A Three-Step Approach to Teaching Logic Models
Bibliographic Data
| ID | 12470920 |
|---|---|
| Authors | Ralph Renger (0000-0002-0224-1444, College of Public Health at University of Arizona, 1435 N. Fremont Ave., Tucson, AZ 85712, USA, corresponding author), Allison Titcomb |
| Year | 2002 |
| Volume | 23 |
| Issue | 4 |
| Pages | 493-503 |
| Publication date | 2002-12-01 |
| 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 | 10.1177/109821400202300409 |
| OpenAlex | W4242010489 |
| Language | EN |
| Citations received | 42 |
| References cited | 9 |
Developing a logic model is an essential first step in program evaluation. Our experience has been that there is little guidance to teach students how to develop a logic model that is true to its intended purpose of providing a clear visual representation of the underlying rationale that is not shrouded by including the elements of evaluation. We have developed a three-step approach that begins with developing the visual representation of the underlying rationale, central to which is the identification of Antecedent conditions. Step 2 ensures that program activities Target antecedent conditions, while Step 3 focuses on Measurement issues, depicting indicators and objectives for outcomes being included in the evaluation plan. We have coined this method of teaching the ATM approach. We hope that teachers of evaluation will find the ATM approach useful in the form presented here or at least stimulate thought as to how to adapt the approach to meet individual teaching needs
Antecedent (behavioral psychology · Identification (biology · Logic model · Management science · Plan (archaeology · Representation (politics · Sociology · Computer Science · Educational Assessment and Improvement · Engineering · Evaluation and Performance Assessment · Psychology · Student Assessment and Feedback · Artificial Intelligence
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| Unique citing works | 42 |
|---|---|
| Citations per year | 1,83 |
| Citation span | 2003 - 2026 (24) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 42 |