Factorial Composition of Counsellor Effectiveness Scale
Bibliographic record
Abstract
The study developed a set of items that could measure counsellor effectiveness. It reduced the initial set of variablesrelated to counsellor effectiveness to such number of variables that are generally perceived as indicative ofcounsellor effectiveness and determined the factorial composition of the scale. in order to identify the major factorsthat underpin counsellor effectiveness with a view of developing an homogeneous items of counsellor effectivenessscale suitable for Nigeria Schools. The study design was exploratory using Principal Component Analysis (PCA)with interaction. The sample size consisted of 50 counsellors selected by convenience sampling from the populationof counsellors in 148 schools in Ondo State as at the time of the study. Alongside, an initial 800 counsellee who weresecondary school students were purposively selected from 12 secondary schools in six local government areas of thestate. The instrument used for this study was Questionnaire that measured counsellor effectiveness. This consisted ofan initial number of 51 items describing an effective counsellor in terms of personality characteristics, personalqualities, and performance indicators. These were derived from available description and characteristics ofcounsellor effectiveness in literatures. The responses were coded and analyzed using Principal Component Analysis.Items that failed to meet the baseline scores of eligibility into the final list were dropped. The final list consisted of35 items were subjected to Principal Component Analysis which provided the initial factor. These were rotated usingOrthogonal (Varimax) Method which yielded five underlying components of counsellor effectiveness. These wereidentified as expertness, sense of responsibility, pleasantness and integrity. The fifth factor could not beconceptualized from the regression weights and factor loadings of the items.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".