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Research Methodology, Data Collection, Analysis & Visualization Training

June 3 - June 14

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.

Inspection & Sampling Techniques
June 3 - June 14

Organizer

ASPM
+254 737 022726
info@aspm.co.ke

Venue

Nairobi
Nairobi Kenya + Google Map
+254 737 022726

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