Date & Time: March 6th-7th. 9am-1pm

Room 20.287 (Campus Ciutadella) and via Zoom

Course description

There is an abundance of survey data on various topics, collected in many countries of the world over the last several decades. Combining these data creates new opportunities for social science research, as it extends the scope of possible analyses in terms of geographical and/or time coverage. However, combining data that were not intended to be analyzed jointly is associated with challenges primarily related to the variation in data quality and comparability. This workshop (1) provides a conceptual overview of the issues involved in ex-post harmonization of survey data, and (2) presents ways of addressing these issues, focusing on applications aimed at harmonizing data on political attitudes.

Prerequisites

Familiarity with basics of survey research and statistical concepts.

Software

Exercises and illustrations will use R. Some level of familiarity with R is expected as there will be no time to provide an intro to R in class. R needs to be installed beforehand.

Day 1

1. Cross-national surveys: overview of available data sources

2. Survey data quality and comparability: Total Survey Error framework, cross-survey differences in measurement and representation, survey quality and how to measure it

Day 2

3. Framework for survey data harmonization: measurement model, time trend model, representativeness adjustments

4. Using harmonized data: examples and cautionary tales

Readings:

Dubrow, J.K. & Tomescu-Dubrow, I. (2016). The rise of cross-national survey data harmonization in the social sciences: emergence of an interdisciplinary methodological field. Quality and Quantity 50, 1449–1467. https://doi.org/10.1007/s11135-015-0215-z

Valgarðsson, V. O., & Devine, D. (2022). What Satisfaction with Democracy? A Global Analysis of “Satisfaction with Democracy” Measures. Political Research Quarterly, 75(3), 576–590. https://doi.org/10.1177/10659129211009605

Claassen, C. (2020). In the Mood for Democracy? Democratic Support as Thermostatic Opinion. American Political Science Review, 114(1), 36-53. https://doi.org/10.1017/S0003055419000558

Tai, Y.C., Hu, Y., & Solt, F. (2022). Democracy, Public Support, and Measurement Uncertainty. American Political Science Review, 1-7. http://doi.org/10.1017/S0003055422000429

Kołczyńska, M., Bürkner, P., Kennedy, L., & Vehtari, A. (2020). Modeling public opinion over time and space: Trust in state institutions in Europe, 1989-2019. SocArXiv. https://doi.org/10.31235/osf.io/3v5g7

Instructor

Marta Kołczyńska

Date & Time: March 8th-10th. 9am-1pm

Room 20.287 (Campus Ciutadella) and via Zoom

Course description

Cross-cultural survey research may span multiple countries, regions, and languages. Examples include the European Social Survey (ESS), the European Values Survey (EVS), the Survey of Health Age and Retiring in Europe (SHARE) among others. Creating questionnaires for multiple contexts should consider potential differences in the way respondents answer questionnaires, and methodological procedures, such as the translation of questionnaires. This course will show students how to design questionnaires for multiple populations. The course is divided in three parts: In the first part students will get familiar with three-step procedure to design questionnaires. In the second part, students will learn about gold-standard procedures to translate survey questionnaires. In the third part, students will learn the state of the art on the effects that linguistic elements can have in the way respondents answer questionnaires.

In the lab sessions, we will use the MCSQ https://www.upf.edu/web/mcsq and open-source and open-access research resource to retrieve and analyse past translations decisions in large scale comparative survey projects, such as ESS, EVS, SHARE.

If students are in the process of designing questionnaires, we will work with their real examples.

Prerequisites

No prerequisites. This course is addressed to all researchers with an interest in comparative research in multiple contexts and cultures. It is specially recommended for questionnaire designers, survey practitioners and translators with an interest in the translation of questionnaires. 

Schedule

References:

Hong, Y., Morris, M. W., Chiu, C., & Benet-Martínez, V. (2000). Multicultural minds: A dynamic constructivist approach to culture and cognition. American Psychologist, 55(7), 709–720. https://doi.org/10.1037/0003-066X.55.7.709  

Harkness, J. A., Villar, A., & Edwards, B. (2010). Translation, Adaptation, and Design. In J. A. Harkness, M. Braun, B. Edwards, T. P. Johnson, L. E. Lyberg, P. P. Mohler, … T. W. Smith (Eds.), Survey Methods in Multinational, Multiregional, and Multicultural Contexts (pp. 115–140). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470609927.ch7

Harkness, J. A., Edwards, B., Hansen, S. E., Miller, D. R., & Villar, A. (2010). Designing Questionnaires for Multipopulation Research. In J. A. Harkness, M. Braun, B. Edwards, T. P. Johnson, L. E. Lyberg, P. P. Mohler, … T. W. Smith (Eds.), Survey Methods in Multinational, Multiregional, and Multicultural Contexts (pp. 31–57). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470609927.ch3

Saris, W. E., & Gallhofer, I. (2014). Design, evaluation, and analysis of questionnaires for survey research. In Wiley Series in Survey Methodology (Second, Vol. 548). John Wiley & Sons. https://doi.org/10.1111/j.1751-5823.2008.00054_20.x

Zavala-Rojas, D., Sorato, D., Hareide, L., & Hofland, K. (forthcoming). [MCSQ] Multilingual Corpus of Survey Questionnaires. Meta: Journal Des Traducteurs.

We will see examples from the following literature and depending on the individual interests of each student I will recommend specific readings from the following, among others:

Behr, D. (2018) "Translating questionnaires for cross-national surveys – a description of a genre and its particularities based on the ISO 17100 categorization of translator competences." Translation & Interpreting 10 (2): 5-20.

Cohen, A. B. (2009). Many forms of culture. American Psychologist, 64(3), 194.

Peytcheva, E. (2018). Can the Language of Survey Administration Influence Respondents’ Answers? Advances in Comparative Survey Methods. https://doi.org/doi:10.1002/9781118884997.ch15

Stewart, M. A., Ryan, E. B., & Giles, H. (1985). Accent and Social Class Effects on Status and Solidarity Evaluations. Personality and Social Psychology Bulletin, 11(1), 98–105. https://doi.org/10.1177/0146167285111009

Zavala-Rojas, D., & Saris, W. E. (2018). Measurement Invariance in Multilingual Survey Research: The Role of the Language of the Questionnaire. Social Indicators Research, 140(2), 485–510. https://doi.org/10.1007/s11205-017-1787-x

Instructor

Diana Zavala-Rojas

Date & Time: March 9th-10th. 2pm-6pm

Room 20.287 (Campus Ciutadella) and via Zoom

Course Description

Network Analysis is a rapidly growing field among quantitative social sciences. Networks permeate everyday life and contemporary social phenomena are increasingly represented as networks: from the participation to online social media, to the structure of relations in a working environment, to the description of political movements, to the analysis of the diffusion of banking crises. The boundaries of Network Science go well beyond social sciences, including applications to history, biology, glottology, statistics, and computer science.

Network Analysis focuses on the relationship that exist between units of analysis, and studies those relationships through the tools of Graph Theory. This course will introduce the participants to Applied Network Analysis concentrating on measurement, data collection and manipulation, description and visualization of networks. Some inferential statistical techniques will be presented at the end of the course. Examples will be drawn from economics, political science, and sociology, and replication exercises will be discussed in class and implemented by the participants.

The course has a hands-on character, and the class will apply the concepts presented by the instructor using R packages and RStudio: R will be used most of the time, with some incursion into Gephi, for large networks visualizations. Knowledge of R is therefore recommended to get the best out of this short, applied introduction to Social Network Analysis.

Software

R.

Prerequisites

The course is directed to anyone involved and/or interested in network analysis, especially professionals with a social science background, and students of statistics, economics, political science, and sociology. Prerequisites for the course are basic knowledge of statistics. Previous use of R and R-Studio is strongly recommended. Students may work through one or more R tutorials prior to the first class meeting. Some resources can be found here: https://rstudio.cloud/learn/primers

Schedule

The course will cover the following topics:

•          General introduction to Network Analysis

o          History of (Social) Network Analysis

o          Terminology and basic definitions

o          Software

o          Data

o          References

•          Types of Networks

o          Directed and undirected graphs

o          Weighted and unweighted graphs

o          Two mode networks

o          Multiplex

•          Network Data

o          Imputing network data

o          Manipulating network objects: Nodes, Edges and Graph Attributes

o          Generating networks: Random Graphs, Small-World models, Preferential Attachment

•          Visualizing Networks

o          Introduction to layout algorithms

o          Elements of networks visualizations

o          Decorating Graphs Layouts

o          Visualizing Large Networks

•          Centrality

o          Degree centrality

o          Closeness centrality

o          Betweenness centrality

o          Eigenvector centrality

•          Network Cohesion

o          Subgraphs and Censuses

o          Density and relative frequencies of edges

o          Connectivity and cuts

o          Assortativity and segregation

•          Graph Partitioning

o          Hierarchical clustering

o          Spectral partitioning

o          Modularity and communities in networks

•          Statistical Models for Network Data

o          Correlation networks

o          Propensity score with network data

References:

While not required, the following references are useful background readings for the course:

Jackson M. (2019), The Human Network, Atlantic Books, London.

de Martí, J. and Y. Zenou (2011), “Social networks” In: I. Jarvie and J. Zamora-Bonilla (Eds.), Handbook of Philosophy of Social Science, London: SAGE Publications, Chap. 16, 339-361.

De Benedictis L. and L. Tajoli (2011), “The World Trade Network.” The World Economy, 34, 8, 1417-1454.

Arpino B, L, De Benedictis, A. Mattei (2018), Implementing propensity score matching with network data: the effect of the General Agreement on Tariffs and Trade on bilateral trade, Journal of the Royal Statistical Society: Series C, 66(3), 537-554.

Instructor

Luca De Benedictis