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continuous variable

Can Likert Scale Data ever be Continuous?

by Karen Grace-Martin  51 Comments

A very common question is whether it is legitimate to use Likert scale data in parametric statistical procedures that require interval data, such as Linear Regression, ANOVA, and Factor Analysis.

A typical Likert scale item has 5 to 11 points that indicate the degree of something. For example, it could measure agreement with a statement, such as 1=Strongly Disagree to 5=Strongly Agree. It can be a 1 to 5 scale, 0 to 10, etc. [Read more…] about Can Likert Scale Data ever be Continuous?

Tagged With: ANOVA, continuous variable, Factor Analysis, Likert Scale, linear regression, Model Assumptions, Nonparametric statistics

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A Strategy for Converting a Continuous to a Categorical Predictor

by Jeff Meyer  Leave a Comment

At times it is necessary to convert a continuous predictor into a categorical predictor.  For example, income per household is shown below.Stage 2

This data is censored, all family income above $155,000 is stated as $155,000. A further explanation about censored and truncated data can be found here. It would be incorrect to use this variable as a continuous predictor due to its censoring.

[Read more…] about A Strategy for Converting a Continuous to a Categorical Predictor

Tagged With: Censored, continuous predictor, continuous variable, LOWESS, pairwise, polynomial regression, predictor variable, smoothing

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A Useful Graph for Interpreting Interactions between Continuous Variables

by Jeff Meyer  4 Comments

What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?Stage 2

Let’s start by looking at a model without an interaction.  In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.

[Read more…] about A Useful Graph for Interpreting Interactions between Continuous Variables

Tagged With: continuous predictor, continuous variable, explaining statistics, interaction, interpreting, predictor variable

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Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

by Jeff Meyer  24 Comments

We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?

What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? [Read more…] about Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

Tagged With: categorical variable, continuous variable, interaction, Interpreting Interactions, linear regression

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Differences Between the Normal and Poisson Distributions

by Karen Grace-Martin  4 Comments

The normal distribution is so ubiquitous in statistics that those of us who use a lot of statistics tend to forget it’s not always so common in actual data.

And since the normal distribution is continuous, many people describe all numerical variables as continuous. I get it: I’m guilty of using those terms interchangeably, too, but they’re not exactly the same.

Numerical variables can be either continuous or discrete.

The difference? Continuous variables can take any number within a range. Discrete variables can only be whole numbers.

So 3.04873658 is a possible value of a continuous variable, but not discrete.

Count variables, as the name implies, are frequencies of some event or state. Number of arrests, fish [Read more…] about Differences Between the Normal and Poisson Distributions

Tagged With: continuous variable, discrete, negative binomial, normal distribution, normality, numeric variable, Poisson Regression

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The Impact of Removing the Constant from a Regression Model: The Categorical Case

by Jeff Meyer  5 Comments

by Jeff MeyerStage 2

In a simple linear regression model, how the constant (a.k.a., intercept) is interpreted depends upon the type of predictor (independent) variable.

If the predictor is categorical and dummy-coded, the constant is the mean value of the outcome variable for the reference category only. If the predictor variable is continuous, the constant equals the predicted value of the outcome variable when the predictor variable equals zero.

Removing the Constant When the Predictor Is Categorical

When your predictor variable X is categorical, the results are logical. Let’s look at an example. [Read more…] about The Impact of Removing the Constant from a Regression Model: The Categorical Case

Tagged With: categorical predictor, constant, continuous variable, Dummy Coded, Interpreting intercept, linear regression, predictor variable

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