Training On Quantitative Data Management And Analysis With Spss

  • Overview

INTRODUCTION

The course is aimed for trainers in the training industry who need to advance their competencies in order to assist their own and their employers' growth as trainers. Faculty members of training institutions, trainers, and training staff from internal training centers, institutions, and anyone interested in training and consulting are the target audience.

COURSE OBJECTIVE

The participant should be able to do the following operations on data at the end of the course:

  • Define variables, recode variables, construct dummy variables, pick and weight cases, split files.
  • Creating graphs in SPSS, including column charts, line graphs, scatterplots, and box plots.
  • Executing the fundamental data analysis steps: frequencies, descriptive, explore, means, and cross-tabulation.
  • Testing the assumption of normalcy
  • Identifying outliers in a data set and transforming variables
  • performing the three primary one-sample analyses: the chi square for goodness of fit, the one-sample t-test, and the binomial test.
  • Performing the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis

DURATION

5 Days

WHO SHOULD ATTEND

Project staff, researchers, managers, decision-makers, and development professionals who are in charge of projects and programs in an organization are the course's target audience.

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

  • Our seasoned instructors, who have years of experience as seasoned professionals in their respective fields of work, will be teaching this course. A combination of practical exercises, theory, group projects, and case studies are used to teach the course.
  • The participants receive training manuals and supplementary reading materials.
  • Participants who complete this course successfully will receive a certificate.
  • We can also create a course specifically for your organization to match your needs. To learn more, get in touch with us at training@dealsontrainers.org.
  • The training will take place at DEALSON TRAINERS IN NAIROBI, KENYA in Nairobi, Kenya.
  • The training fee includes lunch, course materials, and lodging for the training session. Upon request, we may arrange for our participants' lodging and transportation to the airport.
  • Payment must be made to our bank account before the training begins, and documentation of payment should be emailed to training@dealsontrainers.org

Course Schedule:
Dates Duration Fees Location Action