Item response analysis to explore psychometric properties of the Persian version of Troutman successful aging inventory: Rasch partial credit model

2020 ◽  
pp. 135910532093117
Author(s):  
Mozhgan Seif ◽  
Abdolrahim Asadollahi ◽  
Mahsa Yarelahi ◽  
Elham Rezaian
Keyword(s):  
Older Adults ◽  
Item Response ◽  
Successful Aging ◽  
Rasch Analysis ◽  
Response Analysis ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Men And Women ◽  
Persian Version ◽  
Successive Response

The study aimed to evaluate Persian version of the TSAI-2011 to determine successful aging issues in older adults. In a psychometric study, the instrument was completed by 400 men and women aged 60 and above and the Rasch partial credit model was used. The PCM indicated that items 1 and 20 were misfitting. Also, it successive response categories for all items were located in the expected order and version of TSAI with 22-items had more internal consistency. Although Rasch analysis indicated to relevant of TSAI 22-Items, it should be evaluated in further studies and divergent cultures.

scholarly journals Scale Alignment in the Between-Item Multidimensional Partial Credit Model

2021 ◽  
pp. 014662162110131
Author(s):  
Leah Feuerstahler ◽  
Mark Wilson
Keyword(s):  
Item Response ◽  
Kindergarten Readiness ◽  
Latent Trait ◽  
Individual Development ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Models ◽  
Item Response Models ◽  
Item Parameters ◽  
Polytomous Item Response

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.

Using Item Response Theory Models to Evaluate the Practice Environment Scale

2014 ◽  
Vol 22 (2) ◽  
pp. 323-341 ◽  
Author(s):  
Dheeraj Raju ◽  
Xiaogang Su ◽  
Patricia A. Patrician
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Information Criterion ◽  
Partial Credit Model ◽  
Practice Environment ◽  
Partial Credit ◽  
Response Theory ◽  
Environment Scale ◽  
Graded Response ◽  
Item Response Theory Models

Background and Purpose: The purpose of this article is to introduce different types of item response theory models and to demonstrate their usefulness by evaluating the Practice Environment Scale. Methods: Item response theory models such as constrained and unconstrained graded response model, partial credit model, Rasch model, and one-parameter logistic model are demonstrated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices are used as model selection criterion. Results: The unconstrained graded response and partial credit models indicated the best fit for the data. Almost all items in the instrument performed well. Conclusions: Although most of the items strongly measure the construct, there are a few items that could be eliminated without substantially altering the instrument. The analysis revealed that the instrument may function differently when administered to different unit types.

scholarly journals Item Response Theory Models for the Fuzzy TOPSIS in the Analysis of Survey Data

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Bartłomiej Jefmański ◽  
Adam Sagan
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Partial Credit Model ◽  
Response Model ◽  
Partial Credit ◽  
Triangular Fuzzy Numbers ◽  
Response Theory ◽  
Irt Models

The fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is an attractive tool for measuring complex phenomena based on uncertain data. The original version of the method assumes that the object assessments in terms of the adopted criteria are expressed as triangular fuzzy numbers. One of the crucial stages of the fuzzy TOPSIS is selecting the fuzzy conversion scale, which is used to evaluate objects in terms of the adopted criteria. The choice of a fuzzy conversion scale may influence the results of the fuzzy TOPSIS. There is no uniform approach in constructing and selecting the fuzzy conversion scale for the fuzzy TOPSIS. The choice is subjective and made by researchers. Therefore, the aim of the article is to present a new, objective approach to the construction of fuzzy conversion scales based on Item Response Theory (IRT) models. The following models were used in the construction of fuzzy conversion scales: Polychoric Correlation Model (PM), Polytomous Rasch Model (PRM), Rating Scale Model (RSM), Partial Credit Model (PCM), Generalized Partial Credit Model (GPCM), Graded Response Model (GRM), Nominal Response Model (NRM). The usefulness of the proposed approach is presented on the example of the analysis of a survey’s results on measuring the quality of professional life of inhabitants of selected communes in Poland. The obtained results indicate that the choice of the fuzzy conversion scale has a large impact on the closeness coefficient values. A large difference was also observed in the spreads of triangular fuzzy numbers between scales based on IRT models and those used in the literature on the subject. The use of the fuzzy TOPSIS with fuzzy conversion scales built based on PRM, RSM, PCM, GPCM, and GRM models gives results with a greater range of variability than in the case of fuzzy conversion scales used in empirical research.

scholarly journals The Performance of the Semigeneralized Partial Credit Model for Handling Item-Level Missingness

2020 ◽  
Vol 80 (6) ◽  
pp. 1196-1215
Author(s):  
Sherry Zhou ◽  
Anne Corinne Huggins-Manley
Keyword(s):  
Item Response ◽  
Likelihood Estimation ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Generalized Partial Credit Model ◽  
Full Information Maximum Likelihood ◽  
Response Data ◽  
Item Level ◽  
Generalized Partial Credit ◽  
Item Data

The semi-generalized partial credit model (Semi-GPCM) has been proposed as a unidimensional modeling method for handling not applicable scale responses and neutral scale responses, and it has been suggested that the model may be of use in handling missing data in scale items. The purpose of this study is to evaluate the ability of the unidimensional Semi-GPCM to aid in the recovery of person parameters from item response data in the presence of item-level missingness, and to compare the performance of the model with two other proposed methods for handling such missingness: a multidimensional modeling approach for missingness and full information maximum likelihood estimation. The results indicate that the Semi-GPCM performs acceptably in an absolute sense when less than 30% of the item data is missing but does not outperform the other two methods under any particular conditions. We conclude with a discussion about when practitioners may or may not want to use the Semi-GPCM to recover person parameters from item response data with missingness.

scholarly journals Item-Focused Trees for the Detection of Differential Item Functioning in Partial Credit Models

2017 ◽  
Vol 78 (5) ◽  
pp. 781-804 ◽  
Author(s):  
Stella Bollmann ◽  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Differential Item Functioning ◽  
Present Article ◽  
Item Response ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Models ◽  
Item Response Models ◽  
Item Functioning ◽  
Alternative Approaches ◽  
Ordered Response

Various methods to detect differential item functioning (DIF) in item response models are available. However, most of these methods assume that the responses are binary, and so for ordered response categories available methods are scarce. In the present article, DIF in the widely used partial credit model is investigated. An item-focused tree is proposed that allows the detection of DIF items, which might affect the performance of the partial credit model. The method uses tree methodology, yielding a tree for each item that is detected as DIF item. The visualization as trees makes the results easily accessible, as the obtained trees show which variables induce DIF and in which way. In the present paper, the new method is compared with alternative approaches and simulations demonstrate the performance of the method.

scholarly journals THE ESTIMATION OF TEACHING PROFESSION EDUCATION (TPE) PARTICIPANTS’ ABILITY IN INDONESIA: AN ASSESSMENT MODEL WITH PARTIAL SCORING

2019 ◽  
Vol 77 (6) ◽  
pp. 790-805
Author(s):  
Lian Gafar Otaya ◽  
Badrun Kartowagiran ◽  
Heri Retnawati
Keyword(s):  
Item Response ◽  
Lesson Plan ◽  
Sampling Technique ◽  
Teaching Profession ◽  
Lesson Plans ◽  
Assessment Model ◽  
Partial Credit Model ◽  
Item Response Model ◽  
Partial Credit ◽  
Information Function

The ability of TPE participants in arranging lesson plans is essential to be mastered as an indicator of pedagogical competence which should be possessed before conducting a teaching process in the class. This research aimed to analyze the estimation of TPE participants’ ability in composing a lesson plan which uses the assessment with partial scoring. This research used quantitative descriptive explorative approach. Data were collected through document study of lesson plan made by TPE participants assessed by lecturers in a workshop. In the sample were 236 respondents selected by using purposive sampling technique. The data analysis was performed to estimate participants’ skill using Item Response Theory through Partial Credit Model (PCM). The estimation result produced the interpretation of items with 5 categories of scoring 0, 1, 2, 3, 4 and has 4 thresholds such as the threshold of category 0 to category 1, category 1 to category 2, category 2 to category 3, and from category 3 to category 4. Besides that the information function value of the instrument and Standard Error of Measurement based on the analysis obtained items in the instrument which have higher information function value compared to the mistakes of the measurement or the estimation of measurement which has relatively small errors, so it can be stated that learning model is using accurate partial scoring to estimate the ability of TPE participants in composing lesson plans. Keywords: teaching profession, partial scoring, item response model, partial credit model.

scholarly journals Cross-Cultural Equivalence of De Jong Gierveld Loneliness Scale Among Older Native and Diasporic Chinese Adults

2020 ◽  
Author(s):  
Sie-Long Cheung ◽  
Hans J S M Hobbelen ◽  
Cees P van der Schans ◽  
Wim P Krijnen
Keyword(s):  
Older Adults ◽  
The Netherlands ◽  
Item Response ◽  
Goodness Of Fit ◽  
Local Community ◽  
Cross Cultural ◽  
Dimensional Structure ◽  
Response Analysis ◽  
Loneliness Scale ◽  
Multiple Group

Abstract Background and Objectives Loneliness is prevalent among older adults and known to be detrimental to mental health. The objective of this study was to determine the psychometric properties of the Chinese 6-item De Jong Gierveld Loneliness Scale (DJGLS) in the older native and diasporic Chinese community. Research Design and Methods Participants were recruited from a local community in urban Tianjin, China and urban Chinese communities of older adults in the Netherlands. Scale properties, including reliability, were calculated with Cronbach’s alpha and multiple-group confirmatory factor analysis to examine the 2-dimensional structure of the scale and the cross-cultural equivalence between both countries. Item response analysis was employed to plot the relationships between the item response and expected total scale score. Results A total of 193 older adults from China and 135 older adults from the Netherlands were included. The Cronbach’s alphas were 0.68 (China) and 0.71 (the Netherlands). The DJGLS’s 2-dimensional structure was validated by the goodness of fit and the factor loadings. Cross-cultural equivalence was demonstrated with the multiple-group confirmatory analysis. In addition, sufficient discriminative power of the individual items was demonstrated by item response analysis in both countries. Discussion and Implications This study is the first to provide a detailed item behavior analysis with an item response analysis of the DJGLS. In conclusion, the findings of this study suggest that the DJGLS has an adequate and similar item and scalar equivalence for use in Chinese populations.

Entwicklungsschritte für einen computeradaptiven Test zur Erfassung von Angst (A-CAT1)

Diagnostica ◽  
2005 ◽  
Vol 51 (2) ◽  
pp. 88-100 ◽  
Author(s):  
Otto B. Walter ◽  
Janine Becker ◽  
Herbert Fliege ◽  
Jakob Bjorner ◽  
Mark Kosinski ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Theory ◽  
Generalized Partial Credit Model ◽  
Generalized Partial Credit

Zusammenfassung. Die empirische Erfassung psychischer Merkmale erfolgt in der Regel mit Instrumenten, die auf der Grundlage der klassischen Testtheorie entwickelt wurden. Seit den 60er Jahren bietet sich hierzu mit der Item Response Theory (IRT) eine Alternative an, die verschiedene Vorteile verspricht. Auf ihrer Grundlage können u.a. computeradaptive Tests (CATs) entwickelt werden, welche die Auswahl der vorgelegten Items dem Antwortverhalten der Patienten anpassen und damit eine höhere Messgenauigkeit bei reduzierter Itemzahl ermöglichen sollen. Wir haben verschiedene Schritte zur Entwicklung eines CAT zur Erfassung von Angst unternommen, um zu prüfen, ob sich die theoretischen Vorzüge der IRT auch in der praktischen Umsetzung bestätigen lassen. In dem vorliegenden Beitrag wird die Entwicklung der zu Grunde liegenden Itembank dargestellt. Hierfür wurde auf Daten von N = 2348 Patienten zurückgegriffen, die an der Medizinischen Klinik mit Schwerpunkt Psychosomatik der Charité zwischen 1995 und 2001 im Rahmen der Routinediagnostik ein umfangreiches Set etablierter konventioneller Fragebögen computergestützt beantwortet hatten. Diese beinhalteten 81 Items, die in einem Expertenrating für das Merkmal Angst als relevant angesehen wurden. Die Eigenschaften dieser Items wurden anhand ihrer residualen Korrelationen nach konfirmatorischer Faktorenanalyse (MplusTM), ihrer Antwortkategorienfunktion (TestgrafTM) und ihrer Diskriminationsfähigkeit (ParscaleTM) überprüft. Es verblieben 50 Items, die für die Anwendung eines polytomen Zwei-Parameter-Modells (Generalized-Partial-Credit-Model) als geeignet angesehen werden können. Orientiert man sich an einer Reliabilität von ρ ≥ .90 und legt für den computeradaptiven Testalgorithmus einen Standardfehler von ≤ .32 fest, so zeigen Simulationsstudien, dass die Merkmalsausprägung für Angst im Bereich von ± 2 Standardabweichungen um den Mittelwert der Stichprobe mit ca. 7 Items ermittelt werden kann. Zudem legen die Simulationsstudien nahe, dass der CAT-Algorithmus das Merkmal in den oberen und unteren Ausprägungen besser zu differenzieren vermag als die konventionell berechnete Summen-Skala des STAI (State).

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