The University of Southampton

ENVS1005 Quantitative Methods

Module Overview

You will be introduced to a number of key statistical concepts and data presentation formats. Beginning with exposure to a variety of data types defining the nature and properties of data you are likely to encounter. Emphasis is placed on distinguishing between population parameters and sample statistics and exploring the nature of distributions. Aided via the introduction of a powerful graphics package, a component part of SPSS, a dedicated statistical software. You will become familiar with the concept of central tendency and the measurement of variation, and how these may be presented graphically. Emphasis is placed on information transfer to aid presentations, essays, reports and dissertation. In addition to computer technology, you will hone skills in the use of hand calculator for use in laboratory and the field applications. A significant portion of the unit is given to developing your understanding of a variety of common statistical procedures including establishing the presence and strength of a relationships and standard approaches for determining if significant differences exist between groups within a variety of experimental designs. Central to this is the concept of hypotheses testing.

Aims and Objectives

Module Aims

The overall aim of the unit is to empower you with the ability to understand statistical nomenclature in the literature and to carry out statistical analysis and data presentation for use throughout your undergraduate career and beyond. Emphasis is placed on knowledge and skill transfer.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The need for both a multi-disciplinary and an interdisciplinary approach in advancing knowledge and understanding of Earth systems, drawing, as appropriate, from the natural and the social sciences
  • The processes which shape the natural world at different temporal and spatial scales and their influence on and by human activities
  • The terminology, nomenclature and classification systems used in environmental science
  • Methods of acquiring, interpreting and analysing environmental science information with a critical understanding of the appropriate contexts for their use
  • The contribution of environmental science to the development of knowledge of the world we live in
  • The applicability of environmental science to the world of work
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Receiving and responding to a variety of information sources (e.g. textual, numerical, verbal, graphical)
  • Appreciating issues of sample selection, accuracy, precision and uncertainty during collection, recording and analysis of data in the field and laboratory
  • Preparing, processing, interpreting and presenting data, using appropriate qualitative and quantitative techniques and packages including geographic information systems
  • Solving numerical problems using computer and non-computer-based techniques
  • Developing the skills necessary for self-managed and lifelong learning (e.g. working independently, time management and organisation skills)
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Planning, conducting, and reporting on environmental investigations, including the use of secondary data
  • Collecting, recording and analysing data using appropriate techniques in the field and laboratory
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Recognising and using subject-specific theories, paradigms, concepts and principles
  • Analysing, synthesising and summarising information critically, including prior research
  • Collecting and integrating several lines of evidence to formulate and test hypotheses
  • Applying knowledge and understanding to complex and multidimensional problems in familiar and unfamiliar contexts


Lecture 1: Defining Levels of Measurement: Categorical (Nominal), Ordinal, Interval and Ratio Scales. Continuous and Non-continuous (discreet) variables. The significance of different levels of measurements to statistical analysis and questionnaire design. Lecture 2: Consideration of the term 'Measurement of Central Tendency'. Examination of the Mean, Median and Mode. Introduction to distributions and the role of the Histogram. Kurtosis in distributions. Computer Workshop: Introduction to SSPS data entry and coding. The Data Editor. Data Entry format. Variable names, variable and value labels. Defining variable type, missing values, column format and decimal places. Scales of measurement: Nominal, Ordinal, Interval and Ratio scales. Saving and retrieving data. Printing output. SPSS Help facility. Lectures 3 & 4: The nature of variation. The measurement of variation within a population. Consideration of the range, interquartile range, percentiles, standard deviation and variation. Introduction to populations and samples. Defining population parameters and sample statistics. Standard errors. Computer Workshop: The use of the scientific calculator. Data editing, manipulation and recoding in SPSS. Blocking, copying and paste operations. Data deletion. Reports, case selection and case summaries. Data file information. Recoding variables. Lectures 5 & 6: Frequency distributions. Introduction to the normal distributions. The area under the normal curve, the Z distribution. Computer Workshop: Tabulations and Graphics. Describing categorical data. Data summary, frequencies and frequency tabulated output. Bar and Pie charts. Chart values. Introduction to crosstabs and contingency tables. Describing interval data. Frequencies and frequency statistics: Histograms. Quartiles and Percentiles. Measures of dispersion and central tendency. Descriptives and descriptive statistics. Stem and leaf plots. Boxplots. Lecture 7: Introduction to Correlation. Measure of association and association strength in categorical and interval data. Introduction to the Spearman Rank Correlation Coefficient and the Product Moment Correlation Coefficient (r). Introduction to the Coefficient of Determination (r2). Lecture 8: Introduction to simple linear regression. The concept of cause and effect. Dependency. The construction of simple 'Scatter Plots'. The definition of Independent and Dependent variables. The regression equation. Simple application to scatter plots. Derivation of the 'Best Fit Line'. Computer Workshop: Construction of simple 'Scatter plots'. Application of the Spearman Rank Correlation Coefficient and the Product Moment Correlation Coefficient (r). Application of simple Linear Regression. Lectures 9 & 10: Introduction to Hypothesis Testing. Significance test theory. Parametric and Non-parametric statistics and data assumptions. Hypothesis testing via introduction to at t-test for small samples. Computer Workshop: Examining if the difference between sample means is statistically significant. Application of a test of hypothesis with reference to the t-test. Lectures 11 and 12: Further applications of significance testing. The F-test for Homogeneity of Variances. Further t-test applications: One sample t-test. Independent samples and matched pair designs. Computer Workshop: Application of the F-test for Homogeneity of Variance. One sample, independent samples and matched pair t-testing using SPSS. Lecture 13: Introduction to the Analysis of Variance (ANOVA). Oneway design. Lecture 14: Nested ANOVA design Computer workshop: Introduction to simple oneway analysis of variance. ANOVA options. Oneway ANOVA post hoc multiple comparisons. Scheffe and Tukey posteriori tests. ANOVA output. Simple nested ANOVA designs Lectures 15 & 16: Twoway analysis of Variance (ANOVA) with and without replication. Computer workshop: Simple application of Twoway analysis of variance with and without replication. Lectures 17 & 18: Tests of association. Measure of association in categorical data. Introduction to the Chi-Square analysis. Simple and more complex designs. Contingency tables. Yates correction factor. Computer workshop: Further crosstab operations. Crosstab layering. Data sets and contingency tables. Case weighting. Association in categorical data. Binomial test for one dichotomous variable. Chi-square test for one sample. Imposed selected expected frequencies. Chi-square test for multiple unrelated samples. Large contingency tables. Crosstab statistics and chisquare. Cell displays. Chi-square output. Lectures 19 & 20: Introduction to Non-parametric statistics. Mann-Whitney and Wilcoxan independent and paired designs. Kruskal Wallis and Friedman non-parametric alternatives to ANOVA. Computer workshop: Applications of Mann-Whitney, Wilcoxan, Kruskal Wallis and Friedman tests using SPSS. Final two weeks will involve revision and formal assessment.

Special Features


Learning and Teaching

Teaching and learning methods

The unit will be delivered by the Unit Co-ordinator via a combination of lecture and computer workshop. There will be two lecture sessions dealing with statistical theory early in the week, followed by a computer workshop when the theory will be tested in a series of applied computer exercises. Both the lecture and workshop sessions will have dedicated learning materials that will build into a valuable portfolio. Learning activities include: • Attendance at lecture and workshop sessions • Self-directed learning – it is expected that exercises presented at each workshop will be completed within the intervening week prior to commencing the following workshop. Tutorial support provided by the Unit Co-ordinator will be availed in the interim in order to maintain progress within the unit.

Wider reading or practice17
Practical classes and workshops24
Preparation for scheduled sessions25
Completion of assessment task25
Total study time150

Resources & Reading list

Comprehensive, stand alone, learning materials are provided at each lecture and workshop. The materials are designed to build into a valuable reference portfolio for use during the remainder of the degree programme.. 

Gray C.D. and Kinnear P.R. (2012). IBM SPSS Statistics 19 Made Simple. 


Assessment Strategy



Mock exam


MethodPercentage contribution
Exam  (120 minutes) 50%
Exam  (120 minutes) 50%


MethodPercentage contribution
Exam  (120 minutes) 50%
Exam  (120 minutes) 50%

Repeat Information

Repeat type: Internal & External

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