Duration: 40 hours (5 days)
Number of Participants: 15-20
Pre-requisite: Master’s degree with background of basic statistics.
Background in social science research will be an asset. In addition, all participants will have to take a screening test to be eligible for the training.
Dr. Dirgha Ghimire, Research Associate Professor, University of Michigan
Dr. Keshav Pokhrel, Asst. Professor of Statistics, University of Michigan-Dearborn, USA
Dr. Prem Bhandari, Senior Research Scientist, ISER-N
Mr. Bishnu Adhikari, Data Manager, ISER-N
Ms. Rija Manandhar, Senior Research Officer, ISER-N
This course focuses on various techniques commonly used for the analysis of survey data. First, this course will focus on data structure, data importation and data management. Then, under data analysis techniques, we will include univariate (data description and summarization), bi-variate (mean comparisons, chi-square and correlation), and multivariate (linear and binary logistic regression) analysis techniques. Emphasis will be given to choosing appropriate statistical tools for data analysis and interpretation of the results. R and SPSS, statistical software to analyze social science data, will be extensively used. More importantly, this workshop will focus on practical issues associated with choosing appropriate statistical tools, analysis of data, and interpretation of the results with reference to scientific articles published in highly referred sociological and population journals around the globe.
This interactive course uses a combination of lecture, in class exercises, group exercises and individual participation. Individual consultation will be provided to assist participants with the assignments leading to a grant proposal. Assignments throughout the week should be emailed or handed in as hard copy the day they are due. Feedback will be provided in a timely manner to aid in future assignments.
Upon completion of this course, participants will:
- Learn application of the survey data analysis techniques.
- Gain experience on generating statistical tables and interpreting results