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• W2147130791 abstract "The present work describes the process of developing an item bank and short forms that measure the impact of asthma on quality of life (QoL) that avoids confounding QoL with asthma symptomatology and functional impairment. Using a diverse national sample of adults with asthma (N = 2032) we conducted exploratory and confirmatory factor analyses, and item response theory and differential item functioning analyses to develop a 65-item unidimensional item bank and separate short form assessments. A psychometric evaluation of the RAND Impact of Asthma on QoL item bank (RAND-IAQL) suggests that though the concept of asthma impact on QoL is multi-faceted, it may be measured as a single underlying construct. The performance of the bank was then evaluated with a real-data simulated computer adaptive test. From the RAND-IAQL item bank we then developed two short forms consisting of 4 and 12 items (reliability = 0.86 and 0.93, respectively). A real-data simulated computer adaptive test suggests that as few as 4–5 items from the bank are needed to obtain highly precise scores. Preliminary validity results indicate that the RAND-IAQL measures distinguish between levels of asthma control. To measure the impact of asthma on QoL, users of these items may choose from two highly reliable short forms, computer adaptive test administration, or content-specific subsets of items from the bank tailored to their specific needs. The present work describes the process of developing an item bank and short forms that measure the impact of asthma on quality of life (QoL) that avoids confounding QoL with asthma symptomatology and functional impairment. Using a diverse national sample of adults with asthma (N = 2032) we conducted exploratory and confirmatory factor analyses, and item response theory and differential item functioning analyses to develop a 65-item unidimensional item bank and separate short form assessments. A psychometric evaluation of the RAND Impact of Asthma on QoL item bank (RAND-IAQL) suggests that though the concept of asthma impact on QoL is multi-faceted, it may be measured as a single underlying construct. The performance of the bank was then evaluated with a real-data simulated computer adaptive test. From the RAND-IAQL item bank we then developed two short forms consisting of 4 and 12 items (reliability = 0.86 and 0.93, respectively). A real-data simulated computer adaptive test suggests that as few as 4–5 items from the bank are needed to obtain highly precise scores. Preliminary validity results indicate that the RAND-IAQL measures distinguish between levels of asthma control. To measure the impact of asthma on QoL, users of these items may choose from two highly reliable short forms, computer adaptive test administration, or content-specific subsets of items from the bank tailored to their specific needs. According to the U.S. National Heart, Lung, and Blood Institute's (NHLBI) Asthma Guidelines, the goal of asthma treatment is to improve the quality of life (QoL) of people who have asthma, while working toward controlling symptoms, reducing the risk of exacerbations, and preventing asthma-related death [[1]National Heart, Lung, and Blood Institute National asthma education and prevention program, third expert panel on the diagnosis and management of asthma. Expert panel report 3: guidelines for the diagnosis and management of asthma. National Heart, Lung, and Blood Institute, (US): Bethesda, MD2007Google Scholar]. Recently, leaders in the asthma field noted important limitations of existing asthma-specific QoL measures [[2]Wilson S.R. et al.Asthma outcomes: quality of life.J Allergy Clin Immunol. 2012; 129: S88-S123Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar]. Most notably, past efforts to measure asthma QoL have resulted in tools that can confound QoL with symptoms (e.g., shortness of breath, wheezing), functional impairment (e.g., limitations in daily activities), and control (i.e., the extent to which symptoms, functional impairments, and risks of negative events are minimized and goals for treatment are met). The majority of these assessments also lack the patient's perception of the impact or bother of asthma symptoms on his or her life. In light of these limitations, the Asthma Quality of Life Subcommittee of the 2010 NHLBI Asthma Outcomes Workshop declined to recommend any existing instrument as a core outcome measure of asthma-specific QoL [1National Heart, Lung, and Blood Institute National asthma education and prevention program, third expert panel on the diagnosis and management of asthma. Expert panel report 3: guidelines for the diagnosis and management of asthma. National Heart, Lung, and Blood Institute, (US): Bethesda, MD2007Google Scholar, 2Wilson S.R. et al.Asthma outcomes: quality of life.J Allergy Clin Immunol. 2012; 129: S88-S123Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar]. Instead, the Subcommittee strongly recommended development of new instruments that incorporate the patient's perspective and are able to measure the impact of asthma on QoL as a construct that is distinct from asthma symptoms or functional status. The primary objective of the present work responds to this recommendation by developing new freely available instrumentation for measuring the impact of asthma on QoL that avoids confounding QoL with asthma symptomatology and functional impairment, and includes many domains of life important to people with asthma. Our developmental process began with formative work, a detailed description of which can be found in Eberhart et al. [[3]Eberhart N.K. Sherbourne C.D. Edelen M.O. Stucky B. Lara M. Development of a measure of asthma-specific quality of life among adults.Qual Life Res. 2013; https://doi.org/10.1007/s11136-013-0510-xCrossref PubMed Scopus (15) Google Scholar]. Briefly, although the development of our item pool incorporated literature review and expert recommendations, the majority of its content was generated based on feedback from adults with asthma who participated in focus groups. Salient themes generated from focus group discussions included both general (e.g., enjoyment of life) and specific (e.g., sleep difficulty, affect, medication, physical activities, social relations, health) areas of impact. Using the focus group transcripts, we followed a well-defined item development and refinement process, to arrive at a set of items in standard format representing a wide range of content regarding the impact of asthma on QoL. This paper describes the development and psychometric properties of an item bank to measure the impact of asthma on QoL in adults. Using data from a large national field test of adults with asthma, we evaluated the pool of candidate items using modern psychometric methods, including item response theory (IRT) and computerized adaptive tests. Our analytic plan adheres to many guidelines used by the patient reported outcomes measurement information system (PROMIS) collaborative [[4]Reeve B.B. et al.Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS).Med Care. 2007; 45: S22-S31Crossref PubMed Scopus (1090) Google Scholar]. Following these guidelines the graded response model (GRM [[5]Samejima F. Estimation of latent ability using a response pattern of graded scores.Psychometrika Monogr Suppl. 1969; 34: 100Google Scholar]), is used to “calibrate” (or characterize) the strength of the relationship between items and the construct being measured (here the impact of asthma on QoL) and the location on the construct's scale where the item is most informative. The collection of calibrated items is referred to as an “item bank.” Item banks – large sets of items that each measures the same underlying construct – have many advantages over traditional scales. Because not all the items in the bank need to be administered in order to produce a reliable score, item banks provide a very flexible assessment environment. For example, one of the unique features of item banks is that items can be administered adaptively (i.e., with computer adaptive testing), often resulting in reduced overall test lengths. However, for situations in which it is impractical to administer a computer adaptive test, reliable subsets of items can be drawn from the bank to produce traditional, brief fixed-length instruments (i.e., short forms) that can be administered via computer or paper and pencil. Items may be selected for short forms to achieve various measurement goals. For example, if the goal is to assess the impact of asthma on QoL among a non-clinical sample of people with a wide range of potential asthma impact, one would select items that optimize measurement precision across the entire impact continuum. Alternatively, a study involving treatment-seeking patients with severe asthma may benefit most from items that provide precision at the higher end of the asthma impact continuum, whereas an intervention study aimed at improving the social QoL for people with asthma might select a short form that over-represents content specific to that goal (e.g., items that assess the impact of asthma on social activities). The present work develops an item bank and separate short forms that measure the impact of asthma on QoL. Our short forms were selected to be brief and representative with respect to breadth of content while maintaining acceptable measurement precision across the entire impact continuum. Other short forms that focus on a particular QoL component (e.g., impact of asthma on social concerns) can be generated from the item bank if desired. A national sample of adults (ages 18+) with asthma (N = 2032) was recruited by Harris Interactive, a global interactive media and services company, and all survey measures were completed via internet assessment. All study procedures were approved by the institution's IRB. Participants were eligible for the study if (1) they had been told by a doctor or other health professional that they had asthma, and (2) they reported still having asthma. To assure that we would have variability across a range of asthma severity, we also required that 90% of the sample had experienced an episode of asthma or an asthma attack during the prior 12 months [[6]CDC America breathing easier 2010: CDC’s national asthma control program at a glance. National Center for Environmental Health (NCEH): Division of Environmental Hazards and Health Effects, 2010: 4Google Scholar]. We sampled Hispanic, Black, Asian and non-Hispanic Whites, oversampling minorities to have at least 200 participants in each group. Similarly, we targeted at least 200 participants within each of four age groups (18–34, 35–49, 50–64 and 65+), and achieved a distribution of about 40% men to reflect the distribution of individuals with asthma in the general U.S. adult population. Because of concerns about confounding Chronic Obstructive Pulmonary Disease (COPD) with asthma, we limited the proportion of the sample that had comorbid COPD. As incentives, participants received points through Harris that can be redeemed for rewards such as an Amazon gift card. Table 1 describes the demographic characteristics and health service utilization patterns of participants. In the past year, 39% (N = 785) of the sample had visited an emergency room or urgent care facility, and 19% had spent at least one night in a hospital because of asthma. A subset of the sample reported having chronic medical conditions; 10% (N = 194) of the sample had chronic heart disease and 14% (N = 287) had COPD.Table 1Characteristics of the exploratory and confirmatory samples.Exploratory group (N = 1500)Confirmatory group (N = 532)Female %6061Age mean (SD; range)43.3 (14.9; 18–86)43.0 (14.3; 18–77)Race/Ethnicity, % Non-Hispanic White8180 African American1923 Hispanic1014 Asian129 Other31Education % <High school graduate33 High school graduate1514 <BA/BS degree3638 BA/BS degree2522 Graduate degree2223Employment % Full-time5053 Part-time108 Not employed1718 Retired/student/homemaker2321Income % <$25,0001919 $25,000–$49,9992622 $50,000–$99,9993134 >$100,0002425 Open table in a new tab Our pool of candidate items consisted of 112 items measuring various aspects of the impact of asthma on QoL. These items were developed using focus groups, literature review, expert input, and cognitive interviews (see Eberhart et al. [[3]Eberhart N.K. Sherbourne C.D. Edelen M.O. Stucky B. Lara M. Development of a measure of asthma-specific quality of life among adults.Qual Life Res. 2013; https://doi.org/10.1007/s11136-013-0510-xCrossref PubMed Scopus (15) Google Scholar], for a detailed description of the item development process). In cases where items were selected from the literature review, permission to use such items was sought and items were incorporated into the preliminary item bank only if permission was granted [7Adams R. et al.Assessment of an asthma quality of life scale using item-response theory.Respirology. 2005; 10: 587-593Crossref PubMed Scopus (9) Google Scholar, 8Barley E.A. Quirk F.H. Jones P.W. Asthma health status measurement in clinical practice: validity of a new short and simple instrument.Respir Med. 1998; 92: 1207-1214Abstract Full Text PDF PubMed Scopus (114) Google Scholar, 9Benninger M.S. Senior B.A. The development of the Rhinosinusitis Disability Index.Arch Otolaryngol Head Neck Surg. 1997; 123: 1175-1179Crossref PubMed Scopus (248) Google Scholar, 10Hyland M.E. et al.Measurement of psychological distress in asthma and asthma management programmes.Br J Clin Psychol. 1995; 34: 601-611Crossref PubMed Scopus (53) Google Scholar, 11Marks G.B. Dunn S.M. Woolcock A.J. A scale for the measurement of quality of life in adults with asthma.J Clin Epidemiol. 1992; 45: 461-472Abstract Full Text PDF PubMed Scopus (348) Google Scholar, 12Sibbald B. Collier J. D'Souza M. Questionnaire assessment of patients' attitudes and beliefs about asthma.Fam Pract. 1986; 3: 37-41Crossref PubMed Scopus (31) Google Scholar, 13Tu S.P. et al.A new self-administered questionnaire to monitor health-related quality of life in patients with COPD. Ambulatory Care Quality Improvement Project (ACQUIP) investigators.Chest. 1997; 112: 614-622Crossref PubMed Scopus (91) Google Scholar, 14Yeatts K.B. et al.Construction of the pediatric asthma impact scale (PAIS) for the patient-reported outcomes measurement information system (PROMIS).J Asthma. 2010; 47: 295-302Crossref PubMed Scopus (79) Google Scholar, 15Hyland M.E. The living with asthma questionnaire.Respir Med. 1991; 85 ([discussion 33-7]): 13-16Abstract Full Text PDF PubMed Scopus (104) Google Scholar]. Items were standardized to have a consistent timeframe (past 4-weeks), orientation (first-person), and response format (5-point Likert-type) reflecting magnitude (i.e., “not at all” to “very much”) or frequency of impact (i.e., “never” to “almost always”). The order of item administration was randomized to avoid serial effects [[16]Steinberg L. Context and serial-order effects in personality measurement - limits on the generality of measuring changes the measure.J Pers Soc Psychol. 1994; 66: 341-349Crossref Scopus (63) Google Scholar]. The Asthma Control Test (ACT) [[17]Nathan R.A. et al.Development of the asthma control test: a survey for assessing asthma control.J Allergy Clin Immunol. 2004; 113: 59-65Abstract Full Text Full Text PDF PubMed Scopus (2034) Google Scholar] is a five-item measure that includes content on asthma symptoms, use of rescue medication, impact on functioning, and a self-rating of asthma control. Each item uses a 5-point Likert response scale. The total score ranges from 5 (poor control) to 25 (good control) and the validated score categories are 5–15 (poorly controlled), 16–19 (somewhat controlled), and 20–25 (well controlled) [[18]Schatz M. et al.Validation of the asthma impact survey, a brief asthma-specific quality of life tool.Qual Life Res. 2007; 16: 345-355Crossref PubMed Scopus (17) Google Scholar]. We collected information on demographics, asthma symptoms, co-morbid health conditions (e.g., COPD, sinusitis, etc), and asthma-related health care use (e.g., emergency department, hospitalization). We began by randomly splitting our total sample into exploratory (N = 1500) and confirmatory (N = 532) subsamples. Analyses initially used only the exploratory subsample; the confirmatory subsample was set aside to be used as an independent check on the validity of the dimensionality findings. The goal of the factor analyses was to identify unidimensional sets of items that could form the basis for the item bank(s). All factor analyses were conducted using the computer program Mplus [[19]Muthén L.K. Muthén B.O. Mplus user's guide: statistical analysis with latent variables.6th ed. Muthén & Muthén, Los Angeles2010Google Scholar] and the mean and variance adjusted weighted least squares algorithm (WLSMV) that is appropriate for categorical response items. Model fit was evaluated with commonly used model fit indices (RMSEA ≤ 0.08, TLI ≥ 0.95, CFI ≥ 0.95) [9Benninger M.S. Senior B.A. The development of the Rhinosinusitis Disability Index.Arch Otolaryngol Head Neck Surg. 1997; 123: 1175-1179Crossref PubMed Scopus (248) Google Scholar, 20Hu L.T. Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives.Struct Equ Model A Multidiscip J. 1999; 6: 1-55Crossref Scopus (61696) Google Scholar]. Given that preliminary qualitative item development work generated a number of distinct content categories (see Eberhart et al. [[3]Eberhart N.K. Sherbourne C.D. Edelen M.O. Stucky B. Lara M. Development of a measure of asthma-specific quality of life among adults.Qual Life Res. 2013; https://doi.org/10.1007/s11136-013-0510-xCrossref PubMed Scopus (15) Google Scholar],) we anticipated that multiple dimensions might be needed to explain item responses. However, through the course of considering several multiple EFA solutions the model fit evidence overwhelmingly supported a single-factor solution (these analyses are described in more detail in the results section). Thus analyses proceeded assuming a single latent dimension for the item pool. Following EFA, we used modification indices from 1-factor CFA models to identify clusters or pairs of items with excess dependence. The presence of local dependence violates the IRT assumption of unidimensionality and could result in misleading score estimates; therefore it is necessary to identify and minimize local dependence in the item bank. Thus, for a given pair of locally dependent items or group of items, we considered the items' factor loadings and wording to determine which item(s) to remove. This model fitting process was repeated iteratively until no additional local dependence was identified, at which point a final 1-factor CFA model was fit to the confirmatory sample to cross-validate dimensionality findings. Once a provisional set of unidimensional items was identified, the complete sample (N = 2032) was used to test for differential item functioning (DIF) using item response theory (IRT) within the computer program IRTPRO [[21]Cai L. du Toit S.H.C. Thissen D. IRTPRO Version 2: flexible, multidimensional, multiple categorical IRT modeling. Scientific Software International, Chicago, IL2011Google Scholar]. Item-level DIF indicates that responses to an item for members of subgroups vary in a way that is not predicted by the IRT model after accounting for group-level mean and variances differences. Thus, the IRT model does not hold and the item should be considered for removal. DIF was tested according to age groups, education, race/ethnicity, and gender. Analysis of DIF used three steps. First, two-group chi-square tests were evaluated across subgroups and the combined significance tests for all comparisons per grouping variable were adjusted using the Benjamini-Hochberg procedure [[22]Benjamini Y. Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing.J R Stat Soc Series B Methodol. 1995; 57: 289-300Google Scholar] at p < 0.05. Next, to evaluate the magnitude of DIF, items demonstrating significant DIF after p-value correction were further probed by computing the weighted area between the expected score curves based on an approach by Raju (1988) [[23]Raju N.S. The area between 2 item characteristic curves.Psychometrika. 1988; 53: 495-502Crossref Scopus (249) Google Scholar]. Our experience indicates that using corrected significance tests when coupled with effect size indicators is useful in revealing the items most likely to result in bias across subgroups. Finally, for items that met both criteria we examined plots of expected item scores generated from the parameters of the DIF model that best fit the data prior to selecting items for removal. To characterize the final set of items as an item bank we calibrated the data using the graded IRT model (GRM) [[5]Samejima F. Estimation of latent ability using a response pattern of graded scores.Psychometrika Monogr Suppl. 1969; 34: 100Google Scholar]. For each item, the GRM characterizes the relationship of the item responses with the underlying latent construct with a unique slope parameter (a), and indicates each response option's location along the continuum (of asthma impact) with four threshold parameters (bk) (for five response option items). To ensure that there was adequate power to estimate the threshold parameters, prior to conducting the IRT analysis we evaluated the response coverage across all response options. IRT-based assessments of latent constructs (here the negative impact of asthma on QoL) allow the items' slope or discrimination parameters to vary as a function of the strength of the relationship between the item and the construct, as opposed to Rasch-based IRT techniques that select items having an equivalent relation to the construct so that their slope parameters may be fixed to equality (1). In addition, the IRT model is scaled by assuming a normal underlying distribution (N(0,1)), though graphical illustrations of score precision and tables of score estimates reported here follow standard PROMIS conventions and translate the Z-score metric to a T-score scale with a mean of 50 and a standard deviation of 10. The performance of the item bank was evaluated using “real-data” computer adaptive test simulations from the full sample of participants. A real-data computer adaptive test assesses the performance of the item bank as if the respondents in the calibration sample had received the items adaptively and stopped when a predetermined level of precision was reached (i.e., a computer adaptive test administration) rather than answering every item in the bank. Computer adaptive test simulations were conducted to (1) evaluate the overall performance of the item bank; (2) provide an expectation of the number of items administered under typical computer adaptive test conditions; (3) indicate the items most routinely administered in computer adaptive test scenarios; and (4) provide a comparison to short form scores. In this simulation, the computer adaptive test was programmed to stop administering items after achieving a score standard error of 0.316 (which corresponds to a reliability of 0.90) or completing administration of 12 items, whichever came first. Two short forms were created to provide highly reliable fixed-length assessments of the impact of asthma on QoL. The goal for the first short form was to generate a reliable assessment across the range of asthma impact scores using the fewest, most widely relevant items. Thus items were selected that were reflective of the impact of asthma on ‘global’ aspects of QoL and provided information (i.e., according to the IRT model) across the range of impact of asthma on QoL. In order to provide a more content-diverse short form, the second (longer) short form added items from several content domains that Eberhart et al. [[3]Eberhart N.K. Sherbourne C.D. Edelen M.O. Stucky B. Lara M. Development of a measure of asthma-specific quality of life among adults.Qual Life Res. 2013; https://doi.org/10.1007/s11136-013-0510-xCrossref PubMed Scopus (15) Google Scholar] identified based on a series of focus groups as being of particular interest to adults with asthma (e.g., physical limitations, social concerns (see Eberhart et al. [[3]Eberhart N.K. Sherbourne C.D. Edelen M.O. Stucky B. Lara M. Development of a measure of asthma-specific quality of life among adults.Qual Life Res. 2013; https://doi.org/10.1007/s11136-013-0510-xCrossref PubMed Scopus (15) Google Scholar])). Following standard practice (for examples, see Irwin et al. [[24]Irwin D.E. et al.An item response analysis of the pediatric PROMIS anxiety and depressive symptoms scales.Qual Life Res. 2010; 19: 595-607Crossref PubMed Scopus (291) Google Scholar]; DeWitt et al. [[25]DeWitt E.M. et al.Construction of the eight-item patient-reported outcomes measurement information system pediatric physical function scales: built using item response theory.J Clin Epidemiol. 2011; 64: 794-804Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar]), we used the sum of the item responses to generate IRT-based scores for each short form [[26]Thissen D. Wainer H. Test scoring. L. Erlbaum Associates, Mahwah, N.J2001: 1Google Scholar] and rescaled them to a T-score metric with a mean of 50 and a standard deviation of 10. For example, a score of 40 is one standard deviation below the mean and suggests less impact of asthma on QoL. We compared the precision of the computer adaptive test and short form scores to scores based on the full bank (which in this case represents the gold standard) using root mean square errors (RMSE). The RMSE is the square root of the average error variance (the squared standard error of measurement of the score) across respondents, and indicates the average precision of the IRT score estimates. Finally, to provide an initial indication of validity, scores for the short forms and real-data computer adaptive test were compared against asthma control categories derived from the ACT. Initial EFA solutions using the 112-item pool and the exploratory sample (N = 1500) focused on capturing the relationships among the item responses using high and low-dimensional models. Through the course of considering multiple factor solutions the model fit evidence overwhelmingly supported a single-factor solution (χ2 = 37,898, df = 6104; CFI = 0.945; TLI = 0.944; RMSEA = 0.059), which accounted for 71% of the total variance explained. Consistent with the goal of producing a unidimensional set of items, we next used the exploratory sample to identify and remove items with local dependence. Using results from a 1-factor CFA model involving all 112 items, we elected to remove 26 items because of local dependence. For example, the item pair: “I had to make compromises because of the cost of treating my asthma” and “The cost of treating my asthma was a burden to me” displayed strong local dependence because of the overlapping content. For this particular item pair, we elected to remove the latter item because it was also locally dependent with several other items. A 1-factor CFA model using the remaining 86 items revealed the presence of additional, though weaker, local dependence from which 14 more items were removed. A final 1-factor CFA of the remaining 72 items did not reveal any additional instances of problematic local dependence, and fit was acceptable in both the exploratory (χ2 = 15,683, df = 2484, CFI = 0.965, TLI = 0.964, RMSEA = 0.060) and confirmatory (χ2 = 6282, df = 2484; CFI = 0.971, TLI = 0.970, RMSEA = 0.054) samples. DIF was evaluated among the remaining 72 items using the combined sample (N = 2032) by gender, age (18–34, 35–54, and 55–64 years), race/ethnicity (African American, Asian, Hispanic, and non-Hispanic White), and educational status (up to completion of high school versus attending some high school and greater). DIF comparisons of educational status did not indicate the need for any item removal. DIF tests according to gender resulted in multiple items with statistically significant DIF. However, closer examination indicated only minor gender DIF impact (wABC's < 0.20) that was not substantial enough to warrant item removal. Age and race/ethnicity DIF comparisons led to a total of seven items being removed. The item “I worried about becoming addicted to my asthma medication” had significant race/ethnicity DIF between White and Asian subgroups (wABC = 0.31), and between Black and Asian subgroups (wABC = 0.34), and was removed. Six additional items were removed because of age DIF. Fig. 1 contains two example items that were removed because of age DIF (“I was bothered by the unwanted attention I got because of my asthma” and “It was hard for me to admit that I have asthma”). These items illustrate age bias such that at a given level of impact on the T-score metric (x-axis), higher item responses (y-axis) are more likely from younger individuals. This means that, given mean and variance group differences, younger individuals are more likely to report being bothered by unwanted attention and not wanting to admit having asthma. For these six DIF items it was typically the younger (18–34) to older (55–64) age group comparison that resulted in significant and problematic DIF with wABC effect sizes ranging from 0.31 to 0.56. Our analytic process resulted in a 65-item unidimensional bank of items measuring the Impact of Asthma on QoL (hereafter referred to as the RAND-IAQL). Final IRT item parameters are presented in Table A1; items are sorted by magnitude of the slope parameter. Fig. 2 displays the item bank's reliability" @default.
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• W2147130791 title "Developing an item bank and short forms that assess the impact of asthma on quality of life" @default.
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