EES403 module 2015

Course Overview

  • Course Title: Applied Economic Statistics (EES 403)

  • Institution: Kenyatta University, Institute of Open Distance & e-Learning in collaboration with the School of Economics, Department of Econometrics and Statistics.

  • Written by: Mr. Steve Makambi

  • Edited by: Dr. Susan Okeri

Course Description

  • An advanced course designed to familiarize students with the practical applications of statistical techniques.

  • Focus on designing experiments/surveys, creating instruments for data collection, collecting data, and conducting hypotheses testing using inferential statistics.

  • Expected Outcomes: By the end of the course, students will:

    • Have skills in sampling.

    • Understand methods & procedures for data collection and analysis.

    • Construct survey questionnaires.

    • Write research reports.

    • Test hypotheses using non-parametric methods.

Table of Contents

  • Lecture 1: Sampling

    • Main Concepts

    • Sampling Procedure

    • Sampling Designs

  • Lecture 2: Data Collection Methods

    • Types of Data

    • Personal Observation Method

    • Personal Interviewing Method

    • Mail Questionnaire

  • Lecture 3: Questionnaires

  • Lecture 4: Data Editing and Analysis

  • Lecture 5: Index Numbers

  • Lecture 6: Non-parametric Tests

  • Lecture 7: Writing a Research Report

Lecture 1: Sampling

Introduction and Objectives

  • Recognize appropriate sampling methods.

  • Understand factors determining sample selection and economic trade-offs related to sampling.

Sampling Concepts

  • Population/Universe: All items under consideration; denoted by N.

  • Census: Complete coverage of all items in a population. Typically done when:

    • The universe is small.

    • Required by law (e.g., population census every ten years).

  • Sample Design: A definitive plan/method to obtain a sample from the population.

  • Sample Size: Number of items selected from the universe. Recommended size: n >= 30.

Features of a Good Sample

  • Represents the population accurately.

  • Minimizes sampling error.

  • Is viable within budget constraints.

Sampling Procedure

Steps in Selecting a Sample:

  1. Define objectives of the study.

  2. Clearly define the target population.

  3. Identify the sampling unit for information/data collection.

  4. Create a sampling frame which may include a list and details of units in the target population.

Issues with Sampling Frame

  • Missing Units: May or may not cover the entire population.

  • Clusters: Problem when units are grouped instead of listed individually.

  • Foreign Members: Include incorrect entries not part of desired population.

  • Overlapping Membership: Potential for duplicates in sample.

Determining Sample Size

  • Depends on desired precision and available funding.

  • Trade-off between sample size and cost.

Sampling Designs

  • Classifications:

    • Probability Methods:

      • Simple Random Sampling

      • Systematic Sampling

      • Stratified Sampling

      • Cluster Sampling

    • Non-Probability Methods:

      • Convenience Sampling

      • Judgment Sampling

      • Quota Sampling

Lecture 2: Data Collection Methods

Objectives

  • Differentiate between primary and secondary data.

  • Apply various primary data collection techniques.

Factors Determining Data Collection Method

  • Nature, scope, and objectives of the study.

  • Availability of funds (cost implications).

  • Required time for data collection.

Types of Data

  1. Secondary Data: Collected from past records.

  • Advantages: Saves time/money, may be reliable.

  • Disadvantages: May not suit the current study purpose, accessibility issues.

  1. Primary Data: Collected directly from the source.

Data Collection Techniques

  • Personal Observation Method: Uses sensory engagement to collect data.

  • Personal Interviewing Method: Involves direct communication for data collection.

  • Mail Questionnaire: Sent to respondents for self-completion.

Lecture 3: Questionnaires

Overview

  • Definition: A tool for gathering data through questions.

Designing the Questionnaire

  • Elements: Content, Structure, Format, Sequence of questions.

  • Types include Closed-ended, Open-ended, and Contingency questions.

Lecture 4: Data Editing and Analysis

Objectives

  • Organize and interpret data collected.

Data Editing

  • Review for completion, consistency, accuracy, and homogeneity.

Data Analysis

  • Qualitative vs Quantitative analysis, use of descriptive statistics.

Lecture 5: Index Numbers

Introduction

  • Definition and purpose.

Construction Steps

  1. Define purpose of the index.

  2. Collect necessary data.

  3. Select base period and items.

  4. Obtain price quotations.

  5. Choose appropriate formula.

Types of Index Numbers

  1. Price Index

  2. Quantity Index

  3. Value Index

  4. Composite Index

Lecture 6: Non Parametric Tests

Overview

  • Introduction to non-parametric statistical methods.

Types and Procedures for Various Tests

  • Wilcoxon Rank Sum Test, Mann-Whitney U-Test, Sign Test, Kruskal-Wallis Test, Spearman Rank Correlation, and Runs Test with conditions and applications for each.

Lecture 7: Writing a Research Report

Objectives

  • Understand structure and necessary components of a report.

Structure

  1. Preliminaries (Title, Dedication, Acknowledgement, Abstract)

  2. Main Body (Introduction, Literature Review, Methodology, Results & Discussion, Summary & Conclusions)

  3. References and Appendices including citation styles, e.g., APA.

Important Considerations

  • Clarity, consistency, and coherence throughout the document.