Annotation for the course
«Applied Informatics for Social Scientists»
Department: Sociology
Kafedra: Methods and Techniques of Sociological Research
Program: Bachelor level, Sociology, second year
Author: Nikiforova, IS, Ph.D.
Lecturers:
Lissovsky, Alexander Vl., Candidate in Psychology, St. Petersburg State University
Nikiforova, Irina S., Ph.D., Sociology of S&T, Georgia Institute of Technology
1. Explanatory note
This course («Applied Informatics») requires proficiency in MS Office: Excel and Word and in mathematics at the level of Calculus and Linear Algebra, covered during the first year of study. The knowledge of informatics is helpful but not required. However, proficiency in English is a prerequisite for academic learning, understanding instructors' explanations, and reporting the results of analyses.
2. Teaching goals for the course
The course provides an introduction to the most popular programs for data analysis (SPSS and R) for social scientists and serves as one of the foundational courses in methods. The goal of the course is to introduce students to research tools and develop their computational thinking early on in the program. Computation thinking and proficiency in statistical software are part of the analytical skills that students will bring into data analysis and modeling courses. The course prepares students for independent problem solving and the use of the basic statistical tools in research.
Students will learn statistical and computer science terminology in English which will help them to read articles, work and study abroad, and be part of the international research community.
3. Thematic plan
Theme / Class hours / Self study1. / Introduction to statistical software for sociologists / 2 / 6
2. / Preparing data for analysis / 2 / 6
3. / SPSS: import/export and transformation of data in SPSS / 4 / 6
4. / SPSS: Descriptive statistics and graphics / 4 / 6
5. / SPSS: Crosstabs and correlation tables, correlation coefficients and Chi-Square / 4 / 6
6. / SPSS: Comparing means, t-tests; ANOVA / 8 / 6
7. / SPSS: Linear regression / 10 / 6
8. / SPSS and MS Office: Basic graphs / 4 / 6
9. / Displaying results in PowerPoint presentations / 4 / 6
10. / R: Introduction to R / 2 / 6
11. / R: Data input/output and simple operations / 4 / 6
12. / R: Data structures / 4 / 6
13. / R: Data transformations / 4 / 6
14. / R: Basic programming / 4 / 6
15. / R: Descriptive statistics and graphics / 4 / 6
16. / R. Advanced graphics / 2 / 6
Total (34 hrs of seminars, 32 hrs of practical work) / 66 / 96
4. Brief topic overview
1: Introduction to statistical software for sociologists
Data analysis in sociology. Overview of statistical software: BMDP, SAS, SPSS, Statistica, and R.
2: Preparing data for analysis
Data types and frequent mistakes in data coding. Data cleaning. Creation of a database.
3: Import/export, overview and transformation of data in SPSS
Data import/export from Excel to SPSS. Exploring the variables. Copying and transforming data. Creating new variables. Recoding Variables. Dealing with scaled questions. Weighting variables.
4: Descriptive statistics and graphics
Measuring central tendency and dispersion. Calculating and interpreting the mean, median and mode. Measures of dispersion and variation. Quartiles. Standard deviation. The coefficient of variation.
5: Crosstabs and correlation tables, correlation coefficients and Chi-Square
Hypothesis testing. Comparing proportions. Chi-square. Associations in nominal, ordered and interval variables. Two-dimensional scatter plots. Nonlinearity. Heterogeneity. Causes and correlations.
6: SPSS: Comparing means, t-tests; analysis of variance (ANOVA)
Confidence intervals. Z-test. Student's t-distribution and t-test. Analysis of variance.
7: SPSS: Linear regression
Linear regression models. Method of least-squares. R-squared. Regression assumptions. Nominal independent variables in the regression. Multicollinearity. Interactions.
8: SPSS and MS Office: Basic graphs
Types of graphs: pie charts, histograms, linear graphs. Graphs for different data types. Characteristics of good graphs. Readability and typical mistakes.
9: Displaying results in PowerPoint presentations
Advantages of different types of presentations for communicating data results.
10: R: Introduction to R; description and classification of programming languages.
Computers and computing environments. Introduction to informatics. Algorithms. Programs. Scripts. Functions. Programming paradigms. Advantages and disadvantages or R.
11: R: Data input/output and simple operations
Starting R. Data input/output. Working with files and scripts. Installing packages. R as a calculating environment. Simple commands. Exiting R.
12: R: Data structures
Types of objects in R: numeric (integer, double), complex, logical, character, raw. Special variables. Data types: vector, factor, matrix, list and data frames. Working with different data types.
13: R: Data transformations
Data transformations. Data sorting, indexing, filtering, conditional manipulations.
14: R: Basic programming
Logical and mathematical operations. Grouping, looping and conditional execution. Control statements: if, if else, for, while, repeat, break, next, switch. Writing functions in R. Recursion.
15: R: Descriptive statistics and graphics
Descriptive statistical commands. Correlations. Basic graphs. Low-level plotting commands.
16: R. Advanced graphics
Advance graphics with packages Lattice, ggplot2, and vcd.
5. Forms of control
Type of testing / Form of testing / ParametersCurrent / Weekly Homeworks and Labs / 40%
Intermediate / Test / 30%
Final test / Test / 30%
6. Literature
Main textbook
Pallant, Julie. (2007). A Step by Step Guide to Data Analysis using SPSS for Windows.
Jones, O. (2009). Introduction to scientific programming and simulation using R. Boca Raton, FL: Chapman & Hall/CRC.
Other books
Adler, J. (2010). R in a nutshell. Sebastopol, CA: O'Reilly.
Teetor, Paul. (2011). R Cookbook. Sebastopol, CA: O'Reilly Media.
Rachad, Antonius. (2003). Interpreting Quantitative Data with SPSS.
Tyrrell, Sydney. (2009). SPSS: Stats Practically Short and Simple.
7. Contact person
Irina Nikiforova,
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