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How satisfied are you with your job? Estimating the reliability of scores on a single‐item job satisfaction measure
Author(s) -
Ock Jisoo
Publication year - 2020
Publication title -
international journal of selection and assessment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.812
H-Index - 61
eISSN - 1468-2389
pISSN - 0965-075X
DOI - 10.1111/ijsa.12285
Subject(s) - reliability (semiconductor) , psychology , variance (accounting) , trait , statistics , job satisfaction , measure (data warehouse) , consistency (knowledge bases) , explained variation , econometrics , social psychology , mathematics , computer science , data mining , artificial intelligence , quantum mechanics , power (physics) , physics , accounting , business , programming language
Estimating the reliability of scores on single‐item measures can be difficult because commonly used internal consistency estimates of reliability cannot be calculated. When longitudinal data is available, statistical models can be used to decompose the variability in the latent variable at each wave into trait versus state variance. Then, reliability can be estimated as a ratio of the sum of the trait variance that is captured in repeated assessments over the total variance. The current study used latent trait‐state‐error models on a nine‐year longitudinal data ( N = 5,003) to estimate the test–retest reliability of scores on a single‐item measure of job satisfaction. Results showed that job satisfaction scores were somewhat unreliable ( r xx = .49–.59) and amenable to change.