1. An investigation into the internal structure of the learning potential construct as measured by the APIL Test Battery

2. The Reality

3. Need for method to identify individuals who will gain maximum benefit from affirmative development opportunities Ideally: Assess core/fundamental abilities and potentialies, not influenced by exposure/opportunities Taylor – Apil-B Test Battery (Vygotsky, Snow et al., Ackerman) Dependent on an understanding of the reasons underlying training performance Can the underlying explanatory model developed by Taylor successfully explain variance in learning performance? Relevance

4. Primary: a.Explicate the structural model underlying the APIL test battery; and b.Evaluate the fit of the model on empirical data Secondary: a.Determine whether static measures of the two latent learning dispositions (abstract reasoning capacity and information processing capacity) would significantly explain variance in learning performance when added to a model already containing dynamic measures (transfer of knowledge and automatization) of the two latent learning competencies Objectives of research

5. Taylor’s: Underlying model Information Processing Capacity Transfer of Knowledge Automatization Abstract Thinking Capacity Job Competency Potential

6. Primary: a.Explicate the structural model underlying the APIL test battery; and b.Evaluate the fit of the model on empirical data Secondary: a.Determine whether static measures of the two latent learning dispositions (abstract reasoning capacity and information processing capacity) would significantly explain variance in learning performance when added to a model already containing dynamic measures (transfer of knowledge and automatization) of the two latent learning competencies Objectives of research

7. 1.Does the basic learning potential structural model provide an adequate explanation of the covariance observed between the measures of learning performance, the learning competencies and learning potential? 2.Is the extent to which transfer of knowledge occurs, determined by the level of abstract thinking capacity? 3.Is the extent to which automatization occurs determined by the level of information processing capacity? 4.Is the extent to which transfer of knowledge occurs determined by the extent to which automatization occurs? 5.Does transfer of knowledge determine job competency potential targeted by the affirmative training intervention? 6.Does automatization determine job competency potential targeted by the affirmative training intervention? 7.Is the influence of abstract thinking capacity on the job competencies targeted by the training intervention mediated by transfer of knowedge? 8.Is the influence of information processing capacity on the job competencies targeted by the training intervention mediated by automatization? Research problems

8. Additional 3: 1.Do the dynamic measures of the two latent learning competencies each explain unique variance in composite measure of the job competency potential targeted by the affirmative training intervention? 2.Do the static measures of the two latent learning dispositions explain variance in a composite measure of the job competency potential targeted by the affirmative training intervention when added to a model already containing dynamic measures of the two latent learning competencies 3.Do the two dynamic measures of the two latent learning competencies and the static measures of the two latent learning dispositions each explain unique variance in a composite measure of the job competency potential targeted by the affirmative training intervention? Research problems

9. Measuring Instruments/Operationalisation/ Sampling VariablesMeasure Abstract Thinking Capacity:Concept Formation Test Transfer of Knowledge:Knowledge Transfer Test Information Processing Capacity:Flexibility-Accuracy-Speed Test Automatization:Curve of Learning Test Job Competency Potential:Specific Crimes Statutory Law Non probability sample of 434 new recruits

10. Measurement model fits data reasonably well, but not perfectly Two indicator variables caused concern – Transfer of Knowledge and Job Competency Potential Integrity of structural relations threatened Results: Measurement Model

11. Results: structural model Information Processing Capacity Transfer of Knowledge Automatization Abstract Thinking Capacity Job Competency Potential

12. Transfer of Knowledge should not be included in a model already containing automatization Automatization explains unique variance in job competency potential if included in a model containing Transfer of Knowledge Abstract Reasoning Capacity and/or Information Processing Capacity should be included in a model already containing automatization Abstract Reasoning Capacity should not be included in model already containing Automatization and Information Processing Capacity Information Processing Capacity explains uniqu variance in Learning Performance in model already containing Automatization Automatization does not significantly explain unique variance in Learning Performance in model already containing Information Processing Capacity Results: Regression Analysis

13. Information Processing Capacity Transfer of Knowledge Automatization Abstract Thinking Capacity Job Competency Potential

14. Use of APIL battery for affirmative development selection is partially justified Not all scores in APIL need not be considered when estimating future training performance Reasonable model fit – claim that specific indicator variables used to reflect the specific latent variables comprising the learning potential structural model does not seem unreasonable Information processing capacity – best predictor of learning performance Conclusions and Implications

15. Thank You! Johan de Goede Email: johan@thehumanroute.com Cell: +27 (0) 74 938 4108