Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey

Chien, Tsair‐Wei; Shao, Yang; Kuo, Shu‐Chun

doi:10.1186/s12874-016-0276-2

Cited by 18 publications

(28 citation statements)

References 33 publications

(36 reference statements)

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“…To answer the third research question, we collected data from previous studies regarding a PSC survey in a hospital [ 6 , 7 , 37 ]. The sample size was 2237 with 97 departments.…”

Section: Methodsmentioning

confidence: 99%

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture

Chien

Shao²,

Jen

2017

Health Qual Life Outcomes

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BackgroundMany quality-of-life studies have been conducted in healthcare settings, but few have used Microsoft Excel to incorporate Cronbach’s α with dimension coefficient (DC) for describing a scale’s characteristics. To present a computer module that can report a scale’s validity, we manipulated datasets to verify a DC that can be used as a factor retention criterion for demonstrating its usefulness in a patient safety culture survey (PSC).MethodsMicrosoft Excel Visual Basic for Applications was used to design a computer module for simulating 2000 datasets fitting the Rasch rating scale model. The datasets consisted of (i) five dual correlation coefficients (correl. = 0.3, 0.5, 0.7, 0.9, and 1.0) on two latent traits (i.e., true scores) following a normal distribution and responses to their respective 1/3 and 2/3 items in length; (ii) 20 scenarios of item lengths from 5 to 100; and (iii) 20 sample sizes from 50 to 1000. Each item containing 5-point polytomous responses was uniformly distributed in difficulty across a ± 2 logit range. Three methods (i.e., dimension interrelation ≥0.7, Horn’s parallel analysis (PA) 95% confidence interval, and individual random eigenvalues) were used for determining one factor to retain. DC refers to the binary classification (1 as one factor and 0 as many factors) used for examining accuracy with the indicators sensitivity, specificity, and area under receiver operating characteristic curve (AUC). The scale’s reliability and DC were simultaneously calculated for each simulative dataset. PSC real data were demonstrated with DC to interpret reports of the unit-based construct validity using the author-made MS Excel module.ResultsThe DC method presented accurate sensitivity (=0.96), specificity (=0.92) with a DC criterion (≥0.70), and AUC (=0.98) that were higher than those of the two PA methods. PA combined with DC yielded good sensitivity (=0.96), specificity (=1.0) with a DC criterion (≥0.70), and AUC (=0.99).ConclusionsAdvances in computer technology may enable healthcare users familiar with MS Excel to apply DC as a factor retention criterion for determining a scale’s unidimensionality and evaluating a scale’s quality.Electronic supplementary materialThe online version of this article (10.1186/s12955-017-0784-8) contains supplementary material, which is available to authorized users.

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“…To answer the third research question, we collected data from previous studies regarding a PSC survey in a hospital [ 6 , 7 , 37 ]. The sample size was 2237 with 97 departments.…”

Section: Methodsmentioning

confidence: 99%

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture

Chien

Shao²,

Jen

2017

Health Qual Life Outcomes

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“…Person-fit statistics have a rich place in the education literature on detecting aberrant responses (e.g., Karabatsos, 2003;Meijer & Sijtsma, 2001), and they have also begun to emerge in psychological research when evaluating scores from psychological scales. For instance, person-fit statistics have been used to refine scales for health outcomes, anxiety and depression, mindfulness, personality, and attitudes (Chien, Shao, & Kuo, 2017;Edelen & Reeve, 2007;Ferrando, 2004;Pallant & Tennant, 2007;Van Dam, Earleywine, & Borders, 2010). Beyond identifying person misfit, person-fit statistics have been used in moderated multiple-regression analysis to evaluate validity estimates (McGrath, Mitchell, Kim, & Hough, 2010).…”

mentioning

confidence: 99%

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Hong

Cheng

2018

Behav Res

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Self-report data are common in psychological and survey research. Unfortunately, many of these samples are plagued with careless responses, due to unmotivated participants. The purpose of this study was to propose and evaluate a robust estimation method to detect careless or unmotivated responders, while leveraging item response theory (IRT) person-fit statistics. First, we outlined a general framework for robust estimation specific for IRT models. Subsequently, we conducted a simulation study covering multiple conditions in order to evaluate the performance of the proposed method. Ultimately, we showed that robust maximum marginal likelihood (RMML) estimation significantly improves detection rates for careless responders and reduces bias in item parameters across conditions. Furthermore, we applied our method to a real data set, to illustrate the utility of the proposed method. Our findings suggest that robust estimation coupled with person-fit statistics offers a powerful procedure to identify careless respondents for further review and to provide more accurate item parameter estimates in the presence of careless responses.

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“…In the current study, we programmed an MS Excel module, as well as an online representation on the Google, maps to deal with continuous item responses using the probabilistic modeling like Likert-type responses in a healthcare setting [1,16]. The average scores shown in Figure 3 are meaningful when data form a one-dimensional measurement(see Figure 3), and the states shown at the right-hand side of the scatter plot beyond 2.0 in Figure 6 on the Google maps [39] can be easily interpreted as an unusual pattern in response to domains.…”

Section: Discussionmentioning

confidence: 99%

“…A Microsoft Excel module was programmed to deal with Rasch [15] continuous item responses (CIR for short) [16], see Multimedia Additional file 1, for examining scale unidimensionality with the criterion of item Infit mean square error (Infit MNSQ) less than 1.5 [17]. The Wright Map (or named person-item map) [18,19] were drawn using the CIR module.…”

Section: Examination Of Item Scores Addiblementioning

confidence: 99%

Inpatient Perception on Hospitalization Experience across US 50 States: Analysis of the Historical Series from 2007 To 2014

Chen¹,

Chien²,

Tang³

2020

SARJAMS

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Background: There are many ways to gather and assess patient expectations, experience, and satisfaction, but few use visual representations to report their results on the Google maps and help patients select the best hospitals for consultation. Objectives: To analyze 10-dimensional scores of states in the USA about inpatients' perceptions of their hospitalization experience to develop a method for analyzing data on the Google maps and getting feedback on the smartphones. Methods: We downloaded HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data from the 2007 to 2014 summary of survey results to study (1) whether the 10 dimensional scores can be combined to determine its unidimensionality using Rasch continuous item responses, (2) what type of trends about inpatient perception on hospitalization experience that can be reported with an individual and an overall base, (3) what an online dashboard that can be designed using the Google maps for comparing results of each US state, and (4) how to demonstrate an online assessment that uses smartphones for gathering perceptions of their hospitalization experience in the future. Results: The ten core dimensional scores of each US state about inpatient hospitalization experience reported by the HCAHPS can be unidimensional. The improvement was evident of inpatient perception on hospitalization experience in the historical series. Online visual representation of the Google maps can be easy to build and allows for real-time identification in comparison with the performance of each state. A smartphone app was designed to get feedback directly from patients. Conclusions: We verified that the 10-dimensional scores reporting patient satisfaction in US states could be a unidimensional scale and use Rasch continuous item responses to show results on the Google maps.Keywords: Smart phone, unidimensionality, HCAHPS, hospitalized experience perception, the Google maps SUDPAR.Psychometrically, we are interested in identifying aberrant survey respondents and selecting the best and worst respondents [12]. A method for distinguishing the best and worst clusters is required using the multiple variables of the assessed dimensional scores for an investigation.

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Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey

Cited by 18 publications

References 33 publications

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Inpatient Perception on Hospitalization Experience across US 50 States: Analysis of the Historical Series from 2007 To 2014

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