Research Methodology, Data Collection, Analysis & Visualization Training

Research Methodology, Data Collection, Analysis & Visualization Course

Upon successful completion of this course participants should be able to:

• Identify different research methods and their theoretical underpinnings.
• Demonstrate an ability to identify, analyze and synthesize literature related to a research question.
• Critically analyze and demonstrate an ability to formulate viable research questions.
• Demonstrate an understanding and ability to undertake the range of tasks necessary to completing a research project.
• Identify and engage with the range of ethical issues involved in the conduct of a research project.
• Show an understanding of cross cultural contexts and the nuances/implications of cross cultural research.
• Discuss the relevance of reliable data collection process in Research and informed decision making
• Use relevant computer packages for data management and extrapolation
• To introduce the concepts of monitoring and evaluation and the value of implanting it in programs/projects
• To focus participant’s attention on monitoring and evaluation study designs
• M&E Data Management and Analysis
• Get hands on skills on SPSS

Who Should Attend?

The course is recommended for the following;

• Departmental heads
• Data manager
• Research students
• Project or Departmental managers
• Project officers
• Staffs involved in making informed decision
• Anyone involved in development project
• Anyone who aspires to be a good manager
• Researchers & M&E officers.
• Embrace use of data in informed decision making.

  Main Training Modules

Introduction to research

• Introduction to research
• Different types of research
• Formulation of research problem statement
• Formulation of research hypothesis

Research Design

• Quantitative Research Approaches
• Qualitative Research Approaches

Sampling

• Sampling Techniques
• Probability
• Non-probability
• Sample size determination

Data Collection Methods in Research

• Quantitative data collection methods
• Qualitative data collection
• Creating an evaluation framework

Data Collection tools in Research

• Survey Questionnaire design
• FGD guide design
• KII guide design

 Introduction to SPSS Statistics

• Explain how IBM SPSS Statistics is used for basic analysis
• Explain the basic steps in data analysis
• Understand the primary windows in IBM SPSS Statistics
• Understand the different components of dialog boxes

 Reading data

• Import data from different types of file formats
• Describe choices on the File menu for reading data
• Read Microsoft Excel files
• Read files from a Microsoft Access database
• Read delimited text files

Defining Variable Properties

• Describe and define variable properties in the Variable View window
• Use the Define Variable Properties dialog box
• Save variable properties with data in an IBM SPSS Statistics data file
• Use the Variables utility to view variable properties interactively
• Use the Display Data Dictionary facility and the Codebook procedure to view variable properties

 Working with the Data Editor

• Use features in the Data Editor
• Insert, delete, and move variables and cases
• Use the Split Screen view
• Copy information from one dataset to another
• Use the Copy Data Properties feature

 Modifying data values: Recode

• Use Visual Binning to reclassify values of an ordinal or scale variable
• Use Recode Into a Different Variable to reclassify values of a nominal variable
• Use Automatic Recode to create a numeric variable from a string variable

Modifying data values: Compute

• Describe the features of Compute Variable
• Create new variables with numeric expressions
• Create new variables with conditional numeric expressions

Summarizing individual variables

• Define levels of measurement
• Use the Frequencies procedure to produce tables and charts appropriate for nominal variables
• Use the Frequencies procedure to produce tables and charts appropriate for ordinal variables
• Use the Frequencies and Descriptive procedure to produce tables and charts for scale variables

Describing relationships between variables

• Select the appropriate procedure to summarize the relationship between two variables
• Use the Crosstabs procedure to summarize the relationship between categorical variables
• Use the Means procedure to summarize the relationship between a scale and a categorical variable

Selecting cases for analyses

• Select cases in a data file using various methods
• Describe and use the features of the Select Cases dialog box
• Describe and use the features of the Split File dialog box

 Creating and editing charts

• Present results with charts
• Use the Chart Builder to create various types of graphs
• Format and edit graphs in the Chart Editor

Way forward After the Training

Participants will develop a work plan through the help of facilitators that stipulates application of skills acquired in improving their organizations. ASPM will monitor implementation progress after the training

Training Evaluation:

Participants will undertake a simple assessment before the training to gauge knowledge and skills on data analysis & management, another assessment will be done after the training in-order to demonstrate knowledge gained through the training.