Latest Module Specifications
Current Academic Year 2025 - 2026
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Description This module will introduce students to the most common quantitative analytic techniques in psychology. It will familiarise students with basic statistical tests, their appropriate usage and how to compute the tests using R. The module also supports the development of skills including data analytics and visualisation. A strong emphasis is placed on enabling students to integrate theory and statistical reasoning skills, including analytic competence, using laboratory experiments, practical laboratory classes and online labs. The content of laboratory activities will be informed by other core modules taken in Year 1 and through the use of real-world datasets. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Demonstrate the ability to import, clean, and manipulate datasets in R 2. Identify and use basic data analysis and visualization tools to describe, conduct and interpret basic descriptive statistics such as central tendency and variance. 3. Describe statistical concepts such as the normal distribution, probability, sampling distributions, confidence intervals, and hypothesis testing 4. Conduct, Report, and Interpret inferential tests such as correlation, simple regression, and t-tests 5. Use APA conform reporting for statistical results. 6. Gain first hand research experience by participating in psychological research projects within the school including those of staff, post-graduate students and undergraduate final year projects. 7. Use searching skills to effectively use library and online psychology resources. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
R and Rstudio Introduction to the R language environment; entering, importing, and saving data. Experimental and Survey design Descriptive statistics Measures of central tendency & dispersion. Describing and exploring Data using graphs Introduction to data visualisation using for spread and inferential statistics. Sampling distributions Normal distribution, skewed data, and z-scores; the student-t distribution; the Central Limit Theorem and sample error. Statistical Inference Inferential analysis using correlation, simple regression, and t-tests, and pitfalls in their interpretation. Theoretical and Practical Foundations of Hypothesis testing The null hypothesis and underpinning assumptions; probability and statistical significance; Type I and Type II error. Inferential statistics Parametric and non-parametric tests of difference and association for two conditions; assessing normality. Report writing Writing APA-style psychology research reports, including appropriate citations and references. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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