Methods and Statistical Techniques for Researchers

Code

570525

Credits

5cr

Content

1. Bases of scientific research

1.1. Scientific method: need, characteristics, principles
1.2. Approaches to scientific research
1.3. Hypothesis, theories, models
1.4. Variables: definitions and classifications
1.5. Validity: internal, external

2. Phases of scientific research

2.1. Research problem and bibliographic search
2.2. Research design
2.3. Sampling
2.4. Data collection
2.5. Data organization
2.6. Data analysis: principles and options
2.7. Interpretation of results
2.8. Dissemination: scientific articles

3. Research designs and their analyses
3.1. Independent groups designs
3.2. Matched groups designs
3.3. Repeated measures designs
3.4. Factorial designs
3.5. Mixed factorial designs
3.6. Single-case experimental designs
3.7. Nonexperimental designs: studying association

4. Linear regression
4.1. Bases: estimation, predictive capacity, assumptions, diagnostics
4.2. Simple linear regression
4.3. Multiple linear regression: selecting predictors, collinearity
4.4. Analysis of covariance: statistical control of extraneous variables

5. Extensions of regression model
5.1. Polynomial regression
5.2. Linearizing nonlinear models
5.3. Intrinsically nonlinear models

Methodology

The course consists of four types of sessions: (a) theoretical: presenting the main content of the course via slides with explanations and examples; (b) exercises: interpreting output of statistical software implementing the data analytical techniques which are object of the theoretical sessions; (c) practice: interacting with the software to obtain results for different analytical techniques; and (d) discussion of scientific articles, related to language and cognitive sciences, to identify their key elements and to critically assess the data analytical steps taken. In terms of the software to learn to use, the University of Barcelona pays a licence for IBM SPSS, whereas R (https://cran.r-project.org/) and jamovi (https://www.jamovi.org/) can be downloaded for free.

Evaluation: Continuous

The continuous evaluation will consist of both practical activities that are carried out throughout the course and an exam at the end of the course. The practical activities entail both interpreting output from statistical software (SPSS, R, and jamovi) and interacting with the software (i.e., clicking) to obtain the results. These practical activities allow scoring 4 points out of the 10 points for the course. The final exam entails applying the knowledge constructed to specific situations and it is answered with all materials (slides, books, articles) available, in order to represent a real-world situation in which researchers have access to knowledge, but need to decide how to apply it correctly. The exam allows scoring 6 points out of the 10 points of the course.

Re-evaluation: if students do not sum at least 5 out of the 10 points of the course, they will answer a new exam (similar in characteristics to the final exam answered) that entails a new opportunity to score 10 points.

Evaluation: Single-test

The single-test evaluation consists only of answering the final exam, which entails applying the knowledge constructed to specific situations and it is answered with all materials (slides, books, articles) available, in order to represent a real-world situation in which researchers have access to knowledge, but need to decide how to apply it correctly. The exam allows scoring all 10 points of the course.

Learning goals

Knowledge

Describe the main research methods and techniques in the domain of language and cognition

Skills

Use analytical tools in an appropriate way, at an expert level

Competences

Assess critically scientific research carried out in the domain of language and cognition

Equality policy

In agreement with the University of Barcelona Equality Policy (Pla d’Igualtat), I will try to ensure that everyone feels equally welcomed and encouraged to contribute to class discussions, ask questions and give feedback. Regarding the content and the materials suggested for study, I will avoid gender, ethnolinguistic, cultural and geographic biases, and I will include work by a diverse range of scientists irrespective of their gender, socio-economic class, ethnic origins or country of affiliation. For the evaluation and grading, I will try to avoid the multiple unconscious biases that might interfere and to be as objective as possible in my assessment.

Artificial Intelligence Usage

The use of generative artificial intelligence (AI) tools as learning support is permitted in continuous assessment activities, unless expressly stated otherwise by the instructor responsible for the activity. The use of these tools is not permitted during in-person assessment tests or in any activity where the instructor has explicitly restricted their use.

If any generative AI tool is used, and in accordance with the guide “Good Practices for the Use of Generative Artificial Intelligence at the University of Barcelona (UB)”, a declaration of use must be attached. This declaration must specify in detail which tool was used, for what purpose, at which stage of the work it was employed, and how the results were reviewed or verified.

Readings

Ato y Vallejo (2007). Diseños experimentales en Psicología. Madrid: Pirámide.

Balluerca y Vergara (2002). Diseños de investigación experimental en Psicología. Madrid: Prentice Hall.

Cohen, J. & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.

Chalmers, A.F. (1999). What is this thing called science? Indianapolis: Hackett Pub. Traducción española: ¿Qué es esa cosa llamada ciencia? Madrid: Siglo XXI.

Chaterjee, S., & Hadi, A. S. (2006). Regression analysis by example (4th ed.) London: John Wiley & Sons.

Field, A. (2005). Discovering statistics using SPSS. London: Sage.

Field, A. & Hole, G. (2003). How to design and report experiments. London: Sage.

Fox, J. (2016). Applied regression analysis and generalized linear models (3rd Ed.). London, UK: Sage.

Keppel., G. (1991). Design and analysis: A researcher’s handbook (3rd ed.). Englewood Cliffs Prentice Hall.

Kirk, R.E. (1995). Experimental Design. Procedures for the Behavioral Sciences. International Thomson Publishing Company.

Maxwell S.E. y Delaney, H.D. (2004). Designing experiments and analyzing data. Lawrence Erlbaum Associates.

Mendenhall, W., & Sincich, T. (2012). A second course in statistics: Regression analysis (7th ed.). Boston, MA: Prentice Hall.

Pedhazur E.J. & Pedhazur-Schmelkin, L. (1991). Measurement, design, and analysis: an integrated approach. Lawrence Erlbaum Associates.

Peña, D. (2002). Regresión y diseño de experimentos. Madrid: Alianza Editorial.

Peró, M., Leiva, D., Guàrdia, J. y Solanas, A. (Eds.) (2012). Estadística aplicada a las ciencias sociales mediante R y R-Commander. Madrid: Garceta. (Cap. 9).

Salafranca, Ll., Sierra, V., Núñez, M. I., Solanas, A. y Leiva, D. (2005). Análisis estadístico mediante aplicaciones informáticas. SPSS, StatGraphic, Minitab y Excel. Barcelona: Edicions Universitat de Barcelona.

Solanas, A., Salafranca, Ll., Fauquet, J. y Núñez, M. I. (2005). Estadística descriptiva en ciencias el comportamiento. Madrid: Thomson.

Wright, D. B., & London, K. (2009). Modern regression techniques using R: A practical guide for students and researchers. London, UK: Sage.

Fox, J. (2016). Applied regression analysis and generalized linear models (3^rd ed.). London, UK: Sage.

Mendenhall, W., & Sincich, T. (2003). A second course in statistics: Regression analysis. Upper Saddle River,NJ: Pearson Education International.

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th Ed.). Boston, MA: Pearson. (Chapter 6)

Schedule: Wednesdays 10-13h

#	Day	Room	Time schedule	Activity
1	7-Oct	Quanti.Psy. section: Biblioteca	1h 15min 1h 30min	Bases of scientific research Phases of the scientific research
2	14-Oct	Quanti.Psy. section: Biblioteca	1h 00min 1h 00min 0h 45min	Data analysis principles Between-groups designs: Parametric tests Between-groups designs: Nonparametric tests
3	21-Oct	1308	1h 15min 1h 30min	Exercise: Between-groups designs Practice: Between-groups designs
4	28-Oct	1308	0h 45min 0h 45min 1h 15min	Repeated measures designs: Parametric tests Repeated measures designs: Nonparametric tests Practice: Repeated measures designs
5	4-Nov	Quanti.Psy. section: Biblioteca	0h 45min 0h 45min 0h 30min 0h 45min	Exercise: Repeated measures designs Factorial designs Mixed factor designs Single-case experimental designs
6	11-Nov	1308	1h 00min 0h 45min 1h 00min	Nonexperimental designs: Association Exercise: Mixed factor designs Practice: Mixed factor designs
7	18-Nov	1308	1h 45min 1h 00min	Discussing article #1: Methodological and analytical (ANOVA; t-test; chi-square) features Solving questions; more software
8	25-Nov	Quanti.Psy. section: Biblioteca	1h 30min 1h 15min	Regression: concept Simple linear regression
9	2-Dic	Quanti.Psy. section: Biblioteca	1h 1h 45min	Exercise: Simple linear regression Multiple regression
10	9-Dec	1308	0h 45min 1h 30min 0h 30min	Exercise: Multiple regression Practice: Simple + Multiple Regression Multiple regression: Mediation + Moderation
11	16-Dec	Quanti.Psy. section: Biblioteca	0h 45 min 1h 00 min 0h 30min 0h 30min	ANCOVA Exercise: ANCOVA Polynomial regression (+ alternatives) Exercise: Polynomial regression
12	13-Jan	1308	1h 00min 1h 00min 0h 45min	Practice: ANCOVA Practice: Polynomial regression Nonlinear regression: Introduction; Linearizable + Intrinsically nonlinear
13	20-Jan	Quanti.Psy. section: Biblioteca	1h 45min 1h 00min	Discussing article #2: Methodological and analytical (ANCOVA) features Discussing article #3: Regression analysis
14	27-Jan	1308	1h 1h 45min	Nonlinear regression: Illustration with SPSS Exam