Developing an identity fraud measurement model: a factor analysis approach
Bibliographic record
Abstract
Purpose Though many studies and reports have been published about the scale of identity fraud (IDF), no work has been done on developing models to measure IDF. The purpose of this paper is to propose a measurement model for IDF and test the validity of that measurement model. Design/methodology/approach After providing a background on the concepts of IDF, the paper discusses the related term, identity theft. Next, a measurement model is developed, based on the current practice of measurement of IDF in four countries. Exploratory factor analysis (EFA) is used in identifying the indicators and factors of IDF. After the EFA is conducted, confirmatory factor analysis is employed to test the validity of the measurement model. These tests are conducted using the data collected from Canadian financial institutions. Findings The review of the current empirical studies suggests that IDF should be assessed using a measurement model with 33 indicators to measure five factors of IDF. However, the analysis of Canadian financial institutions suggests that a measurement model that includes 27 indicators and four factors is most appropriate for the data. Research limitations/implications The measurement model developed in the present paper is based on an examination of a sample of financial institutions in Canada. Hence, the results of this paper cannot be generalized to organizations in other sectors of the economy. Further studies in other sectors of the economy are required to identify industry‐specific measurement model. Practical implications This paper is the first approach toward developing a model for measuring IDF. Originality/value This paper is the first study that attempts to scientifically identify and validate a measurement system in the area of IDF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".