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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23
No such Code for pgprog: MA Research Methods

Department: Psychology

PSYC40130: Applied Statistics

Type Tied Level 4 Credits 30 Availability Not available in 2021/22
Tied to C8K107 Research Methods (Developmental Psychology)
Tied to C8K009 Developmental Psychopathology
Tied to C8K109 Cognitive Neuroscience
Tied to C8K409 Behavioural Science
Tied to MA Research Methods

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To introduce students to the theory and application of statistical methods using relevant software
  • To develop students' confidence and competence in the use of statistics and the analysis of data relevant to Psychologists

Content

  • Data collection and validation
  • Data manipulation
  • Presentation of data using graphics
  • Basic parametric and non-parametric techniques
  • Analysis of Variance (Anova) (One way / Two way, Mixed Models, Hierarchical, Covariance)
  • Regression (Linear, Non-Linear, Logistic)
  • Factor Analysis
  • Multidimensional Scaling
  • Cluster Analysis
  • Analysis of Power
  • Meta-analysis
  • Additional statistical techniques may be introduced as appropriate

Learning Outcomes

Subject-specific Knowledge:
  • range of widely-used statistical tests
  • importance of the role of statistics in any successful data analysis
  • limitations of the statistical techniques covered
  • advantages and limitations of statistical software
Subject-specific Skills:
  • use and appllication of a wide variety of statistical techniques
  • effective use of statistical applications software
  • analysing data and presenting accurage and relevant conclusions
Key Skills:
  • implement genral IT and research skills
  • manage their own time and resources
  • work to deadlines and within defined parameters

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

  • The objectives will be met in lecture and practical sessions. Students will be taught a variety of parametric and non-parametric statistical data analysis methods, illustrated by examples. These examples will be in the form of data sets that will be analysed, and in the form of existing research papers that contain results from a statistical analysis, which are discussed to evaluate the results. In practical sessions students will have hands on training in data analysis using SPSS, R and JASP.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 22 1 per week 2 44
Practicals 22 1 per week 1 22
Preparation & Reading 234
Total 300

Summative Assessment

Component: Examination Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Examination 2 hours 100%
Component: Online Test Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Online Test 2 hours 100%

Formative Assessment:

Formative student assessments (both written and oral) will be undertaken throughout the duration of the module. These will be assessed by the tutor to enable students to gauge their own individual rate of progress.


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