# Quantitative Data Management and Analysis with SPSS Course

Training, Policy/Research

Start date

23/09/2019

End date

27/09/2019

## Overview

Course Date: 23rd – 27th, 2019 for 5 Days

Register here as individual or group to attend: http://tinyurl.com/yxldcwz9

Organizer: Foscore Development Center

Course Fee: \$ 1,000

Introduction

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

General Notes

• All our courses can be Tailor-made to participants needs
• The participant must be conversant with English
• Presentations are well guided, practical exercise, web based tutorials and group work. Our facilitators are expert with more than 10years of experience.
• Upon completion of training the participant will be issued with Foscore development center certificate (FDC-K)
• Training will be done at Foscore development center (FDC-K) center in Nairobi Kenya. We also offer more than five participants training at requested location within Kenya, more than ten participant within east Africa and more than twenty participant all over the world.
• Course duration is flexible and the contents can be modified to fit any number of days.
• The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance and other personal expenses.
• Accommodation, pickup, freight booking and Visa processing arrangement, are done on request, at discounted prices.
• One year free Consultation and Coaching provided after the course.
• Register as a group of more than two and enjoy discount of (10% to 50%) plus free five hour adventure drive to the National game park.
• Payment should be done two week before commence of the training, to FOSCORE DEVELOPMENT CENTER account, so as to enable us prepare better for you.
• For any enquiry to: [email protected] or +254712260031
• Website: www.fdc-k.org
• Register online to attend- http://tinyurl.com/yxldcwz9
• View Research and Data Analysis courses calendar 2019 – http://bit.ly/2rfjxn4
• View All Courses calendar 2019 - http://bit.ly/2mB2p5w

## What you will learn

• 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