Recently we bought 10 coin like vibrating motors from Precision Microdrives. In the following video you will one of them in use. It is connected…
Month: December 2015
I recently asked which programming language I should learn next year (i.e., 2016). In this post I will evaluate the alternatives that I have by asking the question in different places around the internet. The post will end with the choice I made and how to install the language
To summarize my earlier post, I mainly use programming for creating Psychology experiments and, thus, need a powerful language. Furthermore, in Psychological experiments stimuli are typically being presented (e.g., sounds, images, text, or video). Responses need to be collected from the keyboard, mouse and specially built equipment (e.g., via USB; Arduino). For some experiments timing of the presentation and collection of responses might be significant. The language should, of course, be free, open source, and work on a computer running Windows, Linux, and OS-X. However, mobile platforms such as Smartphones and Tablets might also be interesting in the future. Note that all languages considered are more or less general purpose languages and might, therefore, be attractive to anyone that want to extend their stack and learn a new programming language 2016.
One of the most valuable answers I got was that I should look for a functional language.
In this tutorial you will get to know how to use the PsychoPy function TrialHandler to create trials and correct responses to your targets in these trials. PsychoPy is an application for creating experiments for Psychology experiments. The application is written in Python, an easy programming language to learn. You can learn more about PsychoPy in my two previous posts (Free and Useful software – PsychoPy and Python apps and libraries…”).
Obviously; rpy2 requires that you have both R (version 3.2+) and Python (versions 2.7 and 3.3) installed. There are pre-compiled binaries available for Linux and Windows (unsupported and unofficial, however). In this short tutorial, I will show you how to do carry out a repeated measures ANOVA (rmANOVA) using the r-packages ‘afex‘ and ‘emmeans‘, Python, and rpy2. The post is now updated and you will find a YouTube video going through the rpy2 examples found in this blog post. You will also find another YouTube Video in which you will learn two methods to show R plots inline in Jupyter Notebooks. Make sure you check them out!
R is a free and open-source statistical programming environment. Being open-source and free it has a large and helpful online community (for instance, see StackOverflow). When I went from carrying out analysis in SPSS to do them in R, I searched for good books targeted to Psychologists. The following 4 R books are useful and good for Psychologists that want to learn R.
The first book, Discovering Statistics Using R, may be a really good start if you are an undergraduate and have no experience of programming or statistics. The next two books are from intermediate to advanced level. The last book is, at the moment, free and is also a great introduction to statistics.
Do you also find it time-consuming to make your manuscripts follow American Psychological Associations (APA) guidelines? Have you searched the internet for a good .docx/.doc APA template? After reading this post, you might not have to search any more. r-package that makes your manuscript conform to APA guidelines! In this post I am briefly going to describe the package and what I think of it.is an
In this post you will find some really good Python Blogs and Resources. Most of these really helped me when moving from proprietary software to free and Open Source software (e.g., PsychoPy written in Python). The links are divided into two categories: general and research. In the ‘general’ category you will find good Python resources that might be more general and introducing. That is, more helpful for general problems and you might want to start here. In the ‘research’ category there are links to good Python resources that are more specific to use in research (e.g., PsychoPy and data analysis). The links in the ‘research’ category mainly contains links to Python blogs.
In this post, I will describe the existing free Python applications and libraries for creating experiments. So far, I have only used PsychoPy but I plan to test most of them. At least the ones that seem to still be maintained. All applications and libraries are open-source which makes it possible to download the source code and add your own stuff to it.
This post is my first on R and it will describe a method to reverse scores using R.
Reverse scoring in R
Many instruments (i.e., questionnaires) contain items are phrased so that a strong agreement indicates something negative (e.g., “When there is music in the room I find it hard to concentrate on reading”). These items need to be reversed so that the data will be correct later for statistical analysis.
For more information on reverse scoring, please see my earlier post: Reverse scoring in Python. Since I was more familiar with Python compared to R, and I had no a clue on how to do this in SPSS, I wrote a Python script. The Python script used a function that used Pandas DataFrame and it reversed the scores nice and quickly.
However, in R was pretty much as simple to do reverse scoring as in Python. In the following script a data frame is generated with column names (i.e., columnNames,’Q1′ to ‘Q6’) and some data is generated using replicate and sample (100 responses, on the 6 questions). After that you will find two methods, that are pretty much the same, for reversing the can be found. The methods only differ in how the columns are selected. The first are select based on the index of the column and the second select based on the column names to be reversed (might be preferable if you know the names of columns but not the indices).