This study aims to investigate the performance of test equating methods extended to mixed-format tests within the framework of Item Response Theory (IRT). To this end, a simulation study was conducted to compare equating errors of the mean/mean, mean/sigma, robust mean/sigma, Haebara, and Stocking-Lord methods under different conditions. Using 40-item tests, the effects of anchor length (10%, 20%, and 30%) and ability distribution (normal, negatively skewed, and positively skewed) were examined on a sample of 1000 participants. We used the common-item nonequivalent group design. The tests were developed using the three parameter logistic model for dichotomous simulated data and the generalized partial credit model for polytomous simulated data. The results of the study revealed that the robust mean/sigma method generally had the highest equating errors. When all conditions were evaluated, the least equating error occurred with the "Stocking-Lord" method in the case of positively skewed groups and a long anchor test (30%). Moreover, the results indicated that the groups with similar ability distributions (normal-normal, negatively skewednegatively skewed, and positively skewed-positively skewed) produced less equation errors than the groups with different ability distributions (negatively skewed-normal, positively skewed-normal, and positively skewed-negatively skewed).
A r ş . G ö r . Me r v e Ş A H İ N -A r ş . G ö r . İ b r a h i m U Y S A LÖ ğ r e t me n A d a y l a r ı n ı n Ö l ç me v e D e ğ e r l e n d i r me K o n u s u n d a k i Ö z -Y e t
Abstract-Semantic Role Labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates. In this paper, we introduce and assess the idea of using SRL on generic Multi-Document Summarization (MDS). We score sentences according to their inclusion of frequent semantic phrases and form the summary using the top-scored sentences. We compare this method with a term-based sentence scoring approach to investigate the effects of using semantic units instead of single words for sentence scoring. We also integrate our scoring metric as an auxiliary feature to a cutting edge summarizer with the intention of examining its effects on the performance. The experiments using datasets from the Document Understanding Conference (DUC) 2004 show that the SRL-based summarization outperforms the term-based approach as well as most of the DUC participants.
İbrahim UYSAL Güler DUMAN Elif YAZICI Merve ŞAHİN ÖzSon yıllarda bilgi ve iletişim teknolojilerinde yaşanan gelişmeler, zorbalığının yeni bir biçimi olan siber zorbalık kavramını ortaya çıkarmıştır. Siber zorbalık insanların hayatlarını olumsuz yönde etkileyen toplumsal bir sorun haline gelmiştir. Siber zorbalığın olumsuz yönlerini azaltmada ve siber zorbalık ile başa çıkmada anahtar unsur ise siber zorbalık duyarlılığına sahip olmaktır. Bu araştırmanın amacı, öğretmen adaylarının siber zorbalık duyarlılıklarını cinsiyet ve bölüm değişkenleri açısından incelemektir. Tarama modelinin kullanıldığı bu araştırma, Batı Karadeniz Bölgesindeki bir üniversitenin Eğitim Fakültesinde öğrenim görmekte olan 296 öğretmen adayı üzerinde gerçekleştirilmiştir. Araştırmada veri toplama aracı olarak Tanrıkulu, Kınay ve Arıcak (2013) tarafından geliştirilen "Siber Zorbalığa ilişkin Duyarlılık Ölçeği" kullanılmıştır ve nicel veriler 2012-2013 öğretim yılı yaz döneminde toplanmıştır. Verilerin analizinde "Mann Whitney U Test" ve "Doğrulayıcı Faktör Analizi" kullanılmıştır. Araştırma sonucunda öğretmen adaylarının siber zorbalık duyarlılıklarının yüksek olduğu fakat cinsiyet ve bölüme göre anlamlı bir şekilde farklılaşmadığı sonucuna ulaşılmıştır. Çalışmanın siber zorbalığa ilişkin farkındalığı da ortaya çıkardığı dikkate alınarak bu farkındalığın çeşitli değişkenlerle birlikte incelenmesi ve sonuçların karşılaştırılması önerilebilir. Ayrıca nitel ve nicel çalışmalar aynı araştırmada kullanılarak siber zorbalık duyarlılığı konusunda derinlemesine bilgi edinilebilir. Cyberbullying has become a social concern affecting people's lives negatively similar to bullying. The sensibility towards cyberbullying is the key factor to minimize the negative effects of cyberbullying and get ready for managing cyberbullying. This study aimed to examine preservice sensibility towards cyberbullying in terms of demographic variables such as gender and department. A survey method was adopted in the study and data were collected from 296 pre-service teachers studying at education faculty of a university in the western Black Sea region. "Cyberbullying Sensibility Scale" developed by Tanrıkulu, Kınay and Arıcak (2013) was used as the data collection instrument to collect the quantitative data during the spring term of 2012-2013 academic year. In the data analysis process "Mann Whitney U Test" and "Confirmatory Factor Analysis" was computed. The results indicated that although the pre-service teachers had a high level of sensibility towards cyberbullying, their sensibility levels did not differ depending on their genders and departments. Reflecting on these results, the study revealed pre-service teachers' sensibility towards cyberbullying, so future studies could be conducted across different settings with different variables in order to present a more complete picture of cyberbullying. Also, mixed methods studies could be carried out to provide in-depth information about sensibility towards cyberbullying.
The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.
ÖzBu araştırma, araştırma görevlilerinin yaşadıkları mesleki sorunların önem düzeyini "karşılaştırmalı yargılar kanunu" kapsamında yer alan ikili karşılaştırma yoluyla ölçeklemeyi amaçlamaktadır. Bu kapsamda araştırma görevlilerinin mesleki sorunları cinsiyet, öğrenim durumu ve kadro türü değişkenlerine göre incelenmiştir. Araştırma genel tarama modeli ile desenlenmiştir. Araştırmanın örneklemini Türkiye'de Yüksek Öğretim Kurumuna bağlı devlet ve vakıf üniversitelerinde görev yapan 555 araştırma görevlisi oluşturmaktadır. Ölçme aracı iki kısımdan oluşmakta olup birinci kısımda demografik bilgiler (cinsiyet, öğrenim seviyesi ve kadro türü), ikinci kısımda ise araştırma görevlilerinin ikili olarak karşılaştıracakları sekiz mesleki sorun (akademik olmayan işlere yönlendirilme, fiziksel yetersizlikler, mobbing, yabancı dil sorunu, kadro güvencesi olmaması, ekonomik sorunlar, ödeneklerin yetersizliği ve idare tarafından verilen fakülte işlerinin yoğunluğu) bulunmaktadır. Ölçekleme Thurstone'un karşılaştırmalı yargılar kanununun III. Hal denklemi kullanılarak tam veri matrisinden gerçekleştirilmiştir. Araştırmanın sonucunda elde edilen bulgulara göre araştırma görevlilerinin en önemli sorunu ekonomiktir. Bunu sırasıyla mobbing, idare tarafından verilen fakülte işlerinin yoğunluğu, ödeneklerin yetersizliği ya da bulunmaması, kadro güvencesi olmaması, akademik olmayan işlere yönlendirme, fiziksel yetersizlikler ve yabancı dil sorunları takip etmiştir. Araştırma görevlilerinin karşılaştıkları mesleki sorunlar cinsiyet değişkenine göre incelendiğinde kadın araştırma görevlileri mobbingi en önemli sorun olarak görürken erkek araştırma görevlileri bu sorunu altıncı sırada görmektedir. Diğer bir değişken olan öğrenim seviyesinde, doktora eğitimini tamamlamış araştırma görevlilerinin en önemli sorununun kadro güvencesinin olmaması olduğu görülmüştür. Kadro türü değişkeni açısından ise 50. maddenin d bendince atanan araştırma görevlilerinin en önemli sorunu kadro güvencesinin olmamasıdır.
Researchers examine assumptions before performing most hypothesis testing. A common assumption is that the data are normally distributed. However, normality tests and descriptive statistics often create dilemmas for researchers, making it difficult to decide whether the data is normally distributed. The aim of the study was to compare univariate normality tests (Anderson-Darling, Cramervon Mises, Jarque-Bera, Kolmogorov-Smirnov, Lilliefors, Pearson chi-square, Shapiro-Francia and Shapiro-Wilk) and descriptive statistics used for normality (standard values of skewness and kurtosis coefficients, skewness coefficient/standard error) according to the value of skewness, sample size, and the continuous-categorical status of the data. The research was a Monte Carlo simulation study. The simulation conditions were determined by the skewness coefficient (-2.5, -1.0, 0.0, 1.0, and 2.5), sample size (20, 30, 50, 100, 500, 1000, and 5000), and continuous or ordinal (number of categories 2, 3, 4, 5, and 7) status of the data. In the study, 210 simulation conditions were studied with fully crossed design. The evaluation criteria were determined as type-1 error and power. As a result of the research, it was determined that Jarque-Bera, the standard value of the skewness coefficient, skewness coefficient/standard error and the standard value of the kurtosis coefficients showed a better performance in terms of type-1 error. In terms of power, there was decrease in the power of all methods when the sample size was small, the data type was continuous, and the skewness coefficient was -1 or +1.
This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance adjusted unweighted least squares (ULSMV), mean and variance adjusted weighted least squares (WLSMV), and Bayesian estimators. As a result of the study, it was revealed that increased average factor loading and sample size had a positive effect on the performance of the estimation methods. According to the research findings, it can be said that the methods are sufficient to estimate average factor loading and interfactor correlations, regardless of the estimation methods, in most of the conditions where the average factor loading is 0.7. In small sample sizes particularly, the interfactor correlation was underestimated for skewed indicator conditions. According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.
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