In this study, a cluster analysis was performed by creating a data set from students' personality traits and academic procrastination behaviours. Correlation analysis was done to examine the relationship between the variables, and the characteristics of the formed clusters and the association of the clusters with the perceived socioeconomic status were examined. Cluster analysis is a simple and practical method for classifying a set of complex data based on certain variables and making them more meaningful and using the results as an aid to decision-making. Clustering algorithms handle such data effectively, making it more meaningful. Following the analysis, it was revealed that two clusters had formed. The first of the clusters includes 65.2 % of the sample population; the level of procrastination and the mean score of neurotic personality traits were calculated higher than the other cluster. The remaining part of the sample population (34.8 %) constitutes the second cluster. The mean scores of studying systematically habits and extroversion, agreeableness, conscientiousness, and openness to experience personality traits of the students forming this cluster are higher than the other cluster. No association was observed between the clusters and the perceived socioeconomic levels of the students. The distributions of socioeconomic levels within the clusters are similar to each other. When the correlations of these variables are examined; positive relationships were found between the level of procrastination and neurotic personality traits. Procrastination behaviour and neurotic personality traits were also negatively correlated with other variables.

Data obtained through questionnaires sometimes respond to the items presented by social norms, so sometimes they do not suit themselves. High social desirability (SD) in non-cognitive measurements will cause item bias. Several ways are used to reduce item bias, including freeing respondents from not writing their names or being anonymous, explaining to the participants to respond to each statement honestly, as they are or according to themselves, and responding to the questionnaire online or offline. This research aims to prove that several methods can minimize the possibility of item bias SD and academic dishonesty (AD). The research was carried out with an experimental study using a factorial design. There were 309 respondents who were willing to be involved in this research. Data analysis was carried out using multivariate ANOVA. The research results show differences for all variables, Self-Deceptive Enhancement (SDE), Impression Management (IM), and AD in the anonymous group. There are differences in AD in the groups that provide a complete explanation and do not explain, and there is an interaction between the average AD based on the anonymous and explanation group.

This study aimed to validate the Learned Helplessness Questionnaire (LHQ), originally developed in Italy, for use in an American context. It examined the LHQ's factor structure, social desirability bias, the relationship between learned helplessness and mastery orientation, and demographic differences in these constructs. Data from 100 adults were collected via an online survey. Confirmatory factor analysis supported the two-factor structure of the LHQ (learned helplessness and mastery orientation), with acceptable fit indices after minor modification. Internal consistency was deemed to be acceptable for both factors. Social desirability bias analysis indicated significant correlations with both constructs, suggesting that participants may have overreported mastery orientation and underreported learned helplessness. A significant negative correlation was found between the two constructs, with participants generally reporting higher levels of mastery orientation. No significant differences were observed across gender or age groups. These findings contribute to the cross-cultural validation of the LHQ, highlighting the importance of social desirability bias in self-reported measures and suggesting potential areas for refinement.