Mojtaba Jahanifar; Ebrahim Khodaie; jalil Younesi; Amin Musavi
Abstract
In order to the better interpreting and comparing scores in test batteries the raw scores in each test are converted to a common scale that called scale score. There are different Linear and nonlinear methods to convert raw scores to scale scores. Conventional methods of non-linear converting raw scores ...
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In order to the better interpreting and comparing scores in test batteries the raw scores in each test are converted to a common scale that called scale score. There are different Linear and nonlinear methods to convert raw scores to scale scores. Conventional methods of non-linear converting raw scores to scale scores are normalization and Arcsine methods. In This study that aimed to compare the standard error of measurement in non-linear converting methods we used 10000 random simulated sample data and 10000 random real sample data from Iran university entrance exam applicants.in order to compare converting methods conditional standard error of measurement that called CSEM, frequency charts and statistical indexes like moments was used. The results showed that both methods have different features. Although scores in both methods have high reliability and accuracy but Arcsine method reducing score error undulation for different score levels, also the mean of standard error of measurement for Arcsine scale scores was less than normalized scale scores.
mojtaba habibi; balal izanluo; ebrahim khodai
Volume 3, Issue 9 , October 2012, , Pages 81-104
Abstract
According to Kerlinger (1P86), factor analysis is a statistical method serving “scientific parsimony principle”. This technique can be used to reduce multiplicity between variables, purify the relationships among variables, and maximize simplification. In fact, this technique determines The ...
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According to Kerlinger (1P86), factor analysis is a statistical method serving “scientific parsimony principle”. This technique can be used to reduce multiplicity between variables, purify the relationships among variables, and maximize simplification. In fact, this technique determines The dependency among measures and variables and facilitates scientific interpretation. The purpose of the present study is to introduce the application of factor analysis for assigning weight to the variables, exploring the simplest and the most feasible indices with highest weight and significance of evaluation of the sample group, and determining the factor loading for each of the scale items on every one of the explored factors. In the present descriptive study, the data related to the psychological and physiological symptoms of stress among 430 high school teachers of West Azerbaijan Province (105 teachers of physical education and 298 teachers of other fields) were utilized. In the analysis of the data using method of factor analysis, weight was assigned to items and in the next stage, the weight of each item based on the weight of that indicator in the related factor would be used instead of constant weight equal one, in order to come up with final scores of subjects. Results show that there is a significant difference between the two methods in determining the weight of the items by Friedman. Referring to the results, it could be concluded that efficacy of factor analysis in comparing with other ranking methods of indicators was in optimum level.
