Data Analysis and Management

1. Statistical and Spatial Data Analysis and Visualization using R

The course will:

• Introduce participants to R as a quantitative data analysis tool

• Enable learner master R software and R-studio as a user interface

• Enable learner import data from various sources

• Introduce basic statistics for exploratory data analysis including methods for describing and summarizing variable distributions

• Provide essential skills for data manipulation including selecting subsets and recoding

• Introduce the visual representation of variables in scatter graphs, bar charts, and histograms

• Introduce users to the use of R for spatial data analysis and map making

2. Qualitative Data Management & Analysis using NVivo

Course Objectives:

• By the end of this program, participants should be able to:
  Understand the fundamentals of qualitative research

• Understand qualitative data collection and analysis methods

• Set up a project in NVivo and explore the function of NVivo

• Create, import and work with codes and nodes in NVivo

• Undertake deductive and inductive qualitative data coding using NVivo

• Manage all project materials conveniently in one project file.

• Deliver quality outputs backed by a transparent discovery and analysis process

• Justify decision making with sound findings and evidence-based recommendations

• Revisit data easily, build up the big picture perspective

• Write a qualitative research report

3. Data Management and Analysis using SPSS

Statistical Package for Social Sciences (SPSS) is a comprehensive and user friendly statistical software for researchers providing visualization and data analytical tools. Participants shall explore the statistical capabilities of SPSS ranging from simple percentages to complex analyses of variance, multiple regressions, and general linear models as well as to generate tabulated reports, charts and plots of distributions and trends. Participants will review several statistical techniques, gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.