PSYCHOMETRIC METHODS IN CLINICAL RESEARCH

Course objectives

General aims Teaching aims to provide students with theoretical and practical skills aimed at understanding and using statistical tools for research and evaluation in psychology, with emphasis on aspects relevant to basic, diagnostic, and intervention effectiveness research. The expected learning outcomes are: competence in critically understanding scientific articles, research reports, and evaluations of effectiveness; competence in planning and conducting statistical analyses. The face-to-face lectures provide students with knowledge of the basic principles guiding the planning, evaluation, and analysis of research designs in psychology, particularly considering the contexts on which the Course of Study focuses. The laboratory offers students the opportunity to plan, execute and interpret the statistical analyses covered in the course, and to touch upon the practical consequences of validity and reliability issues. The laboratory thus ensures the acquisition of practical and technical skills in the field of planning and conducting research, and data analysis. Attendance at laboratory classes should be considered mandatory. Specific aims Knowledge and understanding Passing the exam ensures that you are able to understand and use the most widely used and important methodological tools of data analysis for research and evaluation in psychology and psychopathology, and that you are able to develop analysis and research problems in an original way. Applying knowledge and understanding Passing the exam ensures that you know how to use the specific techniques of research and analysis in the contexts on which the Course of Study focuses, as well as how to apply the same tools of practical analysis to situations specific to other psychological, social and health disciplines. Making judgements Passing the exam implies acquiring the ability to critically and creatively judge research designs and methodologies employed in data analysis, in order to be able to recognize critical issues, limitations and possible improvements in the methods of investigation of psychological processes covered in the Course of Study. These skills are acquired during teaching through the presentation of complex cases of data interpretation, both in lectures and during the laboratory. Communication skills Passing the exam involves the ability to effectively use the communicative tools inherent in scientific publications, projects and research reports. These skills are acquired during teaching through emphasis on scientific terminology and technical rhetoric-and its limitations-both in lectures and in the laboratory. Learning skills Passing the exam implies the acquisition of learning skills that are cross-cutting and common to the logic and practice of scientific inquiry, which will enable the student to deepen throughout his or her academic and professional career the principles and use of research designs and data analysis techniques. Learning skills are acquired during teaching by placing emphasis-in the lectures-on alternative ways of investigating and testing the same empirical hypothesis, and by proposing-in the laboratory-cases of data analysis whose procedures and results can be generalized to similar cases in different contexts and disciplines. Prerequisites For a fruitful study of the subject, and for an adequate understanding of the teaching materials, the following can be pointed out as prerequisites: a) notions of descriptive statistics (important); b) notions of inferential statistics (useful), c) notions about bivariate and univariate statistical tests (useful). The teaching in each case will provide the elements to acquire the listed skills for all students.

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LUIGI LEONE Lecturers' profile

Program - Frequency - Exams

Course program
Course Topics The course covers the following main topics: Different types of scientific research Research validity Research designs addressing threats to validity Reliability, including diagnostic reliability Measurement validity Research designs for groups and single-case designs Inferential statistics Data analysis techniques, further subdivided into: A. Univariate and bivariate statistics B. Regression models C. ANOVA models D. Models with interaction effects E. Non-parametric tests This is a 60-hour course, approximately divided as follows: 12 hours (mainly traditional lectures) focused on: Types of research and research questions Threats to research validity Research designs Measurement reliability Single-case designs 12 hours dedicated to inferential statistics and univariate and bivariate tests 36 hours of laboratory sessions focused on the presentation and explanation of the main statistical models listed above, with special attention to practical aspects such as data coding, data entry, data management, and the selection of appropriate statistical procedures. During the laboratory sessions, particular emphasis will be placed on the interpretation of results and the communication of key findings.
Prerequisites
To fully benefit from the course materials and make the most of the recommended textbooks, students should ideally meet the following prerequisites: a) Basic understanding of common descriptive statistics (essential); b) Basic understanding of inferential statistics (useful); c) Basic understanding of common univariate and bivariate statistical tests (useful). Regardless of the students’ prior mastery of these prerequisites at the beginning of the course, the course will provide the necessary support to help them acquire these foundational concepts.
Books
- Strumenti statistici per la ricerca e la diagnosi in psicologia (2007), a cura di Anna Paola Ercolani, Raffaello Cortina Editore. This book covers mainly the following program topics: 1) Different types of scientific research; 2) Research validity; 3) Research designs for addressing threats to validity; 4) Reliability, and diagnostic reliability; 5) non-parametric tests. - Modelli statistici per l’analisi dei dati nelle scienze sociali (Second Ed., 2017). Marcello Gallucci, Luigi Leone, Manuela Berlingeri. Pearson. This book covers mainly the following program topics: 1) Recap. of univariate and bivariate statistics; 2) Regression; 3) ANOVA; 4) Statistica interaction; 5) factor analysis. The following chapters need to be carefully studied: chapters: 1-9 (up to paragraph 9.3 included); chapter 12. - As an alternative to the second edition above, the first edition would provide an adequate and suffi-cient companion: “Modelli statistici per l’analisi dei dati nelle scienze sociali” (2012). Marcello Gallucci e Luigi Leone. Pearson. In this first edition the following chapters need to be carefully stud-ied 1-9 (up to paragraph 9.3 included); chapter 11. As a suggestion useful to refresh basic statistical concepts (descriptive statistics, null- hypothesis testing and univariate and bivariate tests): “Elementi di statistica per la psicologia”. Anna Paola Ercolani, Alessandra Areni, Luigi Leone. Il Mulino.
Frequency
Class attendance should be considered mandatory for laboratory lessons. This requirement is needed be-cause the practical skills tackled in the laboratory require students to attend all the lessons in order to ac-quire the benefits in terms of skills and abilities the laboratory is supposed to deliver. Attending the tradi-tional lessons is not mandatory, because textbooks and other study materials provided as the course unfolds are sufficient to study the exam’s topics. In any case, class attendance is strongly recommended.
Exam mode
Aims of the Exam The exam aims to assess the extent to which students have acquired the topics discussed in the course, as well as the skills developed throughout the course. Tasks Included in the Exam and Timing There are no mid-course tests, as it is considered preferable to evaluate students’ mastery of the entire syllabus in a single exam session, encompassing both conceptual and practical aspects. Therefore, exams will be held only after the course has concluded. The exam consists of a single test battery covering topics addressed in both the traditional lectures and the laboratory hands-on sessions. Type of Exam, Duration, and Administration The exam consists of 30 multiple-choice items, each with four possible answers (one correct option; no penalties for incorrect answers). The test must be completed within 45 minutes. Twenty items focus on the core issues corresponding to the 3 credits of the traditional portion of the course. Ten items cover topics from the laboratory portion, mainly assessing practical and technical skills such as interpreting software outputs, graphs, and other summary tools essential for effective data analysis. The 20 questions on conceptual and methodological principles assess students’ mastery of the course content in terms of “knowledge and understanding.” The 10 questions related to practical decisions—such as selecting the appropriate test for given data and interpreting results—evaluate students’ ability to “apply knowledge and understanding.” Grading The final grade is calculated as the total number of correct answers. To pass the exam, students must achieve a minimum score of 18 out of 30. This threshold reflects sufficient attainment of the course content and the development of related skills.
Lesson mode
The course is organized as a combination of traditional lectures and practical laboratory sessions. The traditional lectures aim to describe and discuss the main conceptual and theoretical issues related to research methodology, research designs, and measurement concerns. The laboratory sessions provide hands-on experience with the challenges and opportunities presented by different data analyses. The integration and interplay between traditional lectures and laboratory sessions align with the course objectives of acquiring knowledge and understanding, as well as applying and mastering these skills. Discussing recurrent and complex methodological issues commonly encountered in research helps develop the ability to make informed judgments. Exploring different ways to communicate empirical results fosters communication skills. Additionally, applying and discussing various approaches to data analysis and hypothesis testing supports the development of general problem-solving abilities and learning skills, which are essential for addressing diverse contexts and research problems.
VALERIO PELLEGRINI Lecturers' profile

Program - Frequency - Exams

Course program
9.1 Course topics The main general topics that the course addresses are: 1) Different types of scientific research; 2) Research validity; 3) Research designs for addressing threats to validity; 4) Reliability, and diagnostic reliability; 5) Measurement validity; 6) Research designs for groups and single case designs; 7) Inferential statistics; 8) Data analysis techniques, a point that could in turn be segmented as follows: A) Univariate and bivariate statistics; B) Regression models; C) ANOVA models; D) Models with interaction effects; E) Non-parametric tests. This is a sixty hours course. This amount is approximately segmented as follows: 12 hours, mainly tradition-al lessons, devoted to different kinds of research and research questions, threats to research validity, re-search designs, reliability of measures, single-case designs; 12 hours devoted to inferential statistics, uni-variate and bivariate tests; 36 hours of laboratory sessions devoted to description and explanation of the main statistical models mentioned above, with a special emphasis on practical issues of data coding, data entering, data management, and choice of statistical procedures. During the laboratory session, interpreta-tion of the results and how to communicate the main findings would be particularly emphasized.
Prerequisites
8.3 Requirements To more adequately digest the materials provided during the course and the to take full advantage of the study of the books proposed, students should consider that the following prerequisites apply: a) basic understanding of common descriptive statistics (important); b) basic understanding of inferential statistics (useful); c) basic understanding of univariate and bivariate common statistical tests (useful). Regardless of the actual mastering of the mentioned prerequisites at the start of the course, the course would provide stu-dents with the means to fully acquire these notions.
Books
- Strumenti statistici per la ricerca e la diagnosi in psicologia (2007), a cura di Anna Paola Ercolani, Raffaello Cortina Editore. This book covers mainly the following program topics: 1) Different types of scientific research; 2) Research validity; 3) Research designs for addressing threats to validity; 4) Reliability, and diagnostic reliability; 5) non-parametric tests. - Modelli statistici per l’analisi dei dati nelle scienze sociali (Second Ed., 2017). Marcello Gallucci, Luigi Leone, Manuela Berlingeri. Pearson. This book covers mainly the following program topics: 1) Recap. of univariate and bivariate statistics; 2) Regression; 3) ANOVA; 4) Statistica interaction; 5) factor analysis. The following chapters need to be carefully studied: chapters: 1-9 (up to paragraph 9.3 included); chapter 12. - As an alternative to the second edition above, the first edition would provide an adequate and sufficient companion: “Modelli statistici per l’analisi dei dati nelle scienze sociali” (2012). Marcello Gal-lucci e Luigi Leone. Pearson. In this first edition the following chapters need to be carefully studied 1-9 (up to paragraph 9.3 included); chapter 11. As a suggestion useful to refresh basic statistical concepts (descriptive statistics, null- hypothesis testing and univariate and bivariate tests): “Elementi di statistica per la psicologia”. Anna Paola Ercolani, Alessandra Areni, Luigi Leone. Il Mulino.
Frequency
9.3 Attendance Class attendance should be considered mandatory for laboratory lessons. This requirement is needed be-cause the practical skills tackled in the laboratory require students to attend all the lessons in order to ac-quire the benefits in terms of skills and abilities the laboratory is supposed to deliver. Attending the traditional lessons is not mandatory, because textbooks and other study materials provided as the course unfolds are sufficient to study the exam’s topics. In any case, class attendance is strongly recommended.
Exam mode
10.1 Aims of the Exam The exam aims at assessing the extent of students’ acquisition of the topics discussed in the course, and the abilities developed during the course. 10.2 Tasks included in the exam and temporal placement of the exams There are no intermediate-course-tests, because appears advisable to evaluate in a single exam session covering the whole syllabus how much students master the topics of the course, and the interplay between conceptual and practical issues. Therefore, exams would be held only once the course has ended. The exam includes a single test-battery: Questions cover the topics addressed in the traditional lessons and in the laboratory hands-on portion of the course. 10.3 Type of exam, duration and how the test is administered The exam test includes 30 items, with four possible answers (one correct option, no penalties for wrong answers). The test must be completed in 45 minutes. Twenty items tap into the issues that represent the core of the 3 credits of the “traditional” or standard course. Ten items tap into the issues dealt with in the laboratory portion of the course. The items pertaining to the contents discussed in the laboratory portion of the course concern mainly practical and technical abilities, as interpretation of software outputs, interpretation of graphs and other summarizing tools that need to be mastered for a fruitful approach to data analysis. The 20 questions tapping on conceptual issues and methodological principles test to what extent the students master the topics of the course in terms of “knowledge and understanding”. The 10 items tapping on practical decisions on which test appears more correct for the data at hand, and how results should be interpreted test to what extent students have acquired the aims of the course in terms of “applying knowledge and understanding”. 10.4 Grading The final grade would be obtained as a sum-score of correct answers. To pass the exam a score of at least 18 out of 30 needs to be achieved. This score can be interpreted as reflecting a sufficient attainment of the top-ics covered by the course, and of the abilities developed during the course.
Lesson mode
9.2 Organization of the course The course is organized as a combination of traditional lessons, and practical laboratory session. The traditional lessons aim at describing and discussing the main conceptual and theoretical issues concerning re-search methodology, research designs, measurement issues. The laboratory allows for a hands-on experience of the challenges and opportunities provided by different data analyses. The mixture and interplay of the traditional and laboratory lessons are consistent with the aims of the course in terms of knowledge and understanding to be achieved and applying knowledge and understanding to be mastered. The discussion of the recurrent and challenging methodological issues that arise in most research endeavors help to build-up the ability of making informed judgments; discussing different ways to convey empirical results help devel-oping communication skills; instantiating and discussing different approaches to data analyses and hypothe-sis testing help developing general problem-solving abilities and learning skills that are crucial when differ-ent contexts and problems need to be tackled.
  • Lesson code10612023
  • Academic year2025/2026
  • CoursePsychodynamic Psychopathology and Clinical Relationship in Developmental age and Adulthood
  • CurriculumSingle curriculum
  • Year2nd year
  • Semester2nd semester
  • SSDM-PSI/03
  • CFU6