Winter Term 1995
Victoras Radusis
CEU Department of Sociology
for European Studies Department
Central European University

Course Outline

Course purpose: To give an introductory understanding of the main conceps and ideas of the quantitative analysis in the social sciences. To prepare students to be able to understand academic texts that are applying statistical methodology in the analysis of social phenomena.

Number of hours: 1 hour lecture and 1 hour workshop each week

Main texts:
1. Blalock, Hubert M. (1960) Social Statistics. New York, McGraw-Hill.(Library)
2. Henkel, Ramon E. (1990) Tests of Significance. Beverly Hills, Sage Publications. (Library)
3. Agresti, Alan and Finlay, Barbara (1986) Statistical Methods for the Social Sciences. London, Macmillan. (some photocopied parts will be distributed).
4. Reading material prepared by the lecturer.

Week one: Introduction.
Quantitative methods in social sciences and statistics. Population and sample. Parameters and statistics. Descriptive and inferential techniques. Variables and variable values. Discrete and continuous variables. Levels of measurement: Nominal, ordinal, interval, and ratio scales.

[A&F 1.1 1.2 2.1]
[B Introduction 2.2 2.3].

Week two:

Measures of central tendency and dispersion and their use. Some mathematical notations and techniques of presenting regular data. Central tendency: Mean, median, mode. Dispersion: Variance and standard deviation. Probability, random, variable. Sampling procedures (briefly). Graphical presentation of distribution: histogram, and density function. Population, sample, and sampling distribution.

[A&F 2.2 2.3 3.1 3.2 3.3 3.4 4.1 4.3]
[B Ch.4,5,6,7.1 8.1 Ch.9 11.1]
[H p.13-25].

Week three.
Some theoretical distributions. Normal and standard normal distribution. Z scores. The Central Limit Theorem. Statistical inference: Confidence intervals for means and proportions for large samples.

[A&F 4.2 4.4 5.1 5.2 5.3 5.4]
[B Ch.7 11.1 Ch.12]
[H p.27-31].

Week four.
Statistical inference: Testing hypotheses about a single mean. Null vs. alternative (research) hypothesis. The fallacy of affirming the consequent. Significance
levels. One and two tail tests. Single-sample tests about the mean for large and small
samples. Student's distribution. Degrees of freedom.

[A&F 6.1 6.2 6.4 6.6 6.7]
[B 8.2 8.3 8.4 10.2 Ch.11]
[H p.34-45].

Week five.
Hypotheses about the difference of two means. Tests for independent and dependent samples. T-test and Oneway analysis of variance.

[A&F 7.1 7.2]
[B Ch.13]
Iversen, Norporth. Analysis of Variance. Sage Pubns.

Week six.
Nonparametric tests. Crosstabulations: Association of two nominal variables, Chi square. Measures based on Chi square. Proportional reduction in error. Measures of association for ordinal variables.

Liebetrau, Albert M. (1983) Measures of Association. Newbury Park, Sage Publications.
Reynolds. Analysis of Nominal Data. Sage Pubns..
Hildebrand, Laing, Rosenthal. Analysis of Ordinal Data. Sage Pubns..

Week seven.
Correlation and regression analyses.

Week eight.
Regression analysis (2).

Reading list for week seven-eight:
Blalock, Hubert M. (1960) Social Statistics. New York, McGraw-Hill.
Lewis-Beck, Michael S. (1980) Applied Regression. An Introduction. Sage Pubns.
Other books on regression analysis from Sage Pubns.

Week nine.
Brief overview of other statistical methods in the social sciences.


1. Students will be split into 2 workshop groups according to their level/skills in statistics.
2. The final exam will be in the form of a written 3 hour test.

CRC-Curriculum Resource Center
CEU Budapest, Hungary
Modified: April, 1996


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