Race-mixing and science in the United States

Scientific racism was widely used as a justification to oppose race-mixing in the United States. Historians have justly criticized this abuse of science, but have overlooked some of the important ways in which science was used in the 1930s and 1940s to overturn scientific racism and opposition to race-mixing. Of particular importance was the cultural anthropology of Franz Boas and the evolutionary biology of Theodosius Dobzhansky, which supplied arguments against racism and fundamentally altered the scientific understanding of race.     

The information seeking behaviour of the users of digital scholarly journals

The article employs deep log analysis (DLA) techniques, a more sophisticated form of transaction log analysis, to demonstrate what usage data can disclose about information seeking behaviour of virtual scholars – academics, and researchers. DLA works with the raw server log data, not the processed, pre-defined and selective data provided by journal publishers. It can generate types of analysis that are not generally available via proprietary web logging software because the software filters out relevant data and makes unhelpful assumptions about the meaning of the data. DLA also enables usage data to be associated with search/navigational and/or user demographic data, hence the name ‘deep’. In this connection the usage of two digital journal libraries, those of EmeraldInsight, and Blackwell Synergy are investigated. The information seeking behaviour of nearly three million users is analyzed in respect to the extent to which they penetrate the site, the number of visits made, as well as the type of items and content they view. The users are broken down by occupation, place of work, type of subscriber (“Big Deal”, non-subscriber, etc.), geographical location, type of university (old and new), referrer link used, and number of items viewed in a session.     

Nature's agents or agents of empire? Entomological workers and environmental change during the construction of the Panama Canal

This essay examines the role that entomological workers played in U.S. public health efforts during the construction of the Panama Canal (1904-1914). Entomological workers were critical to mosquito control efforts aimed at the reduction of tropical fevers such as malaria. But in the process of studying vector mosquitoes, they discovered that many of the conditions that produced mosquitoes were not intrinsic to tropical nature per se but resulted from the human-caused environmental disturbances that accompanied canal building. This realization did not mesh well with an American ideology of tropical triumphalism premised on the notion that the Americans had conquered unalloyed tropical nature in Panama. The result, however, was not a coherent counternarrative but a set of intra-administrative tensions over what controlling nature meant in Panama. Ultimately, entomological workers were loyal not just to the U.S. imperial mission in Panama but also to a modernist culture of science and to the workings of mosquito ecology as they understood them.     

Modeling Skipped and Not‐Reached Items Using IRTrees

When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree‐based IRT framework for modeling responses and omissions jointly, taking into account that test takers as well as items can contribute to the two types of omissions. The proposed framework covers several existing models for missing responses, and many IRTree models can be estimated using standard statistical software. Further, simulated data is used to show that ignoring missing responses is less robust than often considered. Finally, as an illustration of its applicability, the IRTree approach is applied to data from the 2009 PISA reading assessment.     

Application of the Levenshtein Distance Metric for the Construction of Longitudinal Data Files

The analysis of longitudinal data in education is becoming more prevalent given the nature of testing systems constructed for No Child Left Behind Act (NCLB). However, constructing the longitudinal data files remains a significant challenge. Students move into new schools, but in many cases the unique identifiers (ID) that should remain constant for each student change. As a result, different students frequently share the same ID, and merging records for an ID that is erroneously assigned to different students clearly becomes problematic. In small data sets, quality assurance of the merge can proceed through human reviews of the data to ensure all merged records are properly joined. However, in data sets with hundreds of thousands of cases, quality assurance via human review is impossible. While the record linkage literature has many applications in other disciplines, the educational measurement literature lacks details of formal protocols that can be used for quality assurance procedures for longitudinal data files. This article presents an empirical quality assurance procedure that may be used to verify the integrity of the merges performed for longitudinal analysis. We also discuss possible extensions that would permit merges to occur even when unique identifiers are not available.     

RIM: A Random Item Mixture Model to Detect Differential Item Functioning

In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized p -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life data set.     

The development of pattern recognition ability in children

Pattern recognition development was studied in high and low ability preschool, kindergarten, and first-grade youngsters. Pattern recognition was assessed by the administration of 96 pattern tasks which measured the ability of a youngster to find: (a) duplicate patterns and elements to a model; (b) the opposite pattern to a model; and (c) duplicate patterns but different elements than in the model. Aside from main effects for grade level, ability level, and pattern task, a significant three-way interaction was noted. This interaction was analyzed in terms of its component simple effects. The implication of these simple interaction effects for Piagetian theory and educational practice were discussed.