جستجوی مقالات مرتبط با کلیدواژه « تحلیل خوشه ای » در نشریات گروه « روانشناسی »

The aim of this study was to determine the number of Internet dependency clusters and then compare different groups in social anxiety and depression. The present study population consisted of undergraduate female dormitory students of Shiraz University. 370 female students were selected using the multi-stage cluster random sampling method. For gathering data, of Young internet addiction test (IAT), Beck depression inventory (BDI), and social anxiety Liebowitz questionnaire (LSAS) were utilized. Data were analyzed by cluster analysis and MANOVA. The results of cluster analysis showed three clusters with the mind, moderate and severe degrees of internet addition (P≤ 0/001). Also, significant differences among 3 profiles were found in depression (P≤ 0/001) and social anxiety (P≤0/01). Mild Internet addiction clusters experience the least amount of depression. According to the results, it's necessary to examine internet addiction as a psychological problem and cultural educational and family systems plan to solve this problem.

This research was conducted to show psycho-social competences profile of students as non-cognitive skills, with person-based analysis and examining provided clusters and their differences based on students’ school satisfaction. 414 school kids of 5th and 6th graders from deprived urban areas completed Zhou & Ee’s (2012) social-emotional competence questionnaire (SECQ) and students’ school satisfaction sub-scale of Huebner’s (2014) Multidimensional Life Satisfaction Survey. Hierarchical cluster analysis showed that 234 (56%) and 180 (44%) clustered in the first and second as having low and high psycho-social competences, respectively. Confirming the obtained results of cluster analysis, students’ school satisfaction was used. The t test result (t=2.38, p= 0.018) indicted that there is a significant difference between groups regarding school satisfaction. The findings of this study demonstrated the competences profile and implicated the importance of psychosocial skills and competences in students' wellbeing at school and beyond i.e. life, concerning mental health in fast changing world; it should be imbedded in educational processes.

Investigating the dimensional-factorial structure of cognitive abilities is an important issue for measuring, interfering and developing these abilities as evidence of its structure's validity from the aspects of content and cognitive processes. This research was conducted with the aim of investigating the dimensional-factorial structure of cognitive abilities and providing a model for this structure in comparison with the presented structures of cognitive abilities such as general intelligence theory (factor g). The research method was descriptive-analytic and modeling the relationship between items and responses. The sample size of the study consisted of 1105 ninth grade students (578 boys and 527 girls) selected by stratified random sampling from among 32 high schools in Ahwaz. Data analysis was performed using cluster analysis methods based on the conditional covariance proximity (HAC/CCPROX) and PolyDETECT, Structural Equation Modeling (SEM) and Monte Carlo simulation. The results of the analysis showed that the structure of cognitive abilities of students is two-dimensional. The first dimension was named non-social cognition and includes six factors namely retrospective memory, prospective memory, inhibitory control, decision-making, planning, and sustain attention. The second dimension was called social cognition, which included only social cognition factor. Also, the validity and reliability of the dimensions and the obtained factors were confirmed. Discussion and The results of this study showed that the unidimensional theory of general intelligence (factor g) is not a complete theory for the structure of cognitive abilities and the factor g can not explain the variance of social cognition. Thus, the theory underlying the present study was called "Thetwo dimensionaltheory of social-nonsocialcognitive abilities". Also, the results of this study confirmed the findings of some studies that non-social cognition and social cognition form two different dimensions of cognitive abilities.

Students' classrooms misbehaviors have an important impact on the teaching and learning effectiveness. The aim of this study was to compare the students’ indiscipline in different clusters based on the early maladaptive schemas and family functioning components. The K-means clustering method was used for student clusters formation based on early maladaptive schemas and family functioning components in a group of 360 girl students in Tabriz city 10th grade students. According to the criteria, the two clusters was the most distinct between students' clustering. The first cluster students with the highest number in comparison to the second cluster have higher mean in all early maladaptive schemas, while in the family function components the cluster has lower mean. Also, according to a MANOVA test results the first cluster students have higher mean in all five types of indiscipline behaviors. The findings emphasize the importance of paying attention to family components and students' schemas on their misbehaviors.

Problematic computer use is a growing social issue which is being debated worldwide. This study examined the prevalence of internet addition among Tabriz university students and its relationship with emotional and social feeling of loneliness.

372 students of Tabriz University (200 male students and 172 female students) were selected by multi-stage random cluster sampling. For gathering data, of Young internet addiction test (1998), loneliness questionnaire Woodworth (1986) emotional and social loneliness Russell questionnaire (1996) were utilized.

Data were analyzed by cluster analysis and MANOVA. The results of cluster analysis showed three clusters with mind (M= 21.6), moderate (M= 36.8) and severe (M= 47.8) degrees of internet addition. Also significant differences among 3 profiles were found in emotional loneliness [F (130/5, P≤ 0/001] and social isolation [F (123.45, P≤ 0/001].

Given the rising percentage of students suffer from Internet addiction and emotional-social isolation, it is therefore necessary to consider the phenomenon of Internet addiction as a psychological problem by cultural and educational planners and the family system. In order to prevent this phenomenon, it is necessary to provide the necessary knowledge at the community level about signs and symptoms of addiction to the Internet.

Different variables such as motivation, self-esteem, and learning methods can affect the learner during learning activity. Therefore, educational research has shown that motivation is one of the essential factors for the improvement of student performances. Using a person-oriented approach, the present study aimed to determine the motivational profiles (clusters) of students and to compare their self-control considering these profiles. This study was conducted with a comparative-descriptive method. The participants were 204 university of Qom students that were choose by multiple cluster sampling method. Participants completed the Tangeny self-control (2002) and Snyder hope scale (1991). The statistical methods was cluster analysis, ANOVA. Cluster analysis revealed three motivational profiles for the students: 38/2% of the subjects were placed in the low quantity motivation group, 38/7% were placed in the moderate quantity motivation group, the high quantity motivation group (25%). The behavioral self-control of low quantity motivation group was lower than the other clusters. The findings of this study will help those involved in the field of education make effective attempts by planning helpful educational program, so promote the students’ learning motivations, particularly the senior ones during the academic years.

Regarding numerous studies in the literature involving Goldberg’s big 5 factor inventory (BFI), it is safe to assume that factor structure of BFI is highly valid. The main goal of this study is to assess whether network analysis can reproduce the results of exploratory factor analysis and if so, will the plotted network of items provide new insights? In the present study, the network structure of 25 items of BFI will be presented by conducting network analysis and cluster analysis; In addition, the results of network analysis will be compared with findings from exploratory factor analysis. The statistical population encompasses all people from different countries who voluntarily referred to website of international personality item pool and took part in the English version of Goldberg’s big 5 factor personality assessment tool. The data sample includes 2800 people from statistical population who referred to this website in the spring of 2010. All the statistical analysis has been carried out in R statistical software (version: 3.4.4). The network analysis using Gaussian graphical modeling alongside cluster analysis was able to accurately categorize the 25 items in 5 subgraphs in complete accordance with exploratory factor analysis results. In addition, items corresponding to extraversion sit in the center of the graph which denotes the importance of extraversion among other factors. Last but not least, the centrality analysis revealed invaluable information regarding the role and importance of items in the personality questionnaire.

Procrastination is a widespread phenomenon that has been associated with the most of cognitive, emotional, and motivational factors, about which a clear and integrated picture is still lacking, though. The aim of this study was to use primary established psychological procrastination-related factors in the base of Ellis cognitive emotional behavioral approach to examine whether reliable subgroups of procrastinators can be identified through cluster analysis. So 900 students from Tehran University (447 females and 453 males) were selected with Random multi-stage and then completed a measure of procrastination and 200 students who got high scores in procrastination on the scale of Solomon and Rothblum (1984) were selected and this sample responded to the scale of self-regulation by Green and Miller (2004), self-efficacy of Midelton and Midgley (1997), the scale of the frustration beliefs of Harrington (2005) and irrational beliefs of  Koopmans et al. (1994). Two clusters were identified: The first cluster with middle scores of self-regulated and self-efficacy and high scores of irrational beliefs and frustration beliefs .The second cluster with low scores of self-regulated, self-efficacy, irrational beliefs and frustration beliefs. Based on the findings, it seems that students with high procrastination, have different motivational and belief profiles and cluster analysis can identify these profiles.

Data mining is the process of sorting and classification of large Data. This paper introduces cluster analysis as a data mining technique and provide examples of its application in clustering of procrastinators. So introduce the cluster analysis and a variety of approaches, how to choose each step of its implementation in a representative sample presented the sample consists of 200 students of Tehran university (100 boys and 100 girls) that get high score in procrastination on the scale of Solomon and Rothblum (1984) and this sample responded to scale of self regulation by Green and Miller (2004), self-efficacy of Midelton and Midgley (1997), the scale of the failure beliefs Harrington (2005) and irrational beliefs Koopmans et al. (1994). The results indicated that different clusters of procrastinators which with identify groups of procrastinators and the characteristics of each of the groups can be more effective ways to reduce procrastination each group presented.