ISSN: 2378-315X BBIJ

Biometrics & Biostatistics International Journal
Research Article
Volume 4 Issue 3 - 2016
Does Differential Item Functioning Occur Across Respondents’ Characteristics in Safety Attitudes Questionnaire?
Heon-Jae Jeong1 and Wui-Chiang Lee2*
1The Care Quality Research Group, Chuncheon, Korea
2Department of Medical Affairs and Planning, Taipei Veterans General Hospital & National Yang-Ming University School of Medicine, Taipei, Taiwan
Received: August 01, 2016 | Published: August 11, 2016
*Corresponding author: Wui-Chiang Lee, Department of Medical Affairs and Planning, Taipei Veterans General Hospital & National Yang-Ming University School of Medicine, Taipei, Taiwan, Tel: +886-2-28757120; Fax: +886-2-28757200; Email:
Citation: Jeong HJ, Lee WC (2016) Does Differential Item Functioning Occur Across Respondents’ Characteristics in Safety Attitudes Questionnaire?. Biom Biostat Int J 4(3): 00097. DOI: 10.15406/bbij.2016.04.00097

Abstract

Statistically, yes. Practically, maybe. A complete overhaul is suggested.

Keywords: Safety culture; Safety Attitudes Questionnaire; Patient safety; Item response theory; Differential item functioning

Introduction

When we administer a survey questionnaire to a population, we implicitly assume that people with the same level of attributes being measured will give the same response to a certain item designed to measure the attributes [1,2]. Otherwise, the survey questionnaire is suspected of having limited value as a measurement instrument for the attributes. Yet this assumption is frequently challenged. For a realistic example, it is common that respondents from a country where humility is much encouraged give a lower score to an item about self-confidence than those from a country where more self-assured people are well-respected, even when their underground trait levels of self-confidence are the same. In more psychometric terminology, this phenomenon—namely, unequal responding patterns among groups—is called differential item functioning (DIF), which is a profound bias threatening survey-based research [3]. If DIF is in doubt, we naturally question whether a difference in survey scores between two groups stems from the real difference in the trait that we want to measure or DIF between the groups, at least to a certain degree [4]. Thus, it is essential to ensure equivalence in the responding pattern for survey items among groups before moving forward to any group-to-group comparison of survey scores and more sophisticated analysis. Unfortunately, however, more often than not, this step is omitted in survey-based studies [5].

In this series of Safety Attitudes Questionnaire–Korean Version (SAQ-K) articles, we have intentionally postponed the discussion on DIF [6-9] because we planned to utilize item response theory (IRT) for DIF detection. Using IRT is known to be a superior method given its conditional invariance property, which enables better decisions on DIF than traditional sum scores of a questionnaire [10]. We waited for the successful application of IRT to SAQ-K, which we achieved in our most recently published article [11]. Thus, we can no longer put off this DIF investigation on SAQ-K.

Various approaches can be used to examine DIF, such as the Mantel-Haenszel (MH) method and logistic regression (LR)-based techniques [12,13]. For our SAQ-K data, we chose the LR approach—more specifically, an iterative hybrid ordinal logistic regression—because it can effectively handle the polytomous property of SAQ-K items (5-point Likert scale). The use of the MH method is somewhat limited to dichotomous variables [3,14,15]. In addition, the LR method has a higher power than the MH method in detecting items with DIF, albeit a downside of the power does exist (as discussed in a later section) [2,16,17].

Although various group criteria can be tested with DIF, we focused on a single criterion: job type. Specifically, we analyzed DIF between physicians and nurses, the groups that constitute the majority of healthcare professionals in a healthcare organization. This study is not a complete overhaul of DIF for SAQ-K; rather, we hope the findings of this study will guide or at least ignite further studies in the item functioning of patient safety culture survey instruments like SAQ. For readers not familiar with the approaches introduced in this article, we provide a brief overview.

A Brief Introduction to DIF and Its Detection

For easier understanding, let us begin with an item with a dichotomous response: correct or incorrect, coded as 1 and 0, respectively. In Figure 1, the two lines in each graph indicate the two groups: A (solid) and B (dashed). The x-axis is the latent trait level that the item is supposed to measure, and the y-axis is the log odds of the correct answer. The graphs depict different types of DIF (graphs are purposefully simplified to the level of linear function).

If the two groups respond to the item in the same manner (no DIF), the relationship between the log odds of the correct answer (y-axis) and latent trait level shows the pattern in graph on the left. Because the lines from groups A and B are superimposed, they appear as one single line.

If one group shows higher log odds scores over the entire range of latent traitlevel as shown in the middle graph, the phenomenon is called uniform DIF. On the contrary, if the slopes of the graphs are significantly different (meaning the lines might eventually cross somewhere in the latent trait continuum—maybe even outside the graph), and as such each group is favored over the different latent trait region, the condition is called non-uniform DIF (as in the graph on the right)[13, 17]. In epidemiologic terminology, uniform DIF corresponds to confounding, and non-uniform DIF can be said to be effect modification [3].

Note: x-axis: latent trait level; y-axis:log odds of correct response; solid line: group A; dashed line: group B.

Figure 1: Three Types of Differential Item Functioning.

Here, we describe the discussed DIF types in the form of logistic regression models (Table 1). The essence of the LR-based approach for DIF detection is to examine whether a model has a better fit than the nested model, which can be tested with a likelihood ratio χ2 test. Uniform DIF is investigated by comparing the log likelihood of model 1 with 2 (degree of freedom (df)=1) and non-uniform DIF by comparing model 2 with 3 (df=1).The comparison between model 1 and model 3 (df=2) is supposed to detect the total DIF effect—both uniform and non-uniform DIF [2, 3, 15, 17]. For all three models, the number of response options for a particular item is the same; therefore, the df is determined solely by the number of regression coefficients in the models compared [2].

Table 1: Logistic Models for DIF Detection.

Logistic Model

DIF Type*

Model 1

logit P( correct response )= α k + β 1 ( trait ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaciiBai aac+gacaGGNbGaamyAaiaadshaqaaaaaaaaaWdbiaacckacaGGqbWa aeWaaeaacaWGJbGaam4BaiaadkhacaWGYbGaamyzaiaadogacaWG0b GaaiiOaiaadkhacaWGLbGaam4CaiaadchacaWGVbGaamOBaiaadoha caWGLbaacaGLOaGaayzkaaGaeyypa0JaeqySde2aaSbaaKqbGeaaca WGRbaajuaGbeaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaigdaaKqb agqaamaabmaabaGaamiDaiaadkhacaWGHbGaamyAaiaadshaaiaawI cacaGLPaaaaaa@5CFF@

No DIF (a)

Model 2

logit P( correct response )= α k + β 1 ( trait )+ β 2 ( group ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaciiBai aac+gacaGGNbGaamyAaiaadshaqaaaaaaaaaWdbiaacckacaGGqbWa aeWaaeaacaWGJbGaam4BaiaadkhacaWGYbGaamyzaiaadogacaWG0b GaaiiOaiaadkhacaWGLbGaam4CaiaadchacaWGVbGaamOBaiaadoha caWGLbaacaGLOaGaayzkaaGaeyypa0JaeqySde2aaSbaaKqbGeaaca WGRbaajuaGbeaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaigdaaKqb agqaamaabmaabaGaamiDaiaadkhacaWGHbGaamyAaiaadshaaiaawI cacaGLPaaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaikdaaKqbagqa amaabmaabaGaam4zaiaadkhacaWGVbGaamyDaiaadchaaiaawIcaca GLPaaaaaa@676A@

Uniform DIF (b)

Model 3

logit P( correct response )= α k + β 1 ( trait )+ β 2 ( group )+ β 3 ( trait×group ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaciiBai aac+gacaGGNbGaamyAaiaadshaqaaaaaaaaaWdbiaacckacaGGqbWa aeWaaeaacaWGJbGaam4BaiaadkhacaWGYbGaamyzaiaadogacaWG0b GaaiiOaiaadkhacaWGLbGaam4CaiaadchacaWGVbGaamOBaiaadoha caWGLbaacaGLOaGaayzkaaGaeyypa0JaeqySde2aaSbaaKqbGeaaca WGRbaajuaGbeaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaigdaaKqb agqaamaabmaabaGaamiDaiaadkhacaWGHbGaamyAaiaadshaaiaawI cacaGLPaaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaikdaaKqbagqa amaabmaabaGaam4zaiaadkhacaWGVbGaamyDaiaadchaaiaawIcaca GLPaaacqGHRaWkcqaHYoGydaWgaaqcfasaaiaaiodaaKqbagqaamaa bmaabaGaamiDaiaadkhacaWGHbGaamyAaiaadshacqGHxdaTcaWGNb GaamOCaiaad+gacaWG1bGaamiCaaGaayjkaiaawMcaaaaa@78AA@

Non-uniform DIF (c)

Note: DIF Type* is valid when the model has a better fit than the immediate nested model.

This LR approach has been reported to show a good power for detecting DIF, but it has also raised the issue of inflated type I error rates, especially on large samples.A large sample may make the analysis so sensitive that items with a very small amount of DIF that might have been ignored are stigmatized as DIF items. Combined with some researchers’ tradition of always dropping DIF-suspected items (even when the items still measure the intended latent trait), such inflated type I error creates inefficiency in instrument development and analysis.

Therefore, it is highly recommended to check the effect size in the model comparison process, such as through R2 statistics. In the realm of logistic regression, R2 calculation and interpretation have never been straightforward; thus, several pseudo R2 statistics have been applied, such as Coxand Snell’s, Nagelkerke’s, and McFadden’s R2s. Observing these values suggests the magnitude of DIF for each item, but universal agreement on this is ideal, and knowing how to interpret it has not been defined yet.

In addition, proportional change in the regression coefficient between models can be used as both one of the DIF detection criteria and an effect size measure. To illustrate, uniform DIF is strongly suspected when a change in β1 equal to or larger than 10 percent occurs between model 1 and model 2, albeit this 10 percent threshold is subjective. In simple language, this test criterion asks whether including a group term (β2) influences the relationship between latent trait level and response [2].

Other methods for DIF detection are available, such as testing the significance of regression coefficients in the models, but obviously no silver bullet that can be applied to every dataset has yet been invented. As such, varying methods have been utilized across studies, and the choice of method is left to the researcher’s discretion. One thing for sure is that we should take advantage of a statistical significance-based approach like the likelihood ratio χ2 test in conjunction with effect size measures like pseudo R2 statistics or proportional β1 change.

Thus far, we have described DIF detection with dichotomous response items. The methods for ordinal response, such as a 5-point Likert scale,are simply an expansion of what we have described. Instead of the logit model shown above, we simply mobilized the proportional-odds cumulative-logit model (proportional-odds logistic regression model)[11, 14, 18-20]. Therefore, we will use the above model number for corresponding models for the cumulative-logit model in this article.

In closing this brief introduction section, we must mention that the examples included here assumed the DIF detection between only two groups (groups A and B). However, this LR approach can easily be extended to multiple group comparisons (e.g., groups A, B, C, and D). Thus,we add β2 and β3 terms for each group with a dichotomized indicator variable (1 or 0) and then compare two times the difference in log likelihoods of the above models to a χ2 distribution, with the number of groups to investigate minus one as the degree of freedom [20].

Methods

We used the same dataset from our previous studies on SAQ-K; the survey was conducted in a large metropolitan hospital in Seoul from October through November 2013. Detailed information as to survey process and participants can be found in our previous articles [6-8]. Note that this study used questionnaires collected only from doctors and nurses as these were groups to be compared.

SAQ-K consists of 34 items in six domains. The definition and number of items of each domain are summarized in Table 2 [6,21].

Table 2: SAQ Domain Definitions and Number of Items.

Saq Domain

Definition

Number of Items

Teamwork Climate (TC)

Perceived quality of collaboration between personnel

5

Safety Climate (SC)

Perception of a strong and proactive organizational commitment to safety

6

Job Satisfaction (JS)

Positivity about the work experience

5

Stress Recognition (SR)

Acknowledgment of how performance is influenced by stressors

4

Perception of Management (PM)

Approval of managerial action

10

Working Conditions (WC)

Perceived quality of the work environment and logistical support

4

DIF was tested within each domain, meaning six sets of tests were conducted. Items that revealed significance in any of the three likelihood ratio χ2 tests with an alpha of 0.01 (Model 1 versus 2, Model 2 versus 3, and Model 1 versus 3) were flagged as having DIF.

In what follows, we describe the magnitude of DIF. For individual items, we calculated McFadden’s pseudo R2 statistics for the defined model comparisons. Among the various pseudo R2 statistics, we chose McFadden’s because, despite being a subjective decision, McFadden’s pseudo R2 statistic satisfies most of Kvalseth’s eight criteria for a reliable R2 [22,23], suggesting that it can serve as a decent measure. We then applied Zumbo et al.’s guideline to evaluate R2: Below 0.13 is negligible, between 0.13 and 0.26 is moderate, and above 0.26 is large DIF [17,24].

In addition, proportional β1 change was obtained for additional information about the amount of uniform DIF: A 10% change was regarded as a meaningful size of uniform DIF [2,25].

For individual participants, we calculated the difference between the DIF-adjusted (purified) SAQ-K score and the initial unadjusted score for each domain. Finally, we drew test characteristic curves to show the impact of DIF for each group.

In sum, we began from DIF detection and magnitude evaluation for each item, then moved to investigate the aggregate impact of DIF items on domain score for each individual participant and each group (i.e., doctors and nurses).

Results

Characteristics of Respondents

Of the 1,381 questionnaires returned, we analyzed 987 questionnaires collected from 378 doctors and 609 nurses. Table 3 shows the characteristics of the respondents, inclusive of gender, work year, and job type.

Table 3: Characteristics of Respondents.

Characteristics

N

%

Gender

Male

230

23.3

Female

757

76.7

Work years

Less than 6 months

68

6.9

7–11 months

109

11.0

1–2 years

177

17.9

3–4 years

225

22.8

5–10 years

250

25.3

11–20 years

117

11.9

More than 21 years

41

4.2

Job type

Physician

378

38.3

Nurse

609

61.7

Total

987

100

DIF Detection and Effect Size Analysis

Table 4 shows the results of the logistic regression approach for DIF detection. DIF items revealed from the likelihood ratio χ2 tests are highlighted in bold. Among the 34 SAQ-K items, 15 were flagged as DIF items; they all showed statistical significance from the total DIF effect test comparing Models 1 and 3: Pr( χ 13 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIXaGaaG4maaqaaiaaikdaaaaaaa@3ABF@ , 1) was smaller than 0.01. Ten items showed significance only in Pr( χ 12 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIXaGaaGOmaaqaaiaaikdaaaaaaa@3ABE@ , 1) statistic from the comparison of Models 1 and 2, suggesting the typical uniform DIF manner. One item (SC6) showed significance only in Pr( χ 23 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIYaGaaG4maaqaaiaaikdaaaaaaa@3AC0@ , 1), suggesting it has an archetypal nonuniform DIF nature. Four items (TC1, JS1, JS4, and WC3) were significant for both Pr( χ 12 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIXaGaaGOmaaqaaiaaikdaaaaaaa@3ABE@ , 1) and Pr( χ 23 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIYaGaaG4maaqaaiaaikdaaaaaaa@3AC0@ , 1) statistics.

Unlike the likelihood ratio χ2 tests that raised the DIF flag for approximately half of the items, McFadden’s pseudo R2 statistics were negligible for all items (<0.13) [17,24]; the largest was only 0.0651 in pseudo R2 between Models 1 and 2 for TC5. To verify this result, we calculated other pseudo R2 statistics, such Cox and Snell’s R2 and Nagelkerke’s R2, but they all yielded negligible values. For a proportional β1 change, two items were higher than 10%: TC6 (10.78%) and SC1 (11.87%). These two items had statistical significance only from the χ 12 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIXaGaaGOmaaqaaiaaikdaaaaaaa@3ABE@ , not the χ 23 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeq4Xdm 2aa0baaKqbGeaacaaIYaGaaG4maaqaaiaaikdaaaaaaa@3AC0@ , suggesting an apparent uniform DIF.

Table 4: Likelihood Ratio χ2 Test Results, McFadden’s Pseudo R2, and Percent Change of β1 Change.

ID

Items

χ2 Statistics

McFadden's Pseudo R2

  Δ β 1 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeuiLdq KaeqOSdi2aaSbaaKqbGeaacaaIXaaajuaGbeaaaaa@3B23@ (%)

Chi12

Chi23

Chi13

R12

R23

R13

Teamwork Climate

TC1

Nurse input is well received in this clinical area

.0000

.0000

.0000

.0071

.0084

.0155

.6600

TC2

Disagreements in this clinical area are resolved appropriately (i.e., not who is right, but what is best for the patient)

.2171

.9322

.4651

.0006

.0000

.0006

.0300

TC3

I have the support I need from other personnel to care for patients

.9734

.1957

.4327

.0000

.0007

.0007

.0000

TC4

It is easy for personnel here to ask questions when there is something that they do not understand

.2740

.5781

.4709

.0005

.0001

.0006

.2200

TC5

The physicians and nurses here work together as a well-coordinated team

.0000

.0125

.0000

.0628

.0023

.0651

10.7800

Safety Climate

SC1

I would feel safe being treated here as a patient

.0000

.0789

.0000

.0461

.0013

.0473

11.8700

SC2

Medical errors are handled appropriately in this clinical area

.0018

.8938

.0074

.0041

.0000

.0041

.3100

SC3

I know the proper channels to direct questions regarding patient safety in this clinical area

.0508

.0783

.0315

.0014

.0011

.0025

.1500

SC4

I receive appropriate feedback about my performance

.0000

.6333

.0000

.0131

.0001

.0132

1.2700

SC5

I am encouraged by my colleagues to report any patient safety concerns I may have

.0010

.5548

.0038

.0042

.0001

.0043

1.5800

SC6

The culture in this clinical area makes it easy to learn from the errors of others

.7935

.0011

.0046

.0000

.0044

.0044

.0700

Job Satisfaction

JS1

I like my job

.0000

.0085

.0000

.0138

.0025

.0163

1.3300

JS2

Working here is like being part of a family

.1776

.8104

.3916

.0006

.0000

.0007

.7100

JS3

This is a good place to work

.0372

.7643

.1091

.0016

.0000

.0016

.1000

JS4

I am proud to work in this clinical area

.0075

.0004

.0000

.0027

.0048

.0075

2.0600

JS5

Morale in this clinical area is high

.1621

.0923

.0912

.0007

.0011

.0018

.6400

Stress Recognition

SR1

When my workload becomes excessive, my performance is impaired

.0004

.0607

.0003

.0000

.0014

.0064

.4400

SR2

I am less effective at work when fatigued

.9492

.8186

.9721

.0000

.0000

.0000

.0300

SR3

I am more likely to make errors in tense or hostile situations

.5271

.9680

.8181

.0000

.0000

.0001

.1600

SR4

Fatigue impairs my performance during emergency situations (e.g., emergency resuscitation, seizure)

.6489

.1608

.3372

.0000

.0007

.0008

.1500

Perception of Management

PM1

Unit management supports my daily efforts

.5967

.3684

.5801

.0001

.0003

.0004

.1400

PM2

Hospital management supports my daily efforts

.0033

.0915

.0032

0035

.0012

.0046

.3900

PM3

Unit management doesn't knowingly compromise patient safety

.1514

.5124

.2883

.0008

.0002

.0010

.1000

PM4

Hospital management doesn't knowingly compromise patient safety

.8516

.1809

.4015

.0000

.0007

.0007

.0300

PM5

Unit management is doing a good job

.8760

.9452

.9856

.0000

.0000

.0000

.0300

PM6

Hospital management is doing a good job

.0000

.7495

.0002

.0066

.0000

.0066

2.1100

PM7

Problem personnel are dealt with constructively by our unit management

.0762

.7073

.1935

.0013

.0001

.0014

.2200

PM8

Problem personnel are dealt with constructively by our hospital management

.2687

.2487

.2789

.0005

.0005

.0010

.3100

PM9

I get adequate, timely info about events that might affect my work from unit management

.3319

.0901

.1485

.0004

.0011

.0015

.2600

PM10

I get adequate, timely info about events that might affect my work from hospital management

.6260

.8889

.8794

.0001

.0000

.0001

.0900

Working Condition

WC1

The levels of staffing in this clinical area are sufficient to handle the number of patients

.0000

.9814

.0000

.0167

.0000

.0167

1.5700

WC2

This hospital does a good job of training new personnel

.0000

.8747

.0000

.0080

.0000

.0080

2.1600

WC3

All the necessary information for diagnostic and therapeutic decisions is routinely available to me

.0000

.0061

.0000

.0084

.0033

.0117

.8100

WC4

Trainees in my discipline are adequately supervised

.6223

.9509

.8840

.0001

.0000

.0001

.0900

Note: Chi12, Chi23, and Chi13 denote the likelihood ratio χ2 statistic between Models 1 and 2, Models 2 and 3, and Models 1 and 3, respectively; R12, R23, R13 indicate McFadden’s pseudo R<sup>2</sup> from a comparison Models 1 and 2, Models 2 and 3, and Models 1 and 3, respectively; Δ β 1 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeuiLdq KaeqOSdi2aaSbaaKqbGeaacaaIXaaajuaGbeaaaaa@3B23@ is proportional change in β1 and shown as a percentage.

Graphical Analyses of DIF Effects

From the above analyses, we generated various graphs. Due to space constraints, we present only those graphs with the most distinctive results here. Again, the purpose of this study is to test DIF in SAQ-K; what follows mainly explains how to interpret such graphical results and perform various diagnostic analyses for readers.

Figure 2 depicts DIF pattern by item; we selected two items (SC1 and SC6) as examples. The upper-left graph (SC1) shows a typical uniform DIF pattern; the item score (y-axis) for doctors (solid line) is higher than that for nurses (dashed line) over the entire range of latent trait level (x-axis). On the other hand, in the upper-right graph (SC6), two lines cross at around zero in the latent trait continuum; in the higher latent trait region, nurses’ score was larger than doctors’ and the lower latent trait level region doctor’s score was larger than nurses’, suggesting typical nonuniform DIF pattern.

We also depicted item response functions to deeply understand how DIF arises in the SAQ-K items with categorical response. In the graphs for item response function, there should be five curves (lower-right graph, SC6), each of which corresponds to each of the response options of the 5-point Likert scale (1 through 5 in our case). The reason why there are only four curves for SC1 (lower-left graph) was that too few respondents chose the first category; thus, it was collapsed into the second category. Eventually, the analysis for SC1 was done with four response categories (1 and 2 together, 3, 4, and 5 in the Likert scale). The x-axis is respondents’ latent trait as always, and the y-axis is the probability of a category being chosen.

In the item response function of SC1, for every response category, the solid line (doctors) was located to the left of the corresponding dashed line (nurses), suggesting that over the entire range of the latent trait level, doctors had a greater propensity to choose a higher response option than nurses. On the contrary, for the item response function of SC6, in the latent trait region higher than zero, dashed lines (nurses) are located to the left of the solid lines (doctors); in the lower latent trait region, the solid lines (doctors) are on the left. These item response functions for each category clearly explain how different DIF patterns could be generated behind the curtain. Other DIF items basically share similar patterns of the above two items, albeit the magnitudes varied.

Figure 2: Item True Score Functions and Item Response Functions of DIF Items.
Now, we turn to DIF impact on individual respondents.Figure 3 shows the difference in latent trait level between the initial IRT-based trait estimate (DIF was ignored) and the purified trait estimate (DIF was accounted for). Unlike Figure 2, where item characteristics from the entire population were presented, Figure 3 shows an individual respondent’s information: Each circle and triangle stands for the latent trait estimate of a domain for a single participant. In a word, Figure 3 shows the expected amount of change in latent trait estimates when DIF is accountedfor. In both graphs, the y-axis is the difference (initial – purified) and the x-axis of the right graph is the initial latent trait level.Here, we present the TC domain as it shows the most overt pattern.

Note: The y-axis is a purified score that accounts for DIF subtracted from the naïve score that ignores DIF.

Figure 3: Difference between Initial and Purified TC Domain Scores for Each Respondent.

The box plot on the left shows that the median difference was around -0.03, and the interquartile range(i.e., the middle 50% of respondents, depicted as the box)ranged from approximately -0.06 to 0.08. This is a typical right-skewed distribution with outliers at high values. The right graph shows the initial – purified difference against the initial latent trait level that ignored DIF. The interpretation of this graph is as follows: Across the entire latent trait continuum, doctors (circle) show a positive difference, suggesting that accounting for DIF leads to lower scores than the initial scores; for nurses (triangle), the pattern is reversed.

What is delineated above corresponds well with the test characteristic curves (TCC) in Figure 4. The y-axis of TCC is the possible score from a domain.As there are five items with the 5-point Likert scale ranging from 1 to 5, the minimum value is 5 and the maximum is 25 for the y-axis (left graph). Given the two DIF items (TC1 and TC5), the y-axis of the right graph ranges from 2 to 10. The point here is that, for DIF items, we can observea clear difference in TCC curves between doctors and nurses, although the absolute magnitude was not that much. On the other hand, the left graph for all items displayeda much smaller difference than the right graph. This can be explained as the DIF impact being diluted by the non-DIF items, resulting in the overall score difference between the two groups becoming minimal.

Note: The y-axis is the sum of the item scores of the TC domain.

Figure 4: Test Characteristic Curves for All Items and DIF Items.

Discussion

This study aimed to test whether SAQ-K is a DIF-free instrument. To this end, we utilized the LR approach to handle the categorical response with a 5-point Likert scale of SAQ-K and revealed that 15 items had a statistically significant but practically minimal amount of DIF, thereby answering our research questions. Although providing detailed contextual implications of all DIF items is beyond the scope of this article, a few things about the SAQ-specific results are worth mentioning here.

First, TC1 (“The physicians and nurses here work together as a well-coordinated team”) was tagged as a typical uniform DIF item that favored doctors across the entire TC trait continuum to a considerable degree (proportional change was bigger than 10%). Previous studies (for the hospital’s internal use and, thus, not published) that did not account for DIF reported that the raw score from doctors was much higher than that of nurses. Some researchers have suggested that this phenomenon stems from the difference between doctors and nurses in how they define a “well-coordinated team.” For instance, doctors may think of a good team as simply “doctors order and nurses follow them well” whereas nurses point out that their active participation in the decision-making process is essential for good teamwork [26]. Therefore, the detected DIF might have stemmed from the difference in how different groups interpret the item differently.

The other uniform DIF item, SC1 (“I would feel safe being treated here as a patient”) also showed a higher score from doctors than nurses. The perception of the word “safe” might have differed between these groups. Doctors might perceive safe not as “free from medical errors,” as SAQ originally intended, but as “clinical quality of treatment is reliable.” Although this interpretation is just our retrospective conjecture, it is worth conducting an in-depth investigation. Ultimately, an item’s definition should be clearly presented in the survey instrument to prevent DIF that could have surely been avoided. We strongly recommend conducting a thorough pilot study for every instrument development process and resolving any discordance in item interpretation among different groups before rolling out the instrument widely.

Another issue to consider is the number of items to include on a survey questionnaire. For an instrument like the Patient-Reported Outcomes Measurement Information System (PROMIS), a relatively large number of items are included in a domain, at least in the item bank [27]. Yet, for many instruments designed for healthcare professionals, the number of items is reduced as much as possible considering their busy schedules. The SAQ-K is no exception; indeed, five out of six SAQ domains have only 4 to 6 items, and only the perception of management domain has 10 items. The problem is evident when multiple items show DIF; in other words, too few items are DIF free. Those non-DIF items serve as an anchor to calibrate DIF items across groups. Therefore, having enough DIF-free items in an instrument gives more stability. As it is practically not easy to increase the number of items in an instrument in a hospital setting, replacing DIF items with newly developed and tested non-DIF items might be a way to solve the issue.

In the statistical DIF detection process, type I and II errors (false positive and false negative, respectively) for DIF items can result from the impact of the other DIF items. DIF detection begins with measuring the latent trait; this trait level measurement itself is affected by DIF items [10,28]. To handle this issue, we applied Crane et al.’s approach, iterative detection and updated latent trait ability estimation [2]. In that approach, latent trait measurement is conducted with IRT and DIF is detected based upon the measurement. Then, the DIF items are purified. Using the new values, these steps are repeated until the same DIF items are flagged twice in a row [20]. The fundamental strength of this approach is that we retain all DIF items while checking whether the type I and II errors in DIF detection steps influenced the initial findings [4,29]. Considering the small number of items of SAQ domains, we think utilizing this approach while retaining all items is quite appropriate and, indeed, recommended.

The determination of a threshold to detect DIF has been actively discussed among researchers. Traditionally, we use a predetermined threshold, like alpha of 0.01, and compare the likelihood ratio statistic to the threshold. A newly developed method is to utilize Monte Carlo simulation to set the empirical threshold [20]. To illustrate, instead of using a certain alpha value like 0.01, we can generate many simulated datasets from the original data and calculate the statistic from each dataset. Then the 99th percentile of the statistics is the empirical threshold of DIF detection. For example, we run 1,000 cycles and the 10th smallest value is the threshold (empirical alpha value 0.01). If the value is 0.007, we can say we may expect to better control type I error with this threshold than with the nominal alpha, 0.01. The same logic can be applied to effect size measures too, but we do not recommend doing so because simulated R<sup>2</sup> is not meaningful in evaluating the magnitude of DIF.

In this particular study, we tried to detect DIF mainly using statistical significance, which naturally led us to address less the domain-or instrument-level impact of DIF items. Although not described in this article, we found that the favored group varies across items. To illustrate, for a certain uniform DIF item, doctors’ scores can be higher; for another item, the opposite is true. In the domain level, these opposite DIF effects may be canceled out to some degree, and the total DIF effects get smaller, maybe even reaching a negligible level. For nonuniform DIF items, it is much more complicated. Therefore, caution should be exercised when evaluating the total amount of impact that DIF brings to a domain or instrument as a whole. At the population level, the proportion of respondents who answer a certain response category also influence the final effect. Let us assume that an item shows DIF mainly in a trait range corresponding to high response options (e.g., 4 or 5 on a 5-point Likert scale) and not much difference in the trait region of the lower response category (e.g., 1 or 2). If most respondents choose the lower response category, the population level effect of DIF would be minimized. Of course, in these examples of domain (instrument) level and population level, the direction of the DIF effect change can be reversed: The DIF impact would be more amplified instead of being canceled out or reduced.

This study is rather preliminary in that it uses a dataset from a single hospital, but we have shown that the methodology described here worked well in a healthcare setting. To build a broad and concrete knowledge base, further studies in different organizations are needed. Also, there are many different group variables in healthcare, such as work experience (duration), seniority, and full time versus part time. Even the size of a clinical area (number of healthcare workers) might cause DIF. Doctors and nurses are just one example. All these group variables are worth checking with DIF to maximize the effectiveness of an instrument, although this would be a huge undertaking. Obviously, it cannot be done in one night, but it should be done.

Conclusion

This is the fifth episode of the SAQ-K series. We are not sure whether it ends as a pentaptych or goes further. One thing is for sure: Safety culture plays a key role at some point in every accident causation [30]; thus, we have to shape the culture in an effort to improve patient safety. Thus, we developed and utilized instruments like SAQ. Thus far, the medical society has neglected DIF in safety culture measurements. Maybe we have been too busy solving impending problems threating patients, but that should not be an excuse any longer. We all know that, if we cannot measure safety culture, we cannot manage it. This study showed that we may not be fine with the measurement part; thus, a complete overhaul of the measurement instruments should be the first priority. We urge our colleague researchers to participate in this endeavor. Of course we will do so as well. We promise.

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