Teaching plan for the course unit


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General information


Course unit name: Workshop on Data Analysis

Course unit code: 568388

Academic year: 2015-2016

Coordinator: David Leiva Ureńa

Department: Department of Methodology of the Behavioural Sciences

Credits: 2,5

Single program: S



Estimated learning time

Total number of hours 62.5


Face-to-face learning activities



-  Lecture with practical component




-  Group tutorial



Supervised project


Independent learning






Further recommendations

Basic knowledge of applied statistics (i.e., an introductory course on descriptive statistics and inference) is advisable.



Competences to be gained during study


To have the capacity to apply the acquired knowledge and problem solving skills in new or not well known environments in wider or multidisciplinary contexts related to their area of study.


To be able to communicate the conclusions, knowledge and ultimate reasons that support them to specialized and non-specialized public in a clear and unambiguous way.


To be able to have the learning abilities to continue learning in a way that will probably be self-directed and autonomous.


To be able to apply information and communication technologies with different goals and purposes (relationship with other professionals, get information, knowledge diffusion…).


To identify the different techniques of data analysis in behaviour and cognition and to be able to apply them.


To show abilities in the writing of research papers and books, in different formats, particularly in the American Psychological Association, in the different areas related to the research in behaviour and cognition, including the knowledge of the steps required to their publication.





Learning objectives


Referring to knowledge

  • To know main tools for statistical analysis under the statistical environment R.


  • To be acquainted with the most common meta-analytical procedures when combining results from a set of original articles


  • To know the particularities of single-case designs and how these features affect the possibilities of data analysis.


Referring to abilities, skills

  • To use R (and R-Commander) for analyzing data for descriptive statistics and inference.


  • To develop basic programming skills with R.


  • To design and create scientific documents using some tools for Literate Programming with R as Sweave, Knitr and R Markdown.


  • To use efficiently  R tools for meta-analysis.


  • To display efficient use of analytical tools in R for single-case designs.


Referring to attitudes, values and norms

  • To maintain their knowledge updated in order to apply the best available analytical option for quantitative integration and single-case designs.



Teaching blocks


1. R for researchers

1.1. R and R-Commander

1.2. Statistics with R

1.3. Introduction to R programming

2. Meta-analysis

2.1. Integrating results in psychology

2.2. Steps in a meta-analysis

2.3. Advanced topics in meta-analysis

2.4. Metafor: R package for meta-analysis

3. Single-case designs

3.1. Single-case designs features

3.2. Visual analysis and visual aids

3.3. Quasi-statistical procedures: nonoverlap and raw indices

3.4. Standardized mean difference

3.5. Regression analysis

3.6. Simulation modeling analysis

3.7. Randomization tests

3.8. Single-case designs analysis with R and R-Commander



Teaching methods and general organization


The theoretical explanations in all three parts will be complemented with examples. The use of R and R-Commander in the sessions will enable the students to play with the topics discussed during the course. The three-way interaction between the professors, the students, and the R program is intended to offer the possibility for a procedural and not only declarative knowledge of the workshop topics.



Official assessment of learning outcomes


The assessment is based on two types of tasks: a set of activities to be done during sessions and a course work consisting of three different parts. Weights are as follows:
- Activities during the sessions: 50% of the grade.
- Course work: 50% of the grade.



Reading and study resources

Consulteu la disponibilitat a CERCABIB


Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single case experimental designs: Strategies for studying behavior change (3rd ed.). Boston, MA: Pearson.


  Recommended readings for Part 3: Single-case designs

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester, UK: John Wiley & Sons.

  Recommended readings for Part 2

Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.)(2009). The handbook of research synthesis and meta-analysis (2nd ed.). New York: Russell Sage Foundation.


  Recommended readings for Part 2

Crawley, M.J. (2012). The R Book (2nd ed.). Chichester: John Wiley & Sons.


  Additional readings for Part 1

Hedges, L. V., & Olkin, I. (1985). Statistical Methods for Meta-analysis. New York: Academic Press.


  Recommended readings for Part 2

Lipsey, M. W., & Wilson, D. B. (2001). Practical Meta-analysis. Thousand Oaks, CA: Sage Publications.


  Recommended readings for Part 2

Verzani, J. (2005). Using R for Introductory Statistics. Boca Raton: Chapman & Hall/CRC.  EnllaƧ

  Recommended readings for Part 1


Bulté, I., & Onghena, P. (2012). When the truth hits you between the eyes: A software tool for the visual analysis of single-case experimental data. Methodology, 8, 104-114.


  Recommended readings for Part 3: Visual analysis

Fox, J. (2005). The R Commander: A basic-statistics Graphical User Interface to R. Journal of Statistical Software, 14, 1–42.


  Recommended readings for Part 1

Kratochwill, T. R., & Levin, J. R. (2010). Enhancing the scientific credibility of single-case intervention research: Randomization to the rescue. Psychological Methods, 15, 124-144.


  Recommended readings for Part 3: Randomization tests

Edició en paper.  EnllaƧ

Maggin, D. M., Swaminathan, H., Rogers, H. J., O’Keefe, B. V., Sugai, G., & Horner, R. H. (2011). A generalized least squares regression approach for computing effect sizes in single-case research Application examples. Journal of School Psychology, 49, 301-321.


  Recommended readings for Part 3: Regression analysis

Edició en paper.  EnllaƧ

Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5, 299–314.


  Recommended readings for Part 1

Parker, R. I., & Vannest, K. J. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40, 357-367.


  Recommended readings for Part 3: Procedures related to visual analysis

Edició en paper.  EnllaƧ

Solanas, A., Manolov, R., & Onghena, P. (2010). Estimating slope and level change in N=1 designs. Behavior Modification, 34, 195-218.


  Recommended readings for Part 3: Procedures related to visual analysis

Edició en paper.  EnllaƧ