Course

Unit 1

Descriptive and Inferential Statistics

Definition and distinction between descriptive and inferential statistics. Measures of central tendency and variability. Concepts of population, sample, and sampling distributions. Introduction to hypothesis testing and confidence intervals. Hands-on exercises using Excel or Jamovi to calculate descriptive statistics and confidence intervals.

Unit 2

Data and Graphical Representation

Types of data: Nominal, ordinal, interval, and ratio scales. Techniques for organising and summarising data. Graphical methods: Histograms, bar charts, pie charts, box plots, and scatterplots. Interpretation and presentation of data visuals. Practical exercises using beginner-friendly software to create interactive graphs and charts.

Unit 3

Foundations of Probability Theory

Basic concepts: Sample space, events, and probability axioms. Conditional probability and independence. Common probability distributions: Binomial, normal, and t-distributions. The role of probability in statistical inference. Simulations of probability experiments using RStudio Cloud or online probability simulators.

Unit 4

Parametric and Non-Parametric Tests

Introduction to parametric tests: Assumptions and applications. Common parametric tests: t-tests (independent and paired samples), ANOVA. Conditions required for using parametric tests. Introduction to non-parametric tests: When and why to use them. Common non-parametric tests: Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis Test, Chi-square test. Conducting tests using beginner-friendly software (Jamovi, SPSS) with guided outputs and interpretation exercises.

Unit 5

Estimation, Effect Sizes, and Statistical Power

Concept of point and interval estimation. Understanding and calculating effect sizes.Concept of statistical power and factors influencing it. Methods for determining appropriate sample sizes for research studies. Introduction to power analysis. Interactive exercises in software to calculate effect sizes, simulate power, and determine sample size.

Unit 6

Data Analysis Using Statistical Software

Introduction to statistical software (e.g., SPSS, R, or SAS). Data entry, manipulation, and management. Performing descriptive and inferential statistical analyses. Interpreting and reporting output from statistical software. Ethical considerations in data analysis and reporting. Beginner-level guided tutorials for performing analyses, generating graphs, and interpreting output. Online, cloud-based platforms such as RStudio Cloud and Jamovi Cloud will be used for hands-on practice.

Text Books / References

Textbooks

1. Flick, U. (2017). Introducing research methodology: A beginner’s guide to doing a research project (2nd ed.). Sage Publications.
2. Somekh, B., & Lewin, C. (2012). Theory and methods in social research (2nd ed.). Sage Publications.
3. Howell, D. C. (2012). Statistical methods for psychology (8th ed.). Cengage Learning.
4. Bear, G. G., King, B. M., & Minium, E. W. (2008). Statistical reasoning in psychology and education. Wiley India Private Limited.
5. Gupta, S. P. (1999). Statistical methods (3rd ed.). Sultan Chand & Sons.

Suggested Readings

• Heiman, G. (2013). Basic statistics for the behavioural sciences (7th ed.). Cengage Learning.
• Garrett, H. E. (2006). Statistics in psychology and education. Paragon International Publishers.
• Agresti, A., & Finlay, B. (2013). Statistical methods for the social sciences. Pearson Education.

This course provides a comprehensive foundation in research methodology, emphasising the application of statistical techniques in psychological research. Students will explore descriptive and inferential statistics, data representation, probability theory, and both parametric and non-parametric testing methods. Additionally, the course covers essential concepts such as effect sizes, statistical power, sample size determination, and the utilisation of computer software for data analysis. Students will also gain practical exposure to beginner-friendly statistical software and online tools (SPSS, Jamovi, RStudio Cloud, or Excel) to perform analyses, visualise data, and simulate statistical concepts interactively.

Course Objectives:

• To introduce students to the fundamental concepts of descriptive and inferential statistics.
• To develop proficiency in organising, summarising, and graphically representing data.
• To provide a foundational understanding of probability theory and its applications in hypothesis testing.
• To familiarise students with parametric and non-parametric tests, including their assumptions and appropriate usage.
• To equip students with the skills to estimate effect sizes, understand test power, and determine appropriate sample sizes for research studies.
• To introduce the use of statistical software for data analysis and interpretation.
• To enable students to perform statistical analyses and create visualisations using beginner-friendly software and online interactive tools.

Course Outcomes:

• CO1: Apply descriptive and inferential statistical methods to analyse research data.
• CO2: Effectively organise and present data using appropriate graphical techniques.
• CO3: Understand and apply the principles of probability theory in the context of research.
• CO4: Select and perform suitable parametric and non-parametric tests based on data characteristics and research questions.
• CO5: Calculate and interpret effect sizes, assess statistical power, and determine necessary sample sizes for various research designs.

CO-PO Mapping