Econ 148: Analytical and statistical packages for economics 1


Class schedule: Monday & Tuesday | 17:00 - 18:00
Laboratory schedule: Wednesday | 16:00 - 19:00
Instructor: Christopher Llones
e-mail: christopher.llones@vsu.edu.ph
Pre-requisites: Econ 115 - Econometrics
Course credits: 3 units
Number of hours: 2 hrs lectures and 3 hrs laboratory per week

Course description

Introduction to survey research and survey data analysis using R programming.

Course objectives

  • Create a valid and reliable survey questionnaire and survey codebook.
  • Perform exploratory data analysis on survey data using R programming.
  • Perform Parametric and Non-Parametric Test on Means using R.
  • Perform correlation analysis and build a regression model explaining relationship of certain economic variables in R.
  • Perform regression analysis on limited dependent variables in R.
  • Perform various techniques of multivariate data analysis in the R statistical software.

Course outline

Week Topics Lessons Description
Week 2-4 Module 1: survey research design
  1. Methods of data collection
  2. Sampling design in surveys
  3. Measurement issues in survey research
  4. Questionnaire construction
  5. Basics of interviewing
  6. Creating a codebook
  7. Data entry
  1. Discuss the various methods of data collection including survey, observation and experimental methods.
  2. Discuss different ways for gathering a sample; random and non-random sampling. Discuss rudimentary formulas for sample size calculation.
  3. Discuss the issues in assigning numbers to represent quantities of attributes. Discuss the various scales of measurement. Discuss criteria in constructing good measurement of variables: reliablity and validity.
  4. Discuss the various advantages and disadvantages of interviews and questionnaire over other methods of data collection.
  5. Discuss the do’s and dont’s of an interviewer’s conduct.
  6. Discuss the importance of creating a codebook for survey data.
  7. Discuss rudimentary of data entry.
Week 5-6 Module 2: exploratory data analysis
  1. Rudiments of EDA
  2. Charts and tables
  3. Measures of central tendency
  4. Dispersion, parameters, skewness and kurtosis
  5. Contingency tables and scatter plot
  1. Discuss EDA as the first step in data analysis.
  2. Discuss various techniques in summarizing and visualizing data.
  3. Discuss the various measures of central tendency and data location.
  4. Discuss the relevance of various dispersion, parameters, skewness and kurtosis.
  5. Discuss the relevance of contingency tables and scatterplots for summarizing and visualizing data.
Week 7-8 Module 3: test on means
  1. Parametric test on means
  2. Non-parametric test on means
  1. Discuss the various t-tests and ANOVA and perform them on a sample data with R.
  2. Perform various non-parametric equivalent of the t-tests and ANOVA on sample data with R.
Week 9-11 Module 4: correlation and regression analysis
  1. Correlation analysis
  2. Review of Regression analysis
  1. Discuss the various types of correlation analysis procedures. Interpret the correlation coefficient.
  2. Discuss the various aspects of regression model building.
Week 12-15 Module 5: limited-dependent variable models
  1. Review of binary dependent regression
  2. Extension to the logit model.
  3. Censored and truncated regression models.
  4. Count dependent variable models.
  1. Discuss the various aspects of the logit and probit.
  2. Discuss the multinomial and ordinal logit models.
  3. Discuss the Tobit regression model for censored data and truncated regression models.
  4. Discuss Poisson and Negative Binomial regression models in the regression analysis of count-dependent variable models.
Week 16-17 Module 6: multivariate statistical analysis
  1. Cluster analysis
  2. Principal component analysis
  3. Exploratory factor analysis
  4. Confirmatory factor anlysis
  5. Structural equation modelling
  1. Understand and apply different clustering methods. Analyze and evaluate the quality and effectiveness of different clusters in dataset.
  2. Learn to perform and interpret principal component analysis to reduce the dimensionality of dataset. Develop the ability to identify and retain significant components for simplifying data without losing critical information.
  3. Identify and estimate underlying factor structures within a set of observed variables.
  4. Understand model-based factor anlaysis and develop proficiency in evaluating model fit and making necessary adjustments to improve analysis.
  5. Apply SEM techniques to understand relationships among variables and construct theoretical models.