## Part 1 – An overview of SurveyMonkey’s export options

Part 1 – An overview of SurveyMonkey’s export options

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# Videos

## Part 1 – An overview of SurveyMonkey’s export options

## Part 2 – Exporting data with the ‘Actual answer text’ option

## Part 3 – Exporting data with the ‘Numerical values’ option

## The basics of SPSS syntax

## Exploring the grammatical structure of syntax procedures

## Getting help with SPSS syntax

## Useful syntax procedures

## Different methods for filtering data using syntax

## Working with variables in SPSS syntax

## Automating and scheduling jobs with SPSS syntax

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

## Introduction to the Neural Networks module in SPSS Statistics – part one

## Making sense of odds ratios and relative risk estimates in SPSS Statistics

## Introducing survival analysis

## Exploring the life tables procedure

## The Kaplan Meier procedure

## Introduction to Cox regression

## Cox regression with multiple variables

## Introducing Bayesian analysis with SPSS

## Prior probabilities and Bayes’ theorem

## Bayesian Estimation and Hypothesis Testing in SPSS

## Performing Bayesian Analyses in SPSS

## Performing tests of significance

## Introduction to linear regression

## Modelling non-linear relationships with SPSS

## An introduction to moderation analysis

## An introduction to mediation analysis

## Introduction to structural equation modelling with SPSS Amos – part one

## Introduction to structural equation modelling with SPSS Amos – part two

## Multivariate analysis with Camo Analytics Unscrambler part one

## Multivariate analytics with Camo Analytics Unscrambler part two

## Multivariate analysis with Camo Analytics Unscrambler part three

## Multivariate analysis with Camo Analytics Unscrambler part four

## 6 secrets of building better models part one: bootstrap aggregation

## 6 secrets of building better models part two: boosting

## 6 secrets of building better models part three: feature engineering

## 6 secrets of building better models part four: ensemble modelling

## 6 secrets of building better models part five: meta models

## 6 secrets of building better models part six: split models

## A first look at SPSS Modeler v18.2

## How to change the appearance of your output in SPSS Statistics

## How to merge files in SPSS Statistics

## How to create grouped or banded variables in SPSS Statistics

## How to recode your data in SPSS Statistics

Part 1 – An overview of SurveyMonkey’s export options

Part 2 – Exporting data with the ‘Actual answer text’ option

Part 3 – Exporting data with the ‘Numerical values’ option

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.

Explains what the DATASET ACTIVATE command does and explores the grammatical structure of syntax procedures.

Shows how to add comments to syntax without creating errors and explains how to use syntax Help resources to run different procedures.

Shows how read different data files with syntax, how to create and add labels to variables and how to recode data using syntax.

Explores different methods for filtering data and introduces the TEMPORARY command.

Introduces the TO and ALL commands, working with temporary variables, renaming and deleting variables, reading or saving data with the DROP and KEEP subcommands and modifying output with syntax.

Shows how to automate syntax jobs with the Production facility and creating batch scheduled jobs for timed execution.

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 »

In this two-part video series Jarlath Quinn explores how to work with the Neural Networks module in SPSS Statistics. Watch part two here. Part 1: Introduces the concept of Neural Networks Shows how to build a basic Neural Network model to predict credit worthiness Explains the how to interpret the default output generated by the procedure

In this video, Jarlath Quinn explains how odds and relative risks are calculated and how you can code and arrange your variables so that interpretation is as straightforward as possible.

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.

This video explores the Life Tables procedure and introduces many of the key concepts of survival analysis such as cumulative survival, hazard rates and survival curves

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.

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.

This video shows how to create a more complex Cox Regression model. Also introduces the Cox Regression with Time Dependent Covariates procedure.

Explores the historical and theoretical context of the classical ‘Frequentist’ statistical approach and its Bayesian counterpart.

Introduces the concept of Prior Probabilities and how these are utilised in Bayes Theorem.

Introduces credible intervals and the use of Bayes Factor as an alternative to P values.

Shows how to perform a Bayesian Analysis in SPSS Statistics and how to interpret the output.

A video guide to performing tests of significance in the SPSS Custom Tables module.

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.

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.

The video explores moderation analysis, which enables analysts to identify interaction effects that alter the relationship between a dependent and independent variable.

This video explores mediation analysis – an analytical approach used to test if a third factor could represent the underlying cause of a relationship between an independent and dependent variable.

In these videos Jarlath Quinn introduces the concept of Structural Equation Modelling with SPSS AMOS. In the first video Jarlath uses the example of creating a simple linear model to illustrate the functionality in the AMOS interface.

In these videos Jarlath Quinn introduces the concept of Structural Equation Modelling with SPSS AMOS. In the second video Jarlath shows how to perform a basic Confirmatory Factor Analysis in AMOS.

Watch the other videos in this series

Watch the rest of the videos in this series

Watch the other videos in this series

Watch the rest of the videos in this series

Bootstrap aggregation, also called bagging, is a random ensemble method designed to increase the stability and accuracy of models. It involves creating a series of models from the same training data set by randomly sampling with replacement the data.

Boosting is another ensemble model-building method that was designed to help develop strong classification models from weak classifiers. Boosting methods focus on error (or misclassifications) that occur in prediction.

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.

Ensemble modelling refers to the practice of combining the predictions of separate models on the old principle that “two heads are better than one”. Ensemble methods can be particularly effective when combining models that have been created using completely different algorithms.

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.

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.

In this video Jarlath Quinn takes a first look at SPSS Modeler v18.2 and demonstrates some of the new functionality that’s included within this release.

We’re often asked how you can change the appearance of the tables that SPSS generates as output. In this video Jarlath Quinn demonstrates two different ways to do this, either by choosing a different table look in the edit / options function, or by editing the table properties directly yourself.

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’).

SPSS users often want to be able to create grouped or banded data from continuous fields such as, for example, creating age groups or income bands from continuous fields. In this video Jarlath Quinn demonstrates how to use the visual binning procedure within SPSS Statistics to do this.

Recoding your data means changing the values of a variable so that they represent something else. Within SPSS Statistics there is more than one type of recode that can be performed.

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