Equidistant likert as weighted sum of response categories

Introduction: Addition of scores of Likert items may not be meaningful since equidistant property is not satisfied. This implies computation of mean, standard deviation, correlation, regression and Cronbach alpha using sum of item variances and test variance could be problematic. Objective: Avoiding...

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Autores:
Chakrabartty, Satyendra Nath
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9835
Acceso en línea:
https://hdl.handle.net/11323/9835
https://repositorio.cuc.edu.co/
Palabra clave:
Likert items
Weighted sum
Monotonic
Equidistant
Normal distribution
Ítems tipo Likert
Suma ponderada
Monotónico
Equidistante
Distribución normal
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
Description
Summary:Introduction: Addition of scores of Likert items may not be meaningful since equidistant property is not satisfied. This implies computation of mean, standard deviation, correlation, regression and Cronbach alpha using sum of item variances and test variance could be problematic. Objective: Avoiding limitation of summative Likert scores by transforming raw item scores to continuous monotonic scores satisfying equidistant property and evaluate the methods with respect to desired properties and testing normality of transformed test scores. Methodology: The methodological paper gives three methods of transforming discrete, ordinal item scores to continuous scores by weighted sum where weights consider frequencies of different response-categories of different items and generate continuous data satisfying equidistant and monotonic properties. Results and discussions: All the proposed methods avoided major limitations of summative Likert scores, generates continuous data satisfying equidistant and monotonic properties. The method based on frequencies of response-categories for different items (Method 3) passed the normality test unlike the Method 1 and Method 2. Normally distributed transformed scores in Method 3 facilitate undertaking analysis under parametric set up. Conclusions: Proposed methods having high correlations with summative Likert scores, retained similar factor structure and provides reconciliation to the debate on ordinal vs. interval nature of data generated from a Likert questionnaire. Considering the theoretical advantages, the Method 3 is recommended for scoring Likert items primarily due to Normal distribution of individual scores facilitating meaningfulness of operations and to undertake parametric statistical analysis