In this course, students will gain skills in data preparation and data entry, data cleaning, descriptive and inferential statistics, as well as presentation and conveying results.
Whether you are writing your dissertation, masters thesis, doctoral thesis or any other type of academic research paper assignment, this mini-course will get you off to a great start. This is a crash course focused on teaching you everything you need to know to get started with the analysis of questionnaire data, the basics of designing survey data-entry instruments, specifically teaching the data processing methods of translating the answers on your survey research questionnaires into a dataset, cleaning up the dataset, analysing to the presentation of your quantitative research results using the free public domain statistical software package of EpiData.
At the end of the course, what will you be able to DO?
KNOW the basics of data pre-processing and how to get data ready for analysis
KNOW the basics of data-entry validation rules for accurate and efficient data entry procedures.
BE ABLE to set up a dataset and perform simple summary statistics for analysis
GRASP use of EpiData to Analyze data & perform data-entry
We all know that every student at some point in their academic career is required to undertake research for their dissertation or other academic assignments. That's the focus of the course, not question design or interpretation of survey data.
This practical comprehensive hands-on course targets undergraduate, post-graduate, MA or PhD students; program managers, monitoring & evaluation professionals or anyone who needs to perform research data or statistical analyses of survey questionnaire methodology where the data are subsequently transformed into tables and figures which gives you the first comprehensible results. Participants will pick up a basic and straightforward technical skillset to effectively analyse and summarize the results of the study using descriptive analysis.
On completion of the course, participants will have the necessary familiarity with EpiData Software to move on to further EpiData courses or continue learning themselves.
Simply put, I will walk you through the 3 stages of data processing methods; namely:
Input – After data collection, the raw data must be fed into the cycle for processing, which is referred to as input.
Processing – Once the raw data is provided, it is processed using a suitable or chosen statistical tool. This is an important step because it outputs the processed data that will be used later.
Output – Using tables and charts, present the data results in a logical order. This is the result. The raw data that was provided in the first stage has now been "processed," and is now useful and informative, and it is no longer referred to as data.
Welcome, and see you in class.
Raymond Wamalwa
