From parallel strands to an integrated research design This is the third post in the series Research Questions That Actually Work. The first post focused on quantitative research questions and the discipline of measurable design. The second examined qualitative research questions and the logic of meaning, context, and interpretive inquiry….
Research Questions That Actually Work, Part II: How to Write a Good Qualitative Research Question
From broad curiosity to meaningful and interpretable inquiry This is the second post in the series Research Questions That Actually Work. The first post examined quantitative research questions and the logic of measurable, answerable design. This post turns to qualitative research questions, where the central task is to formulate a…
Research Questions That Actually Work, Part I: How to Write a Good Quantitative Research Question
From broad interest to measurable, answerable design Many weak empirical studies do not fail because the software is wrong or the regression is badly coded. They fail earlier, when a broad topic is mistaken for a quantitative research question. A student says, “I want to study inflation and poverty,” or…
Quick start with R: Improving our regression model (Part 29)
Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command. Here are the data again. height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175)<br /> bodymass = c(82, 49, 53,…
Quick start with R: Diagnosing our regression model (Part 28)
Last time we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175)…
Quick start with R: More on regression (Part 27)
In my last blog we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(176, 154, 138, 196, 132, 176, 181, 169,…
Quick start with R: Scatterplot with regression line (Part 26)
Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people. Copy and paste the following code to the R command line to create this variable. height <- c(176,…
Quick start with R: Symbol colours and legend in qplot (Part 25)
In Blog 24 we saw how to use qplot to map symbol size to a categorical variable. Now we see how to control symbol colours and create legend titles. Copy in the same dataset of Blog 24 (a medical data set relating to patients in a randomised controlled trial: M…
Quick start with R: Symbol sizes in qplot (Part 24)
In Blog 24, let’s see how to use qplot to map symbol colour to a categorical variable. Copy in the following dataset (a medical dataset relating to patients in a randomised controlled trial): M <- structure(list(PATIENT = structure(c(32L, 15L, 41L, 42L, 44L, 17L, 31L, 10L, 38L, 18L, 22L, 30L), .Label…
Quick start with R: Using qplot() function (Part 23)
In Part 23, let’s see how to use qplot to create a simple scatterplot. The qplot (quick plot) system is a subset of the ggplot2 (grammar of graphics) package which you can use to create nice graphs. It is great for creating graphs of categorical data, because you can map…