scholarly journals Fitting Rasch Model using Appropriateness Measure Statistics

2005 ◽  
Vol 8 (1) ◽  
pp. 100-110 ◽  
Author(s):  
José Antonio López Pina ◽  
M. Dolores Hidalgo Montesinos
Keyword(s):  
Standard Deviation ◽  
Rasch Model ◽  
Mean Square ◽  
Sample Sizes ◽  
Fit Statistics ◽  
Item Fit ◽  
Estimated Parameters ◽  
Distributional Properties ◽  
Expected Values

In this paper, the distributional properties and power rates of the Lz, Eci2z, and Eci4z statistics when they are used as item fit statistics were explored. The results were compared to t-transformation of Outfit and Infit mean square. Four sample sizes were selected: 100, 250, 500, and 1000 examinees. The abilities were uniform and normal with mean 0 and standard deviation 1, and uniform and normal with mean –1 and standard deviation 1. The pseudo-guessing parameter was fixed at .25. Two ranges of difficulty parameters were selected: ±1 logits and ±2 logits. Two test lengths were selected: 15 and 30 items. The results showed important differences between the T-infit, T-outfit, Lz, Eci2z, and Eci4z statistics. The T-oufit, T-infit, and Lz statistics showed poor standardization with estimated parameters because their distributional properties were not close to the expected values. However, the Eci2z and Eci4z statistics showed satisfactory standardization on all conditions. Further, the power rates of Eci2z and Eci4z were 5% to 10% higher than the power rates of Lz, T-outfit, and T-infit to detect items that do not fit Rasch model.

scholarly journals Practical Significance of Item Misfit in Educational Assessments

2017 ◽  
Vol 41 (5) ◽  
pp. 388-400 ◽  
Author(s):  
Carmen Köhler ◽  
Johannes Hartig
Keyword(s):  
Item Response ◽  
Correlation Coefficients ◽  
Model Fit ◽  
Practical Significance ◽  
Fit Statistics ◽  
Item Fit ◽  
Estimated Parameters ◽  
Educational Test ◽  
Educational Assessments ◽  
Using Data

Testing item fit is an important step when calibrating and analyzing item response theory (IRT)-based tests, as model fit is a necessary prerequisite for drawing valid inferences from estimated parameters. In the literature, numerous item fit statistics exist, sometimes resulting in contradictory conclusions regarding which items should be excluded from the test. Recently, researchers argue to shift the focus from statistical item fit analyses to evaluating practical consequences of item misfit. This article introduces a method to quantify potential bias of relationship estimates (e.g., correlation coefficients) due to misfitting items. The potential deviation informs about whether item misfit is practically significant for outcomes of substantial analyses. The method is demonstrated using data from an educational test.

Validity and Reliability of Green Competencies Instrument for Automobile Technology Programme Using Rasch Model

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Igogbe Regina Onyilo ◽  
Mahyuddin Arsat ◽  
Nor Fadila Amin
Keyword(s):  
Rasch Model ◽  
Point Of View ◽  
Validity And Reliability ◽  
Automobile Engineering ◽  
Engineering Technology ◽  
Fit Statistics ◽  
Item Fit ◽  
Point Measure ◽  
Technology Programme ◽  
Automobile Technology

This article aims to determine the validity of developed constructs and check the reliability of the newly developed instrument named as Questionnaire on Green Competencies for Automobile Engineering Technology (QGCAET) for the Automobile Technology Programme in Nigerian Universities. The instrument consists of 170 elements measuring four constructs namely Technical Green Competencies; Managerial Green Competencies; Personal Green Competencies and Social Green Competencies and was administered to 299 respondents made of Lecturers, Technologists and Final- Year Students of Automobile Engineering and Technology programme in Nigeria universities. The Rasch model was used to examine the validity and reliability of the items. From the analysis point of view, the polarity of the elements indicates that the correlation of the point measure (PTMEA CORR) of 170 elements of green competencies is between 0.00 and 0.55. The summary statistics show that the reliability of the items and the separation of the items of the green competencies instrument are 0.98 and 6.46, respectively. Similarly, the item reliability of each construct is between 0.96 and 0.99, and the reliability of the person is between 0.79 and 1.97, respectively. In terms of item fit statistics, a total of 157 items are found to be fit to achieve the objectives of the study. The result also indicates that the range of fit for the four (4) identified green competencies constructs is between 0.61 and 1.49 signifying that all the constructs are in harmony in measuring the items in the constructs, so suitable in achieving the objectives of the research.

scholarly journals Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model

2021 ◽  
Author(s):  
Samantha Estrada
Keyword(s):  
Rasch Model ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Fit Indices ◽  
Fit Statistics ◽  
Item Fit ◽  
Q Index ◽  
The Rasch Model

To understand the role of fit statistics in Rasch measurement is simple: applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the current study was to assess the Q-Index robustness (Ostini and Nering, 2006), and its performance was compared to the current popular fit statistics known as MSQ Infit, MSQ Outfit, and standardized Infit and Outfit (ZSTDs) under varying conditions of test length, sample size, item difficulty (normal and uniform), and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. This study provides applied researchers guidelines the robustness and appropriateness of the use of the Q-Index, which is an alternative to the currently available item fit statistics. The Q-Index was slightly more sensitive to the levels of multidimensionality set in the study while MSQ Infit, Outfit, and standardized Infit and Outfit (ZSTDs) failed to identify the multidimensional conditions. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated β=.20 across all fit indices.

scholarly journals The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Cem Kadilar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Long Memory ◽  
Moving Average ◽  
Mean Square ◽  
Sample Sizes ◽  
Advantages And Disadvantages

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.

scholarly journals Evaluation of Hajj Instrument (HAJI) Psychometric Properties Using Rasch Measurement

2016 ◽  
Vol 12 (8) ◽  
pp. 212
Author(s):  
Muhammad Iqbal Tariq Idris ◽  
Abdul Hafidz Omar ◽  
Dayang Hjh Tiawa Awang Hj Hamid ◽  
Fahmi Bahri Sulaiman
Keyword(s):  
Physical Activity ◽  
Psychometric Properties ◽  
Healthy Eating ◽  
Rasch Model ◽  
Rasch Measurement ◽  
Mental Toughness ◽  
Validity And Reliability ◽  
Item Fit ◽  
The Rasch Model ◽  
Physical Care

<p>Hajj Instrument (HAJI) was developed to determine hajj pilgrim’s wellness. This study used Rasch measurement to evaluate the psychometric properties including validity and reliability of the HAJI. The respondents involved in this study were 300 comprised of Malaysian hajj pilgrims. HAJI consists of eight constructs namely physical care, physical activity, healthy eating, knowledge, mental toughness, intrapersonal, interpersonal and relationship with Creator and natures. Validity of each construct and content was determined through dimensionality, item fit and item polarity while the reliability was achieved by administered person and item separation. The results showed that the reliability for both item and person were 0.99 and 0.96 respectively. Besides, there were no items need to be dropped based on PTMEA CORR and INFIT MNSQ results. The study revealed that the items of HAJI fit the Rasch model as well as able to measure hajj pilgrim’s wellness. </p>

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

Clinical evaluation of the repeatability of ocular aberrometry obtained with a new pyramid wavefront sensor

2018 ◽  
Vol 29 (6) ◽  
pp. 585-592 ◽  
Author(s):  
Ana B Plaza-Puche ◽  
Liberdade C Salerno ◽  
Francesco Versaci ◽  
Daniel Romero ◽  
Jorge L Alio
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Pupil Size ◽  
Pupil Diameter ◽  
Intraclass Correlation ◽  
Wavefront Sensor ◽  
Mean Square ◽  
The Subject ◽  
Healthy Eyes

Purpose:To evaluate the intrasubject repeatability of the ocular aberrometry obtained with a new ocular pyramidal aberrometer technology in a sample of normal eyes.Methods:A total of 53 healthy eyes of 53 subjects with ages ranging from 18 to 45 years were included in this study. In all cases, three consecutive acquisitions were obtained. Intrasubject repeatability of the measurements with a pyramidal aberrometer was calculated. Intrasubject repeatability for 4.0- and 6.0-mm pupils was evaluated within the subject standard deviation (Sw) and intraclass correlation coefficient.Results:Low values of the Swand intraclass correlation coefficient outcomes close to 1 were observed for the sphere and cylinder at 3.0-mm pupil size. Most low Swand intraclass correlation coefficient values close to 1 were observed for total, low-order aberrations and higher-order aberrations root mean square and for each Zernike coefficient analysis (intraclass correlation coefficient ⩾0.798) at 4.0-mm pupil size, with more limited outcomes for the aberrometric coefficient of Z(4, 4) with an intraclass correlation coefficient of 0.683. For a 6.0 mm pupil diameter, low Swand intraclass correlation coefficient values close to 1 were observed for all aberrometric parameters or Zernike coefficients analyzed (intraclass correlation coefficient ⩾0.850).Conclusion:The new pyramidal aberrometer Osiris provides repeatable and consistent measurements of ocular aberrometry measurements in normal eyes.

scholarly journals Q-Matrix Refinement Based on Item Fit Statistic RMSEA

2018 ◽  
Vol 43 (7) ◽  
pp. 527-542 ◽  
Author(s):  
Chunhua Kang ◽  
Yakun Yang ◽  
Pingfei Zeng
Keyword(s):  
Search Algorithm ◽  
Delta Method ◽  
Mean Square ◽  
Simulation Studies ◽  
Response Data ◽  
Item Fit ◽  
Dina Model ◽  
Q Matrix ◽  
Diagnosis Model ◽  
Effectiveness And Efficiency

A Q-matrix, which reflects how attributes are measured for each item, is necessary when applying a cognitive diagnosis model to an assessment. In most cases, the Q-matrix is constructed by experts in the field and may be subjective and incorrect. One efficient method to refine the Q-matrix is to employ a suitable statistic that is calculated using response data. However, this approach is limited by its need to estimate all items in the Q-matrix even if only some are incorrect. To address this challenge, this study proposes an item fit statistic root mean square error approximation (RMSEA) for validating a Q-matrix with the deterministic inputs, noisy, “and” (DINA) model. Using a search algorithm, two simulation studies were performed to evaluate the effectiveness and efficiency of the proposed method at recovering Q-matrices. Results showed that using RMSEA can help define attributes in a Q-matrix. A comparison with the existing Delta method and residual sum of squares (RSS) method revealed that the proposed method had higher mean recovery rates and can be used to identify and correct Q-matrix misspecifications. When no error exists in the Q-matrix, the proposed method does not modify the correct Q-matrix.

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