Making Statistics More Meaningful for Policy Research and Program Evaluation
Bibliographic Data
| ID | 12470616 |
|---|---|
| Authors | Henry May (0000-0002-5965-3933, Consortium for Policy Research in Education, University of Pennsylvania, 3440 Market Street, Suite-560, Philadelphia, PA 19104, USA, corresponding author) |
| Year | 2004 |
| Volume | 25 |
| Issue | 4 |
| Pages | 525-540 |
| Publication date | 2004-12-01 |
| 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 | 10.1177/109821400402500408 |
| OpenAlex | W2006074222 |
| Language | EN |
| Citations received | 4 |
| References cited | 7 |
This article focuses on the use of statistics in policy and evaluation research and the need to present statistical information in a form that is meaningful to mixed audiences. Three guidelines for formulating and presenting meaningful statistics are outlined. Understandability ensures that knowledge of statistical methods is not required for comprehending the information presented. Interpretability ensures that statistical information can be explained using familiar, non-abstract units. Comparability ensures that the magnitudes of different estimates can be directly compared within and across studies. Examples for improving popular effect size estimates from linear and non-linear models are included, and a general approach to presenting statistical information meaningfully for consumers of policy and evaluation research is explained
Comparability · Data science · Econometrics · Interpretability · Machine learning · Management science · Statistical analysis · Statistics · Computer Science · Evaluation and Performance Assessment · Health Policy Implementation Science · Mathematics · Psychometric Methodologies and Testing
| Citations per year | 0.2 |
|---|---|
| Citation span | 2006 - 2010 (5) |
| Highly cited | No |