Psychology of typical and atypical development

LUIGI LEONE
SARA MANGANELLI

The course aims to provide knowledge on the key points of research methodology, research designs,
and measurement-related issues, to deepen understanding of the use and interpretation of basic
statistics indices, and additionally, to provide knowledge on the use, setup, and interpretation of
major multivariate analysis techniques. These objectives will be achieved through lectures and
practical laboratory activities, structured as follows.
In the first part of the course, thematic cores related to research methodology will be discussed
(types of research, research validity; research designs for validity; reliability and validity of
measurements, research designs on groups and single cases).
In the second part of the course, concepts of descriptive statistics and techniques for univariate and
bivariate data analysis will be revisited (variables and levels of measurement, measures of central
tendency and variability, use of z-scores and percentiles, percentage calculation, contingency tables,
correlation and simple regression), with examples of their use in clinical practice and research.
The third part of the course will address non-parametric tests and the main multivariate statistical
techniques (univariate analysis of variance, statistical interaction tests, multiple regression).
Each described topic includes a practical exercise to be carried out in the classroom during
laboratory hours, using material provided by the teacher, presenting statistical or methodological
problems and asking students to perform one or more of the following activities: define the
variables involved and specify their characteristics, define the experimental design, identify any
confounding variables and explain how to control them, calculate statistical indices, test hypotheses,
and comment on the results. Each exercise is followed by the teacher's presentation of the correct
procedures for solving the proposed problems and a discussion of the errors made by the students.
1.Knowledge and understanding. Passing the exam would imply being capable of understanding
and using the most widely used methodological tools for data analyses in basic and applied research
in psychology and psychopathology; passing the ex-am would also imply the ability to elaborate
autonomously on research issues.

2.Applying knowledge and understanding. Passing the exam would imply mastering the research
techniques generally applied in the research contexts tapped by the Degree Course, to which this
specific course belongs. Students would also master how to apply the technical tools acquired to
various contexts, as other psychology domains, social science contexts, and health-related research
contexts.
3.Making judgements. Passing the exam would imply mastering the ability to make informed
judgments and evaluations on research projects, research designs, and methodological issues in data
analyses; such abilities would allow to detect pitfalls, limitations, and potential improvements and
developments in the research endeavors pertaining to psychological, social and health-related
processes. These abilities would be attained through discussions on complex research issues, and
complex analytical results. Discussions would be held in the traditional lessons as well as during the
laboratory sessions.
4.Communication skills. Passing the exam would attest that students master the communication
abilities and tools needed for an efficacious scientific communication. These abilities would be
attained during the traditional lessons and the laboratory sessions by emphasizing and instantiating
the use of scientific terminology and technically-oriented rhetoric.
5.Learning skills. Passing the exam would attest the acquisition of skills conducive to further
learning on the general topic of scientific methodology; such abilities would allow students to tackle
new complex issues during their academic and professional lives, in the domains of research
designing, and data analyzing. During the traditional les-sons such abilities would be transmitted by
emphasizing different perspective on data analysis and research planning, and showing – during the
laboratory session – alternative and complementary approaches to research questions, which can be
applicable to new and different contexts and domains.

The course aims at providing the students with theoretical and practical knowledge that would help them to understand how to use the statistical tools employed in basic and applied (diagnostic, evidence based treat-ments) psychological research. The expected outcomes would be: competent and critical reading of research papers, research reports and efficacy evaluations; competent planning of statistical analyses and competent execution of statistical analyses.
The traditional lessons would allow students to master the main principles behind a correct planning, eval-uation and execution of research designs and statistical analyses in psychology.
Laboratory lessons would provide students with hand-on opportunities to plan, execute, and interpret the analyses discussed, and to directly experience the practical side of issues of validity and reliability. The la-boratory thus ensures acquiring the practical and technical ability to manage research designs, data collec-tions and data analyses. Attendance to the laboratory lessons and experiences should be considered manda-tory.


8.2 Specific aims

8.2.1 Knowledge and understanding
Passing the exam would imply being capable of understanding and using the most widely used methodo-logical tools for data analyses in basic and applied research in psychology and psychopathology; passing the exam would also imply the ability to elaborate autonomously on research issues.




8.2.2 Applying knowledge and understanding
Passing the exam would imply mastering the research techniques generally applied in the research contexts tapped by the Programme, to which this specific course belongs; students would also master how to apply the technical tools acquired to various contexts, as other psychology domains, social science contexts, and health-related research contexts.

8.2.3 Making judgements
Passing the exam would imply mastering the ability to make informed judgments and evaluations on re-search projects, research designs, and methodological issues in data analyses; such abilities would allow to detect pitfalls, limitations, and potential improvements and developments in the research endeavors pertain-ing to psychological, social and health-related processes. These abilities would be attained through discus-sions on complex research issues, and complex analytical results. Discussions would be held in the tradi-tional lessons as well as during the laboratory sessions.



8.2.4 Communication skills
Passing the exam would attest that students master the communication abilities and tools needed for an ef-ficacious scientific communication. These abilities would be attained during the traditional lessons and the laboratory sessions by emphasizing and instantiating the use of scientific terminology and technically-oriented rhetoric.



8.2.5 Learning skills
Passing the exam would attest the acquisition of skills conducive to further learning on the general topic of scientific methodology; such abilities would allow students to tackle new complex issues during their aca-demic and professional lives, in the domains of research designing, and data analyzing. During the tradi-tional lessons such abilities would be transmitted by emphasizing different perspective on data analysis and research planning, and showing – during the laboratory session – alternative and complementary approaches to research questions, which can be applicable to new and different contexts and domains.

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 un-derstanding 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.

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 tradi-tional 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, univa-riate 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 enter-ing, data management, and choice of statistical procedures. During the laboratory session, interpretation of the results and how to communicate the main findings would be particularly emphasized.

- 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 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.

The course is organized as a combination of traditional lessons, and practical laboratory session. The tradi-tional 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 de-veloping communication skills; instantiating and discussing different approaches to data analyses and hy-pothesis testing help developing general problem solving abilities and learning skills that are crucial when different contexts and problems need to be tackled.

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.

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 cov-ering the whole syllabus how much students master the topics of the course, and the interplay between con-ceptual and practical issues. Therefore, exams would be held only once the course has ended. The exam in-cludes a single test-battery: Questions cover the topics addressed in the traditional lessons and in the la-boratory 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 an-swers). 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 labora-tory 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 prac-tical decisions on which test appears more correct for the data at hand, and how results should be inter-preted 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 topics covered by the course, and of the abilities developed during the course.