The impact of adopting shareholder primacy corporate governance on the growth of the financial market in developing countries. By

176^ Created using ‘Item Characteristic Curves" from the Wolfram Demonstrations Project’ coded by Vincent Kieftenbeld < http://demonstrations.wolfram.com/ItemCharacteristicCurves/>

177^ Bryce B Reeve and Peter Fayers, ‘Applying item response theory modelling for evaluating questionnaire item and scale properties’ in P Fayers and R Hays (eds.), Assessing Quality of Life in Clinical Trials: Methods of Practice (2nd edn, OUP 2005)

178^ Fumiko Samejima, ‘Estimation of latent ability using a response pattern of graded scores.’ (1969) 34 (4) Psychometrika Monograph Supplement 100.

179^ Adapted from ‘Whats’ beyond Concerto: An introduction to the R package catR’ - Overview of polytomous IRT models available at < http://www.psychometrics.cam.ac.uk/uploads/documents/catr/catr-workshop-session-4>; See also Cees A. W. Glas, ‘Bayesian Estimation Methods for Multidimensional Models for Discrete and Continuous Responses’ (2006) available at last accessed on 1 May 2015.

180^ See Remo Ostini and Michael Nering, Polytomous Item Response Theory Models (SAGE 2006) 65

181^ F Baker and S Kim, Item Response Theory (2nd edn, Marcel Dekker 2004)

182^ Wim van der Linden and Ronald K. Hambleton, ‘Item Response Theory: Brief History, Common Models, and Extensions.’ in Wim van der Linden and Ronald K. Hambleton, Handbook of Modern Item Response Theory (Springer 1997) 1-28; This can also be done using Maximum Likelihood Estimator as discussed in R. Darrell Bock and Murray Aitkin, ‘Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm.’ (1981) 46 (4)Psychometrika 443-459.

183^ Andrew D. Martin and Kevin M. Quinn, ‘Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999’ (2002) 10 (2) Political Analysis 134-153.

184^ See generally Christophe Andrieu, ‘Monte Carlo Methods for Absolute Beginners’ in Olivier Bousquet, Ulrike von Luxburg and Gunnar Rätsch (eds) Advanced Lectures on Machine Learning (Springer 2004) 113-145.

185^ James H Albert, ‘Bayesian estimation of normal ogive item response curves using Gibbs sampling’, (1992) 17 Journal of Educational Statistics 251–269. In this paper he provided the first MCMC algorithm for estimation of the parameters of the normal ogive model, complete with a small block of code for the computer software MATLAB that has been the basis for a good deal of the subsequent work. Albert used data augmentation, which is one common approach to MCMC estimation for latent-variable problems. In few years’ time the method was refined by Mark Patz and Brian Junker in their papers R Patz and B Junker, ‘A straightforward approach to Markov chain Monte Carlo methods for item response models’, (1999) 24 Journal of Educational and Behavioral Statistics 146–178 and R Patz and B Junker, ‘Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses’ (1999) 24 Journal of Educational and Behavioral Statistics 342–366 where they provided an alternative MCMC algorithm based on Metropolis-Hastings within Gibbs sampling, the other common approach to complex MCMC estimation, for the 2PL and 3PL models.

186^ See generally Markov Chains (Open University 1988)

187^ Thissen and Steinberg (n 165) 148-178

188^ See generally Howard Wainer et al., Testlet Response Theory and its Applications (Cambridge University Press 2007); Eric Bradlow et al., ‘A Bayesian random effects model for testlets’, (1999) 64 Psychometrika 153–168; Howard Wainer et al., ‘Testlet response theory: an analog for the 3-PL useful in testlet-based adaptive testing’, in Wim van der Linden and Cees Glas (eds.), Computerized Adaptive Testing: Theory and Practice (Kluwer Academic Publishers 2000) 245–270; Xiaohui Wang et al., ‘A general Bayesian model for testlets: theory and applications’ (2002) 26 Applied Psychological Measurement 109–128; For a review of Bayesian techniques especially multilevel modelling see Jean Paul Fox, ‘Multilevel IRT using dichotomous and polytomous response data’ (2005) 58 British Journal of Mathematical and Statistical Psychology 145–172 and Jean Paul Fox and Cees Glas, ‘Bayesian estimation of a multilevel IRT model using Gibbs sampling’ (2001) 66 Psychometrika 269–286.

189^ See generally Xiaohue Wang et al., ‘A Bayesian method for studying DIF: a cautionary tale filled with surprises and delights’ (2008) 33 Journal of Behavioral and Educational Statistics 363–384.

190^ See S Geman and D Geman, ‘Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images’ (1984) 6 IEEE Transactions on pattern analysis 721-741

191^ See W R Gilks et al., ‘Introducing Markov Chain Monte Carlo’ in W R Gilks et al. (eds.) Markov Chain Monte Carlo in Practice (Chapman and Hill 1996)

192^ Philip Resnik and Eric Hardisty, ‘Gibbs Sampling for the Uninitiated’ (2010) last accessed on 1 May 2015

193^ S. McKay Curtis, ‘BUGS Code for Item Response Theory’ (2010) 36 Journal of Statistical Software 1-34 available at