
Statistical and Psychometric Models
Code: 102570Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Psychology | OB | 2 |
Contact lecturer
- Name :
- José Blas Navarro Pastor
- Email :
- joseblas.navarro@uab.cat
Teaching staff
- Saiko Allende Leal
- Joan Llosada Gistau
- José Blas Navarro Pastor
- Carme Viladrich Segués
- Eduardo Doval Diéguez
- Clara Pretus Gomez
- Marina Bosque Prous
- Eva Penelo Werner
- Alfred Pardo Garrido
Group languages
You can consult this information at the end of the document.
Prerequisites
It is highly recommended to have acquired the competences worked on in the two previous methodological subjects: "Research Methods, Design and Techniques" and "Data Analysis". Therefore, students have to be able to understand and apply the methodology used for research in psychology, as well as basic descriptive and inferential data analysis techniques.
This course uses the statistical software JAMOVI, and attending classes requires a laptop computer.
Objectives
“Statistical and psychometric models" is taught in the second semester of the second year, after having completed the two previous subjects on methodology, through which the students must have acquired the foundations of research methodology and data analysis.
On the basis of these previous subjects, in the current subject students will now move on to more complex statistical models, of a multivariable nature, introducing analytical solutions to three common phenomena in psychological research: interaction between variables; statistical control of confusing variables; and reduction in the dimensionality of data.
The training objectives of this subject are:
1. To learn the concept of a statistical model as an approach to the multidimensionality of research in psychology.
2. To understand the relationship between the research design used and the corresponding data analysis.
3. To know when and how to apply data-reduction techniques.
At the end of the course, students must be able to:
1. Specify the statistical model appropriate to the objectives and hypotheses of psychological research when research design allows this.
2. Distinguish between models that respond to a predictive hypothesis and those that respond to an explanatory hypothesis.
3. If necessary, include interaction variables and/or adjustment variables in the model.
4. Decide on the need to keep terms of interaction and/or adjustment variables in the model.
5. Correctly estimate and interpret the coefficients of a regression model.
6. Delimit the main aspects to be diagnosed when validating the model.
7. Know how to apply a principal-components analysis to reduce data dimensionality; correctly determine the number of components retained; optimal rotation of the said components; and perform an adequate interpretation of their meaning.
8. Understand the statistical analysis carried out in research papers that use predictive or explanatory statistical models, or data-reduction models.
9. Know the basic statistical vocabulary in Catalan, Spanish and English.
10. Know the basic elements of statistical analysis software.
Learning outcomes
- Relate the results obtained by applying data analysis techniques to the theoretical approaches that originated the research hypothesis/es.
- Identify key models and psychometric analysis techniques and interpret the results obtained adequately.
- Assess and contrast models, tools and techniques and decide which are best suited to psychometric analysis.
- Assess and contrast models, tools and techniques and decide which are most suitable for statistical analysis.
- Describe the main features of the probability of statistical inference, estimation and hypothesis testing in the development of psychometric tests.
- Make adequate use of data analysis tools in the development of psychometric tests.
- Correctly interpret the results obtained from the application of psychometric evidence presented.
- Draw reasoned conclusions from the results obtained after applying psychometric methods and techniques to respond to a research hypothesis.
- Draw reasoned conclusions from the results obtained after applying statistical methods and technic which can respond to a research hypothesis.
- Describe statistical indicators of reliability and validity based on test theory.
- Use the scoring criteria and interpretation of scores to draw conclusions about the characteristics of the people tested.
- Use different ICTs for different purposes.
- Use computer programmes for data management and analysis.
- Maintain a favourable attitude towards the permanent updating through critical evaluation of scientific documentation, taking into account its origin, situating it in an epistemological framework and identifying and contrasting its contributions in relation to the available disciplinary knowledge.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Identify the general linear models and some techniques for multivariable statistical analysis and interpret the results obtained adequately.
- Adequately interpret the results obtained from the application of the linear model and the techniques for reduction of dimensionality.
Contents
U1. Unifactorial Confirmatory Factor Analysis (CFA)
U2. Multifactorial Confirmatory Factor Analysis (CFA)
U3. Unidimensional Exploratory Factor Analysis (EFA)
U4. Multidimensional Exploratory Factor Analysis (EFA)
U5. Internal Consistency
U6. Consistency or Agreement
U7. Regression Models for Continuous Responses
U8. Categorical predictors
U9. Predictive models
U10. Explanatory models
U11. Model diagnosis and results comunication
U12. Regression Models for Binary Responses
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Practical review of the main analytical procedures of the course through the resolution of the practices | 10 | 0.4 | 2, 7, 9, 10, 12 |
| Self-study: Completion of summaries, diagrams and conceptual maps | 40 | 1.6 | 1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 16, 17 |
| Supervision of the resolution of the practices carried out autonomously | 7.5 | 0.3 | 1, 2, 7, 12, 16, 17 |
| Practical classes (small groups): approach and resolution of different practical problems of investigation analysis | 26 | 1.04 | 1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17 |
| Bibliographic and documentary consultations | 7 | 0.28 | 1, 2, 3, 4, 7, 9, 10, 13, 15 |
| Theoretical classes: master class with multimedia support | 18 | 0.72 | 1, 2, 5, 6, 7, 8, 10, 12, 13, 14, 16, 17 |
| Monitoring and participation in discussion forums through the virtual campus | 7.5 | 0.3 | 10, 15 |
| Reading the "Theory Schemes" for the preparation of theoretical classes | 30 | 1.2 | 1, 2, 7, 10, 12 |
This course provides different activities based on active-learning methodologies that are centred on the student (ALM). This involves a \"hybrid\" approach in which we combine traditional teaching resources with other resources aimed at encouraging meaningful and cooperative learning.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Evidence 3. Individual submission in the practical group session of the analysis results of a practical problem related to topics 712 (approx. weeks 1317); Written feedback provided in two weeks | 15 | 0 | 0 | 2, 3, 5, 6, 10, 11, 12, 15, 16 |
| Evidence 1. Individual submission, during the practical session with the assigned group, of the analysis results of a practical case related to topics 1 to 6 (approximately during weeks 4 to 7); Written feedback will be provided within two weeks after | 15 | 0 | 0 | 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17 |
| Evidence 4: Writted evidence consisting of a set of multiple choice questions related to units 7-12, as well as tables witth Jamovi (2nd assessment period); Written feedback provided in two weeks | 40 | 2 | 0.08 | 5, 6, 10, 12, 15, 16 |
| Evidence 2: Written evidence consisting of a set of multiple choice questions related to units 1-6, as well as to the Jamovi tables that make the previous analysys (1st assessment period); Written feedback provided in two weeks | 40 | 2 | 0.08 | 1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17 |
EV1 and EV3 consist of performing analyses with Jamovi based on a data matrix provided by the teaching team. It is carried out individually in the practical classroom. In order for an evidence to be evaluated, it will be necessary to have attended 2/3 of its practices in person. The weight of each of these evidences is 15%. The results of this evidence will be returned as a group on the first day of practice after the date of completion, and will consist of reviewing the solution and the most common errors.
The EV2 and EV4 (individual exams) consist of a set of approximately 25 multiple choice questions (three answer options, penalty for errors; two errors discount one correct, according to the usual criteria k-1). Students will be allowed to bring printed the material prepared by the teaching team as well as notes of the student's own elaboration. Electronic devices will not be available except for a calculator (not a mobile phone). At demand of the teaching staff, the students could have the statement and some Jamovi results tables a few hours before. The return of the results of this evidence will be carried out in groups and will consist of resolving each of the evaluation questions referring to the result tables obtained with Jamovi.
All responses to the assessment tasks must be original. Submissions identified as having been taken from other sources, or responses that have been copied or plagiarized, will not be accepted. Failure to comply with this requirement will result in the invalidation of the corresponding assessment task. More than one breach of this requirement will result in a final course grade of 0, in accordance with the assessment regulations of the UAB and the Bachelor's Degree in Psychology. These measures will apply to all individuals involved in the assessment misconduct. In accordance with the Faculty's assessment guidelines, to pass the course through continuous assessment (on a 0–10 grading scale), students must obtain an overall grade of at least 5.0 and a minimum grade of 4.0 in each on-site assessment activity that contributes 20% or more to the continuous assessment grade. If these requirements, together with any additional course-specific requirements, are not met, the maximum final grade that may be recorded in the student's academic transcript will be 4.5. In accordance with UAB regulations, students who have not passed the course through continuous assessment may be eligible for reassessment provided that they: (1) have completed assessment tasks accounting for at least two-thirds of the total course grade, and (2) have obtained a continuous assessment grade of 3.5 or higher. Assessment tasks EV2 and/or EV4 may be reassessed. The grade obtained in the reassessed task(s) will replace the previous grade, and the final course grade will be recalculated according to the criteria described above.
A student who has submitted evidence of learning with a weight equal to or greater than 4 points (40%) will be recorded as 'evaluable'.
No unique final synthesis test for students who enroll for the second time or more is anticipated.
The presentation of the translation of the of the statements of the in-person assessment tests will be carried out if the requirements established in Article 263 of the academic regulations are met and the request is made in week 4 online (e-form) (more information on the faculty website).
The single assessment(AU) will take place on the same day and at the same location as the exam for the second assessment period of the students. All course content will be assessed.The same evidence will be used, and the final grade for the students will be calculated as described for continuous assessment, with the same weightings as indicated in the Continuous assessment. The total duration will be 3–4 hours. The single assessment must be requested online (e-form) during the specific period (more information on the faculty website).
Use of Artificial Intelligence: In accordance with the model established by the faculty regarding the use of artificial intelligence, it is considered permitted solely as a tool to support study and self-directed learning, but not allowed during the completion of any assessment evidence. All evaluations are conducted in person, and the use of any software other than JAMOVI is not authorized. Therefore, the use of AI-based technologies is restricted to the context of personal study, under the responsibility of the student, and it must be understood that such tools cannot be considered sources whose content has been validated by the teaching staff.Any irregularity committed in an assessment activity (including academic misconduct, plagiarism, or the improper use of artificial intelligence, unless such use is expressly authorized in the course syllabus) that may lead to a significant alteration of the assessment outcome will result in a grade of 0 (zero) for that assessment activity. If the course syllabus specifies that obtaining a minimum grade in that assessment activity is a mandatory requirement to pass the course, or if multiple irregularities are committed in the assessment activities of the same course, the final grade for the course will be 0 (zero). In addition, students who engage in any of these irregularities may be subject to disciplinary proceedings in accordance with the University's regulations.0.
Link to the assessment guidelines of the Faculty of Psychology and Speech Therapy: https://www.uab.cat/doc/DOC_PautesAvaluacio_2026
Bibliography
Reference manuals:
Abad, F.J., Olea, J., Ponsoda, V. & García, C. (2011). Medición en ciencias sociales y de la salud. Madrid: Síntesis.
Kleinbaum, D.G., Kupper, L.L., Nizam, A., Muller, K. & Rosenberg, E.S. (2012). Applied Regression Analysis and other Multivariable Methods. (5ª ed.). Boston (MA): Cengage Learning, Inc.
Kleinbaum, D, G., Klein, M. (2010). Logistic Regression : A Self-Learning Text (3rd Edition). New York: Springer.
Ajenjo, C., Miguel, F.J., Griera, O. (2021). Manual d'ús de Jamovi per anàlisi de dades en estudis socials. Bellaterra: Universitat Autònoma de Barcelona.
Losilla, J.M., Vives, J. (2023). Análisis de Datos con jamovi. Bellaterra: Universitat Autònoma de Barcelona.
Other references:
Domènech, J.M. & Granero, R. (2004). Anàlisi de dades en Psicologia (Vols. 1 i 2) (2ª Ed.). Barcelona: Signo.
Martínez Arias, R. (1995). Psicometría: Teoría de los tests psicológicos y educativos. Madrid: Síntesis.
Meltzoff, J. (2000). Crítica a la investigación. Psicología y campos afines. Madrid: Alianza Editorial. (Traducción del original de 1998).
Viladrich, M.C. & Doval, E. (Eds.) (2008). Psicometria. Barcelona: Edicions UOC.
Software
Jamovi
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TE) Theory | 1 | Catalan | second semester | morning-mixed |
| (TE) Theory | 2 | Catalan/Spanish | second semester | morning-mixed |
| (TE) Theory | 3 | Catalan | second semester | morning-mixed |
| (TE) Theory | 4 | Catalan/Spanish | second semester | morning-mixed |
| (TE) Theory | 5 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 111 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 112 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 113 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 114 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 211 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 212 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 213 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 214 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 311 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 312 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 313 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 314 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 411 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 412 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 413 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 414 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 511 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 512 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 513 | Catalan | second semester | morning-mixed |