Item Response Theory Parameters Research Articles

Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis

A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on this topic in the framework of item response theory have shown that imputation procedures are in general associated with more accurate estimates of item location and discrimination parameters under several missing data generating mechanisms. This paper proposes a model-based multiple imputation procedure for multiple categorical items (dichotomous, multinomial or Likert-type) which relies on the results of latent class analysis to impute missing item responses. The effectiveness of the proposed technique is assessed in the estimation of item response theory parameters using a range of ad hoc measures. The accuracy of the method is assessed with respect to other single and multiple imputation procedures, under different missing data generating mechanisms and different rate of missingness (5% to 30%). The simulation results indicate that the proposed technique performs satisfactorily under all conditions and has the greatest potential with severe rates of missingness and under non ignorable missing data mechanisms. The method was implemented in R code with a function that calls scripts from a latent class analysis routine.

Examination of Polytomous Items’ Psychometric Properties According to Nonparametric Item Response Theory Models in Different Test Conditions

Tests used for such purposes as determining educational quality, defining educational needs, hiring an employee, student selection and placement and performing guidance and clinic services have an important place in education and psychology. Of course, they should have certain psychometric features related to test scores' validity and reliability. Various test theories have helped to create more valid and reliable measurements and, as a result, to make better decisions regarding individuals. In education and psychology, Classical Test Theory (CTT) and Item Response Theory (IRT) are both widely used. CTT assumes that an individual's observed score is the total of the true score and the error score, while IRT estimates an individual's ability or latent trait from responses to test items (Embretson & Reise, 2000).When IRT assumptions and model-data fit are ensured, item and ability parameters' invariance occurs; this is known as the most important advantage IRT has over CTT. Item and ability parameters' invariance means estimating ability parameters independently of item sample and estimating item parameters independently of ability sample. IRT's invariance feature makes it very practicable in many applications, for instance, test development, computerized adaptive testing, bias studies, test equating and item mapping (Hambleton & Swaminathan, 1985). IRT is classified under two main categories as parametric IRT (PIRT) and nonparametric IRT (NIRT) (Olivares, 2005; Sijtsma & Molenaar, 2002).To analyse ordered items, such as Likert-type attitude items, partial credit cognitive items or not ordered graded items such as multiple-choice test items, item response models are developed towards polytomous items in IRT (Ostini & Nering, 2006). In these models developed for polytomous items, a non-linear relationship between an individual's latent trait and the possibility of choosing a certain category of item answer is explained (Embretson & Reise, 2000). Graded Response Model (GRM), part of IRT models developed for polytomous items, is often preferred by researchers for applications since it is more useful in presentations, portfolios, essays and Likert-type items with ordered item categories (DeMars, 2010; Ostini & Nering, 2006). To scale tests that consist of polytomous items by making true estimates according to GRM, evaluating PIRT's assumptions and model-data fit is necessary. And to provide these assumptions and model-data fit, large samples are needed. At this point, NIRT models draw attention because they provide a practical advantage in determining psychometric properties of tests with fewer items and respondents (Stout, 2001).NIRT models are defined as statistical scaling methods that require fewer assumptions than PIRT models for measuring persons and items (Stochl, 2007). With their wide application area, NIRT models are used in ordinal scales, applied research areas, sociology, marketing research and health research on quality of life (Sijtsma, 2005). The literature reveals that two models, namely, the Mokken model and nonparametric regression estimation models, are employed. These two models are themselves divided into sub-models. The Mokken model consists of the sub-models Monotone Homogeneity Model (MHM) and the Double Monotonicity Model (DMM). Nonparametric regression estimation models consist of such sub-models as the Kernel Smoothing Approach Model (KSAM), the Isotonic Regression Estimation and the Smoothed Isotonic Regression Estimation models (Lee, 2007; Sijtsma & Molenaar, 2002). Along with theoretical studies being conducted, new sub-models are being added to nonparametric regression estimation models.As a NIRT model, MHM requires unidimensionality, local independence and monotonicity assumptions, and it defines the relationship that latent variables and items with homogeneous (unidimensional) and monotone item characteristic curve (ICC) have (Meijer & Baneke, 2004; Sijtsma & Molenaar, 2002). …

Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis

BackgroundShared decision-making is a cornerstone of patient-centred care. The 9-item Shared Decision-Making Questionnaire (SDM-Q-9) is a brief self-assessment tool for measuring patients’ perceived level of involvement in decision-making related to their own treatment and care. Information related to the psychometric properties of the SDM-Q-9 for multiple sclerosis (MS) patients is limited. The objective of this study was to assess the performance of the items composing the SDM-Q-9 and its dimensional structure in patients with relapsing-remitting MS.MethodsA non-interventional, cross-sectional study in adult patients with relapsing-remitting MS was conducted in 17 MS units throughout Spain. A nonparametric item response theory (IRT) analysis was used to assess the latent construct and dimensional structure underlying the observed responses. A parametric IRT model, General Partial Credit Model, was fitted to obtain estimates of the relationship between the latent construct and item characteristics. The unidimensionality of the SDM-Q-9 instrument was assessed by confirmatory factor analysis.ResultsA total of 221 patients were studied (mean age = 42.1 ± 9.9 years, 68.3% female). Median Expanded Disability Status Scale score was 2.5 ± 1.5. Most patients reported taking part in each step of the decision-making process. Internal reliability of the instrument was high (Cronbach’s α = 0.91) and the overall scale scalability score was 0.57, indicative of a strong scale. All items, except for the item 1, showed scalability indices higher than 0.30. Four items (items 6 through to 9) conveyed more than half of the SDM-Q-9 overall information (67.3%). The SDM-Q-9 was a good fit for a unidimensional latent structure (comparative fit index = 0.98, root-mean-square error of approximation = 0.07). All freely estimated parameters were statistically significant (P < 0.001). All items presented standardized parameter estimates with salient loadings (>0.40) with the exception of item 1 which presented the lowest loading (0.26). Items 6 through to 8 were the most relevant items for shared decision-making.ConclusionsThe SDM-Q-9 presents appropriate psychometric properties and is therefore useful for assessing different aspects of shared decision-making in patients with multiple sclerosis.

Evaluation of Northwest University, Kano Post-UTME Test Items Using Item Response Theory

High-stakes testing is used for the purposes of providing results that have important consequences. Validity is the cornerstone upon which all measurement systems are built. This study applied the Item Response Theory principles to analyse Northwest University Kano Post-UTME Economics test items. The developed fifty (50) economics test items was administered to a sample of 600 students. The data obtained was analysed using XCALIBRE 4 and SPSS 20v softwares to determine items parameters base on IRT models. Indicate that, the test measure single trait by satisfying the condition of unidimensionality. Similarly, the goodness of fit test revealed that, the two parameter IRT model was more suitable since no misfit item was observed and the test reliability was 0.86. The mean examinee ability was 0.07 (SD=0.94). The mean item difficulty was -0.63(SD=2.54) and mean item discrimination was 0.28 (SD=0.04). 16 (33%) items were identified as “problematic” based on difficulty indices, 35(71%) also failed to meet the set standards on the basis of discrimination parameters. it can be concluded that, using the IRT approach, the NWU Post-UTME items are not stable as far as item difficulty and discrimination indices are concerned. It is recommended that, the Post-UTME items should be made to pass through all process of standardisation and validation; test development and content experts should be involve in developing and validating the test items in order to obtain valid and reliable results which will lead to valid inferences

Multiple Equating of Separate IRT Calibrations.

When test forms are calibrated separately, item response theory parameters are not comparable because they are expressed on different measurement scales. The equating process includes the conversion of item parameter estimates on a common scale and the determination of comparable test scores. Various statistical methods have been proposed to perform equating between two test forms. This paper provides a generalization to multiple test forms of the mean-geometric mean, the mean-mean, the Haebara, and the Stocking-Lord methods. The proposed methods estimate simultaneously the equating coefficients that permit the scale transformation of the parameters of all forms to the scale of the base form. Asymptotic standard errors of the equating coefficients are derived. A simulation study is presented to illustrate the performance of the methods.

An Approach to Scoring and Equating Tests With Binary Items: Piloting With Large-Scale Assessments.

This article describes an approach to test scoring, referred to as delta scoring (D-scoring), for tests with dichotomously scored items. The D-scoring uses information from item response theory (IRT) calibration to facilitate computations and interpretations in the context of large-scale assessments. The D-score is computed from the examinee's response vector, which is weighted by the expected difficulties (not "easiness") of the test items. The expected difficulty of each item is obtained as an analytic function of its IRT parameters. The D-scores are independent of the sample of test-takers as they are based on expected item difficulties. It is shown that the D-scale performs a good bit better than the IRT logit scale by criteria of scale intervalness. To equate D-scales, it is sufficient to rescale the item parameters, thus avoiding tedious and error-prone procedures of mapping test characteristic curves under the method of IRT true score equating, which is often used in the practice of large-scale testing. The proposed D-scaling proved promising under its current piloting with large-scale assessments and the hope is that it can efficiently complement IRT procedures in the practice of large-scale testing in the field of education and psychology.

Development and community-based validation of eight item banks to assess mental health

There is a need for precise but brief screening of mental health problems in a range of settings. The development of item banks to assess depression and anxiety has resulted in new adaptive and static screeners that accurately assess severity of symptoms. However, expansion to a wider array of mental health problems is required. The current study developed item banks for eight mental health problems: social anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder, adult attention-deficit hyperactivity disorder, drug use, psychosis and suicidality. The item banks were calibrated in a population-based Australian adult sample (N=3175) by administering large item pools (45–75 items) and excluding items on the basis of local dependence or measurement non-invariance. Item Response Theory parameters were estimated for each item bank using a two-parameter graded response model. Each bank consisted of 19–47 items, demonstrating excellent fit and precision across a range of −1 to 3 standard deviations from the mean. No previous study has developed such a broad range of mental health item banks. The calibrated item banks will form the basis of a new system of static and adaptive measures to screen for a broad array of mental health problems in the community.

Development of the Contact Lens User Experience: CLUE Scales.

The field of optometry has become increasingly interested in patient-reported outcomes, reflecting a common trend occurring across the spectrum of healthcare. This article reviews the development of the Contact Lens User Experience: CLUE system designed to assess patient evaluations of contact lenses. CLUE was built using modern psychometric methods such as factor analysis and item response theory. The qualitative process through which relevant domains were identified is outlined as well as the process of creating initial item banks. Psychometric analyses were conducted on the initial item banks and refinements were made to the domains and items. Following this data-driven refinement phase, a second round of data was collected to further refine the items and obtain final item response theory item parameters estimates. Extensive qualitative work identified three key areas patients consider important when describing their experience with contact lenses. Based on item content and psychometric dimensionality assessments, the developing CLUE instruments were ultimately focused around four domains: comfort, vision, handling, and packaging. Item response theory parameters were estimated for the CLUE item banks (377 items), and the resulting scales were found to provide precise and reliable assignment of scores detailing users' subjective experiences with contact lenses. The CLUE family of instruments, as it currently exists, exhibits excellent psychometric properties.

The COPD-SIB: a newly developed disease-specific item bank to measure health-related quality of life in patients with chronic obstructive pulmonary disease.

BackgroundHealth-related quality of life (HRQoL) is widely used as an outcome measure in the evaluation of treatment interventions in patients with chronic obstructive pulmonary disease (COPD). In order to address challenges associated with existing fixed-length measures (e.g., too long to be used routinely, too short to ensure both content validity and reliability), a COPD-specific item bank (COPD-SIB) was developed.MethodsItems were selected based on literature review and interviews with Dutch COPD patients, with a strong focus on both content validity and item comprehension. The psychometric quality of the item bank was evaluated using Mokken Scale Analysis and parametric Item Response Theory, using data of 666 COPD patients.ResultsThe final item bank contains 46 items that form a strong scale, tapping into eight important themes that were identified based on literature review and patient interviews: Coping with disease/symptoms, adaptability; Autonomy; Anxiety about the course/end-state of the disease, hopelessness; Positive psychological functioning; Situations triggering or enhancing breathing problems; Symptoms; Activity; Impact.ConclusionsThe 46-item COPD-SIB has good psychometric properties and content validity. Items are available in Dutch and English. The COPD-SIB can be used as a stand-alone instrument, or to inform computerised adaptive testing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12955-016-0500-0) contains supplementary material, which is available to authorized users.

Item response theory analysis of Centers for Disease Control and Prevention Health-Related Quality of Life (CDC HRQOL) items in adults with arthritis.

BackgroundExamine the feasibility of performing an item response theory (IRT) analysis on two of the Centers for Disease Control and Prevention health-related quality of life (CDC HRQOL) modules – the 4-item Healthy Days Core Module (HDCM) and the 5-item Healthy days Symptoms Module (HDSM). Previous principal components analyses confirm that the two scales both assess a mix of mental (CDC-MH) and physical health (CDC-PH). The purpose is to conduct item response theory (IRT) analysis on the CDC-MH and CDC-PH scales separately.Methods2182 patients with self-reported or physician-diagnosed arthritis completed a cross-sectional survey including HDCM and HDSM items. Besides global health, the other 8 items ask the number of days that some statement was true; we chose to recode the data into 8 categories based on observed clustering. The IRT assumptions were assessed using confirmatory factor analysis and the data could be modeled using an unidimensional IRT model. The graded response model was used for IRT analyses and CDC-MH and CDC-PH scales were analyzed separately in flexMIRT.ResultsThe IRT parameter estimates for the five-item CDC-PH all appeared reasonable. The three-item CDC-MH did not have reasonable parameter estimates.ConclusionsThe CDC-PH scale is amenable to IRT analysis but the existing The CDC-MH scale is not. We suggest either using the 4-item Healthy Days Core Module (HDCM) and the 5-item Healthy days Symptoms Module (HDSM) as they currently stand or the CDC-PH scale alone if the primary goal is to measure physical health related HRQOL.

A two-step item response theory procedure for a better measurement of marketing constructs

In order to broaden the set of methods for developing better multi-item marketing scales, this article proposes an item response theory (IRT) procedure that consists of two successive steps. The first exploratory step uses a non-parametric IRT model for dimensionality detection and scale purification. The second confirmatory step employs a parametric IRT model for evaluating, and likely refining, the resulting scale. This two-step IRT procedure also involves a wide range of new parametric and non-parametric IRT-based interpretive tools (that is, goodness-of-fit indices, scale-score reliability coefficients and item response/information functions) that allows scale developers and users to work in a highly informed manner. To illustrate the benefits of the proposed procedure, the article presents an extended application to the topical construct of emotional attachment.

A New Extension of the Binomial Error Model for Responses to Items of Varying Difficulty in Educational Testing and Attitude Surveys.

We put forward a new item response model which is an extension of the binomial error model first introduced by Keats and Lord. Like the binomial error model, the basic latent variable can be interpreted as a probability of responding in a certain way to an arbitrarily specified item. For a set of dichotomous items, this model gives predictions that are similar to other single parameter IRT models (such as the Rasch model) but has certain advantages in more complex cases. The first is that in specifying a flexible two-parameter Beta distribution for the latent variable, it is easy to formulate models for randomized experiments in which there is no reason to believe that either the latent variable or its distribution vary over randomly composed experimental groups. Second, the elementary response function is such that extensions to more complex cases (e.g., polychotomous responses, unfolding scales) are straightforward. Third, the probability metric of the latent trait allows tractable extensions to cover a wide variety of stochastic response processes.

Rasch Model Parameter Estimation in the Presence of a Nonnormal Latent Trait Using a Nonparametric Bayesian Approach.

Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A number of methods, including Ramsay curve item response theory, have been developed to reduce such bias, and have been shown to work well for relatively large samples and long assessments. An alternative approach to the nonnormal latent trait and IRT parameter estimation problem, nonparametric Bayesian estimation approach, has recently been introduced into the literature. Very early work with this method has shown that it could be an excellent option for use when fitting the Rasch model when assumptions cannot be made about the distribution of the model parameters. The current simulation study was designed to extend research in this area by expanding the simulation conditions under which it is examined and to compare the nonparametric Bayesian estimation approach to the Ramsay curve item response theory, marginal maximum likelihood, maximum a posteriori, and the Bayesian Markov chain Monte Carlo estimation method. Results of the current study support that the nonparametric Bayesian estimation approach may be a preferred option when fitting a Rasch model in the presence of nonnormal latent traits and item difficulties, as it proved to be most accurate in virtually all scenarios that were simulated in this study.

Confirmatory Factor Analysis of the Patient Reported Outcomes Measurement Information System (PROMIS) Adult Domain Framework Using Item Response Theory Scores.

To guide measure development, National Institutes of Health-supported Patient reported Outcomes Measurement Information System (PROMIS) investigators developed a hierarchical domain framework. The framework specifies health domains at multiple levels. The initial PROMIS domain framework specified that physical function and symptoms such as Pain and Fatigue indicate Physical Health (PH); Depression, Anxiety, and Anger indicate Mental Health (MH); and Social Role Performance and Social Satisfaction indicate Social Health (SH). We used confirmatory factor analyses to evaluate the fit of the hypothesized framework to data collected from a large sample. We used data (n=14,098) from PROMIS's wave 1 field test and estimated domain scores using the PROMIS item response theory parameters. We then used confirmatory factor analyses to test whether the domains corresponded to the PROMIS domain framework as expected. A model corresponding to the domain framework did not provide ideal fit [root mean square error of approximation (RMSEA)=0.13; comparative fit index (CFI)=0.92; Tucker Lewis Index (TLI)=0.88; standardized root mean square residual (SRMR)=0.09]. On the basis of modification indices and exploratory factor analyses, we allowed Fatigue to load on both PH and MH. This model fit the data acceptably (RMSEA=0.08; CFI=0.97; TLI=0.96; SRMR=0.03). Our findings generally support the PROMIS domain framework. Allowing Fatigue to load on both PH and MH improved fit considerably.

Development and initial testing of the Penn Parkinson's Daily Activities Questionnaire.

The aim of this work was to describe the development and psychometric analysis of the Penn Parkinson's Daily Activities Questionnaire. The questionnaire is an item response theory-based tool for rating cognitive instrumental activities of daily living in PD. Candidate items for the Penn Parkinson's Daily Activities Questionnaire were developed through literature review and focus groups of patients and knowledgeable informants. Item selection and calibration of item-response theory parameters were performed using responses from a cohort of PD patients and knowledgeable informants (n = 388). In independent cohorts of PD patients and knowledgeable informants, assessments of test-retest reliability (n = 50), and construct validity (n = 68) of the questionnaire were subsequently performed. Construct validity was assessed by correlating questionnaire scores with measures of motor function, cognition, an existing activities of daily living measure, and directly observed daily function. Fifty items were retained in the final questionnaire item bank. Items were excluded owing to redundancy, difficult reading level, and when item-response theory parameters could not be calculated. Test-retest reliability was high (intraclass correlation coefficient = 0.97; P < 0.001). The questionnaire correlated strongly with cognition (r = 0.68; P < 0.001) and directly observed daily function (r = 0.87; P < 0.001), but not with motor impairment (r = 0.08; P = 0.53). The questionnaire score accurately discriminated between PD patients with and without dementia (receiver operating characteristic curve = 0.91; 95% confidence interval: 0.85-0.97). The Penn Parkinson's Daily Activities Questionnaire shows strong evidence of reliability and validity. Item response theory-based psychometric analysis suggests that this questionnaire can discriminate across a range of daily functions.

A Comprehensive Regression-Based Approach for Identifying Sources of Person Misfit in Typical-Response Measures.

This article proposes a general parametric item response theory approach for identifying sources of misfit in response patterns that have been classified as potentially inconsistent by a global person-fit index. The approach, which is based on the weighted least squared regression of the observed responses on the model-expected responses, can be used with a variety of unidimensional and multidimensional models intended for binary, graded, and continuous responses and consists of procedures for identifying (a) general deviation trends, (b) local inconsistencies, and (c) single response inconsistencies. A free program called REG-PERFIT that implements most of the proposed techniques has been developed, described, and made available for interested researchers. Finally, the functioning and usefulness of the proposed procedures is illustrated with an empirical study based on a statistics-anxiety scale.

Measurement of latent cognitive abilities involved in concept identification learning

Introduction: We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET). Method: Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and were fitted to 35,553 Army soldiers’ PCET data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Results: Data were consistent with a hypothesis-testing model with multiple latent abilities—abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set-shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. ion was most precisely assessed for participants with abilities ranging from 1.5 standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors. Conclusions: The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set-shifting ability, but reliable assessment is limited to the impaired range of ability, reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.

Testing item response theory invariance of the standardized Quality-of-life Disease Impact Scale (QDIS(®)) in acute coronary syndrome patients: differential functioning of items and test.

The Quality-of-life (QOL) Disease Impact Scale (QDIS(®)) standardizes the content and scoring of QOL impact attributed to different diseases using item response theory (IRT). This study examined the IRT invariance of the QDIS-standardized IRT parameters in an independent sample. The differential functioning of items and test (DFIT) of a static short-form (QDIS-7) was examined across two independent sources: patients hospitalized for acute coronary syndrome (ACS) in the TRACE-CORE study (N = 1,544) and chronically ill US adults in the QDIS standardization sample. "ACS-specific" IRT item parameters were calibrated and linearly transformed to compare to "standardized" IRT item parameters. Differences in IRT model-expected item, scale and theta scores were examined. The DFIT results were also compared in a standard logistic regression differential item functioning analysis. Item parameters estimated in the ACS sample showed lower discrimination parameters than the standardized discrimination parameters, but only small differences were found for thresholds parameters. In DFIT, results on the non-compensatory differential item functioning index (range 0.005-0.074) were all below the threshold of 0.096. Item differences were further canceled out at the scale level. IRT-based theta scores for ACS patients using standardized and ACS-specific item parameters were highly correlated (r = 0.995, root-mean-square difference = 0.09). Using standardized item parameters, ACS patients scored one-half standard deviation higher (indicating greater QOL impact) compared to chronically ill adults in the standardization sample. The study showed sufficient IRT invariance to warrant the use of standardized IRT scoring of QDIS-7 for studies comparing the QOL impact attributed to acute coronary disease and other chronic conditions.

A Comparison of Magical Ideation in Nonclinical Adolescent and Adult Groups : An Item Response Theory Based Differential Item Functioning Analysis

Objectives : We examined magical ideation in adolescent and adult group by Magical Ideation Scale (MIS). We also explored how adolescents and adults respond differently to each items of MIS. Methods : 310 nonclinical adults and 310 Year 10 students participated in this study, and completed MIS and Symptom Checklist 90-revision (SCL-90-R). Total scores of MIS were compared between adults and adolescents. The item characteristics of MIS were evaluated by item response theory (IRT). Differential item functioning (DIF) was detected using the parameters of IRT. Results : Total score of MIS was higher in adolescents than in adults, but there was no statistical significance. Item 5, 10, 13, and 16 showed significant difference on item difficulty parameters and were identified as DIF. Among DIF items, item 5 was more dif ficult for adolescents than adults. Item 10, 13, and 16 were more difficult for adults than adolescents. The modified MIS score ex cluding 4 DIF items was significantly higher in adolescents than adults. Conclusion : The influence of age on response to DIF items should be considered when comparing MIS scores between adoles cents and adults. (Korean J Schizophr Res 2015;18:28-34)

Use of an Item Bank to Develop Two Short-Form FAMCARE Scales to Measure Family Satisfaction With Care in the Setting of Serious Illness

ContextFamily satisfaction is an important and commonly used research measure. Yet current measures of family satisfaction are lengthy and may be unnecessarily burdensome—particularly in the setting of serious illness. ObjectivesTo use an item bank to develop short forms of the Family Satisfaction with End-of-Life Care (FAMCARE) scale, which measures family satisfaction with care. MethodsTo shorten the existing 20-item FAMCARE measure, item response theory parameters from an item bank were used to select the most informative items. The psychometric properties of the new short-form scales were examined. The item bank was based on data from family members from an ethnically diverse sample of 1983 patients with advanced cancer. ResultsEvidence for the new short-form scales supported essential unidimensionality. Reliability estimates from several methods were relatively high, ranging from 0.84 for the five-item scale to 0.94 for the 10-item scale across different age, gender, education, ethnic, and relationship groups. ConclusionThe FAMCARE-10 and FAMCARE-5 short-form scales evidenced high reliability across sociodemographic subgroups and are potentially less burdensome and time-consuming scales for monitoring family satisfaction among seriously ill patients.