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Register a new userThe Differences in Item Difficulty Parameter Estimations for Simulated Data having (MC1- PL) Model in Item Response Theory
الفروق في تقديرات معلم صعوبة الفقرة لبيانات محاكاة توائم النموذج التعويضي (MC1- PL) لنظرية استجابة الفقرة
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هدفت الدراسة الحالية إلى الكشف عن الفروق في تقديرات معلم صعوبة الفقرة لبيانات محاكاة توائم النموذج التعويضي (MC1- PL) باختلاف عدد الأبعاد للاختبار و قوة الارتباط بين هذه الأبعاد و اختلاف برمجية التقدير. و استخدمت في الدراسة بيانات محاكاة لعينة حجمها (1000) مفحوص تم توليدها باستخدام برمجية (RESGENT)، تقيس بعدين مرتبطين من الفقرات، تم من خلالهما خلق حالات مختلفة من تعددية البعد داخل الفقرات (ثلاثة أبعاد، بعدان، بعد واحد) بواقع (7) فقرات ثنائية التدريج لكل بعد، و عند ثلاثة مستويات مختلفة لقوة الارتباط بين الأبعاد (0.0, 0.50, 0.86) على الترتيب. و حللت البيانات باستخدام البرمجيات الإحصائية (NOHARM؛ Bilog- MG3). أظهرت النتائج أن الفروق في تقديرات معلم صعوبة الفقرات المشكلة للاختبار متعدد الأبعاد داخل الفقرات لم تكن دالة إحصائيا عند مستوى الدلالة ( ) عند الحالات المختلفة لبعدية الأبعاد و باختلاف قوة الارتباط بين الأبعاد، و كذلك باختلاف برمجية التقدير Bilog–MG3)؛ NOHARM)، و كانت التقديرات بشكل عام مرتفعة و متسقة. و توصي الدراسة بضرورة الاعتماد على هاتين البرمجيتين الإحصائيتين في تحليل البيانات التي توائم النموذج، و خاصة عندما تتحقق الافتراضات التي اعتمدتها الدراسة الحالية في بيانات حقيقية.
This study aimed at finding out the differences in item difficulty parameter estimations for Simulated Data having (MC1- PL) Model in Item Response Theory (IRT) according to the differences in a test Dimensions (3D; 2D; 1D), correlations between these dimensions (0.0, 0.50, 0.86), and the statistical program used in analyzing the data (NOHARM ؛Bilog- MG3). The Monte Carlo simulations data having (MC1- PL) Model in IRT using (RESGENT) program; that fully filled in a 21-item was used to achieve the study aims. Data were analyzed using the statistical programs (NOHARM ؛Bilog- MG3). The results revealed no statistically significant differences in item difficulty parameter estimations that construct the multidimensional test within items due to differences in a test, correlations between these dimensions, and the statistical program used in analyzing the data (NOHARM ؛Bilog- MG3), as well as the estimations were consistent and high. Finally, the study recommends using these statistical programs for a data having (MC1- PL) Model, especially when similar assumptions are satisfied in a real data.
References used
Ackerman, T. Using multidimensional item response theory to understand what items and tests are measuring. Applied Measurement in Education, 7, (1994). 255- 278
(Capar, N. (2000). Analyzing multidimensional response data structure represented by unidimensional IRT models increase the precision of scoring using out-of-scale information. Paper presented at the annual meting of the Florida educational research association (45th Tallahassee, FL, November 8-10, 2000
(Dawadi, B. (1999). Robustness of the polytomous IRT to violations of the unidimensionality assumption. Paper presented at the annual meeting of the American educational research association (Montreal, Canada, April 19-23, 1999
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هدفت هذه الدراسة إلى استقصاء أثر حجم العينة في تقدير معلمة صعوبة الفقرة ( Item Difficulty)
و الخطـأ المعيـاري تقـديرها فـي (Standard Error of Estimation)
باستخدام نظرية الاستجابة للفقرة (Theory Response Item) و لتحقيق أهـداف هـذه
الدراسة تم اشتقا
ق معلمة الصعوبة، و الخطأ المعياري في تقديرها باسـتخدام اختبـار
تحصيلي في الرياضيات للصف العاشر الأساسي تكون في صورته النهائية من (80)
فقرة من نوع الاختبار من المتعدد.
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