Quantitative Data Management and Analysis with SPSS Online Training

The training is essential in the development of better understanding of the concepts of statistics. It will provide the participants with a general idea of computer assisted data analysis. Additionally, the training will also focus on developing skills that are crucial to the transformation of data using SPSS.

Course Objective:

• Performing operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files
• Building charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams
• Performing the basic data analysis procedures: Frequencies, Descriptives, Explore, Means, Crosstabs
• Testing the hypothesis of normality
• Detecting the outliers in a data series
• Transform variables
• Performing the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit
• Performing the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis

Duration

5 days

Who should attend?

The course targets project staff, researchers, managers, decision makers, and development practitioners who are responsible for projects and programs in an organization.

Course content

• Introduction
• Defining Variables
• Variable Recoding
• Dummy Variables
• Selecting Cases
• File Splitting
• Data Weighting
• Creating Charts in SPSS
• Column Charts
• Line Charts
• Scatterplot Charts
• Boxplot Diagrams
• Simple Analysis Techniques
• Frequencies Procedure
• Descriptive Procedure
• Explore Procedure
• Means Procedure
• Crosstabs Procedure
• Assumption Checking. Data Transformations
• Checking for Normality - Numerical Methods
• Checking for Normality - Graphical Methods
• Detecting Outliers - Graphical Methods
• Detecting Outliers - Numerical Methods
• Detecting Outliers - How to Handle the Outliers
• Data Transformations
• One-Sample Tests
• One-Sample T Test - Introduction
• One-Sample T Test - Running the Procedure
• Introduction to Binomial Test
• Binomial Test with Weighted Data
• Chi Square for Goodness-of-Fit
• Chi Square for Goodness-of-Fit with Weighted Data
• Pearson Correlation - Introduction
• Pearson Correlation - Assumption Checking
• Pearson Correlation - Running the Procedure
• Spearman Correlation - Introduction
• Spearman Correlation - Running the Procedure
• Partial Correlation - Introduction
• Chi Square For Association
• Chi Square For Association with Weighted Data
• Loglinear Analysis - Introduction
• Loglinear Analysis - Hierarchical Loglinear Analysis
• Loglinear Analysis - General Loglinear Analysis
• Tests for Mean Difference
• Independent-Sample T Test - Introduction
• Independent-Sample T Test - Assumption Testing
• Independent-Sample T Test - Results Interpretation
• Paired-Sample T Test - Introduction
• Paired-Sample T Test - Assumption Testing
• Paired-Sample T Test - Results Interpretation
• One-Way ANOVA - Introduction
• One-Way ANOVA - Assumption Testing
• One-Way ANOVA - F Test Results
• One-Way ANOVA - Multiple Comparisons
• Two-Way ANOVA - Introduction
• Two-Way ANOVA - Assumption Testing
• Two-Way ANOVA - Interaction Effect
• Two-Way ANOVA - Simple Main Effects
• Three-Way ANOVA - Introduction
• Three-Way ANOVA - Assumption Testing
• Three-Way ANOVA - Third Order Interaction
• Three-Way ANOVA - Simple Second Order Interaction
• Three-Way ANOVA - Simple Main Effects
• Three-Way ANOVA - Simple Comparisons
• Multivariate ANOVA - Introduction
• Multivariate ANOVA - Assumption Checking
• Multivariate ANOVA - Result Interpretation
• Analysis of Covariance (ANCOVA) - Introduction
• Analysis of Covariance (ANCOVA) - Assumption Checking
• Analysis of Covariance (ANCOVA) - Results Intepretation
• ANOVA - Introduction
• ANOVA - Assumption Checking
• ANOVA - Results Interpretation
• ANOVA - Introduction
• ANOVA - Assumption Checking
• ANOVA - Interaction
• ANOVA - Simple Main Effects
• Mixed ANOVA - Introduction
• Mixed ANOVA - Assumption Checking
• Mixed ANOVA - Interaction
• Mixed ANOVA - Simple Main Effects
• Predictive Techniques
• Simple Regression - Introduction
• Simple Regression - Assumption Checking
• Simple Regression - Results Interpretation
• Multiple Regression - Introduction
• Multiple Regression - Assumption Checking
• Multiple Regression - Results Interpretation
• Regression with Dummy Variables
• Sequential Regression
• Binomial Regression - Introduction
• Binomial Regression - Assumption Checking
• Binomial Regression - Goodness-of-Fit Indicators
• Binomial Regression - Coefficient Interpretation
• Binomial Regression - Classification Table
• Multinomial Regression - Introduction
• Multinomial Regression - Assumption Checking
• Multinomial Regression - Goodness-of-Fit Indicators
• Multinomial Regression - Coefficient Interpretation
• Multinomial Regression - Classification Table
• Ordinal Regression - Introduction
• Ordinal Regression - Assumption Testing
• Ordinal Regression - Goodness-of-Fit Indicators
• Ordinal Regression - Coefficient Interpretation
• Ordinal Regression - Classification Table
• Scaling Techniques
• Reliability Analysis
• Multidimensional Scaling - Introduction
• Multidimensional Scaling - PROXSCAL
• Data Reduction
• Principal Component Analysis - Introduction
• Principal Component Analysis - Running the Procedure
• Principal Component Analysis - Testing For Adequacy
• Principal Component Analysis - Obtaining a Final Solution
• Principal Component Analysis - Interpreting the Final Solutions
• Principal Component Analysis - Final Considerations
• Correspondence Analysis - Introduction
• Correspondence Analysis - Running the Procedure
• Correspondence Analysis - Results Interpretation
• Correspondence Analysis - Imposing Category Constraints
• Grouping Methods
• Cluster Analysis - Introduction
• Cluster Analysis - Hierarchical Cluster
• Discriminant Analysis - Introduction
• Discriminant Analysis - Simple DA
• Discriminant Analysis - Multiple DA
• Multiple Response Analysis

Note

• All our courses can be Tailor-made to participants needs
• Course duration is flexible, and the contents can be modified to fit any number of days.
• Presentations are well guided, practical exercise, web-based tutorials and group work. Our facilitators are expert with more than 10years of experience.
• One-year free Consultation and Coaching provided after the course.