## How to.

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Generating high quality data from your online quantitative surveys is key to getting powerful insights and making data-driven decisions. Find out how.

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This post describes how to interpret the coefficients, also known as parameter estimates, from logistic regression (a.k.a. binary logit).

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Logistic regression (a.k.a. binary logit or binary logistic regression) is a predictive modeling technique used to predict outcomes involving two options

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When segmenting a market, a practical challenge is to work out the number of segments. There are eight approaches to choosing the number of segments.

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I'll show you three simple methods of analyzing free-form text data from open ended survey questions and explain when you might use each.

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In this post I explore the implications of using hierarchical Bayes versus using latent class analyis for data which contains segments.

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Most data scientists automatically use tidy data. In this post, I'll explain what tidy data is and why you'll want to consider an alternative, numerate data

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Traditionally, the primary statistic of interest for categorical data is the percentage of the cases in the data that fall into each category. However, there…

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How To Convert Text Dates To Numeric? Formatting and cleaning data is a crucial and often time-consuming step in any data analysis. One frequent step in this pr...

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Cluster analysis techniques require users to specify the number of clusters that they require. Six classes of approaches to choosing the number of clusters:

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Open-Ended Survey Questions occur when you run a survey and ask a respondent to enter some text as an answer, learn how to categorize these questions.

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Most of the widely used cluster analysis algorithms can be highly misleading or can simply fail when most or all the observations have some missing values.

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Latent class analysis requires the analyst to specify the number of classes prior to the application of the technique. Learn more about the seven approaches

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A practical challenge when working out how to segment is that there are usually lots of possible variables, and you need to reduce that number.

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Golden questions are used to allocate people to segments. They are also known as self-selection questions. The main applications of golden questions are:

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How to enter data from Paper Surveys. Displayr's Matt Engdahl explains the process in a step by step guide.

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This article discusses how to work out which segmentation variables are appropriate from a list of variables.

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This page lists the key frameworks and processes for identifying relevant variables to use when segmenting a market. The best way to identify relevant

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Sample size: What is the smallest number of data points required in the survey or experiment? Data scientist Tim Bock explains in this informative article.

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How to do statistical testing of the Net Promoter Score. An informative article by Displayr's Justin Yap, including practical examples and code.

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How to create color-coded Word Cloud, with colors are based on sentiment. Step-by-step instructions with a free tool for creating sentiment in Word Clouds.

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Data checking is a bore (and a step we skip at our peril!). The laundry-list of data checking tasks can be seemingly endless: identifying bogus respondents,…

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This posts answers the most common questions about how to interpret R-Squared. The basic mistake that people make with R-squared is to try and work out if a

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An exploration of what processes can be automated in PowerPoint, and how different analysis software packages leverage this.

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Although PLS and Johnson's Relative Weights are both techniques for dealing with correlations between predictors, they give fundamentally different results.

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Why is Multiple Linear Regression the standard technique taught for Key Driver Analysis when it gets it so wrong? The better method is Johnson’s Relative We

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This post describes the single biggest time saving technique that I know about for highlighting significant results on a table. That is just one table that show...

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Most cluster analysis algorithms ignore all of the data for cases with any missing data. I explain & compare the five options for dealing with missing data.

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Shapley regression has been gaining popularity in recent years and has been (re-)invented multiple times. However, relative weights, should be used instead.

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Studies have shown that Shapley regression and Relative Weights provide surprisingly similar scores, despite being constructed in very different ways.

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Why do some tweets sizzle while others fizzle? Sometimes it’s obvious. But if you have a large quantity of tweet text, or other text for…

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