Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Research Methods for Clinical and Health Psychology
Code: 43881Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Research in Clinical Psychology and Health | OP | 1 |
Contact lecturer
- Name :
- Eva Penelo Werner
- Email :
- eva.penelo@uab.cat
Teaching staff
- José Blas Navarro Pastor
Group languages
You can consult this information at the end of the document.
Prerequisites
Knowledge of module 1, especially those related to research methodology and research designs, for their direct link with statistical modeling, those related to descriptive and bivariate analysis, and about functioning of the statistical software used.
Objectives
Provide the necessary skills (theoretical and instrumental) so that the student is able to:
- Analyze the psychometric properties of a questionnaire relative to internal structure and reliability
- Analyze the data of a research using linear or logistic regression models, both in order to predict the response and to study the influence of an exposure on the response
- Incorporate the phenomena of interaction and confusion into the statistical modeling process
- Perform the diagnosis of the conditions of application of linear and logistic regression models
- Distinguish a moderator variable from a mediator variable and to estimate structural equation models (SEM) for the analysis of mediation models
- Interpret the results of the regression models and SEM, being able to select those most suitable to be included in the research report
Learning outcomes
- Recognize research designs that involve a data analysis with multivariate quantitative methods.
- Understand the general limitations of models of statistical analysis explained in the module: relevant research methods and types of analyzable response variables.
- Understand the limitations of theoretical conclusions which may be derived from the numerical results obtained using the statistical analysis models explained in the module.
- Know the main techniques of single-stage sampling, know how to decide the most appropriate to the objectives of a research in a specific field, and know how to calculate the sample size needed to acquire a certain statistical power.
- Recognize research designs that involve a data analysis using structural equation models for the analysis of mediator variables between exposure and response.
- Choose the most appropriate statistical model according to the research question, the data collection design and the measurement scale for the variables involved.
- To estimate the multivariate statistical models that the module contemplates using computer programs of statistical analysis.
- Evaluate the adjustment indices obtained using the computer after carrying out a statistical or psychometric analysis to test the adequacy of the chosen model.
- Select all the results produced by the computer after carrying out a statistical analysis, and the appropriate indices that should appear in a publication.
- Interpret the statistical results and the effects of the magnitude of an effect taking into consideration the sample size and statistical potential.
- Interpret and discuss the results of the research in applied psychology focusing on the design, method and analyses carried out.
- Draw practical conclusions from the results and evaluate their implications.
- Use scientific terminology to argue the results of research in the context of scientific production, to understand and interact effectively with other professionals.
- Apply the outstanding ethical principles and act accordingly to the deontological code for the profession in the scientific research practic.
- Communicate and justify conclusions clearly and unambiguously to both specialised and non-specialised audiences.
- Continue the learning process, to a large extent autonomously.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
Contents
Block A
- Internal structure: principal components analysis (A1) and confirmatory factor analysis and measurement invariance (A2)
- Reliability (A3)
Block B
- Linear regression: predictive models and to evaluate effects
- Statistical modeling in the presence of interaction and confusion
- Diagnosis of the linear regression model
Block C
- Logistic regression: predictive models and to evaluate effects
- Logistic regression and diagnostic tests
- Diagnosis of the logistic regression model
Block D
- Moderation vs mediation
- Structural equation models for the analysis of mediating variables
Note: the content schedule may be subject to change.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Master class + practical sessions with statistical program (9 sessions of variable duration depending on the contents of each block) | 30 | 1.2 | |
| In-person and/or virtual tutors | 6 | 0.24 | |
| Reading of texts, study and personal work, preparation of individual and/or group reports | 110 | 4.4 |
Directed sessions:
- Master classes. Using a material published by the teachers, explanation is made based on examples and matrices of real research data in psychology. Each master class is combined with a space dedicated to the debate and practical exercise with students, who are expected to provide feedback on the understanding, usefulness and applicability of the presented concepts.
- Practical sessions. The results presented in the master class are replicated using statistical software. New exercises with a similar structure are also added.
Materials are in Spanish and English; statements of written learning outcomes or tests are in Spanish; statistical software user-interface can be in English.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| EvA Practical report on internal structure and reliability (individual, written, on-line delivery, for CA: at the end of the 3 class sessions of this block A); written feedback along with the grade | 30 | 0 | 0 | 1, 2, 3, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17 |
| EvC Test on logistic regression (individual, written, face-to-face, at the end of the 5 class sessions of blocks B and C [week after evB]); feedback via groupal mentoring | 25 | 2 | 0.08 | 1, 2, 3, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17 |
| EvD Summary-report on mediation (CA group or SA individual, written, on-line delivery, for CA: at the end of the class session of this block D); written feedback along with the grade | 10 | 0 | 0 | 2, 3, 5, 6, 11, 13, 14, 15, 16, 17 |
| EvB Test on linear regression (individual, written, face-to-face, at the end of the 5 class sessions of blocks B and C); feedback via groupal mentoring | 35 | 2 | 0.08 | 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 |
The evaluation process, whether continuous-assessment (CA) or single-assessment (SA), consists of 4 evaluative elements. For continuous assessment, see the table of assessment activities below (the detailed schedule will be provided before starting the subject). Single assessment will only differ in autor-ship and date of completion/delivery: individual the last day of face-to-face evaluation of the continuous assessment for the 4 learning evidence.
The final grade will be obtained as the weighted average of the 4 evaluation evidences. The module will be passed with grades equal to or greater than 5 points (on a scale of 0 to 10 points), with a minimum of 4 points in EvB and in EvC; otherwise the maximum grade in the course will be 4.5.
The resit process will be the same for the continuous assessment and for the single assessment. Students who have obtained a final grade between 3.5 and less than 5 points and who have carried out evaluation evidence weighing at least 2/3 of the total grade, will be able to take resit (at the end of the subject), to carry out again evidences B and/or C that have not been successfully passed. The maximum grade that can be obtained in each evidence recovered will be 6 points. The grade obtained in the evidence/s recovered will replace the respective original grade and the final grade will be recalculated.
A student who has presented evidence that exceeds 40% of the total may not be qualified as \"Not Evaluable\".
No unique final synthesis test for students who enroll for the second time or more is anticipated.
In this subject, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the development of assessments, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. Students must clearly identify which sections have been based on, generated, reviewed or any other action taken using AI technology, specify the tools employed, and include a critical reflection on how these tools have influenced both the process and the final outcome of the assessment. Lack of transparency regarding AI use, failure to verify the accuracy of AI-generated statements, citation of non-existent references, or the use of data produced by AI will be considered a breach of academic integrity and will result in a grade penalty for the assignment, or more serious sanctions in severe cases.
The document with the evaluation guidelines of the faculty can be found at: https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html
Bibliography
Abad, Francisco J.; Olea, Julio; Ponsoda, Vicente; García, Carmen. (2011). Medición en ciencias sociales y de la salud. Síntesis. [Electronic resurce available at: UAB Library]
American Educational Research Association, American Psychological Association, National Council on Measurement in Education (2014). The standards for educational and psychological testing. Author. [https://www.testingstandards.net/open-access-files.html]
Ato, Manuel; Vallejo, Guillermo. (2011). Los efectos de terceras variables en la investigación psicológica. Anales de Psicología, 27, 550-561. [https://revistas.um.es/analesps/article/view/123201/115851]
Bandalos, Deborah L. (2018). Measurement theory and applications for the social sciences. Guilford Press. [ISBN 1462532136] [Electronic resurce available at: UAB Library]
Kleinbaum, David G.; Kupper, Lawrence L.; Nizam, Azhar; Rosenberg, Eli S. (2014). Applied regression analysis and other multivariable methods. (5ª ed.). Brooks/Cole. [ISBN 1285051084]
Kleinbaum, David G.; Klein, Mitchel. (2010). Logistic regression. A Self-learning text. 3rd ed. Springer. [https://www.springer.com/gp/book/9781441917416; https://link.springer.com/book/10.1007/978-1-4419-1742-3]
Shmueli, Galit. (2010). To explain or to predict? Statistical Science, 25, 289-310. https://dx.doi.org/10.1214/10-STS330
Software
Stata
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 |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | Spanish | second semester | afternoon |