Download Quantitative Methods for Social Sciences

The development of this course has been funded by the Curriculum Resource Center (“CRC”) at the Central European University (“CEU”),
whose programs are partially funded by the Higher Education Support Program (“HESP”). The opinions expressed herein are the author’s own
and do not necessarily express the views of CEU.
Lecturer:
Host Institution:
Course Title:
Year of CDC Grant:
Volodymyr Shportyuk
National University "Kyiv-Mohyla Academy"
Quantitative Methods for Social Sciences
2000 / 2001
I Introduction
The course "Quantitative Methods for Social Sciences" is an advanced teaching course of research
methods in behavioral sciences, with particular attention to the opportunities and challenges that
sociologists and economists face in applying the methods of science to the study of human behavior
and economic processes. The research process in behavioral sciences is complicated and non-linear
thus it is often difficult for students to understand and see its strengths (as well as weaknesses). This
course attempts to immerse students in the research process by starting with elementary points and
building further on them:
1.Selecting a topic;
2. Reviewing the literature;
3. Formulating a researchable question;
4. Choosing an appropriate research design;
5. Collecting data;
6. Analyzing and interpreting data;
7. Reporting research results.
This course presents a level of research methodology sufficient for the performance of supervised
research required for a master's thesis. Further, students are encouraged to apply the newly learned
research methods in various areas.
II Objectives of the course
The academic aims of the course are to

develop skills for using modern statistical software for social research and other disciplines;

build up skills in developing creative and logical thinking, practical problem solving.
By the end of this course students should be able to

understand basic statistical concepts and methodologies;

formulate research questions in order to improve their own understanding and the knowledge
of others in behavioral disciplines;

view scientific research from a scientific and applied point of view;

understand the importance of theory in the research process, and extrapolating from theory to
research hypotheses;

describe and apply the research process, from the principles of measurement, sampling, and
data collection, through to the selection of appropriate quantitative analysis, evaluation and
writing final research reports;

critically evaluate the use of statistical methods in areas relevant to their studies.
III Course Detail
The emphasis in this course is on working with real data, understanding statistical concepts
and putting them to work in analyzing the data sets prepared for this class -- leaving the chores of
actual computation to the computer.
We will use STATISTICA-for-Windows. All data files we will use in this class have been set
up in a special format to make reading and analyzing data even simpler.
3.1 Lecture synopsis
Lecture 1.
Lecture 2.
Lecture 3.
Lecture 4.
Lecture 5.
Sampling Design. Considerations in Designing Experiments.
Parameter Estimation.
Introduction
The Bias and Mean Square Error of Point Estimators
Some Common Unbiased Point Estimators
Evaluating the Goodness of a Point Estimator
Confidence Intervals
Large-Sample Confidence Intervals
Selecting the Sample Size
Small-Sample Confidence Intervals.
Properties of Point Estimators and Methods of Estimation.
Introduction
Relative Efficiency
Consistency
Sufficiency
Confidence Intervals. Hypothesis Testing.
Elements of a Statistical Test
Common Large-Sample Tests
Calculating Type II Error Probabilities
Relationships Between Hypothesis Testing Procedures and Confidence Intervals
Another Way to Report the Results of a Statistical Test:
Attained Significance Levels or p-Values
Small-Sample Hypothesis Testing.
Testing Hypotheses Concerning Variances.
Linear Models and Estimation by Least Squares.
Linear Statistical Models
The Method of Least Squares
Properties of the Least Squares Estimators for the Simple Linear Regression Model
Inferences Concerning the Parameters
Inferences Concerning Linear Functions of the Model Parameters:
Simple Linear Regression
Predicting a Particular Value of Y Using Simple Linear Regression
Correlation
Some Practical Examples
Lecture 6.
Lecture 7.
Lecture 8.
Lecture 9.
The Analysis of Variance
The Analysis of Variance Procedure
Comparison of More than Two Means:
Analysis of Variance for a One-Way Layout
An Analysis of Variance Table for a One-Way Layout
A Statistical Model for the One-Way Layout
Proof of Additivity of the Sums of Squares and E(MST) for a One-Way Layout
Estimation in the One-Way Layout
A Statistical Model for Randomized Block Design
The Analysis of Variance for Randomized Block Design
Estimation in Randomized Block Design
Selecting the Sample Size
Simultaneous Confidence Intervals for More than One Parameter
Analysis of Variance Using Linear Models
Analysis of Categorical Data.
A Description of the Experiment
The Chi-Square Test
A Test of a Hypothesis Concerning Specified Cell Probabilities:
A Goodness-of-Fit Test
Contingency Tables
Other Applications
Nonparametric Statistics.
A General Two-Sample Shift Model
The Sign Test for a Matched Pairs Experiment
The Wilcoxon Signed-Rank Test for a Matched Pairs Experiment
The Use of Ranks for Comparing Two Population Distributions
The Mann-Whitney U Test: Independent Random Samples
The Kruskal-Wallis
Inferences about a Mean Vector. Comparisons of Several Multivariate Means.
Paired Comparisons and a Repeated Measures Design.
Comparing Mean Vectors from Two Populations.
Comparison of Several Multivariate Population Means (One-Way MANOVA).
Simultaneous Confidence Intervals for Treatment Effects.
Two-Way Multivariate Analysis of Variance.
Profile Analysis. Repealed Measures, Designs, and Growth Curves.
Perspectives and a Strategy for Analyzing Multivariate Models.
Lecture 10.
Multivariate Linear Regression Models.
The Classical Linear Regression Model. Least Squares Estimation.
Inferences about the Regression Model.
Inferences from the Estimated Regression Function.
Model Checking and Other Aspects of Regression.
Multivariate Multiple Regression. The Concept of Linear Regression.
Comparing the Two Formulations of the Regression Model.
Lecture 11.
Analysis of Covariance Structure. Principal Components.
Population Principal Components.
Summarizing Sample Variation by Principal Components.
Graphing the Principal Components. Large-Sample Inferences.
Monitoring Quality with Principal Components.
The Geometry of the Sample Principal Component Approximation.
Factor Analysis and Inference for Structured Covariance Matrices.
Lecture 12.
Lecture 13.
Lecture 14.
Lecture 15.
The Orthogonal Factor Model. Methods of Estimation.
Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis.
Structural Equation Models.
Some Computational Details for Maximum Likelihood Estimation.
Canonical Correlation Analysis.
Canonical Variates and Canonical Correlations.
Interpreting the Population Canonical Variables.
The Sample Canonical Variates and Sample Canonical Correlations.
Additional Sample Descriptive Measures.
Large-Sample Inferences.
Discriminant Analysis.
Classification and Grouping Techniques. Discrimination and Classification.
Separation and Classification for Two Populations.
Classifications with Two Multivariate Normal Populations.
Evaluating Classification Functions.
Fisher's Discriminant Function — Separation of Populations.
Classification with Several Populations.
Fisher's Method for Discriminating among Several Populations. Final Comments.
Cluster Analysis.
Clustering, Distance Methods and Ordination. Similarity Measures.
Hierarchical Clustering Methods. Nonhierarchical Clustering Methods.
Multidimensional Scaling. Correspondence Analysis.
Biplots for Viewing Sample Units and Variables.
Lecture 16.
Course Summary.
Tutorials are held in computer labs using “Statistica” software.