Ordered logit is a type of regression analysis designed for modeling ordinal dependent variables—that is, variables with discrete, ranked categories where the order matters but the intervals between them may not be equal. It’s particularly useful when your outcome has more than two levels, such as Net Promoter Score categories, customer satisfaction ratings, or survey responses on Likert scales (e.g., "Strongly disagree" to "Strongly agree").
Using Displayr
Regression Analysis Techniques: Which One Is Right for You?
Regression analysis techniques are how researchers quantify a model. The regression produces a formula, then this formula is used to make predictions and draw conclusions about the relative effectiveness of specific factors.
So, if you've ever wondered how businesses anticipate future sales, understand customer behavior, or optimize marketing spend, the answer is regression analysis. It's a powerful tool that makes it possible to see the variables that have the biggest influence. But it's not a one-size-fits-all solution. There are many different regression analysis techniques available, and choosing the right one for your specific situation is key to getting meaningful results.
Let's explore some key regression analysis techniques you should know.
What is Regression Analysis?
At its core, regression analysis helps us understand and model the relationship between variables. It's a statistical method that estimates how changes in one or more independent variables (predictors) affect a dependent variable (outcome). This allows us to make predictions and gain insights into the underlying drivers of various phenomena. To learn more about the core principles of this technique, check out How Does Linear Regression Work?
Some common dependent variables that come up for market researchers when performing regression analysis include:
- NPS
- Product quality
- Overall satisfaction with a product/service
Meanwhile, typical independent variables might be:
- Demographics
- Measurements of efforts in certain areas (e.g., ad spend on different media)
- Measurements of performance in different areas (e.g., an airline measuring customer satisfaction as a dependent variable might measure satisfaction with food, satisfaction with the in-flight entertainment, etc).
Displayr makes regression simple.
How Does Regression Analysis Work?
Modern data analysis software tools (like Displayr) make it easy for you to perform regression analysis without fully understanding how it works. And while it has been invaluable to democratize such a powerful technique, having a basic understanding of how regression works is helpful, particularly if you ever come into any issues when doing your own regression.
As mentioned above, researchers use regression analysis to make predictions. At its core, it works by identifying relationships between variables—figuring out which ones are driving change and how strong that influence is. This lets you move from simply observing data to actually explaining and forecasting outcomes.
But knowing how to apply regression effectively starts with choosing the right type of model for your data and goals.
Why Use Regression Analysis?
Regression analysis is how market researchers can predict what's going to happen next. Examples of regression problems include predicting sales from ad spend, modeling customer satisfaction from service ratings, or forecasting churn based on usage patterns. More broadly, businesses use regression analysis for various reasons:
- Prediction: Forecast future sales, demand, or customer behavior.
- Understanding Relationships: Identify the factors that most influence a particular outcome.
- Decision-Making: Optimize marketing spend, product development, or resource allocation based on data-driven insights.
Types of Regression Techniques
Linear Regression
The most common technique, linear regression models the relationship between a continuous dependent variable and one or more independent variables using a linear equation. It's used when you expect a straight-line relationship between the variables. For example, you might predict sales based on advertising expenditure. If you have multiple predictors, you'll use multiple linear regression.
Logistic Regression
Logistic regression is used when the outcome is binary or categorical, such as predicting whether a customer will churn or not. This is particularly helpful in market research, where categorical data is common. GLM logistic regression in R (and r glm logistic regression) can be used in this approach.
Ordered Logit Regression
Stepwise Regression
Stepwise regression is based on the Akaike Information Criterion (AIC). When dealing with many potential predictors, stepwise regression can automatically select the most relevant variables for your model.
Robust Regression
Robust regression is designed to handle data that contains outliers or violations of standard regression assumptions. Unlike traditional linear regression, which can be heavily influenced by extreme values, robust regression techniques reduce the impact of outliers by using alternative methods for estimating coefficients—such as minimizing absolute differences rather than squared ones. While it retains the core structure of linear regression, it offers more reliable results when data quality is uneven or noisy.
Driver Analysis Techniques
Methods like Shapley regression and relative importance analysis help identify which predictors have the most influence on the outcome, providing actionable insights for businesses. Displayr has features to calculate relative importance, making this technique accessible to users. You can learn more about these approaches here.
How to Select the Right Regression Model
As you will have noticed, each regression analysis technique is nuanced - built to handle different data types and answer specific questions. This means it can be difficult to select the technique that's going to work for your data. Choosing the right technique depends on:
- Your data type
- Variable relationships
- Analysis goals
- Your intent (whether it's for prediction, explanation, or pattern detection)
There are some basic rules of thumb you can follow to help guide how you choose the right regression model. For example, standard linear regression assumes relationships are linear, errors are independent, and data is normally distributed, while logistic regression is used when the outcome is categorical, and ordered logit is ideal for ordinal outcomes where the order matters but the spacing between categories may not be equal. Techniques like robust regression are better suited when your data contains outliers or violates standard assumptions.
Fortunately, there is one foolproof way to choose the correct regression analysis technique every time. Displayr's regression tool guides you through the process of selecting your regression. What's more, it automatically resolves common issues such as outliers, multicollinearity, heteroscedasticity, and categorical outcome variables.
