The analysis of formative measurement in IS research
Bibliographic record
Abstract
Formative measurement is a valuable alternative to reflective measurement when developing indicators of latent variables in structural equation models (SEM). Our goal is to further guide IS research in the application of formative measures by comparing the two dominant analysis techniques: component-(e.g., partial least squares -PLS) and covariance-based SEM techniques. We demonstrate that covariance-based techniques can be appropriate for formative measurement despite a near absence of their use within IS research, which favors PLS. In addition, we discuss the advantages and disadvantages of using either technique and offer six prescriptions to consider when choosing one technique over another for formative measurement analysis. We present these and other contributions towards encouraging the continued and expanded use of formative measurement in IS research and the diversity of techniques to analyze formative measures.
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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 | 0.252 | 0.568 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".