Translating data between MySQL and Stata

As web-based and other electronic data collection methods become more widely used in research, the opportunities to use statistical software in conjunction with conventional database systems are increasing. Among such systems, MySQL is particularly well suited for research purposes. For example, MySQL's ENUM and SET column types are ideal for storing data collected via the multiple choice questions typically used in social surveys. At the same time, Stata is uniquely suited for working in conjunction with a database; for example, its implementation of characteristics makes it possible to preserve (in a usable form) important information about how the database and front-end application are constructed (e.g., column types and other attributes). In this presentation, we shall describe a Python script we have developed for translating data from MySQL to Stata, and will indicate briefly how we are using it in the development of tools for the collection and management of research data.

Should we teach primary pupils about chemical change?

Thirty six pupils from three 6th grade classes (ages 11/12, n=75) in Greece were interviewed pre and post intervention in a piece of research on explanations of chemical phenomena. Software concerning chemical phenomena was incorporated in a teaching scheme, where the particle theory was used. After 13 hours intervention, pupils' explanations were categorized in five discrete categories. Only a few pupils could give satisfactory explanations, involving the integration of particle ideas at the level of atoms. The idea of chemical change seemed to be very difficult for the majority of pupils. Implications for the teaching of chemical phenomena at young ages are discussed

Primary teachers' particle ideas and explanations of physical phenomena: the effect of an in-service training course

This paper presents a study concerning Greek primary school teachers' (n=162) ideas about the particulate nature of matter and their explanations of physical phenomena. The study took place during an in-service training course where the effectiveness of a specially designed intervention was tested. A key feature was an approach based on the concept of a substance and its states rather than 'solids, liquids and gases'. Pre-intervention, the teachers held misconceptions similar to those of pupils. Also, there seemed to be some relationship between the teachers' particle model ideas and their explanations of phenomena. Post-intervention, the teachers' descriptions and explanations were found to be significantly improved, with almost zero correlation between pre and post intervention scores. Implications for science education are discussed

Little Things

We present LITTLE THINGS (Local Irregulars That Trace Luminosity Extremes, The HI Nearby Galaxy Survey) that is aimed at determining what drives star formation in dwarf galaxies. This is a multi-wavelength survey of 37 Dwarf Irregular and 4 Blue Compact Dwarf galaxies that is centered around HI-line data obtained with the National Radio Astronomy Observatory (NRAO) Very Large Array (VLA). The HI-line data are characterized by high sensitivity (less than 1.1 mJy/beam per channel), high spectral resolution (less than or equal to 2.6 km/s), and high angular resolution (~6 arcseconds. The LITTLE THINGS sample contains dwarf galaxies that are relatively nearby (less than or equal to 10.3 Mpc; 6 arcseconds is less than or equal to 300 pc), that were known to contain atomic hydrogen, the fuel for star formation, and that cover a large range in dwarf galactic properties. We describe our VLA data acquisition, calibration, and mapping procedures, as well as HI map characteristics, and show channel maps, moment maps, velocity-flux profiles, and surface gas density profiles. In addition to the HI data we have GALEX UV and ground-based UBV and Halpha images for most of the galaxies, and JHK images for some. Spitzer mid-IR images are available for many of the galaxies as well. These data sets are available on-line.Comment: In press in A