"The level of collapse we are allowed: Comparison of different response scales in Safety Attitudes Questionnaire"

Jeong, Heon-Jae; Lee, Wui-Chiang

doi:10.15406/bbij.2016.04.00100

Cited by 66 publications

(71 citation statements)

References 13 publications

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“…Of course, we did not miss the issue of how items are functioning differently across different groups [6]. Exploring different response options other than a 5-point Likert scale was a truly enjoyable experience [7,8].…”

Section: Resultsmentioning

confidence: 99%

“…A dozen of our previous articles on the Safety Attitudes Questionnaire-Korean version (SAQ-K) introduced several new approaches to analyze collected data [1][2][3][4][5] and even novel methods to improve the instrument itself [6][7][8][9]. Yet we might not have answered a fundamental question: How are safety attitudes as a whole constructed in our minds?…”

Section: Introductionmentioning

confidence: 99%

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The Pure and the Overarching: An Application of Bifactor Model to Safety Attitudes Questionnaire

Jeong¹

2016

BBIJ

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Although this article is long, we believe that it is worth reading. It answers the most fundamental question that patient safety professionals have asked: How do we measure the overall safety attitudes score of each person? Broadly speaking, we showed how to structuralize various safety-related traits in one's mind, using the Safety Attitudes Questionnaire-Korean version as an example. We applied the bifactor model, which explicitly contains a general safety attitudes dimension that governs all SAQ-K items as well as six original SAQ-K domains. The major finding regarding the model structure is that several items might not fall under a specific SAQ domain, although they are still largely governed by safety attitudes in general; yet the stress recognition domain might not be part of the general construct-namely, safety attitudes. However, the more important information that we intended to share was that the bifactor model can effectively take control of the seemingly inadequate items or domains and calculate domain-specific scores and general safety attitudes score by providing different weights for each item. Thus, we can obtain much more purified domains scores from the data, compared to the traditional mean score approach. In addition, the item response theory-based approach used in this article gives more solid theoretical strength in handling the ordinal data and also offers the possibility of adding or dropping items or even a domain, while still allowing a longitudinal analysiscomparing scores from different versions of safety attitudes questionnaire. In addition to these theoretical strengths, the bifactor model provides exceptional computational efficiency compared to the other models we have tried, thereby allowing us to unlock an extremely large and complex dataset that could not be analyzed thus far. We hope the approach introduced in this article can help all the patient safety professionals achieve more precise and valid information from their already collected safety attitudes data as well as data collected in the future, ultimately saving more lives. DisclaimerThe target audience of this article is those who actually use safety culture assessment tools in the healthcare setting, and we assumed they possess basic-to mediumlevel knowledge in statistics and psychometrics. Thus, we intentionally avoided showing complex formulae; instead, we tried to simplify, and sometimes oversimplify, the concepts introduced in this article to facilitate understanding. Terminology was also carefully chosen for those who did not major in statistics. In addition, we did not follow a typical research article style. We intentionally allocated many paragraphs usually reserved for the discussion section to the results section in order to facilitate understanding. Anyone curious about full mathematical descriptions are asked to please contact the authors. IntroductionA dozen of our previous articles on the Safety Attitudes Questionnaire-Korean version (SAQ-K) introduced several new approaches to analyze collected d...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Pure and the Overarching: An Application of Bifactor Model to Safety Attitudes Questionnaire

Jeong¹

2016

BBIJ

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“…Those two approaches are, by definition, built on two different paradigms. Traditional factor analysis assumes the measurement scale is linear [21]basically, a ratio scale or at least an interval scale where we can freely obtain central tendency and variation [3]. However, in many survey instruments, including the TPSC we analyzed, a response is measured on an ordinal scale, usually Likert, which is not linear at all.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, Jeong and Lee (2016) showed that the number of response options causes significant differences in the time to complete surveys; for example, a 3-point Likert scale is much faster to complete than a 5-point Likert scale [2]. In addition to reducing the time required, they also showed that a 3-point and a 5-point Likert scale yielded very similar results in a population level [3]. Although their studies used a specific instrument, the Safety Attitudes Questionnaire-Korean version (SAQ-K), the results suggest that we can modify and streamline survey questionnaires without undermining their validity.…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach to Develop a Parsimonious Survey Questionnaire—Taiwanese Patient Safety Culture Survey as an Example

Liao¹,

Lee²,

You³

et al. 2017

BBIJ

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This study aims to suggest a method to choose what items can be removed from a survey instrument, thereby lessening survey participants' burden. We used the Taiwanese Patient Safety Culture (TPSC) survey as an example-in particular, the emotional exhaustion (EE) and work-life balance (WLB) domains. A traditional factor analysis approach, whereby a Likert scale is treated as a linear variable, was applied, and an item response theory (IRT) graded response model (GRM), where a response is treated as an ordinal scale, was used. From both methods, five out of nine EE items and three of seven WLB items were filtered out for removal; these items provided significantly smaller information for domain score estimation than other items. To check whether the downsized versions showed sufficient validity, we tested the factor structure of the remaining items and compared fit indices between the models with all original items and the new models with the remaining items. The new models in both EE and WLB domains showed better model fit than the models with all original items. In this study, we showed how to select more important items while ensuring that the selected items as a whole can provide a better fit compared to the original instrument. This interesting phenomenon may be due to the translation step and different cultural soils where surveys are administered. We strongly recommend checking the possibility that items can be removed when adopting and validating a survey instrument-not only to shorten the overall length, but also to increase validity. IntroductionHealthcare professionals tend to work within extremely tight schedules. Yet those in the field of improving the quality and safety of care almost always want to administer survey questionnaires to these professionals to collect information on various aspects of healthcare. Such information is certainly crucial, but it is also certain that the surveys would put much burden on healthcare professionals. In this regard, it is our obligation to control the burden expected when administering a survey. From a more practical standpoint, fatigue from frequent surveys may result in a low response rate that endangers the representativeness of the survey itself [1], which could lead to less useful survey results.In addition to being frequently asked to participate in surveys, the burden of completing a survey also resides in the characteristics of each questionnaire, such as how many questions are asked, how long each item is, and even what measurement scale is used. Indeed, Jeong and Lee (2016) showed that the number of response options causes significant differences in the time to complete surveys; for example, a 3-point Likert scale is much faster to complete than a 5-point Likert scale [2]. In addition to reducing the time required, they also showed that a 3-point and a 5-point Likert scale yielded very similar results in a population level [3]. Although their studies used a specific instrument, the Safety Attitudes Questionnaire-Korean version (SAQ-K), the resu...

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“…Methodologies analyzing the survey responses have also evolved to get the most out of collected responses. For example, Jeong et al, in their series of articles, proposed several novel approaches to process and analyze the already collected safety culture questionnaire data and how to present them most effectively [12][13][14][15][16][17][18][19][20][21]. These efforts certainly helped us depict how various aspects of safety culture are intertwined in healthcare professionals' minds [22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Indicator for How Fairly Hospitals Treat Healthcare Professionals’ Safety Improvement Efforts: Differences in Hospital Responses to Medical Error Reports Depending the Type of Healthcare Professional Reporting the Event

Jeong¹

2017

BBIJ

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Error reports and subsequent improvement actions are critical components in patient safety, possibly preventing the same events from occurring again. This study investigates how fairly or differently hospitals respond to healthcare professionals' medication error reports according to their job types (i.e., physicians, nurses, and pharmacists). Data from a national voluntary medication error reporting system (MEDMARX) in the US from 1997 through 2007 were utilized for this study. A logistic regression model was applied to understand the difference in the odds that an error report leads a hospital to take actions depending on types of reporters. Actions were acknowledged only when solid changes took place following reports. Because harmful errors generally induce more actions than nonharmful errors, we examined the effect of the reporter separately in harmful and nonharmful error reports. In addition, the differences in responses among different types of hospital owners-namely, government hospitals (GH) and nongovernment hospitals (NGH)-were analyzed to see whether they influence responses to error reports. Phases of the medication use processes such as prescribing, transcribing, dispensing, administering, and monitoring medication related to reporter type (e.g., pharmacists tend to detect most prescribing errors) and, thus, are controlled in the analysis. In addition, to address any clustering of reports within hospitals, the model included a random intercept for hospitals. Ultimately, 605,422 medication error reports were collected from 574 hospitals (153 GH and 421 NGH) for analysis. For nonharmful error reports, compared to hospital responses to reports from physicians, reports from nurses induced significantly fewer actions in both GH (OR=0.63, 95% CI: 0.52-0.77) and NGH (OR=0.62, 95% CI: 0.53-0.72). The ORs for pharmacists (compared to physicians' reports) were 0.25 (95% CI: 0.20-0.30) in GH and 0.56 (95% CI: 0.48-0.66) in NGH. For harmful errors, in GH, reports from nurses and pharmacists were also less likely to lead to action compared to physicians ' reports (nurses: OR=0.58, pharmacists: OR=0.60,. Ideally, error reports should initiate improvement actions regardless of types of reporters. However, this study shows that hospitals are less likely to respond to reports from nurses and pharmacists compared to those from physicians. When healthcare professionals' reports are not addressed well, it can decrease the professionals' morale. Healthcare organizations with error reporting systems need to take full advantage of these underutilized error reports, not only because they are a rich potential source of information, but also because they propel healthcare providers to pursue safety improvements.

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"The level of collapse we are allowed: Comparison of different response scales in Safety Attitudes Questionnaire"

Cited by 66 publications

References 13 publications

The Pure and the Overarching: An Application of Bifactor Model to Safety Attitudes Questionnaire

The Pure and the Overarching: An Application of Bifactor Model to Safety Attitudes Questionnaire

A Practical Approach to Develop a Parsimonious Survey Questionnaire—Taiwanese Patient Safety Culture Survey as an Example

A Novel Indicator for How Fairly Hospitals Treat Healthcare Professionals’ Safety Improvement Efforts: Differences in Hospital Responses to Medical Error Reports Depending the Type of Healthcare Professional Reporting the Event

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