Effects of Reducing the Length of the Questionnaire by Multiple Matrix Sampling on the Validity of Structural Equation Modeling for Factors Affecting Job Morale

Pimpilai Jaikaew,Suntonrapot Damrongpanit

doi:10.13189/ujer.2018.060715

Abstract

The research was designed to examine the effects of question setting using different conditions into 10 sets on the validity of structural equation modeling for factors affecting job morale. The data was collected from 690 personnel working in regional Statistical Offices around Thailand by using cluster random sampling. The tool used in collecting data was the questionnaire with 95 items and 5 levels of rating scale. The discrimination value was between 0.244 and 0.860 and the reliability: α was from 0.699 to 0.900. Data analysis was conducted by the use of descriptive statistics and structural equation modeling by Mplus 7.4 program. The findings showed that the structural equation modeling with question setting in 3 sets of sub-questionnaires, cooperated with item sampling without replacement and non-fixing core items, conformed to the empirical data the most and that overall relationship between the structural model parameter and the model parameter from the complete questionnaire was high in a positive way.

Highlights

Structural Equation Modeling or SEM is one of the widely popular statistical techniques used in both behavioral sciences and social sciences [52] due to its two distinguished characteristics
From the previously mentioned principle of MMS, when considering all 95 items according to the conceptual framework, the researcher could design the set of questions into 10 models based on two reasons
The findings revealed that 3 sets of question setting provided better harmonized index than that in 2 sets of item sampling, whereas 2 sets of item sampling provided better index of consistency than that in the complete questionnaire

Summary

Introduction

Structural Equation Modeling or SEM is one of the widely popular statistical techniques used in both behavioral sciences and social sciences [52] due to its two distinguished characteristics. One is that it can be used to analyze the model with latent variable (the variable which is studied with abstract characteristics and cannot be completely measured as it needs the measurement index called “observed variable”) and analyze for the effect at the same time [33] Another is that it has relaxed assumptions [11] as well as an opportunity for model modification provided to the researcher in case of incomplete conformation of the model the empirical data [56]. With these two distinguished characteristics; SEM is widely accepted and better applied in current research. SEM provides reliability of the findings, some of its limitations are still found in the model using a great number of both latent and observed variables

Methods

Results

Conclusion