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Tien Yin Wong - One of the best experts on this subject based on the ideXlab platform.
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an evaluation of the reliability and validity of the visual functioning questionnaire vf 11 using rasch analysis in an asian population
Investigative Ophthalmology & Visual Science, 2009Co-Authors: Ecosse L Lamoureux, Konrad Pesudovs, Julian Thumboo, Seangmei Saw, Tien Yin WongAbstract:family support, and nursing home placement. 2‐12 A major limitation common to several studies reporting on visual functioning or quality of life (QoL) has been the use of a mean or summary score. Summary scoring, termed Likert scoring, allocates an ordinal assignment of a numerical value to a participant’s response and assumes a score based on an Interval Scale. The validity of such summary scores has been questioned by Rasch analysis—usually referred as the Rasch measurement model. 1,13‐17 The model assumes that the probability that a respondent will select an item is a logistic function of the relative distance between the item location and the respondent location on a linear Scale. In other words, the probability that a person will affirm a category within an item is a logistic function of the difference between the person’s level of, for example, visual functioning and the level of visual functioning necessary to perform this item, and only a function of that difference. Thus, Rasch analysis is taken as a criterion for the structure of the responses that should be satisfied rather than a simple statistical description of the responses. Both person ability and item difficulty must refer to one trait being measured (i.e., visual functioning) which supports the concept of unidimensionality, which is central to the Rasch model. Once the data fit the Rasch model, estimates of measures on an
Gerold Stucki - One of the best experts on this subject based on the ideXlab platform.
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the extended barthel index ebi can be reported as a unidimensional Interval Scaled metric a psychometric study
Physikalische Medizin Rehabilitationsmedizin Kurortmedizin, 2019Co-Authors: Roxanne Maritz, Alan Tennant, Gerold Stucki, Carolina S Fellinghauer, Birgit ProdingerAbstract:Background The Extended Barthel Index (EBI), consisting of the original Barthel Index plus 6 cognitive items, provides a tool to monitor patients’ outcomes in rehabilitation. Whether the EBI provides a unidimensional metric, thus can be reported as a valid sum-score, remains to be examined. Objective To examine whether the EBI can be reported as unidimensional Interval-Scaled metric for neurological and musculoskeletal rehabilitation. Methods Rasch analysis of a calibration sample of 800 cases from neurological or musculoskeletal rehabilitation in 2016 in Switzerland. Results In the baseline analysis no fit to the Rasch Model was achieved. When accommodating local dependencies with a testlet approach satisfactory fit to the Rasch Model was achieved, and an Interval Scale transformation table was created. Conclusion The results support the reporting of adapted EBI total scores for both rehabilitation groups by applying the Interval Scaled transformation table presented in this study.
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items from patient oriented instruments can be integrated into Interval Scales to operationalize categories of the international classification of functioning disability and health
Journal of Clinical Epidemiology, 2009Co-Authors: Alarcos Cieza, Roger Hilfiker, Annelies Boonen, Somnath Chatterji, Nenad Kostanjsek, Bedirhan Ustun, Gerold StuckiAbstract:Objective: To exemplify the construction of Interval Scales for specified categories of the International Classification of Functioning, Disability and Health (ICF) by integrating items from a variety of patient-oriented instruments. Study Design and Setting: Psychometric study using data from a convenience sample of 122 patients with rheumatoid arthritis. Patients completed six different patient-oriented instruments. The contents of the instrument items were linked to the ICF. Rasch analyses for ordered-response options were used to examine whether the instrument items addressing the ICF category b130: Energy and drive functions constitute a psychometrically sound Interval Scale. Results: Nineteen items were linked to b130: Energy and drive functions. Sixteen of the 19 items fit the Rasch model according to the chi-square (c 2 ) statistic (c 2532 538.25, P 5 0.21) and the Z-fit statistic (ZMean 50.451, ZSD 51.085 and ZMean 5 � 0.223, ZSD 5 1.132 for items and persons, respectively). The Person Separation Index rb was 0.93. Conclusion: The ICF category Interval Scales to operationalize single ICF categories can be constructed. The original format of the items included in the Interval Scales remains unchanged. This study represents a step forward in the operationalization and future imple
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interpretation of change scores in ordinal clinical Scales and health status measures the whole may not equal the sum of the parts
Journal of Clinical Epidemiology, 1996Co-Authors: Gerold Stucki, Lawren H Daltroy, Jeffrey N Katz, Magnus Johannesson, Matthew H LiangAbstract:The objective of this study was to analyze the problem of interpreting change scores of ordinal health status measures for clinical research or practice. Methods used included exploration of the generation of change scores in the physical ability Scale of the SF-36, one of the most widely used generic health status instruments. Resulting data are presented as the ranking of items according to baseline score; a percentage of patients with severe difficulty and Rasch analysis provided the same rank order of item difficulty. On the Interval Scale provided by the Rasch model a concentration of items reflecting moderate difficulty occurred. This "inflates" numerical gains for patients with moderate disability compared to patients with very severe or minor physical disability. Calibration of change scores using patient perception of the level of change in function showed important variation of numerical gains with baseline. We conclude that numerically equal gains may differ in their meaning depending on baseline health status. It is recommended that distribution of baseline health status measures and distribution of responders by baseline status be reported in evaluative studies.
Chanseok Jeong - One of the best experts on this subject based on the ideXlab platform.
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a high quality supplier selection model for supply chain management and iso 9001 system
Production Planning & Control, 2003Co-Authors: Museong Lee, Younghae Lee, Chanseok JeongAbstract:Supplier selection process for supply chain management (SCM) and ISO 9001 quality management system environments is considered. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. This paper proposes a high-quality-supplier selection (HQSS) model to deal with supplier selection problems in supply chain management. In selecting a supplier, quality management factors are considered first, and then price, delivery, etc. Quality management factors include a quality management audit, product testing, engineering work force, capability index, training time, etc., based on a five-Interval Scale. Next, the HQSS model determines the final solution by considering factors such as price, production lead-time, and delivery time.
Naoichi Chino - One of the best experts on this subject based on the ideXlab platform.
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adl structure for stroke patients in japan based on the functional independence measure
American Journal of Physical Medicine & Rehabilitation, 1995Co-Authors: Tetsuya Tsuji, Shigeru Sonoda, Kazuhisa Domen, Eiichi Saitoh, Naoichi ChinoAbstract:The difficulty patterns of FIM (Functional Independence Measure) in Japan were determined and compared with patterns found in the United States to assess whether FIM can be used for worldwide comparisons of ADL (the activities of daily living). The FIM was measured for 190 stroke patients in several hospitals throughout Japan. The scores at admission and discharge were converted to an Interval Scale by Rasch analysis. Right and left brain lesion patients were analyzed separately. The FIM items were divided into two groups : motor items and cognitive items to minimize misfit. A degree of misfit was acceptable, except for bowel and bladder management, stairs, bathing, and expression. Motor items, eating, and bowel and bladder management were the easiest ; stairs, bathing, and tub/shower transfers were the most difficult. The difficulty patterns of patients with left and right hemisphere lesions were almost identical. Bathing and tub/shower transfer were more difficult for Japanese patients than for those studied in the United States. Concerning the cognitive items, expression was easiest for patients with right hemisphere lesions but most difficult for those with left hemisphere lesions. Social interaction was easier for Japanese patients with left hemisphere lesions than the other patients. The item difficulty patterns in Japan differs slightly from those in the United States because of cultural differences. As countries show different patterns of difficulty, we must be careful when making international comparisons of FIM data converted by Rasch analysis.
Museong Lee - One of the best experts on this subject based on the ideXlab platform.
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a high quality supplier selection model for supply chain management and iso 9001 system
Production Planning & Control, 2003Co-Authors: Museong Lee, Younghae Lee, Chanseok JeongAbstract:Supplier selection process for supply chain management (SCM) and ISO 9001 quality management system environments is considered. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. This paper proposes a high-quality-supplier selection (HQSS) model to deal with supplier selection problems in supply chain management. In selecting a supplier, quality management factors are considered first, and then price, delivery, etc. Quality management factors include a quality management audit, product testing, engineering work force, capability index, training time, etc., based on a five-Interval Scale. Next, the HQSS model determines the final solution by considering factors such as price, production lead-time, and delivery time.
