Karen Grace-Martin

Can Likert Scale Data ever be Continuous?

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 indicat... Read >

Circular Statistics

Circular variables, which indicate direction or cyclical time, can be of great interest to biologists, geographers, and social scientists. The defining characteristic of circular variables is that the beginning and end of their scales meet. For example, c... Read >

Limitations of Common Solutions to Missing Data

A previous article discussed some of the causes of missing data and some of the consequences of analyzing only complete cases. This newsletter will discuss some other common ways of dealing with missing data, with a discussion of their advantages and disa... Read >

Missing Data Mechanisms

As almost any researcher can attest, missing data are a widespread problem. Data from surveys, experiments, and secondary sources are often missing some data. The impact of the missing data on the results of statistical analysis depends on the mechanism w... Read >

Introduction to Logistic Regression

Researchers are often interested in setting up a model to analyze the relationship between some predictors (i.e., independent variables) and a response (i.e., dependent variable). Linear regression is commonly used when the response variable is continuous... Read >

Regression Models for Count Data

One of the main assumptions of linear models such as linear regression and analysis of variance is that the residual errors follow a normal distribution. To meet this assumption when a continuous response variable is skew, a transformation of the response... Read >

Specifying Fixed and Random Factors in Mixed or Multi-Level Models

Since SAS introduced Proc Mixed about fifteen years ago, S-Plus, Stata and SPSS have implemented procedures to analyze mixed models, greatly broadening the options available to researchers. These programs require correctly specifying the fixed and random... Read >

Interpreting Interactions in Regression

Adding interaction terms to a regression model can greatly expand understanding of the relationships among the variables in the model and allows more hypotheses to be tested. A previous article, Interpreting Regression Coefficients, discussed how to inter... Read >

Interpreting Regression Coefficients

Linear regression is one of the most popular statistical techniques used by researchers. Despite its popularity, interpretation of the regression coefficients of any but the simplest models is sometimes difficult. This article explains how to interpret th... Read >

Assessing the Fit of Regression Models

A well-fitting regression model results in predicted values close to the observed data values. The mean model, which uses the mean for every predicted value, generally would be used if there were no informative predictor variables. The fit of a proposed r... Read >

The Many Names of Independent Variables

Statistical models, such as general linear models (linear regression, ANOVA, mixed models) and generalized linear models (logistic, Poisson, proportional hazard regression, etc.) all have the same general form. On the left side of the equation is one or m... Read >

Multiple Imputation in a Nutshell

Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be told that you should never do it for a variety of very good reasons. Mean imputation, in which each missing va... Read >