The Effect of Response Model Misspecification and Uncertainty on the Psychometric Properties of Estimates. In: Millsap RE, van der Ark LA, Bolt DM, Woods CM, editors. New Developments in Quantitative Psychology. New York: Springer; 2013. pp. 85-114. [DOI: 10.1007/978-1-4614-9348-8_7]

Sources of Error in IRT Trait Estimation

In item response theory (IRT), item response probabilities are a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (a) random error, (b) the trait score estimation method, (c) errors in item parameter estimation, and (d) model misspecification. This study investigated the relative effects of these error sources on the bias and confidence interval coverage rates for trait scores. Our results showed that overall, bias values were close to 0, and coverage rates were fairly accurate for central trait scores and trait estimation methods that did not use a strong Bayesian prior. However, certain types of model misspecifications were found to produce severely biased trait estimates with poor coverage rates, especially at extremes of the latent trait continuum. It is demonstrated that biased trait estimates result from estimated item response functions (IRFs) that exhibit systematic conditional bias, and that these conditionally biased IRFs may not be detected by model or item fit indices. One consequence of these results is that certain types of model misspecifications can lead to estimated trait scores that are nonlinearly related to the data-generating latent trait. Implications for item and trait score estimation and interpretation are discussed.

Using Confidence Intervals for Assessing Reliability of Real Tests

Test authors report sample reliability values but rarely consider the sampling error and related confidence intervals. This study investigated the truth of this conjecture for 116 tests with 1,024 reliability estimates (105 pertaining to test batteries and 919 to tests measuring a single attribute) obtained from an online database. Based on 90% confidence intervals, approximately 20% of the initial quality assessments had to be downgraded. For 95% confidence intervals, the percentage was approximately 23%. The results demonstrated that reported reliability values cannot be trusted without considering their estimation precision.

Big Data Methods

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.