Interpretation of Formative Measurement in Information Systems Research1
Bibliographic record
Abstract
Within the Information Systems literature, there has been an emerging interest in the use of formative measurement in structural equation modeling (SEM). This interest is exemplified by descriptions of the nature of formative measurement (e.g., Chin 1998a), and more recently the proper specification of formatively measured constructs (Petter et al. 2007) as well as application of such constructs (e.g., Barki et al. 2007). Formative measurement is a useful alternative to reflective measurement. However, there has been little guidance on interpreting the results when formative measures are employed. Our goal is to provide guidance relevant to the interpretation of formative measurement results through the examination of the following six issues: multicollinearity; the number of indicators specified for a formatively measured construct; the possible co-occurrence of negative and positive indicator weights; the absolute versus relative contributions made by a formative indicator; nomological network effects; and the possible effects of using partial least squares (PLS) versus covariance-based SEM techniques. We provide prescriptions for researchers to consider when interpreting the results of formative measures as well as an example to illustrate these prescriptions.
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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 | 0.367 | 0.638 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".