programme

Quantitative Research Methods in Education

Home/ Quantitative Research Methods in Education
Course TypeCourse CodeNo. Of Credits
Foundation ElectiveSES3012052

 

Semester and Year Offered: Semester 1/2

Course Coordinator and Team: Nivedita Sarkar (C)

Email of course coordinator: niveditasarkar@aud.ac.in

Pre-requisites: Pre-doctoral

Aims/Outline:

  • Familiarize students with quantitative research methods and their application in diverse research settings.
  • Familiarize students with various statistical concepts and how to do the analyses with the help of those.
  • Enable students to comprehend and interpret statistical results presented in academic papers, reports and studies and skills for presentation of own research findings.
  • Familiarize students with data sets available in the country to pursue their own research.
  • Familiarize students with statistical software packages.

Course Outcomes:

By the end of the course the participants will be able to:

  • understand various statistical concepts introduced in the course.
  • equip students with statistical tools to conduct empirical research in the area education.

Brief description of modules/ Main modules:

Unit 1: Approaches to Educational Research

The course will begin with a brief discussion on the epistemological and ontological underpinnings of quantitative, qualitative and mixed methods, reasons as to why one should choose quantitative or qualitative approach and typical scenarios where the two approaches are combined, so as to give students a rounded understanding of research methods. This unit will also introduce the concepts of criterion of research design; Cross sectional and longitudinal research designs; Experimental/ quasi- experimental methods.The unit concludes with introducing students to data sets that are available in India for education research. The topics will include:

  • Research Methods in the Social Sciences
  • Steps in Conducting Research: Sampling and its types
  • Quantifying social phenomenon: Data and Data Types, Classification and standardization and Scales of measurement
  • Graphical representation of data
  • Meaning and function of research design; criterion of research design; Cross sectional and longitudinal research designs; Experimental/ quasi- experimental methods.
  • Designing a survey questionnaire
  • Introduction to various education related data sets and handling data

Unit 2: Revisiting Basic and Descriptive Statistics

The second unit will delve in discussing descriptive statistics viz. types of variables, frequency distribution, and graphical representation of data, measures of central tendency, measures of dispersion, it will also introduce the concept of normal probability distributions, which is the most important distribution in statistics and is the foundational base for inferential statistics, z- score problems, sampling distributions and the central limit theorem along with correlation analysis, parametric and non-parametric tests. Students will be introduced to both Excel and SPSS in this unit and these sessions will continue throughout the course. The topics will include:

  • Using descriptive Statistics to understand data characteristics: Measures of central tendency and variability
  • Normal probability distribution
  • Sampling Distribution: Basic Concepts and Types of error
  • Test of significance
  • Measures of Correlation; Simple Partial and Multiple
  • Hypothesis Testing – Large and Small Samples (z and t tests)
  • Parametric and non- parametric techniques (Chi Square, ANOVA)

Unit 3: Regression Analysis

Bivariate linear regression model will be discussed in this unit. The discussion will be quite detailed since understanding of bivariate regression is essential to further understand multivariate regression and advanced statistical techniques. We will initiate the discussion with the use of straight line to describe a particular form of relationship between two continuous variables and scatter plots to check if the relationship is approximately linear, followed by the use of least squares method to estimate the best line to describe a relationship, variability of data about the straight line, Pearson’s correlation to measure the strength of linear association between two variables and statistical tests for significance for a regression analysis. This unit will further discuss about the multiple regression analysis and conclude with some discussion about dummy variable regression model.

  • Two Variable Regression Analysis: some basic ideas
  • The method of Ordinary Least Squares
  • Properties of the OLS estimator: GAUSS MARKOV Theorem
  • Multiple regression analysis: The problem of estimation
  • Relaxing the assumption of Classical Regression Models (Multi-collinearity, Heteroscedasticity; Autocorrelation)
  • Dummy Variable Regression models
  • Using SPSS for statistical analysis

Assessment Details with weights:

Assignment/Class test

30%

SPSS/STATA assessments

30%

End term exam

40%

 

Reading List:

  • Creswell, J. W. (2003). Research Design: Qualitative,Quantitative and Mixed Methods Approaches. Second Edition. University of Nebraska
  • Healey, J. Ninth Edition. Statistics- A Tool for Social Research, Wadsworth CengageLearning, Student Copy ISBN-978-1-111-18636-4
  • Cohen, L., L. Manion and K. Morrison.(2000). Fifth Edition.Research Methods in Education.Routledge Falmer
  • Healey, J. Ninth Edition. Statistics- A Tool for Social Research, Wadsworth
  • Cengage Learning, Student Copy ISBN-978-1-111-18636-4.
  • Healey, J. Ninth Edition. Statistics- A Tool for Social Research, Wadsworth
  • Cengage Learning, Student Copy ISBN-978-1-111-18636-4.
  • Wooldridge, Jeffrey M. (2010). Econometrics. Cengage Learning. (pp. 22-436)
  • Gujarati, D. N. (2003).Basic Econometrics, Fourth edition.McGraw-Hill. New York. (pp. 1-505)