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Course Detail

Course Name Research Methodology II: Adavanced Quantitative Data Analysis
Course Code 24CLT514
Program M. Sc. Cognitive Sciences, Learning and Technology
Semester II
Credits 3
Campus Amritapuri


Unit I

Unit I – Basic Concepts of Statistical Tests
Basic concepts of statistical tests:

  • Levels of measurement
  • Population vs sample
  • Normal distribution
  • Measures of central tendency and dispersion
  • Probability and Bayesian statistics
Unit II

Unit II – Descriptive Statistics versus Inferential Statistics

Descriptive Statistics:

  • Purpose of descriptive statistics: summarizing and presenting data
  • Graphs: bar chart, pie chart, histogram
  • Frequency tables

Inferential statistics:

  • Purpose of inferential statistics
  • Null hypothesis (H0) and alternative hypothesis (HA)
  • Type I and type II errors
  • Significance level Alpha (α)
  • Critical values (t Z) and the confidence interval
  • One-tailed and two-tailed tests of significance
Unit III

Unit III – Inferential Statistics Part 1: Measuring Differences Between Groups

  • Parametric vs non-parametric tests
  • Measuring differences between groups: t-test, univariate analysis of variance (ANOVA), multivariate analysis of variance (MANOVA)
  • Non-parametric equivalents: Chi-square test, McNemar’s test, Mann-Whitney U test, Wilcoxon signed rank test, Kruskal-Wallis H test
Unit IV

Unit IV – Inferential Statistics Part 2: Exploring Relationships Between Variables

  • Cross-tabulation
  • Correlation (Spearman’s rank correlation and Pearson’s product-moment correlation)
  • Linear regression
  • Multiple regression analysis
  • Binary logistic regression analysis
Unit V

Unit V – Effect Size and Test Power; Validating Psychometric Scales

Effect size and test power:

  • What is the effect size, and why report it?
  • Effect sizes of the different statistical tests
  • Test power (1-β)
  • Using G*Power to calculate test power and the sample size
  • Reporting the effect size and test power in a research paper
  • How to report the results of significance tests in APA format
  • How to conduct a systematic literature review and meta-analysis

Validating psychometric scales:

  • Avoiding errors in measurement
  • Testing for validity (e.g., convergent vs discriminant validity)
  • Testing for reliability (Cronbach’s Alpha)
  • Performing factor analysis to confirm subscales

Course Objectives and Outcomes

Prerequisite: Good reading and writing skills in English; basic knowledge of research methodology

Course Objectives:

  • Gain an in-depth understanding of concepts of quantitative data analysis, such as measures of central tendency and dispersion, levels of measurements, normal distribution of the data, and type I and II errors, so that they can be applied in one’s research study
  • Gain the ability to distinguish between parametric and non-parametric tests and know when to use which
  • Develop more advanced skills in summarizing and presenting data as graphs and frequencies in descriptive statistics using computer software programs (e.g., SPSS)
  • Know how to measure group differences and test for significance using inferential statistics using computer software programs (e.g., SPSS): Chi-square tests, t-tests, Mann-
  • Whitney U tests, Kruskal-Wallis H tests, as well as ANOVA (Univariate Analysis of Variance) and MANOVA (Multivariate Analysis of Variance)
  • Know how to explore the relationships between variables using cross-tabulations, correlations, and different types of regression analyses
  • Gain the ability to calculate effect sizes and test power, as well as the ability to report results from statistical tests in a scientific research paper
  • Understand the foundations of validating research instruments in order to test for the validity and reliability

Course Outcomes:

  • CO1: Understand the basic concepts of statistical significance tests, such as levels of measurement, measures of central tendency and dispersion, the normal distribution, significance level Alpha, as well as Type I and Type II Errors
  • CO2: Knowledge of how to apply statistical tests for hypothesis testing
  • CO3: Knowledge of how to perform and interpret quantitative statistical analyses with computer software programs (e.g., SPSS)
  • CO4: Knowledge of how to use computer software programs (e.g., SPSS) to summarize and present data, as well as to do inferential statistical tests for measuring differences between groups and for exploring the relationship between variables
  • CO5: Understand the concepts of effect size and test power to interpret the results of statistical tests CO6: Knowledge of how to test the psychometric properties of the research instruments used to validate them for a specific population
  • CO7: Understand how to report the results of statistical tests in a research paper


  • Proficiency in computer software programs to perform statistical tests (e.g., SPSS)
  • Ability to use statistical tests appropriate for the research question
  • Distinguish between parametric and non-parametric tests
  • Make graphs with software programs (e.g., SPSS)
  • Perform inferential statistics to test hypotheses, such as finding differences between groups and relationships between variables
  • Calculate effect size and required sample size
  • Test psychometric properties of standardized psychometric scales
  • Interpret results of statistical tests
  • Report results of statistical tests

Program outcome PO – Course Outcomes CO Mapping


Evaluation Pattern:

Assessment Internal External
Midterm Exam 30
*Continuous Assessment (CA) 20
End Semester 50

*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar


Reference Books

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