COLLABORATION ON VARIOUS STAGES OF PREPARING TRAINING LINGUISTICS MAJORS FOR DOING SCIENTIFIC RESEARCH

A.I. Dashkina; A. Y. Fyodorova

doi:10.5862/jhss.244.19

COLLABORATION ON VARIOUS STAGES OF PREPARING TRAINING LINGUISTICS MAJORS FOR DOING SCIENTIFIC RESEARCH

St Petersburg State Polytechnical University Journal Humanities and Social Sciences ◽

10.5862/jhss.244.19 ◽

2016 ◽

Vol 244 (2) ◽

pp. 155-163

Author(s):

A.I. Dashkina ◽

A. Y. Fyodorova

Keyword(s):

Scientific Research

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Building a Successful Career in Scientific Research

10.1017/cbo9781139165471 ◽

2006 ◽

Cited By ~ 2

Author(s):

Phil Dee

Keyword(s):

Scientific Research ◽

Successful Career

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Social-Scientific Research Competency

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000451 ◽

2019 ◽

Vol 35 (5) ◽

pp. 737-750 ◽

Cited By ~ 3

Author(s):

Christopher Gess ◽

Christoph Geiger ◽

Matthias Ziegler

Keyword(s):

Higher Education ◽

Test Scores ◽

Research Performance ◽

Scientific Research ◽

Research Education ◽

Crystallized Intelligence ◽

Research Competency ◽

Social Scientific Research ◽

Social Scientific ◽

Education Courses

Abstract. Although the development of research competency is an important goal of higher education in social sciences, instruments to measure this outcome often depend on the students’ self-ratings. To provide empirical evidence for the utility of a newly developed instrument for the objective measurement of social-scientific research competency, two validation studies across two independent samples were conducted. Study 1 ( n = 675) provided evidence for unidimensionality, expected differences in test scores between differently advanced groups of students as well as incremental validities over and above self-perceived research self-efficacy. In Study 2 ( n = 82) it was demonstrated that the competency measured indeed is social-scientific and relations to facets of fluid and crystallized intelligence were analyzed. Overall, the results indicate that the test scores reflected a trainable, social-scientific, knowledge-related construct relevant to research performance. These are promising results for the application of the instrument in the evaluation of research education courses in higher education.

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