Analysis of rating scales for the measurement of attitudes and perceptions / Lee Ong Kim

Lee, Ong Kim (2006) Analysis of rating scales for the measurement of attitudes and perceptions / Lee Ong Kim. Asian Journal of University Education (AJUE), 2 (1). pp. 57-78. ISSN 1823-7797

Abstract

It is still common today to see questionnaires with Likert Scale items concerning very different variables being used to capture data on aspects as varied as possible that are to be investigated by the research work. This is perfectly alright if each of the questions is to be treated as standing on its own and is not intended to add up to a measure of a single variable. This, however, has the problem of inadequate sampling of items to come to any meaningful measure of persons on that set of multiple variables, with as small a Standard Error of Measurement (SEM) as possible. Each variable to be measured is best put on a single rating scale, with items being replicated a sufficient number of times to reduce the SEM. There can be more than one rating scale in one questionnaire, but they should obviously be placed in separate sections, and their analyses
done separately. This paper discusses a specific example of the
measurement of attitude towards teaching and perceptions of subjects’ own teaching knowledge and skills, and how to measure their changes over time, through the anchoring of item calibrations, using a Rasch model.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Lee, Ong Kim
oklee@nie.edu.sg
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Education
Journal or Publication Title: Asian Journal of University Education (AJUE)
UiTM Journal Collections: UiTM Journal > Asian Journal of University Education (AJUE)
ISSN: 1823-7797
Volume: 2
Number: 1
Page Range: pp. 57-78
Keywords: Rating scales, Measurement of Attitudes, Teaching, Rasch model, Singapore
Date: June 2006
URI: https://ir.uitm.edu.my/id/eprint/299
Edit Item
Edit Item

Download

[thumbnail of 299.pdf] Text
299.pdf

Download (253kB)

ID Number

299

Indexing

Statistic

Statistic details