Version:1.0
Release:2020-03-25
Availability:available
UniData provides two kind of data access:
- open data
(all users can use the data - an expense allowance could be required) - restricted data
(the data use is limited - see below)
Data Kind:
individual data
Time Dimensions:
longitudinal (panel)
The longitudinal dataset is part of the OpenTeQ project, a cluster RCT conducted on 198 Italian middle schools to evaluate the impact of a light-touch training intervention on teachers relational skills.
A two-wave longitudinal survey was conducted via CATI in 2016 and 2017, involving all the Italian language and math teachers operating in the 7th grade classes of the enrolled schools. In both occasions, they were asked to self-assess their efficacy in teaching using the Ohio State Teacher Efficacy Scale (OSTES). The two waves have been finally merged into a unique longitudinal dataset of 1,912 participants (response rate of 92%). These data are used to investigate the longitudinal invariance of the OSTES, evaluating whether different testing procedures led to similar conclusions about model fit and invariance of measurement and structural parameters.
To ensure the complete reproducibility of findings, the Mplus 7 syntaxes for all model specifications are made available with the data.
Data are available for free (registration required). After logging in, please click on the green button 
in the box at the right.
Keywords: teacher efficacy, Longitudinal measurement invariance, middle school, reproducibility
Topic Classification:
Nations: Italy
Geographical Cover:City of Milano City of Roma City of Napoli Province of Como Province of Pavia Province of Novara Province of Verona Province of Bologna Province of Pesaro-Urbino Province of Ancona Province of Bari
Geographical Unit: not applicable
Analysis Unit: individual
Universe: Teachers from the 198 schools involved in the controlled experimentation
Sample Procedure: 1,912 individuals. All secondary schools of first grade located in the 11 selected provinces (Milano, Roma, Napoli, Como, Pavia, Novara, Verona, Bologna, Pesaro-Urbino, Ancona and Bari) and which had at least four first classes (grade 6) available to be directly involved in the training during the 2015/2016 school year were invited to participate in the experimentation. The complete list of schools with these characteristics was provided to the Research Group by the Italian Ministry of Education, for a total of 619 institutions. At the end of the contact and enrollment phases of the schools, 198 institutions decided to formally join. All Italian and mathematics teachers working in the second classes of these schools (Grade 7) were involved in the two waves of the survey
Weight: No weight used
Collection Mode: Computer-Assisted Telephone Interviewing (CATI)
Collection Size: UniData supplies: 1 dataset in SPSS format; 1 methodological notes in PDF format (ita); 1 codebook in PDF format (ita); 1 folder including the documents (data and syntaxes) for the reproducibility of findings (4 file)
Documentation:
| Codebook (pdf): |
|
| DDI Documentation: |  |
Publications:
- Gerosa, T. (2021). Measurement Invariance with ordered Categorical Variables. Applications in Longitudinal Survey Research. In A. Cernat & J.W. Sakshaug (Eds.), Measurement of Error in Longitudinal Data (pp. 259-288). Oxford University Press
Data Use Restriction:
Data are released in according to Creative Commons – Attribution 4.0 Licence, available here.
Source Contact: Tiziano Gerosa - Università degli Studi di Milano-Bicocca
Citation:
Argentin, Gianluca; Gerosa, Tiziano. (2016-2017) OpenTeQ – Teacher Efficacy Scale. UniData - Bicocca Data Archive, Milan. Study Number SN218. Data file version 1.0 doi:10.20366/unimib/unidata/SN218-1.0
Deposit Requirement:
The user is obliged to quote all data and documents disseminated by UniData and used in the own publications, using the information previously showed. The user is also obliged to send UniData the bibliographic citations related to the publications where the requested data and documents are used.
Disclaimer:
Neither the depositor nor UniData bear any responsibility for the analysis or interpretation of the data produced by the user.