Advanced Statistical Analysis Using IBM SPSS Statistics V25

Duration: 
2 days
Codes: 
0G09
Versions: 
V25

Overview

This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.

Audience

Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.

Skills Gained

  • Introduction to advanced statistical analysis
  • Group variables: Factor Analysis and Principal Components Analysis
  • Group similar cases: Cluster Analysis
  • Predict categorical targets with Nearest Neighbor Analysis
  • Predict categorical targets with Discriminant Analysis
  • Predict categorical targets with Logistic Regression
  • Predict categorical targets with Decision Trees
  • Introduction to Survival Analysis
  • Introduction to Generalized Linear Models
  • Introduction to Linear Mixed Models

Prerequisites

  • Experience with IBM SPSS Statistics (navigation through windows; using dialog boxes)
  • Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V25) course.

Course Outline

Introduction to advanced statistical analysis

  • Taxonomy of models
  • Overview of supervised models
  • Overview of models to create natural groupings

    Group variables: Factor Analysis and Principal Components Analysis
  • Factor Analysis basics
  • Principal Components basics
  • Assumptions of Factor Analysis
  • Key issues in Factor Analysis
  • Improve the interpretability
  • Use Factor and component scores

    Group similar cases: Cluster Analysis
  • Cluster Analysis basics
  • Key issues in Cluster Analysis
  • K-Means Cluster Analysis
  • Assumptions of K-Means Cluster Analysis
  • TwoStep Cluster Analysis
  • Assumptions of TwoStep Cluster Analysis

    Predict categorical targets with Nearest Neighbor Analysis
  • Nearest Neighbor Analysis basics
  • Key issues in Nearest Neighbor Analysis
  • Assess model fit

    Predict categorical targets with Discriminant Analysis
  • Discriminant Analysis basics
  • The Discriminant Analysis model
  • Core concepts of Discriminant Analysis
  • Classification of cases
  • Assumptions of Discriminant Analysis
  • Validate the solution

    Predict categorical targets with Logistic Regression
  • Binary Logistic Regression basics
  • The Binary Logistic Regression model
  • Multinomial Logistic Regression basics
  • Assumptions of Logistic Regression procedures
  • Testing hypotheses

    Predict categorical targets with Decision Trees
  • Decision Trees basics
  • Validate the solution
  • Explore CHAID
  • Explore CRT
  • Comparing Decision Trees methods

    Introduction to Survival Analysis
  • Survival Analysis basics
  • Kaplan-Meier Analysis
  • Assumptions of Kaplan-Meier Analysis
  • Cox Regression
  • Assumptions of Cox Regression

    Introduction to Generalized Linear Models
  • Generalized Linear Models basics
  • Available distributions
  • Available link functions

    Introduction to Linear Mixed Models
  • Linear Mixed Models basics
  • Hierachical Linear Models
  • Modeling strategy
  • Assumptions of Linear Mixed Models

  • Thinking about Onsite?

    If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

    Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

    Upcoming Dates

    • GREEN This class is Guaranteed To Run.
    • SPVC - Self-Paced Virtual Class.
    • Click a Date to Enroll.
    Course Location Days Cost Date
    Onsite
    Onsite2 1000 £1000 2019-06-16