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    Effect of the Jigsaw Technique on Achievement and Attitude in Teaching Statistics to Nursing Students
    (Lippincott Williams & Wilkins, 2025) Kocak, Duygu
    Background:Negative attitudes toward statistics can hinder nursing students' performance, but the cooperative Jigsaw technique can boost collaboration, improving both achievement and attitudes.Purpose:This study explored the effect of the Jigsaw technique on nursing students' achievement in statistics and their attitudes toward the subject.Methods:This research used a pretest-posttest control group experimental design. The experimental group was taught using the Jigsaw technique.Results:The Jigsaw technique significantly improved nursing students' statistical achievement compared to traditional methods, and this effect persisted in the follow-up test. Additionally, the Jigsaw method led to a positive shift in students' attitudes toward statistics, while no changes were observed in the control group.Conclusions:The Jigsaw technique is an effective method for improving statistical achievement and attitudes in nursing students. It can be incorporated into nursing education to enhance performance and reduce negative perceptions.
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    Öğe
    The Effects of Model Based Missing Data Methods on Guessing Parameter in Case of Ignorable Missing Data
    (Pegem Akad Yayincilik Egitim Danismanlik Hizmetleri Tic Ltd Sti, 2018) Kocak, Duygu
    The present study aims to investigate the effects of model based missing data methods on guessing parameter in case of ignorable missing data. For this purpose, data based on Item Response Theory with 3 parameters logistic model were created in sample sizes of 500, 1000 and 3000; and then, missing values at random and missing values at completely random were created in ratios of 2.00%, 5.00% and 10.00%. These missing values were completed using expectation-maximization (EM) algorithm and multiple imputation methods. It was concluded that the performance of EM algorithm and multiple imputation methods was efficient depending on the rate of missing values on the data sets with missing values completely at random. When the missing value rate was 2.00%, both methods performed well in all sample sizes; however, they moved away from reference point as the number of missing values increased. On the other hand, it was also found that when the sample size was 3000, the cuts were closer to reference point even when the number of missing values was high. As for missing values at random mechanism, it was observed that both methods performed efficiently on guessing parameter when the number of missing values was low. Yet, this performance deteriorated considerably as the number of missing values increased. Both EM algorithm and multiple imputation methods did not perform effectively on guessing parameter in missing values at random mechanism.