Measuring socioeconomic status: Reliability and preliminary validity of different approaches
Bibliographic record
Abstract
This study investigated issues related to commonly used socioeconomic status (SES) measures in 140 participants from three cities (Atlanta, Boston, and Toronto) in two countries (United States and Canada). Measures of SES were two from the United States (four-factor Hollingshead scale, Nakao and Treas scale) and one from Canada (Blishen, Carroll, and Moore scale). Reliability was examined both within (interrater agreement) and across (intermeasure agreement) measures. Interrater reliability and classification agreement was high for the total sample (range r = .86 to .91), as were intermeasure correlations and classification agreement (range r = .81 to .88). The weakest agreement across measures was found when families had one wage earner who was female. Validity data for these SES measures with academic and intellectual measures also were obtained. Some support for a simplified approach to measuring SES was found. Implications of these findings for the use of SES in social and behavioral science research are discussed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".