The basics of SPSS syntax

SPSS Syntax has long been exploited by expert analysts due to its flexibility, power and ease of learning. Syntax vastly increases users’ productivity by making it easier to automate commonly used procedures.

Introduction to the Neural Network module in SPSS Statistics – part two

In this two-part video series Jarlath Quinn explores how to work with the Neural Networks module in SPSS Statistics (watch part one here) Part 2: Shows how to create a partition variable to control which cases are used in Training and Testing  Explores the effect of choosing different activation functions Explains and demonstrates different model training …

Introduction to the Neural Network module in SPSS Statistics – part two Read More »

Introducing survival analysis

This video looks at the key concepts that underpin survival analysis such as time-to-event data, censored cases and the different survival analysis procedures available in IBM SPSS Statistics.

The Kaplan Meier procedure

This video looks at the non-parametric Kaplan Meier procedure including tests of equality of survival distributions and the difference between survival and hazard functions.

Introduction to Cox regression

This video introduces the Cox Regression method and explains the proportional hazards model. Shows how to interpret the output from Cox Regression using a simple model with a single predictor.

Introduction to linear regression

In classical statistics, linear regression is regarded as the ‘gateway to predictive modelling’. For decades students have been taught about regression from theory to practice simply because it still one of the most versatile and simple ways to understand and predict the effect of key factors on critical outcomes.

Modelling non-linear relationships with SPSS

In this video Jarlath Quinn shows how you can move beyond simple linear regression to model curvilinear relationships using techniques such as variable transformations and quadratic regression before finally exploring how log-log regression can be used to model price elasticity of demand.

6 secrets of building better models part three: feature engineering

Feature Engineering is really just a fancy term for creating new data. Very often we can help an algorithm build better models by preparing the input data in a way that allows it to detect a clearer signal in the often noisy data. In machine learning variables are often referred to as ‘features’, so feature engineering refers to the transformation of variables or the creation of new ones.

6 secrets of building better models part five: meta models

The idea of meta modelling is to build a predictive model using the predictions or scores generated by another model. By adding the predictive scores generated by an initial modelling algorithm to an existing pool of predictor fields, a second algorithm can then exploit these scores in to build a final more accurate model.

6 secrets of building better models part six: split models

Split models or split population modelling is another technique that allows the user to build multiple models which can then be combined to create a single prediction. The idea with split modelling is that if the data represent different populations or contain separate groups that behave in very different ways, assuming that a single model can explain all the inherent variability across these distinct populations might be unreasonable.

How to merge files in SPSS Statistics

In this video Jarlath Quinn demonstrates how to merge data files within SPSS Statistics using each of the two main methods, either adding cases (combining files with the same fields but additional rows) or adding variables (combining files by joining variables to a target file using something like an ID field as a ‘keyed variable’).

How to select cases in SPSS Statistics

In this video Jarlath Quinn demonstrates how to use SPSS Statistics to define data filters in order to select particular cases for analysis. This can be done either to create a temporary selection or to create a permanent new file with only a subsection of cases included within it.

How to reverse a scale in SPSS Statistics

In this video Jarlath Quinn demonstrates how to reverse the values of a rating scale (such as an agreement scale or a satisfaction scale) in SPSS Statistics, so that the highest value becomes the lowest value and vice versa.

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