TRAINING COURSE ON QUANTITATIVE DATA MANAGEMENT AND ANALYSIS WITH SPSS

Start Date: 02/09/2019 End Date: 06/09/2019 for 5 days__
__Register online: https://bit.ly/2M4eorv

Organizer: DATA-AFRIQUE CONSULTANCY (www.data-afriqueconsultancy.org)

Course fee: KSH 70,000, USD 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**

By the end of this course the participant should be able to:

- 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, Descriptive, Explore, Means, Cross tabs.
- 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 Procedures
- 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 test
- 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
- Test for Mean Difference
- Independent –sample T-test –Introduction
- Independent –sample T-test – Assumption testing
- Independent –sample T-test – resulting 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 – Results Interpretation
- Analysis of Covariance (ANCOVA) – Introduction
- Analysis of Covariance (ANCOVA) – Assumption Checking
- Analysis of Covariance (ANCOVA) – Results Interpretation
- ANOVA – Introduction
- ANOVA – Assumption Checking
- ANOVA – Results Interpretation
- 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
- 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**

This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Upon successful completion of this course, participants will be issued with a certificate.

We can also do this as tailor-made course to meet organization-wide needs. Contact us to find out more:

The training will be conducted at DATA-AFRIQUE TRAINING CENTRE, NAIROBI KENYA.

The training fee covers tuition fees, training materials, lunch and training venue. Accommodation and airport transfer are arranged for our participants upon request.

Payment should be sent to our bank account before start of training and proof of payment sent to: