TRAINING COURSE ON QUANTITATIVE DATA ANALYSIS WITH SPSS

Course Date Location Course fee: Registrations (Group)
Start Date: 19/12/2022 End Date: 23/12/2022 Mombasa $ 1,000 Register as Individual Register for Online Training Register as a Group

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

 

Our 2022 Group Rates (in USD)

# of Days
Group #
5 DAYS PER PERSON
10 DAYS PER PERSON
#
PAXS
USD
USD
1. 1 1000.00 2000.00
2. 2 - 10 800.00 1,600.00
3. 11 - 20 750.00 1,500.00
4. 21 - 30 700.00 1,400.00
5. 32 - 40 650.00 1,300.00
6. 41 - 50 600.00 1,200.00
7. 51 > Above 500.00 1000.00