A Three-Step Approach to Teaching Logic Models
Dados Bibliográficos
| ID | 12470920 |
|---|---|
| Autores | Ralph Renger (0000-0002-0224-1444, College of Public Health at University of Arizona, 1435 N. Fremont Ave., Tucson, AZ 85712, USA, autor correspondente), Allison Titcomb |
| Ano | 2002 |
| Volume | 23 |
| Fascículo | 4 |
| Páginas | 493-503 |
| Data de publicação | 2002-12-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | American Journal of Evaluation (JOURNAL) |
| Identificadores do periódico | ISSN: 1098-2140 • E-ISSN: 1557-0878 |
| Editora | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/109821400202300409 |
| OpenAlex | W4242010489 |
| Idioma | EN |
| Citações recebidas | 42 |
| Referências citadas | 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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| Obras citantes distintas | 42 |
|---|---|
| Citações por ano | 1,83 |
| Intervalo de citações | 2003 - 2026 (24) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 42 |