Author Information
Karen Grace-Martin
Member since 18th September 2008
Karen Grace-Martin, founder of The Analysis Factor, has helped social science researchers practice statistics for 9 years, as a statistical consultant at Cornell University and in her own business. She knows the kinds of resources and support that researchers need to practice statistics confidently, accurately, and efficiently, no matter what their statistical background. To answer your questions, receive advice, and view a list of resources to help you learn and apply appropriate statistics to your data, visit www.analysisfactor.com.
Displaying 1 to 12 (of 12 articles)
21st October 2008
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...
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06th October 2008
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...
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06th October 2008
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...
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06th October 2008
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...
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06th October 2008
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...
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06th October 2008
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...
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29th September 2008
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...
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29th September 2008
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...
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25th September 2008
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...
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25th September 2008
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...
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25th September 2008
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...
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18th September 2008
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...
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