Five-Point Likert Scale

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One of the most common scaled-response formats, the Five-point Likert scale is most commonly used. It was developed by the American educator and organizational psychologist Rensis Likert in 1932 as an attempt to improve the levels of measurement in social research through the use of standardized response categories in survey questionnaires. The benefit of a likert scale is that the questions used are usually easy to understand and so lead to the reliable answers. On the other hand there is a disadvantage that only a few options are presented with which respondents may not completely agree. With a five-point scale the points can be labeled as agree strongly, agree somewhat, neutral, disagree somewhat, disagree strongly. It is interesting to…show more content…
As a pre testing only the sample of 45 students is selected to check the reliability from the different public and private universities. By using the Cronbach’s Alpha, the result of the reliability coefficient is 0.859. As a rule of thumb, a designed questionnaire is recommended, if α is 0.7 or higher. 3.4.4 Cronbach’s Alpha Cronbach’s Alpha measures how well a set of items (or variables) measure a single unidimensional latent construct. It determines the internal consistency or average correlation of items in survey instrument to gauge its reliability when data have a multidimensional structure, Cronbach’s alpha will usually be low. Technically speaking, Cronbach’s alpha is not a statistical test – it is a coefficient of reliability or consistency. We will check the reliability of the questionnaire by Cronbach’s alpha during pretesting. α=(N.c ̅)/(v ̅+(N-1)c ̅ ) Where N is the number of components…show more content…
The analysis separates the effects of the factors, which are of basic interest, from the errors. Thus the factor analysis derives the new variables, called factors, which give a better understanding of data. The main objective of factor analysis is to describe the relationship between a large number of measured traits and a small number of unobserved factors. Application Factor analysis is used to identify “factors” that explain a variety of results on different tests. It is not a technique of data analysis. It has been widely from the 20th century (spearman, 1904). Social scientist are using it extensively for examining patterns of interrelationships, instrument development, data reduction, organization and explanation of data, hypothesis testing, and exploring relationships in new domains of interest. Factor analysis is an established analytical technique that has been studied widely by statisticians, mathematicians, and research methodologists. For example, intelligence research has found that people who get high scores on oral test they are also good on other tests that require unwritten abilities. Researchers explain this by using factor analysis to separate one factor, often called crystallized intelligence or verbal intelligence that represents the degree to which someone is able to solve problems involving verbal

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