Identifying Sources of Difference in Reliability in Content Analysis
Bibliographic record
Abstract
This paper reports on a case study which identifies and illustrates sources of difference in agreement in relation to reliability in a context of quantitative content analysis of a transcript of an online asynchronous discussion (OAD). Transcripts of 10 students in a month-long online asynchronous discussion were coded by two coders using an instrument with two categories, five processes, and 19 indicators of Problem Formulation and Resolution (PFR). Sources of difference were identified in relation to: coders; tasks; and students. Reliability values were calculated at the levels of categories, processes, and indicators. At the most detailed level of coding on the basis of the indicator, findings revealed that the overall level of reliability between coders was .591 when measured with Cohen’s kappa. The difference between tasks at the same level ranged from .349 to .664, and the difference between participants ranged from .390 to .907. Implications for training and research are discussed. Keywords: content analysis; online discussions; reliability; Cohen's kappa; sources of difference; coding
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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 | 0.203 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".