Multivariate analysis variable presentation

Multivariate analysis, types of Multivariate analysis, different variables of Multivariate analysis etc

1. MULTIVARIATE ANALYSIS PRESENTED BY Nitin Maharjan Shrawak Maharjan Sujan Bhandari Palistha Maharjan

2. INTRODUCTION Statistical method used to analyze data sets that involve multiple variables. Considers three or more variables simultaneously. Particularly valuable when studying complex systems or phenomena where multiple factors influence the outcomes. Hours spend on Instagram per day vs. Self-esteem score 01 - MALE 02 - FEMALE

3. 01 - POWERFUL FRAMEWORK 02 - COMPREHENSIVE APPROACH 03 - POWERFUL TOOL It provides a powerful framework for researchers and analysts to gain a deeper understanding of complex relationships within data sets It offers a comprehensive approach to understanding complex relationships within datasets with multiple variables It offers a powerful tool for researchers and analysts seeking to uncover nuanced insights from complex datasets USAGE OF MULTIVARIATE ANALYSIS

4. COMMON TECHNIQUES IN MULTI VARIATE ANALYSIS Factor Analysis Cluster Analysis Principal Component Analysis Multivariate Analysis of Variance (MONAVA) Canonical Correlation Analysis (CCA)

5. WHAT IS FACTOR ANALYSIS? Consider you have 50 students, and we need to put them in 6 rooms. We need to see which students have the same habits so they can stay in the same room Now consider variables are your rooms and items to measure these variables are students Factor analysis helps us in identifying these things

6. A technique that is used to reduce a large number of variables into fewer number of factors This technique extracts maximum common variance from all variables and puts them into a common score FACTOR ANALYSIS

7. SPSS (Statistical Package for the Social Sciences) is a powerful software program widely used for statistical analysis in various fields, including social sciences, business, and health research. It provides a user-friendly interface, making it accessible to researchers, analysts, and students. SPSS

8. SPSS

9. STEPS FOR FACTOR ANALYSIS USING SPSS STEP 1: CHOOSING FACTOR VARIABLES STEP 2: LAUNCHING FACTOR ANALYSIS •Open your dataset in SPSS. •Identify the continuous variables you want to include in the factor analysis. •Ensure that the variables are appropriate for factor analysis (interval or ratio scale). •Navigate to the "Analyze" menu. •Select "Dimension Reduction" and then choose "Factor..."

10. STEPS FOR FACTOR ANALYSIS USING SPSS STEP 3: SPECIFYING PARAMETERS •In the "Factor Analysis" dialog box: •Move the variables from the left to the "Variables" box on the right. •Select the extraction method (e.g., Principal Components or Principal Axis Factoring). •Specify the number of factors to extract. •Choose a rotation method (e.g., Varimax or Promax). •Set other options based on your analysis needs. •Click the "OK" button to run the factor analysis.

11. STEPS FOR FACTOR ANALYSIS USING SPSS STEP 4: INTERPRETING THE OUTPUT •Examine the output, which typically includes several tables and charts: •Descriptive Statistics: Provides mean, standard deviation, etc., for each variable. •KMO and Bartlett's Test: Checks for the suitability of the data for factor analysis. •Total Variance Explained: Indicates the proportion of variance explained by the extracted factors. •Eigenvalues: Show the variance explained by each factor. •Scree Plot: Visual aid for determining the number of factors to retain. •Factor Loadings Table: Displays the correlation between each variable and each factor. •Rotated Component Matrix: If rotation is applied, this matrix simplifies the factor structure. •Interpret the factor loadings, consider the eigenvalues, and decide on the number of factors to retain. •Review the rotated component matrix if rotation was applied for a clearer interpretation of factors.

12. APPLICATIONS Psychometrics: Market Research: Social Sciences: Finance: In psychology, factor analysis is used to understand the latent constructs underlying observed behaviors or traits. In market research, it can be used to identify underlying factors influencing consumer preferences. Used to explore underlying factors influencing various social phenomena. In finance, it can be applied to identify latent factors influencing stock prices.

13. •Technique of grouping individuals objects or cases into relatively homogeneous groups that are often referred to as clusters. •the number of mutually exclusive and collectively exclusive clusters in the Determine population. •Reduces a large number of variables or cases into a smaller number of factors or clusters. •Two approaches of clustering: Hierarchical clustering approach Non-hierarchical clustering approach CLUSTER ANALYSIS

14. STEPS FOR CLUSTER ANALYSIS USING SPPS Step 1: Step 2: Step 3: Step 4: Choosing Cluster Variables Selecting Cluster Method Specifying Parameters Interpreting the Output 1 2 3 4

15. MDS is a statistical technique used to visualize and analyze relationships within complex data. B y representing high-dimensional data in a lower-dimensional space, MDS allows researchers to identify patterns, similarities, and dissimilarities between objects or variables. I t is a valuable tool for understanding and interpreting complex data sets in social sciences, marketing research, and network analysis. T echniques for mapping relationships in complex data include classical metric scaling, non-metric scaling, and indirect scaling methods MULTIDIMENSIONAL SCALING (MDS)

16. First, the distances between points on the map should reflect the dissimilarity between the corresponding objects or variables in the high-dimensional space. Second, the configuration of points should be as simple and interpretable as possible. Finally, the map should be robust and stable, meaning it should not change drastically with small changes in the dataset or analysis parameters. BASIC THREE PRINCIPLES OF MAPPING RELATIONSHIPS USING MULTIDIMENSIONAL SCALING

17. ADVANTAGES AND DISADVANATAGES ADVANTAGES Visualization of complex relationships Preservation of proximity Non-metric and metric solutions Useful for exploratory data analysis Applicability to various fields DISADVANTAGES Sensitivity to Input Data Computational complexity Interpretability challenges Subjectivity in parameter tuning Limited to distance information

18. THANK YOU Any Questions ?