Adopting standard variable labels solves many of the problems with sharing and reusing data

Datasets and analysis scripts are becoming more available online, but most datasets are still unclear and difficult to use due to poor meta-data. Adopting standard variable label solves most of these problems and is easily implemented if we set the labels at the time of publication, that is, for authors to also establish standard variable labels when they establish for example, question wording. This simple step involves little effort but facilitates the sharing of datasets and analysis scripts enormously. Current initiatives to improve meta-data rely on users spending much time creating new meta-data for each variable, which is time consuming, unenjoyable, and hinders adoption. Some suggestions are made on how brief, unique, and clear variable labels can be developed, especially using the last two digits of the year the scale was published in. Standards for dataset and analysis script etiquette are the future, and the final section of the manuscript examines other easy places simple standards can save time and frustration for (re)users.

Ethnic Labeling in Mexican-Origin Youth: A Qualitative Assessment

Outcomes are reported from a qualitative investigation addressing ethnic label selection, meaning, use, and influences upon Mexican-origin youth. Participants selected multiple labels with distinct meanings and influences. Findings indicate a need for school counselors to honor student label selection and to advocate for variable label use by school professionals and in school documentation. School counselors can provide resources and venues to facilitate student exploration of ethnic labels, as one key component of ethnic identity development.

Human Limitations in Using a Portable Light Pen Device for Manually Scanning Bar Code Patterns

In Experiment I a total of 30 subjects used the device to scan bar code patterns of varying position, orientation and width. The strongest main effects on the dependent variable, proportion of labels scanned in error, were observed for the independent variable, label width. Label widths of 0.1 inch produced error rates of 58.2 percent compared to error rates of 29.6 percent and 24.8 percent for 0.2 inch and 0.3 inch width labels, respectively. Experiment II examined learning effects and showed that label scanning accuracy leveled off after scanning 400 to 500 labels.