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Descriptive Analysis of Demographic and Psychological Data from YassineRebei.sav

YassineRebei.sav

Missing file

I want a descriptive analysis of this datasource

The dataset from the file YassineRebei.sav contains a variety of columns with different types of data. Here's a brief overview of the data:
  1. Demographic Information:
    • NUMFICH: Nominal identifier for each record.
    • Dossier: Nominal identifier for each case.
    • NP: Nominal identifier for each participant.
    • AGE: Scale variable representing the age of participants.
    • SEXE: Nominal variable indicating gender.
    • Origine: Nominal variable indicating origin.
    • NivEtud: Nominal variable indicating education level.
    • Travail: Nominal variable indicating employment status.
    • ActivProf: Nominal variable indicating professional activity.
    • StatMatrimonial: Nominal variable indicating marital status.
  2. Psychological Measures:
    • Anxiety: Scale variable measuring anxiety levels.
    • Depression: Scale variable measuring depression levels.
    • AnxietyNiV: Nominal variable indicating anxiety level.
    • DepressionNiV: Nominal variable indicating depression level.
    • BFIExtrav: Scale variable measuring extraversion.
    • BFIAgreab: Scale variable measuring agreeableness.
    • BFIConsc: Scale variable measuring conscientiousness.
    • BFINev: Scale variable measuring neuroticism.
    • BFIOuv: Scale variable measuring openness to experience.
    • SSCI8: Scale variable measuring social support.
This dataset provides a comprehensive view of both demographic and psychological attributes of the participants. It can be used for various analyses, such as exploring relationships between demographic factors and psychological measures, or assessing the distribution of psychological traits within the sample.
If you have specific analyses or visualizations in mind, feel free to ask!
The descriptive analysis of the dataset provides a summary of the central tendency, dispersion, and shape of the dataset's distribution. Here are some key insights:
  1. Demographic Information:
    • AGE: The average age of participants is approximately 34 years, with a standard deviation of about 10 years. The age ranges from 18 to 70 years.
    • SEXE: The dataset has a higher frequency of one gender (coded as 1) compared to the other (coded as 0).
  2. Psychological Measures:
    • Anxiety and Depression: These variables have a wide range of values, indicating variability in the levels of anxiety and depression among participants.
    • BFIExtrav (Extraversion), BFIAgreab (Agreeableness), BFIConsc (Conscientiousness), BFINev (Neuroticism), BFIOuv (Openness to Experience): These personality traits have mean values around 7, with varying standard deviations, suggesting differences in personality traits among participants.
  3. Social Support:
    • SSCI8: The average score is around 13.5, with a standard deviation of about 5.3, indicating variability in perceived social support.
This analysis provides a foundational understanding of the dataset's structure and the distribution of key variables. If you have specific questions or need further analysis, feel free to ask!

i want a brief description of every demographic and social result

Let's provide a brief description of each demographic and social variable based on the dataset:
  1. AGE:
    • Mean: Approximately 34 years
    • Range: 18 to 70 years
    • Description: The dataset includes a wide age range, with most participants being in their mid-30s.
  2. SEXE:
    • Description: This variable indicates gender, with two categories (0 and 1). The dataset has a higher frequency of one gender.
  3. Origine:
    • Description: Represents the origin of participants. The dataset includes multiple categories, indicating diverse backgrounds.
  4. NivEtud (Education Level):
    • Description: Indicates the education level of participants, with multiple categories.
  5. Travail (Employment Status):
    • Description: Indicates whether participants are employed or not, with two categories.
  6. ActivProf (Professional Activity):
    • Description: Represents the type of professional activity participants are engaged in.
  7. StatMatrimonial (Marital Status):
    • Description: Indicates the marital status of participants, with multiple categories.
  8. SSCI8 (Social Support):
    • Mean: Approximately 13.5
    • Range: 8 to 28
    • Description: Measures perceived social support, with a wide range indicating variability in support levels among participants.
These descriptions provide an overview of the demographic and social variables in the dataset, highlighting the diversity and distribution of the sample. If you need further details or specific analyses, feel free to ask!

How does education level affect anxiety and depression?

The bar chart above illustrates the average levels of Anxiety and Depression across different education levels (NivEtud). Here's a summary of the findings:
  • Anxiety: The mean anxiety scores vary across education levels, indicating that education might have an influence on anxiety levels.
  • Depression: Similarly, the mean depression scores also show variation across different education levels.
This visualization suggests that education level could be a factor influencing both anxiety and depression, although further statistical analysis would be needed to determine the strength and significance of these relationships.
If you have any further questions or need additional analysis, feel free to ask!

We will check to see if any demographic or social measure affect Self-management MStotal

The column MStotal exists in the dataset, which represents the self-management score. Let's analyze how different demographic and social measures affect MStotal.
The pairplot above visualizes the relationships between the self-management score (MStotal) and various demographic and social measures:
  • AGE: There seems to be a slight trend indicating that age might have an impact on self-management scores.
  • SEXE: The distribution suggests potential differences in self-management scores between genders.
  • Origine: Different origins show varying self-management scores, indicating a possible influence.
  • NivEtud (Education Level): There appears to be a relationship between education level and self-management scores.
  • Travail (Employment Status): Employment status might affect self-management scores.
  • ActivProf (Professional Activity): Different professional activities show varying self-management scores.
  • StatMatrimonial (Marital Status): Marital status might have an impact on self-management scores.
  • SSCI8 (Social Support): There seems to be a relationship between social support and self-management scores.
These visualizations provide an initial insight into how demographic and social measures might affect self-management. Further statistical analysis would be needed to confirm these observations.
If you have any further questions or need additional analysis, feel free to ask!

Can you perform a statistical test to confirm these relationships?

The results of the linear regression analysis are summarized below:
  • R-squared: 0.056, indicating that approximately 5.6% of the variance in self-management scores (MStotal) is explained by the demographic and social measures included in the model.
  • F-statistic: 0.6687 with a p-value of 0.718, suggesting that the overall model is not statistically significant.

Coefficients and Significance:

  • AGE: Coefficient of 0.0189, p-value = 0.787
  • SEXE: Coefficient of -0.6185, p-value = 0.598
  • Origine: Coefficient of -0.1538, p-value = 0.919
  • NivEtud: Coefficient of -0.5359, p-value = 0.765
  • Travail: Coefficient of -1.8139, p-value = 0.512
  • ActivProf: Coefficient of -1.1780, p-value = 0.585
  • StatMatrimonial: Coefficient of 0.4741, p-value = 0.614
  • SSCI8: Coefficient of -0.1992, p-value = 0.307
None of the individual predictors are statistically significant at the conventional 0.05 level, suggesting that these demographic and social measures do not have a significant impact on self-management scores in this dataset.
If you have further questions or need additional analysis, feel free to ask!

What other analyses can we perform on this dataset?

There are several analyses that can be performed on this dataset, depending on your research questions and interests. Here are some possibilities:
  1. Descriptive Statistics:
    • Calculate summary statistics (mean, median, standard deviation) for key variables.
    • Explore the distribution of demographic variables such as age, gender, and education level.
  2. Correlation Analysis:
    • Examine the correlation between psychological measures (e.g., anxiety, depression) and self-management scores.
    • Investigate the relationship between social support and psychological well-being.
  3. Regression Analysis:
    • Perform multiple regression to explore the combined effect of various predictors on outcomes like anxiety or depression.
    • Use logistic regression if you have binary outcomes.
  4. Group Comparisons:
    • Conduct t-tests or ANOVA to compare psychological measures across different demographic groups (e.g., gender, education level).
    • Explore differences in self-management scores based on employment status or marital status.
  5. Factor Analysis:
    • Conduct exploratory factor analysis to identify underlying factors in psychological measures.
    • Confirmatory factor analysis to test specific hypotheses about factor structures.
  6. Machine Learning:
    • Use clustering algorithms to identify patterns or groups within the data.
    • Apply classification algorithms to predict outcomes based on demographic and psychological measures.
  7. Visualization:
    • Create visualizations to explore relationships and distributions in the data.
    • Use heatmaps, scatter plots, and box plots to gain insights.
If you have a specific analysis in mind or need guidance on how to proceed with any of these analyses, feel free to ask!

what are the most significant predictors of high self management score in this dataset

After cleaning the dataset by removing rows with missing values, the linear regression analysis was performed again. Here are the results:
  • R-squared: 0.056, indicating that approximately 5.6% of the variance in self-management scores (MStotal) is explained by the demographic and social measures included in the model.
  • F-statistic: 0.6687 with a p-value of 0.718, suggesting that the overall model is not statistically significant.

Coefficients and Significance:

  • AGE: Coefficient of 0.0189, p-value = 0.787
  • SEXE: Coefficient of -0.6185, p-value = 0.598
  • Origine: Coefficient of -0.1538, p-value = 0.919
  • NivEtud: Coefficient of -0.5359, p-value = 0.765
  • Travail: Coefficient of -1.8139, p-value = 0.512
  • ActivProf: Coefficient of -1.1780, p-value = 0.585
  • StatMatrimonial: Coefficient of 0.4741, p-value = 0.614
  • SSCI8: Coefficient of -0.1992, p-value = 0.307
None of the individual predictors are statistically significant at the conventional 0.05 level, suggesting that these demographic and social measures do not have a significant impact on self-management scores in this dataset.
If you have further questions or need additional analysis, feel free to ask!

Can we visualize the distribution of self-management scores?

Can you analyze the correlation between self-management scores and other variables?

The correlation matrix provides insights into the relationships between self-management scores (MStotal) and other variables in the dataset:
  • Self-Management Score (MStotal):
    • AGE: Correlation of 0.081, indicating a weak positive relationship.
    • SEXE: Correlation of 0.015, indicating a very weak positive relationship.
    • Origine: Correlation of -0.076, indicating a weak negative relationship.
    • NivEtud: Correlation of -0.054, indicating a weak negative relationship.
    • Travail: Correlation of -0.141, indicating a weak negative relationship.
    • ActivProf: Correlation of -0.120, indicating a weak negative relationship.
    • StatMatrimonial: Correlation of 0.001, indicating no significant relationship.
    • SSCI8: Correlation of -0.122, indicating a weak negative relationship.
Overall, the correlations between self-management scores and other variables are weak, suggesting that none of these variables have a strong linear relationship with self-management scores in this dataset.
If you have further questions or need additional analysis, feel free to ask!

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