By understanding these factors, educators, mental healthprofessionals, and parents can develop targeted interventions and support systems topromote the well-being and academic success of
Significance of the Study 0G G55 S9 9 9 TH 0.00 0060996 8 E Scope of F€S€AaTFCHI: G5 (55c Họ TH Họ TT 0.000 0000 00900896 9 IL Literature ]ẹ€VI€W - 5 << << cọ HH 0 00.09104040 080 10 A Perfectionism in University Students - <5 G5 1 1950156 10 1 Define perfectionism and its CẽaSSIÍICAfIOH - s25 5E k**VE+EEseeseesekrsee 10 B OCD in University Student 0- G5 G5 5 9 99 0009 0096 5.0 13 1 Define OCD and its SYMPtOMS - - 5 c1 3221133339 EESESEEeeeerreeeerrkre 13 2 The factors contributing to the development and maintenance of OCD in
The impact of OCD on students’ daily activities, academic performance, and P15190)130)19 2227277577
OCD significantly impacts students' daily lives, academics, and relationships The persistent obsessions and compulsions consume time and energy, disrupting daily routines and activities This hinders time management, assignment completion, and studying, affecting academic performance OCD also strains relationships, causing students to withdraw and avoid social events due to fears related to their obsessions The preoccupation with rituals and compulsions can lead to social isolation, impacting friendships and overall well-being.
14 relationships, as well as limit opportunities for social and emotional growth A study conducted by Storch et al (2007) investigated the impact of OCD on academic and social functioning in college students The findings revealed that students with OCD reported higher levels of impairment in academic performance, as well as lower levels of social functioning compared to their peers without OCD The study emphasized the negative impact of OCD on various aspects of students’ lives and emphasized the importance of early intervention and support Understanding the impact of OCD on students' daily activities, academic performance, and relationships is crucial in providing appropriate support and intervention Cognitive-behavioral therapy (CBT) and medication management are commonly used treatments that can help students manage their symptoms and improve their overall functioning.
Relationship between Perfectionism and OCD s ô<< ô5< ssses<sse 15 1 The potential bidirectional relationship between perfectionism and OCD
How perfectionism may exacerbate OCD symptoms and vice versa
The relationship between perfectionism and Obsessive-Compulsive Disorder (OCD) is intricate and multifaceted, with each having the potential to amplify the other.
Perfectionism, defined by an intense desire for flawlessness and high standards, can significantly exacerbate the symptoms of OCD For example, a perfectionist student may constantly worry about making a mistake in their homework or tests This obsessive worry can lead to compulsive behaviors, such as repeatedly checking their work to ensure it's flawless, even when it disrupts their daily life or causes significant distress On the other hand, the symptoms of OCD can also intensify perfectionistic tendencies A person with OCD might have a compulsion to arrange things in a certain way or perform tasks in a specific order to prevent something bad from happening.This compulsion can reinforce their desire for everything to be 'perfect' or ‘just right’,thereby intensifying their perfectionistic tendencies This vicious cycle between perfectionism and OCD can lead to a significant increase in distress and impairment in daily functioning It underscores the importance of addressing both conditions in treatment to break this cycle and alleviate the associated distress and impairment.
The potential implications of this relationship for students’ well-being and
The relationship between perfectionism and OCD can have significant implications for students’ well-being and academic success Firstly, the presence of perfectionism and OCD symptoms can contribute to increased stress and anxiety levels among students. The constant need for perfection and the fear of making mistakes can create a constant state of pressure and self-criticism, negatively impacting their mental health This can lead to decreased overall well-being and an increased risk of developing other mental health issues Moreover, the presence of OCD symptoms can also affect students' academic success The repetitive behaviors and intrusive thoughts associated with OCD can consume a significant amount of time and mental energy, making it challenging for students to focus on their studies and complete tasks efficiently This can result in decreased productivity, difficulty meeting deadlines, and lower academic performance Additionally, the negative impact of perfectionism on students’ well- being can further hinder their academic success The fear of failure and the constant pursuit of perfection can lead to avoidance of challenging tasks or taking risks in fear of not meeting their own high standards This can limit students' growth and learning opportunities, preventing them from reaching their full potential It is crucial for educators, parents, and mental health professionals to recognize the potential
Perfectionism and OCD share a complex relationship, impacting students in multifaceted ways By addressing both conditions, educators can foster healthier coping mechanisms, stress management, and a balanced academic approach By promoting a growth mindset, self-compassion, and focusing on progress rather than perfection, institutions can enhance students' overall well-being and academic achievements.
D Factors Affecting Perfectionism and OCD in Students
Various factors can contribute to the development and exacerbation of perfectionism and OCD symptoms in students The environment in which students grow up plays a crucial role High parental expectations, academic pressure, and a competitive school environment can shape perfectionistic tendencies Students who constantly receive messages that their worth is tied to their achievements may internalize these expectations and develop an intense fear of failure Research by Stoeber and Stoeber
(2009) supports the influence of environmental factors on the prevalence of perfectionism Moreover, exposure to stressful or traumatic events can trigger OCD symptoms in susceptible students Traumatic experiences such as bullying, abuse, or major life changes can disrupt the sense of control and safety, leading to the development of obsessive thoughts and compulsive behaviors as a coping mechanism. Cognitive factors also play a significant role in the development of perfectionism and OCD Maladaptive beliefs, such as the need for control, the importance of preventing harm, or the need for symmetry and order, can contribute to the manifestation of these conditions For example, individuals with OCD may believe that if they don't perform certain rituals or compulsions, something terrible will happen Similarly, perfectionistic tendencies can be reinforced by cognitive distortions, such as all-or- nothing thinking or the belief that mistakes are unacceptable Frost and Steketee
(1997) found that cognitive processes contribute to perfectionism in individuals with OCD Furthermore, genetic and biological factors contribute to the vulnerability to perfectionism and OCD Research suggests that there is a genetic predisposition to these conditions, with certain genetic variations increasing the likelihood of their development Additionally, neurochemical imbalances, particularly involving serotonin, have been associated with OCD symptoms Taylor and Asmundson (2004) highlight the significance of genetic factors and abnormal serotonin levels in the
17 manifestation of OCD symptoms Understanding these factors can help educators, parents, and mental health professionals identify at-risk students and provide appropriate support and interventions By addressing these underlying factors through therapy, stress management techniques, and promoting a balanced approach to academic and personal growth, it is possible to promote healthier coping strategies, reduce the impact of perfectionism and OCD symptoms, and enhance students’ overall well-being and academic success.
Logistic regression method is a statistical analysis method based on analyzing the relationship between a dependent binary variable and 1 or more independent variables at the definitional, ordinal and proportional levels This method is used effectively in the healthcare field This review is based on the document “Understanding Logistic Regression Analysis" by Sandro Sperandei Logistic regression is similar to multiple linear regression, but it is used when the response variable is official The result of this method is the impact of each variable on the odds ratio of the observed event of interest This method has the ability to avoid confounding effects by analyzing the relationship of variables with each other However, interpreting the results of logistic regression is complicated especially when dealing with proportions and probabilities, continuous explanatory variables, variables with more than two levels, and the choice of variables to include model Therefore, it is necessary to set it up correctly in logistic regression analysis Sperandei uses a fictional study to illustrate the application of logistic regression In this study, the effects of two pharmacological treatments on Staphylococcus Aureus (SA) endocarditis were compared The odds ratio (OR) of death for patients receiving standard treatment was calculated, showing that these patients were 3.71 times more likely to die than patients receiving new treatment. Regression is a powerful tool however it requires careful model construction and interpretation.
0.3 ` @ Correct predictions (actual responses), yi
0.1- bự, — Estimated logistic regression line, p(x) oo ® ® ¢ @ Logit, F(x)
Figure 1 Single-variate logistic regression
Green represents actual responses as well as accurate predictions x red represents wrong predictions The full black line is the estimated logistic regression line () And in gray are the points on this line that correspond to and values in the second column of the probability matrix The black dashed line is logit() A value of slightly above 2 corresponds to the threshold ()=0.5, which is ()=0 This value is the boundary between points classified as Os and those predicted as 1s.
In a survey of 150 VNUIS students, factors such as stress, anxiety, academic pressure, perfectionism, and OCD symptoms will be assessed through online administration. This study aims to understand the prevalence and relationships between these factors among VNUIS students for a comprehensive understanding of their mental health and well-being The findings from this survey can help inform interventions and support programs to address these issues and promote a healthy academic environment.
Logistic regression, a statistical technique, will be employed to analyze responses from a survey administered to 150 VNUIS students This analysis aims to establish the variables that influence perfectionism and their correlation with the manifestation of OCD symptoms The independent variables considered in the regression model encompass various factors, including personality traits and environmental influences.
19 as stress, anxiety, academic pressure, and perfectionism itself The dependent variable will be a binary variable indicating the presence or absence of OCD Research suggests that perfectionism is a cognitive variable that plays a role in obsessive- compulsive disorder (OCD) While perfectionism is a personality trait, OCD is a diagnosable mental health condition However, there is evidence to suggest that perfectionism can be a trait seen in some individuals with OCD Understanding the relationship between perfectionism and OCD symptoms can provide insights into the specific role of perfectionism in certain forms of obsessive-compulsive symptoms By conducting logistic regression analysis, researchers can examine the associations between the independent variables (stress, anxiety, academic pressure, and perfectionism) and the presence of OCD symptoms This analysis can help identify which factors are significantly related to the presence of OCD symptoms among VNUIS students The findings can contribute to a better understanding of the complex relationship between perfectionism and OCD and inform interventions and support strategies for students experiencing these symptoms It is important to note that the results of the logistic regression analysis should be interpreted cautiously, as correlation does not imply causation Additionally, the study's findings may be limited to the specific sample of VNUIS students and may not be generalizable to other populations Further research is needed to validate and expand upon these findings.Overall, the use of logistic regression in analyzing the data from the survey of VNUIS students can provide valuable insights into the relationship between perfectionism and the presence of OCD symptoms This analysis can contribute to the understanding of the factors associated with OCD and inform interventions to support the mental health and well-being of students.
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Logistic regression, a statistical method, can be employed to construct a model that gauges the probability of a VNUIS student developing OCD This technique examines the correlation between independent variables (e.g., stress, anxiety, academic pressure, perfectionism) and a binary dependent variable These independent variables will be measured using a survey distributed among VNUIS students The dependent variable will be a binary indicator denoting the presence or absence of OCD symptoms.
20 the presence or absence of OCD symptoms By estimating the odds of developing OCD for a given level of each independent variable, the logistic regression model can provide insights into the relationship between these factors and the likelihood of developing OCD The model can estimate the impact of each independent variable on the odds of developing OCD, taking into account the interplay between these variables The results of the logistic regression analysis can be used to develop a predictive model that considers the levels of stress, anxiety, academic pressure, and perfectionism to determine the likelihood of a VNUIS student developing OCD This model can be valuable in identifying individuals who may be at a higher risk of developing OCD and can inform targeted interventions and support strategies to address their mental health needs It is important to note that the logistic regression model should be validated using additional data to ensure its accuracy and generalizability Additionally, the model's predictions should be interpreted with caution, as correlation does not imply causation Further research and validation are necessary to refine and enhance the predictive model for OCD among VNUIS students.
Use of a holdout sample of VNUIS students
Testing of the accuracy of the model
Adherence to the ethical principles of the Declaration of Helsinki
Refer to the Institutional Review Board of Vietnam National University, Hanoi.
Publication in scientific research competition of VNUIS 2024.
Completion within over a half year.
This research project is expected to make a significant contribution to the understanding of OCD among Vietnamese university students and contribute to the broader understanding of OCD and its risk factors.
The survey included 150 VNUIS students, including 86 females and 64 males aged 18 to 25 years old Results showed a common occurrence of symptoms related to OCD and nepotism, all in this age group This idea highlights the potential importance of these aspects of mental health in young adults in college settings Understanding the prevalence of OCD and perfectionism in this age group is important for promoting students' mental health and academic success.
The research describes a research project on factors affecting perfectionism and OCD in students Here's the Instruments section based on the information provided:
A report survey was developed with 20 questions to assess: The survey design likely included input from a New York Psychology Master to ensure its relevance and effectiveness in identifying OCD and perfectionism in students.
The project employed a logistic regression model to analyze the survey data.
This model is designed to predict the probability of a particular outcome (having OCD in this case) based on a set of independent variables (survey responses).
Feature selection: Choosing the most relevant survey questions that might be indicative of OCD.
Data pre-processing: Cleaning and preparing the survey data for analysis (e.g., handling missing values, normalization).
Model building: Training the logistic regression model using the pre-processed data.
Model evaluation: Splitting the data into training and testing sets to assess the model's accuracy in predicting OCD.
Diagnosis: Applying the trained model to new data (potentially future student surveys) to estimate the probability of having OCD.
Data Analysis Procedures 08
Feature selection: Choosing the most relevant survey questions that might be indicative of OCD.
Data pre-processing: Cleaning and preparing the survey data for analysis (e.g., handling missing values, normalization).
Model building: Training the logistic regression model using the pre-processed data.
Model evaluation: Splitting the data into training and testing sets to assess the model's accuracy in predicting OCD.
Diagnosis: Applying the trained model to new data (potentially future student surveys) to estimate the probability of having OCD.
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The survey was administered to 150 students The target population was targeted at university students (VNUIS students were mentioned).
The survey's development was collaborative with the MA in Psychology in New York to ensure the survey's suitability and effectiveness in identifying OCD and perfectionism in students By incorporating the expertise of an expert in the field, the survey has been carefully designed to capture relevant aspects and symptoms associated with these conditions This collaboration helped ensure that the survey items were comprehensive and accurate in assessing OCD and perfectionism in students To ensure broad reach and maximize participation, the survey was distributed to students through a variety of channels, both online and offline This approach provides flexibility and convenience because students can access and complete the survey through mediums such as email and social media By using multiple distribution channels, the survey aimed to appeal to a diverse range of students and increase the likelihood of a representative sample Data collection was performed in a secure manner to protect participant confidentiality and privacy Completed surveys are collected and stored using a secure data management system, following established guidelines for data protection This ensures that information provided by participants remains confidential and protected from unauthorized access Overall, the survey development process included collaboration with the Master of Psychology,
To ensure accurate data collection and protect participants' privacy, the survey employed diverse distribution channels, prioritized confidentiality measures through data security protocols, and fostered an appropriate survey design to encourage participation These actions aimed to enhance survey quality and safeguard participant information.
1 Meaning of our data analysis:
Data analysis (analysis method using Logistic regression algorithm) allows us to find specific information from collected data (collected through a survey of 150 students, each There are 20 questions) This helps us better understand a problem or research situation (students with symptoms often suffer from OCD and perfectionism) We discovered patterns (150 students) or hidden patterns in data So that we can predict (diagnose OCD) and explain and treat OCD and perfectionism The relationships between variables (20 questions correspond to 20 independent variables, and the dependent variable is the diagnostic result) in the data The interaction and influence of different factors on the outcome of the psychological diagnosis of OCD and globalism.
Data analysis reliability is crucial for accurate and meaningful decision-making To ensure reliability, statistical principles and testing methods are employed Reliability requires a randomly selected, representative sample and data processing involving checking, cleaning, and normalization Logistic regression is appropriate for the data type and research goals Statistical testing, such as the widely used Wald test, is used to evaluate the reliability of logistic regression results by estimating standard errors and calculating z-scores, indicating the significance of coefficients.
24 value is greater than a determined threshold of 1.645, indicating statistical significance at the 0.05 level, indicating that the coefficient is significantly different To ensure reliability, we evaluate the analysis results thoroughly and clearly state them, including the analysis method using the Logistic regression algorithm, assumptions and limitations of the above study.
To select the most suitable features for the model, several steps were taken based on previous research projects and best practices First, the internal consistency of the survey responses was analyzed using Cronbach's alpha, which is a commonly used measure of reliability and internal consistency in exit surveys This analysis helps determine whether the survey questions reliably measure the same latent variables, such as perfectionism and OCD symptoms High Cronbach's alpha values indicate that the survey items are consistent and can be considered reliable measures of the constructs of interest Additionally, exploratory factor analysis was conducted to identify underlying factors or dimensions in the OCD and perfectionism questionnaires This analysis helps group related questions together and potentially reduces the number of features used in the model By identifying underlying factors, the survey can capture essential aspects of OCD and perfectionism, which in turn can help select the most appropriate questions for the model Based on the results of factor analysis and existing research on OCD assessment, a combination of questions representing the most relevant aspects of OCD and perfectionism was selected This selection process ensures that the model includes questions that are highly indicative of the concepts being measured, thereby improving the accuracy and effectiveness of the model in identifying OCD and spontaneity all of the students In summary, the feature selection process includes analyzing survey responses for internal consistency using Cronbach's alpha, performing exploratory factor analysis to identify underlying dimensions, and selecting incorporated questions representing the most relevant aspects of OCD and perfectionism based on current and factor analysis learn These measures were taken to ensure that the selected characteristics were reliable, valid, and comprehensive in assessing OCD and perfectionism in students.
Missing Values: Analyze missing data patterns to understand reasons for missingness (e.g., random, systematic) Apply appropriate techniques based on the pattern (e.g., mean/median imputation, listwise deletion for minimal missingness).
Outlier Detection: Identify and address outliers in the data that might skew the model's results This involves winsorizing (capping) outliers or removing extreme cases if justified.
Normalization: If the data for different survey questions uses different scales (e.g., Likert scale with varying ranges), apply normalization techniques (e.g., z-scores, min- max scaling) to ensure all features are on a comparable scale for the model.
To build a model to predict the incidence of obsessive disorder (OCD) in VNU students based on a survey of 150 students, we can follow the following steps.
First, divide the data into two parts: training set and testing set The training set is the larger portion (75%) and is used to train the model, while the test set is the smaller portion (25%) and is used to evaluate the model's performance on unknown data see. Make sure that both sets have a balanced ratio of classes (OCD vs non-OCD) to ensure balance during training and assessment.
Next, we process the data by checking and handling missing values (if any) in the data.
If necessary, we can also standardize the data to ensure that the attributes have the same range of values.
Then, we build a logistic regression model to predict the incidence of OCD in students. This model estimates coefficients (weights) for each question (characteristic) in the survey The goal is to find optimal coefficients so that the model has the best ability to predict the likelihood of having OCD based on the survey questions.
To enhance model generalization and prevent overfitting, regularization techniques are crucial L1 (Lasso) or L2 (Ridge) regularization effectively minimizes the impact of irrelevant or highly correlated independent variables in prediction outcomes By reducing the influence of such variables, these techniques help ensure model robustness and improve its performance on unseen data.
Finally, we evaluate the performance of the model using the test set We can use
26 metrics such as accuracy, sensitivity, specificity and Fl-score to measure the model's predictive ability.
The model building process can be refined and adjusted based on the evaluation results We can test and compare different models to find the best model for the problem of predicting the incidence of OCD in VNU students based on survey data.
Evaluate the model's performance on the testing set using metrics like:
Accuracy: The ratio of correct predictions to the total number of predictions Used to evaluate overall model performance.
Sensitivity: The model's ability to detect true positives Often used in critical situations, for example disease detection.
Specificity: The model's ability to detect true negatives Used to evaluate the ability to eliminate unwanted cases.
Precision: The proportion of correct predictions among positive predictions Useful when we are interested in avoiding false positive predictions.
Fl-score (Fl-score): Combination of sensitivity and positive accuracy Used to evaluate the overall performance of the model, especially when the data is unbalanced.
Use the trained logistic regression model on new data (e.g., future student surveys) to estimate the probability of having OCD for each participant.
Set a threshold probability (e.g., 0.5) to classify participants as potentially having OCD(above the threshold) or unlikely to have OCD (below the threshold) This is a preliminary assessment, and the model's output should not be used for definitive diagnosis Encourage participants with a high probability to seek professional evaluation from a qualified mental health professional.
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Explore the possibility of using alternative classification algorithms (e.g., decision trees, random forests) if the logistic regression model's performance is not satisfactory.
Conduct internal validation techniques (e.g., cross-validation) to ensure the model's generalizability and avoid overfitting on the specific training data used.
The research was conducted on a sample of 150 students from Vietnam National University, Hanoi, who participated in a quick survey (the survey included 20 questions).
This study aimed to investigate perfectionism, obsessive-compulsive disorder (OCD), and mild symptoms in a sample of 150 students We collected data on these indicators and conducted statistical analyzes to better understand the status of these factors in the student sample To measure levels of perfectionism, we converted the prevalence of OCD into percentage form An OCD prevalence of 1.5% is converted to 0.015 Next, we calculate the difference between each OCD prevalence value and the mean With the average value of OCD incidence being 0.01, we have a difference of 0.015 - 0.01 = 0.005 We squared this difference to calculate the variance, which results in 0.000025.
We then calculate the average of the squares by dividing the sum of the squares by the number of students in the sample For our sample of 150 students, we calculated the mean of squares to be 0.000025 / 150 = 0.0000001667 Finally, we took the square root of the mean square to calculate the standard deviation of OCD prevalence in the student sample The result is the standard deviation value, which measures the variability in OCD prevalence within the sample We gained a clearer view of perfectionism and obsessive-compulsive disorder in our sample of students Note, however, that these results are only estimates based on the sample and may not be absolutely accurate for the entire population.
Perfectionism: Report the 25th percentile (Q1), 50th percentile (Median or Q2), and 75th percentile (Q3) of perfectionism in the student sample.
4 Proportions: a Obsessive-compulsive disorder: The prevalence of OCD in the student sample was 1.5% (about 2 students). b Mild symptoms: The proportion of students with mild symptoms was 35% (about
This is a Spearman correlation matrix plot, also known as a heat plot of the Spearman correlation coefficient This chart is used to visualize the degree of correlation between
20 variables in a survey data set of 150 students:
Feature 1 Feature 2 Feature 4 Feature 5 Feature 6 Feature 7 Feature & Feature 9 ir Feature 10 Feature 11 Feature 12 Feature 13 Feature 14 Feature 15 Feature 16 Feature 17 Feature 18 Feature 19 Feature 20
Figure 2 Heatmap of the Spearman correlation coefficient.
Figure 3 The pie chart represents our diagnostic results.
The pie chart represents our diagnostic results The chart clearly shows that 63.5% do not have OC psychology, 35% have mild symptoms and 1.5% are diagnosed with OCD and perfectionism.
Details of the illustrative chart:
Data: The chart shows the percentage of macOS users across three levels of OCD:
Each level of OCD is represented by a different color:
The chart has labels for each segment, showing the percentage of people at the corresponding level of OCD.
The graph is captioned "Suffering from OCD," indicating that the graph is showing data on rates by severity of OCD.
Based on the data shown in the graph, it can be seen that the majority of people do not have OCD (63.5%) However, there is also a significant proportion of macOS users who have mild symptoms (35.0%) or OCD (1.5%).
Practice the Logistic Regression model simulating the diagnosis of 150 students participating in an interview survey: whether they have OCD with symptoms or not:
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Figure 4 Logistic Regression chart of students participating in the survey
The data includes a total of 3000 values including 150 samples and 20 attributes each. The output of this Logistic regression algorithm shows that with a threshold of 0.5, there are 60 sample values greater than the threshold (60 values corresponding to 1-2% of students participating in the survey) and 1200 values close to the threshold of 0.5
(60 values corresponding to 1-2% of students participating in the survey) 1200 values
31 equivalent to 30 - 40% of the total 150 students participating in the survey).
Performance data and accuracy index of this Logistic Regression model in a study of 150 students participating in a survey on OCD and globalism:
Figure 5 Graph of indicators evaluating the model's accuracy
Performance indicators evaluating my model:
Accuracy: The ratio of correct predictions to the total number of predictions Used to evaluate overall model performance.
Sensitivity: The model's ability to detect true positives Often used in critical situations, for example disease detection.
Specificity: The model's ability to detect true negatives Used to evaluate the ability to eliminate unwanted cases.
Precision: The proportion of correct predictions among positive predictions Useful when we are interested in avoiding false positive predictions.
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20 questions in the survey: These 20 variables can be numeric or categorical variables, depending on how the data is collected.
For example, number of points per question, time to answer each question, etc.
Category variables: For example, gender, age, education level, etc.
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Use training data to develop a logistic regression model that predicts the probability of disease based on specific attributes.* Ensure that the input variables are relevant and avoid multicollinearity by removing redundant variables.* Address overfitting by employing regularization techniques or data augmentation to improve model accuracy and generalization.
Using the test set, evaluate the performance of the Logistic Regression model using measures such as accuracy, sensitivity, specificity and Fl-score This helps determine the model's predictive ability and evaluate its fit to the data.
For the rate of 1.6% out of 150 survey participants with OCD: You can use the Logistic Regression model to predict the probability of having OCD for each student. You can then apply a defined threshold to classify the predictions into two groups: disease or no disease The predicted probability of a student having OCD exceeds the threshold of 0.5, you can predict that the student has OCD.
For the rate of 36% of the 150 students participating in the survey having OCD symptoms: Similarly, you can use the Logistic Regression model to predict the probability of having OCD symptoms for each student A defined threshold was then applied to classify the predictions into two groups: OCD symptoms present or OCD symptoms absent The predicted probability of a student having OCD symptoms crosses the threshold of 0.36, you can predict that the student has OCD symptoms.
Evaluate inference: Evaluate the accuracy of the inference model by comparing the model's predictions with reality You can calculate the accuracy, sensitivity, specificity, and Fl-score for both classification cases to evaluate the performance of the inference model.
The coefficients in Logistic Regression models indicate the influence of independent variables on the dependent variable, with positive coefficients representing positive correlations and negative coefficients representing negative correlations The absolute value of coefficients determines the importance of each independent variable in the model, with higher absolute values indicating greater predictive power Consider the sign of the coefficient to discern the direction of correlation: positive coefficients imply that increasing the independent variable increases the likelihood of the dependent variable, while negative coefficients imply the opposite To derive practical meaning from the coefficients, relate them to the context of the research, considering the significance of the corresponding questions or attributes in predicting the dependent variable.
34 or having symptoms OCD It is important to remember that the Logistic Regression model only provides an approximate view of the relationship between variables and the dependent variable The coefficients can be affected by noise and model assumptions Therefore, it is necessary to consider the limitations and uncertainties of the results The meaning of the results and the importance of the Logistic Regression model, it is necessary to consider its limitations For example, the Logistic Regression model assumes a linear relationship between the independent variable and the dependent variable, which may not be appropriate in cases where the relationship is non-linear Additionally, the model may be affected by confounding variables, over- importance of some variables, or lack of evidence of a disruptive relationship This should be considered in the interpretation of results.
The research project aims to study the factors that contribute to the development and exacerbation of perfectionism and obsessive-compulsive disorder (OCD) ) in college students This study provides a comprehensive overview of OCD, including its symptoms and prevalence in school children It highlights the significant impact OCD has on students' daily activities, academic performance and relationships The study also examined the relationship between perfectionism and OCD, highlighting the mutual influence these constructs can have on each other Collecting data for research poses challenges due to the sensitive nature of OCD However, under the guidance of a trained psychologist, the research team collected survey responses from 150 participants The team applied logistic regression analysis to the data to identify significant factors associated with OCD diagnosis Research shows a complex interaction between perfectionism and OCD Each has the potential to amplify the other, suggesting a two-way relationship Perfectionism can contribute to the development of OCD, and OCD symptoms can also make perfectionistic tendencies worse This relationship can lead to worsening of both conditions Research highlights the potential impacts of this relationship on students' academic success and well-being. The presence of symptoms of perfectionism and OCD can contribute to increased levels of stress and anxiety in students, negatively affecting their mental health and academic performance This study highlights factors that influence perfectionism and OCD in students By understanding these factors, educators, mental health professionals, and parents can develop targeted intervention and support systems that promote children's well-being and academic success Children are affected by perfectionism and OCD.
VI Limitations of the Study
This research project has limitations that should be considered Firstly, the study will be limited to VNUIS students, which may limit the generalizability of the findings to
J9 other populations Different student populations may have unique characteristics and experiences that could impact the factors associated with OCD Secondly, the study will be cross-sectional, capturing data at a specific point in time This design does not allow for the determination of causal relationships between the factors and OCD. Longitudinal studies would be needed to establish causal relationships and better understand the temporal dynamics of OCD development Another limitation is that the study will rely on self-reported data, which may be subject to recall bias Participants may not accurately remember or report their experiences, leading to potential inaccuracies in the collected data Additionally, self-report measures may not capture the full complexity of OCD symptoms and related factors.Despite these limitations, this research project is expected to contribute to the understanding of OCD among Vietnamese university students and inform the development of intervention programs. However, caution should be exercised when generalizing the findings beyond the specific population and context of VNUIS students.
This research project may be limited by the availability of resources, such as funding and time These limitations can impact the scope and scale of the study, potentially affecting the sample size and data collection methods Additionally, the study may be limited by the availability of data, such as data on the prevalence of OCD among VNUIS students The lack of comprehensive data on OCD within this specific population may restrict the depth of analysis and generalizability of the findings. Furthermore, the expertise of the researchers involved in the study may also be a limitation The effectiveness of the research and interpretation of the results can be influenced by the knowledge and experience of the researchers Despite these limitations, this research project is expected to provide valuable insights into the factors associated with OCD among Vietnamese university students The findings can still contribute to the understanding of OCD and inform the development of intervention programs tailored to the specific needs of VNUIS students.
This research project may face limitations due to the availability of resources, such as funding and time However, the researchers will work diligently to secure the
36 necessary resources to support the study Additionally, the availability of data on the prevalence of OCD among VNUIS students may pose a limitation ( Indian J Psychiatry 2010 Jan; 52(Suppll): S200-5S209) The researchers will make efforts to collect this data to enhance the understanding of OCD in this specific population. Furthermore, the expertise of the researchers in the field of OCD may also be a limitation (World J Psychiatry 2012 Dec 22) To address this, the researchers will actively work to build their expertise and knowledge in the field Despite these limitations, the research project is expected to make a significant contribution to the understanding of OCD among Vietnamese university students and the broader understanding of OCD and its risk factors.
Research project investigates factors influencing perfectionism and obsessive- compulsive disorder (OCD) among Vietnamese university students The main goal of the study was to understand the relationship between perfectionism and OCD and how the two concepts may influence each other The research method included surveying
150 students, assessing factors such as stress, anxiety and academic pressure The data collected were then analyzed using a logistic regression model to identify factors that may contribute to the diagnosis of OCD This study provides a comprehensive overview of OCD, including its symptoms and prevalence in school children It highlights the impact of OCD on students' daily activities, academic performance, and relationships The study also examined the relationship between perfectionism and OCD, highlighting how these constructs may influence each other Although limited due to the sensitive nature of the mental health issue, which may lead to difficulties in data collection, this study is expected to contribute significantly to the understanding of OCD among students Vietnamese university student This understanding is important for informing the development of intervention programs The paper also describes the detailed methodology for the study, including data collection and analysis procedures, model development and validation, and strategies to address limitations The research results are expected to have important implications in the prevention and treatment of OCD in Vietnamese university students In conclusion,
37 this study provides valuable information about factors influencing perfectionism and OCD in college students By understanding these factors, educators, mental health professionals, and parents can develop targeted intervention and support systems that promote children's well-being and academic success Children are affected by perfectionism and OCD.
Research project investigates factors influencing perfectionism and obsessive- compulsive disorder (OCD) among Vietnamese university students The main goal of the study was to understand the relationship between perfectionism and OCD and how the two concepts may influence each other The research method included surveying