CODE 102300 ACADEMIC YEAR 2021/2022 CREDITS 6 cfu anno 3 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI 8766 (L-35) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR SECS-S/01 TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW Experts introduce or present advances on statistical techniques that they use in their work by illustrating their applications through concrete examples. AIMS AND CONTENT LEARNING OUTCOMES Provide statistical tools relevant to specific applications and the experience of on-field experts. AIMS AND LEARNING OUTCOMES At the end of the course the student will be able to: identify the degree of applicability of the analytical techniques identify the main issues in data analysis in the applied contexts listed below identify the adequate statistical methods carry out the analyses with the adequate software interpret the results of the analyses and assess their validity summarize the results in a semi-professional report that describes and provides an explanation for the results Measurement models in psychometrics (3 CFU) The course introduces to statistical issues in psychometric theory and to the use of statistical software (R) for carrying out basic psychometric analyses. Demography in Italy and in the world: topics, data and measurements (1 CFU) To illustrate via a complex example the issues related to the communication of demographic data to the general population. Official statistics (1 CFU) The system of official statistics. Multiple statistical tests in biomedical research (1 CFU) To understand the problem related to performing multiple statistical hypothesis tests and to learn how to handle it critically. Further seminar activities (not evaluated) could be organised each year. Usually they are presented by data scientists who work in applied contexts such as companies, consumer companies, public bodies. TEACHING METHODS Combination of traditional lectures and lab sessions with the softwaresMatlab and R. SYLLABUS/CONTENT Psychometrics Classical test theory Psychological variables or constructs Definition of the content domain of a construct and its operationalizations Measurement models in psychology: reflective indicators models and formative indicators models Item analysis and reliability Exploratory factor analysis Confirmatory factor analysis Structural Equation Models Item Response Theory Applications in R (packages 'psych', 'lavaan', 'mirt') will be shown. Demography in Italy and in the world: topics, data and measurements The course is based on the careful reading and analysis of a highly data-driven volume. Collecting, analysing, and presenting data to help society organise itself to turn opportunities into new realities. The reference text to be analysed has changed over the years Official statistics Official statistics, statistical sources, official data, SISTAN, legislation on statistics, codes of statistics. Multiple statistical tests in biomedical research Statistical hypothesis tests are characterised by a probability of a false positive (significance level) and a probability of a false negative (1-test power). When several tests are performed, the probability that at least one true hypothesis will be rejected increases rapidly. In experimental research, this is the case, for example, when several markers are tested, a diagnostic test is repeated several times, several drugs are compared to a standard, several evaluation criteria are used to assess the effectiveness of a treatment, etc. The significance of the observed results is determined by the level of significance. The significance of the observed results is largely overestimated if the results of the most significant tests are interpreted without taking into account the multiplicity of tests performed. This problem is even more crucial if only the most significant results are reported and disseminated. In the case of multiple tests, several measures of error are available, in particular the probability of encountering at least one false positive (FWER, familywise error rate) and the false discovery rate (FDR). Each has a different meaning and is more or less appropriate depending on whether the purpose of the research is more exploratory or confirmatory. Several correction methods for multiple tests are available in the statistical literature. Each correction method aims to control the FWER or FDR and each has different properties. In the context of evaluating different efficacy criteria in clinical trials, the FDA (US Food and Drug Administration) has made preliminary guidelines available. Practical examples with R software: inflation of the significance level of multiple tests combined, correction of the significance level of multiple tests. RECOMMENDED READING/BIBLIOGRAPHY Psychometrics Furr, R. M., & Bacharach, V. R. (2018). Psychometrics: An introduction. 3rd (or 4th) edition.Sage Handouts and other teaching material (e.g., R codes) will be shared online. Demography in Italy and in the world: topics, data and measurements Information about recommended readings will be provided by the teacher Official statistics Reading and study materials will be provided by the teacher Multiple statistical tests in biomedical research Dmitrienko, D’Agostino, 2013, Traditional multiplicity adjustment methods in clinical trials. Stat Med 32 Goeman J, Solari A, 2014, Multiple Hypothesis Testing in Genomics. Stat Med 33 FDA, 2017, Multiple Endpoints in Clinical Trials. Draft Guidance for Industry. UCM536750 TEACHERS AND EXAM BOARD CARLO CHIORRI Ricevimento: Meetings with students can be scheduled on e-mail request, in order to agree on date, time, place and/or platform (e.g., through Microsoft Teams / Skype / Zoom / Google Meet / you name it). Teachers' contacts E-mail EVA RICCOMAGNO Ricevimento: By appointment arranged by email with Luca Oneto luca.oneto@unige.it and Fabrizio Malfanti <fabrizio.malfanti@intelligrate.it> For organizational issues contact by email Eva Riccomagno <riccomagno@dima.unige.it> FRANCESCO PORRO Exam Board CARLO CHIORRI (President) FRANCESCO PORRO EVA RICCOMAGNO (President Substitute) SARA SOMMARIVA (President Substitute) LESSONS LESSONS START The class will start according to the academic calendar. Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Psychometrics Written paper and oral examination Demography Multiple choice-test Official statistics Multiple-choice test and oral examination Multiple statistical tests in biomedical research Multiple-choice test and oral examination The final mark is the weighted average of the marks of the three parts. The weights are proportional to the hours of classroom lectures. ASSESSMENT METHODS Psychometrics: The student will be provided with a dataset and will be asked to perform some of the analyses presented during the course. On the basis of what has been learnt and the study material, in this written assignment the student will be asked to carry out, report, interpret, and comment on the results and highlight any critical points. In the oral examination, any issues in the written test will be commented on and the student's knowledge of psychometric theory will be investigated. Demography: The student's ability to find precise information and data in a complex popular text, as well as identifying the statistical methodology used for the analyses in the text. Official Statistics: The student's ability to recognise official data and its uses. Multiple statistical tests in biomedical research: the ability to apply in a theoretical context the mathematical/statistical techniques learned, interpret the results, recognise their validity and reliability. Exam schedule Data appello Orario Luogo Degree type Note 11/05/2022 09:00 GENOVA Compitino 16/06/2022 09:00 GENOVA Compitino 13/07/2022 09:00 GENOVA Compitino FURTHER INFORMATION Web pages: http://www.onairweb.com/corsoPR/ https://www.dropbox.com/s/groq642v7rbviha/Lezioni%20SMID%202016.zip?dl=0 Prerequisites: Applied Statistics 1 Attendance is highly recommended.
