Basic Statistics for Extension Professionals

 

  1. Course Title: EE 106– Basic Statistics for Extension Professionals
  2. Course Aim: this is a tailor made course aimed at introducing students to some basic statistics essential for performing their role as change agent at community level

 

  • Course Expected Learning outcomes:

    By the end of the course students should be able to:

  • Describe some basic concepts in statistics
  • Interpret the results of statistical analyses;
  • Describe the basic math underlying the leading statistical procedures of social science
  • Use statistical concepts to handle observations in some extension research activities
  • Understand how to conduct   hypothesis testing  and estimate population parameter;
  • Develop positive attitude towards using statistics in agricultural extension.
  • Apply statistical knowledge and skills in the real extension situation;
  1. course status:            Elective
  2. Credit rating:            5 Credits
  3. Total hours Spent:            75 hours

                                    Lecture                                    32 hours

Seminars/Tutorials                   20 hours

Practical                                  0 hours

Assignment                             15 hours

Independent Research                        8 hours

Pre-requisite:  None

vii Course Content:

  • Descriptive Statistics for extension research: Definitions of relevant statistical terminologies; introduction to elementary statistics: data collection, organisation, presentation, analysis, frequency distribution; statistical measures of central tendency and dispersion, measures of correlations, measures of symmetry and skewness,
  • Statistical Inferences for extension research: Elementary probability theory; introduction to probability distributions; discrete distributions
  • Sampling Procedures and Distributions for extension research: Random and non-random sampling distributions
  • Hypothesis testing for extension research: null and alternative hypotheses, level of significance, type one error, one tail and two tail tests. Introduction to non-parametric statistics: sign tests, rank-sum tests and randomness tests.
  • Introduction to Regression Analysis; simple linear regression, How to Manipulate SPSS Software: Launching SPSS, how to enter data directly to SPSS, data management and analysis

 

Viii Teaching and Learning Activities

Teaching will involve lectures, practical, group assignments and seminar presentations, individual assignments to capture self- reading. Use of case studies in teaching for some practical aspects will be employed.

  • Assessment Methods

The assessments will be through continuous assessments were written timed tests (theory and practical), quizzes, seminar presentation, practical reports, and submission of individual/group assignment papers will be used. The assessment will also include final University written examination.

 

 

 

  • Reading List

 

  • Nesselroader, K.P. (2019). Statistical applications for the behavioural and social sciences, 2nd Edition. Wiley
  • Hagle, Timothy M. (1995) Basic Math for Social Scientists: Concepts Sage University Papers Series. Quantitative Applications in the Social Sciences.Sage Publications, Inc.
  • William H.Greene (2003), Econometrics Analysis, 5th edition.
  • Robert J. Beaver, Barbara M. Beaver, & William Mendenhall (2006). Introduction to probability and statistics (12th ed.)
  • Creswell, J W (2008) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches.SAGE Publ.
  • Bruce L. Bowermem, Richard T.O’Connell, Micheal L. Hand, Business Statistics in Practice (2nd Ed.)
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