Durham University
Programme and Module Handbook

Postgraduate Programme and Module Handbook 2019-2020 (archived)

Module SOCI59215: Statistical Exploration and Reasoning

Department: Sociology

SOCI59215: Statistical Exploration and Reasoning

Type Open Level 4 Credits 15 Availability Available in 2019/20 Module Cap
Tied to


  • None


  • None.

Excluded Combination of Modules

  • None


  • To enable students to understand how to use statistical techniques for exploration and description of data sets;
  • To enable students to make appropriate statistical inferences about associations between social phenomena.


  • The nature of data – spreadsheets and what they contain.
  • Exploring and describing data.
  • Populations, sample data and sampling distributions.
  • Basic inference I: point estimates and confidence intervals.
  • Basic inference II: significance tests.
  • Cross-tabulation and Chi-Square tests.
  • Differences in means: t-tests and ANOVA.
  • Correlation and simple linear regression.
  • Causal claims, statistical control and multiple linear regression.
  • Discussion of formative assignments, review, questions and answers.

Learning Outcomes

Subject-specific Knowledge:
  • At the end of this module students will be able to:
  • Understand the concept of statistical inference and carry out basic inferential procedures.
  • Understand the concept of statistical association and how to use statistical methods to test for such association.
Subject-specific Skills:
  • Set up and navigate an SPSS spreadsheet.
  • Calculate basic descriptive statistics for a set of data and construct appropriate graphical representations.
  • Obtain and interpret confidence intervals and significance tests for single variables.
  • Construct and interpret contingency tables.
  • Carry out and interpret tests for differences in means across two or more groups.
  • Estimate and interpret simple and multiple linear regression models.
  • Distinguish correlation and cause.
  • Use SPSS to execute the procedures covered in the module.
Key Skills:

    Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

    • The package SPSS will be used as this is now the best platform for the course content.
    • Assessment: Students will be required to produce a report by analysing a real large-scale secondary dataset .

    Teaching Methods and Learning Hours

    Activity Number Frequency Duration Total/Hours
    Lectures 10 weekly 1 10
    Practicals 10 weekly 1 10
    Preparation & Reading 130
    Other: 150

    Summative Assessment

    Component: Assessment Component Weighting: 100%
    Element Length / duration Element Weighting Resit Opportunity
    Project based on data provided to students and analysed and interpreted by themselves 3,000 100%

    Formative Assessment:

    This will be a short formative assignment in which students must carry out a series of SPSS operations and interpret the results of the operations. Although not all procedures will be covered in this work, and different data will be used, the formative assignment follows the similar format as the summative assignment. It is therefore aimed to assist students to become familiar with requirements and expectations of summative work. To be submitted in session eight. Students will receive individual feedback on the formative.

    Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University