Constructing knowledge: The role of graphs and tables in hard and soft psychology.

Smith, Laurence D.; Best, Lisa A.; Stubbs, D. Alan; Archibald, Andrea Bastiani; Roberson‐Nay, Roxann

doi:10.1037/0003-066x.57.10.749

Cited by 114 publications

(116 citation statements)

References 51 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…Why would psychologists consider journals that typically include papers that contain more graphs than tables as reporting 'harder' science (Smith et al, 2002), but also be more likely to cite articles that contain fewer graphs than tables, and more model diagrams? The graphism thesis contains an internal contradiction; it assumes that scientists frequently use graphs to convince their peers of the strength of their findings, and also are routinely duped by this same persuasion technique.…”

Section: Discussionmentioning

confidence: 99%

The Consequences of Predicting Scientific Impact in Psychology Using Journal Impact Factors

Hegarty

Walton

2012

Perspect Psychol Sci

View full text Add to dashboard Cite

An academic journal's impact factor (hereafter JIFs) is an average measure of the citation count of individual articles published in that journal. JIF is used to assess merit, predict impact, and allocate resources, but the actual number of citations to individual articles is only modestly correlated with the JIFs of the journals in which they are published. We counted Psycinfo citations to 1,134 papers published in nine leading psychology journals (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). Both article length, r =.31, and reference list length, r = .41, predicted log-transformed citation counts better than JIF, r = .27. Articles with fewer graphs and more structural equation models were more frequently cited. Citation count was better predicted by a model based on article length and citation count rather than JIF. When JIF was used to predict citation count, the impact of women authors and social science research was underestimated. These findings distinguish impact in science, as measured by JIF, from actual impact in psychology, and show the unintended consequences of using a measure of the former to predict the latter.

show abstract

Section: Discussionmentioning

confidence: 99%

The Consequences of Predicting Scientific Impact in Psychology Using Journal Impact Factors

Hegarty

Walton

2012

Perspect Psychol Sci

View full text Add to dashboard Cite

show abstract

“…A well-designed graph can be a vivid, memorable, and easy-to-understand depiction of quantitative information (Larkin & Simon, 1987;Shah, Freedman, & Vekiri, 2005;Smith, Best, Stubbs, Archibald, & Roberson-Nay, 2002;Tufte, 2001). For those reasons, graphs are used extensively in textbooks, scientific journals, and the popular print media (Shah et al, 2005;Zacks, Levy, Tversky, & Schiano, 2002).…”

Section: Introductionmentioning

confidence: 99%

Bar and Line Graph Comprehension: An Interaction of Top‐Down and Bottom‐Up Processes

Shah

Freedman

2011

Topics in Cognitive Science

149

116

View full text Add to dashboard Cite

This experiment investigated the effect of format (line vs. bar), viewers' familiarity with variables, and viewers' graphicacy (graphical literacy) skills on the comprehension of multivariate (three variable) data presented in graphs. Fifty-five undergraduates provided written descriptions of data for a set of 14 line or bar graphs, half of which depicted variables familiar to the population and half of which depicted variables unfamiliar to the population. Participants then took a test of graphicacy skills. As predicted, the format influenced viewers' interpretations of data. Specifically, viewers were more likely to describe x-y interactions when viewing line graphs than when viewing bar graphs, and they were more likely to describe main effects and ''z-y'' (the variable in the legend) interactions when viewing bar graphs than when viewing line graphs. Familiarity of data presented and individuals' graphicacy skills interacted with the influence of graph format. Specifically, viewers were most likely to generate inferences only when they had high graphicacy skills, the data were familiar and thus the information inferred was expected, and the format supported those inferences. Implications for multivariate data display are discussed.

show abstract

“…In addition, Smith et al (2000) asked the same respondents to rate the scientific 'hardness' of the subfields of 25 journals published by the American Psychological Association. Again, they found an almost perfect linear relationship and a correlation of .93 between the mean fractional graph areas and the 'hardness' attributed to the scientific disciplines (which ranged from behavioral neuroscience to educational psychology, see also Smith, Best, Stubbs, Archibald, & Roberson-Nay 2002). Thus, there was a strong linear and positive relationship between perceived 'hardness' and the use of graphs both between different scientific disciplines and between different subfields of one single discipline (psychology).…”

Section: Graphs Visualizing Empirical Data As Plausibility Cuesmentioning

confidence: 78%

“…Thus, there was a strong linear and positive relationship between perceived 'hardness' and the use of graphs both between different scientific disciplines and between different subfields of one single discipline (psychology). In contrast, the prevalence of the two other major devices to describe quantitative information or relationships, equations and tables, has been found to be either uncorrelated with hardness (Arsenault, Smith, & Beauchamp 2006), or even inversely related (Smith et al 2002).…”

Section: Graphs Visualizing Empirical Data As Plausibility Cuesmentioning

confidence: 99%

Comprehending conflicting science-related texts: graphs as plausibility cues

et al. 2013

View full text Add to dashboard Cite

When reading conflicting science-related texts, readers may attend to cues which allow them to assess plausibility. One such plausibility cue is the use of graphs in the texts, which are regarded as typical of 'hard science'. The goal of our study was to investigate the effects of the presence of graphs on the perceived plausibility and situation model strength for conflicting science-related texts, while including the influence of readers' domain knowledge and their knowledge about scientific visualization conventions as potential moderators of these effects. In an experiment mimicking web-based informal learning, 77 university students read texts on controversial scientific issues which were presented with either graphs or tables. Perceived plausibility and situation model strength for each text were assessed immediately after reading; reader variables were assessed several weeks prior to the experiment proper. The results suggest that graphs can indeed serve as plausibility cues and thus boost situation model strength for texts which contain them. This effect was mediated by the perceived plausibility of the information in the texts with graphs. However, whether readers use graphs as plausibility cues in texts with conflicting information seems to depend also on their amount of experience with scientific texts and graphs.

show abstract

Constructing knowledge: The role of graphs and tables in hard and soft psychology.

Cited by 114 publications

References 51 publications

The Consequences of Predicting Scientific Impact in Psychology Using Journal Impact Factors

The Consequences of Predicting Scientific Impact in Psychology Using Journal Impact Factors

Bar and Line Graph Comprehension: An Interaction of Top‐Down and Bottom‐Up Processes

Comprehending conflicting science-related texts: graphs as plausibility cues

Contact Info

Product

Resources

About