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, 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 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: training@data-afriqueconsultancy.org
• 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 7 DAYS before start of training and proof of payment sent to: training@data-afriqueconsultancy.org