Web-based computer adaptive assessment of individual perceptions of job satisfaction for hospital workplace employees
1 Department of Management, Chi-Mei Medical Center, Tainan, Taiwan
2 Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan
3 Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
4 Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li, Taiwan
5 Assessment Research Center, The Hong Kong Institute of Education, Hong Kong, China
6 Institute of Biomedical Engineering, Southern Taiwan University, Tainan, Taiwan
7 Division of Nephrology, Department of Medicine; Chi-Mei Medical Center, Tainan, Taiwan
8 Department of Sports Management, College of Leisure and Recreation Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
9 Department of Family Medicine, Chi-Mei Medical Center, Tainan, Taiwan
Citation and License
BMC Medical Research Methodology 2011, 11:47 doi:10.1186/1471-2288-11-47Published: 17 April 2011
To develop a web-based computer adaptive testing (CAT) application for efficiently collecting data regarding workers' perceptions of job satisfaction, we examined whether a 37-item Job Content Questionnaire (JCQ-37) could evaluate the job satisfaction of individual employees as a single construct.
The JCQ-37 makes data collection via CAT on the internet easy, viable and fast. A Rasch rating scale model was applied to analyze data from 300 randomly selected hospital employees who participated in job-satisfaction surveys in 2008 and 2009 via non-adaptive and computer-adaptive testing, respectively.
Of the 37 items on the questionnaire, 24 items fit the model fairly well. Person-separation reliability for the 2008 surveys was 0.88. Measures from both years and item-8 job satisfaction for groups were successfully evaluated through item-by-item analyses by using t-test. Workers aged 26 - 35 felt that job satisfaction was significantly worse in 2009 than in 2008.
A Web-CAT developed in the present paper was shown to be more efficient than traditional computer-based or pen-and-paper assessments at collecting data regarding workers' perceptions of job content.