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!
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 »
Are you struggling with standardizing your data or detecting outliers in R? If so, z-scores might be just what you need! In this blog post, we will learn how to calculate z-score in R using different data structures, such as vectors, matrices, and data frames. We will also have a quick look at the z-score
How to Calculate Z Score in R Read More »
This tutorial covers the critical topic of standardizing data in R. Standardizing data involves transforming data such that the mean of the data becomes zero and the standard deviation becomes one. This technique is widely used in data analysis and modeling, including psychology, biology, and finance.
Through this post, you will learn about the circumstances when and why we may want or need to standardize data in R. We will also discuss what it means to standardize variables and which R functions are best suited for standardization. Additionally, the tutorial provides a step-by-step guide on how to standardize data in R. Starting with standardizing a vector, we move on to standardizing a matrix, standardizing data in a dataframe, and finally, standardizing only numeric columns in a dataframe.
Whether you are a seasoned data analyst, a research professional, or a student, this tutorial will equip you with essential skills to standardize data in R. These skills will enable you to understand better and analyze your data, paving the way for better insights and informed decision-making.
How to Standardize Data in R with scale() & dplyr Read More »
This post will teach you how to make a residual plot in R. Moreover, you will learn when to use different residual plots and how to interpret them. Residual plots are a graphical tool that we can use to evaluate the quality of our regression models. These plots are good for identifying issues with the
How to Make a Residual Plot in R & Interpret Them using ggplot2 Read More »
This post will teach you how to carry out the Kruskal-Wallis test in R. The Kruskal-Wallis test is a non-parametric statistical test used to determine whether there is a significant difference in the median values of two or more independent groups. This method is used when the analyzed data is not normally distributed, or the
How to do a Kruskal-Wallis Test in R Read More »
In this post, you will learn how to create a Psychomotor vigilance task in PsychoPy using the Builder GUI and some Python code.
Psychomotor Vigilance Task (PVT) in PsychoPy (Free Download) Read More »