When we attempt to build a model that captures these different aspects of age and development, it seems to me we have some choices:ġ) We can use DOL and CGA both as terms in the modelĢ) We can use DOL, CGA, and the interaction term CGA*DOL as terms in the modelģ) We can use DOL, CGA, and gestational age at birth (GAB) as terms in the modelĤ) We can use DOL, GAB, and the interaction term DOL*GAB as terms in the modelĥ) We could derive another term such as CGA-DOL, or GAB+DOL, to replace GAB or CGA, respectively… Biologically, these are distinct factors and we want to account for both of them. Day of life (DOL) is meaningful because it captures the amount of time out of the womb and in the world. Gestational age at birth (time since conception/length of pregnancy) is typically the variable that one knows and uses to convert day of life into corrected gestational age, but it is also an important variable on its own as it directly indicates how developed an infant typically is at birth.Ĭorrected gestational age (CGA) is meaningful because it captures developmental age.
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Conversely, a two week old baby born at 30 weeks of pregnancy and a two week old full term baby have the same day of life, but different corrected gestational ages and very different biology. A full term baby born at 40 weeks of pregnancy would have a day of life of 0 at birth, and a corrected gestational age of 40 weeks. The other is “corrected gestational age,” which is the number of days since conception.Įxamples: 40 weeks after conception, an infant born prematurely at 36 weeks of pregnancy will have a day of life of 28, and a corrected gestation age of 40 weeks. One is their day of life (the is straightforward – the number of days since they were born). When the population being studied is premature infants, there are a couple ways to think about age. I have a scenario in which I need to incorporate age into a model – where the concept of age is not so straight forward – and I’d be interested in what you think. The result is as shown in range E4:G16 of Figure 4.įigure 4 – Interaction via Extract Columns data analysis tool Since neither Money nor Quality has yet been added to the output, these too are copied over along with the interaction. Now, select both Money and Quality from the list box in the dialog box that appears as shown on the right side of Figure 4 (by clicking on Money and, while holding down the Ctrl key, clicking on Quality) and press the Add Inter button. Now enter A3:D19 into the Input Range of the dialog box that appears (as described in Figure 4 of Categorical Coding in Regression) and press the OK button. We can use the Real Statistics Extract Columns from a Data Range data analysis tool to automate the process of creating the interaction between two variables.įor example, to create the interaction between Money and Quality in Example 1, press Ctrl-m and select Extract Columns from a Data Range from the menu. 05 = α), but with an Adjusted R Square value of 77.4%, not quite as good as the model with interaction. This model is also a good fit for the data (p-value = 0.000499 <.
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We can also run the Regression data analysis tool on the original data without the interaction variable, obtaining the output in Figure 3.įigure 3 – Regression without interaction This model is almost a perfect fit for the data (99.7% Adjusted R Square), and shows that we can predict the percentage of votes a candidate will get via the formula: The resulting output is shown in Figure 2. We now use the Regression data analysis tool on the interaction model. Interaction is simply the product of the money and quality values. To capture the interaction between money and quality, we add an independent variable called “Interaction” (as described in the table on the right of Figure 1).
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Determine the relationship between votes, money and quality.įigure 1 – Data for Example 1 plus interaction model The table on the left of Figure 1 shows the percentage of votes 10 candidates received in different elections along with the amount of money spent and their quality. Studied in Multiple Regression Analysis where x 3 = x 1 Įxample 1: We postulate that the amount of votes a candidate gets depends on the amount of amount of money they spend and their quality (position on issues, ability to debate, charisma, organizational abilities, etc.).
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This is equivalent to a usual multiple regression model Sometimes the dependent variable depends not just on the independent variables but also on the interaction between the variables.