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Register a new userأثر حجم العينة على تقدير صعوبة الفقرة و الخطأ المعياري في تقديرها باستخدام نظرية الاستجابة للفقرة
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Damascus University ورقة بحثية
Publication date
2007
fields
Education
and research's language is
العربية
Authors
أحمد محمود الثوابية( باحث )
Created by
Shamra Editor
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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 اشتقاق 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.
No English abstract
References used
الدوسري، راشد . ( 2004 ) . القياس والتقويم التربوي الحديث . عمان: دار الفكر.
عدس، عبد الرحمن ومنيزل، عبد الله . ( 2002 ) . مقدمة في الإحصاء التربوي. عمان: دار الفكر .
.Baker, F. (2001) .The Basics of Item Response Theory Maryland: Universities of Maryland
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The research aims to estimate the effect of sample size on the statistical test
power (t) for one sample, two interrelated samples, two independent samples,
and on the statistical test power of one-way analysis of variance test (F) to
compare the
averages. The descriptive method was used, and different sizes
of samples (300) items, where it was generated using the program (PASS 14),
and taken into account to be realized in this data the set of assumptions
needed to make test (t) and (F), with respect to random testing, categorical
level of measurement, normal distribution, and equinoctial variance.
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 th
ese
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.
The research aims to develop some formulas of sample size and characterization and comparison among themselves to determine the best formula of formulas to calculate the sample size and the creation of a modified reflected well on the sample size, in
addition to specifying individual gratification I and II for the relevant formulas and mathematical equations can predict the sample size, however the size of the community. The researcher through the study the following results:
The results were identical to the formula related to the size and the sample size when consolidation requirements.
Sample size did not increase with increasing size of the moral community at first gratification.
No moral differences between sample volume according to the size of the community when individual gratification.
Moral differences exist between sample size and average total inspection according to the size of the community when individual gratification.
We got a mathematical models of the relationship between size and the sample size and the size of the community and the average total inspection.
We have developed a comprehensive table gives sample size corresponding to the size of the community can be accessible to researchers to take advantage of it and apply the formulas as long as it originally relied upon certain conditions.
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting th
e ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.
تهدف هذه الدراسة إلى تحليل سلوك الطلب على النقود في سورية اعتماداً على بيانات ربع سنوية
تشمل الفترة ١٩٧٤-١٩٩٤ . و استخدم الناتج المحلي الإجمالي بأسعار ١٩٨٥ ، و معدل التضخم، في
تقدير دوال الطلب على النقود الحقيقية. و قد توطدت علاقة طلب على النقود مس
تقرة طويلة الأجل
بين هذه المتغيرات. و قدرت هذه العلاقة باستخدام نموذج تصحيح الخطأ و أسلوب التكامل المشترك.
و نتيجة لانغلاق الاقتصاد السوري مالياً، فقد أخفق معدل الفائدة، و سعر الصرف في تفسير سلوك
الطلب على النقود.
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