SUBJECT

General Research Methods and Multivariate Statistics

lecture

master

Faculty of Education and Psychology

2

Semester 1

Autumn semester

Aim of the course:

Students are introduced to the most common multivariate analyses used within the field of psychology. This course is designed to provide students with a working knowledge of the basic concepts underlying the most important multivariate techniques, with an overview of actual applications.                                                                                 

Learning outcome, competences

knowledge:

• students are expected to know the most frequently used multivariate statistical analyses in psychological research and their practical applications and applicability
• students are expected to know the assumptions of the most frequently used multivariate statistical analyses
• students are expected to know how to report the learned analyses in APA format
• students are expected to know the limitations of the learned analyses

attitude:

• students are expected to gain confidence in making their own decisions about statistical procedures
• students are expected to think creatively and flexibly while applying the learnt knowledge in practice

skills:

• We aim to prepare students to use their statistical knowledge flexibly and be able to compose their MA theses.
• Students are acquiring the judicious selection of analyses, with the applicability and interpretation of them

Content of the course

Topics of the course

• Introduction to multivariate statistics and multivariate data.
• Correlation and simple linear regression analysis.
• Multiple linear regression analysis.
• Logistic regression analysis.
• Introduction to analysis of variance (ANOVA).
• Factorial ANOVA, analysis of covariance (ANCOVA)
• Multivariate analysis of variance (MANOVA)
• Principal component analysis and exploratory factor analysis.
• To fulfil the students’ interest the following topics could also be covered optionally: hierarchical cluster analysis, non-hierarchical cluster analysis, discriminant analysis, analyzing missing data, residual analysis, configuration analysis, introduction to multilevel linear models, confirmatory factor analysis.

Learning activities, learning methods

• interactive lecture is the method of instruction

• students are acquiring the judicious selection of analyses, with the usage and interpretation of them  through several educatory examples
• students get Power Point presentations and detailed written handouts of the material

Evaluation of outcomes

Learning requirements, mode of evaluation, and criteria of evaluation:

The grade consists of the result of the final exam that should have to be passed

mode of evaluation:

• 5-level grading, based on the achieved scores in percentages

criteria of evaluation:

• GRADING based on scores achieved:

0-50 % = 1 (failed)

51-65 % = 2 (passed)

66-79 % = 3

80-89 % = 4

90-100 % = 5

Compulsory reading list

• Field A. (2013). Discovering Statistics Using IBM SPSS Statistics 4th edition, Sage Publications.
• Chapter 4. – Exploring Data with graphs pp. 121-163.
• Chapter 5. – The beast of bias pp. 163-211.
• Chapter 7. – Correlation pp. 270-292.
• Chapter 8. – Regression pp. 314-356.
• Chapter 19. – Logistic regression pp. 775-799.
• Chapter 11. – Comparing several means: ANOVA (GLM 1)  pp. 460-477.
• Chapter 12. – Analysis of covariance, ANCOVA (GLM 2) pp. 488-506.
• Chapter 13. – Factorial ANOVA (GLM 3) pp. 520-542.
• Chapter 14. – Repeated-measures designs (GLM 4) pp. 555-590.
• Chapter 16. – Multivariate Analysis of Variance – MANOVA pp. 623-664.
• Chapter 17. -  Exploratory factor analysis pp. 686-706.

Recommended reading list

• Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston: Pearson Education.
• Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research, The Guilford Press, 40-156.
• Field A. (2013). Discovering Statistics Using IBM SPSS Statistics 4th edition, Sage Publications. – Chapter 20. Multilevel linear models pp. 814-866.
• Vargha, A., Torma, B. & Bergman, L. R. (2015). ROPstat: a general statistical package useful for conducting person-oriented analyses. Journal for Person-Oriented Research, 1 (1-2), 87-98. http://www.person-research.ouradmin.se/articles/volume1_1_2/filer/20.pdf
• Vargha, A., Bergman, L. R. & Takács, S. (2016). Performing cluster analysis within a person-oriented context: Some methods for evaluating the quality of cluster solutions. Journal for Person-Oriented Research, 2 (1-2), 78–86. DOI: 10.17505/jpor.2016.08. http://www.personresearch.ouradmin.se/articles/volume2_1_2/filer/5.pdf