In this category you will find posts that are related to programming and should be interesting for psychologists, cognitive scientists, and neuroscientists. Well, almost every researcher would probably find some of the information useful at some time!
Every research psychologist, cognitive scientist, and neuroscientist, should know how to program.. Knowing how to program and write scripts will make many of a researchers everyday tasks much easier. For instance, instead of going through line after line of raw data you can write a Python script that runs through each cell in each column. Furthermore, you get the possibility to use more advanced, and cutting edge, statistical techniques by using R statistical programming environment.
Another example might be to create experiments using PsychoPy (either by coding using Python or using the drag-and-drop interface) and the cheap and open-source Arduino microcontroller. Also, coding is fun and relaxing!
In this post, you will learn how to report correlation according to APA. Adhering to APA (American Psychological Association) guidelines is important wen you report your analysis and write up your results . Whether you are conducting research in psychology, cognitive hearing science, or cognitive science, APA style is often required by journals and conferences. […]
Report Correlation in APA Style using R: Text & Tables Read More »
In this post, we will learn how to transform data from wide to long in R. Wide-to-long format conversion is often an important data manipulation technique in data analysis. In R, we can use many packages and their functions to transform data from a wide to long format. These functions include the tidyr package’s pivot_longer()
Wide to Long in R using the pivot_longer & melt functions Read More »
In this blog post, you will learn how to carry out a countif function using base R and dplyr. Countif is a powerful function that allows you to count the number of times a certain condition is met in a dataset. Countif is particularly useful in cognitive hearing science, where researchers often need to analyze
Countif function in R with Base and dplyr Read More »
Discover how to analyze non-parametric data using the Wilcoxon Signed-Rank Test in Python. Learn how to interpret the results and compare different Python packages for running the test. Get started now!
Wilcoxon Signed-Rank test in Python Read More »
In this blog post, you will learn how to test for the normality of residuals in R. Testing the normality of residuals is a step in data analysis. It helps determine if the residuals follow a normal distribution, which is important in many fields, including data science and psychology. Approximately normal residuals allow for powerful
Test for Normality in R: Three Different Methods & Interpretation Read More »
This blog will teach you how to carry out the Durbin-Watson Test in R. Have you ever run a linear regression model in R and wondered if the model’s assumptions hold? One common assumption of a linear regression model is the independence of observations, which means that the residuals (the differences between predicted and actual
Durbin Watson Test in R: Step-by-Step incl. Interpretation Read More »
In this blog post, we will learn how to sum across columns in R. Summing over columns can be a useful data analysis technique in various fields, including data science, psychology, and hearing science. The post will show several examples of how to sum across columns in R, including summing across a matrix, multiple columns
Sum Across Columns in R – with dplyr & base Functions Read More »
In this blog post, you will learn how to plot the prediction interval in R. If you work with data or are interested in statistical analysis, you know that making predictions is an essential part of the process. Moreover, if you are new to prediction intervals or want to refresh your knowledge, this post will
Plot Prediction Interval in R using ggplot2 Read More »
This blog post will teach us how to use probit regression in R, a statistical modeling technique for analyzing binary response variables. Probit regression can be useful when the outcome variable is dichotomous. That is when the outcome variable takes only two possible values, such as “success” or “failure,” “yes” or “no,” or “tinnitus” or
Probit Regression in R: Interpretation & Examples Read More »
Sankey plots are an essential tool for data visualization in science and business. Whether you are exploring complex data flows, identifying patterns, or communicating insights, Sankey diagrams make it easy to visualize connections and gain meaningful insights. Discover four methods to create stunning Sankey plots in R and elevate your data analysis game!
How to Create a Sankey Plot in R: 4 Methods Read More »