Research Methods for Clinical and Health Psychology
Code: 43881 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 4316222 Research in Clinical Psychology and Health | OT | 0 | 2 |
Contact
- Name:
- Eva Penelo Werner
- Email:
- eva.penelo@uab.cat
Use of Languages
- Principal working language:
- spanish (spa)
Other comments on languages
Materials are in Spanish and English; statements of written learning outcomes or tests are in Spanish; Stata user-interface is in EnglishTeachers
- José Blas Navarro Pastor
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 software Stata.
Objectives and Contextualisation
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
Competences
- Analyze critically the most current theories, models and methods of psychological research in the field of clinical and health psychology.
- Analyze data and interpret results on research in clinical and health psychology.
- 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.
- Discuss the results the results on clinical and health psychology research, and contrast them with existing scientific literature and draw conclusions and practical applications.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- Use scientific terminology to argue the results of research in the context of scientific production, to understand and interact effectively with other professionals.
Learning Outcomes
- Apply the outstanding ethical principles and act accordingly to the deontological code for the profession in the scientific research practic.
- Choose the most appropriate statistical model according to the research question, the data collection design and the measurement scale for the variables involved.
- Communicate and justify conclusions clearly and unambiguously to both specialised and non-specialised audiences.
- Continue the learning process, to a large extent autonomously.
- Draw practical conclusions from the results and evaluate their implications.
- Evaluate the adjustment indices obtained using the computer after carrying out a statistical or psychometric analysis to test the adequacy of the chosen model.
- Interpret and discuss the results of the research in applied psychology focusing on the design, method and analyses carried out.
- Interpret the statistical results and the effects of the magnitude of an effect taking into consideration the sample size and statistical potential.
- 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.
- Recognize research designs that involve a data analysis with multivariate quantitative methods.
- Select all the results produced by the computer after carrying out a statistical analysis, and the appropriate indices that should appear in a publication.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- To estimate the multivariate statistical models that the module contemplates using computer programs of statistical analysis.
- 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.
- Use scientific terminology to argue the results of research in the context of scientific production, to understand and interact effectively with other professionals.
Content
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
Methodology
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 ends with a space dedicated to the debate with students, who are expected to provide feedback on the understanding, usefulness and applicability of the presented concepts.
- Practical sessions with Stata. The results presented in the master class are replicated using Stata. New exercises with a similar structure are also added.
N.B. The proposed teaching and assessment methodologies may experience some modifications as a result of the restrictions on face-to-face learning imposed by the health authorities. The teaching staff will use the Moodle classroom or the usual communication channel to specify whether the different directed and assessment activities are to be carried out on site or online, as instructed by the Faculty.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Master class + practical sessions with Stata | 30 | 1.2 | 1, 16, 15, 9, 2, 14, 5, 8, 7, 13, 3, 4, 10, 11, 12, 17, 6 |
| Type: Autonomous | |||
| Elaboration of reports | 15.5 | 0.62 | 1, 16, 15, 9, 2, 14, 5, 8, 7, 13, 3, 4, 10, 11, 12, 17, 6 |
| Personal work | 100 | 4 | 1, 16, 15, 9, 2, 14, 5, 8, 7, 13, 3, 4, 10, 11, 12, 17, 6 |
Assessment
The continuous evaluation process will integrate 4 evaluative elements.
The final grade of the continuous evaluation 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 3 points on avergage in EvB and EvC; otherwise the maximum grade in the course will be 4.5.
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 (week 14), 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.
The document with the evaluation guidelines of the Faculty can be found at: https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| EvA Practical report on internal structure and reliability (individual on-line delivery, week 2-4) | 25 | 0 | 0 | 1, 16, 3, 10, 12, 17, 6 |
| EvB Test on linear regression (individual, written, face-to-face week 12) | 35 | 2.5 | 0.1 | 1, 16, 15, 9, 2, 14, 5, 8, 7, 13, 3, 4, 10, 11, 12, 17, 6 |
| EvC Test on logistic regression (individual, written, face-to-face week 13) | 25 | 2 | 0.08 | 1, 16, 9, 2, 14, 5, 8, 7, 3, 4, 11, 12, 17 |
| EvD Summary-report on mediation (group on-line delivery: teams of 2 students, week 7-8) | 15 | 0 | 0 | 1, 16, 15, 9, 2, 14, 5, 8, 7, 13, 3, 4, 10, 11, 12, 17, 6 |
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]
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