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Unit information: Psychological Statistics and Research Tools in 2016/17

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Unit name Psychological Statistics and Research Tools
Unit code PSYCM0041
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Stollery
Open unit status Not open
Pre-requisites

n/a

Co-requisites

n/a

School/department School of Psychological Science
Faculty Faculty of Life Sciences

Description

The central aim of this unit is to extend coverage of the research tools psychologists will encounter during their literature search, research and psychological assessment activities, focusing on multivariate methods to structure large sets of variables and their relationships. The unit is designed to update and extend students’ knowledge and understanding of generic analysis methods routinely used in psychological investigations, covering topics from more complex forms of multi-factor mixed samples analysis of variance (ANOVA) and analysis of variance with covariance (ANCOVA), to calculation and reporting of effect size and study power, and by extension meta-analysis techniques. Students will also be introduced to state-of-the art computational modelling and programming techniques (e.g., Structural Equation Modelling) and recent advances in non-frequentist statistical reasoning (Bayesian statistical analysis); an approach becoming more prominent in psychology. Each session dealing with theoretical-conceptual issues is supported by a practical session where experience with the relevant software for conducting these analysis is undertaken. The aim is to provide a firm foundation for understanding how the variety of analysis tools currently being used in psychology can be integrated and to introduce students to statistical techniques likely to become more prevalent in the future. The unit finishes with a coursework clinic for each of the forms of analysis to aid in the comprehension of the technique and a final reading week when coursework is submitted.

Intended learning outcomes

On completion of the unit, the students will:

  • Have developed an excellent understanding of the range of conventional and contemporary techniques for analysing complex numerical multivariate data.
  • Have acquired an in-depth knowledge of the interrelationship between experimental design and data analysis in psychology.
  • Have understood how different methodologies of data collection feed into subsequent data analysis.
  • Have acquired the skill of writing of a concise report that conveys the essence of the results from a complex multivariate analysis.
  • Have improved their generic (e.g., time management; project management) and content specific (e.g., statistical software) transferable skills.

Teaching details

Teaching consist of a blend of lectures and practical experience within the software environment given by research active academic staff who use the analysis software as part of their research. Before submission of the coursework, students be able to attend an advice clinic on each of the statistical techniques to enable them to fully appreciate the requirements of an APA submission in their chosen area and/or gain further clarification of the analysis techniques that have been covered. The final week of the unit will free of teaching to enable students to fully focus on their submission. The unit includes 48 hours of scheduled lectures and lab work.

Assessment Details

The unit is assessed by coursework (100%).

Student are given a study description and a complex set of numerical data and have to determine the appropriate analysis, undertake this analysis, and report the findings from the study in the APA (American Psychological Association) format; such as would be found in an APA research paper describing the study (2,000 words maximum).

Reading and References

  • Blunch, N.J. (2013). Introduction to structural equation modelling. (2nd ed). London: Sage. Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge: London Field, A. (2013). Discovering statistics using IBM SPSS statistics. (4th ed). London: Sage Howell, D. C. (2002). Statistical methods for psychology. Pacific Grove: Duxbury. Kruschke, J.K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, & Stan. New York: Academic Press. Tabachnick, B. G. & Fiddell, L. S. (2001) Using multivariate statistics. (4th ed). Harper and Row. Zyphur, M. J., & Oswald, F. L. (2015). Bayesian estimation and inference: A user’s guide. Journal of Management, 41(2), 390-420. doi: 10.1177/0149206313501200 Other references to on-line publications will be made during the unit.

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