A thirteen-item cyberbullying sensibility scale (CSS), developed by Tanrıkulu, Kınay, and Arıcak (2013) and extensively used by researchers, was used to measure the cyberbullying sensibility levels of high school students. Unlike other similar concepts, such as cyberbullying and cyber victimization, there are no scales developed to measure the cyberbullying sensibility among university students. In this study, the data obtained from 727 university students were analyzed based on item response theory (IRT) techniques, and psychometric evidences were obtained to evaluate whether it is appropriate to use the scale on the university students. Accordingly, a parameterization of CSS items was performed by using the graded response model. Using the discrimination parameters and item fit statistics, some items were removed from the original scale and a seven-item CSS version was developed since preliminary exploratory and confirmatory factor analyses provide inadequate evidence for the validity of a one-dimensional structure of cyberbullying sensibility. However, an IRT-based item removal process yielded an acceptable improvement. In this way, despite the six items being removed from the original CSS form, the scale retained 64% of the information it provided. The reliability values computed based on the classical approach and IRT were above .8 after the item elimination process with only a minor drop. With the validation process, the CSS will be a valuable measurement tool to determine the level of cyberbullying sensibility among university students and allow academicians to conduct research with this population.
Life satisfaction is an important factor for mental health and has many positive effects on people. Considering its importance, different measurement tools were developed over the last 5 years. Among these tools, the Satisfaction with Life Scale (SWLS) is the most prominent and adapted to diverse populations, including university students. On the other hand, all these studies were conducted using the classical testing approach, while item response theory was rarely preferred. Regarding this gap, this study aimed to evaluate the psychometric properties of the Turkish version of SWLS by using a graded response model (GRM), which is a member of a broader family of the modern psychometric approach called Item Response Theory (IRT). For this purpose, the data were collected from 471 university students (male = 83, female = 388) aged between 17 and 37 years (M = 21.23, SD = 2.32). IRT based analysis provided satisfactory results on the psychometric properties of the SWLS. It was found that the scale's reliability was acceptable across a wide range of ability spectrums and items fit the GRM well and did not show gender-based differential item functioning. As a result, the psychometric quality of SWLS was further proved in the IRT context.
ÖZET ADAPTION OF MARYLAND SAFE AND SUPPORTIVE SCHOOLS CLIMATE SURVEY INTO TURKISH CULTURE:VALIDITY AND REABILITY STUDY ABSTRACT The aim of this research is to adapt the Maryland Safe and Supportive School Climate Scale (MGDOI) to Turkish and to determine its validity and reliability. 395 high school students participated in this study. Confirmatory factor analysis was performed to test the construct validity of MGDOI. The Cronbach Alpha coefficient was calculated to determine the internal consistency of the scale. In addition, the Mc Donald ve Guttman 6 values were calculated, taking into account the multilevel structure of the scale. In order to calculate the criterion validity of the scale, the Pearson correlation coefficient between MGDOI and High School Life Quality Scale (LYKÖ) was calculated and found to be 0.65. It was found that the scale adapted to Turkish had the same sub-dimensions as the original scale. Furthermore, as a result of the data analysis, MGDOI was found to be valid and reliable for Turkish high school students.Key Words: School climate, test adaptation * Prof. Dr., Marmara Üniversitesi, Psikolojik Danışmanlık ve Rehberlik, h.eksi70@gmail.com ** Arş. Gör., Marmara Üniversite, Psikolojik Danışmanlık ve Rehberlik, tugbaturkk@hotmail.com *** Arş. Gör. Dr., Marmara Üniversitesi, Psikolojik Danışmanlık ve Rehberlik, akifavcu@yahoo.com Halil EKŞİ, Tuğba TÜRK, Akif AVCU 1883 1.GİRİŞOkul iklimi; öğretmen, öğrenci, yönetici, ebeveyn ve diğer okul personelinin davranışlarını etkileyen, bir okulu diğerinden ayırt eden, okula ait kişiliktir ve bir kavram olarak okuldaki insan ilişkilerine gönderme yapmakta ve ön plana çıkartmaktadır. Olumlu okul iklimi öğrenciler, öğretmenler, yöneticiler ve ebeveynler için iyi karşılandıklarına ilişkin bir algı oluşturur ve bireyler arasındaki saygılı etkileşimle karakterize edilir. Öğrenciler başarı için motive olmuşlardır, öğretmenler ve yöneticiler okulun ve öğrenmenin önemini iletirler. Okul temiz, iyi bakımlı ve caziptir. Bu özelliklerin birleşimi sonucunda okul iyi bir yer olarak algılanır (Çalık ve Kurt, 2010; Lehr, 2005: 471). Freiberg & Stein (1999: 11) okul iklimini okulun kalbi ve ruhu olarak görmektedirler. Okul iklimi öğrenci, öğretmen, yönetici ve diğer personelin okulu sevmelerini ve her gün okula gelmek için sabırsızlanmalarını temin etmektedir. Okul iklimi kendimizin ötesinde bir düşünsel ya da nesnel varlığa ait olma duygusu oluştururken, kişinin değerli, saygın ve önemli hissetmesine yardım eden bir okul niteliğidir.Okul iklimi okul kültürüyle ilişkili olmakla birlikte bu iki kavram farklıdır. Fult (2011)'e göre okul iklimi ve okul kültürünü birbirinden net bir şekilde ayırırken ihtiyatlı olmak gerekmektedir. Genellikle alan yazında bu iki kavram birbirinin yerine kullanılmaktadır, ancak okul ikliminin yapısının daha iyi anlaşılabilmesi için aradaki farkın anlaşılması önemli görünmektedir. Erickson (1987) okul kültürünü, okula kimlik kazandıran ve okuldaki tüm bireylerden yapması beklenilen ortak fikirler, varsayımlar, değerler ve inançlar...
When performing regression analysis, one way to examine the normality of data is to screen outliers. Outliers, on the other hand, do not always have an effect on regression results. In reality, cases with a large amount of residuals that affect regression analysis results are referred to as influential cases. It is important to detect them in the dataset because they can lead to erroneous conclusions. The influence of influential cases has already gotten a lot of attention in the regression literature, while it has gotten a lot less attention in factor analysis. The aim of this paper is to show how influential cases affect factor analysis results when they are detected using the Forward Search algorithm. The data was collected from 686 university students ranging in age from 17 to 30. The data was gathered using the Self-Regulation Scale (SRS). The results revealed that the removal of influential cases had an effect on the observed correlation matrice for the SRS items, the factorability results, the number of dimensions extracted, CFA fit indices, and the amount of factor loadings and associated errors. Later, in light of related literature, these results were discussed and the researchers were recommended to consider the effect of influential when applying factor analysis.
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