Quantitative Data Analysis (Theory)

Paper Code: 
25CPSY611
Credits: 
04
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

To develop an understanding of various statistical techniques in terms of their assumptions, applications and limitations

 

Course Outcomes: 

Course

Course Outcomes

Learning and

teaching strategies

Assessment Strategies

Course Code

Course Title

25CPSY611

Quantitative Data

CO121:

Understanding the

Approach in teaching:

Class      test, Semester end

 

Analysis

nature of measurement

Interactive

examinations,

 

(Theory)

and its various levels.

Lectures,

Quiz, Solving

 

 

CO122: Developing

Discussion,

problems    in

 

 

skills to use

Reading

tutorials,

 

 

quantitative techniques

assignments,

Assignments,

 

 

such as measures of

Team teaching

Presentation,

 

 

central tendency,

Learning

Individual

 

 

variability, and

activities for

and      group

 

 

correlation.

CO123: Knowing how

the students:

Self-learning

projects

 

 

to use the normal

assignments,

 

 

 

probability curve as a

Effective

 

 

 

model in scientific

questions,

 

 

 

theory

Simulation,

 

 

 

CO124: Grasping

Seminar

 

 

 

concepts related to

presentation,

 

 

 

hypothesis testing and

Giving tasks,

 

 

 

developing related computational

Field practical

 

 

 

skills

 

 

 

 

CO125: Learning basic

 

 

 

 

techniques of

 

 

 

 

descriptive and

 

 

 

 

inferential statistics.

 

 

 

 

CO126: Contribute

 

 

 

 

effectively in course-

 

 

 

 

specific interaction

 

 

 

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 and 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 Behavioural Sciences. New York: McGraw Hill.

 

Suggested Readings:

 

· 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:

· Quantitative research. A course  offered  on Coursera. Access via: https://www.coursera.org/learn/quantitative-research

Psychological research specialization. A course offered on Coursera. Access via https://www.coursera.org/specializations/psychological-research

Academic Year: 
2025-2026
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