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PSYC40130: Applied Statistics

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Type	Tied
Level	4
Credits	30
Availability	Available in 2023/24
Module Cap
Location	Durham
Department	Psychology

Prerequisites

None

Corequisites

None

Excluded Combinations 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	Monitored
Lectures	22	1 per week	2	44	Yes
Practicals	22	1 per week	1	22	Yes
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.

More information

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