INGLESE

Psychometrics and measurement in psychology - Building a test -
Construct, dimensions, facets & indicators - Example: The Big Five - Likert scale - Content Validity & Facet analysis - Reliability - Classical Theory of Reliability
Reliability and Validity - Applications of Reliability: Split Half and Cronbach’s Alpha - Spearman-Brown Prophecy Formula - Attenuation paradox & Bandwidth-Fidelity Trade-off
Generalizability Theory - Validity: Face Validity, Criterion Validity, Construct Validity & MultiMethod-MultiTrait Matrix, Nomological Validity

Mandatory texts:

(1) John, O.P. & Bennet-Martínez, V. (2014). Measurement: Reliability, construct validation, and scale construction. In: Reis, Harry T. & Judd, Charles M. (Eds), Handbook of research methods in social and personality psychology (Cap. 18, pp. 473-503). Second Edition. Cambridge: Cambridge University Press.

(2) Tabachnick, B.G. & Fidell, L.S. (2013). Principal components and factorial analysis. In: Using multivariate statistics (Cap. 13, pp. 612.680). Boston: Pearson.
Downloadable by:
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiOgaqsvIDyAhWk_7sIHVQOAQYQFjACegQIBBAD&url=http%3A%2F%2Febook.stkip-pgri-sumbar.ac.id%2Febook%2Fkomputer-informasi-referensi-umum%2F6th-edition-using-multivariate-statistics-pearson%2Fdownload&usg=AOvVaw3fDp8kBAL6eUN4tv_lzu72
Mandatory ppt slides:

(3) Notes from the lessons (proff. Gnisci, Irpino, Senese).
----------
Article useful for exercises and report:

- Boateng, G.O., Neilands, T.B., Frongillo, E.A., Melgar-Quiñonez, H. R. & Young, S. L. (2018) Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Front. Public Health 6:149. doi: 10.3389/fpubh.2018.00149

- Sergi, I, Gnisci, A., Senese, V. P., & Perugini, M. (2019). The HEXACO-Middle School Inventory (MSI): A personality inventory for children and adolescents. European Journal of Psychological Assessment. doi: 10.1027/1015-5759/a000538

- Senese, V.P., Ruotolo, F., Ruggiero, G., & Iachini, T. (2012). The Italian version of the Weinstein Noise Sensitivity Scale: Measurement Invariance Across Age, Gender, and Context. European Journal of Psychological Assessment, 28(2), 118-124. doi: 10.1027/1015-5759/a000099

It aims to provide students with the knowledge and skills to build a measurement instrument (for example, a Likert-type questionnaire) and determine its reliability and validity.

Fundamentals of descriptive and inferential statistics and research methods in social sciences.

Face-to-Face lessons with ppt slides and use of e-learning platform and mixed teaching, according to the COVID situation.

Classroom exercises using JASP statistical software.

Multiple choice written exam with 31 questions (each correct answer gets 1 point; a blank or a wrong answer gets 0 points)
and preparation of a report on data analysis conducted by the student, to be discussed if the written exam is passed.

PROF. AUGUSTO GNISCI Room #15, II floor, Viale Ellittico 31, 81100, Caserta. Availability:
Tuesday, 15:00-17:00 (contact the teacher by e-mail previously).
Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

PROF. ANTONIO IRPINO: Viale Lincoln 5, Office B1, 81100 Caserta (CE), Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

PROF. V. P. SENESE: Associate Professor at the Department of Psychology, University of Campania «Luigi Vanvitelli», head of the «Psychometric Laboratory» (http://psiclab.altervista.org/).
e-mail: Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.
Availability: see https://calendly.com/senese (Ms-Teams)

GENERAL ASPECTS OF PSYCHOLOGICAL TESTS
I. Introduction to psychological tests: 1. The birth of the tests 2. Normal distribution 3. Transformation of scores and standardization of the measures
II. Psychological test construction models: 1. Indicators, constructs and dimensions 2. Regressive and factorial models 3. Analysis of the items 4. Functions of the tests
PSYCHOMETRIC ASPECTS OF PSYCHOLOGICAL TESTS
III. Dimensionality of psychological tests: 1. Dimensionality of the tests 2. Principal Components Analysis (ACP) 3. Eigenvalues and communality 4. Criteria for the identification of the number of factors 5. Orthogonal and oblique rotation 6. Elimination of the items 7. Interpretation of factors 8. Factorial Scores 9. Common Factors Analysis (AFC) 10. Conditions of applicability of dimensional analysis IV. Reliability of psychological tests: 1. Classical theory 2. Types of reliability 2. Coefficients based on two measurements (stability or test-retest and equivalence) 3. Coefficients based on one measurement (split-half and internal consistency) 4. Coefficient Cronbach’s alpha of 5. Reliability for observational measures, point by point and global coefficients
V. Validity of psychological tests: 1. Content validity 2. Face validity 3. Criterion validity: concurrent and predictive 4. Convergent and divergent construct validity 5. The Multi-Trait-Multi-Method matrix (MTMM)

English

Psychometrics and measurement in psychology - Building a test -
Construct, dimensions, facets & indicators - Example: The Big Five - Likert scale - Content Validity & Facet analysis - Reliability - Classical Theory of Reliability
Reliability and Validity - Applications of Reliability: Split Half and Cronbach’s Alpha - Spearman-Brown Prophecy Formula - Attenuation paradox & Bandwidth-Fidelity Trade-off
Generalizability Theory - Validity: Face Validity, Criterion Validity, Construct Validity & MultiMethod-MultiTrait Matrix, Nomological Validity

Mandatory texts:

(1) John, O.P. & Bennet-Martínez, V. (2014). Measurement: Reliability, construct validation, and scale construction. In: Reis, Harry T. & Judd, Charles M. (Eds), Handbook of research methods in social and personality psychology (Cap. 18, pp. 473-503). Second Edition. Cambridge: Cambridge University Press.

(2) Tabachnick, B.G. & Fidell, L.S. (2013). Principal components and factorial analysis. In: Using multivariate statistics (Cap. 13, pp. 612.680). Boston: Pearson.
Downloadable by:
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiOgaqsvIDyAhWk_7sIHVQOAQYQFjACegQIBBAD&url=http%3A%2F%2Febook.stkip-pgri-sumbar.ac.id%2Febook%2Fkomputer-informasi-referensi-umum%2F6th-edition-using-multivariate-statistics-pearson%2Fdownload&usg=AOvVaw3fDp8kBAL6eUN4tv_lzu72
Mandatory ppt slides:

(3) Notes from the lessons (proff. Gnisci, Irpino, Senese).
----------
Article useful for exercises and report:

- Boateng, G.O., Neilands, T.B., Frongillo, E.A., Melgar-Quiñonez, H. R. & Young, S. L. (2018) Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Front. Public Health 6:149. doi: 10.3389/fpubh.2018.00149

- Sergi, I, Gnisci, A., Senese, V. P., & Perugini, M. (2019). The HEXACO-Middle School Inventory (MSI): A personality inventory for children and adolescents. European Journal of Psychological Assessment. doi: 10.1027/1015-5759/a000538

- Senese, V.P., Ruotolo, F., Ruggiero, G., & Iachini, T. (2012). The Italian version of the Weinstein Noise Sensitivity Scale: Measurement Invariance Across Age, Gender, and Context. European Journal of Psychological Assessment, 28(2), 118-124. doi: 10.1027/1015-5759/a000099

It aims to provide students with the knowledge and skills to build a measurement instrument (for example, a Likert-type questionnaire) and determine its reliability and validity.

Fundamentals of descriptive and inferential statistics and research methods in social sciences.

Face-to-Face lessons with ppt slides and use of e-learning platform and mixed teaching, according to the COVID situation.

Classroom exercises using JASP statistical software.

Multiple choice written exam with 31 questions (each correct answer gets 1 point; a blank or a wrong answer gets 0 points)
and preparation of a report on data analysis conducted by the student, to be discussed if the written exam is passed.

PROF. AUGUSTO GNISCI Room #15, II floor, Viale Ellittico 31, 81100, Caserta. Availability:
Tuesday, 15:00-17:00 (contact the teacher by e-mail previously).
Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

PROF. ANTONIO IRPINO: Viale Lincoln 5, Office B1, 81100 Caserta (CE), Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

PROF. V. P. SENESE: Associate Professor at the Department of Psychology, University of Campania «Luigi Vanvitelli», head of the «Psychometric Laboratory» (http://psiclab.altervista.org/).
e-mail: Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.
Availability: see https://calendly.com/senese (Ms-Teams)

GENERAL ASPECTS OF PSYCHOLOGICAL TESTS
I. Introduction to psychological tests: 1. The birth of the tests 2. Normal distribution 3. Transformation of scores and standardization of the measures
II. Psychological test construction models: 1. Indicators, constructs, and dimensions 2. Regressive and factorial models 3. Analysis of the items 4. Functions of the tests
PSYCHOMETRIC ASPECTS OF PSYCHOLOGICAL TESTS
III. Dimensionality of psychological tests: 1. The dimensionality of the tests 2. Principal Components Analysis (ACP) 3. Eigenvalues and communality 4. Criteria for the identification of the number of factors 5. Orthogonal and oblique rotation 6. Elimination of the items 7. Interpretation of factors 8. Factorial Scores 9. Common Factors Analysis (AFC) 10. Conditions of applicability of dimensional analysis IV. Reliability of psychological tests: 1. Classical theory 2. Types of reliability 2. Coefficients based on two measurements (stability or test-retest and equivalence) 3. Coefficients based on one measurement (split-half and internal consistency) 4. Coefficient Cronbach’s alpha of 5. Reliability for observational measures, point by point and global coefficients
V. Validity of psychological tests: 1. Content validity 2. Face validity 3. Criterion validity: concurrent and predictive 4. Convergent and divergent construct validity 5. The Multi-Trait-Multi-Method matrix (MTMM)