Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…

This paper analyses the reliability and accuracy of the relationships between migration and employment status when estimated using a linked data set. The analysis will be carried out using a new source, the "Labour and Geographical Mobility Statistics," which is provided by the Spanish Statistical Office. This statistic is constructed by…

It is often difficult and costly to obtain individual-level student achievement data, yet, researchers are frequently reluctant to use school-level achievement data that are widely available from state websites. We argue that public-use aggregate school-level achievement data are, in fact, sufficient to address a wide range of evaluation questions…

In the nonequivalent groups with anchor test (NEAT) design, the standard error of linear observed-score equating is commonly estimated by an estimator derived assuming multivariate normality. However, real data are seldom normally distributed, causing this normal estimator to be inconsistent. A general estimator, which does not rely on the…

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…

Every measurement in science, every experimental decision, result and information drawn from it has to cope with something that has long been named by the term "error". In fact, errors describe our limitations when it comes to experimental science and science looks back on a long tradition to cope with them. The widely known way to cope…

Assessing measurement equivalence in the framework of the common factor linear models (CFL) is known as factorial invariance. This methodology is used to evaluate the equivalence among the parameters of a measurement model among different groups. However, when dichotomous, Likert, or ordered responses are used, one of the assumptions of the CFL is…

Maximum likelihood is the most common estimation method in structural equation modeling. Standard errors for maximum likelihood estimates are obtained from the associated information matrix, which can be estimated from the sample using either expected or observed information. It is known that, with complete data, estimates based on observed or…

To use environmental program evaluation to increase effectiveness, predictive power, and resource allocation efficiency, evaluators need good data. Data require sufficient credibility in terms of fitness for purpose and quality to develop the necessary evidence base. The authors examine elements of data credibility using experience from critical…

Since meta-analysis was described in 1976 (Glass) as the application of familiar experimental methods to the integration of available research, at least five coherent approaches to meta-analysis have appeared in common use. These approaches can be divided into two broad groups. In the first group (including procedures by Robert Rosenthal, Larry…

In this paper, we use two complementary Italian data sources (the 1995 ISTAT and Bank of Italy household surveys) to generate household-specific nondurable expenditure in the Bank of Italy sample that contains relatively high-quality income data. We show that food expenditure data are of comparable quality and informational content across the two…

Many times in both educational and social science research it is impossible to collect data that is complete. When administering a survey, for example, people may answer some questions and not others. This missing data causes a problem for researchers using structural equation modeling (SEM) techniques for data analyses. Because SEM and…