EMPOWERING DEVELOPMENT CONFERENCE 17 JULY 2015 STIAS STELLENBOSCH

1. EMPOWERING DEVELOPMENT CONFERENCE17 JULY 2015STIASSTELLENBOSCH

2. A SUMMARY OF THE BURGER, DU TOIT & PRINSLOO AFFIRMATIVE DEVELOPMENTLEARNING POTENTIAL STRUCTURAL MODELS

3. Employment Equity Act • In Chapter 1 of the EEA the purpose of the Act is defined as [Republic of South Africa, 1998, p. 5]: The purpose of this Act is to achieve equity in the workplace by- (a) promoting equal opportunity and fair treatment in employment through the elimination of unfair discrimination; and (b) implementing affirmative action measures to redress the disadvantages in employment experienced by designated groups, in order to ensure their equitable representation in all occupational categories and levels of the workforce.

4. Adverse impact • Job selection first and foremost is a flow intervention aimed at enhancing the effectiveness and efficiency [profitably] with which organisations transform scare resources into products/services. • In pursuit of this objective in South Africa job selection procedures typically can comply with the first EEA aim but not with the second aim. • The demographic profiles of South African organisations in the private sector do not reflect the demographic profile of the labour market • When maximising performance, job selection, even when predictive bias is eliminated, typically creates adverse impact against currently under-represented groups. • Urgentappropriate/intellectually honest attention to the problem is required. • The concern exists that the required sense of urgency is lacking and that the appropriate response is not sufficiently aggressive. • The need for a sense of urgency and the need for an aggressive intellectually honest, appropriate response is motivated by the following four considerations:

5. 1: High Gini coefficient

6. Problems derived from the high Gini coefficient • Poverty [in the face of affluence] • Crime • Reliance on social grants [but a narrow tax base] • Social unrest • Smothering of economic growth [due to the cause and the consequences] • Dissuading foreign investment • These problems are complexly structurally inter-related • See Van Heerden [2013]. Elaboration and empirical testing of the De Goede learning potential structural model. MCom [Industrial Psychology] Stellenbosch University • These aspects are important because they define the context within which solutions need to be implemented.

7. Three further reasons why an urgent and appropriate response to AI is required • Increasing governmental frustration and increasing impatience with the slow pace of transformation in the private sector • If it would in addition be assumed that inherent capability is not related to gender, race, language or culture then the current adverse impact implies a wastage of human resources. • The inappropriate response to adverse impact [inappropriate because the treatment is not based on an accurate diagnosis of the problem] via affirmative action as it is traditionally interpreted results in the gradual implosion of organisations

8. Two balls that organisations [and HR] need to keep in the air Getting all segments of the South African labour market constructively active in the labour market Sustaining and improving organisational productivity and competitiveness In the interest of/in service of society

9. How to respond? • Selection is based on [clinically or mechanically] derived criterion inferences. • When criterion inferences are derived without predictive bias [i.e. fairly in the Cleary sense of the term] from predictor information the group-specific expected criterion distributions and the group-specific observed criterion distributions will coincide in terms of the mean but ²E[Y|X]<²Y [as a function of the validity coefficient. • If Y differs across groups E[Y|X] will differ in the same manner across groups • Since selection decisions are based on [clinically or mechanically] derived criterion inferences, differences in selection ratios are inevitable when Y and therefore E[Y|X]differ across groups. • AI therefore occurs fundamentally because of systematic differences in the level/location of criterion distributions.

10. AI in the final analysis is due to differences in the group-specific criterion distributions Differences in predictor distributions could be due to measurement bias or could reflect differences in the latent means

11. AI in the final analysis is due to differences in the group-specific criterion distributions If the differences in the intercept of the regression of Y on X would be ignored when deriving criterion inferences from X, these inferences would be predictively biased and the selection would discriminate unfairly against the red group. This problem can, however, be corrected

12. Why the differences in Y and E[Y|X] • Selection instruments should therefore not be blamed as the fundamental source of adverse impact in personnel selection [although AI occurs in selection] in South Africa and consequently the solution should not be sought in selection practices. • What should be blamed then and where should the solution be sought? • What caused the differences in the group-specific criterion distributions?

13. AI in the final analysis is due to differences in the group-specific criterion distributions [http://files.unistel.co.za/articles/MediaArticles2008/Ype%20Poortinga%202008.pdf] • “In the South African context it does not seem unreasonable to attribute at least some part of the systematic group-related differences in criterion distributions to a socio-political system that systematically denied the members of specific groups the opportunity to develop and acquire those abilities required to succeed. The current selection procedures are just honest messengers revealing a tragic truth. The solution therefore is not to be found in strategies to convince the messenger to alter its message. The difference in criterion distributions observed between protected and non-protected groups reflect bona fide differences on numerous critical dispositions and attainments required to succeed in the world of work, which have resulted from the systemic denial of access to developmental opportunities. To deny the criterion differences and the differences in the underlying competency potential is to deny the history that caused it.”

14. The need for affirmative/empowering development • The solution rather lies in affirmative development interventions aimed at developing those attainments [and possibly, dispositions] needed to succeed on the criterion provided that it can be assumed that the lack of opportunity only prevented the crystalised abilities from developing but that it did not [irreparably] damage the components of the psychological learning mechanism [i.e. the other latent variables comprising the learning potential structural model]. • Resources are, however, limited. • Selection of those most likely to benefit optimally from an affirmative development opportunity is therefore required as well as post-selection steps to increase the probability that those admitted onto the programme successfully complete the programme. • The level of learning performance that learners on an affirmative development programme achieve is not the outcome of a random event. • Rather it is determined by a complex nomological network of latent variables characterising the learner and his/her learning environment.

15. The need for a learning potential structural model • Rational and purposeful attempts at increasing the probability of the successful completion of an affirmative development programme are dependent on a valid understanding of this nomological net. • Selection would predominantly attempt to increase the level of learning performance by influencing the level of non-malleable learning competency potential latent variables by controlling the flow of learners into the programme • Post-selection interventions would attempt to increase the level of learning performance by influencing the level of malleable learning competency potential latent variables by changing specific characteristics of learners and/or characteristics of the learning context.

16. The need for a learning potential structural model • The focal latent variable in the nomological net is learning performance. • A distinction, however, needs to be made in attempts to model the nomological net between classroom learning performance, learning performance during evaluation and action learning on the job. • Learning performance should, moreover, be conceptualised in terms of a structurally inter-linked set of latent learning competencies [behaviours] and latent learning outcomes. • This applies to classroom learning performance, learning performance during evaluation and action learning on the job

17. Richelle Burger (2012)

18. Richelle Burger (2012)

19. Definitions of latent variables • Learning performance [LP]: Refers to the level of performance in a formal test or examination that requires the learner to respond to unfamiliar [learning material] by transferring insights developed during the training programme • Time cognitively engaged [TCE]:Refers to the extent to which learners are spending time attending to and expending mental effort in their learning tasks. • Academic self-leadership [ASL]: Refers to the strength of the tendency of the learner to direct and motivate themselves to behave and perform so as to achieve academic success. • Leaning motivation [LM]: Refers to the strength of the desire of the learner to learn the learning material. • Academic self-efficacy [ASE]:Refers to the strength of the tendency of the learner to positively evaluate their capacity to achieve success in academic learning tasks. • Conscientiousness [CON]:Refers to the strength of the tendency of learners to be prepared, diligent, thorough, self-disciplined, organised and punctual.

20. Berne Du Toit (2014)

21. Berne Du Toit (2014)

22. Definitions of latent variables • Learning [or mastery] goal orientation[LGO]: Refers to the extent to which learners have as their goal to truly understand or master the task at hand. Learners with a high standing on this latent variable are interested in self-improvement and tend to compare their current level of achievement to their own prior achievement rather than to those of others. • Meta-cognive knowledge [MCK]: Refers to the extent to which the learner is knowledgeable about the cognitive processes necessary for understanding and learning • Meta-cognitive regulation [MCR]: Refers to the extent to which the learner displays competence at regulating and influencing these cognitive processes underpinning learning. • Cognitive engagement [CE]:Refers to the extent to which the learner is psychologically present and focused on learning. Engagement in learning refers to the intensity and emotional quality of an individual’s involvement in initiating and carrying out learning activities.

23. Jessica Prinsloo (2013)

24. Definitions of latent variables • Hope [HOPE]: Refers to the strength of the combined tendency to be passionately determined to pursue and achieve goals [a sense of willpower] and to be convinced of one’s ability to find alternative pathways when obstacles are met in pursuit of the goals [a sense of waypower]. • Optimism [OPT]:Refers to the strength of the tendency to explain setbacks and failure in terms of external, temporary and specific causes and the tendency to explain gains and successes to personal/internal, permanent and general causes. • Resilience [RESIL]:Refers to the strength of the tendency to “bounce back” from challenging positive or negative events that hold the risk of negative consequences.

25. Sample information

26. Model fit

27. Burger: significance of the path-specific hypotheses   TCE= Time Cognitively Engaged; ASE= Academic Self-efficacy; LM= Learning Motivation; ASL= Academic Self-leadership; LP= learning performance; CON=Conscientiousness * represents statistically significant unstandardised ij estimates (p < ,05).It has become common practice to interpret the test-statistics calculated by LISREL to determine the statistical significance of unstandardised measurement model parameter estimates as Student t values. Strictly speaking, however, given the sample sizes typically involved, when performing SEM, the values that are calculated should be interpreted as z-scores (Guilford & Fruchter, 1978). Moreover, since the alternative hypotheses are typically formulated as directional alternative hypotheses the test of the significance of the unstandardised parameter estimates should be treated as a directional test. Assuming a 5% significance level the critical z-score should therefore be |1.6449| rather than |1.96|.A critical z-value of 1.96 would be appropriate if the alternative hypothesis would be formulated as a non-directional hypothesis.

28. Du Toit: significance of the path-specific hypotheses   * represents statistically significant unstandardised ij estimates (p < ,05). TCE= Time Cognitively Engaged; ASE= Academic Self-efficacy; CE=Cognitive engagement; LM= Learning Motivation; ASL= Academic Self-leadership; LP= learning performance; LGO=Learning goal orientation; MCK= Meta-cognitive knowledge; MCR= Meta-cognitive regulation

29. Prinsloo: significance of the path-specific hypotheses   TCE= Time Cognitively Engaged; ASE= Academic Self-efficacy; CON= Conscientiousness; LM= Learning Motivation; ASL= Academic Self-leadership; LP= learning performance; HOPE=Hope; RES= Resilience; OPT= Optimism * represents statistically significant unstandardised ij estimates (p < ,05).

30. Combined findings • Statistically significant path • Statistically insignificant path • Significant path inappropriate sign [2] HOPE RESIL CE OPT [2] [2] [3] [3] [3] CON ASE LP L_MOT TCE [2/1] [2/1] [2] ASL LGO [1/2] [2] MCK MCR

31. Managerial implications? • To some degree validly understanding the psychological mechanism that underpins learning performance offers the possibility of selection. • Selection, however, raises a number of questions/choices • What should the criterion be [or on what should the selection decision be based?] • E[LP|Xi]; implies a two-stage job selection procedure • E[JP|Xi]; implies a single-stage job selection procedure in which the criterion is post-development job performance • The latter holds the disadvantage that prediction errors are compounded • Should criterion inferences be derived mechanically or a clinically? • When developing an actuarial prediction model should it be based on a explanatory learning potential structural model in which the insignificant paths are pruned away or should it be based on a predictive regression model containing the predictors that significantly explain unique variance in the criterion? • The former implies deriving latent score estimates via the measurement model parameter estimates and propagating these via the structural equations through the structural model

32. Two or single-stage selection? E[Z|Q] E[Y|Z,Xi] Applicant Pool π1 Training Job [Y] Evaluation [Z] Applicant Pool π2 Learning performance + other KSA’s [Z+Xi] Learning potential [Q] E[Y|Q,Xi]

33. EEA paragraph 20(3) “For purposes of this Act, a person may be suitably qualified for a job as a result of any one of, or any combination of that person’s- [a] formal qualifications; [b] prior learning; [c] relevant experience; or [d] capacity to acquire, within a reasonable time, the ability to do the job.”

34. Managerial implications? • The latter brings with it the choice to [a] derive latent criterion score estimates via the structural equation and latent predictor score estimates [derived from the measurement model] or to more conventionally [b] derive observed criterion score estimates via the observed score regression equation and observed predictor scores. • Should only non-malleable learning competency potential latent variables be considered for selection or also malleable learning competency potential variables? • Should a purely construct-orientated approach/logic to selection be utilised or a combination of a construct-orientated and a content-orientated approach/logic? • Learning motivation is a malleable learning competency potential latent variable • According to the expectancy theory of motivation motivational effort is determined by the multiplicative combination of P[ELP] and Valence[LP]. The valence of performance is in turn determined by the multiplicative combination of P[LPR] and Valence[R]. • LM is therefore determined by P[ELP]*P[PR]*Valence[R].

35. Managerial implications? • LM can therefore be affected by enhancing each of these components by making high LP [learning performance during evaluation] instrumental in the attainment of highly valenced rewards, by making high LP [classroom learning performance] intrinsically rewarding, by making high classroom learning performance instrumental in achieving highlearning performance during evaluation and by enhancing the expectancy of high LP. • According to the model LM is affected by ASE, LGO, HOPE and CON • The latter three latent variables should probably be influenced via selection [although PSYCAP can to some degree be considered malleable] but ASE presents a portal to influence P[ELP]. • The trainer-instructor should probably play a significant role in enhancing ASE through the four sources of ASE information identified by Bandura (1977), namely, performance accomplishments, vicarious experience, verbal persuasion and physiological states.

36. Extending the learning potential structural model • The De Goede [2007] research study focused on the cognitive learning competency potential latent variables [abstract thinking capacity & information processing capacity] underpinning the cognitive learning competencies [transfer & automisation] proposed by Taylor [1989, 1992, 1994, 1997]. • Due to the problems associated with the appropriate operationalisation of the two cognitive learning competencies most of the subsequent studies [Burger [2012]; Prinsloo [2013], Van Heerden [2013], Du Toit [2014], Mahembe [2014], Pretorius [2015]] purposefully, excluded transfer and automisation and the cognitive mechanism underpinning them from the learning potential structural models that were empirically tested. • It is clearly not possible to attain a convincing explanation of learning performance during evaluation if these two cognitive learning competencies remain excluded. • To bring them back requires [a] solving the operationalisation problem and [b] elaborating the detail of this part of the psychological mechanism.

37. MCK MCR Thoughts on the cognitive learning mechanism Transfer TCE Gf PriorK Info proc capacity Autom PostK ? LP