Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

Background. Visual displays can facilitate risk communication and promote better health choices. Their effectiveness in improving risk comprehension is influenced by graph literacy. However, the construct of graph literacy is still insufficiently understood, partially because existing objective measures of graph literacy are either too difficult or too long. Objectives. We constructed a new 4-item Short Graph Literacy (SGL) scale and examined how SGL scores relate to key cognitive, affective, and conative precursors of health behavior change described in common health behavior theories. Methods. We performed secondary analyses to adapt the SGL scale from an existing 13-item scale. The initial construction was based on data collected in a laboratory setting in Germany (n = 51). The scale was then validated using data from nationally representative samples in Germany (n = 495) and the United States (n = 492). To examine how SGL scores relate to precursors of health behavior change, we performed secondary analyses of a third study involving a nationwide US sample with 47% participants belonging to racial/ethnic minorities and 46% with limited formal education (n = 835). Results. Graph literacy was significantly associated with cognitive precursors in theoretically expected ways (e.g., positive associations with risk comprehension and response efficacy and a negative association with cognitive risk perception). Patterns for affective precursors generally mirrored those for cognitive precursors, although numeracy was a stronger predictor than graph literacy for some affective factors (e.g., feelings of risk). Graph literacy had predictive value for most cognitive and affective precursors beyond numeracy. In addition, graph literacy (but not numeracy) predicted key conative precursors such as defensive processing. Conclusions. Our data suggest that the SGL scale is a fast and psychometrically valid method for measuring objective graph literacy. Our findings also highlight the theoretical and practical relevance of graph literacy.

[1]  M. Becker.  The Health Belief Model and Sick Role Behavior* , 1974 .

[2]  E. Hilgard.  The trilogy of mind: cognition, affection, and conation. , 1980, Journal of the history of the behavioral sciences.

[3]  W. Mcguire.  Public communication as a strategy for inducing health-promoting behavioral change. , 1984, Preventive medicine.

[4]  Baruch Fischhoff,et al.  Characterizing Perceived Risk , 1985 .

[5]  K. Witte.  Putting the fear back into fear appeals: The extended parallel process model , 1992 .

[6]  S. Epstein,et al.  Conflict Between Intuitive and Rational Processing: When People Behave Against Their Better Judgment , 1994 .

[7]  D. Watson,et al.  Constructing validity: Basic issues in objective scale development , 1995 .

[8]  E. Seydel,et al.  Protection Motivation Theory , 2022 .

[9]  M. Conner,et al.  The theory of planned behaviour and health behaviours. , 1996 .

[10]  Lisa M. Schwartz,et al.  The Role of Numeracy in Understanding the Benefit of Screening Mammography , 1997, Annals of Internal Medicine.

[11]  N. Weinstein.  What does it mean to understand a risk? Evaluating risk comprehension. , 1999, Journal of the National Cancer Institute. Monographs.

[12]  R. Schwarzer.  Social-Cognitive Factors in Changing Health-Related Behaviors , 2001 .

[13]  B. Rimer,et al.  General Performance on a Numeracy Scale among Highly Educated Samples , 2001, Medical decision making : an international journal of the Society for Medical Decision Making.

[14]  Christopher K. Hsee,et al.  Risk as Feelings , 2001, Psychological bulletin.

[15]  Winston R. Sieck,et al.  Foreground:background salience: Explaining the effects of graphical displays on risk avoidance , 2003 .

[16]  D. Streiner.  Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency , 2003, Journal of personality assessment.

[17]  Bracha Kramarski,et al.  Enhancing Mathematical Reasoning in the Classroom: The Effects of Cooperative Learning and Metacognitive Training , 2003 .

[18]  Ian Spence,et al.  No Humble Pie: The Origins and Usage of a Statistical Chart , 2005 .

[19]  N. Weinstein,et al.  Risk perceptions: assessment and relationship to influenza vaccination. , 2007, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[20]  M. Schapira,et al.  Predictors of pessimistic breast cancer risk perceptions in a primary care population , 2004, Journal of General Internal Medicine.

[21]  I. Lipkus.  Numeric, Verbal, and Visual Formats of Conveying Health Risks: Suggested Best Practices and Future Recommendations , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[22]  E. Peters,et al.  Associations of Perceived Risk and Worry with Cancer Health-protective Actions , 2007, Journal of health psychology.

[23]  Erika A. Waters,et al.  Reducing aversion to side effects in preventive medical treatment decisions. , 2007, Journal of experimental psychology. Applied.

[24]  V. Reyna,et al.  How numeracy influences risk comprehension and medical decision making. , 2009, Psychological bulletin.

[25]  P. Slovic,et al.  The Use of Narrative Evidence and Explicit Likelihood by Decisionmakers Varying in Numeracy , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[26]  M. Galesic,et al.  Who profits from visual aids: overcoming challenges in people's understanding of risks [corrected]. , 2010, Social science & medicine.

[27]  B. Zikmund‐Fisher,et al.  The Effect of Format on Parents' Understanding of the Risks and Benefits of Clinical Research: A Comparison Between Text, Tables, and Graphics , 2010, Journal of health communication.

[28]  H. de Vries,et al.  Measuring risk perceptions of skin cancer: reliability and validity of different operationalizations. , 2011, British journal of health psychology.

[29]  M. Galesic,et al.  Graph Literacy , 2011, Medical decision making : an international journal of the Society for Medical Decision Making.

[30]  Priti Shah,et al.  Bar and Line Graph Comprehension: An Interaction of Top-Down and Bottom-Up Processes , 2011, Top. Cogn. Sci..

[31]  M. Wakefield,et al.  Mass media campaigns designed to support new pictorial health warnings on cigarette packets: evidence of a complementary relationship , 2011, Tobacco Control.

[32]  Michael Siegrist,et al.  Risk communication with pictographs: The role of numeracy and graph processing , 2011, Judgment and Decision Making.

[33]  Dora Agapito,et al.  The Cognitive-Affective-Conative Model of Destination Image: A Confirmatory Analysis , 2013 .

[34]  William J. Burns,et al.  Risk Perception and the Economic Crisis: A Longitudinal Study of the Trajectory of Perceived Risk , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[35]  A. Meade,et al.  Identifying careless responses in survey data. , 2012, Psychological methods.

[36]  E. Peters.  Beyond Comprehension , 2012 .

[37]  V. Reyna,et al.  Individual Differences in Numeracy and Cognitive Reflection, with Implications for Biases and Fallacies in Probability Judgment. , 2012, Journal of behavioral decision making.

[38]  Mirta Galesic,et al.  When Higher Bars Are Not Larger Quantities: On Individual Differences in the Use of Spatial Information in Graph Comprehension , 2012, Spatial Cogn. Comput..

[39]  Edward T. Cokely,et al.  Measuring Risk Literacy: The Berlin Numeracy Test , 2012, Judgment and Decision Making.

[40]  H. de Vries,et al.  Thinking versus feeling: Differentiating between cognitive and affective components of perceived cancer risk , 2012, Psychology & health.

[41]  W. Gaissmaier,et al.  Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information. , 2012, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[42]  Toru Ishikawa,et al.  Landmark Selection in the Environment: Relationships with Object Characteristics and Sense of Direction , 2012, Spatial Cogn. Comput..

[43]  Edward T. Cokely,et al.  Individual Differences in Graph Literacy: Overcoming Denominator Neglect in Risk Comprehension , 2012 .

[44]  S. Vernon,et al.  Construct definition and scale development for defensive information processing: an application to colorectal cancer screening. , 2013, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[45]  Erika A. Waters,et al.  “Don’t Know” Responses to Risk Perception Measures , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.

[46]  Paul K. J. Han,et al.  Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers , 2013, BMC Medical Informatics and Decision Making.

[47]  Joseph Sharit,et al.  The roles of health literacy, numeracy, and graph literacy on the usability of the VA's personal health record by veterans , 2014 .

[48]  Edward T. Cokely,et al.  The Influence of Skills, Message Frame, and Visual Aids on Prevention of Sexually Transmitted Diseases , 2014 .

[49]  R. García-Retamero,et al.  Feeling the Numbers: On the Interplay Between Risk, Affect, and Numeracy , 2014 .

[50]  Laura D. Scherer,et al.  Blocks, Ovals, or People? Icon Type Affects Risk Perceptions and Recall of Pictographs , 2014, Medical decision making : an international journal of the Society for Medical Decision Making.

[51]  Erika A. Waters,et al.  “I Don’t Know” My Cancer Risk , 2015, Medical decision making : an international journal of the Society for Medical Decision Making.

[52]  E. Peters,et al.  Journal of Personality and Social Psychology Multiple Numeric Competencies: When a Number Is Not Just a Number , 2014 .

[53]  Antonio Maldonado,et al.  Improving risk understanding across ability levels: Encouraging active processing with dynamic icon arrays. , 2015, Journal of experimental psychology. Applied.

[54]  Rocio Garcia-Retamero,et al.  Measuring Graph Literacy without a Test , 2016, Medical decision making : an international journal of the Society for Medical Decision Making.

[55]  J. Sharit,et al.  The Association of Graph Literacy With Use of and Skills Using an Online Personal Health Record in Outpatient Veterans , 2016, Journal of health communication.

[56]  Shahrul Mt-Isa,et al.  Literature review of visual representation of the results of benefit–risk assessments of medicinal products , 2016, Pharmacoepidemiology and drug safety.

[57]  Michael Siegrist,et al.  High Numerates Count Icons and Low Numerates Process Large Areas in Pictographs: Results of an Eye‐Tracking Study , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[58]  J. DeFrank,et al.  Anticipated regret and health behavior: A meta-analysis. , 2015, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[59]  J. Gore,et al.  Relevance of graph literacy in the development of patient-centered communication tools. , 2016, Patient education and counseling.

[60]  Erika A. Waters,et al.  “I don’t know” My Cancer Risk: Implications for Health Behavior Engagement , 2016, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[61]  Yasmina Okan,et al.  How People with Low and High Graph Literacy Process Health Graphs: Evidence from Eye‐tracking , 2016 .

[62]  Edward T. Cokely,et al.  Training Graph Literacy: Developing the RiskLiteracy.org Outreach Platform , 2017, CogSci.

[63]  Edward T. Cokely,et al.  Designing Visual Aids That Promote Risk Literacy: A Systematic Review of Health Research and Evidence-Based Design Heuristics , 2017, Hum. Factors.

[64]  Erika A. Waters,et al.  Combining risk communication strategies to simultaneously convey the risks of four diseases associated with physical inactivity to socio-demographically diverse populations , 2018, Journal of Behavioral Medicine.

[65]  Erika A. Waters,et al.  Examining the Interrelations Among Objective and Subjective Health Literacy and Numeracy and Their Associations with Health Knowledge , 2018, Journal of General Internal Medicine.

[66]  David Russell,et al.  The impact of home care nurses’ numeracy and graph literacy on comprehension of visual display information: implications for dashboard design , 2018, J. Am. Medical Informatics Assoc..