Group items tagged

In most social research the data analysis involves three major steps, done in roughly this order: Cleaning and organizing the data for analysis (Data Preparation) Describing the data (Descriptive Statistics) Testing Hypotheses and Models (Inferential Statistics)

"Too much time is spent manipulating data just to get analysis and visualization tools to read it. Wrangler is designed to accelerate this process: spend less time fighting with your data and more time learning from it."

"Curriculum Reports replace the AAMC's Curriculum Directory and feature: -Graphical interpretations of aggregate and historical curriculum-related data, collected annually by the Liaison Committee on Medical Education (LCME), -Representation of 100 percent of U.S. medical schools, and -A section on Educational Technology, based on results from the annual Group on Information Resources (GIR) Annual Survey. "

This is a brilliant tool, and its not necessary to leave any data in the public domain as the resulting file can be saved to file. Lots of fonts etc.

Our on-line multilevel modelling course is now available: 'LEMMA' (The Learning Environment for Multilevel Methodology and Applications) contains a set of graduated modules starting from an introduction to quantitative research progressing to multilevel modelling of continuous data.

Title * MRC Psycholinguistic Database Machine Usable Dictionary [Electronic resource] : expanded Shorter Oxford English Dictionary entries / Max Coltheart and Michael Wilson Author Coltheart, M. (Max), 1939-; Wilson, Michael John, 1939- Availability Freely available for non-commercial use provided that this header is included in its entirety with any copy distributed. This resource is freely available, you should be able to download it now. Languages English; Editorial Practice * Encoding format: MS Word and UNIX OTA keywords Dictionaries LC keywords Psycholinguistics -- Dictionaries Extent * designation: Text data * size: (16 files : ca. 12.5 megabytes) Creation Date [198?] Source Description MRC Psycholinguistic Database Machine Usable Dictionary : expanded Shorter Oxford English Dictionary entries Coltheart, M. (Max), 1939-; Wilson, Michael John, 1939- s.n. s.l.: s.d [Note: For additional information see: Coltheart, Max.--"MRC Psycholinguistic database" in Quarterly Journal of Experimental Psychology 33A (1981):497-505.--Catalogued on RLIN] Notes * Mode of access: Offline. Application to OTA * Title proper taken from electronic text

Clinical Document Collection: A collection of 7673 initial visit notes was obtained from the Columbia University Medical Center Milstein Hospitalist Service. This includes all resident and attending initial visit notes and initial consult notes for inpatient admissions of all types from late 2006 through early 2007. They are not filtered and should therefore be representative of all patients admitted to the Hospitalist Service. All notes from the Service are entered through semi-structured entry templates in a system called eNote11. PMH was entered into a coded field in eNote templates, but as free text within that field. The advantage for this analysis was that these lists were in the doctor's own words without any limits on structure or content imposed by the information system. The notes were stored using the Clinical Document Architecture (CDA) XML schema. This allowed for a simple XSL transformation to filter protected health information (PHI) and convert sections of interest to text. A small Java application was written to perform this XSLT on each document and do basic preprocessing to prepare the text for natural language processing analysis. Data Preparation: The corpus was then parsed with the MedLEE natural language processor12 to obtain the semantic structure and UMLS codes of concepts represented in these notes. MedLEE output was generated as XML and a Java postprocessor was used to validate the XML output. Each note section was divided into a text section with numbered phrase tags around identifiable phrases and a structured element containing references describing the tagged phrases. Reference tags were named with the phrase's semantic type. MedLEE assigned a UMLS code to the phrase whenever it could map the clinical information detected to known UMLS concepts. MedLEE results were then merged into one large XML file to facilitate querying across all documents with XQuery.