The results of any statistical analysis should include the confidence intervals for estimated parameters. How confident are you that you can explain what they mean? Even those of us who have a solid understand of confidence intervals can get tripped up by the wording. Let’s look at an example.
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Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies. The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set […]
The following statement might surprise you, but it’s true. To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is: Continuous Unbounded Measured on an interval or ratio scale The normality assumption is about the errors in the model, which have the same […]
by Christos Giannoulis Many data sets contain well over a thousand variables. Such complexity, the speed of contemporary desktop computers, and the ease of use of statistical analysis packages can encourage ill-directed analysis. It is easy to generate a vast array of poor ‘results’ by throwing everything into your software and waiting to see what […]
Many of us love performing statistical analyses but hate writing them up in the Results section of the manuscript. We struggle with big-picture issues (What should I include? In what order?) as well as minutia (Do tables have to be double-spaced?).
By Karen Grace-Martin Confounding variable is one of those statistical terms that confuses a lot of people. Not because it represents a confusing concept, but because of how it’s used. (Well, it’s a bit of a confusing concept, but that’s not the worst part). First, it has slightly different meanings to different types of researchers. […]
Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly. But we can use a set of questions on a scale, called indicators, to represent the construct together by combining them into a latent factor.Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.
Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too!
When our research question is focused on the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. A few examples are: Poisson, negative binomial, zero-inflated Poisson and truncated negative binomial.There are specific requirements as to which count model to use. The models are not interchangeable. But regardless of the model we use, there is a very important prerequisite that they all share.
There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results. Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the […]
