Annotation for the Course Data Analysis

Annotation for the course “Data Analysis”

Department: Sociology

Chair: Methods and Techniques of Sociological Research

Specialization/profession: Sociology, Baccalaureate, third year

The author: AlexanderVl. Lissovsky, candidate degree in psychology (St. Petersburg State University), MA in sociology (Duke University, NC, USA), senior researcher degree in psychology.

Thelecturer: AlexanderVl. Lissovsky (see above)

1. Explanatory note

Requirements and prerequisites for students:

1. Students should get at least a satisfactory grade (no lower than 5 out of 10) for the course “Statistics – 1”, which is a required course for the first year curriculum in sociology.
2. Students should be able to understand English terminology related to sociological methods, statistical analysis, to ask questions and to participate in discussions during lab sessions and to write analytical research papers in English.

The purpose of the course is to train students in advanced methods of multidimensional statistical analysis of comparative surveys data. The major data source is European Social Survey (ESS), which provides five waves of data, collected every two years, starting from 2002. The last wave took place in 2010 and the important advantage of ESS data is a wide scope of social problems represented in ESS: starting from politics and ending with mundane household activities. Students will perform statistical analysis in SPSS (versions 19-21). SPSS is the most user-friendly system for statistical data-analysis compared to other available (R, Stata, SAS, Stastica etc.).

Every student is required to choose a country for his analytical report. The 5th wave of ESS was conducted in Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Lithuania, Netherlands, Norway, Poland, Portugal, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom, so every country will be analyzed by 2 or 3 studentsdepending on the number of students in a class.

At the second stage students are united in groups of 3or 4 members (the members of a group should choose different countries) and they perform comparative analysis for the chosen countries. Every group submits a written analytical report and makes a presentation.

Students are required to use for their analysis following methods:

Regression (OLS and logistical). Factor analysis.Cluster analysis.

Students can choose variables for their analysis, but they need to get an approval for their lists of variables from the lecturer. Students are advised to choose variables and topics related to their term papers and diploma projects.

The important consideration: analytical projects should be oriented on substantial analysis of sociological data, statistical data analysis is not an ultimate purpose – it is an important tool among other methods of social research.

1. Teaching tasks for the course

Students should develop and improve following competencies:

1. Selecting and checking data from comparative cross-cultural surveys.
2. Applying multidimensional statistical analysis.
3. Preparing analytical reports.
4. Working in teams.
5. Presenting the results of their analysis.

1. Distribution of hours

4. Coursecontent

1. Data selection and checking

Typical problems with comparative surveys data.Data selection (Select, Split file).Data changing-recoding (Compute, Recode).Missing data treatment.

1. Regression

OLS – assumptions and applicability. Multicollinearity problem (limitations of formal diagnostics with VIF and tolerance).Logistics regressions – assumptions and applicability.Introduction to multilevel regression.

1. Factor analysis

Basic assumptions.Factor extraction (principal components and maximum likelihood).Rotation (orthogonal and oblique).Factor interpretation.Using factor scores as variables.

1. Cluster analysis

Empirical classifications of respondents – possible usages. Different measures of proximity for cluster analysis. Different methods of cluster analysis (Two-steps, K-means, Hierarchical).Choosing among possible cluster solutions. Cluster interpretation.

1. Essentials of comparative analysis

What is comparable and what is not. Variable selection for comparative analysis.How to evaluate cross-country similarities-dissimilarities.Reporting results of cross-cultural analysis.

1. Project presentations

Literaturereview.Choosing what results are worth reporting. Formats for reporting different types of analysis. Typical mistakes in presentations.

5.Assessment

6.The reading materials

Main textbook:

Andy P. Field. Discovering statistics using SPSS (and sex and drugs and rock 'n' roll) (3rd edition) SAGE(2009)

Other books:

Antonius R. Interpreting Quantitative Data with SPSS. Sage Publications Ltd, 2003.

Ember C.R., Ember M. Cross-cultural Research Methods. - Plymouth: AltaMira Press, 2009.

Hantaris L. International comparative research: theory, methods and practice. - Palgrave Macmillan, 2009.

Landman, T. Issues and Methods in Comparative Politics. - Routledge, 2008.

Lieberman E.V. Nested Analysis as a Mixed-Method Strategy for Comparative Research // American Political Science Review. - Vol. 99, No. 3. - August 2005.

Miller R.L., Acton C., Fullerton D.A., Maltby J. SPSS for Social Scientists. Palgrave Macmillan, 2002.

Stevens, James. Applied multivariate statistics for the social sciences 5th ed. Routledge, 2009.

7.Contact person:

AlexanderVladimirovichLissovsky,,