Understanding Data: An Information Literacy Perspective

Elaine M. Lasda BergmanAssociate Librarian

Dewey Graduate LibraryUNL 299: Information Literacy in Mathematics and Statistics

February 18, 2016

https://upload.wikimedia.org/wikipedia/en/0/09/DataTNG.jpg

Selecting Data

• What is your question? • What are the variables needed in the dataset

that will answer this question? • How will you need to manipulate the data to

arrive at meaningful conclusions? • What format does the dataset need to be in to

maniuplate the data?

Do do you have to collect your own data?

• First party data – Collected by entity doing the analysis– Unique; often a direct relationship to data source– Trustworthy (?)– Smaller dataset (mostly)

• Second party data – Access is from another platform, but you have access to it

• Repositories– Creator of original platform has direct relationship to data source– Trustworthy (?)

• Third party data– Access from another platform – Collected anonymously; without user consent

• “data exhaust”– Large, aggregated datasets– Trustworthy (?)

Data Collection

• Empirical• Survey• Ethnographic• Focus Group• Qualitative vs. Quantitative

Variable Types

• NOT all data is numerical!– Nominal – Ordinal– Discrete– Continuous

• How does these datatypes affect the creation of descriptive statistics about the data?

Dataset Architecture

• .csv/Spreadsheet• RDBs• Linked Datasets• OODBs• “Big Data”• Others…

Metadata

• Data about data• “Attributes” of variables

https://library.uoregon.edu/datamanagement/metadata.html

• How does Metadata aid in our use of a dataset?

Evaluation of Data

• GIGO: data cleaning• Current? • Authoritative?• Format• Replicable?

Tools for Analysis

• R• Stata• SPSS• Excel• Weka• HadoopEtc, etc etc

Ethics

• In data collection• In data usage• In data storage and preservation• In data disposal

Questions?

elasdabergman@albany.edu