Advanced Data Analysis Training Using SPSS

Training Overview

This training program is designed for users who have a foundational understanding of SPSS and basic statistics. It focuses on more advanced statistical techniques and analyses that can be performed using SPSS, enabling participants to explore complex datasets, derive deeper insights, and solve real-world analytical problems with more sophisticated methods.

Who can enroll?

• Students
• Corporate and Govt Employees
• Experienced data analysts
• Researchers and social scientists
• Business analysts with intermediate knowledge of SPSS
• Professionals looking to advance their data analytics capabilities

1: Recap of SPSS Basics and Intermediate Techniques

• Quickly review the foundational features and refresh skills required for advanced analysis.
• Review of SPSS Interface and Basic Operations:
• Data View, Variable View, Output Window
• Importing and Exporting Data
• Data Management (recoding, variable creation, missing value handling)
• Basic Statistical Techniques:
• Descriptive statistics (mean, median, mode, standard deviation)
• T-Tests and Chi-Square Tests
• Correlation analysis
• Visualizations Review:
• Graphical representation of data (bar charts, histograms, scatter plots)

2:Advanced Regression Techniques

• Learn how to conduct advanced regression analyses for prediction and explanatory modeling.
• Multiple Linear Regression:
• Assumptions of multiple linear regression
• Interpreting coefficients, R-squared, and significance
• Handling multicollinearity (Variance Inflation Factor - VIF)
• Model diagnostics (residual analysis)
• Logistic Regression:
• Understanding binary and multinomial logistic regression
• Model fitting and interpretation (odds ratios, Wald test)
• Handling categorical predictors and outcomes
• Assumptions and diagnostics for logistic regression
• Stepwise Regression:
• Forward, backward, and stepwise selection
• Choosing variables based on statistical criteria (AIC, BIC)
• Overfitting and model validation

3:Factor and Principal Component Analysis

• Explore advanced multivariate techniques to reduce data dimensions and identify underlying structures.
• Factor Analysis:
• Introduction to Factor Analysis (Exploratory Factor Analysis - EFA)
• Factor extraction methods (Principal Axis Factoring, Maximum Likelihood)
• Rotation techniques (Varimax, Promax)
• Interpreting factor loadings and communalities
• Determining the number of factors to retain (Eigenvalue criteria, Scree plot)
• Principal Component Analysis (PCA):
• Understanding PCA for dimensionality reduction
• Eigenvalues and Eigenvectors
• Interpreting principal components and variance explained
• Visualizing PCA output (scree plot, component plot)

4: Multivariate Analysis

• Learn how to analyze more complex relationships between multiple variables simultaneously.
• Multivariate Analysis of Variance (MANOVA):
• Understanding the differences between ANOVA and MANOVA
• Conducting MANOVA in SPSS
• Interpreting Wilks' Lambda, Pillai’s Trace, etc.
• Post-hoc tests and follow-up analyses
• Discriminant Analysis:
• Introduction to Discriminant Analysis
• Classifying cases into groups based on predictor variables
• Evaluating model accuracy (cross-validation, confusion matrix)
• Interpreting group means and discriminant functions
• Multidimensional Scaling (MDS):
• Understanding MDS for visualizing data similarity or dissimilarity
• Classical vs. Non-metric MDS
• Interpreting MDS results and stress values

5: Time Series Analysis

• Learn to analyze and forecast time-dependent data using SPSS.
• Introduction to Time Series Analysis:
• Components of time series data (trend, seasonality, cycles, noise)
• Preparing time series data in SPSS (stationarity, seasonality)
• Decomposition of time series data
• Autoregressive Integrated Moving Average (ARIMA) Models:
• Understanding ARIMA (p, d, q) parameters
• Conducting ARIMA analysis in SPSS
• Model fitting and diagnostic checks (ACF, PACF)
• Forecasting and residual analysis
• Exponential Smoothing Methods:
• Simple Exponential Smoothing (SES)
• Holt’s Linear Trend Method
• Holt-Winters Seasonal Method

6: Structural Equation Modeling (SEM)

• Explore how to use SEM for complex relationships involving latent variables and measurement models.
• Introduction to Structural Equation Modeling (SEM):
• What is SEM and when to use it?
• Overview of latent variables, observed variables, and path models
• Understanding direct and indirect relationships
• Estimating and interpreting SEM models
• Conducting SEM in SPSS:
• Using AMOS (SPSS’s SEM tool)
• Model specification and fitting
• Goodness-of-fit indices (CFI, RMSEA, Chi-square)
• Interpreting path coefficients and model fit
• Confirmatory Factor Analysis (CFA):
• Understanding CFA and its role in SEM
• Conducting CFA in SPSS AMOS
• Evaluating factor structure and model fit

7:Advanced Multivariate and Non-Parametric Methods

• Learn to apply advanced non-parametric methods and multivariate techniques to handle complex data.
• Non-Parametric Tests:
• Wilcoxon signed-rank test
• Kruskal-Wallis test
• Friedman test
• Mann-Whitney U test
• Advanced Cluster Analysis:
• K-Means Clustering and Hierarchical Clustering
• Determining the optimal number of clusters (Elbow method, Silhouette score)
• Visualizing and interpreting clusters
• Canonical Correlation Analysis:
• Understanding the relationship between two sets of variables
• Conducting canonical correlation in SPSS
• Interpreting canonical coefficients and correlations

8:Reporting and Presentation of Advanced Analysis

• Learn how to effectively communicate the results of complex analyses.
• Interpreting Output from Advanced Models:
• Detailed interpretation of regression, SEM, and multivariate analysis results
• Communicating statistical significance and model fit
• Visualizing Results:
• Advanced charting techniques for complex analyses (factor plots, regression plots)
• Creating high-quality reports and presentation-ready visuals
• Exporting Results and Creating Reports:
• Exporting SPSS output to Excel, Word, and PowerPoint
• Preparing clear and concise reports for stakeholders

9: Hands-on Practice and Case Studies

• Apply advanced analysis methods on real-world datasets.
• Work through a comprehensive case study involving regression, factor analysis, time series, or SEM.
• Hands-on practice with the SPSS interface to conduct complex data analysis.