Analysis of Questionnaire/Survey Data, Oct 2023

Topics covered: Basics of Survey design, Stats Refresher, Chi-squared test, Correspondence Analysis, Analysis of Variance, Correlation, Regression, Clustering/Segmentation, Factor analysis (more details with dates at bottom)

Do you create consumer surveys or questionnaires yet feel lost or under-equipped in trying to make the most out of the data? Once you have collected data from hundreds, perhaps thousands of consumers – where do you begin in trying to draw meaningful conclusions, that can influence business decision making and deliver true insights? Do you feel somewhat anxious with the many statistical methods that you’ve heard about but don’t really understand how to apply them, or what differentiates one method from another?

If you feel like one of the above applies to you, don’t worry — you’re not alone! Perhaps the best place to start is to think about the question you’re trying to answer with your survey, i.e., what is your overall objective? Below are four scenarios that our common in our experience (or scroll down to see course content):

Scenario 1:

Exploratory survey looking to understand relationships between several variables (most likely of different types, for example some are qualitative or quantitative)
To analyse data in this scenario: You are likely to need cross tabulations including chi-square tests, analysis of variance (ANOVA), correspondence analysis and even clustering methods.

Scenario 2:

I have liking or preference data and want to relate this data to demographic, usage and attitude and purchase data.
To analyse data in this scenario: You are likely to want to know about regression, factor analysis, and/or multiple correspondence analysis and clustering methods

Scenario 3:

I’m trying to measure a latent variable (e.g., complexity or satisfaction) through several survey items, and/or perhaps using a published questionnaire such as the Big 5 Personality Traits.
How do I analyse the data?
To analyse data in this scenario: You will need to know about factor analysis and clustering methods

Scenario 4:

I’m in the early stage of product development and want to survey consumers regarding their associations and expectations to a specified concept that we’ve developed (e.g., “fresh”, “raw” or “natural”). How do I develop a survey, how many respondents are required and how to analyse the data?
You will likely need to know about analysis of variance, regression and clustering methods, and analysing textual data

This comprehensive online course covers all of the above methods and more (see topic list below), enabling you to gain full insight into the methods you need to answer business questions. No matter what your survey is, there is a method to objectively combine the data using statistical methods.

Some of the analysis methods above also fall under the category of Machine Learning techniques.

After the initial pre-requisites session, the remote learning package consists of lectures delivered online together with workshops (some of them for you to complete  afterwards in your own time – solutions and data sets provided), plus a pdf of the lecture notes and email support whilst you are completing the workshop after the course for up to 2 weeks. More than just a lecture and as close to face-to-face training as it can be!

By the end of the course you will have practised several techniques with worked examples using the XLSTAT statistical software and have worked solutions to use, for when you repeat these analyses on your own as part of your day job.

Participants can choose to attend either any of the modules. If you only choose to attend later modules, it is expected you will have a good understanding of basic statistics including variability, statistical significance, ANOVA etc, and have experience of using XLSTAT. No previous experience of statistics or of using XLSTAT software is required if you attend all modules.