Module Code and Title:       STS101 Introduction to Statistics

Programme:                          BSc in Environmental Management

Credit Value:                         12

Module Tutor(s):                   Leishipem Khamrang (Coordinator), Kinley Dorji

General objective: This module aims to provide students with the basic statistical concepts and its relevance to environmental studies. Practical learning using empirical data and the real-word data is a major emphasis of the module. The module will thus not only expose students to the availability and the uses of quantitative information related to environmental issues but also help them better learn the current state of the environment. 

Learning Outcomes – On completion of the module, students will be able to:

1. Describe the relevance of statistics in environmental studies.
2. Define essential statistical concepts and terms.
3. Choose appropriate statistical tests and techniques for analysis of data.
4. Organize and present quantitative data using appropriate statistical techniques.
5. Apply statistical techniques for analysing data using spreadsheet and statistical software.
6. Test hypotheses using appropriate statistical tests and techniques and draw correct inferences. 
7. Interpret the outputs of statistical analysis, in numerical terms and through graphs.
8. Analyse environmental related data such as rainfall, temperature and water quality.

Learning and Teaching Approach:

Assessment Approach:

1. Class test (Practical): 15% 

Students will undertake two class tests (lab tests) – one before the midterm (7.5%) and one after the midterm (7.5%). These lab tests will cover proficiency of using statistical software for generation of outputs, interpretation of the outputs – contingency tables and graphs. 

2. Written assignment (Analysis of statistical report): 10%

To assess statistical proficiency, students will individually analyse different statistical reports such as, Bhutan Living Standards Survey, Labour Force Survey, GNH Survey report, Poverty Analysis Report, Bhutan (PAR) etc. They will discuss the statistical tools and techniques employed in the reports and make inferences based on the results presented in the reports. The report analysis will be 500-600 words in length. The report analysis will be evaluated on:  

3%       Exposition on the techniques, tools and methods employed in the reports

5%       Interpretation: accuracy, completeness and robustness

2%       Language, flow and articulation 

3. Written assignment (application of statistical techniques): 20%

Students will individually submit a project on quantitative data analysis using primary data or secondary data. Students will decide a topic and identify suitable statistical tools and technique(s) for the data analysis. The project will be 750-1000 words in length. This assignment consists of two parts: presentation (10%) and project report (10%). Duration of the presentation will be 10 minutes. 

Presentation

4%       Content analysis and discussion (accuracy, completeness, argument and justification) 

4%       Delivery (Language, flow, presentation skills and visual aids)

2%       Time management and concluding remarks.

Project report

4%       Accuracy and completeness 

4%       Quality of analysis: contents analysis and discussion of the findings

2%       Mechanics (Language, organization and referencing

Students will take a written exam of 1.5-hr duration covering topics up to the mid-point of the semester. The exam will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

5. Semester-End Examination: 40%

Students will take a written exam of 2.5-hr duration encompassing all the subject matter covered in the semester. This assessment is comprehensive and summative in nature, and will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

Overview of assessment approaches and weighting

Pre-requisites: None

Subject Matter:

1. Unit I: Introduction to using statistics
   1. The research process; making observations, generating theories and testing them
   2. Introduction to data collection and analysis
      1. Populations and samples
      2. What to measure: variables, measurement error, validity and reliability
      3. How to measure: correlational research methods, experimental research methods, randomization
2. Unit II: Basics of SPSS
   1. Overview of the SPSS environment
   2. Data editor
   3. Variable view
   4. Syntax window, outputs
   5. File management
3. Unit III: Exploring data with graphs
   1. Art of presenting data properly and reading graphs accurately
   2. Chart making in SPSS
   3. Types of charts, their uses and suitability for different purposes (column and bar graphs, histograms, boxplots, line charts, scatterplots)
4. Unit IV: Descriptive Statistics
   1. Measures of central tendency and dispersion
      1. Mean, median, mode, quartile, deciles and percentiles
      2. Range and coefficient of range
      3. Mean deviation, variance, standard deviation and coefficient of variance 
   2. Analysing data: frequency distribution (types, centre, dispersion)
5. Unit V. Inferential statistics: Correlation 
   1. Karl Pearson’s Correlation Co-efficient
   2. Spearman’s Rank Correlation
   3. Kendall’s Rank Correlation
   4. Testing the significance of correlation coefficient
   5. Fitting statistical model
6. Unit VI: Inferential statistics: Tests of hypotheses 
   1. Basics concept of hypothesis 
   2. Critical regions, critical values, p-values and decision rule
   3. Confidence intervals 
   4. Using statistical models to test research questions
      1. Test statistics
      2. Null hypothesis and alternative hypothesis
      3. One- and two-tailed tests
      4. Types I and type II errors
      5. Effect size
      6. Statistical power
   5. Applications of t-test
   6. Applications of chi-squared test

List of practical works:

1. Data presentation – group and ungrouped data, frequency tables
2. Analysis of weather variable data and vegetation data (tree height, DBH, Biomass)
3. Correlation exercises (correlation coefficient and t-test) using data related to social infrastructure and level of living, access to irrigation and agriculture production, application of fertilizers and agriculture production
4. Measures of central tendency and dispersion exercises using socio-economic data, such as, literacy rate, urbanization, households’ access to safe drinking water, happiness index, labour force participation rate etc. 
5. Hypothesis testing exercises: t-test and chi-squared test

Reading List:

Essential Reading

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4^th ed.)  New Delhi, India: Sage Publications. 

Gupta, S.C. (2018). Fundamentals of statistics (7^th ed.). Mumbai, India: Himalaya Publishing House.

Manly, B.F.J. (2009). Statistics for environmental science and management. London, England: Chapman & Hall.

Additional Reading

Rumsey, D.J. (2011). Statistics for dummies (2^nd ed.). Hoboken, NJ: Wiley Publishing. 

Rumsey, D.J. (2009). Statistics II for dummies. Hoboken, NJ: Wiley Publishing. 

Urdan, T.C. (2017). Statistics in plain English (4^th ed.). New Jersey: Lawrence Erlbaum Associates, Inc. 

Twonend, J. (2002). Practical statistics for environmental and biological scientists. West Sussex, UK: John Wiley and Sons.

Kothari, C. R. & Garg, G. (2019). Research methodology: Methods and techniques (4^th ed.) New Delhi, India: New Age International Publishers.  

Date: June 2021