Enhancing the Effectiveness of Logic Models
Bibliographic Data
| ID | 12470526 |
|---|---|
| Authors | Nathan D Jones (0000-0002-5264-206X, Claremont Graduate University, corresponding author), Natalie D Jones (Claremont Graduate University), Tarek Azzam (0000-0003-3864-0217, Claremont Graduate University, corresponding author), Dana Linnell Wanzer (0000-0001-9732-4121, Claremont Graduate University), Darrel Skousen (Claremont Graduate University), Ciara Knight (Claremont Graduate University), Nina Sabarre (Claremont Graduate University) |
| Year | 2019 |
| Volume | 41 |
| Issue | 3 |
| Pages | 452-470 |
| Publication date | 2019-04-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Evaluation (JOURNAL) |
| Journal identifiers | ISSN: 1098-2140 • E-ISSN: 1557-0878 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1098214018824417 |
| OpenAlex | W2934599828 |
| Language | EN |
| Citations received | 2 |
| References cited | 37 |
One of the most widely used communication tools in evaluation is the logic model. Despite its extensive use, there has been little research into the visualization aspect of the logic model. To assess the impact that design modifications would have on its effectiveness, we applied established visualization principles to revise a program model. Participants were randomly assigned to one of the six conditions to examine the effectiveness (i.e., visual efficiency, comprised of accuracy, response time, and mental effort; credibility; aesthetics) of variations to a logic model. The results demonstrated that the revisions to the model increased accuracy, perceived message credibility, and were considered more aesthetically pleasing; furthermore, revisions decreased mental effort and reduced the amount of time taken to review the model. Together, the findings from the study support the claim that visual efficiency can be improved by modifying a logic model’s formatting and design
Algorithm · AND gate · Cognitive science · Credibility · Disk formatting · Logic gate · Logic model · Machine learning · Mental model · Visualization · Behavioral and Psychological Studies · Computer Science · Data Visualization and Analytics · Psychology · Visual and Cognitive Learning Processes · Artificial Intelligence
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| Unique citing works | 2 |
|---|---|
| Citations per year | 0,4 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |