PSYCHOMETRICS FOR COGNITIVE AND CLINICAL SCIENCES

Single discipline educational activity

Course Sheet

Course code:

CH0002

Academic Year of enrolment:

2022

Year of supply:

2022/2023

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in COMPUTATIONAL COGNITIVE SCIENCE

Disciplinary Sector:

Psychometrics

Language:

Italian

Location:

CHIETI

Credits:

Programme Year:

Cycle:

First semester

Hours of classroom activity:

Individual study hours:

136

Prerequisites:

Borrowed from

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PSYCHOMETRICS FOR COGNITIVE AND CLINICAL NEUROSCIENCE

PSYCHOLOGY

Objectives

The course aims to provide the theoretical foundations as well as the practical skills needed for the application of statistical analysis methods and techniques in neuro-cognitive research and clinical contexts. It also includes the study of the relationship between brain and behavior. The course also foresees the familiarization with informatic/digital procedures for the analysis of quantitative data in research as well as clinical contexts. Concerning the expected educational achievements, the student is expected to show to have achieved the following goals:
1) Knowledge about the concepts, the operationalization and the instruments of psychometric measurement of general psychological constructs;
2) Knowledge about the use of the general psychological constructs of intelligence and personality for the characterization of individual differences;
3) The capacity to detect deficits in patients and the dissociation between psychological/cognitive test scores by using principal statistical methods, and to identify typical and atypical psychological and cognitive functioning in a clinical context;
4) The capacity to use quantitative, neuro-cognitive datasets and to implement principal statistical procedures and models for data analysis (data with multiple categorical factors, repeated measures and continuous variables) starting from an hypothesis;
5) The understanding of the use of informatic support (software, electronic datasets, platforms for data sharing) for the statistical analysis is described above by the points 3) and 4);
6) The capacity to integrate and interpret the results obtained through the procedures described above by points 1-5) at a statistical, conceptual and clinical level; to be able to communicate this interpretation, using a disciplinary lexicon, to specialists as well as non-specialists.

Contents

The topics treated during the course include:

1. The foundations of the measurement of psychological constructs, particularly cognitive tests (general cognition abilities) and non-cognitive tests (general personality traits), and the application of behavioral data in the neurosciences (educational objectives 1 and 2);

2. Methods for the statistical analysis of single cases, and to identify psychological/cognitive deficits in patients (educational objectives 3, 5 and 6);

3. Statistical analysis of experimental datasets with single/multiple factors and the integration of continuous cognitive variables in neuroscience for hypothesis testing: regression, analysis of variance and analysis of covariance (educational objectives 4, 5 and 6);

4. The application of specialized software for the analysis of quantitative data and becoming familiar with the use of an open access data sharing platform (educational objectives 5 and 6).

Extended Syllabus

The topics treated during the course include the following:

1. The psychometric measurement of psychological constructs and their application in the neurosciences
- Cognitive tests (general cognitive abilities): intelligence (WAIS-IV)
- Non-cognitive tests (general personality traits): the big five personality model (NEO)

2. Methods for the statistical analysis of single cases in neuropsychology, and to identify psychological/cognitive deficits in patients and the dissociation between scores on multiple tests
- Evaluations and standardized scores
- Intra-individual comparisons
- The use of a control sample (t-tests and Monte Carlo simulations)

3. Experimental statistical analysis: from single factors to multiple factors and the integration of continuous variables (e.g., scores of psychological tests or behavioral data) in the analysis of variance in neuroscience
- Simple and multiple regression
- Analysis of variance, ANOVA (single factors, factorial designs and interactions, repeated measures)
- Analysis of covariance, ANCOVA (factors, continuous variables and their interaction)
- Translating an hypothesis in an experimental design and a statistical model

4. The application of software for the analysis of quantitative data and becoming familiar with open access data sharing platforms
- JASP
- OSF

Recommended Bibliography

“STATISTICA SPERIMENTALE
UNIVARIATA: UNA GUIDA PRATICA CON L’AUSILIO DEL SOFTWARE JASP (Di Plinio & Ebisch, 2021)” available on the e-learning platform: https://elearning.unich.it
(Contenuti nr. 3 and 4)

Elementi di statistica per la psicologia. Anna Paola Ercolani, Alessandra Areni e Luigi Leone. ISBN 978-88-15-12169-1. Il Mulino, Bologna, 2018. (Capitoli 6 e 7; Contenuti 3 and 4).

Introducing ANOVA and ANCOVA: a GLM approach. Andrew Rutherford. SAGE publications, 2001. ISBN 0 7619 5160 1. (optional book to support the course; contenuti 3 and 4)

Articles and material available at the e-learning page (https://elearning.unich.it) of the course (Contenuto 2):
- Crawford, J. R., & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12(4), 482-486.
- Crawford, J. R., & Garthwaite, P. H. (2002). Investigation of the single case in neuropsychology: Confidence limits on the abnormality of test scores and test score differences. Neuropsychologia, 40(8), 1196-1208.
- Crawford, J. R. & Garthwaite, P.H. (2005). Testing for suspected impairments and dissociations in single-case studies in neuropsychology: Evaluation of alternatives using Monte Carlo simulations and revised tests for dissociations”. Neuropsychology,19, 318-331.
- Manuale di neuropsicologia Clinica ed elementi di riabilitazione. Vallar G. & Papagno C. Il Mulino, 2018. ISBN edizione digitale: 9788815350084. ISBN edizione a stampa: 9788815278708. (Chapter 5: Approcci statistici in ambito neuropsicologico: dalla valutazione della normalità e della patologia alla stima delle variabili latenti)

- Handbook of Psychological Assessment, 6th Edition. Gary Groth-Marnat, A. Jordan Wright. ISBN: 978-1-118-96064-6 May 2016. (Opzionale: Chapter 5 e 10; Contenuto nr. 1).

Additional teaching materials (slides, exercises, teaching material in pdf, web links to free/open source programs) will be available at the e-learning platform: https://elearning.unich.it

Methods of Provision

Conventional

Teaching Methods

The course consists of 64 hours of frontal teaching, divided in lessons of 2 or 3 hours, twice or three times a week, depending on the academic calendar. Frontal teaching will consist partially of theoretical lessons. During the lessons, considerable time will also be spent on practical exercises (>16 hours) with the aim to consolidate the achieved theoretical knowledge to provide the opportunity to acquire familiarity, experience and autonomy in the application and the understanding of statistical techniques. The exercises will be performed at the group and the individual level in an interactive way with the teacher and the other students. The use of a personal laptop could be useful as a support for the exercises. Participation in the lessons is optional for the students, but given the complexity of the topics and the course content, it is strongly recommended to participate regularly and continuously.

In addition to the frontal teaching described above, the online e-learning platform will be used to support teaching ( https://elearning.unich.it ), which allows to provide material for exercising and studying autonomously. This material will be treated also during the frontal teaching hours.
- Software (free) for the statistical analysis of single cases in neuropsychology: https://homepages.abdn.ac.uk/j.crawford/pages/dept/SingleCaseMethodology...
- Software (free) for experimental statistical analysis (JASP open source)
https://jasp-stats.org
- Online platform (free) for datasets for the exercises (Open Science Framework) https://osf.io

Evaluation methods

Verification of learning:

The exam is composed of two parts (total end score: 30 points).

1) Written test (optional partial test; educational objectives 1-5): The evaluation of the achievements of the students will take place using a multiple choice questionnaire with 10 questions (10 points, 1/3 of the total end score) for a duration of 20 minutes. Each question will be associated with four alternative answers, whereas one of these is correct. The choice of each correct answer will lead to the attribution of one point, whereas a wrong answer (or indicating multiple answers or no answer) will lead to the attribution of zero points. The topics of the written exam reflect those of the course program at both a theoretical and a practical level (contenuti 1 and 2 of the course).

2) Oral examination (final test; educational objectives 4-6 or course contenuti 3 and 4): The preparation of the students will be evaluated in an interview by the teacher (20 points, 2/3 of the total end score) for 20 minutes. The aim of the interview is to examine the capacity of the student to read and interpret the quantitative results of a statistical analysis, and to communicate the statistical results in theoretical and clinical terms in an appropriate disciplinary language suitable to inform specialists as well as non-specialists. At the application level, the students will be required to determine what is the appropriate statistical model to answer a clinical or experimental question starting from an hypothesis and dataset (e.g. indicate in a determined context which is the suitable test, model, identify the variables, factors and the factor levels to select).
Evaluation:
Final vote

Contacts/More Information

E-mail of the teacher: s.ebisch@unich.it

In addition to the receiving hours of the teacher, the teacher also will be available to elucidate questions of the students in the context of the lessons. The students are recommended to regularly access and check the e-learning page for updates, communications about the content of the lessons and required preparations, slides of the lessons, etc.
Web page teacher: https://www.dnisc.unich.it/home-ebisch-sjoerd-johannes-hendrikus-4237

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

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