Quantitative data analysis

Paper Code: 
CPSY 611
Credits: 
04
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

Learning Outcomes

Learning and teaching strategies

Assessment Strategies

  
 

CO55: Understanding the nature of measurement and its various levels.

CO56: Developing skills to use quantitative techniques such as measures of central tendency, variability, and correlation.

CO57: Knowing how to use the normal probability curve as a model in scientific theory

CO58: Grasping concepts related to hypothesis testing and developing related computational

skills

CO59: Learning basic techniques of descriptive and inferential statistics.

 

Approach in teaching:

Interactive Lectures, Discussion, Tutorials, Reading assignments, Demonstration, Team teaching

Learning activities for the students:

Self-learning assignments, Effective questions, Simulation, Seminar presentation, Giving tasks, Field practical

Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects

 

 

  

 

12.00
Unit I: 
Introduction:

Introduction to Statistics; Descriptive and Inferential Statistics; Measures of central tendency and variability: Characteristics and computation of mean, median and mode; Characteristics and computation of standard deviation & variance

12.00
Unit II: 
Hypothesis Testing about the Difference between Two Independent Means:

Null and the Alternative Hypotheses; One-Tailed and Two-Tailed Tests; Concept of confidence interval and df; Computation and Interpretation of t values

12.00
Unit III: 
Analysis of Variance (ANOVA):

Purpose and Assumptions; one-way and two-way Analysis of Variance

12.00
Unit IV: 
Correlation and regression:

Correlation: Meaning, types and computation; Regression and Prediction: Regression equations, linear regression

12.00
Unit V: 
Nonparametric Approaches to Data:

Introduction and assumptions; Comparison with Parametric Tests; Mann Whitney U Test, Chi-square test

Essential Readings: 
  • Garrett, H.E. (2005). Statistics in Psychology and Education. New Delhi: Paragon International Publishers.
  • Mangal, S.K. (2002). Statistics in Psychology and Education. New Delhi: Prentice Hall India.
  • Minium, E.W. , King B.M. & Bear, G. (1995). Statistical Reasoning in Psychology and Education. New York : John Wiley & Sons.
  • Seigel S. (1988). Nonparametric Statistics in Behavioral Sciences. New York: McGraw Hill.
References: 
  • Freund, R. J., & Wilson, W. J. (2003). Statistical methods. Elsevier.
  • Ott, R. L., & Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Cengage Learning.
  • Singh, A.K. (2017). Tests, Measurements and Research Methods in Behavioural Science. Patna : Bharti Bhavan.
  • Welkowitz, J., Ewen, R.B. & Chocen J. (1982). Introduction to Statistics for Behavioural Sciences. New York: Academic Press.

E Resources : 

  • ResearchGate
  • JSTOR
  • Proquest
  • Shodhganga
  • Delnet
  • Google Scholar
  • National Digital Library (NPTEL)

Academia Journals :

  • Perspectives on Psychological Science
  • Quarterly Journal of Experimental Psychology
  • Journal of Computational and Graphical Statistics
Academic Year: 
2023-2024
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