Kenett, R. S., and G. Shmueli, "Rejoinder: On Information Quality", JRSS A, vol. 177, issue 1, pp. 35-38, 2014.

Shmueli, G., "Research Dilemmas With Behavioral Big Data", Big Data, vol. 5, issue 2, pp. 98-119, 2017.

Shmueli, G., "To Explain or To Predict?", Working Paper (RHS 06-099) Smith School of Business, University of Maryland, 2009.

Shmueli, G., "To Explain or To Predict?", Statistical Science, vol. 25, issue 3, pp. 289-310, 2010. Stat Science published.pdf (293.36 KB)

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big To Fail: Large Samples and the P-Value Problem", Information Systems Research, vol. 24, issue 4, pp. 906-917, 2013. Article (269.29 KB)

Shmueli, G., and O. Koppius, "The Challenge of Prediction in Information Systems Research", Working Paper RHS 06-152: Smith School of Business, University of Maryland, 2009.

Shmueli, G., and I. Yahav, The Forest or the Trees? Tackling Simpson's Paradox with Classification and Regression Trees: Indian School of Business, 2014.

Kenett, R. S., and G. Shmueli, From Quality to Information Quality in Official Statistics: Indian School of Business, 04/2014.

Kenett, R. S., and G. Shmueli, "On Information Quality", Working Paper RHS 06-100: Smith School of Business, University of Maryland, 2011.

Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", Working Paper RHS 06-138: Smith School of Business, University of Maryland, 2010.

Shmueli, G., "Statistical Inference with Large (eCommerce) Datasets", Working Paper RHS 06-61: Smith School of Business, University of Maryland, 2008.

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big to Fail: Larger Samples and False Discoveries", Working Paper RHS 06-068: Smith School of Business, University of Maryland, 2009.