Преподавател(и):

проф. Енчо Герганов  д-р

Описание на курса:

Компетенции:

After successfully taken of the course students

1) will know:

- the four kinds of data according to the Coombs’ theory as a main framework of

psychological scaling methods;

- the different methods of collecting different kinds of data and methods of data analysis

- the broad areas of application of scaling methods in psychology and social practice

2) will be able:

- to design scaling experiments for different purposes;

- to apply different methods for gathering data and to analyze these data up to obtaining scales;

- to work with different scaling and statistical software and to interpret the results in adequate theoretical frameworks.

Предварителни изисквания:
School level of mathematical knowledge and skills. Basic knowledge and skills of introductory statistics.

Форми на провеждане:
Редовен

Учебни форми:
Лекция

Език, на който се води курса:
Английски

Теми, които се разглеждат в курса:

1. The nature of measurement. Basic concepts of the General measurement theory.

Isomorphic characteristics. Types of scales. An overview of the Coombs’ theory of data.The four kinds of data as a framework of the methods of scaling

Lecture 2

2. Preferential choice data. The unfolding technique in one dimension. General idea of multidimensional unfolding. Methods of collecting preferential choice data – method of paired comparisons, method of rank order,method of successive categories

Lecture 2

3. Online experiment for collecting of preferential choice data. Application of the method of paired comparison. Analysis of collected data by the Module PAIRED COMPARISON DATA of the Nishisato’s DUAL SCALING Software.

First online experiment + Laboratory work

2

3/3

4 Online experiment for collecting of preferential choice data. Application of both the method of rank order and the method of successive categories. Analysis of collected data by the Module RANK ORDER DATA of the Nishisato’s DUAL SCALING Software and the Module CORRESPONDANCE ANALYSIS of STATISTICA

Second online experiment + Laboratory work

2

5. Single stimulus data. Scalogram analysis. Item operative curves in deterministic models.Triangular analysis. Application of BIGSTEP software for doing triangular analysis

Lecture + Laboratory work

2

6. Probabilistic models of single stimulus data. Basic concepts of Item Response Theory. Rush models. Two and three parametric models of Item Operative Curves.

Lecture 2

7. Analysis of test data by the methods of classical test theory and item response theory. Application of MICROCAT software and the IRT module of SYSTAT statistical package

Laboratory work 2

8. Casus 1 Examination

2

9. Stimuli comparison data. The law of comparative judgment. Method of paired

comparisons. Online experiment for collecting stimulus comparison data. Data

analysis by the Module PAIRED COMPARISON DATA of Nishisato’s DUAL SCALING software.

Lecture + Third online experiment + Laboratory work

2

10. The law of categorical judgment. Method of successive categories. Online experiment for collecting successive categories data. Data analysis by both the Module SUCCESSIVE CATEGORIES DATA of Nishisato’s DUAL SCALING software and the Module CORRESPONDENCE ANALYSIS of STAITISTICA package

Lecture + Fourth online experiment + Laboratory work

2

11. Similarities data. Basic concepts and algorithms of Multidimensional scaling

Lecture 2

12. Methods of collecting similarities data. Method of successive categories. Method of free classification. Matrices of confusability. Online experiment for collecting similarity data. Analysis of similarity data by the Module MULTIDIMENSIONAL SCALING of SPSS

Fifth online experiment + Laboratory work

2

13. Casus 2. Examination

2

14. Some interrelations among data matrices and among methods

Discussion + examination

2

15. Final test. Laboratory working casus Examination 2

4/3

Литература по темите:

Basic

Borg, I. and Groenen, P.J. F. (2005). Modern Multidimensional Scaling. Springer

Coombs, C.H. (1964). A theory of data. New York: Wiley

Guilford, J. P. (1954). Psychometric methods. N. Y., McGraw-Hill

Maranell G. M.(Ed.) (2007). Scaling: A Sourcebook for Behavioural Scientists

Nishisato Sh. (1994). Elements of Dual Scaling: An Introduction to Practical Data Analysis. LAWRENCE ERLBAUM ASSOCIATES, PUBLISHERS 1994 Hillsdale, New Jersey Hove and London

Torgerson W. S. (1958). Theory and methods of scaling. New York: Wiley

Additional

Baker, F.B. (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and

Evaluation

Carroll, J. D. and Wish, M. (1974). Multidimensional and perceptual models and measurement methods. In Handbook of Perception (vol. 2), N. Y., Academic Press, 1974.

Coombs, C. H.(1952). A theory of psychological scaling. Ann Arbor

Guttmann, L. (1950). Measurement and prediction. Princeton university press, Princeton

Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32

Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis. Psychometrika, 29

Lazarsfeld, P. F., Henry, N. W. (1968). Latent structure analysis. Boston

Margenau, H. (1950). The nature of physical reality. N. Y., McGraw-Hill

Miller, G. A.(1969). A psychological method to investigate verbal concepts. J. of Math. Psychol., 6

Nakanishi M. and Cooper L.G. (2003). Metric Unfolding Revisited: Straight Answers to Basic Questions. Department of Statistics Papers. University of California, Los Angeles. Paper 2003010112, pp.46

Thurstone, L. L. (1927). A law of comparative judgment. Psychol. Rew., 34

Средства за оценяване:

Two casuses for measuring of knowledge

One laboratory working casus for measuring of skills