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LAIS 609C (3): Data Analysis

PREREQUISITES:

LAIS 605 (previously LIBR 600) or permission of the iSchool Graduate Advisor

GOAL: The goals of this course, Making Sense with Data (MSD), are to support learners in acquiring basic knowledge and skill-set that will allow them to identify, prepare, and analyze data in order to answer questions of interest, and to communicate findings with stakeholders. Thus, the course will focus on alignment of questions, data, and methods; understanding the nature of common descriptive and inferential statistics; choice, preparation, and analysis of data; and verbal and visual communication.

OBJECTIVES:

Upon completion of this course students will be able to:

  • Formulate research questions in a way that is both informative and answerable
  • Understand the affordances and constraints of different types and sources of data
  • Identify relevant data that are appropriate for chosen questions
  • Convert and prepare data so they are ready to be analyzed
  • Understand the principles, inputs, and outputs of different descriptive statistics such as central tendency, variance and correlations.
  • Understand the principles, inputs, and outputs of different inferential statistics such as t-test and ANOVA
  • Understand the nature of null and alternative hypotheses
  • Operationalize research questions by identifying relevant dependent variables, independent variables, and covariates.
  • Identify and estimate source of noise, error, and randomness.
  • Use the R software to analyze data in order to answer the chosen question
  • Use the web to find, understand, and apply additional R functions as needed.
  • Synthesize and interpret research findings to answer the given questions.
  • Communicate research findings in a meaningful, succinct, and accurate way using written, oral, and visual representations.

CONTENT:

  • Introduction to R (commands, files, markdown)
  • Finding and using R commends from the web
  • Central tendency and graphing with one variable
  • Relationship between two variables
  • Sampling
  • Point and range estimation
  • Hypothesis testing: z test, t test, ANOVA, chi-square
  • P value and its limitations
  • Type I and II errors
  • Data preparation
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