The Academic Buoyancy Scale (ABS) (Dehghanizadeh & Hosseinchari, 2012) and the Grade Point Average (GPA) of the first semester of the students were considered as the screening tools (diagnosing academic problems) and as the pretest scales the second semester GPA was considered as the posttest measure. By simple random clustering method, the selected students were placed into two groups of experimental and control (n=23/group). Subsequently, 46 students with academic buoyancy and academic achievement problems were identified. This number was confirmed by GPower software concerning the effect size, the alpha 0.05, and the test power indices. The sample size was estimated to range between 32 and 50 individuals using Cohen’s table. Then, 4 classes were considered among them. Accordingly, among all schools in the region one, 9 high schools were randomly recruited. The study participants were selected using a multiphase cluster sampling method in the final forth cluster of a school, as well as a simple cluster sampling approach. The research statistical population consisted of all the second–grade female high school students in Kerman City, Iran, in the 2015–2016 academic year. Methods: This was an experimental study with a pretest–posttest and a control group design. Therefore, the current research aimed to investigate the effects of self–regulation learning strategies training on academic buoyancy and academic achievement among female students with academic buoyancy and academic achievement problems. On this basis, learning self–regulation strategies significantly impact eliminating or decreasing students’ numerable academic problems, such as academic languish and poor academic achievement, which encompass numerous female students. Self–regulation learning strategies include overt approaches (controlling the task situation & others in the task setting) and covert methods (meta–cognitive control, motivation control & emotion control). Besides, it refers to cognitive, metacognitive, motivational, behavioral, and affective activities. Self–regulation is a multi–dimensional concept, i.e., its aspects have concurrent inner and independent relations. A dissertation is a good time to learn a new method, as it can allow you to answer a big question.Background & Objectives: Academic buoyancy, academic achievement, and self–regulation are to some extent correlated with each other. However, its possible that your professor is suggesting SEM because s/he has questions that can only be answered in SEM. If you can answer your question using either package, it's your choice. You've got to use something you're comfortable with and can defend. If you want to test constraints on any parameters, include individuals who have missing data under full information methods, or include item level behavior variables in a factor model, SEM allows you a lot more flexibility. Present predict Behavior, Attitude, and Emotion will fit equally well in either framework. MANOVA/MANCOVA methods will have restrictions on how the set of dependent variables are related/treated. The benefit to using SEM like methods is how general and flexible they are. ANOVA and regression give the same answers when provided the same data, as to MANCOVA and identically specified multi-level models/heirarchical linear models/SEMs. MANOVA, regression, and SEM are all special cases of the general linear model.
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