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Article 1: Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16, 650–674. doi:10.1177/1094428113493929
Summary: Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet intercorrelations between predictors (multicollinearity) undermine the interpretation of MLR weights in terms of predictor contributions to the criterion. Alternative indices include validity coefficients, structure coefficients, product measures, relative weights, all-possible-subsets regression, dominance weights, and commonality coefficients. This article reviews these indices.

Article 2: Oosterhuis, H. E. M., van der Ark, L. A., & Sijtsma, K. (2016). Sample size requirements for traditional and regression-based norms. Assessment, 23, 191–202. doi:10.1177/1073191115580638
Summary: Sample size requirements are presented for each norming method, test length, and number of answer categories.

Article 3: Breen, R., Holm, A., & Karlson, K. B. (2014). Correlations and nonlinear probability models. Sociological Methods & Research, 43, 571–605. doi:10.1177/0049124114544224
Summary: Although the parameters of logit and probit and other nonlinear probability models (NLPMs) are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, the authors argue that they may also be usefully interpreted in terms of the correlations between the dependent variable of the latent variable model and its predictor variables.