Impact measurement among social purpose organizations: which practices are associated with useful, non-burdensome impact measurement
Bibliographic record
Abstract
Purpose The benefits and challenges of impact measurement for social purpose organizations are well known. Measuring impact can equip managers with information to further their organizations’ purposes. Measurement can also be costly and time-consuming. The many tools and techniques give managers a choice; however, the techniques are not appropriately scaled to the financial and human resources available. This study aims to identify and validate a minimum set of essential impact measurement practices associated with useful, non-burdensome impact measurement among social purpose organizations. Design/methodology/approach The authors use data from a sample of social purpose organizations that answered questions about impact measurement practices based on the common approach to impact measurement’s common foundations model and three questions about impact measurement’s perceived benefits and value. The authors use factor analysis (first confirmatory factor analysis and then exploratory factor analysis) to identify the minimum set of impact measurement practices associated with the useful, non-burdensome impact measurement. Findings The authors found that the Common Foundations 21 practices are correlated and consistent with the perception that measurement is useful and not burdensome. However, the model that underpins the Common Foundations had a poor fit when tested with confirmatory factor analysis. The authors present and validate a revised model with a high goodness of fit. The revised model identifies ten impact measurement practices that, when implemented, are highly correlated with useful, non-burdensome measurement. Originality/value To the best of the authors’ knowledge, this study is the first to empirically examine a minimum set of impact measurement practices associated with the benefits of measurement while reducing the burden. These findings are of practical value to social purpose organizations looking to benefit from impact measurement whose financial and human resources are limited. The authors offer them ten essential impact measurement practices. The findings offer a validated instrument for assessing if an organization’s impact measurement practices will likely lead to useful, non-burdensome impact measurement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".