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Regular version of the site
Bachelor 2017/2018

## Data Analysis in Sociology

Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Sociology and Social Informatics)
Area of studies: Sociology
When: 2 year, 3, 4 module
Mode of studies: offline
Language: English
ECTS credits: 4

### Course Syllabus

#### Abstract

This course lasts for three years. The 1st year aims at beginners. The course goes from introductory topics (variable types, hypothesis testing, descriptive statistics) to some statistics and methods (chi-square, t-test, nonparametric statistics, one-way ANOVA, and linear regression). The course covers the building blocks of quantitative data analysis with the goal of training students to be informed consumers and producers of quantitative research. This course is also the starting point for students interested in pursuing advanced methods training or planning to use quantitative methods in their own research.

#### Learning Objectives

• develop skills necessary to solve typical problems in analysing social data in R software environment

#### Expected Learning Outcomes

• Conduct statistical analyses in RStudio
• Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
• Give meaningful interpretation of statistical results: regression coefficients, tables, plots and diagrams (produced in R)
• Perform data transformations
• Represent graphically the results of the statistical analyses
• Create analytical reports describing all the stages of analysis and interpreting its results

#### Course Contents

• Research hypotheses vs. statistical hypotheses. Variable types
The cycle of research. Data analysis as part of the research process. Posing and testing hypotheses. Research hypotheses vs. statistical hypotheses testing. Directed and non-directed hypotheses. Dependent and independent variables. Variable scales: nominal, ordinal, continuous (interval and ratio). Descriptive statistics of a variable depending on its type. Getting to know R and RStudio.
• Central tendency measures
Mean, median, mode. Standard normal distribution and its use. Z-scores. Moments of distributions. Distribution plots and reading them. Sources of bias in data. Interpretation of z-scores. Mean as a data model.
• Chi-square
Observed and expected frequencies. Measures of association for categorical variables. Reading and interpreting chi-square tests. Assumptions of chi-square. Independence. Standardised residuals. Odds ratio. Chi-square and other association measures in R.
• Two means comparison
Independent and paired samples. Assumptions behind the t-test. One-sample t-test. Two-sample t-tests. Nonparametric tests for two samples and for multiple samples. Reading and interpreting means comparison. Confidence intervals. Means comparison in R
• One-way ANOVA
Assumptions and usage of ANOVA. Between-group and within-group variance, their ratio. Planned and non-planned comparisons; corrections. Post hoc comparisons for equal and unequal variances. Reading and interpreting ANOVA. One-way ANOVA in R. Presenting the results of ANOVA. Getting to know RMarkdown: reports and slide shows.
• Linear regression
Correlations. Research problems for correlational analysis. Correlation coefficients for different types of data. ANOVA, correlation, regression as linear models. Building a linear regression. Ordinary least squares. Fitting the regression line. Assumptions behind linear regression. Reading and interpreting regressions. Presenting and interpreting a linear regression. Categorical predictors in a linear regression. Dummy-coding. Linear regression in R. Plotting linear regressions in R (case studies).
• Linear regression with multiple predictors
The concept of interaction effects for categorical by categorical, categorical by continuous, continuous by continuous variables. Effect coding. Centring. Multicollinearity. Reading and interpreting interaction models in a linear regression. Testing for interactions in R. Reporting and interpreting a linear regression with interactions.

#### Assessment Elements

• Projects
• In-class activity
• Exam

#### Interim Assessment

• Interim assessment (4 module)
0.2 * Exam + 0.2 * In-class activity + 0.6 * Projects

#### Recommended Core Bibliography

• Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
• Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling, Fourth Edition (Vol. Fourth edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1078917