation of the structure of graphics. Wilkinson breaks down the notion of a graphic into its constituent parts—e.g., the data, scales, coordinates, geometries, aesthetics—much like conventional grammar breaks down a sentence into nouns, verbs, punctuation, and other elements of writing. The popularity and utility of this approach has been implemented in a number of software packages, including the popular ggplot2 package 12 currently available in R. 13 (Although the grammar of graphics approach is not explicitly adopted here, the term geometry is used consistently with Wilkinson to refer to different geometrical representations, whereas the term aesthetics is not used consistently with the grammar of graphics and is used simply to describe something that is visually appealing and effective.) By understanding basic visual design principles and their implementation, many figure authors may find new ways to emphasize and convey their information.

The Ten Principles

Principle #1 diagram first.

The first principle is perhaps the least technical but very important: before you make a visual, prioritize the information you want to share, envision it, and design it. Although this seems obvious, the larger point here is to focus on the information and message first, before you engage with software that in some way starts to limit or bias your visual tools. In other words, don't necessarily think of the geometries (dots, lines) you will eventually use, but think about the core information that needs to be conveyed and what about that information is going to make your point(s). Is your visual objective to show a comparison? A ranking? A composition? This step can be done mentally, or with a pen and paper for maximum freedom of thought. In parallel to this approach, it can be a good idea to save figures you come across in scientific literature that you identify as particularly effective. These are not just inspiration and evidence of what is possible, but will help you develop an eye for detail and technical skills that can be applied to your own figures.

Principle #2 Use the Right Software

Effective visuals typically require good command of one or more software. In other words, it might be unrealistic to expect complex, technical, and effective figures if you are using a simple spreadsheet program or some other software that is not designed to make complex, technical, and effective figures. Recognize that you might need to learn a new software—or expand your knowledge of a software you already know. While highly effective and aesthetically pleasing figures can be made quickly and simply, this may still represent a challenge to some. However, figure making is a method like anything else, and in order to do it, new methodologies may need to be learned. You would not expect to improve a field or lab method without changing something or learning something new. Data visualization is the same, with the added benefit that most software is readily available, inexpensive, or free, and many come with large online help resources. This article does not promote any specific software, and readers are encouraged to reference other work 14 for an overview of software resources.

Principle #3 Use an Effective Geometry and Show Data

Geometries are the shapes and features that are often synonymous with a type of figure; for example, the bar geometry creates a bar plot. While geometries might be the defining visual element of a figure, it can be tempting to jump directly from a dataset to pairing it with one of a small number of well-known geometries. Some of this thinking is likely to naturally happen. However, geometries are representations of the data in different forms, and often there may be more than one geometry to consider. Underlying all your decisions about geometries should be the data-ink ratio, 7 which is the ratio of ink used on data compared with overall ink used in a figure. High data-ink ratios are the best, and you might be surprised to find how much non-data-ink you use and how much of that can be removed.

Most geometries fall into categories: amounts (or comparisons), compositions (or proportions), distributions , or relationships . Although seemingly straightforward, one geometry may work in more than one category, in addition to the fact that one dataset may be visualized with more than one geometry (sometimes even in the same figure). Excellent resources exist on detailed approaches to selecting your geometry, 15 and this article only highlights some of the more common geometries and their applications.

Amounts or comparisons are often displayed with a bar plot ( Figure 1 A), although numerous other options exist, including Cleveland dot plots and even heatmaps ( Figure 1 F). Bar plots are among the most common geometry, along with lines, 9 although bar plots are noted for their very low data density 16 (i.e., low data-ink ratio). Geometries for amounts should only be used when the data do not have distributional information or uncertainty associated with them. A good use of a bar plot might be to show counts of something, while poor use of a bar plot might be to show group means. Numerous studies have discussed inappropriate uses of bar plots, 9 , 17 noting that “because the bars always start at zero, they can be misleading: for example, part of the range covered by the bar might have never been observed in the sample.” 17 Despite the numerous reports on incorrect usage, bar plots remain one of the most common problems in data visualization.

Examples of Visual Designs

(A) Clustered bar plots are effective at showing units within a group (A–C) when the data are amounts.

(B) Histograms are effective at showing the distribution of data, which in this case is a random draw of values from a Poisson distribution and which use a sequential color scheme that emphasizes the mean as red and values farther from the mean as yellow.

(C) Scatterplot where the black circles represent the data.

(D) Logistic regression where the blue line represents the fitted model, the gray shaded region represents the confidence interval for the fitted model, and the dark-gray dots represent the jittered data.

(E) Box plot showing (simulated) ages of respondents grouped by their answer to a question, with gray dots representing the raw data used in the box plot. The divergent colors emphasize the differences in values. For each box plot, the box represents the interquartile range (IQR), the thick black line represents the median value, and the whiskers extend to 1.5 times the IQR. Outliers are represented by the data.

(F) Heatmap of simulated visibility readings in four lakes over 5 months. The green colors represent lower visibility and the blue colors represent greater visibility. The white numbers in the cells are the average visibility measures (in meters).

(G) Density plot of simulated temperatures by season, where each season is presented as a small multiple within the larger figure.

For all figures the data were simulated, and any examples are fictitious.

Compositions or proportions may take a wide range of geometries. Although the traditional pie chart is one option, the pie geometry has fallen out of favor among some 18 due to the inherent difficulties in making visual comparisons. Although there may be some applications for a pie chart, stacked or clustered bar plots ( Figure 1 A), stacked density plots, mosaic plots, and treemaps offer alternatives.

Geometries for distributions are an often underused class of visuals that demonstrate high data density. The most common geometry for distributional information is the box plot 19 ( Figure 1 E), which shows five types of information in one object. Although more common in exploratory analyses than in final reports, the histogram ( Figure 1 B) is another robust geometry that can reveal information about data. Violin plots and density plots ( Figure 1 G) are other common distributional geometries, although many less-common options exist.

Relationships are the final category of visuals covered here, and they are often the workhorse of geometries because they include the popular scatterplot ( Figures 1 C and 1D) and other presentations of x - and y -coordinate data. The basic scatterplot remains very effective, and layering information by modifying point symbols, size, and color are good ways to highlight additional messages without taking away from the scatterplot. It is worth mentioning here that scatterplots often develop into line geometries ( Figure 1 D), and while this can be a good thing, presenting raw data and inferential statistical models are two different messages that need to be distinguished (see Data and Models Are Different Things ).

Finally, it is almost always recommended to show the data. 7 Even if a geometry might be the focus of the figure, data can usually be added and displayed in a way that does not detract from the geometry but instead provides the context for the geometry (e.g., Figures 1 D and 1E). The data are often at the core of the message, yet in figures the data are often ignored on account of their simplicity.

Principle #4 Colors Always Mean Something

The use of color in visualization can be incredibly powerful, and there is rarely a reason not to use color. Even if authors do not wish to pay for color figures in print, most journals still permit free color figures in digital formats. In a large study 20 of what makes visualizations memorable, colorful visualizations were reported as having a higher memorability score, and that seven or more colors are best. Although some of the visuals in this study were photographs, other studies 21 also document the effectiveness of colors.

In today's digital environment, color is cheap. This is overwhelmingly a good thing, but also comes with the risk of colors being applied without intention. Black-and-white visuals were more accepted decades ago when hard copies of papers were more common and color printing represented a large cost. Now, however, the vast majority of readers view scientific papers on an electronic screen where color is free. For those who still print documents, color printing can be done relatively cheaply in comparison with some years ago.

Color represents information, whether in a direct and obvious way, or in an indirect and subtle way. A direct example of using color may be in maps where water is blue and land is green or brown. However, the vast majority of (non-mapping) visualizations use color in one of three schemes: sequential , diverging , or qualitative . Sequential color schemes are those that range from light to dark typically in one or two (related) hues and are often applied to convey increasing values for increasing darkness ( Figures 1 B and 1F). Diverging color schemes are those that have two sequential schemes that represent two extremes, often with a white or neutral color in the middle ( Figure 1 E). A classic example of a diverging color scheme is the red to blue hues applied to jurisdictions in order to show voting preference in a two-party political system. Finally, qualitative color schemes are found when the intensity of the color is not of primary importance, but rather the objective is to use different and otherwise unrelated colors to convey qualitative group differences ( Figures 1 A and 1G).

While it is recommended to use color and capture the power that colors convey, there exist some technical recommendations. First, it is always recommended to design color figures that work effectively in both color and black-and-white formats ( Figures 1 B and 1F). In other words, whenever possible, use color that can be converted to an effective grayscale such that no information is lost in the conversion. Along with this approach, colors can be combined with symbols, line types, and other design elements to share the same information that the color was sharing. It is also good practice to use color schemes that are effective for colorblind readers ( Figures 1 A and 1E). Excellent resources, such as ColorBrewer, 22 exist to help in selecting color schemes based on colorblind criteria. Finally, color transparency is another powerful tool, much like a volume knob for color ( Figures 1 D and 1E). Not all colors have to be used at full value, and when not part of a sequential or diverging color scheme—and especially when a figure has more than one colored geometry—it can be very effective to increase the transparency such that the information of the color is retained but it is not visually overwhelming or outcompeting other design elements. Color will often be the first visual information a reader gets, and with this knowledge color should be strategically used to amplify your visual message.

Principle #5 Include Uncertainty

Not only is uncertainty an inherent part of understanding most systems, failure to include uncertainty in a visual can be misleading. There exist two primary challenges with including uncertainty in visuals: failure to include uncertainty and misrepresentation (or misinterpretation) of uncertainty.

Uncertainty is often not included in figures and, therefore, part of the statistical message is left out—possibly calling into question other parts of the statistical message, such as inference on the mean. Including uncertainty is typically easy in most software programs, and can take the form of common geometries such as error bars and shaded intervals (polygons), among other features. 15 Another way to approach visualizing uncertainty is whether it is included implicitly into the existing geometries, such as in a box plot ( Figure 1 E) or distribution ( Figures 1 B and 1G), or whether it is included explicitly as an additional geometry, such as an error bar or shaded region ( Figure 1 D).

Representing uncertainty is often a challenge. 23 Standard deviation, standard error, confidence intervals, and credible intervals are all common metrics of uncertainty, but each represents a different measure. Expressing uncertainty requires that readers be familiar with metrics of uncertainty and their interpretation; however, it is also the responsibility of the figure author to adopt the most appropriate measure of uncertainty. For instance, standard deviation is based on the spread of the data and therefore shares information about the entire population, including the range in which we might expect new values. On the other hand, standard error is a measure of the uncertainty in the mean (or some other estimate) and is strongly influenced by sample size—namely, standard error decreases with increasing sample size. Confidence intervals are primarily for displaying the reliability of a measurement. Credible intervals, almost exclusively associated with Bayesian methods, are typically built off distributions and have probabilistic interpretations.

Expressing uncertainty is important, but it is also important to interpret the correct message. Krzywinski and Altman 23 directly address a common misconception: “a gap between (error) bars does not ensure significance, nor does overlap rule it out—it depends on the type of bar.” This is a good reminder to be very clear not only in stating what type of uncertainty you are sharing, but what the interpretation is. Others 16 even go so far as to recommend that standard error not be used because it does not provide clear information about standard errors of differences among means. One recommendation to go along with expressing uncertainty is, if possible, to show the data (see Use an Effective Geometry and Show Data ). Particularly when the sample size is low, showing a reader where the data occur can help avoid misinterpretations of uncertainty.

Principle #6 Panel, when Possible (Small Multiples)

A particularly effective visual approach is to repeat a figure to highlight differences. This approach is often called small multiples , 7 and the technique may be referred to as paneling or faceting ( Figure 1 G). The strategy behind small multiples is that because many of the design elements are the same—for example, the axes, axes scales, and geometry are often the same—the differences in the data are easier to show. In other words, each panel represents a change in one variable, which is commonly a time step, a group, or some other factor. The objective of small multiples is to make the data inevitably comparable, 7 and effective small multiples always accomplish these comparisons.

Principle #7 Data and Models Are Different Things

Plotted information typically takes the form of raw data (e.g., scatterplot), summarized data (e.g., box plot), or an inferential statistic (e.g., fitted regression line; Figure 1 D). Raw data and summarized data are often relatively straightforward; however, a plotted model may require more explanation for a reader to be able to fully reproduce the work. Certainly any model in a study should be reported in a complete way that ensures reproducibility. However, any visual of a model should be explained in the figure caption or referenced elsewhere in the document so that a reader can find the complete details on what the model visual is representing. Although it happens, it is not acceptable practice to show a fitted model or other model results in a figure if the reader cannot backtrack the model details. Simply because a model geometry can be added to a figure does not mean that it should be.

Principle #8 Simple Visuals, Detailed Captions

As important as it is to use high data-ink ratios, it is equally important to have detailed captions that fully explain everything in the figure. A study of figures in the Journal of American Medicine 8 found that more than one-third of graphs were not self-explanatory. Captions should be standalone, which means that if the figure and caption were looked at independent from the rest of the study, the major point(s) could still be understood. Obviously not all figures can be completely standalone, as some statistical models and other procedures require more than a caption as explanation. However, the principle remains that captions should do all they can to explain the visualization and representations used. Captions should explain any geometries used; for instance, even in a simple scatterplot it should be stated that the black dots represent the data ( Figures 1 C–1E). Box plots also require descriptions of their geometry—it might be assumed what the features of a box plot are, yet not all box plot symbols are universal.

Principle #9 Consider an Infographic

It is unclear where a figure ends and an infographic begins; however, it is fair to say that figures tend to be focused on representing data and models, whereas infographics typically incorporate text, images, and other diagrammatic elements. Although it is not recommended to convert all figures to infographics, infographics were found 20 to have the highest memorability score and that diagrams outperformed points, bars, lines, and tables in terms of memorability. Scientists might improve their overall information transfer if they consider an infographic where blending different pieces of information could be effective. Also, an infographic of a study might be more effective outside of a peer-reviewed publication and in an oral or poster presentation where a visual needs to include more elements of the study but with less technical information.

Even if infographics are not adopted in most cases, technical visuals often still benefit from some text or other annotations. 16 Tufte's works 7 , 24 provide great examples of bringing together textual, visual, and quantitative information into effective visualizations. However, as figures move in the direction of infographics, it remains important to keep chart junk and other non-essential visual elements out of the design.

Principle #10 Get an Opinion

Although there may be principles and theories about effective data visualization, the reality is that the most effective visuals are the ones with which readers connect. Therefore, figure authors are encouraged to seek external reviews of their figures. So often when writing a study, the figures are quickly made, and even if thoughtfully made they are not subject to objective, outside review. Having one or more colleagues or people external to the study review figures will often provide useful feedback on what readers perceive, and therefore what is effective or ineffective in a visual. It is also recommended to have outside colleagues review only the figures. Not only might this please your colleague reviewers (because figure reviews require substantially less time than full document reviews), but it also allows them to provide feedback purely on the figures as they will not have the document text to fill in any uncertainties left by the visuals.

What About Tables?

Although often not included as data visualization, tables can be a powerful and effective way to show data. Like other visuals, tables are a type of hybrid visual—they typically only include alphanumeric information and no geometries (or other visual elements), so they are not classically a visual. However, tables are also not text in the same way a paragraph or description is text. Rather, tables are often summarized values or information, and are effective if the goal is to reference exact numbers. However, the interest in numerical results in the form of a study typically lies in comparisons and not absolute numbers. Gelman et al. 25 suggested that well-designed graphs were superior to tables. Similarly, Spence and Lewandowsky 26 compared pie charts, bar graphs, and tables and found a clear advantage for graphical displays over tabulations. Because tables are best suited for looking up specific information while graphs are better for perceiving trends and making comparisons and predictions, it is recommended that visuals are used before tables. Despite the reluctance to recommend tables, tables may benefit from digital formats. In other words, while tables may be less effective than figures in many cases, this does not mean tables are ineffective or do not share specific information that cannot always be displayed in a visual. Therefore, it is recommended to consider creating tables as supplementary or appendix information that does not go into the main document (alongside the figures), but which is still very easily accessed electronically for those interested in numerical specifics.

Conclusions

While many of the elements of peer-reviewed literature have remained constant over time, some elements are changing. For example, most articles now have more authors than in previous decades, and a much larger menu of journals creates a diversity of article lengths and other requirements. Despite these changes, the demand for visual representations of data and results remains high, as exemplified by graphical abstracts, overview figures, and infographics. Similarly, we now operate with more software than ever before, creating many choices and opportunities to customize scientific visualizations. However, as the demand for, and software to create, visualizations have both increased, there is not always adequate training among scientists and authors in terms of optimizing the visual for the message.

Figures are not just a scientific side dish but can be a critical point along the scientific process—a point at which the figure maker demonstrates their knowledge and communication of the data and results, and often one of the first stopping points for new readers of the information. The reality for the vast majority of figures is that you need to make your point in a few seconds. The longer someone looks at a figure and doesn't understand the message, the more likely they are to gain nothing from the figure and possibly even lose some understanding of your larger work. Following a set of guidelines and recommendations—summarized here and building on others—can help to build robust visuals that avoid many common pitfalls of ineffective figures ( Figure 2 ).

Overview of the Principles Presented in This Article

The two principles in yellow (bottom) are those that occur first, during the figure design phase. The six principles in green (middle) are generally considerations and decisions while making a figure. The two principles in blue (top) are final steps often considered after a figure has been drafted. While the general flow of the principles follows from bottom to top, there is no specific or required order, and the development of individual figures may require more or less consideration of different principles in a unique order.

All scientists seek to share their message as effectively as possible, and a better understanding of figure design and representation is undoubtedly a step toward better information dissemination and fewer errors in interpretation. Right now, much of the responsibility for effective figures lies with the authors, and learning best practices from literature, workshops, and other resources should be undertaken. Along with authors, journals play a gatekeeper role in figure quality. Journal editorial teams are in a position to adopt recommendations for more effective figures (and reject ineffective figures) and then translate those recommendations into submission requirements. However, due to the qualitative nature of design elements, it is difficult to imagine strict visual guidelines being enforced across scientific sectors. In the absence of such guidelines and with seemingly endless design choices available to figure authors, it remains important that a set of aesthetic criteria emerge to guide the efficient conveyance of visual information.

Acknowledgments

Thanks go to the numerous students with whom I have had fun, creative, and productive conversations about displaying information. Danielle DiIullo was extremely helpful in technical advice on software. Finally, Ron McKernan provided guidance on several principles.

Author Contributions

S.R.M. conceived the review topic, conducted the review, developed the principles, and wrote the manuscript.

Steve Midway is an assistant professor in the Department of Oceanography and Coastal Sciences at Louisiana State University. His work broadly lies in fisheries ecology and how sound science can be applied to management and conservation issues. He teaches a number of quantitative courses in ecology, all of which include data visualization.

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