Academic journal article Journal of Nursing Measurement

Meta-Analysis of the Reliability and Validity of Part B of the Index of Work Satisfaction across Studies

Academic journal article Journal of Nursing Measurement

Meta-Analysis of the Reliability and Validity of Part B of the Index of Work Satisfaction across Studies

Article excerpt

Nurses' job satisfaction is a crucial factor in health care organizations. This study uses meta-analysis for reliability generalization and synthesis of construct validity of Part B of the Index of Work Satisfaction (IWS), a measure of job satisfaction. Meta-analysis was performed including assessments of study quality and descriptive coding of studies. Rater reliability was assessed for all coding and extraction of data. The mean reliability of Part B scores of the IWS based on 14 studies was .78 (df = 13, p < .05). The mean score reliability was .77 for university settings, .73 for community/acute care hospitals, .77 for multi-site studies, and .90 for other settings. For studies rated high and low quality, the mean score reliability was .77 and .83, respectively. Scores on Part B of the IWS correlated -.38 with turnover intent, .60 with organizational commitment, and -.53 with job stress. Scores on Part B of the IWS are reliable for measuring job satisfaction of nurses across samples. Construct validity needs additional testing.

Keywords: meta-analysis; job satisfaction; index of work satisfaction; reliability generalization

Job satisfaction is a concept of broad interest to people employed in all organizations, as well as to those who study organizations. In today's market, job satisfaction is a crucial factor in health care organizations, specifically in the nursing community. There are many tools for measuring job satisfaction, but the "Index of Work Satisfaction (IWS) is the most frequently used measuring tool" (Stamps, 1997, p. 59). The IWS consists of two parts, Part A and Part B, which will be discussed in more detail in the background section of this article. The primary populations tested with the IWS are staff nurses, nurse managers, licensed practical nurses, and nurses' aides.

The purpose of this study was to examine the reliability generalization and synthesis of construct validity of Part B of the IWS across samples of registered staff nurses in the US. This part of the IWS is the most commonly used to measure job satisfaction in research studies. Part B consists of six subscales that measure various aspects of job satisfaction and are combined for an overall score. Researchers tend to report on the reliability of this overall Part B score rather than the individual subscales that make it up. The two research questions that guided the study were as follows:

1. What is the reliability of scores of Part B of the IWS across samples of registered staff nurses in the USA?

2. What is the evidence for construct validity of Part B scores of the IWS across samples of registered staff nurses in the USA?

BACKGROUND

Reliability Generalization and Meta-Analysis

Reliability and validity are not fixed properties of an instrument, but vary based on the samples used. It is the scores from the test administration and the use of those scores rather than the instrument itself to which reliability and validity apply. This has been emphasized by the American Psychological Association (1985), which defined reliability as "the degree to which test scores are free from errors of measurement" (p. 19). It follows, then, that characteristics of the sample involved in the use of an instrument will affect the reliability and validity of the scores obtained. Use of meta-analysis techniques to synthesize validity information across studies has been accepted in the literature for some time (Hunter & Schmidt, 2004). The same strategies have not been applied to reliability information. This has led to recent interest in reliability generalization, a term coined by Vacha-Haase (1998) to refer to the application of meta-analytic techniques to quantitatively examine the variability in reliability estimates as a function of sample characteristics. Reliability generalization can assist in identifying the "typical" score reliability across samples and factors that contribute to variability across studies. …

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