Interactions existed between body composition and birth weight on fasting glucose in men and two-hour glucose in women and between gender and birth weight on both outcome measures. Body composition was a significant predictor of both glucose measures in both genders. Birth weight significantly predicted two-hour glucose levels in men (adjusted p=0.03). A lifecourse approach was used to estimate proportions of variance in plasma glucose levels accounted for by each stage of the lifecourse. We studied 169 men and 219 women from the Newcastle Thousand Families cohort who attended for clinical examination, including measurements of fasting and 2 h post oral glucose load) at age 50. This study aimed to determine and quantify influences on plasma glucose levels at age 50. Your interpretations are going to be about mean ranks, not means.Suboptimal nutrition in early life is suggested to influence plasma glucose levels in later life. Just as a log transformation on a dependent variable puts all the means and coefficients on a log(DV) scale, the rank transformation puts everything on a rank scale. The thing to remember though, is that all results need to be interpreted in terms of the ranks. The basic idea is a rank transformation: transform each ordinal outcome score into the rank of that score and run your regression, two-way ANOVA, or other model on those ranks. Rank transformationsĪnother model-based approach combines the advantages of ordinal logistic regression and the simplicity of rank-based non-parametrics. They are a very good tool to have in your statistical toolbox. But they are also sometimes exactly what you need. These models are complex, have their own assumptions, and can take some practice to interpret. The most commonly available in software is the cumulative link function, which allows you to measure the effect of predictors on the odds of moving into any next-highest-ordered category. There are a few different ways of specifying the logit link function so that it preserves the ordering in the dependent variable. One of the most commonly used is ordinal models for logistic (or probit) regression. There aren’t many tests that are set up just for ordinal variables, but there are a few. You can’t, for example, include interactions among two independent variables or include covariates. Sure you can compare groups one-way ANOVA style or measure a correlation, but you can’t go beyond that. The limitation of these tests, though, is they’re pretty basic. So while we think of these tests as useful for numerical data that are non-normal or have outliers, they work for ordinal variables as well, especially when there are more than just a few ordered categories.Ĭommon rank-based non-parametric tests include Kruskal-Wallis, Spearman correlation, Wilcoxon-Mann-Whitney, and Friedman.Įach test has a specific test statistic based on those ranks, depending on whether the test is comparing groups or measuring an association. Ranks are themselves ordinal–they tell you information about the order, but no distance between values. Many non-parametric descriptive statistics are based on ranking numerical values. So think long and hard about whether you’re able to justify this assumption. And that can be very difficult to justify. This approach requires the assumption that the distance between each set of subsequent categories is equal. Treat ordinal variables as numericīecause the ordering of the categories often is central to the research question, many data analysts do the opposite: ignore the fact that the ordinal variable really isn’t numerical and treat the numerals that designate each category as actual numbers. The biggest advantage to this approach is you won’t violate any assumptions. For example, when there are few categories and the order isn’t central to the research question. This can make a lot of sense for some variables. There are many options for analyzing categorical variables that have no order. One simple option is to ignore the order in the variable’s categories and treat it as nominal. Ordinal variables are fundamentally categorical. Here are five options when your dependent variable is ordinal. Some are better than others, but it depends on the situation and research questions. There are not a lot of statistical methods designed just for ordinal variables.īut that doesn’t mean that you’re stuck with few options.
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